@Preamble{"\input bibnames.sty"}
@String{ack-nhfb = "Nelson H. F. Beebe,
University of Utah,
Department of Mathematics, 110 LCB,
155 S 1400 E RM 233,
Salt Lake City, UT 84112-0090, USA,
Tel: +1 801 581 5254,
FAX: +1 801 581 4148,
e-mail: \path|beebe@math.utah.edu|,
\path|beebe@acm.org|,
\path|beebe@computer.org| (Internet),
URL: \path|https://www.math.utah.edu/~beebe/|"}
@String{j-TIST = "ACM Transactions on Intelligent Systems and
Technology (TIST)"}
@Article{Yang:2010:IAT,
author = "Qiang Yang",
title = "Introduction to {ACM TIST}",
journal = j-TIST,
volume = "1",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858949",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2010:IAT,
author = "Huan Liu and Dana Nau",
title = "Introduction to the {ACM TIST} special issue {AI} in
social computing and cultural modeling",
journal = j-TIST,
volume = "1",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858950",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bainbridge:2010:VWC,
author = "William Sims Bainbridge",
title = "Virtual worlds as cultural models",
journal = j-TIST,
volume = "1",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858951",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Feldman:2010:SCR,
author = "Michal Feldman and Moshe Tennenholtz",
title = "Structured coalitions in resource selection games",
journal = j-TIST,
volume = "1",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858952",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2010:OFU,
author = "Fang Wu and Bernardo A. Huberman",
title = "Opinion formation under costly expression",
journal = j-TIST,
volume = "1",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858953",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Roos:2010:ESD,
author = "Patrick Roos and J. Ryan Carr and Dana S. Nau",
title = "Evolution of state-dependent risk preferences",
journal = j-TIST,
volume = "1",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858954",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Goolsby:2010:SMC,
author = "Rebecca Goolsby",
title = "Social media as crisis platform: The future of
community maps\slash crisis maps",
journal = j-TIST,
volume = "1",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858955",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2010:AIS,
author = "Meng Wang and Bo Liu and Xian-Sheng Hua",
title = "Accessible image search for colorblindness",
journal = j-TIST,
volume = "1",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1858948.1858956",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Tue Nov 23 12:18:28 MST 2010",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2010:PSI,
author = "Yixin Chen",
title = "Preface to special issue on applications of automated
planning",
journal = j-TIST,
volume = "1",
number = "2",
pages = "9:1--9:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869398",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Porteous:2010:API,
author = "Julie Porteous and Marc Cavazza and Fred Charles",
title = "Applying planning to interactive storytelling:
Narrative control using state constraints",
journal = j-TIST,
volume = "1",
number = "2",
pages = "10:1--10:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869399",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bryce:2010:PIB,
author = "Daniel Bryce and Michael Verdicchio and Seungchan
Kim",
title = "Planning interventions in biological networks",
journal = j-TIST,
volume = "1",
number = "2",
pages = "11:1--11:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869400",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Refanidis:2010:CBA,
author = "Ioannis Refanidis and Neil Yorke-Smith",
title = "A constraint-based approach to scheduling an
individual's activities",
journal = j-TIST,
volume = "1",
number = "2",
pages = "12:1--12:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869401",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Benaskeur:2010:CRT,
author = "Abder Rezak Benaskeur and Froduald Kabanza and Eric
Beaudry",
title = "{CORALS}: a real-time planner for anti-air defense
operations",
journal = j-TIST,
volume = "1",
number = "2",
pages = "13:1--13:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869402",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Talamadupula:2010:PHR,
author = "Kartik Talamadupula and J. Benton and Subbarao
Kambhampati and Paul Schermerhorn and Matthias
Scheutz",
title = "Planning for human-robot teaming in open worlds",
journal = j-TIST,
volume = "1",
number = "2",
pages = "14:1--14:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869403",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cirillo:2010:HAT,
author = "Marcello Cirillo and Lars Karlsson and Alessandro
Saffiotti",
title = "Human-aware task planning: An application to mobile
robots",
journal = j-TIST,
volume = "1",
number = "2",
pages = "15:1--15:??",
month = nov,
year = "2010",
CODEN = "????",
DOI = "https://doi.org/10.1145/1869397.1869404",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:50 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2011:ISI,
author = "Daqing Zhang and Matthai Philipose and Qiang Yang",
title = "Introduction to the special issue on intelligent
systems for activity recognition",
journal = j-TIST,
volume = "2",
number = "1",
pages = "1:1--1:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889682",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2011:LTR,
author = "Yu Zheng and Xing Xie",
title = "Learning travel recommendations from user-generated
{GPS} traces",
journal = j-TIST,
volume = "2",
number = "1",
pages = "2:1--2:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889683",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Farrahi:2011:DRL,
author = "Katayoun Farrahi and Daniel Gatica-Perez",
title = "Discovering routines from large-scale human locations
using probabilistic topic models",
journal = j-TIST,
volume = "2",
number = "1",
pages = "3:1--3:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889684",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hsu:2011:PMC,
author = "Jane Yung-Jen Hsu and Chia-Chun Lian and Wan-Rong
Jih",
title = "Probabilistic models for concurrent chatting activity
recognition",
journal = j-TIST,
volume = "2",
number = "1",
pages = "4:1--4:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889685",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhou:2011:RPA,
author = "Yue Zhou and Bingbing Ni and Shuicheng Yan and Thomas
S. Huang",
title = "Recognizing pair-activities by causality analysis",
journal = j-TIST,
volume = "2",
number = "1",
pages = "5:1--5:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889686",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ward:2011:PMA,
author = "Jamie A. Ward and Paul Lukowicz and Hans W.
Gellersen",
title = "Performance metrics for activity recognition",
journal = j-TIST,
volume = "2",
number = "1",
pages = "6:1--6:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889687",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wyatt:2011:ICC,
author = "Danny Wyatt and Tanzeem Choudhury and Jeff Bilmes and
James A. Kitts",
title = "Inferring colocation and conversation networks from
privacy-sensitive audio with implications for
computational social science",
journal = j-TIST,
volume = "2",
number = "1",
pages = "7:1--7:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889688",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bao:2011:FRC,
author = "Xinlong Bao and Thomas G. Dietterich",
title = "{FolderPredictor}: Reducing the cost of reaching the
right folder",
journal = j-TIST,
volume = "2",
number = "1",
pages = "8:1--8:??",
month = jan,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1889681.1889689",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Wed Jan 26 14:40:51 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hua:2011:ISI,
author = "Xian-Sheng Hua and Qi Tian and Alberto {Del Bimbo} and
Ramesh Jain",
title = "Introduction to the special issue on intelligent
multimedia systems and technology",
journal = j-TIST,
volume = "2",
number = "2",
pages = "9:1--9:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899413",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2011:ALM,
author = "Meng Wang and Xian-Sheng Hua",
title = "Active learning in multimedia annotation and
retrieval: a survey",
journal = j-TIST,
volume = "2",
number = "2",
pages = "10:1--10:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899414",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Active learning is a machine learning technique that
selects the most informative samples for labeling and
uses them as training data. It has been widely explored
in multimedia research community for its capability of
reducing human annotation effort. In this article, we
provide a survey on the efforts of leveraging active
learning in multimedia annotation and retrieval. We
mainly focus on two application domains: image/video
annotation and content-based image retrieval. We first
briefly introduce the principle of active learning and
then we analyze the sample selection criteria. We
categorize the existing sample selection strategies
used in multimedia annotation and retrieval into five
criteria: risk reduction, uncertainty, diversity,
density and relevance. We then introduce several
classification models used in active learning-based
multimedia annotation and retrieval, including
semi-supervised learning, multilabel learning and
multiple instance learning. We also provide a
discussion on several future trends in this research
direction. In particular, we discuss cost analysis of
human annotation and large-scale interactive multimedia
annotation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shao:2011:VIG,
author = "Yuanlong Shao and Yuan Zhou and Deng Cai",
title = "Variational inference with graph regularization for
image annotation",
journal = j-TIST,
volume = "2",
number = "2",
pages = "11:1--11:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899415",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Image annotation is a typical area where there are
multiple types of attributes associated with each
individual image. In order to achieve better
performance, it is important to develop effective
modeling by utilizing prior knowledge. In this article,
we extend the graph regularization approaches to a more
general case where the regularization is imposed on the
factorized variational distributions, instead of
posterior distributions implicitly involved in EM-like
algorithms. In this way, the problem modeling can be
more flexible, and we can choose any factor in the
problem domain to impose graph regularization wherever
there are similarity constraints among the instances.
We formulate the problem formally and show its
geometrical background in manifold learning. We also
design two practically effective algorithms and analyze
their properties such as the convergence. Finally, we
apply our approach to image annotation and show the
performance improvement of our algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yu:2011:CBS,
author = "Jie Yu and Xin Jin and Jiawei Han and Jiebo Luo",
title = "Collection-based sparse label propagation and its
application on social group suggestion from photos",
journal = j-TIST,
volume = "2",
number = "2",
pages = "12:1--12:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899416",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online social network services pose great
opportunities and challenges for many research areas.
In multimedia content analysis, automatic social group
recommendation for images holds the promise to expand
one's social network through media sharing. However,
most existing techniques cannot generate satisfactory
social group suggestions when the images are classified
independently. In this article, we present novel
methods to produce accurate suggestions of suitable
social groups from a user's personal photo collection.
First, an automatic clustering process is designed to
estimate the group similarities, select the optimal
number of clusters and categorize the social groups.
Both visual content and textual annotations are
integrated to generate initial predictions of the group
categories for the images. Next, the relationship among
images in a user's collection is modeled as a sparse
graph. A collection-based sparse label propagation
method is proposed to improve the group suggestions.
Furthermore, the sparse graph-based collection model
can be readily exploited to select the most influential
and informative samples for active relevance feedback,
which can be integrated with the label propagation
process without the need for classifier retraining. The
proposed methods have been tested on group suggestion
tasks for real user collections and demonstrated
superior performance over the state-of-the-art
techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2011:DML,
author = "Lei Wu and Steven C. H. Hoi and Rong Jin and Jianke
Zhu and Nenghai Yu",
title = "Distance metric learning from uncertain side
information for automated photo tagging",
journal = j-TIST,
volume = "2",
number = "2",
pages = "13:1--13:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899417",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Automated photo tagging is an important technique for
many intelligent multimedia information systems, for
example, smart photo management system and intelligent
digital media library. To attack the challenge, several
machine learning techniques have been developed and
applied for automated photo tagging. For example,
supervised learning techniques have been applied to
automated photo tagging by training statistical
classifiers from a collection of manually labeled
examples. Although the existing approaches work well
for small testbeds with relatively small number of
annotation words, due to the long-standing challenge of
object recognition, they often perform poorly in
large-scale problems. Another limitation of the
existing approaches is that they require a set of
high-quality labeled data, which is not only expensive
to collect but also time consuming. In this article, we
investigate a social image based annotation scheme by
exploiting implicit side information that is available
for a large number of social photos from the social web
sites. The key challenge of our intelligent annotation
scheme is how to learn an effective distance metric
based on implicit side information (visual or textual)
of social photos. To this end, we present a novel
``Probabilistic Distance Metric Learning'' (PDML)
framework, which can learn optimized metrics by
effectively exploiting the implicit side information
vastly available on the social web. We apply the
proposed technique to photo annotation tasks based on a
large social image testbed with over 1 million tagged
photos crawled from a social photo sharing portal.
Encouraging results show that the proposed technique is
effective and promising for social photo based
annotation tasks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2011:IAK,
author = "Jinhui Tang and Richang Hong and Shuicheng Yan and
Tat-Seng Chua and Guo-Jun Qi and Ramesh Jain",
title = "Image annotation by {$k$NN}-sparse graph-based label
propagation over noisily tagged web images",
journal = j-TIST,
volume = "2",
number = "2",
pages = "14:1--14:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899418",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we exploit the problem of annotating
a large-scale image corpus by label propagation over
noisily tagged web images. To annotate the images more
accurately, we propose a novel k NN-sparse graph-based
semi-supervised learning approach for harnessing the
labeled and unlabeled data simultaneously. The sparse
graph constructed by datum-wise one-vs- k NN sparse
reconstructions of all samples can remove most of the
semantically unrelated links among the data, and thus
it is more robust and discriminative than the
conventional graphs. Meanwhile, we apply the
approximate k nearest neighbors to accelerate the
sparse graph construction without loosing its
effectiveness. More importantly, we propose an
effective training label refinement strategy within
this graph-based learning framework to handle the noise
in the training labels, by bringing in a dual
regularization for both the quantity and sparsity of
the noise. We conduct extensive experiments on a
real-world image database consisting of 55,615 Flickr
images and noisily tagged training labels. The results
demonstrate both the effectiveness and efficiency of
the proposed approach and its capability to deal with
the noise in the training labels.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tong:2011:APL,
author = "Xiaofeng Tong and Jia Liu and Tao Wang and Yimin
Zhang",
title = "Automatic player labeling, tracking and field
registration and trajectory mapping in broadcast soccer
video",
journal = j-TIST,
volume = "2",
number = "2",
pages = "15:1--15:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899419",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we present a method to perform
automatic player trajectories mapping based on player
detection, unsupervised labeling, efficient
multi-object tracking, and playfield registration in
broadcast soccer videos. Player detector determines the
players' positions and scales by combining the ability
of dominant color based background subtraction and a
boosting detector with Haar features. We first learn
the dominant color with accumulate color histogram at
the beginning of processing, then use the player
detector to collect hundreds of player samples, and
learn player appearance codebook by unsupervised
clustering. In a soccer game, a player can be labeled
as one of four categories: two teams, referee or
outlier. The learning capability enables the method to
be generalized well to different videos without any
manual initialization. With the dominant color and
player appearance model, we can locate and label each
player. After that, we perform multi-object tracking by
using Markov Chain Monte Carlo (MCMC) data association
to generate player trajectories. Some data driven
dynamics are proposed to improve the Markov chain's
efficiency, such as label consistency, motion
consistency, and track length, etc. Finally, we extract
key-points and find the mapping from an image plane to
the standard field model, and then map players'
position and trajectories to the field. A large
quantity of experimental results on FIFA World Cup 2006
videos demonstrate that this method can reach high
detection and labeling precision, reliably tracking in
scenes of player occlusion, moderate camera motion and
pose variation, and yield promising field registration
results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:NJD,
author = "Qingzhong Liu and Andrew H. Sung and Mengyu Qiao",
title = "Neighboring joint density-based {JPEG} steganalysis",
journal = j-TIST,
volume = "2",
number = "2",
pages = "16:1--16:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899420",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib/",
abstract = "The threat posed by hackers, spies, terrorists, and
criminals, etc. using steganography for stealthy
communications and other illegal purposes is a serious
concern of cyber security. Several steganographic
systems that have been developed and made readily
available utilize JPEG images as carriers. Due to the
popularity of JPEG images on the Internet, effective
steganalysis techniques are called for to counter the
threat of JPEG steganography. In this article, we
propose a new approach based on feature mining on the
discrete cosine transform (DCT) domain and machine
learning for steganalysis of JPEG images. First,
neighboring joint density features on both intra-block
and inter-block are extracted from the DCT coefficient
array and the absolute array, respectively; then a
support vector machine (SVM) is applied to the features
for detection. An evolving neural-fuzzy inference
system is employed to predict the hiding amount in JPEG
steganograms. We also adopt a feature selection method
of support vector machine recursive feature elimination
to reduce the number of features. Experimental results
show that, in detecting several JPEG-based
steganographic systems, our method prominently
outperforms the well-known Markov-process based
approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bhatt:2011:PTM,
author = "Chidansh Bhatt and Mohan Kankanhalli",
title = "Probabilistic temporal multimedia data mining",
journal = j-TIST,
volume = "2",
number = "2",
pages = "17:1--17:??",
month = feb,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1899412.1899421",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Oct 1 16:23:55 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Existing sequence pattern mining techniques assume
that the obtained events from event detectors are
accurate. However, in reality, event detectors label
the events from different modalities with a certain
probability over a time-interval. In this article, we
consider for the first time Probabilistic Temporal
Multimedia (PTM) Event data to discover accurate
sequence patterns. PTM event data considers the start
time, end time, event label and associated probability
for the sequence pattern discovery. As the existing
sequence pattern mining techniques cannot work on such
realistic data, we have developed a novel framework for
performing sequence pattern mining on probabilistic
temporal multimedia event data. We perform probability
fusion to resolve the redundancy among detected events
from different modalities, considering their
cross-modal correlation. We propose a novel sequence
pattern mining algorithm called Probabilistic Interval
based Event Miner (PIE-Miner) for discovering frequent
sequence patterns from interval based events. PIE-Miner
has a new support counting mechanism developed for PTM
data. Existing sequence pattern mining algorithms have
event label level support counting mechanism, whereas
we have developed event cluster level support counting
mechanism. We discover the complete set of all possible
temporal relationships based on Allen's interval
algebra. The experimental results showed that the
discovered sequence patterns are more useful than the
patterns discovered with state-of-the-art sequence
pattern mining algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ling:2011:ISI,
author = "Charles X. Ling",
title = "Introduction to special issue on machine learning for
business applications",
journal = j-TIST,
volume = "2",
number = "3",
pages = "18:1--18:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961190",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Dhar:2011:PFM,
author = "Vasant Dhar",
title = "Prediction in financial markets: The case for small
disjuncts",
journal = j-TIST,
volume = "2",
number = "3",
pages = "19:1--19:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961191",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2011:LBC,
author = "Szu-Hao Huang and Shang-Hong Lai and Shih-Hsien Tai",
title = "A learning-based contrarian trading strategy via a
dual-classifier model",
journal = j-TIST,
volume = "2",
number = "3",
pages = "20:1--20:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961192",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2011:CCD,
author = "Bin Li and Steven C. H. Hoi and Vivekanand
Gopalkrishnan",
title = "{CORN}: Correlation-driven nonparametric learning
approach for portfolio selection",
journal = j-TIST,
volume = "2",
number = "3",
pages = "21:1--21:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961193",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bonchi:2011:SNA,
author = "Francesco Bonchi and Carlos Castillo and Aristides
Gionis and Alejandro Jaimes",
title = "Social Network Analysis and Mining for Business
Applications",
journal = j-TIST,
volume = "2",
number = "3",
pages = "22:1--22:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961194",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2011:HMF,
author = "Richong Zhang and Thomas Tran",
title = "A helpfulness modeling framework for electronic
word-of-mouth on consumer opinion platforms",
journal = j-TIST,
volume = "2",
number = "3",
pages = "23:1--23:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961195",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ge:2011:MLC,
author = "Yong Ge and Hui Xiong and Wenjun Zhou and Siming Li
and Ramendra Sahoo",
title = "Multifocal learning for customer problem analysis",
journal = j-TIST,
volume = "2",
number = "3",
pages = "24:1--24:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961196",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hsu:2011:ISI,
author = "Chun-Nan Hsu",
title = "Introduction to special issue on large-scale machine
learning",
journal = j-TIST,
volume = "2",
number = "3",
pages = "25:1--25:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961197",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:PPL,
author = "Zhiyuan Liu and Yuzhou Zhang and Edward Y. Chang and
Maosong Sun",
title = "{PLDA+}: Parallel latent {Dirichlet} allocation with
data placement and pipeline processing",
journal = j-TIST,
volume = "2",
number = "3",
pages = "26:1--26:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961198",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chang:2011:LLS,
author = "Chih-Chung Chang and Chih-Jen Lin",
title = "{LIBSVM}: a library for support vector machines",
journal = j-TIST,
volume = "2",
number = "3",
pages = "27:1--27:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961199",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "LIBSVM is a library for Support Vector Machines
(SVMs). We have been actively developing this package
since the year 2000. The goal is to help users to
easily apply SVM to their applications. LIBSVM has
gained wide popularity in machine learning and many
other areas. In this article, we present all
implementation details of LIBSVM. Issues such as
solving SVM optimization problems theoretical
convergence multiclass classification probability
estimates and parameter selection are discussed in
detail.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gasso:2011:BOL,
author = "Gilles Gasso and Aristidis Pappaioannou and Marina
Spivak and L{\'e}on Bottou",
title = "Batch and online learning algorithms for nonconvex
{Neyman--Pearson} classification",
journal = j-TIST,
volume = "2",
number = "3",
pages = "28:1--28:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961200",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ma:2011:LRE,
author = "Hao Ma and Irwin King and Michael R. Lyu",
title = "Learning to recommend with explicit and implicit
social relations",
journal = j-TIST,
volume = "2",
number = "3",
pages = "29:1--29:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961201",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ma:2011:LDM,
author = "Justin Ma and Lawrence K. Saul and Stefan Savage and
Geoffrey M. Voelker",
title = "Learning to detect malicious {URLs}",
journal = j-TIST,
volume = "2",
number = "3",
pages = "30:1--30:??",
month = apr,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1961189.1961202",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri May 13 11:20:03 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Malicious Web sites are a cornerstone of Internet
criminal activities. The dangers of these sites have
created a demand for safeguards that protect end-users
from visiting them. This article explores how to detect
malicious Web sites from the lexical and host-based
features of their URLs. We show that this problem lends
itself naturally to modern algorithms for online
learning. Online algorithms not only process large
numbers of URLs more efficiently than batch algorithms,
they also adapt more quickly to new features in the
continuously evolving distribution of malicious URLs.
We develop a real-time system for gathering URL
features and pair it with a real-time feed of labeled
URLs from a large Web mail provider. From these
features and labels, we are able to train an online
classifier that detects malicious Web sites with 99\%
accuracy over a balanced dataset.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gomes:2011:ISI,
author = "Carla Gomes and Qiang Yang",
title = "Introduction to special issue on computational
sustainability",
journal = j-TIST,
volume = "2",
number = "4",
pages = "31:1--31:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989735",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Krause:2011:SAO,
author = "Andreas Krause and Carlos Guestrin",
title = "Submodularity and its applications in optimized
information gathering",
journal = j-TIST,
volume = "2",
number = "4",
pages = "32:1--32:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989736",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cattafi:2011:SBP,
author = "Massimiliano Cattafi and Marco Gavanelli and Michela
Milano and Paolo Cagnoli",
title = "Sustainable biomass power plant location in the
{Italian Emilia-Romagna} region",
journal = j-TIST,
volume = "2",
number = "4",
pages = "33:1--33:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989737",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Patnaik:2011:TDM,
author = "Debprakash Patnaik and Manish Marwah and Ratnesh K.
Sharma and Naren Ramakrishnan",
title = "Temporal data mining approaches for sustainable
chiller management in data centers",
journal = j-TIST,
volume = "2",
number = "4",
pages = "34:1--34:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989738",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ramchurn:2011:ABH,
author = "Sarvapali D. Ramchurn and Perukrishnen Vytelingum and
Alex Rogers and Nicholas R. Jennings",
title = "Agent-based homeostatic control for green energy in
the smart grid",
journal = j-TIST,
volume = "2",
number = "4",
pages = "35:1--35:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989739",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mithal:2011:MGF,
author = "Varun Mithal and Ashish Garg and Shyam Boriah and
Michael Steinbach and Vipin Kumar and Christopher
Potter and Steven Klooster and Juan Carlos
Castilla-Rubio",
title = "Monitoring global forest cover using data mining",
journal = j-TIST,
volume = "2",
number = "4",
pages = "36:1--36:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989740",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2011:MMM,
author = "Zhenhui Li and Jiawei Han and Ming Ji and Lu-An Tang
and Yintao Yu and Bolin Ding and Jae-Gil Lee and Roland
Kays",
title = "{MoveMine}: Mining moving object data for discovery of
animal movement patterns",
journal = j-TIST,
volume = "2",
number = "4",
pages = "37:1--37:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989741",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Toole:2011:SCC,
author = "Jameson L. Toole and Nathan Eagle and Joshua B.
Plotkin",
title = "Spatiotemporal correlations in criminal offense
records",
journal = j-TIST,
volume = "2",
number = "4",
pages = "38:1--38:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989742",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ding:2011:SCD,
author = "Wei Ding and Tomasz F. Stepinski and Yang Mu and
Lourenco Bandeira and Ricardo Ricardo and Youxi Wu and
Zhenyu Lu and Tianyu Cao and Xindong Wu",
title = "Subkilometer crater discovery with boosting and
transfer learning",
journal = j-TIST,
volume = "2",
number = "4",
pages = "39:1--39:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989743",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Berry:2011:PPA,
author = "Pauline M. Berry and Melinda Gervasio and Bart
Peintner and Neil Yorke-Smith",
title = "{PTIME}: Personalized assistance for calendaring",
journal = j-TIST,
volume = "2",
number = "4",
pages = "40:1--40:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989744",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reddy:2011:PSA,
author = "Sudhakar Y. Reddy and Jeremy D. Frank and Michael J.
Iatauro and Matthew E. Boyce and Elif K{\"u}rkl{\"u}
and Mitchell Ai-Chang and Ari K. J{\'o}nsson",
title = "Planning solar array operations on the {International
Space Station}",
journal = j-TIST,
volume = "2",
number = "4",
pages = "41:1--41:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989745",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Haigh:2011:RLL,
author = "Karen Zita Haigh and Fusun Yaman",
title = "{RECYCLE}: Learning looping workflows from annotated
traces",
journal = j-TIST,
volume = "2",
number = "4",
pages = "42:1--42:??",
month = jul,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/1989734.1989746",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
bibdate = "Fri Jul 22 08:50:59 MDT 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guy:2011:I,
author = "Ido Guy and Li Chen and Michelle X. Zhou",
title = "Introduction",
journal = j-TIST,
volume = "3",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036265",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lipczak:2011:ETR,
author = "Marek Lipczak and Evangelos Milios",
title = "Efficient Tag Recommendation for Real-Life Data",
journal = j-TIST,
volume = "3",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036266",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Vasuki:2011:SAR,
author = "Vishvas Vasuki and Nagarajan Natarajan and Zhengdong
Lu and Berkant Savas and Inderjit Dhillon",
title = "Scalable Affiliation Recommendation using Auxiliary
Networks",
journal = j-TIST,
volume = "3",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036267",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{McNally:2011:CSC,
author = "Kevin McNally and Michael P. O'Mahony and Maurice
Coyle and Peter Briggs and Barry Smyth",
title = "A Case Study of Collaboration and Reputation in Social
{Web} Search",
journal = j-TIST,
volume = "3",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036268",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhao:2011:WDW,
author = "Shiwan Zhao and Michelle X. Zhou and Xiatian Zhang and
Quan Yuan and Wentao Zheng and Rongyao Fu",
title = "Who is Doing What and When: Social Map-Based
Recommendation for Content-Centric Social {Web} Sites",
journal = j-TIST,
volume = "3",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036269",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:I,
author = "Huan Liu and Dana Nau",
title = "Introduction",
journal = j-TIST,
volume = "3",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036270",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shakarian:2011:GGA,
author = "Paulo Shakarian and V. S. Subrahmanian and Maria Luisa
Sapino",
title = "{GAPs}: Geospatial Abduction Problems",
journal = j-TIST,
volume = "3",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036271",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gal:2011:AAN,
author = "Ya'akov Gal and Sarit Kraus and Michele Gelfand and
Hilal Khashan and Elizabeth Salmon",
title = "An Adaptive Agent for Negotiating with People in
Different Cultures",
journal = j-TIST,
volume = "3",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036272",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Vu:2011:FSK,
author = "Thuc Vu and Yoav Shoham",
title = "Fair Seeding in Knockout Tournaments",
journal = j-TIST,
volume = "3",
number = "1",
pages = "9:1--9:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036273",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cioffi-Revilla:2011:GIS,
author = "Claudio Cioffi-Revilla and J. Daniel Rogers and
Atesmachew Hailegiorgis",
title = "Geographic Information Systems and Spatial Agent-Based
Model Simulations for Sustainable Development",
journal = j-TIST,
volume = "3",
number = "1",
pages = "10:1--10:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036274",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jiang:2011:UMS,
author = "Yingying Jiang and Feng Tian and Xiaolong (Luke) Zhang
and Guozhong Dai and Hongan Wang",
title = "Understanding, Manipulating and Searching Hand-Drawn
Concept Maps",
journal = j-TIST,
volume = "3",
number = "1",
pages = "11:1--11:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036275",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2011:IIS,
author = "Jingdong Wang and Xian-Sheng Hua",
title = "Interactive Image Search by Color Map",
journal = j-TIST,
volume = "3",
number = "1",
pages = "12:1--12:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036276",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Prettenhofer:2011:CLA,
author = "Peter Prettenhofer and Benno Stein",
title = "Cross-Lingual Adaptation Using Structural
Correspondence Learning",
journal = j-TIST,
volume = "3",
number = "1",
pages = "13:1--13:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036277",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Anagnostopoulos:2011:WPS,
author = "Aris Anagnostopoulos and Andrei Z. Broder and Evgeniy
Gabrilovich and Vanja Josifovski and Lance Riedel",
title = "{Web} Page Summarization for Just-in-Time Contextual
Advertising",
journal = j-TIST,
volume = "3",
number = "1",
pages = "14:1--14:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036278",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2011:GPU,
author = "Lei Tang and Xufei Wang and Huan Liu",
title = "Group Profiling for Understanding Social Structures",
journal = j-TIST,
volume = "3",
number = "1",
pages = "15:1--15:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036279",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2011:TWC,
author = "Zhanyi Liu and Haifeng Wang and Hua Wu and Sheng Li",
title = "Two-Word Collocation Extraction Using Monolingual Word
Alignment Method",
journal = j-TIST,
volume = "3",
number = "1",
pages = "16:1--16:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036280",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liao:2011:MCS,
author = "Zhen Liao and Daxin Jiang and Enhong Chen and Jian Pei
and Huanhuan Cao and Hang Li",
title = "Mining Concept Sequences from Large-Scale Search Logs
for Context-Aware Query Suggestion",
journal = j-TIST,
volume = "3",
number = "1",
pages = "17:1--17:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036281",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sukthankar:2011:ARD,
author = "Gita Sukthankar and Katia Sycara",
title = "Activity Recognition for Dynamic Multi-Agent Teams",
journal = j-TIST,
volume = "3",
number = "1",
pages = "18:1--18:??",
month = oct,
year = "2011",
CODEN = "????",
DOI = "https://doi.org/10.1145/2036264.2036282",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun Nov 6 07:22:40 MST 2011",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2012:ISS,
author = "Shixia Liu and Michelle X. Zhou and Giuseppe Carenini
and Huamin Qu",
title = "Introduction to the Special Section on Intelligent
Visual Interfaces for Text Analysis",
journal = j-TIST,
volume = "3",
number = "2",
pages = "19:1--19:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089095",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cui:2012:WSU,
author = "Weiwei Cui and Huamin Qu and Hong Zhou and Wenbin
Zhang and Steve Skiena",
title = "Watch the Story Unfold with {TextWheel}: Visualization
of Large-Scale News Streams",
journal = j-TIST,
volume = "3",
number = "2",
pages = "20:1--20:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089096",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Keyword-based searching and clustering of news
articles have been widely used for news analysis.
However, news articles usually have other attributes
such as source, author, date and time, length, and
sentiment which should be taken into account. In
addition, news articles and keywords have complicated
macro/micro relations, which include relations between
news articles (i.e., macro relation), relations between
keywords (i.e., micro relation), and relations between
news articles and keywords (i.e., macro-micro
relation). These macro/micro relations are time varying
and pose special challenges for news analysis. In this
article we present a visual analytics system for news
streams which can bring multiple attributes of the news
articles and the macro/micro relations between news
streams and keywords into one coherent analytical
context, all the while conveying the dynamic natures of
news streams. We introduce a new visualization
primitive called TextWheel which consists of one or
multiple keyword wheels, a document transportation
belt, and a dynamic system which connects the wheels
and belt. By observing the TextWheel and its content
changes, some interesting patterns can be detected. We
use our system to analyze several news corpora related
to some major companies and the results demonstrate the
high potential of our method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Thai:2012:VAO,
author = "Vinhtuan Thai and Pierre-Yves Rouille and Siegfried
Handschuh",
title = "Visual Abstraction and Ordering in Faceted Browsing of
Text Collections",
journal = j-TIST,
volume = "3",
number = "2",
pages = "21:1--21:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089097",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Faceted navigation is a technique for the exploration
and discovery of a collection of resources, which can
be of various types including text documents. While
being information-rich resources, documents are usually
not treated as content-bearing items in faceted
browsing interfaces, and yet the required clean
metadata is not always available or matches users'
interest. In addition, the existing linear listing
paradigm for representing result items from the faceted
filtering process makes it difficult for users to
traverse or compare across facet values in different
orders of importance to them. In this context, we
report in this article a visual support toward faceted
browsing of a collection of documents based on a set of
entities of interest to users. Our proposed approach
involves using a multi-dimensional visualization as an
alternative to the linear listing of focus items. In
this visualization, visual abstraction based on a
combination of a conceptual structure and the
structural equivalence of documents can be
simultaneously used to deal with a large number of
items. Furthermore, the approach also enables visual
ordering based on the importance of facet values to
support prioritized, cross-facet comparisons of focus
items. A user study was conducted and the results
suggest that interfaces using the proposed approach can
support users better in exploratory tasks and were also
well-liked by the participants of the study, with the
hybrid interface combining the multi-dimensional
visualization with the linear listing receiving the
most favorable ratings.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Candan:2012:PMV,
author = "K. Sel{\c{c}}uk Candan and Luigi {Di Caro} and Maria
Luisa Sapino",
title = "{PhC}: Multiresolution Visualization and Exploration
of Text Corpora with Parallel Hierarchical
Coordinates",
journal = j-TIST,
volume = "3",
number = "2",
pages = "22:1--22:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089098",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The high-dimensional nature of the textual data
complicates the design of visualization tools to
support exploration of large document corpora. In this
article, we first argue that the Parallel Coordinates
(PC) technique, which can map multidimensional vectors
onto a 2D space in such a way that elements with
similar values are represented as similar poly-lines or
curves in the visualization space, can be used to help
users discern patterns in document collections. The
inherent reduction in dimensionality during the mapping
from multidimensional points to 2D lines, however, may
result in visual complications. For instance, the lines
that correspond to clusters of objects that are
separate in the multidimensional space may overlap each
other in the 2D space; the resulting increase in the
number of crossings would make it hard to distinguish
the individual document clusters. Such crossings of
lines and overly dense regions are significant sources
of visual clutter, thus avoiding them may help
interpret the visualization. In this article, we note
that visual clutter can be significantly reduced by
adjusting the resolution of the individual term
coordinates by clustering the corresponding values.
Such reductions in the resolution of the individual
term-coordinates, however, will lead to a certain
degree of information loss and thus the appropriate
resolution for the term-coordinates has to be selected
carefully. Thus, in this article we propose a
controlled clutter reduction approach, called Parallel
hierarchical Coordinates (or PhC ), for reducing the
visual clutter in PC-based visualizations of text
corpora. We define visual clutter and information loss
measures and provide extensive evaluations that show
that the proposed PhC provides significant visual gains
(i.e., multiple orders of reductions in visual clutter)
with small information loss during visualization and
exploration of document collections.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gretarsson:2012:TVA,
author = "Brynjar Gretarsson and John O'Donovan and Svetlin
Bostandjiev and Tobias H{\"o}llerer and Arthur Asuncion
and David Newman and Padhraic Smyth",
title = "{TopicNets}: Visual Analysis of Large Text Corpora
with Topic Modeling",
journal = j-TIST,
volume = "3",
number = "2",
pages = "23:1--23:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089099",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present TopicNets, a Web-based system for visual
and interactive analysis of large sets of documents
using statistical topic models. A range of
visualization types and control mechanisms to support
knowledge discovery are presented. These include
corpus- and document-specific views, iterative topic
modeling, search, and visual filtering. Drill-down
functionality is provided to allow analysts to
visualize individual document sections and their
relations within the global topic space. Analysts can
search across a dataset through a set of expansion
techniques on selected document and topic nodes.
Furthermore, analysts can select relevant subsets of
documents and perform real-time topic modeling on these
subsets to interactively visualize topics at various
levels of granularity, allowing for a better
understanding of the documents. A discussion of the
design and implementation choices for each visual
analysis technique is presented. This is followed by a
discussion of three diverse use cases in which
TopicNets enables fast discovery of information that is
otherwise hard to find. These include a corpus of
50,000 successful NSF grant proposals, 10,000
publications from a large research center, and single
documents including a grant proposal and a PhD
thesis.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:DFE,
author = "Yi Zhang and Tao Li",
title = "{DClusterE}: a Framework for Evaluating and
Understanding Document Clustering Using Visualization",
journal = j-TIST,
volume = "3",
number = "2",
pages = "24:1--24:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089100",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Over the last decade, document clustering, as one of
the key tasks in information organization and
navigation, has been widely studied. Many algorithms
have been developed for addressing various challenges
in document clustering and for improving clustering
performance. However, relatively few research efforts
have been reported on evaluating and understanding
document clustering results. In this article, we
present DClusterE, a comprehensive and effective
framework for document clustering evaluation and
understanding using information visualization.
DClusterE integrates cluster validation with user
interactions and offers rich visualization tools for
users to examine document clustering results from
multiple perspectives. In particular, through
informative views including force-directed layout view,
matrix view, and cluster view, DClusterE provides not
only different aspects of document
inter/intra-clustering structures, but also the
corresponding relationship between clustering results
and the ground truth. Additionally, DClusterE supports
general user interactions such as zoom in/out,
browsing, and interactive access of the documents at
different levels. Two new techniques are proposed to
implement DClusterE: (1) A novel multiplicative update
algorithm (MUA) for matrix reordering to generate
narrow-banded (or clustered) nonzero patterns from
documents. Combined with coarse seriation, MUA is able
to provide better visualization of the cluster
structures. (2) A Mallows-distance-based algorithm for
establishing the relationship between the clustering
results and the ground truth, which serves as the basis
for coloring schemes. Experiments and user studies are
conducted to demonstrate the effectiveness and
efficiency of DClusterE.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2012:TIT,
author = "Shixia Liu and Michelle X. Zhou and Shimei Pan and
Yangqiu Song and Weihong Qian and Weijia Cai and
Xiaoxiao Lian",
title = "{TIARA}: Interactive, Topic-Based Visual Text
Summarization and Analysis",
journal = j-TIST,
volume = "3",
number = "2",
pages = "25:1--25:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089101",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We are building an interactive visual text analysis
tool that aids users in analyzing large collections of
text. Unlike existing work in visual text analytics,
which focuses either on developing sophisticated text
analytic techniques or inventing novel text
visualization metaphors, ours tightly integrates
state-of-the-art text analytics with interactive
visualization to maximize the value of both. In this
article, we present our work from two aspects. We first
introduce an enhanced, LDA-based topic analysis
technique that automatically derives a set of topics to
summarize a collection of documents and their content
evolution over time. To help users understand the
complex summarization results produced by our topic
analysis technique, we then present the design and
development of a time-based visualization of the
results. Furthermore, we provide users with a set of
rich interaction tools that help them further interpret
the visualized results in context and examine the text
collection from multiple perspectives. As a result, our
work offers three unique contributions. First, we
present an enhanced topic modeling technique to provide
users with a time-sensitive and more meaningful text
summary. Second, we develop an effective visual
metaphor to transform abstract and often complex text
summarization results into a comprehensible visual
representation. Third, we offer users flexible visual
interaction tools as alternatives to compensate for the
deficiencies of current text summarization techniques.
We have applied our work to a number of text corpora
and our evaluation shows promise, especially in support
of complex text analyses.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rohrdantz:2012:FBV,
author = "Christian Rohrdantz and Ming C. Hao and Umeshwar Dayal
and Lars-Erik Haug and Daniel A. Keim",
title = "Feature-Based Visual Sentiment Analysis of Text
Document Streams",
journal = j-TIST,
volume = "3",
number = "2",
pages = "26:1--26:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089102",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article describes automatic methods and
interactive visualizations that are tightly coupled
with the goal to enable users to detect interesting
portions of text document streams. In this scenario the
interestingness is derived from the sentiment, temporal
density, and context coherence that comments about
features for different targets (e.g., persons,
institutions, product attributes, topics, etc.) have.
Contributions are made at different stages of the
visual analytics pipeline, including novel ways to
visualize salient temporal accumulations for further
exploration. Moreover, based on the visualization, an
automatic algorithm aims to detect and preselect
interesting time interval patterns for different
features in order to guide analysts. The main target
group for the suggested methods are business analysts
who want to explore time-stamped customer feedback to
detect critical issues. Finally, application case
studies on two different datasets and scenarios are
conducted and an extensive evaluation is provided for
the presented intelligent visual interface for
feature-based sentiment exploration over time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sugiyama:2012:ISS,
author = "Masashi Sugiyama and Qiang Yang",
title = "Introduction to the Special Section on the {2nd Asia
Conference on Machine Learning (ACML 2010)}",
journal = j-TIST,
volume = "3",
number = "2",
pages = "27:1--27:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089103",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hajimirsadeghi:2012:CIL,
author = "Hossein Hajimirsadeghi and Majid Nili Ahmadabadi and
Babak Nadjar Araabi and Hadi Moradi",
title = "Conceptual Imitation Learning in a Human-Robot
Interaction Paradigm",
journal = j-TIST,
volume = "3",
number = "2",
pages = "28:1--28:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089104",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In general, imitation is imprecisely used to address
different levels of social learning from high-level
knowledge transfer to low-level regeneration of motor
commands. However, true imitation is based on
abstraction and conceptualization. This article
presents a model for conceptual imitation through
interaction with the teacher to abstract
spatio-temporal demonstrations based on their
functional meaning. Abstraction, concept acquisition,
and self-organization of proto-symbols are performed
through an incremental and gradual learning algorithm.
In this algorithm, Hidden Markov Models (HMMs) are used
to abstract perceptually similar demonstrations.
However, abstract (relational) concepts emerge as a
collection of HMMs irregularly scattered in the
perceptual space but showing the same functionality.
Performance of the proposed algorithm is evaluated in
two experimental scenarios. The first one is a
human-robot interaction task of imitating signs
produced by hand movements. The second one is a
simulated interactive task of imitating whole body
motion patterns of a humanoid model. Experimental
results show efficiency of our model for concept
extraction, proto-symbol emergence, motion pattern
recognition, prediction, and generation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2012:MRC,
author = "Peipei Li and Xindong Wu and Xuegang Hu",
title = "Mining Recurring Concept Drifts with Limited Labeled
Streaming Data",
journal = j-TIST,
volume = "3",
number = "2",
pages = "29:1--29:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089105",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Tracking recurring concept drifts is a significant
issue for machine learning and data mining that
frequently appears in real-world stream classification
problems. It is a challenge for many streaming
classification algorithms to learn recurring concepts
in a data stream environment with unlabeled data, and
this challenge has received little attention from the
research community. Motivated by this challenge, this
article focuses on the problem of recurring contexts in
streaming environments with limited labeled data. We
propose a semi-supervised classification algorithm for
data streams with REcurring concept Drifts and Limited
LAbeled data, called REDLLA, in which a decision tree
is adopted as the classification model. When growing a
tree, a clustering algorithm based on k -means is
installed to produce concept clusters and unlabeled
data are labeled in the method of majority-class at
leaves. In view of deviations between history and new
concept clusters, potential concept drifts are
distinguished and recurring concepts are maintained.
Extensive studies on both synthetic and real-world data
confirm the advantages of our REDLLA algorithm over
three state-of-the-art online classification algorithms
of CVFDT, DWCDS, and CDRDT and several known online
semi-supervised algorithms, even in the case with more
than 90\% unlabeled data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bifet:2012:ERH,
author = "Albert Bifet and Eibe Frank and Geoff Holmes and
Bernhard Pfahringer",
title = "Ensembles of Restricted {Hoeffding} Trees",
journal = j-TIST,
volume = "3",
number = "2",
pages = "30:1--30:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089106",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The success of simple methods for classification shows
that it is often not necessary to model complex
attribute interactions to obtain good classification
accuracy on practical problems. In this article, we
propose to exploit this phenomenon in the data stream
context by building an ensemble of Hoeffding trees that
are each limited to a small subset of attributes. In
this way, each tree is restricted to model interactions
between attributes in its corresponding subset. Because
it is not known a priori which attribute subsets are
relevant for prediction, we build exhaustive ensembles
that consider all possible attribute subsets of a given
size. As the resulting Hoeffding trees are not all
equally important, we weigh them in a suitable manner
to obtain accurate classifications. This is done by
combining the log-odds of their probability estimates
using sigmoid perceptrons, with one perceptron per
class. We propose a mechanism for setting the
perceptrons' learning rate using the change detection
method for data streams, and also use to reset ensemble
members (i.e., Hoeffding trees) when they no longer
perform well. Our experiments show that the resulting
ensemble classifier outperforms bagging for data
streams in terms of accuracy when both are used in
conjunction with adaptive naive Bayes Hoeffding trees,
at the expense of runtime and memory consumption. We
also show that our stacking method can improve the
performance of a bagged ensemble.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ma:2012:RPC,
author = "Huadong Ma and Chengbin Zeng and Charles X. Ling",
title = "A Reliable People Counting System via Multiple
Cameras",
journal = j-TIST,
volume = "3",
number = "2",
pages = "31:1--31:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089107",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Reliable and real-time people counting is crucial in
many applications. Most previous works can only count
moving people from a single camera, which cannot count
still people or can fail badly when there is a crowd
(i.e., heavy occlusion occurs). In this article, we
build a system for robust and fast people counting
under occlusion through multiple cameras. To improve
the reliability of human detection from a single
camera, we use a dimensionality reduction method on the
multilevel edge and texture features to handle the
large variations in human appearance and poses. To
accelerate the detection speed, we propose a novel
two-stage cascade-of-rejectors method. To handle the
heavy occlusion in crowded scenes, we present a fusion
method with error tolerance to combine human detection
from multiple cameras. To improve the speed and
accuracy of moving people counting, we combine our
multiview fusion detection method with particle
tracking to count the number of people moving in/out
the camera view (`border control'). Extensive
experiments and analyses show that our method
outperforms state-of-the-art techniques in single- and
multicamera datasets for both speed and reliability. We
also design a deployed system for fast and reliable
people (still or moving) counting by using multiple
cameras.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kolomvatsos:2012:FLS,
author = "Kostas Kolomvatsos and Christos Anagnostopoulos and
Stathes Hadjiefthymiades",
title = "A Fuzzy Logic System for Bargaining in Information
Markets",
journal = j-TIST,
volume = "3",
number = "2",
pages = "32:1--32:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089108",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Future Web business models involve virtual
environments where entities interact in order to sell
or buy information goods. Such environments are known
as Information Markets (IMs). Intelligent agents are
used in IMs for representing buyers or information
providers (sellers). We focus on the decisions taken by
the buyer in the purchase negotiation process with
sellers. We propose a reasoning mechanism on the offers
(prices of information goods) issued by sellers based
on fuzzy logic. The buyer's knowledge on the
negotiation process is modeled through fuzzy sets. We
propose a fuzzy inference engine dealing with the
decisions that the buyer takes on each stage of the
negotiation process. The outcome of the proposed
reasoning method indicates whether the buyer should
accept or reject the sellers' offers. Our findings are
very promising for the efficiency of automated
transactions undertaken by intelligent agents.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2012:BMA,
author = "Lixin Shi and Yuhang Zhao and Jie Tang",
title = "Batch Mode Active Learning for Networked Data",
journal = j-TIST,
volume = "3",
number = "2",
pages = "33:1--33:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089109",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We study a novel problem of batch mode active learning
for networked data. In this problem, data instances are
connected with links and their labels are correlated
with each other, and the goal of batch mode active
learning is to exploit the link-based dependencies and
node-specific content information to actively select a
batch of instances to query the user for learning an
accurate model to label unknown instances in the
network. We present three criteria (i.e., minimum
redundancy, maximum uncertainty, and maximum impact) to
quantify the informativeness of a set of instances, and
formalize the batch mode active learning problem as
selecting a set of instances by maximizing an objective
function which combines both link and content
information. As solving the objective function is
NP-hard, we present an efficient algorithm to optimize
the objective function with a bounded approximation
rate. To scale to real large networks, we develop a
parallel implementation of the algorithm. Experimental
results on both synthetic datasets and real-world
datasets demonstrate the effectiveness and efficiency
of our approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shakarian:2012:AGA,
author = "Paulo Shakarian and John P. Dickerson and V. S.
Subrahmanian",
title = "Adversarial Geospatial Abduction Problems",
journal = j-TIST,
volume = "3",
number = "2",
pages = "34:1--34:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089110",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Geospatial Abduction Problems (GAPs) involve the
inference of a set of locations that `best explain' a
given set of locations of observations. For example,
the observations might include locations where a serial
killer committed murders or where insurgents carried
out Improvised Explosive Device (IED) attacks. In both
these cases, we would like to infer a set of locations
that explain the observations, for example, the set of
locations where the serial killer lives/works, and the
set of locations where insurgents locate weapons
caches. However, unlike all past work on abduction,
there is a strong adversarial component to this; an
adversary actively attempts to prevent us from
discovering such locations. We formalize such abduction
problems as a two-player game where both players (an
`agent' and an `adversary') use a probabilistic model
of their opponent (i.e., a mixed strategy). There is
asymmetry as the adversary can choose both the
locations of the observations and the locations of the
explanation, while the agent (i.e., us) tries to
discover these. In this article, we study the problem
from the point of view of both players. We define
reward functions axiomatically to capture the
similarity between two sets of explanations (one
corresponding to the locations chosen by the adversary,
one guessed by the agent). Many different reward
functions can satisfy our axioms. We then formalize the
Optimal Adversary Strategy (OAS) problem and the
Maximal Counter-Adversary strategy (MCA) and show that
both are NP-hard, that their associated counting
complexity problems are \#P-hard, and that MCA has no
fully polynomial approximation scheme unless P=NP. We
show that approximation guarantees are possible for MCA
when the reward function satisfies two simple
properties (zero-starting and monotonicity) which many
natural reward functions satisfy. We develop a mixed
integer linear programming algorithm to solve OAS and
two algorithms to (approximately) compute MCA; the
algorithms yield different approximation guarantees and
one algorithm assumes a monotonic reward function. Our
experiments use real data about IED attacks over a
21-month period in Baghdad. We are able to show that
both the MCA algorithms work well in practice; while
MCA-GREEDY-MONO is both highly accurate and slightly
faster than MCA-LS, MCA-LS (to our surprise) always
completely and correctly maximized the expected benefit
to the agent while running in an acceptable time
period.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2012:LIS,
author = "Xueying Li and Huanhuan Cao and Enhong Chen and Jilei
Tian",
title = "Learning to Infer the Status of Heavy-Duty Sensors for
Energy-Efficient Context-Sensing",
journal = j-TIST,
volume = "3",
number = "2",
pages = "35:1--35:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089111",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the prevalence of smart mobile devices with
multiple sensors, the commercial application of
intelligent context-aware services becomes more and
more attractive. However, limited by the battery
capacity, the energy efficiency of context-sensing is
the bottleneck for the success of context-aware
applications. Though several previous studies for
energy-efficient context-sensing have been reported,
none of them can be applied to multiple types of
high-energy-consuming sensors. Moreover, applying
machine learning technologies to energy-efficient
context-sensing is underexplored too. In this article,
we propose to leverage machine learning technologies
for improving the energy efficiency of multiple
high-energy-consuming context sensors by trading off
the sensing accuracy. To be specific, we try to infer
the status of high-energy-consuming sensors according
to the outputs of software-based sensors and the
physical sensors that are necessary to work all the
time for supporting the basic functions of mobile
devices. If the inference indicates the
high-energy-consuming sensor is in a stable status, we
avoid the unnecessary invocation and instead use the
latest invoked value as the estimation. The
experimental results on real datasets show that the
energy efficiency of GPS sensing and audio-level
sensing are significantly improved by the proposed
approach while the sensing accuracy is over 90\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:AKR,
author = "Weinan Zhang and Dingquan Wang and Gui-Rong Xue and
Hongyuan Zha",
title = "Advertising Keywords Recommendation for Short-Text
{Web} Pages Using {Wikipedia}",
journal = j-TIST,
volume = "3",
number = "2",
pages = "36:1--36:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089112",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Advertising keywords recommendation is an
indispensable component for online advertising with the
keywords selected from the target Web pages used for
contextual advertising or sponsored search. Several
ranking-based algorithms have been proposed for
recommending advertising keywords. However, for most of
them performance is still lacking, especially when
dealing with short-text target Web pages, that is,
those containing insufficient textual information for
ranking. In some cases, short-text Web pages may not
even contain enough keywords for selection. A natural
alternative is then to recommend relevant keywords not
present in the target Web pages. In this article, we
propose a novel algorithm for advertising keywords
recommendation for short-text Web pages by leveraging
the contents of Wikipedia, a user-contributed online
encyclopedia. Wikipedia contains numerous entities with
related entities on a topic linked to each other. Given
a target Web page, we propose to use a content-biased
PageRank on the Wikipedia graph to rank the related
entities. Furthermore, in order to recommend
high-quality advertising keywords, we also add an
advertisement-biased factor into our model. With these
two biases, advertising keywords that are both relevant
to a target Web page and valuable for advertising are
recommended. In our experiments, several
state-of-the-art approaches for keyword recommendation
are compared. The experimental results demonstrate that
our proposed approach produces substantial improvement
in the precision of the top 20 recommended keywords on
short-text Web pages over existing approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhou:2012:LAD,
author = "Ke Zhou and Jing Bai and Hongyuan Zha and Gui-Rong
Xue",
title = "Leveraging Auxiliary Data for Learning to Rank",
journal = j-TIST,
volume = "3",
number = "2",
pages = "37:1--37:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089113",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In learning to rank, both the quality and quantity of
the training data have significant impacts on the
performance of the learned ranking functions. However,
in many applications, there are usually not sufficient
labeled training data for the construction of an
accurate ranking model. It is therefore desirable to
leverage existing training data from other tasks when
learning the ranking function for a particular task, an
important problem which we tackle in this article
utilizing a boosting framework with transfer learning.
In particular, we propose to adaptively learn
transferable representations called super-features from
the training data of both the target task and the
auxiliary task. Those super-features and the
coefficients for combining them are learned in an
iterative stage-wise fashion. Unlike previous transfer
learning methods, the super-features can be adaptively
learned by weak learners from the data. Therefore, the
proposed framework is sufficiently flexible to deal
with complicated common structures among different
learning tasks. We evaluate the performance of the
proposed transfer learning method for two datasets from
the Letor collection and one dataset collected from a
commercial search engine, and we also compare our
methods with several existing transfer learning
methods. Our results demonstrate that the proposed
method can enhance the ranking functions of the target
tasks utilizing the training data from the auxiliary
tasks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Peng:2012:MVC,
author = "Wei Peng and Tong Sun and Shriram Revankar and Tao
Li",
title = "Mining the {``Voice} of the Customer'' for Business
Prioritization",
journal = j-TIST,
volume = "3",
number = "2",
pages = "38:1--38:??",
month = feb,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2089094.2089114",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 16 15:10:10 MDT 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "To gain competitiveness and sustained growth in the
21st century, most businesses are on a mission to
become more customer-centric. In order to succeed in
this endeavor, it is crucial not only to synthesize and
analyze the VOC (the VOice of the Customer) data (i.e.,
the feedbacks or requirements raised by customers), but
also to quickly turn these data into actionable
knowledge. Although there are many technologies being
developed in this complex problem space, most existing
approaches in analyzing customer requests are ad hoc,
time-consuming, error-prone, people-based processes
which hardly scale well as the quantity of customer
information explodes. This often results in the slow
response to customer requests. In this article, in
order to mine VOC to extract useful knowledge for the
best product or service quality, we develop a hybrid
framework that integrates domain knowledge with
data-driven approaches to analyze the semi-structured
customer requests. The framework consists of capturing
functional features, discovering the overlap or
correlation among the features, and identifying the
evolving feature trend by using the knowledge
transformation model. In addition, since understanding
the relative importance of the individual customer
request is very critical and has a direct impact on the
effective prioritization in the development process, we
develop a novel semantic enhanced link-based ranking
(SELRank) algorithm for relatively rating/ranking both
customer requests and products. The framework has been
successfully applied on Xerox Office Group Feature
Enhancement Requirements (XOG FER) datasets to analyze
customer requests.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hua:2012:ISS,
author = "Xian-Sheng Hua and Qi Tian and Alberto {Del Bimbo} and
Ramesh Jain",
title = "Introduction to the {Special Section on Intelligent
Multimedia Systems and Technology Part II}",
journal = j-TIST,
volume = "3",
number = "3",
pages = "39:1--39:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168753",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2012:MRM,
author = "Yi-Hsuan Yang and Homer H. Chen",
title = "Machine Recognition of Music Emotion: a Review",
journal = j-TIST,
volume = "3",
number = "3",
pages = "40:1--40:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168754",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The proliferation of MP3 players and the exploding
amount of digital music content call for novel ways of
music organization and retrieval to meet the
ever-increasing demand for easy and effective
information access. As almost every music piece is
created to convey emotion, music organization and
retrieval by emotion is a reasonable way of accessing
music information. A good deal of effort has been made
in the music information retrieval community to train a
machine to automatically recognize the emotion of a
music signal. A central issue of machine recognition of
music emotion is the conceptualization of emotion and
the associated emotion taxonomy. Different viewpoints
on this issue have led to the proposal of different
ways of emotion annotation, model training, and result
visualization. This article provides a comprehensive
review of the methods that have been proposed for music
emotion recognition. Moreover, as music emotion
recognition is still in its infancy, there are many
open issues. We review the solutions that have been
proposed to address these issues and conclude with
suggestions for further research.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ewerth:2012:RVC,
author = "Ralph Ewerth and Markus M{\"u}hling and Bernd
Freisleben",
title = "Robust Video Content Analysis via Transductive
Learning",
journal = j-TIST,
volume = "3",
number = "3",
pages = "41:1--41:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168755",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Reliable video content analysis is an essential
prerequisite for effective video search. An important
current research question is how to develop robust
video content analysis methods that produce
satisfactory results for a large variety of video
sources, distribution platforms, genres, and content.
The work presented in this article exploits the
observation that the appearance of objects and events
is often related to a particular video sequence,
episode, program, or broadcast. This motivates our idea
of considering the content analysis task for a single
video or episode as a transductive setting: the final
classification model must be optimal for the given
video only, and not in general, as expected for
inductive learning. For this purpose, the unlabeled
video test data have to be used in the learning
process. In this article, a transductive learning
framework for robust video content analysis based on
feature selection and ensemble classification is
presented. In contrast to related transductive
approaches for video analysis (e.g., for concept
detection), the framework is designed in a general
manner and not only for a single task. The proposed
framework is applied to the following video analysis
tasks: shot boundary detection, face recognition,
semantic video retrieval, and semantic indexing of
computer game sequences. Experimental results for
diverse video analysis tasks and large test sets
demonstrate that the proposed transductive framework
improves the robustness of the underlying
state-of-the-art approaches, whereas transductive
support vector machines do not solve particular tasks
in a satisfactory manner.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Suk:2012:VHM,
author = "Myunghoon Suk and Ashok Ramadass and Yohan Jin and B.
Prabhakaran",
title = "Video Human Motion Recognition Using a Knowledge-Based
Hybrid Method Based on a Hidden {Markov} Model",
journal = j-TIST,
volume = "3",
number = "3",
pages = "42:1--42:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168756",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Human motion recognition in video data has several
interesting applications in fields such as gaming,
senior/assisted-living environments, and surveillance.
In these scenarios, we may have to consider adding new
motion classes (i.e., new types of human motions to be
recognized), as well as new training data (e.g., for
handling different type of subjects). Hence, both the
accuracy of classification and training time for the
machine learning algorithms become important
performance parameters in these cases. In this article,
we propose a knowledge-based hybrid (KBH) method that
can compute the probabilities for hidden Markov models
(HMMs) associated with different human motion classes.
This computation is facilitated by appropriately mixing
features from two different media types (3D motion
capture and 2D video). We conducted a variety of
experiments comparing the proposed KBH for HMMs and the
traditional Baum-Welch algorithms. With the advantage
of computing the HMM parameter in a noniterative
manner, the KBH method outperforms the Baum-Welch
algorithm both in terms of accuracy as well as in
reduced training time. Moreover, we show in additional
experiments that the KBH method also outperforms the
linear support vector machine (SVM).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:RVT,
author = "Shengping Zhang and Hongxun Yao and Xin Sun and
Shaohui Liu",
title = "Robust Visual Tracking Using an Effective Appearance
Model Based on Sparse Coding",
journal = j-TIST,
volume = "3",
number = "3",
pages = "43:1--43:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168757",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Intelligent video surveillance is currently one of the
most active research topics in computer vision,
especially when facing the explosion of video data
captured by a large number of surveillance cameras. As
a key step of an intelligent surveillance system,
robust visual tracking is very challenging for computer
vision. However, it is a basic functionality of the
human visual system (HVS). Psychophysical findings have
shown that the receptive fields of simple cells in the
visual cortex can be characterized as being spatially
localized, oriented, and bandpass, and it forms a
sparse, distributed representation of natural images.
In this article, motivated by these findings, we
propose an effective appearance model based on sparse
coding and apply it in visual tracking. Specifically,
we consider the responses of general basis functions
extracted by independent component analysis on a large
set of natural image patches as features and model the
appearance of the tracked target as the probability
distribution of these features. In order to make the
tracker more robust to partial occlusion, camouflage
environments, pose changes, and illumination changes,
we further select features that are related to the
target based on an entropy-gain criterion and ignore
those that are not. The target is finally represented
by the probability distribution of those related
features. The target search is performed by minimizing
the Matusita distance between the distributions of the
target model and a candidate using Newton-style
iterations. The experimental results validate that the
proposed method is more robust and effective than three
state-of-the-art methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ji:2012:CAS,
author = "Rongrong Ji and Hongxun Yao and Qi Tian and Pengfei Xu
and Xiaoshuai Sun and Xianming Liu",
title = "Context-Aware Semi-Local Feature Detector",
journal = j-TIST,
volume = "3",
number = "3",
pages = "44:1--44:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168758",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "How can interest point detectors benefit from
contextual cues? In this articles, we introduce a
context-aware semi-local detector (CASL) framework to
give a systematic answer with three contributions: (1)
We integrate the context of interest points to
recurrently refine their detections. (2) This
integration boosts interest point detectors from the
traditionally local scale to a semi-local scale to
discover more discriminative salient regions. (3) Such
context-aware structure further enables us to bring
forward category learning (usually in the subsequent
recognition phase) into interest point detection to
locate category-aware, meaningful salient regions. Our
CASL detector consists of two phases. The first phase
accumulates multiscale spatial correlations of local
features into a difference of contextual Gaussians
(DoCG) field. DoCG quantizes detector context to
highlight contextually salient regions at a semi-local
scale, which also reveals visual attentions to a
certain extent. The second phase locates contextual
peaks by mean shift search over the DoCG field, which
subsequently integrates contextual cues into feature
description. This phase enables us to integrate
category learning into mean shift search kernels. This
learning-based CASL mechanism produces more
category-aware features, which substantially benefits
the subsequent visual categorization process. We
conducted experiments in image search, object
characterization, and feature detector repeatability
evaluations, which reported superior discriminability
and comparable repeatability to state-of-the-art
works.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Berretti:2012:DFF,
author = "Stefano Berretti and Alberto {Del Bimbo} and Pietro
Pala",
title = "Distinguishing Facial Features for Ethnicity-Based
{$3$D} Face Recognition",
journal = j-TIST,
volume = "3",
number = "3",
pages = "45:1--45:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168759",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Among different approaches for 3D face recognition,
solutions based on local facial characteristics are
very promising, mainly because they can manage facial
expression variations by assigning different weights to
different parts of the face. However, so far, a few
works have investigated the individual relevance that
local features play in 3D face recognition with very
simple solutions applied in the practice. In this
article, a local approach to 3D face recognition is
combined with a feature selection model to study the
relative relevance of different regions of the face for
the purpose of discriminating between different
subjects. The proposed solution is experimented using
facial scans of the Face Recognition Grand Challenge
dataset. Results of the experimentation are two-fold:
they quantitatively demonstrate the assumption that
different regions of the face have different relevance
for face discrimination and also show that the
relevance of facial regions changes for different
ethnic groups.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:GAS,
author = "Ning Zhang and Ling-Yu Duan and Lingfang Li and
Qingming Huang and Jun Du and Wen Gao and Ling Guan",
title = "A Generic Approach for Systematic Analysis of Sports
Videos",
journal = j-TIST,
volume = "3",
number = "3",
pages = "46:1--46:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168760",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Various innovative and original works have been
applied and proposed in the field of sports video
analysis. However, individual works have focused on
sophisticated methodologies with particular sport types
and there has been a lack of scalable and holistic
frameworks in this field. This article proposes a
solution and presents a systematic and generic approach
which is experimented on a relatively large-scale
sports consortia. The system aims at the event
detection scenario of an input video with an orderly
sequential process. Initially, domain
knowledge-independent local descriptors are extracted
homogeneously from the input video sequence. Then the
video representation is created by adopting a
bag-of-visual-words (BoW) model. The video's genre is
first identified by applying the k-nearest neighbor
(k-NN) classifiers on the initially obtained video
representation, and various dissimilarity measures are
assessed and evaluated analytically. Subsequently, an
unsupervised probabilistic latent semantic analysis
(PLSA)-based approach is employed at the same
histogram-based video representation, characterizing
each frame of video sequence into one of four view
groups, namely closed-up-view, mid-view, long-view, and
outer-field-view. Finally, a hidden conditional random
field (HCRF) structured prediction model is utilized
for interesting event detection. From experimental
results, k-NN classifier using KL-divergence
measurement demonstrates the best accuracy at 82.16\%
for genre categorization. Supervised SVM and
unsupervised PLSA have average classification
accuracies at 82.86\% and 68.13\%, respectively. The
HCRF model achieves 92.31\% accuracy using the
unsupervised PLSA based label input, which is
comparable with the supervised SVM based input at an
accuracy of 93.08\%. In general, such a systematic
approach can be widely applied in processing massive
videos generically.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Leung:2012:ISM,
author = "Clement H. C. Leung and Alice W. S. Chan and Alfredo
Milani and Jiming Liu and Yuanxi Li",
title = "Intelligent Social Media Indexing and Sharing Using an
Adaptive Indexing Search Engine",
journal = j-TIST,
volume = "3",
number = "3",
pages = "47:1--47:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168761",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Effective sharing of diverse social media is often
inhibited by limitations in their search and discovery
mechanisms, which are particularly restrictive for
media that do not lend themselves to automatic
processing or indexing. Here, we present the structure
and mechanism of an adaptive search engine which is
designed to overcome such limitations. The basic
framework of the adaptive search engine is to capture
human judgment in the course of normal usage from user
queries in order to develop semantic indexes which link
search terms to media objects semantics. This approach
is particularly effective for the retrieval of
multimedia objects, such as images, sounds, and videos,
where a direct analysis of the object features does not
allow them to be linked to search terms, for example,
nontextual/icon-based search, deep semantic search, or
when search terms are unknown at the time the media
repository is built. An adaptive search architecture is
presented to enable the index to evolve with respect to
user feedback, while a randomized query-processing
technique guarantees avoiding local minima and allows
the meaningful indexing of new media objects and new
terms. The present adaptive search engine allows for
the efficient community creation and updating of social
media indexes, which is able to instill and propagate
deep knowledge into social media concerning the
advanced search and usage of media resources.
Experiments with various relevance distribution
settings have shown efficient convergence of such
indexes, which enable intelligent search and sharing of
social media resources that are otherwise hard to
discover.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chien:2012:ISS,
author = "Steve Chien and Amedeo Cesta",
title = "Introduction to the Special Section on Artificial
Intelligence in Space",
journal = j-TIST,
volume = "3",
number = "3",
pages = "48:1--48:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168762",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wagstaff:2012:DLS,
author = "Kiri L. Wagstaff and Julian Panetta and Adnan Ansar
and Ronald Greeley and Mary Pendleton Hoffer and
Melissa Bunte and Norbert Sch{\"o}rghofer",
title = "Dynamic Landmarking for Surface Feature Identification
and Change Detection",
journal = j-TIST,
volume = "3",
number = "3",
pages = "49:1--49:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168763",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Given the large volume of images being sent back from
remote spacecraft, there is a need for automated
analysis techniques that can quickly identify
interesting features in those images. Feature
identification in individual images and automated
change detection in multiple images of the same target
are valuable for scientific studies and can inform
subsequent target selection. We introduce a new
approach to orbital image analysis called dynamic
landmarking. It focuses on the identification and
comparison of visually salient features in images. We
have evaluated this approach on images collected by
five Mars orbiters. These evaluations were motivated by
three scientific goals: to study fresh impact craters,
dust devil tracks, and dark slope streaks on Mars. In
the process we also detected a different kind of
surface change that may indicate seasonally exposed
bedforms. These experiences also point the way to how
this approach could be used in an onboard setting to
analyze and prioritize data as it is collected.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Estlin:2012:AAS,
author = "Tara A. Estlin and Benjamin J. Bornstein and Daniel M.
Gaines and Robert C. Anderson and David R. Thompson and
Michael Burl and Rebecca Casta{\~n}o and Michele Judd",
title = "{AEGIS} Automated Science Targeting for the {MER
Opportunity Rover}",
journal = j-TIST,
volume = "3",
number = "3",
pages = "50:1--50:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168764",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The Autonomous Exploration for Gathering Increased
Science (AEGIS) system enables automated data
collection by planetary rovers. AEGIS software was
uploaded to the Mars Exploration Rover (MER) mission's
Opportunity rover in December 2009 and has successfully
demonstrated automated onboard targeting based on
scientist-specified objectives. Prior to AEGIS, images
were transmitted from the rover to the operations team
on Earth; scientists manually analyzed the images,
selected geological targets for the rover's
remote-sensing instruments, and then generated a
command sequence to execute the new measurements. AEGIS
represents a significant paradigm shift---by using
onboard data analysis techniques, the AEGIS software
uses scientist input to select high-quality science
targets with no human in the loop. This approach allows
the rover to autonomously select and sequence targeted
observations in an opportunistic fashion, which is
particularly applicable for narrow field-of-view
instruments (such as the MER Mini-TES spectrometer, the
MER Panoramic camera, and the 2011 Mars Science
Laboratory (MSL) ChemCam spectrometer). This article
provides an overview of the AEGIS automated targeting
capability and describes how it is currently being used
onboard the MER mission Opportunity rover.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hayden:2012:UCM,
author = "David S. Hayden and Steve Chien and David R. Thompson
and Rebecca Casta{\~n}o",
title = "Using Clustering and Metric Learning to Improve
Science Return of Remote Sensed Imagery",
journal = j-TIST,
volume = "3",
number = "3",
pages = "51:1--51:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168765",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Current and proposed remote space missions, such as
the proposed aerial exploration of Titan by an aerobot,
often can collect more data than can be communicated
back to Earth. Autonomous selective downlink algorithms
can choose informative subsets of data to improve the
science value of these bandwidth-limited transmissions.
This requires statistical descriptors of the data that
reflect very abstract and subtle distinctions in
science content. We propose a metric learning strategy
that teaches algorithms how best to cluster new data
based on training examples supplied by domain
scientists. We demonstrate that clustering informed by
metric learning produces results that more closely
match multiple scientists' labelings of aerial data
than do clusterings based on random or periodic
sampling. A new metric-learning strategy accommodates
training sets produced by multiple scientists with
different and potentially inconsistent mission
objectives. Our methods are fit for current spacecraft
processors (e.g., RAD750) and would further benefit
from more advanced spacecraft processor architectures,
such as OPERA.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hoi:2012:ISS,
author = "Steven C. H. Hoi and Rong Jin and Jinhui Tang and
Zhi-Hua Zhou",
title = "Introduction to the Special Section on Distance Metric
Learning in Intelligent Systems",
journal = j-TIST,
volume = "3",
number = "3",
pages = "52:1--52:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168766",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhai:2012:MML,
author = "Deming Zhai and Hong Chang and Shiguang Shan and Xilin
Chen and Wen Gao",
title = "Multiview Metric Learning with Global Consistency and
Local Smoothness",
journal = j-TIST,
volume = "3",
number = "3",
pages = "53:1--53:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168767",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In many real-world applications, the same object may
have different observations (or descriptions) from
multiview observation spaces, which are highly related
but sometimes look different from each other.
Conventional metric-learning methods achieve
satisfactory performance on distance metric computation
of data in a single-view observation space, but fail to
handle well data sampled from multiview observation
spaces, especially those with highly nonlinear
structure. To tackle this problem, we propose a new
method called Multiview Metric Learning with Global
consistency and Local smoothness (MVML-GL) under a
semisupervised learning setting, which jointly
considers global consistency and local smoothness. The
basic idea is to reveal the shared latent feature space
of the multiview observations by embodying global
consistency constraints and preserving local geometric
structures. Specifically, this framework is composed of
two main steps. In the first step, we seek a global
consistent shared latent feature space, which not only
preserves the local geometric structure in each space
but also makes those labeled corresponding instances as
close as possible. In the second step, the explicit
mapping functions between the input spaces and the
shared latent space are learned via regularized locally
linear regression. Furthermore, these two steps both
can be solved by convex optimizations in closed form.
Experimental results with application to manifold
alignment on real-world datasets of pose and facial
expression demonstrate the effectiveness of the
proposed method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:TML,
author = "Yu Zhang and Dit-Yan Yeung",
title = "Transfer Metric Learning with Semi-Supervised
Extension",
journal = j-TIST,
volume = "3",
number = "3",
pages = "54:1--54:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168768",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Distance metric learning plays a very crucial role in
many data mining algorithms because the performance of
an algorithm relies heavily on choosing a good metric.
However, the labeled data available in many
applications is scarce, and hence the metrics learned
are often unsatisfactory. In this article, we consider
a transfer-learning setting in which some related
source tasks with labeled data are available to help
the learning of the target task. We first propose a
convex formulation for multitask metric learning by
modeling the task relationships in the form of a task
covariance matrix. Then we regard transfer learning as
a special case of multitask learning and adapt the
formulation of multitask metric learning to the
transfer-learning setting for our method, called
transfer metric learning (TML). In TML, we learn the
metric and the task covariances between the source
tasks and the target task under a unified convex
formulation. To solve the convex optimization problem,
we use an alternating method in which each subproblem
has an efficient solution. Moreover, in many
applications, some unlabeled data is also available in
the target task, and so we propose a semi-supervised
extension of TML called STML to further improve the
generalization performance by exploiting the unlabeled
data based on the manifold assumption. Experimental
results on some commonly used transfer-learning
applications demonstrate the effectiveness of our
method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xu:2012:MLE,
author = "Jun-Ming Xu and Xiaojin Zhu and Timothy T. Rogers",
title = "Metric Learning for Estimating Psychological
Similarities",
journal = j-TIST,
volume = "3",
number = "3",
pages = "55:1--55:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168769",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An important problem in cognitive psychology is to
quantify the perceived similarities between stimuli.
Previous work attempted to address this problem with
multidimensional scaling (MDS) and its variants.
However, there are several shortcomings of the MDS
approaches. We propose Yada, a novel general
metric-learning procedure based on two-alternative
forced-choice behavioral experiments. Our method learns
forward and backward nonlinear mappings between an
objective space in which the stimuli are defined by the
standard feature vector representation and a subjective
space in which the distance between a pair of stimuli
corresponds to their perceived similarity. We conduct
experiments on both synthetic and real human behavioral
datasets to assess the effectiveness of Yada. The
results show that Yada outperforms several standard
embedding and metric-learning algorithms, both in terms
of likelihood and recovery error.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2012:MTP,
author = "Yan-Tao Zheng and Zheng-Jun Zha and Tat-Seng Chua",
title = "Mining Travel Patterns from Geotagged Photos",
journal = j-TIST,
volume = "3",
number = "3",
pages = "56:1--56:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168770",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recently, the phenomenal advent of photo-sharing
services, such as Flickr and Panoramio, have led to
voluminous community-contributed photos with text tags,
timestamps, and geographic references on the Internet.
The photos, together with their time- and
geo-references, become the digital footprints of photo
takers and implicitly document their spatiotemporal
movements. This study aims to leverage the wealth of
these enriched online photos to analyze people's travel
patterns at the local level of a tour destination.
Specifically, we focus our analysis on two aspects: (1)
tourist movement patterns in relation to the regions of
attractions (RoA), and (2) topological characteristics
of travel routes by different tourists. To do so, we
first build a statistically reliable database of travel
paths from a noisy pool of community-contributed
geotagged photos on the Internet. We then investigate
the tourist traffic flow among different RoAs by
exploiting the Markov chain model. Finally, the
topological characteristics of travel routes are
analyzed by performing a sequence clustering on tour
routes. Testings on four major cities demonstrate
promising results of the proposed system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rendle:2012:FML,
author = "Steffen Rendle",
title = "Factorization Machines with {libFM}",
journal = j-TIST,
volume = "3",
number = "3",
pages = "57:1--57:??",
month = may,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2168752.2168771",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:23 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Factorization approaches provide high accuracy in
several important prediction problems, for example,
recommender systems. However, applying factorization
approaches to a new prediction problem is a nontrivial
task and requires a lot of expert knowledge. Typically,
a new model is developed, a learning algorithm is
derived, and the approach has to be implemented.
Factorization machines (FM) are a generic approach
since they can mimic most factorization models just by
feature engineering. This way, factorization machines
combine the generality of feature engineering with the
superiority of factorization models in estimating
interactions between categorical variables of large
domain. libFM is a software implementation for
factorization machines that features stochastic
gradient descent (SGD) and alternating least-squares
(ALS) optimization, as well as Bayesian inference using
Markov Chain Monto Carlo (MCMC). This article
summarizes the recent research on factorization
machines both in terms of modeling and learning,
provides extensions for the ALS and MCMC algorithms,
and describes the software tool libFM.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gabrilovich:2012:ISS,
author = "Evgeniy Gabrilovich and Zhong Su and Jie Tang",
title = "Introduction to the {Special Section on Computational
Models of Collective Intelligence in the Social Web}",
journal = j-TIST,
volume = "3",
number = "4",
pages = "58:1--58:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337543",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Herdagdelen:2012:BGP,
author = "Ama{\c{c}} Herdagdelen and Marco Baroni",
title = "Bootstrapping a Game with a Purpose for Commonsense
Collection",
journal = j-TIST,
volume = "3",
number = "4",
pages = "59:1--59:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337544",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Text mining has been very successful in extracting
huge amounts of commonsense knowledge from data, but
the extracted knowledge tends to be extremely noisy.
Manual construction of knowledge repositories, on the
other hand, tends to produce high-quality data in very
small amounts. We propose an architecture to combine
the best of both worlds: A game with a purpose that
induces humans to clean up data automatically extracted
by text mining. First, a text miner trained on a set of
known commonsense facts harvests many more candidate
facts from corpora. Then, a simple
slot-machine-with-a-purpose game presents these
candidate facts to the players for verification by
playing. As a result, a new dataset of high precision
commonsense knowledge is created. This combined
architecture is able to produce significantly better
commonsense facts than the state-of-the-art text miner
alone. Furthermore, we report that bootstrapping (i.e.,
training the text miner on the output of the game)
improves the subsequent performance of the text
miner.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Carmel:2012:FBT,
author = "David Carmel and Erel Uziel and Ido Guy and Yosi Mass
and Haggai Roitman",
title = "Folksonomy-Based Term Extraction for Word Cloud
Generation",
journal = j-TIST,
volume = "3",
number = "4",
pages = "60:1--60:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337545",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this work we study the task of term extraction for
word cloud generation in sparsely tagged domains, in
which manual tags are scarce. We present a
folksonomy-based term extraction method, called
tag-boost, which boosts terms that are frequently used
by the public to tag content. Our experiments with
tag-boost based term extraction over different domains
demonstrate tremendous improvement in word cloud
quality, as reflected by the agreement between manual
tags of the testing items and the cloud's terms
extracted from the items' content. Moreover, our
results demonstrate the high robustness of this
approach, as compared to alternative cloud generation
methods that exhibit a high sensitivity to data
sparseness. Additionally, we show that tag-boost can be
effectively applied even in nontagged domains, by using
an external rich folksonomy borrowed from a well-tagged
domain.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2012:IOS,
author = "Guan Wang and Sihong Xie and Bing Liu and Philip S.
Yu",
title = "Identify Online Store Review Spammers via Social
Review Graph",
journal = j-TIST,
volume = "3",
number = "4",
pages = "61:1--61:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337546",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online shopping reviews provide valuable information
for customers to compare the quality of products, store
services, and many other aspects of future purchases.
However, spammers are joining this community trying to
mislead consumers by writing fake or unfair reviews to
confuse the consumers. Previous attempts have used
reviewers' behaviors such as text similarity and rating
patterns, to detect spammers. These studies are able to
identify certain types of spammers, for instance, those
who post many similar reviews about one target.
However, in reality, there are other kinds of spammers
who can manipulate their behaviors to act just like
normal reviewers, and thus cannot be detected by the
available techniques. In this article, we propose a
novel concept of review graph to capture the
relationships among all reviewers, reviews and stores
that the reviewers have reviewed as a heterogeneous
graph. We explore how interactions between nodes in
this graph could reveal the cause of spam and propose
an iterative computation model to identify suspicious
reviewers. In the review graph, we have three kinds of
nodes, namely, reviewer, review, and store. We capture
their relationships by introducing three fundamental
concepts, the trustiness of reviewers, the honesty of
reviews, and the reliability of stores, and identifying
their interrelationships: a reviewer is more
trustworthy if the person has written more honesty
reviews; a store is more reliable if it has more
positive reviews from trustworthy reviewers; and a
review is more honest if many other honest reviews
support it. This is the first time such intricate
relationships have been identified for spam detection
and captured in a graph model. We further develop an
effective computation method based on the proposed
graph model. Different from any existing approaches, we
do not use an review text information. Our model is
thus complementary to existing approaches and able to
find more difficult and subtle spamming activities,
which are agreed upon by human judges after they
evaluate our results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lerman:2012:USM,
author = "Kristina Lerman and Tad Hogg",
title = "Using Stochastic Models to Describe and Predict Social
Dynamics of {Web} Users",
journal = j-TIST,
volume = "3",
number = "4",
pages = "62:1--62:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337547",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The popularity of content in social media is unequally
distributed, with some items receiving a
disproportionate share of attention from users.
Predicting which newly-submitted items will become
popular is critically important for both the hosts of
social media content and its consumers. Accurate and
timely prediction would enable hosts to maximize
revenue through differential pricing for access to
content or ad placement. Prediction would also give
consumers an important tool for filtering the content.
Predicting the popularity of content in social media is
challenging due to the complex interactions between
content quality and how the social media site
highlights its content. Moreover, most social media
sites selectively present content that has been highly
rated by similar users, whose similarity is indicated
implicitly by their behavior or explicitly by links in
a social network. While these factors make it difficult
to predict popularity a priori, stochastic models of
user behavior on these sites can allow predicting
popularity based on early user reactions to new
content. By incorporating the various mechanisms
through which web sites display content, such models
improve on predictions that are based on simply
extrapolating from the early votes. Specifically, for
one such site, the news aggregator Digg, we show how a
stochastic model distinguishes the effect of the
increased visibility due to the network from how
interested users are in the content. We find a wide
range of interest, distinguishing stories primarily of
interest to users in the network (``niche interests'')
from those of more general interest to the user
community. This distinction is useful for predicting a
story's eventual popularity from users' early reactions
to the story.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yin:2012:LCT,
author = "Zhijun Yin and Liangliang Cao and Quanquan Gu and
Jiawei Han",
title = "Latent Community Topic Analysis: Integration of
Community Discovery with Topic Modeling",
journal = j-TIST,
volume = "3",
number = "4",
pages = "63:1--63:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337548",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article studies the problem of latent community
topic analysis in text-associated graphs. With the
development of social media, a lot of user-generated
content is available with user networks. Along with
rich information in networks, user graphs can be
extended with text information associated with nodes.
Topic modeling is a classic problem in text mining and
it is interesting to discover the latent topics in
text-associated graphs. Different from traditional
topic modeling methods considering links, we
incorporate community discovery into topic analysis in
text-associated graphs to guarantee the topical
coherence in the communities so that users in the same
community are closely linked to each other and share
common latent topics. We handle topic modeling and
community discovery in the same framework. In our model
we separate the concepts of community and topic, so one
community can correspond to multiple topics and
multiple communities can share the same topic. We
compare different methods and perform extensive
experiments on two real datasets. The results confirm
our hypothesis that topics could help understand
community structure, while community structure could
help model topics.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sizov:2012:LGS,
author = "Sergej Sizov",
title = "Latent Geospatial Semantics of Social Media",
journal = j-TIST,
volume = "3",
number = "4",
pages = "64:1--64:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337549",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multimodal understanding of shared content is an
important success factor for many Web 2.0 applications
and platforms. This article addresses the fundamental
question of geo-spatial awareness in social media
applications. In this context, we introduce an approach
for improved characterization of social media by
combining text features (e.g., tags as a prominent
example of short, unstructured text labels) with
spatial knowledge (e.g., geotags, coordinates of
images, and videos). Our model-based framework GeoFolk
combines these two aspects in order to construct better
algorithms for content management, retrieval, and
sharing. We demonstrate in systematic studies the
benefits of this combination for a broad spectrum of
scenarios related to social media: recommender systems,
automatic content organization and filtering, and event
detection. Furthermore, we establish a simple and
technically sound model that can be seen as a reference
baseline for future research in the field of geotagged
social media.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cortizo:2012:ISS,
author = "Jos{\'e} Carlos Cortizo and Francisco Carrero and
Iv{\'a}n Cantador and Jos{\'e} Antonio Troyano and
Paolo Rosso",
title = "Introduction to the Special Section on Search and
Mining User-Generated Content",
journal = j-TIST,
volume = "3",
number = "4",
pages = "65:1--65:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337550",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The primary goal of this special section of ACM
Transactions on Intelligent Systems and Technology is
to foster research in the interplay between Social
Media, Data/Opinion Mining and Search, aiming to
reflect the actual developments in technologies that
exploit user-generated content.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Paltoglou:2012:TMD,
author = "Georgios Paltoglou and Mike Thelwall",
title = "{Twitter}, {MySpace}, {Digg}: Unsupervised Sentiment
Analysis in Social Media",
journal = j-TIST,
volume = "3",
number = "4",
pages = "66:1--66:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337551",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Sentiment analysis is a growing area of research with
significant applications in both industry and academia.
Most of the proposed solutions are centered around
supervised, machine learning approaches and
review-oriented datasets. In this article, we focus on
the more common informal textual communication on the
Web, such as online discussions, tweets and social
network comments and propose an intuitive, less
domain-specific, unsupervised, lexicon-based approach
that estimates the level of emotional intensity
contained in text in order to make a prediction. Our
approach can be applied to, and is tested in, two
different but complementary contexts: subjectivity
detection and polarity classification. Extensive
experiments were carried on three real-world datasets,
extracted from online social Web sites and annotated by
human evaluators, against state-of-the-art supervised
approaches. The results demonstrate that the proposed
algorithm, even though unsupervised, outperforms
machine learning solutions in the majority of cases,
overall presenting a very robust and reliable solution
for sentiment analysis of informal communication on the
Web.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Trivedi:2012:LSB,
author = "Anusua Trivedi and Piyush Rai and Hal {Daum{\'e} III}
and Scott L. Duvall",
title = "Leveraging Social Bookmarks from Partially Tagged
Corpus for Improved {Web} Page Clustering",
journal = j-TIST,
volume = "3",
number = "4",
pages = "67:1--67:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337552",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Automatic clustering of Web pages helps a number of
information retrieval tasks, such as improving user
interfaces, collection clustering, introducing
diversity in search results, etc. Typically, Web page
clustering algorithms use only features extracted from
the page-text. However, the advent of
social-bookmarking Web sites, such as StumbleUpon.com
and Delicious.com, has led to a huge amount of
user-generated content such as the social tag
information that is associated with the Web pages. In
this article, we present a subspace based feature
extraction approach that leverages the social tag
information to complement the page-contents of a Web
page for extracting beter features, with the goal of
improved clustering performance. In our approach, we
consider page-text and tags as two separate views of
the data, and learn a shared subspace that maximizes
the correlation between the two views. Any clustering
algorithm can then be applied in this subspace. We then
present an extension that allows our approach to be
applicable even if the Web page corpus is only
partially tagged, that is, when the social tags are
present for not all, but only for a small number of Web
pages. We compare our subspace based approach with a
number of baselines that use tag information in various
other ways, and show that the subspace based approach
leads to improved performance on the Web page
clustering task. We also discuss some possible future
work including an active learning extension that can
help in choosing which Web pages to get tags for, if we
only can get the social tags for only a small number of
Web pages.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Potthast:2012:IRC,
author = "Martin Potthast and Benno Stein and Fabian Loose and
Steffen Becker",
title = "Information Retrieval in the {Commentsphere}",
journal = j-TIST,
volume = "3",
number = "4",
pages = "68:1--68:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337553",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article studies information retrieval tasks
related to Web comments. Prerequisite of such a study
and a main contribution of the article is a unifying
survey of the research field. We identify the most
important retrieval tasks related to comments, namely
filtering, ranking, and summarization. Within these
tasks, we distinguish two paradigms according to which
comments are utilized and which we designate as
comment-targeting and comment-exploiting. Within the
first paradigm, the comments themselves form the
retrieval targets. Within the second paradigm, the
commented items form the retrieval targets (i.e.,
comments are used as an additional information source
to improve the retrieval performance for the commented
items). We report on four case studies to demonstrate
the exploration of the commentsphere under information
retrieval aspects: comment filtering, comment ranking,
comment summarization and cross-media retrieval. The
first three studies deal primarily with
comment-targeting retrieval, while the last one deals
with comment-exploiting retrieval. Throughout the
article, connections to information retrieval research
are pointed out.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Carmel:2012:RBN,
author = "David Carmel and Haggai Roitman and Elad Yom-Tov",
title = "On the Relationship between Novelty and Popularity of
User-Generated Content",
journal = j-TIST,
volume = "3",
number = "4",
pages = "69:1--69:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337554",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This work deals with the task of predicting the
popularity of user-generated content. We demonstrate
how the novelty of newly published content plays an
important role in affecting its popularity. More
specifically, we study three dimensions of novelty. The
first one, termed contemporaneous novelty, models the
relative novelty embedded in a new post with respect to
contemporary content that was generated by others. The
second type of novelty, termed self novelty, models the
relative novelty with respect to the user's own
contribution history. The third type of novelty, termed
discussion novelty, relates to the novelty of the
comments associated by readers with respect to the post
content. We demonstrate the contribution of the new
novelty measures to estimating blog-post popularity by
predicting the number of comments expected for a fresh
post. We further demonstrate how novelty based measures
can be utilized for predicting the citation volume of
academic papers.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2012:ERQ,
author = "Xiaonan Li and Chengkai Li and Cong Yu",
title = "Entity-Relationship Queries over {Wikipedia}",
journal = j-TIST,
volume = "3",
number = "4",
pages = "70:1--70:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337555",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Wikipedia is the largest user-generated knowledge
base. We propose a structured query mechanism,
entity-relationship query, for searching entities in
the Wikipedia corpus by their properties and
interrelationships. An entity-relationship query
consists of multiple predicates on desired entities.
The semantics of each predicate is specified with
keywords. Entity-relationship query searches entities
directly over text instead of preextracted structured
data stores. This characteristic brings two benefits:
(1) Query semantics can be intuitively expressed by
keywords; (2) It only requires rudimentary entity
annotation, which is simpler than explicitly extracting
and reasoning about complex semantic information before
query-time. We present a ranking framework for general
entity-relationship queries and a position-based
Bounded Cumulative Model (BCM) for accurate ranking of
query answers. We also explore various weighting
schemes for further improving the accuracy of BCM. We
test our ideas on a 2008 version of Wikipedia using a
collection of 45 queries pooled from INEX entity
ranking track and our own crafted queries. Experiments
show that the ranking and weighting schemes are both
effective, particularly on multipredicate queries.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2012:EFW,
author = "Haofen Wang and Linyun Fu and Wei Jin and Yong Yu",
title = "{EachWiki}: Facilitating Wiki Authoring by Annotation
Suggestion",
journal = j-TIST,
volume = "3",
number = "4",
pages = "71:1--71:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337556",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Wikipedia, one of the best-known wikis and the world's
largest free online encyclopedia, has embraced the
power of collaborative editing to harness collective
intelligence. However, using such a wiki to create
high-quality articles is not as easy as people imagine,
given for instance the difficulty of reusing knowledge
already available in Wikipedia. As a result, the heavy
burden of upbuilding and maintaining the ever-growing
online encyclopedia still rests on a small group of
people. In this article, we aim at facilitating wiki
authoring by providing annotation recommendations, thus
lightening the burden of both contributors and
administrators. We leverage the collective wisdom of
the users by exploiting Semantic Web technologies with
Wikipedia data and adopt a unified algorithm to support
link, category, and semantic relation recommendation. A
prototype system named EachWiki is proposed and
evaluated. The experimental results show that it has
achieved considerable improvements in terms of
effectiveness, efficiency and usability. The proposed
approach can also be applied to other wiki-based
collaborative editing systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lampos:2012:NES,
author = "Vasileios Lampos and Nello Cristianini",
title = "Nowcasting Events from the Social {Web} with
Statistical Learning",
journal = j-TIST,
volume = "3",
number = "4",
pages = "72:1--72:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337557",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present a general methodology for inferring the
occurrence and magnitude of an event or phenomenon by
exploring the rich amount of unstructured textual
information on the social part of the Web. Having
geo-tagged user posts on the microblogging service of
Twitter as our input data, we investigate two case
studies. The first consists of a benchmark problem,
where actual levels of rainfall in a given location and
time are inferred from the content of tweets. The
second one is a real-life task, where we infer regional
Influenza-like Illness rates in the effort of detecting
timely an emerging epidemic disease. Our analysis
builds on a statistical learning framework, which
performs sparse learning via the bootstrapped version
of LASSO to select a consistent subset of textual
features from a large amount of candidates. In both
case studies, selected features indicate close semantic
correlation with the target topics and inference,
conducted by regression, has a significant performance,
especially given the short length --approximately one
year-- of Twitter's data time series.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2012:RUI,
author = "Xuning Tang and Christopher C. Yang",
title = "Ranking User Influence in Healthcare Social Media",
journal = j-TIST,
volume = "3",
number = "4",
pages = "73:1--73:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337558",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Due to the revolutionary development of Web 2.0
technology, individual users have become major
contributors of Web content in online social media. In
light of the growing activities, how to measure a
user's influence to other users in online social media
becomes increasingly important. This research need is
urgent especially in the online healthcare community
since positive influence can be beneficial while
negative influence may cause-negative impact on other
users of the same community. In this article, a
research framework was proposed to study user influence
within the online healthcare community. We proposed a
new approach to incorporate users' reply relationship,
conversation content and response immediacy which
capture both explicit and implicit interaction between
users to identify influential users of online
healthcare community. A weighted social network is
developed to represent the influence between users. We
tested our proposed techniques thoroughly on two
medical support forums. Two algorithms UserRank and
Weighted in-degree are benchmarked with PageRank and
in-degree. Experiment results demonstrated the validity
and effectiveness of our proposed approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Strohmaier:2012:EFI,
author = "Markus Strohmaier and Denis Helic and Dominik Benz and
Christian K{\"o}rner and Roman Kern",
title = "Evaluation of Folksonomy Induction Algorithms",
journal = j-TIST,
volume = "3",
number = "4",
pages = "74:1--74:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337559",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Algorithms for constructing hierarchical structures
from user-generated metadata have caught the interest
of the academic community in recent years. In social
tagging systems, the output of these algorithms is
usually referred to as folksonomies (from
folk-generated taxonomies). Evaluation of folksonomies
and folksonomy induction algorithms is a challenging
issue complicated by the lack of golden standards, lack
of comprehensive methods and tools as well as a lack of
research and empirical/simulation studies applying
these methods. In this article, we report results from
a broad comparative study of state-of-the-art
folksonomy induction algorithms that we have applied
and evaluated in the context of five social tagging
systems. In addition to adopting semantic evaluation
techniques, we present and adopt a new technique that
can be used to evaluate the usefulness of folksonomies
for navigation. Our work sheds new light on the
properties and characteristics of state-of-the-art
folksonomy induction algorithms and introduces a new
pragmatic approach to folksonomy evaluation, while at
the same time identifying some important limitations
and challenges of folksonomy evaluation. Our results
show that folksonomy induction algorithms specifically
developed to capture intuitions of social tagging
systems outperform traditional hierarchical clustering
techniques. To the best of our knowledge, this work
represents the largest and most comprehensive
evaluation study of state-of-the-art folksonomy
induction algorithms to date.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2012:EAL,
author = "Xiaoqin Shelley Zhang and Bhavesh Shrestha and
Sungwook Yoon and Subbarao Kambhampati and Phillip
DiBona and Jinhong K. Guo and Daniel McFarlane and
Martin O. Hofmann and Kenneth Whitebread and Darren
Scott Appling and Elizabeth T. Whitaker and Ethan B.
Trewhitt and Li Ding and James R. Michaelis and Deborah
L. McGuinness and James A. Hendler and Janardhan Rao
Doppa and Charles Parker and Thomas G. Dietterich and
Prasad Tadepalli and Weng-Keen Wong and Derek Green and
Anton Rebguns and Diana Spears and Ugur Kuter and Geoff
Levine and Gerald DeJong and Reid L. MacTavish and
Santiago Onta{\~n}{\'o}n and Jainarayan Radhakrishnan
and Ashwin Ram and Hala Mostafa and Huzaifa Zafar and
Chongjie Zhang and Daniel Corkill and Victor Lesser and
Zhexuan Song",
title = "An Ensemble Architecture for Learning Complex
Problem-Solving Techniques from Demonstration",
journal = j-TIST,
volume = "3",
number = "4",
pages = "75:1--75:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337560",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present a novel ensemble architecture for learning
problem-solving techniques from a very small number of
expert solutions and demonstrate its effectiveness in a
complex real-world domain. The key feature of our
``Generalized Integrated Learning Architecture'' (GILA)
is a set of heterogeneous independent learning and
reasoning (ILR) components, coordinated by a central
meta-reasoning executive (MRE). The ILRs are weakly
coupled in the sense that all coordination during
learning and performance happens through the MRE. Each
ILR learns independently from a small number of expert
demonstrations of a complex task. During performance,
each ILR proposes partial solutions to subproblems
posed by the MRE, which are then selected from and
pieced together by the MRE to produce a complete
solution. The heterogeneity of the learner-reasoners
allows both learning and problem solving to be more
effective because their abilities and biases are
complementary and synergistic. We describe the
application of this novel learning and problem solving
architecture to the domain of airspace management,
where multiple requests for the use of airspaces need
to be deconflicted, reconciled, and managed
automatically. Formal evaluations show that our system
performs as well as or better than humans after
learning from the same training data. Furthermore, GILA
outperforms any individual ILR run in isolation, thus
demonstrating the power of the ensemble architecture
for learning and problem solving.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "75",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2012:LCR,
author = "Zhenxing Wang and Laiwan Chan",
title = "Learning Causal Relations in Multivariate Time Series
Data",
journal = j-TIST,
volume = "3",
number = "4",
pages = "76:1--76:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337561",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many applications naturally involve time series data
and the vector autoregression (VAR), and the structural
VAR (SVAR) are dominant tools to investigate relations
between variables in time series. In the first part of
this work, we show that the SVAR method is incapable of
identifying contemporaneous causal relations for
Gaussian process. In addition, least squares estimators
become unreliable when the scales of the problems are
large and observations are limited. In the remaining
part, we propose an approach to apply Bayesian network
learning algorithms to identify SVARs from time series
data in order to capture both temporal and
contemporaneous causal relations, and avoid high-order
statistical tests. The difficulty of applying Bayesian
network learning algorithms to time series is that the
sizes of the networks corresponding to time series tend
to be large, and high-order statistical tests are
required by Bayesian network learning algorithms in
this case. To overcome the difficulty, we show that the
search space of conditioning sets d-separating two
vertices should be a subset of the Markov blankets.
Based on this fact, we propose an algorithm enabling us
to learn Bayesian networks locally, and make the
largest order of statistical tests independent of the
scales of the problems. Empirical results show that our
algorithm outperforms existing methods in terms of both
efficiency and accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "76",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mandrake:2012:SSD,
author = "Lukas Mandrake and Umaa Rebbapragada and Kiri L.
Wagstaff and David Thompson and Steve Chien and Daniel
Tran and Robert T. Pappalardo and Damhnait Gleeson and
Rebecca Casta{\~n}o",
title = "Surface Sulfur Detection via Remote Sensing and
Onboard Classification",
journal = j-TIST,
volume = "3",
number = "4",
pages = "77:1--77:??",
month = sep,
year = "2012",
CODEN = "????",
DOI = "https://doi.org/10.1145/2337542.2337562",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Nov 6 18:47:26 MST 2012",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Orbital remote sensing provides a powerful way to
efficiently survey targets such as the Earth and other
planets and moons for features of interest. One such
feature of astrobiological relevance is the presence of
surface sulfur deposits. These deposits have been
observed to be associated with microbial activity at
the Borup Fiord glacial springs in Canada, a location
that may provide an analogue to other icy environments
such as Europa. This article evaluates automated
classifiers for detecting sulfur in remote sensing
observations by the hyperion spectrometer on the EO-1
spacecraft. We determined that a data-driven machine
learning solution was needed because the sulfur could
not be detected by simply matching observations to
sulfur lab spectra. We also evaluated several methods
(manual and automated) for identifying the most
relevant attributes (spectral wavelengths) needed for
successful sulfur detection. Our findings include (1)
the Borup Fiord sulfur deposits were best modeled as
containing two sub-populations: sulfur on ice and
sulfur on rock; (2) as expected, classifiers using
Gaussian kernels outperformed those based on linear
kernels, and should be adopted when onboard
computational constraints permit; and (3) Recursive
Feature Elimination selected sensible and effective
features for use in the computationally constrained
environment onboard EO-1. This study helped guide the
selection of algorithm parameters and configuration for
the classification system currently operational on
EO-1. Finally, we discuss implications for a similar
onboard classification system for a future Europa
orbiter.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "77",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{King:2013:ISS,
author = "Irwin King and Wolfgang Nejdl",
title = "Introduction to the special section on {Twitter} and
microblogging services",
journal = j-TIST,
volume = "4",
number = "1",
pages = "1:1--1:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414426",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cheng:2013:CDF,
author = "Zhiyuan Cheng and James Caverlee and Kyumin Lee",
title = "A content-driven framework for geolocating microblog
users",
journal = j-TIST,
volume = "4",
number = "1",
pages = "2:1--2:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414427",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Highly dynamic real-time microblog systems have
already published petabytes of real-time human sensor
data in the form of status updates. However, the lack
of user adoption of geo-based features per user or per
post signals that the promise of microblog services as
location-based sensing systems may have only limited
reach and impact. Thus, in this article, we propose and
evaluate a probabilistic framework for estimating a
microblog user's location based purely on the content
of the user's posts. Our framework can overcome the
sparsity of geo-enabled features in these services and
bring augmented scope and breadth to emerging
location-based personalized information services. Three
of the key features of the proposed approach are: (i)
its reliance purely on publicly available content; (ii)
a classification component for automatically
identifying words in posts with a strong local
geo-scope; and (iii) a lattice-based neighborhood
smoothing model for refining a user's location
estimate. On average we find that the location
estimates converge quickly, placing 51\% of users
within 100 miles of their actual location.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2013:NER,
author = "Xiaohua Liu and Furu Wei and Shaodian Zhang and Ming
Zhou",
title = "Named entity recognition for tweets",
journal = j-TIST,
volume = "4",
number = "1",
pages = "3:1--3:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414428",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Two main challenges of Named Entity Recognition (NER)
for tweets are the insufficient information in a tweet
and the lack of training data. We propose a novel
method consisting of three core elements: (1)
normalization of tweets; (2) combination of a K-Nearest
Neighbors (KNN) classifier with a linear Conditional
Random Fields (CRF) model; and (3) semisupervised
learning framework. The tweet normalization
preprocessing corrects common ill-formed words using a
global linear model. The KNN-based classifier conducts
prelabeling to collect global coarse evidence across
tweets while the CRF model conducts sequential labeling
to capture fine-grained information encoded in a tweet.
The semisupervised learning plus the gazetteers
alleviate the lack of training data. Extensive
experiments show the advantages of our method over the
baselines as well as the effectiveness of
normalization, KNN, and semisupervised learning.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chang:2013:IRR,
author = "Yi Chang and Anlei Dong and Pranam Kolari and Ruiqiang
Zhang and Yoshiyuki Inagaki and Fernanodo Diaz and
Hongyuan Zha and Yan Liu",
title = "Improving recency ranking using {Twitter} data",
journal = j-TIST,
volume = "4",
number = "1",
pages = "4:1--4:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414429",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In Web search and vertical search, recency ranking
refers to retrieving and ranking documents by both
relevance and freshness. As impoverished in-links and
click information is the biggest challenge for recency
ranking, we advocate the use of Twitter data to address
the challenge in this article. We propose a method to
utilize Twitter TinyURL to detect fresh and
high-quality documents, and leverage Twitter data to
generate novel and effective features for ranking. The
empirical experiments demonstrate that the proposed
approach effectively improves a commercial search
engine for both Web search ranking and tweet vertical
ranking.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Han:2013:LNS,
author = "Bo Han and Paul Cook and Timothy Baldwin",
title = "Lexical normalization for social media text",
journal = j-TIST,
volume = "4",
number = "1",
pages = "5:1--5:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414430",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Twitter provides access to large volumes of data in
real time, but is notoriously noisy, hampering its
utility for NLP. In this article, we target
out-of-vocabulary words in short text messages and
propose a method for identifying and normalizing
lexical variants. Our method uses a classifier to
detect lexical variants, and generates correction
candidates based on morphophonemic similarity. Both
word similarity and context are then exploited to
select the most probable correction candidate for the
word. The proposed method doesn't require any
annotations, and achieves state-of-the-art performance
over an SMS corpus and a novel dataset based on
Twitter.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shen:2013:RUT,
author = "Keyi Shen and Jianmin Wu and Ya Zhang and Yiping Han
and Xiaokang Yang and Li Song and Xiao Gu",
title = "Reorder user's tweets",
journal = j-TIST,
volume = "4",
number = "1",
pages = "6:1--6:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414431",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Twitter displays the tweets a user received in a
reversed chronological order, which is not always the
best choice. As Twitter is full of messages of very
different qualities, many informative or relevant
tweets might be flooded or displayed at the bottom
while some nonsense buzzes might be ranked higher. In
this work, we present a supervised learning method for
personalized tweets reordering based on user interests.
User activities on Twitter, in terms of tweeting,
retweeting, and replying, are leveraged to obtain the
training data for reordering models. Through exploring
a rich set of social and personalized features, we
model the relevance of tweets by minimizing the
pairwise loss of relevant and irrelevant tweets. The
tweets are then reordered according to the predicted
relevance scores. Experimental results with real
Twitter user activities demonstrated the effectiveness
of our method. The new method achieved above 30\%
accuracy gain compared with the default ordering in
Twitter based on time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guy:2013:ISS,
author = "Ido Guy and Li Chen and Michelle X. Zhou",
title = "Introduction to the special section on social
recommender systems",
journal = j-TIST,
volume = "4",
number = "1",
pages = "7:1--7:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414432",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Quijano-Sanchez:2013:SFG,
author = "Lara Quijano-Sanchez and Juan A. Recio-Garcia and
Belen Diaz-Agudo and Guillermo Jimenez-Diaz",
title = "Social factors in group recommender systems",
journal = j-TIST,
volume = "4",
number = "1",
pages = "8:1--8:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414433",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article we review the existing techniques in
group recommender systems and we propose some
improvement based on the study of the different
individual behaviors when carrying out a
decision-making process. Our method includes an
analysis of group personality composition and trust
between each group member to improve the accuracy of
group recommenders. This way we simulate the
argumentation process followed by groups of people when
agreeing on a common activity in a more realistic way.
Moreover, we reflect how they expect the system to
behave in a long term recommendation process. This is
achieved by including a memory of past recommendations
that increases the satisfaction of users whose
preferences have not been taken into account in
previous recommendations.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2013:GVR,
author = "Weishi Zhang and Guiguang Ding and Li Chen and
Chunping Li and Chengbo Zhang",
title = "Generating virtual ratings from {Chinese} reviews to
augment online recommendations",
journal = j-TIST,
volume = "4",
number = "1",
pages = "9:1--9:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414434",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative filtering (CF) recommenders based on
User-Item rating matrix as explicitly obtained from end
users have recently appeared promising in recommender
systems. However, User-Item rating matrix is not always
available or very sparse in some web applications,
which has critical impact to the application of CF
recommenders. In this article we aim to enhance the
online recommender system by fusing virtual ratings as
derived from user reviews. Specifically, taking into
account of Chinese reviews' characteristics, we propose
to fuse the self-supervised emotion-integrated
sentiment classification results into CF recommenders,
by which the User-Item Rating Matrix can be inferred by
decomposing item reviews that users gave to the items.
The main advantage of this approach is that it can
extend CF recommenders to some web applications without
user rating information. In the experiments, we have
first identified the self-supervised sentiment
classification's higher precision and recall by
comparing it with traditional classification methods.
Furthermore, the classification results, as behaving as
virtual ratings, were incorporated into both user-based
and item-based CF algorithms. We have also conducted an
experiment to evaluate the proximity between the
virtual and real ratings and clarified the
effectiveness of the virtual ratings. The experimental
results demonstrated the significant impact of virtual
ratings on increasing system's recommendation accuracy
in different data conditions (i.e., conditions with
real ratings and without).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Biancalana:2013:ASR,
author = "Claudio Biancalana and Fabio Gasparetti and Alessandro
Micarelli and Giuseppe Sansonetti",
title = "An approach to social recommendation for context-aware
mobile services",
journal = j-TIST,
volume = "4",
number = "1",
pages = "10:1--10:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414435",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Nowadays, several location-based services (LBSs) allow
their users to take advantage of information from the
Web about points of interest (POIs) such as cultural
events or restaurants. To the best of our knowledge,
however, none of these provides information taking into
account user preferences, or other elements, in
addition to location, that contribute to define the
context of use. The provided suggestions do not
consider, for example, time, day of week, weather, user
activity or means of transport. This article describes
a social recommender system able to identify user
preferences and information needs, thus suggesting
personalized recommendations related to POIs in the
surroundings of the user's current location. The
proposed approach achieves the following goals: (i) to
supply, unlike the current LBSs, a methodology for
identifying user preferences and needs to be used in
the information filtering process; (ii) to exploit the
ever-growing amount of information from social
networking, user reviews, and local search Web sites;
(iii) to establish procedures for defining the context
of use to be employed in the recommendation of POIs
with low effort. The flexibility of the architecture is
such that our approach can be easily extended to any
category of POI. Experimental tests carried out on real
users enabled us to quantify the benefits of the
proposed approach in terms of performance
improvement.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gedikli:2013:IRA,
author = "Fatih Gedikli and Dietmar Jannach",
title = "Improving recommendation accuracy based on
item-specific tag preferences",
journal = j-TIST,
volume = "4",
number = "1",
pages = "11:1--11:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414436",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, different proposals have been made to
exploit Social Web tagging information to build more
effective recommender systems. The tagging data, for
example, were used to identify similar users or were
viewed as additional information about the
recommendable items. Recent research has indicated that
``attaching feelings to tags'' is experienced by users
as a valuable means to express which features of an
item they particularly like or dislike. When following
such an approach, users would therefore not only add
tags to an item as in usual Web 2.0 applications, but
also attach a preference ( affect ) to the tag itself,
expressing, for example, whether or not they liked a
certain actor in a given movie. In this work, we show
how this additional preference data can be exploited by
a recommender system to make more accurate predictions.
In contrast to previous work, which also relied on
so-called tag preferences to enhance the predictive
accuracy of recommender systems, we argue that tag
preferences should be considered in the context of an
item. We therefore propose new schemes to infer and
exploit context-specific tag preferences in the
recommendation process. An evaluation on two different
datasets reveals that our approach is capable of
providing more accurate recommendations than previous
tag-based recommender algorithms and recent
tag-agnostic matrix factorization techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:MRW,
author = "Yu-Chih Chen and Yu-Shi Lin and Yu-Chun Shen and
Shou-De Lin",
title = "A modified random walk framework for handling negative
ratings and generating explanations",
journal = j-TIST,
volume = "4",
number = "1",
pages = "12:1--12:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414437",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The concept of random walk (RW) has been widely
applied in the design of recommendation systems.
RW-based approaches are effective in handling locality
problem and taking extra information, such as the
relationships between items or users, into
consideration. However, the traditional RW-based
approach has a serious limitation in handling
bidirectional opinions. The propagation of positive and
negative information simultaneously in a graph is
nontrivial using random walk. To address the problem,
this article presents a novel and efficient RW-based
model that can handle both positive and negative
comments with the guarantee of convergence.
Furthermore, we argue that a good recommendation system
should provide users not only a list of recommended
items but also reasonable explanations for the
decisions. Therefore, we propose a technique that
generates explanations by backtracking the influential
paths and subgraphs. The results of experiments on the
MovieLens and Netflix datasets show that our model
significantly outperforms state-of-the-art RW-based
algorithms, and is capable of improving the overall
performance in the ensemble with other models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Said:2013:MRC,
author = "Alan Said and Shlomo Berkovsky and Ernesto W. {De
Luca}",
title = "Movie recommendation in context",
journal = j-TIST,
volume = "4",
number = "1",
pages = "13:1--13:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414438",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The challenge and workshop on Context-Aware Movie
Recommendation (CAMRa2010) were conducted jointly in
2010 with the Recommender Systems conference. The
challenge focused on three context-aware recommendation
scenarios: time-based, mood-based, and social
recommendation. The participants were provided with
anonymized datasets from two real-world online movie
recommendation communities and competed against each
other for obtaining the highest accuracy of
recommendations. The datasets contained contextual
features, such as tags, annotation, social
relationsips, and comments, normally not available in
public recommendation datasets. More than 40 teams from
21 countries participated in the challenge. Their
participation was summarized by 10 papers published by
the workshop, which have been extended and revised for
this special section. In this preface we overview the
challenge datasets, tasks, evaluation metrics, and the
obtained outcomes.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bellogin:2013:ECS,
author = "Alejandro Bellog{\'\i}n and Iv{\'a}n Cantador and
Fernando D{\'\i}ez and Pablo Castells and Enrique
Chavarriaga",
title = "An empirical comparison of social, collaborative
filtering, and hybrid recommenders",
journal = j-TIST,
volume = "4",
number = "1",
pages = "14:1--14:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414439",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In the Social Web, a number of diverse recommendation
approaches have been proposed to exploit the user
generated contents available in the Web, such as
rating, tagging, and social networking information. In
general, these approaches naturally require the
availability of a wide amount of these user
preferences. This may represent an important limitation
for real applications, and may be somewhat unnoticed in
studies focusing on overall precision, in which a
failure to produce recommendations gets blurred when
averaging the obtained results or, even worse, is just
not accounted for, as users with no recommendations are
typically excluded from the performance calculations.
In this article, we propose a coverage metric that
uncovers and compensates for the incompleteness of
performance evaluations based only on precision. We use
this metric together with precision metrics in an
empirical comparison of several social, collaborative
filtering, and hybrid recommenders. The obtained
results show that a better balance between precision
and coverage can be achieved by combining social-based
filtering (high accuracy, low coverage) and
collaborative filtering (low accuracy, high coverage)
recommendation techniques. We thus explore several
hybrid recommendation approaches to balance this
trade-off. In particular, we compare, on the one hand,
techniques integrating collaborative and social
information into a single model, and on the other,
linear combinations of recommenders. For the last
approach, we also propose a novel strategy to
dynamically adjust the weight of each recommender on a
user-basis, utilizing graph measures as indicators of
the target user's connectedness and relevance in a
social network.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2013:STC,
author = "Nathan N. Liu and Luheng He and Min Zhao",
title = "Social temporal collaborative ranking for context
aware movie recommendation",
journal = j-TIST,
volume = "4",
number = "1",
pages = "15:1--15:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414440",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Most existing collaborative filtering models only
consider the use of user feedback (e.g., ratings) and
meta data (e.g., content, demographics). However, in
most real world recommender systems, context
information, such as time and social networks, are also
very important factors that could be considered in
order to produce more accurate recommendations. In this
work, we address several challenges for the context
aware movie recommendation tasks in CAMRa 2010: (1) how
to combine multiple heterogeneous forms of user
feedback? (2) how to cope with dynamic user and item
characteristics? (3) how to capture and utilize social
connections among users? For the first challenge, we
propose a novel ranking based matrix factorization
model to aggregate explicit and implicit user feedback.
For the second challenge, we extend this model to a
sequential matrix factorization model to enable
time-aware parametrization. Finally, we introduce a
network regularization function to constrain user
parameters based on social connections. To the best of
our knowledge, this is the first study that
investigates the collective modeling of social and
temporal dynamics. Experiments on the CAMRa 2010
dataset demonstrated clear improvements over many
baselines.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2013:MCM,
author = "Yue Shi and Martha Larson and Alan Hanjalic",
title = "Mining contextual movie similarity with matrix
factorization for context-aware recommendation",
journal = j-TIST,
volume = "4",
number = "1",
pages = "16:1--16:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414441",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Context-aware recommendation seeks to improve
recommendation performance by exploiting various
information sources in addition to the conventional
user-item matrix used by recommender systems. We
propose a novel context-aware movie recommendation
algorithm based on joint matrix factorization (JMF). We
jointly factorize the user-item matrix containing
general movie ratings and other contextual movie
similarity matrices to integrate contextual information
into the recommendation process. The algorithm was
developed within the scope of the mood-aware
recommendation task that was offered by the Moviepilot
mood track of the 2010 context-aware movie
recommendation (CAMRa) challenge. Although the
algorithm could generalize to other types of contextual
information, in this work, we focus on two: movie mood
tags and movie plot keywords. Since the objective in
this challenge track is to recommend movies for a user
given a specified mood, we devise a novel mood-specific
movie similarity measure for this purpose. We enhance
the recommendation based on this measure by also
deploying the second movie similarity measure proposed
in this article that takes into account the movie plot
keywords. We validate the effectiveness of the proposed
JMF algorithm with respect to the recommendation
performance by carrying out experiments on the
Moviepilot challenge dataset. We demonstrate that
exploiting contextual information in JMF leads to
significant improvement over several state-of-the-art
approaches that generate movie recommendations without
using contextual information. We also demonstrate that
our proposed mood-specific movie similarity is better
suited for the task than the conventional mood-based
movie similarity measures. Finally, we show that the
enhancement provided by the movie similarity capturing
the plot keywords is particularly helpful in improving
the recommendation to those users who are significantly
more active in rating the movies than other users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Okada:2013:MDA,
author = "Isamu Okada and Hitoshi Yamamoto",
title = "Mathematical description and analysis of adaptive risk
choice behavior",
journal = j-TIST,
volume = "4",
number = "1",
pages = "17:1--17:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414442",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Which risk should one choose when facing alternatives
with different levels of risk? We discuss here adaptive
processes in such risk choice behavior by generalizing
the study of Roos et al. [2010]. We deal with an n
-choice game in which every player sequentially chooses
n times of lotteries of which there are two types: a
safe lottery and a risky lottery. We analyze this model
in more detail by elaborating the game. Based on the
results of mathematical analysis, replicator dynamics
analysis, and numerical simulations, we derived some
salient features of risk choice behavior. We show that
all the risk strategies can be divided into two groups:
persistence and nonpersistence. We also proved that the
dynamics with perturbation in which a mutation is
installed is globally asymptotically stable to a unique
equilibrium point for any initial population. The
numerical simulations clarify that the number of
persistent strategies seldom increases regardless of
the increase in n, and suggest that a rarity of
dominant choice strategies is widely observed in many
social contexts. These facts not only go hand-in-hand
with some well-known insights from prospect theory, but
may also provide some theoretical hypotheses for
various fields such as behavioral economics, ecology,
sociology, and consumer behavioral theory.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Song:2013:OSM,
author = "Xuan Song and Huijing Zhao and Jinshi Cui and Xiaowei
Shao and Ryosuke Shibasaki and Hongbin Zha",
title = "An online system for multiple interacting targets
tracking: Fusion of laser and vision, tracking and
learning",
journal = j-TIST,
volume = "4",
number = "1",
pages = "18:1--18:??",
month = jan,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2414425.2414443",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multitarget tracking becomes significantly more
challenging when the targets are in close proximity or
frequently interact with each other. This article
presents a promising online system to deal with these
problems. The novelty of this system is that laser and
vision are integrated with tracking and online learning
to complement each other in one framework: when the
targets do not interact with each other, the
laser-based independent trackers are employed and the
visual information is extracted simultaneously to train
some classifiers online for ``possible interacting
targets''. When the targets are in close proximity, the
classifiers learned online are used alongside visual
information to assist in tracking. Therefore, this mode
of cooperation not only deals with various tough
problems encountered in tracking, but also ensures that
the entire process can be completely online and
automatic. Experimental results demonstrate that laser
and vision fully display their respective advantages in
our system, and it is easy for us to obtain a good
trade-off between tracking accuracy and the time-cost
factor.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chopra:2013:ISS,
author = "Amit K. Chopra and Alexander Artikis and Jamal
Bentahar and Frank Dignum",
title = "Introduction to the special section on agent
communication",
journal = j-TIST,
volume = "4",
number = "2",
pages = "19:1--19:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chopra:2013:RDA,
author = "Amit K. Chopra and Alexander Artikis and Jamal
Bentahar and Marco Colombetti and Frank Dignum and
Nicoletta Fornara and Andrew J. I. Jones and Munindar
P. Singh and Pinar Yolum",
title = "Research directions in agent communication",
journal = j-TIST,
volume = "4",
number = "2",
pages = "20:1--20:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Increasingly, software engineering involves open
systems consisting of autonomous and heterogeneous
participants or agents who carry out loosely coupled
interactions. Accordingly, understanding and specifying
communications among agents is a key concern. A focus
on ways to formalize meaning distinguishes agent
communication from traditional distributed computing:
meaning provides a basis for flexible interactions and
compliance checking. Over the years, a number of
approaches have emerged with some essential and some
irrelevant distinctions drawn among them. As agent
abstractions gain increasing traction in the software
engineering of open systems, it is important to resolve
the irrelevant and highlight the essential
distinctions, so that future research can be focused in
the most productive directions. This article is an
outcome of extensive discussions among agent
communication researchers, aimed at taking stock of the
field and at developing, criticizing, and refining
their positions on specific approaches and future
challenges. This article serves some important
purposes, including identifying (1) points of broad
consensus; (2) points where substantive differences
remain; and (3) interesting directions of future
work.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gerard:2013:FVP,
author = "Scott N. Gerard and Munindar P. Singh",
title = "Formalizing and verifying protocol refinements",
journal = j-TIST,
volume = "4",
number = "2",
pages = "21:1--21:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A (business) protocol describes, in high-level terms,
a pattern of communication between two or more
participants, specifically via the creation and
manipulation of the commitments between them. In this
manner, a protocol offers both flexibility and rigor: a
participant may communicate in any way it chooses as
long as it discharges all of its activated commitments.
Protocols thus promise benefits in engineering
cross-organizational business processes. However,
software engineering using protocols presupposes a
formalization of protocols and a notion of the
refinement of one protocol by another. Refinement for
protocols is both intuitively obvious (e.g.,
PayViaCheck is clearly a kind of Pay ) and technically
nontrivial (e.g., compared to Pay, PayViaCheck involves
different participants exchanging different messages).
This article formalizes protocols and their refinement.
It develops Proton, an analysis tool for protocol
specifications that overlays a model checker to compute
whether one protocol refines another with respect to a
stated mapping. Proton and its underlying theory are
evaluated by formalizing several protocols from the
literature and verifying all and only the expected
refinements.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Baldoni:2013:CRS,
author = "Matteo Baldoni and Cristina Baroglio and Elisa Marengo
and Viviana Patti",
title = "Constitutive and regulative specifications of
commitment protocols: a decoupled approach",
journal = j-TIST,
volume = "4",
number = "2",
pages = "22:1--22:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Interaction protocols play a fundamental role in
multiagent systems. In this work, after analyzing the
trends that are emerging not only from research on
multiagent interaction protocols but also from
neighboring fields, like research on workflows and
business processes, we propose a novel definition of
commitment-based interaction protocols, that is
characterized by the decoupling of the constitutive and
the regulative specifications and that explicitly
foresees a representation of the latter based on
constraints among commitments. A clear distinction
between the two representations has many advantages,
mainly residing in a greater openness of multiagent
systems, and an easier reuse of protocols and of action
definitions. A language, named 2CL, for writing
regulative specifications is also given together with a
designer-oriented graphical notation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Falcone:2013:ISS,
author = "Rino Falcone and Munindar P. Singh",
title = "Introduction to special section on trust in multiagent
systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "23:1--23:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2013:FTM,
author = "Jie Zhang and Robin Cohen",
title = "A framework for trust modeling in multiagent
electronic marketplaces with buying advisors to
consider varying seller behavior and the limiting of
seller bids",
journal = j-TIST,
volume = "4",
number = "2",
pages = "24:1--24:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we present a framework of use in
electronic marketplaces that allows buying agents to
model the trustworthiness of selling agents in an
effective way, making use of seller ratings provided by
other buying agents known as advisors. The
trustworthiness of the advisors is also modeled, using
an approach that combines both personal and public
knowledge and allows the relative weighting to be
adjusted over time. Through a series of experiments
that simulate e-marketplaces, including ones where
sellers may vary their behavior over time, we are able
to demonstrate that our proposed framework delivers
effective seller recommendations to buyers, resulting
in important buyer profit. We also propose limiting
seller bids as a method for promoting seller honesty,
thus facilitating successful selection of sellers by
buyers, and demonstrate the value of this approach
through experimental results. Overall, this research is
focused on the technological aspects of electronic
commerce and specifically on technology that would be
used to manage trust.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Erriquez:2013:BUS,
author = "Elisabetta Erriquez and Wiebe van der Hoek and Michael
Wooldridge",
title = "Building and using social structures: a case study
using the agent {ART} testbed",
journal = j-TIST,
volume = "4",
number = "2",
pages = "25:1--25:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article investigates the conjecture that agents
who make decisions in scenarios where trust is
important can benefit from the use of a social
structure, representing the social relationships that
exist between agents. We propose techniques that can be
used by agents to initially build and then
progressively update such a structure in the light of
experience. We describe an implementation of our
techniques in the domain of the Agent ART testbed: we
take two existing agents for this domain (``Simplet''
and ``Connected'') and compare their performance with
versions that use our social structure
(``SocialSimplet'' and ``SocialConnected''). We show
that SocialSimplet and SocialConnected outperform their
counterparts with respect to the quality of the
interactions, the number of rounds won in a
competition, and the total utility gained.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Burnett:2013:STB,
author = "Chris Burnett and Timothy J. Norman and Katia Sycara",
title = "Stereotypical trust and bias in dynamic multiagent
systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "26:1--26:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Large-scale multiagent systems have the potential to
be highly dynamic. Trust and reputation are crucial
concepts in these environments, as it may be necessary
for agents to rely on their peers to perform as
expected, and learn to avoid untrustworthy partners.
However, aspects of highly dynamic systems introduce
issues which make the formation of trust relationships
difficult. For example, they may be short-lived,
precluding agents from gaining the necessary
experiences to make an accurate trust evaluation. This
article describes a new approach, inspired by theories
of human organizational behavior, whereby agents
generalize their experiences with previously
encountered partners as stereotypes, based on the
observable features of those partners and their
behaviors. Subsequently, these stereotypes are applied
when evaluating new and unknown partners. Furthermore,
these stereotypical opinions can be communicated within
the society, resulting in the notion of stereotypical
reputation. We show how this approach can complement
existing state-of-the-art trust models, and enhance the
confidence in the evaluations that can be made about
trustees when direct and reputational information is
lacking or limited. Furthermore, we show how a
stereotyping approach can help agents detect unwanted
biases in the reputational opinions they receive from
others in the society.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Falcone:2013:MKR,
author = "Rino Falcone and Michele Piunti and Matteo Venanzi and
Cristiano Castelfranchi",
title = "From manifesta to krypta: The relevance of categories
for trusting others",
journal = j-TIST,
volume = "4",
number = "2",
pages = "27:1--27:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article we consider the special abilities
needed by agents for assessing trust based on inference
and reasoning. We analyze the case in which it is
possible to infer trust towards unknown counterparts by
reasoning on abstract classes or categories of agents
shaped in a concrete application domain. We present a
scenario of interacting agents providing a
computational model implementing different strategies
to assess trust. Assuming a medical domain, categories,
including both competencies and dispositions of
possible trustees, are exploited to infer trust towards
possibly unknown counterparts. The proposed approach
for the cognitive assessment of trust relies on agents'
abilities to analyze heterogeneous information sources
along different dimensions. Trust is inferred based on
specific observable properties (manifesta), namely
explicitly readable signals indicating internal
features (krypta) regulating agents' behavior and
effectiveness on specific tasks. Simulative experiments
evaluate the performance of trusting agents adopting
different strategies to delegate tasks to possibly
unknown trustees, while experimental results show the
relevance of this kind of cognitive ability in the case
of open multiagent systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2013:ISS,
author = "Qing Li and Xiangfeng Luo and Liu Wenyin and Cristina
Conati",
title = "Introduction to the special section on intelligent
tutoring and coaching systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "28:1--28:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Folsom-Kovarik:2013:TPR,
author = "Jeremiah T. Folsom-Kovarik and Gita Sukthankar and Sae
Schatz",
title = "Tractable {POMDP} representations for intelligent
tutoring systems",
journal = j-TIST,
volume = "4",
number = "2",
pages = "29:1--29:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With Partially Observable Markov Decision Processes
(POMDPs), Intelligent Tutoring Systems (ITSs) can model
individual learners from limited evidence and plan
ahead despite uncertainty. However, POMDPs need
appropriate representations to become tractable in ITSs
that model many learner features, such as mastery of
individual skills or the presence of specific
misconceptions. This article describes two POMDP
representations- state queues and observation chains
-that take advantage of ITS task properties and let
POMDPs scale to represent over 100 independent learner
features. A real-world military training problem is
given as one example. A human study ( n = 14) provides
initial validation for the model construction. Finally,
evaluating the experimental representations with
simulated students helps predict their impact on ITS
performance. The compressed representations can model a
wide range of simulated problems with instructional
efficacy equal to lossless representations. With
improved tractability, POMDP ITSs can accommodate more
numerous or more detailed learner states and inputs.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yen:2013:LIS,
author = "Neil Y. Yen and Timothy K. Shih and Qun Jin",
title = "{LONET}: an interactive search network for intelligent
lecture path generation",
journal = j-TIST,
volume = "4",
number = "2",
pages = "30:1--30:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Sharing resources and information on the Internet has
become an important activity for education. In distance
learning, instructors can benefit from resources, also
known as Learning Objects (LOs), to create plenteous
materials for specific learning purposes. Our
repository (called the MINE Registry) has been
developed for storing and sharing learning objects,
around 22,000 in total, in the past few years. To
enhance reusability, one significant concept named
Reusability Tree was implemented to trace the process
of changes. Also, weighting and ranking metrics have
been proposed to enhance the searchability in the
repository. Following the successful implementation,
this study goes further to investigate the
relationships between LOs from a perspective of social
networks. The LONET (Learning Object Network), as an
extension of Reusability Tree, is newly proposed and
constructed to clarify the vague reuse scenario in the
past, and to summarize collaborative intelligence
through past interactive usage experiences. We define a
social structure in our repository based on past usage
experiences from instructors, by proposing a set of
metrics to evaluate the interdependency such as
prerequisites and references. The structure identifies
usage experiences and can be graphed in terms of
implicit and explicit relations among learning objects.
As a practical contribution, an adaptive algorithm is
proposed to mine the social structure in our
repository. The algorithm generates adaptive routes,
based on past usage experiences, by computing possible
interactive input, such as search criteria and feedback
from instructors, and assists them in generating
specific lectures.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ehara:2013:PRS,
author = "Yo Ehara and Nobuyuki Shimizu and Takashi Ninomiya and
Hiroshi Nakagawa",
title = "Personalized reading support for second-language {Web}
documents",
journal = j-TIST,
volume = "4",
number = "2",
pages = "31:1--31:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A novel intelligent interface eases the browsing of
Web documents written in the second languages of users.
It automatically predicts words unfamiliar to the user
by a collective intelligence method and glosses them
with their meaning in advance. If the prediction
succeeds, the user does not need to consult a
dictionary; even if it fails, the user can correct the
prediction. The correction data are collected and used
to improve the accuracy of further predictions. The
prediction is personalized in that every user's
language ability is estimated by a state-of-the-art
language testing model, which is trained in a practical
response time with only a small sacrifice of prediction
accuracy. The system was evaluated in terms of
prediction accuracy and reading simulation. The reading
simulation results show that this system can reduce the
number of clicks for most readers with insufficient
vocabulary to read documents and can significantly
reduce the remaining number of unfamiliar words after
the prediction and glossing for all users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2013:RCI,
author = "Fei-Yue Wang and Pak Kin Wong",
title = "Research commentary: Intelligent systems and
technology for integrative and predictive medicine: an
{ACP} approach",
journal = j-TIST,
volume = "4",
number = "2",
pages = "32:1--32:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One of the principal goals in medicine is to determine
and implement the best treatment for patients through
fastidious estimation of the effects and benefits of
therapeutic procedures. The inherent complexities of
physiological and pathological networks that span
across orders of magnitude in time and length scales,
however, represent fundamental hurdles in determining
effective treatments for patients. Here we argue for a
new approach, called the ACP-based approach, that
combines artificial (societies), computational
(experiments), and parallel (execution) methods in
intelligent systems and technology for integrative and
predictive medicine, or more generally, precision
medicine and smart health management. The advent of
artificial societies that collect the clinically
relevant information in prognostics and therapeutics
provides a promising platform for organizing and
experimenting complex physiological systems toward
integrative medicine. The ability of computational
experiments to analyze distinct, interactive systems
such as the host mechanisms, pathological pathways, and
therapeutic strategies, as well as other factors using
the artificial systems, will enable control and
management through parallel execution of real and
artificial systems concurrently within the integrative
medicine context. The development of this framework in
integrative medicine, fueled by close collaborations
between physicians, engineers, and scientists, will
result in preventive and predictive practices of a
personal, proactive, and precise nature, including
rational combinatorial treatments, adaptive
therapeutics, and patient-oriented disease
management.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tabia:2013:PBA,
author = "Hedi Tabia and Mohamed Daoudi and Jean-Philippe
Vandeborre and Olivier Colot",
title = "A parts-based approach for automatic {$3$D} shape
categorization using belief functions",
journal = j-TIST,
volume = "4",
number = "2",
pages = "33:1--33:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Grouping 3D objects into (semantically) meaningful
categories is a challenging and important problem in 3D
mining and shape processing. Here, we present a novel
approach to categorize 3D objects. The method described
in this article, is a belief-function-based approach
and consists of two stages: the training stage, where
3D objects in the same category are processed and a set
of representative parts is constructed, and the
labeling stage, where unknown objects are categorized.
The experimental results obtained on the Tosca-Sumner
and the Shrec07 datasets show that the system
efficiently performs in categorizing 3D models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2013:LIC,
author = "Zhengxiang Wang and Yiqun Hu and Liang-Tien Chia",
title = "Learning image-to-class distance metric for image
classification",
journal = j-TIST,
volume = "4",
number = "2",
pages = "34:1--34:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Image-To-Class (I2C) distance is a novel distance used
for image classification and has successfully handled
datasets with large intra-class variances. However, it
uses Euclidean distance for measuring the distance
between local features in different classes, which may
not be the optimal distance metric in real image
classification problems. In this article, we propose a
distance metric learning method to improve the
performance of I2C distance by learning per-class
Mahalanobis metrics in a large margin framework. Our
I2C distance is adaptive to different classes by
combining with the learned metric for each class. These
multiple per-class metrics are learned simultaneously
by forming a convex optimization problem with the
constraints that the I2C distance from each training
image to its belonging class should be less than the
distances to other classes by a large margin. A
subgradient descent method is applied to efficiently
solve this optimization problem. For efficiency and
scalability to large-scale problems, we also show how
to simplify the method to learn a diagonal matrix for
each class. We show in experiments that our learned
Mahalanobis I2C distance can significantly outperform
the original Euclidean I2C distance as well as other
distance metric learning methods in several prevalent
image datasets, and our simplified diagonal matrices
can preserve the performance but significantly speed up
the metric learning procedure for large-scale datasets.
We also show in experiment that our method is able to
correct the class imbalance problem, which usually
leads the NN-based methods toward classes containing
more training images.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Song:2013:FOU,
author = "Xuan Song and Xiaowei Shao and Quanshi Zhang and
Ryosuke Shibasaki and Huijing Zhao and Jinshi Cui and
Hongbin Zha",
title = "A fully online and unsupervised system for large and
high-density area surveillance: Tracking, semantic
scene learning and abnormality detection",
journal = j-TIST,
volume = "4",
number = "2",
pages = "35:1--35:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "For reasons of public security, an intelligent
surveillance system that can cover a large, crowded
public area has become an urgent need. In this article,
we propose a novel laser-based system that can
simultaneously perform tracking, semantic scene
learning, and abnormality detection in a fully online
and unsupervised way. Furthermore, these three tasks
cooperate with each other in one framework to improve
their respective performances. The proposed system has
the following key advantages over previous ones: (1) It
can cover quite a large area (more than 60$ \times
$35m), and simultaneously perform robust tracking,
semantic scene learning, and abnormality detection in a
high-density situation. (2) The overall system can vary
with time, incrementally learn the structure of the
scene, and perform fully online abnormal activity
detection and tracking. This feature makes our system
suitable for real-time applications. (3) The
surveillance tasks are carried out in a fully
unsupervised manner, so that there is no need for
manual labeling and the construction of huge training
datasets. We successfully apply the proposed system to
the JR subway station in Tokyo, and demonstrate that it
can cover an area of 60$ \times $35m, robustly track
more than 150 targets at the same time, and
simultaneously perform online semantic scene learning
and abnormality detection with no human intervention.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tran:2013:CPB,
author = "Vien Tran and Khoi Nguyen and Tran Cao Son and Enrico
Pontelli",
title = "A conformant planner based on approximation:
{CpA(H)}",
journal = j-TIST,
volume = "4",
number = "2",
pages = "36:1--36:??",
month = mar,
year = "2013",
CODEN = "????",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sun May 5 09:06:55 MDT 2013",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article describes the planner C pA( H ), the
recipient of the Best Nonobservable Nondeterministic
Planner Award in the ``Uncertainty Track'' of the 6
$^{th}$ International Planning Competition (IPC), 2008.
The article presents the various techniques that help
CpA( H ) to achieve the level of performance and
scalability exhibited in the competition. The article
also presents experimental results comparing CpA( H )
with state-of-the-art conformant planners.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2013:ISS,
author = "Haifeng Wang and Bill Dolan and Idan Szpektor and
Shiqi Zhao",
title = "Introduction to special section on paraphrasing",
journal = j-TIST,
volume = "4",
number = "3",
pages = "37:1--37:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483670",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Resnik:2013:UTP,
author = "Philip Resnik and Olivia Buzek and Yakov Kronrod and
Chang Hu and Alexander J. Quinn and Benjamin B.
Bederson",
title = "Using targeted paraphrasing and monolingual
crowdsourcing to improve translation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "38:1--38:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483671",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Targeted paraphrasing is a new approach to the problem
of obtaining cost-effective, reasonable quality
translation, which makes use of simple and inexpensive
human computations by monolingual speakers in
combination with machine translation. The key insight
behind the process is that it is possible to spot
likely translation errors with only monolingual
knowledge of the target language, and it is possible to
generate alternative ways to say the same thing (i.e.,
paraphrases) with only monolingual knowledge of the
source language. Formal evaluation demonstrates that
this approach can yield substantial improvements in
translation quality, and the idea has been integrated
into a broader framework for monolingual collaborative
translation that produces fully accurate, fully fluent
translations for a majority of sentences in a
real-world translation task, with no involvement of
human bilingual speakers.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Marton:2013:DPP,
author = "Yuval Marton",
title = "Distributional phrasal paraphrase generation for
statistical machine translation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "39:1--39:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483672",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Paraphrase generation has been shown useful for
various natural language processing tasks, including
statistical machine translation. A commonly used method
for paraphrase generation is pivoting [Callison-Burch
et al. 2006], which benefits from linguistic knowledge
implicit in the sentence alignment of parallel texts,
but has limited applicability due to its reliance on
parallel texts. Distributional paraphrasing [Marton et
al. 2009a] has wider applicability, is more
language-independent, but doesn't benefit from any
linguistic knowledge. Nevertheless, we show that using
distributional paraphrasing can yield greater gains in
translation tasks. We report method improvements
leading to higher gains than previously published, of
almost 2 B leu points, and provide implementation
details, complexity analysis, and further insight into
this method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Madnani:2013:GTP,
author = "Nitin Madnani and Bonnie J. Dorr",
title = "Generating targeted paraphrases for improved
translation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "40:1--40:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483673",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Today's Statistical Machine Translation (SMT) systems
require high-quality human translations for parameter
tuning, in addition to large bitexts for learning the
translation units. This parameter tuning usually
involves generating translations at different points in
the parameter space and obtaining feedback against
human-authored reference translations as to how good
the translations. This feedback then dictates what
point in the parameter space should be explored next.
To measure this feedback, it is generally considered
wise to have multiple (usually 4) reference
translations to avoid unfair penalization of
translation hypotheses which could easily happen given
the large number of ways in which a sentence can be
translated from one language to another. However, this
reliance on multiple reference translations creates a
problem since they are labor intensive and expensive to
obtain. Therefore, most current MT datasets only
contain a single reference. This leads to the problem
of reference sparsity. In our previously published
research, we had proposed the first paraphrase-based
solution to this problem and evaluated its effect on
Chinese--English translation. In this article, we first
present extended results for that solution on
additional source languages. More importantly, we
present a novel way to generate ``targeted''
paraphrases that yields substantially larger gains (up
to 2.7 BLEU points) in translation quality when
compared to our previous solution (up to 1.6 BLEU
points). In addition, we further validate these
improvements by supplementing with human preference
judgments obtained via Amazon Mechanical Turk.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cohn:2013:AAS,
author = "Trevor Cohn and Mirella Lapata",
title = "An abstractive approach to sentence compression",
journal = j-TIST,
volume = "4",
number = "3",
pages = "41:1--41:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483674",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article we generalize the sentence compression
task. Rather than simply shorten a sentence by deleting
words or constituents, as in previous work, we rewrite
it using additional operations such as substitution,
reordering, and insertion. We present an experimental
study showing that humans can naturally create
abstractive sentences using a variety of rewrite
operations, not just deletion. We next create a new
corpus that is suited to the abstractive compression
task and formulate a discriminative tree-to-tree
transduction model that can account for structural and
lexical mismatches. The model incorporates a grammar
extraction method, uses a language model for coherent
output, and can be easily tuned to a wide range of
compression-specific loss functions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Moon:2013:IBM,
author = "Taesun Moon and Katrin Erk",
title = "An inference-based model of word meaning in context as
a paraphrase distribution",
journal = j-TIST,
volume = "4",
number = "3",
pages = "42:1--42:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483675",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Graded models of word meaning in context characterize
the meaning of individual usages (occurrences) without
reference to dictionary senses. We introduce a novel
approach that frames the task of computing word meaning
in context as a probabilistic inference problem. The
model represents the meaning of a word as a probability
distribution over potential paraphrases, inferred using
an undirected graphical model. Evaluated on
paraphrasing tasks, the model achieves state-of-the-art
performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Burrows:2013:PAC,
author = "Steven Burrows and Martin Potthast and Benno Stein",
title = "Paraphrase acquisition via crowdsourcing and machine
learning",
journal = j-TIST,
volume = "4",
number = "3",
pages = "43:1--43:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483676",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "To paraphrase means to rewrite content while
preserving the original meaning. Paraphrasing is
important in fields such as text reuse in journalism,
anonymizing work, and improving the quality of
customer-written reviews. This article contributes to
paraphrase acquisition and focuses on two aspects that
are not addressed by current research: (1) acquisition
via crowdsourcing, and (2) acquisition of passage-level
samples. The challenge of the first aspect is automatic
quality assurance; without such a means the
crowdsourcing paradigm is not effective, and without
crowdsourcing the creation of test corpora is
unacceptably expensive for realistic order of
magnitudes. The second aspect addresses the deficit
that most of the previous work in generating and
evaluating paraphrases has been conducted using
sentence-level paraphrases or shorter; these
short-sample analyses are limited in terms of
application to plagiarism detection, for example. We
present the Webis Crowd Paraphrase Corpus 2011
(Webis-CPC-11), which recently formed part of the PAN
2010 international plagiarism detection competition.
This corpus comprises passage-level paraphrases with
4067 positive samples and 3792 negative samples that
failed our criteria, using Amazon's Mechanical Turk for
crowdsourcing. In this article, we review the lessons
learned at PAN 2010, and explain in detail the method
used to construct the corpus. The empirical
contributions include machine learning experiments to
explore if passage-level paraphrases can be identified
in a two-class classification problem using paraphrase
similarity features, and we find that a
k-nearest-neighbor classifier can correctly distinguish
between paraphrased and nonparaphrased samples with
0.980 precision at 0.523 recall. This result implies
that just under half of our samples must be discarded
(remaining 0.477 fraction), but our cost analysis shows
that the automation we introduce results in a 18\%
financial saving and over 100 hours of time returned to
the researchers when repeating a similar corpus design.
On the other hand, when building an unrelated corpus
requiring, say, 25\% training data for the automated
component, we show that the financial outcome is cost
neutral, while still returning over 70 hours of time to
the researchers. The work presented here is the first
to join the paraphrasing and plagiarism communities.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bouamor:2013:MPA,
author = "Houda Bouamor and Aur{\'e}elien Max and Anne Vilnat",
title = "Multitechnique paraphrase alignment: a contribution to
pinpointing sub-sentential paraphrases",
journal = j-TIST,
volume = "4",
number = "3",
pages = "44:1--44:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483677",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This work uses parallel monolingual corpora for a
detailed study of the task of sub-sentential paraphrase
acquisition. We argue that the scarcity of this type of
resource is compensated by the fact that it is the most
suited type for studies on paraphrasing. We propose a
large exploration of this task with experiments on two
languages with five different acquisition techniques,
selected for their complementarity, their combinations,
as well as four monolingual corpus types of varying
comparability. We report, under all conditions, a
significant improvement over all techniques by
validating candidate paraphrases using a maximum
entropy classifier. An important result of our study is
the identification of difficult-to-acquire paraphrase
pairs, which are classified and quantified in a
bilingual typology.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yu:2013:ISS,
author = "Zhiwen Yu and Daqing Zhang and Nathan Eagle and Diane
Cook",
title = "Introduction to the special section on intelligent
systems for socially aware computing",
journal = j-TIST,
volume = "4",
number = "3",
pages = "45:1--45:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483678",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Schuster:2013:PSC,
author = "Daniel Schuster and Alberto Rosi and Marco Mamei and
Thomas Springer and Markus Endler and Franco
Zambonelli",
title = "Pervasive social context: Taxonomy and survey",
journal = j-TIST,
volume = "4",
number = "3",
pages = "46:1--46:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483679",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "As pervasive computing meets social networks, there is
a fast growing research field called pervasive social
computing. Applications in this area exploit the
richness of information arising out of people using
sensor-equipped pervasive devices in their everyday
life combined with intense use of different social
networking services. We call this set of information
pervasive social context. We provide a taxonomy to
classify pervasive social context along the dimensions
space, time, people, and information source (STiPI) as
well as commenting on the type and reason for creating
such context. A survey of recent research shows the
applicability and usefulness of the taxonomy in
classifying and assessing applications and systems in
the area of pervasive social computing. Finally, we
present some research challenges in this area and
illustrate how they affect the systems being
surveyed.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2013:NLR,
author = "Yue Shi and Pavel Serdyukov and Alan Hanjalic and
Martha Larson",
title = "Nontrivial landmark recommendation using geotagged
photos",
journal = j-TIST,
volume = "4",
number = "3",
pages = "47:1--47:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483680",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online photo-sharing sites provide a wealth of
information about user behavior and their potential is
increasing as it becomes ever-more common for images to
be associated with location information in the form of
geotags. In this article, we propose a novel approach
that exploits geotagged images from an online community
for the purpose of personalized landmark
recommendation. Under our formulation of the task,
recommended landmarks should be relevant to user
interests and additionally they should constitute
nontrivial recommendations. In other words,
recommendations of landmarks that are highly popular
and frequently visited and can be easily discovered
through other information sources such as travel guides
should be avoided in favor of recommendations that
relate to users' personal interests. We propose a
collaborative filtering approach to the personalized
landmark recommendation task within a matrix
factorization framework. Our approach, WMF-CR, combines
weighted matrix factorization and category-based
regularization. The integrated weights emphasize the
contribution of nontrivial landmarks in order to focus
the recommendation model specifically on the generation
of nontrivial recommendations. They support the
judicious elimination of trivial landmarks from
consideration without also discarding information
valuable for recommendation. Category-based
regularization addresses the sparse data problem, which
is arguably even greater in the case of our landmark
recommendation task than in other recommendation
scenarios due to the limited amount of travel
experience recorded in the online image set of any
given user. We use category information extracted from
Wikipedia in order to provide the system with a method
to generalize the semantics of landmarks and allow the
model to relate them not only on the basis of identity,
but also on the basis of topical commonality. The
proposed approach is computational scalable, that is,
its complexity is linear with the number of observed
preferences in the user-landmark preference matrix and
the number of nonzero similarities in the
category-based landmark similarity matrix. We evaluate
the approach on a large collection of geotagged photos
gathered from Flickr. Our experimental results
demonstrate that WMF-CR outperforms several
state-of-the-art baseline approaches in recommending
nontrivial landmarks. Additionally, they demonstrate
that the approach is well suited for addressing data
sparseness and provides particular performance
improvement in the case of users who have limited
travel experience, that is, have visited only few
cities or few landmarks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wei:2013:EPA,
author = "Ling-Yin Wei and Wen-Chih Peng and Wang-Chien Lee",
title = "Exploring pattern-aware travel routes for trajectory
search",
journal = j-TIST,
volume = "4",
number = "3",
pages = "48:1--48:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483681",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the popularity of positioning devices, Web 2.0
technology, and trip sharing services, many users are
willing to log and share their trips on the Web. Thus,
trip planning Web sites are able to provide some new
services by inferring Regions-Of-Interest (ROIs) and
recommending popular travel routes from trip
trajectories. We argue that simply providing some
travel routes consisting of popular ROIs to users is
not sufficient. To tour around a wide geographical
area, for example, a city, some users may prefer a trip
to visit as many ROIs as possible, while others may
like to stop by only a few ROIs for an in-depth visit.
We refer to a trip fitting the former user group as an
in-breadth trip and a trip suitable for the latter user
group as an in-depth trip. Prior studies on trip
planning have focused on mining ROIs and travel routes
without considering these different preferences. In
this article, given a spatial range and a user
preference of depth/breadth specified by a user, we
develop a Pattern-Aware Trajectory Search (PATS)
framework to retrieve the top K trajectories passing
through popular ROIs. PATS is novel because the
returned travel trajectories, discovered from travel
patterns hidden in trip trajectories, may represent the
most valuable travel experiences of other travelers
fitting the user's trip preference in terms of depth or
breadth. The PATS framework comprises two components:
travel behavior exploration and trajectory search. The
travel behavior exploration component determines a set
of ROIs along with their attractive scores by
considering not only the popularity of the ROIs but
also the travel sequential relationships among the
ROIs. To capture the travel sequential relationships
among ROIs and to derive their attractive scores, a
user movement graph is constructed. For the trajectory
search component of PATS, we formulate two trajectory
score functions, the depth-trip score function and the
breadth-trip score function, by taking into account the
number of ROIs in a trajectory and their attractive
scores. Accordingly, we propose an algorithm, namely,
Bounded Trajectory Search (BTS), to efficiently
retrieve the top K trajectories based on the two
trajectory scores. The PATS framework is evaluated by
experiments and user studies using a real dataset. The
experimental results demonstrate the effectiveness and
the efficiency of the proposed PATS framework.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yan:2013:STM,
author = "Zhixian Yan and Dipanjan Chakraborty and Christine
Parent and Stefano Spaccapietra and Karl Aberer",
title = "Semantic trajectories: Mobility data computation and
annotation",
journal = j-TIST,
volume = "4",
number = "3",
pages = "49:1--49:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483682",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the large-scale adoption of GPS equipped mobile
sensing devices, positional data generated by moving
objects (e.g., vehicles, people, animals) are being
easily collected. Such data are typically modeled as
streams of spatio-temporal (x,y,t) points, called
trajectories. In recent years trajectory management
research has progressed significantly towards efficient
storage and indexing techniques, as well as suitable
knowledge discovery. These works focused on the
geometric aspect of the raw mobility data. We are now
witnessing a growing demand in several application
sectors (e.g., from shipment tracking to geo-social
networks) on understanding the semantic behavior of
moving objects. Semantic behavior refers to the use of
semantic abstractions of the raw mobility data,
including not only geometric patterns but also
knowledge extracted jointly from the mobility data and
the underlying geographic and application domains
information. The core contribution of this article lies
in a semantic model and a computation and annotation
platform for developing a semantic approach that
progressively transforms the raw mobility data into
semantic trajectories enriched with segmentations and
annotations. We also analyze a number of experiments we
did with semantic trajectories in different domains.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chin:2013:CPT,
author = "Alvin Chin and Bin Xu and Hao Wang and Lele Chang and
Hao Wang and Lijun Zhu",
title = "Connecting people through physical proximity and
physical resources at a conference",
journal = j-TIST,
volume = "4",
number = "3",
pages = "50:1--50:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483683",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This work investigates how to bridge the gap between
offline and online behaviors at a conference and how
the physical resources in the conference (the physical
objects used in the conference for gathering attendees
together in engaging an activity such as rooms,
sessions, and papers) can be used to help facilitate
social networking. We build Find and Connect, a system
that integrates offline activities and interactions
captured in real time with online connections in a
conference environment, to provide a list of potential
people one should connect to for forming an ephemeral
social network. We investigate how social connections
can be established and integrated with physical
resources through positioning technology, and the
relationship between physical proximity encounters and
online social connections. Results from our two
datasets of two trials, one at the UIC/ATC 2010
conference and GCJK internal marketing event, show that
social connections that are reciprocal in relationship,
such as friendship and exchanged contacts, have
tighter, denser, and highly clustered networks compared
to unidirectional relationships such as follow. We
discover that there is a positive relationship between
physical proximity encounters and online social
connections before the social connection is made for
friends, but a negative relationship for after the
social connection is made. The first indicates social
selection is strong, and the second indicates social
influence is weak. Even though our dataset is sparse,
nonetheless we believe our work is promising and novel
which is worthy of future research.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2013:ISS,
author = "Shanchieh Jay Yang and Dana Nau and John Salerno",
title = "Introduction to the special section on social
computing, behavioral-cultural modeling, and
prediction",
journal = j-TIST,
volume = "4",
number = "3",
pages = "51:1--51:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483684",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hung:2013:OBI,
author = "Benjamin W. K. Hung and Stephan E. Kolitz and Asuman
Ozdaglar",
title = "Optimization-based influencing of village social
networks in a counterinsurgency",
journal = j-TIST,
volume = "4",
number = "3",
pages = "52:1--52:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483685",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article considers the nonlethal targeting
assignment problem in the counterinsurgency in
Afghanistan, the problem of deciding on the people whom
U.S. forces should engage through outreach,
negotiations, meetings, and other interactions in order
to ultimately win the support of the population in
their area of operations. We propose two models: (1)
the Afghan counterinsurgency (COIN) social influence
model, to represent how attitudes of local leaders are
affected by repeated interactions with other local
leaders, insurgents, and counterinsurgents, and (2) the
nonlethal targeting model, a NonLinear Programming
(NLP) optimization formulation that identifies a
strategy for assigning k U.S. agents to produce the
greatest arithmetic mean of the expected long-term
attitude of the population. We demonstrate in an
experiment the merits of the optimization model in
nonlethal targeting, which performs significantly
better than both doctrine-based and random methods of
assignment in a large network.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gintis:2013:MMS,
author = "Herbert Gintis",
title = "{Markov} models of social dynamics: Theory and
applications",
journal = j-TIST,
volume = "4",
number = "3",
pages = "53:1--53:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483686",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article shows how agent-based models of social
dynamics can be treated rigorously and analytically as
finite Markov processes, and their long-run properties
are then given by an expanded version of the ergodic
theorem for Markov processes. A Markov process model of
a simplified market economy shows the fruitfulness of
this approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fridman:2013:UQR,
author = "Natalie Fridman and Gal A. Kaminka",
title = "Using qualitative reasoning for social simulation of
crowds",
journal = j-TIST,
volume = "4",
number = "3",
pages = "54:1--54:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483687",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The ability to model and reason about the potential
violence level of a demonstration is important to the
police decision making process. Unfortunately, existing
knowledge regarding demonstrations is composed of
partial qualitative descriptions without complete and
precise numerical information. In this article we
describe a first attempt to use qualitative reasoning
techniques to model demonstrations. To our knowledge,
such techniques have never been applied to modeling and
reasoning regarding crowd behaviors, nor in particular
demonstrations. We develop qualitative models
consistent with the partial, qualitative social science
literature, allowing us to model the interactions
between different factors that influence violence in
demonstrations. We then utilize qualitative simulation
to predict the potential eruption of violence, at
various levels, based on a description of the
demographics, environmental settings, and police
responses. We incrementally present and compare three
such qualitative models. The results show that while
two of these models fail to predict the outcomes of
real-world events reported and analyzed in the
literature, one model provides good results. We also
examine whether a popular machine learning algorithm
(decision tree learning) can be used. While the results
show that the decision trees provide improved
predictions, we show that the QR models can be more
sensitive to changes, and can account for what-if
scenarios, in contrast to decision trees. Moreover, we
introduce a novel analysis algorithm that analyzes the
QR simulations, to automatically determine the factors
that are most important in influencing the outcome in
specific real-world demonstrations. We show that the
algorithm identifies factors that correspond to
experts' analysis of these events.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Saito:2013:DCI,
author = "Kazumi Saito and Masahiro Kimura and Kouzou Ohara and
Hiroshi Motoda",
title = "Detecting changes in information diffusion patterns
over social networks",
journal = j-TIST,
volume = "4",
number = "3",
pages = "55:1--55:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483688",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We addressed the problem of detecting the change in
behavior of information diffusion over a social network
which is caused by an unknown external situation change
using a small amount of observation data in a
retrospective setting. The unknown change is assumed
effectively reflected in changes in the parameter
values in the probabilistic information diffusion
model, and the problem is reduced to detecting where in
time and how long this change persisted and how big
this change is. We solved this problem by searching the
change pattern that maximizes the likelihood of
generating the observed information diffusion
sequences, and in doing so we devised a very efficient
general iterative search algorithm using the derivative
of the likelihood which avoids parameter value
optimization during each search step. This is in
contrast to the naive learning algorithm in that it has
to iteratively update the pattern boundaries, each
requiring the parameter value optimization and thus is
very inefficient. We tested this algorithm for two
instances of the probabilistic information diffusion
model which has different characteristics. One is of
information push style and the other is of information
pull style. We chose Asynchronous Independent Cascade
(AsIC) model as the former and Value-weighted Voter
(VwV) model as the latter. The AsIC is the model for
general information diffusion with binary states and
the parameter to detect its change is diffusion
probability and the VwV is the model for opinion
formation with multiple states and the parameter to
detect its change is opinion value. The results tested
on these two models using four real-world network
structures confirm that the algorithm is robust enough
and can efficiently identify the correct change pattern
of the parameter values. Comparison with the naive
method that finds the best combination of change
boundaries by an exhaustive search through a set of
randomly selected boundary candidates shows that the
proposed algorithm far outperforms the native method
both in terms of accuracy and computation time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Marathe:2013:AFN,
author = "Achla Marathe and Zhengzheng Pan and Andrea Apolloni",
title = "Analysis of friendship network and its role in
explaining obesity",
journal = j-TIST,
volume = "4",
number = "3",
pages = "56:1--56:??",
month = jun,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2483669.2483689",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:09 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We employ Add Health data to show that friendship
networks, constructed from mutual friendship
nominations, are important in building weight
perception, setting weight goals, and measuring social
marginalization among adolescents and young adults. We
study the relationship between individuals' perceived
weight status, actual weight status, weight status
relative to friends' weight status, and weight goals.
This analysis helps us understand how individual weight
perceptions might be formed, what these perceptions do
to the weight goals, and how friends' relative weight
affects weight perception and weight goals. Combining
this information with individuals' friendship network
helps determine the influence of social relationships
on weight-related variables. Multinomial logistic
regression results indicate that relative status is
indeed a significant predictor of perceived status, and
perceived status is a significant predictor of weight
goals. We also address the issue of causality between
actual weight status and social marginalization (as
measured by the number of friends) and show that
obesity precedes social marginalization in time rather
than the other way around. This lends credence to the
hypothesis that obesity leads to social marginalization
not vice versa. Attributes of the friendship network
can provide new insights into effective interventions
for combating obesity since adolescent friendships
provide an important social context for weight-related
behaviors.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jiang:2013:MSB,
author = "Daxin Jiang and Jian Pei and Hang Li",
title = "Mining search and browse logs for {Web} search: a
survey",
journal = j-TIST,
volume = "4",
number = "4",
pages = "57:1--57:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508038",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Huge amounts of search log data have been accumulated
at Web search engines. Currently, a popular Web search
engine may receive billions of queries and collect
terabytes of records about user search behavior daily.
Beside search log data, huge amounts of browse log data
have also been collected through client-side browser
plugins. Such massive amounts of search and browse log
data provide great opportunities for mining the wisdom
of crowds and improving Web search. At the same time,
designing effective and efficient methods to clean,
process, and model log data also presents great
challenges. In this survey, we focus on mining search
and browse log data for Web search. We start with an
introduction to search and browse log data and an
overview of frequently-used data summarizations in log
mining. We then elaborate how log mining applications
enhance the five major components of a search engine,
namely, query understanding, document understanding,
document ranking, user understanding, and monitoring
and feedback. For each aspect, we survey the major
tasks, fundamental principles, and state-of-the-art
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2013:SAM,
author = "Xi Li and Weiming Hu and Chunhua Shen and Zhongfei
Zhang and Anthony Dick and Anton {Van Den Hengel}",
title = "A survey of appearance models in visual object
tracking",
journal = j-TIST,
volume = "4",
number = "4",
pages = "58:1--58:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508039",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Visual object tracking is a significant computer
vision task which can be applied to many domains, such
as visual surveillance, human computer interaction, and
video compression. Despite extensive research on this
topic, it still suffers from difficulties in handling
complex object appearance changes caused by factors
such as illumination variation, partial occlusion,
shape deformation, and camera motion. Therefore,
effective modeling of the 2D appearance of tracked
objects is a key issue for the success of a visual
tracker. In the literature, researchers have proposed a
variety of 2D appearance models. To help readers
swiftly learn the recent advances in 2D appearance
models for visual object tracking, we contribute this
survey, which provides a detailed review of the
existing 2D appearance models. In particular, this
survey takes a module-based architecture that enables
readers to easily grasp the key points of visual object
tracking. In this survey, we first decompose the
problem of appearance modeling into two different
processing stages: visual representation and
statistical modeling. Then, different 2D appearance
models are categorized and discussed with respect to
their composition modules. Finally, we address several
issues of interest as well as the remaining challenges
for future research on this topic. The contributions of
this survey are fourfold. First, we review the
literature of visual representations according to their
feature-construction mechanisms (i.e., local and
global). Second, the existing statistical modeling
schemes for tracking-by-detection are reviewed
according to their model-construction mechanisms:
generative, discriminative, and hybrid
generative-discriminative. Third, each type of visual
representations or statistical modeling techniques is
analyzed and discussed from a theoretical or practical
viewpoint. Fourth, the existing benchmark resources
(e.g., source codes and video datasets) are examined in
this survey.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cena:2013:PSA,
author = "Federica Cena and Antonina Dattolo and Pasquale Lops
and Julita Vassileva",
title = "Perspectives in {Semantic Adaptive Social Web}",
journal = j-TIST,
volume = "4",
number = "4",
pages = "59:1--59:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2501603",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The Social Web is now a successful reality with its
quickly growing number of users and applications. Also
the Semantic Web, which started with the objective of
describing Web resources in a machine-processable way,
is now outgrowing the research labs and is being
massively exploited in many websites, incorporating
high-quality user-generated content and semantic
annotations. The primary goal of this special section
is to showcase some recent research at the intersection
of the Social Web and the Semantic Web that explores
the benefits that adaptation and personalization have
to offer in the Web of the future, the so-called Social
Adaptive Semantic Web. We have selected two articles
out of fourteen submissions based on the quality of the
articles and we present the main lessons learned from
the overall analysis of these submissions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Biancalana:2013:SSQ,
author = "Claudio Biancalana and Fabio Gasparetti and Alessandro
Micarelli and Giuseppe Sansonetti",
title = "Social semantic query expansion",
journal = j-TIST,
volume = "4",
number = "4",
pages = "60:1--60:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508041",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Weak semantic techniques rely on the integration of
Semantic Web techniques with social annotations and aim
to embrace the strengths of both. In this article, we
propose a novel weak semantic technique for query
expansion. Traditional query expansion techniques are
based on the computation of two-dimensional
co-occurrence matrices. Our approach proposes the use
of three-dimensional matrices, where the added
dimension is represented by semantic classes (i.e.,
categories comprising all the terms that share a
semantic property) related to the folksonomy extracted
from social bookmarking services, such as delicious and
StumbleUpon. The results of an indepth experimental
evaluation performed on both artificial datasets and
real users show that our approach outperforms
traditional techniques, such as relevance feedback and
personalized PageRank, so confirming the validity and
usefulness of the categorization of the user needs and
preferences in semantic classes. We also present the
results of a questionnaire aimed to know the users
opinion regarding the system. As one drawback of
several query expansion techniques is their high
computational costs, we also provide a complexity
analysis of our system, in order to show its capability
of operating in real time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:WMS,
author = "Chao Chen and Qiusha Zhu and Lin Lin and Mei-Ling
Shyu",
title = "{Web} media semantic concept retrieval via tag removal
and model fusion",
journal = j-TIST,
volume = "4",
number = "4",
pages = "61:1--61:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508042",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multimedia data on social websites contain rich
semantics and are often accompanied with user-defined
tags. To enhance Web media semantic concept retrieval,
the fusion of tag-based and content-based models can be
used, though it is very challenging. In this article, a
novel semantic concept retrieval framework that
incorporates tag removal and model fusion is proposed
to tackle such a challenge. Tags with useful
information can facilitate media search, but they are
often imprecise, which makes it important to apply
noisy tag removal (by deleting uncorrelated tags) to
improve the performance of semantic concept retrieval.
Therefore, a multiple correspondence analysis
(MCA)-based tag removal algorithm is proposed, which
utilizes MCA's ability to capture the relationships
among nominal features and identify representative and
discriminative tags holding strong correlations with
the target semantic concepts. To further improve the
retrieval performance, a novel model fusion method is
also proposed to combine ranking scores from both
tag-based and content-based models, where the
adjustment of ranking scores, the reliability of
models, and the correlations between the intervals
divided on the ranking scores and the semantic concepts
are all considered. Comparative results with extensive
experiments on the NUS-WIDE-LITE as well as the
NUS-WIDE-270K benchmark datasets with 81 semantic
concepts show that the proposed framework outperforms
baseline results and the other comparison methods with
each component being evaluated separately.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reddy:2013:ISS,
author = "Chandan K. Reddy and Cristopher C. Yang",
title = "Introduction to the special section on intelligent
systems for health informatics",
journal = j-TIST,
volume = "4",
number = "4",
pages = "62:1--62:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508043",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Batal:2013:TPM,
author = "Iyad Batal and Hamed Valizadegan and Gregory F. Cooper
and Milos Hauskrecht",
title = "A temporal pattern mining approach for classifying
electronic health record data",
journal = j-TIST,
volume = "4",
number = "4",
pages = "63:1--63:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508044",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We study the problem of learning classification models
from complex multivariate temporal data encountered in
electronic health record systems. The challenge is to
define a good set of features that are able to
represent well the temporal aspect of the data. Our
method relies on temporal abstractions and temporal
pattern mining to extract the classification features.
Temporal pattern mining usually returns a large number
of temporal patterns, most of which may be irrelevant
to the classification task. To address this problem, we
present the Minimal Predictive Temporal Patterns
framework to generate a small set of predictive and
nonspurious patterns. We apply our approach to the
real-world clinical task of predicting patients who are
at risk of developing heparin-induced thrombocytopenia.
The results demonstrate the benefit of our approach in
efficiently learning accurate classifiers, which is a
key step for developing intelligent clinical monitoring
systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rashidi:2013:CMM,
author = "Parisa Rashidi and Diane J. Cook",
title = "{COM}: a method for mining and monitoring human
activity patterns in home-based health monitoring
systems",
journal = j-TIST,
volume = "4",
number = "4",
pages = "64:1--64:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508045",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The increasing aging population in the coming decades
will result in many complications for society and in
particular for the healthcare system due to the
shortage of healthcare professionals and healthcare
facilities. To remedy this problem, researchers have
pursued developing remote monitoring systems and
assisted living technologies by utilizing recent
advances in sensor and networking technology, as well
as in the data mining and machine learning fields. In
this article, we report on our fully automated approach
for discovering and monitoring patterns of daily
activities. Discovering and tracking patterns of daily
activities can provide unprecedented opportunities for
health monitoring and assisted living applications,
especially for older adults and individuals with mental
disabilities. Previous approaches usually rely on
preselected activities or labeled data to track and
monitor daily activities. In this article, we present a
fully automated approach by discovering natural
activity patterns and their variations in real-life
data. We will show how our activity discovery component
can be integrated with an activity recognition
component to track and monitor various daily activity
patterns. We also provide an activity visualization
component to allow caregivers to visually observe and
examine the activity patterns using a user-friendly
interface. We validate our algorithms using real-life
data obtained from two apartments during a three-month
period.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wolf:2013:DUR,
author = "Hannes Wolf and Klaus Herrmann and Kurt Rothermel",
title = "Dealing with uncertainty: Robust workflow navigation
in the healthcare domain",
journal = j-TIST,
volume = "4",
number = "4",
pages = "65:1--65:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508046",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Processes in the healthcare domain are characterized
by coarsely predefined recurring procedures that are
flexibly adapted by the personnel to suite-specific
situations. In this setting, a workflow management
system that gives guidance and documents the
personnel's actions can lead to a higher quality of
care, fewer mistakes, and higher efficiency. However,
most existing workflow management systems enforce rigid
inflexible workflows and rely on direct manual input.
Both are inadequate for healthcare processes. In
particular, direct manual input is not possible in most
cases since (1) it would distract the personnel even in
critical situations and (2) it would violate
fundamental hygiene principles by requiring disinfected
doctors and nurses to touch input devices. The solution
could be activity recognition systems that use sensor
data (e.g., audio and acceleration data) to infer the
current activities by the personnel and provide input
to a workflow (e.g., informing it that a certain
activity is finished now). However, state-of-the-art
activity recognition technologies have difficulties in
providing reliable information. We describe a
comprehensive framework tailored for flexible
human-centric healthcare processes that improves the
reliability of activity recognition data. We present a
set of mechanisms that exploit the application
knowledge encoded in workflows in order to reduce the
uncertainty of this data, thus enabling unobtrusive
robust healthcare workflows. We evaluate our work based
on a real-world case study and show that the robustness
of unobtrusive healthcare workflows can be increased to
an absolute value of up to 91\% (compared to only 12\%
with a classical workflow system). This is a major
breakthrough that paves the way towards future
IT-enabled healthcare systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Park:2013:CPC,
author = "Yubin Park and Joydeep Ghosh",
title = "{CUDIA}: Probabilistic cross-level imputation using
individual auxiliary information",
journal = j-TIST,
volume = "4",
number = "4",
pages = "66:1--66:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508047",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In healthcare-related studies, individual patient or
hospital data are not often publicly available due to
privacy restrictions, legal issues, or reporting norms.
However, such measures may be provided at a higher or
more aggregated level, such as state-level,
county-level summaries or averages over health zones,
such as hospital referral regions (HRR) or hospital
service areas (HSA). Such levels constitute partitions
over the underlying individual level data, which may
not match the groupings that would have been obtained
if one clustered the data based on individual-level
attributes. Moreover, treating aggregated values as
representatives for the individuals can result in the
ecological fallacy. How can one run data mining
procedures on such data where different variables are
available at different levels of aggregation or
granularity? In this article, we seek a better
utilization of variably aggregated datasets, which are
possibly assembled from different sources. We propose a
novel cross-level imputation technique that models the
generative process of such datasets using a Bayesian
directed graphical model. The imputation is based on
the underlying data distribution and is shown to be
unbiased. This imputation can be further utilized in a
subsequent predictive modeling, yielding improved
accuracies. The experimental results using a simulated
dataset and the Behavioral Risk Factor Surveillance
System (BRFSS) dataset are provided to illustrate the
generality and capabilities of the proposed
framework.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hoens:2013:RMR,
author = "T. Ryan Hoens and Marina Blanton and Aaron Steele and
Nitesh V. Chawla",
title = "Reliable medical recommendation systems with patient
privacy",
journal = j-TIST,
volume = "4",
number = "4",
pages = "67:1--67:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508048",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One of the concerns patients have when confronted with
a medical condition is which physician to trust. Any
recommendation system that seeks to answer this
question must ensure that any sensitive medical
information collected by the system is properly
secured. In this article, we codify these privacy
concerns in a privacy-friendly framework and present
two architectures that realize it: the Secure
Processing Architecture (SPA) and the Anonymous
Contributions Architecture (ACA). In SPA, patients
submit their ratings in a protected form without
revealing any information about their data and the
computation of recommendations proceeds over the
protected data using secure multiparty computation
techniques. In ACA, patients submit their ratings in
the clear, but no link between a submission and patient
data can be made. We discuss various aspects of both
architectures, including techniques for ensuring
reliability of computed recommendations and system
performance, and provide their comparison.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Khan:2013:VOM,
author = "Atif Khan and John A. Doucette and Robin Cohen",
title = "Validation of an ontological medical decision support
system for patient treatment using a repository of
patient data: Insights into the value of machine
learning",
journal = j-TIST,
volume = "4",
number = "4",
pages = "68:1--68:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508049",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we begin by presenting OMeD, a
medical decision support system, and argue for its
value over purely probabilistic approaches that reason
about patients for time-critical decision scenarios. We
then progress to present Holmes, a Hybrid Ontological
and Learning MEdical System which supports decision
making about patient treatment. This system is
introduced in order to cope with the case of missing
data. We demonstrate its effectiveness by operating on
an extensive set of real-world patient health data from
the CDC, applied to the decision-making scenario of
administering sleeping pills. In particular, we clarify
how the combination of semantic, ontological
representations, and probabilistic reasoning together
enable the proposal of effective patient treatments.
Our focus is thus on presenting an approach for
interpreting medical data in the context of real-time
decision making. This constitutes a comprehensive
framework for the design of medical recommendation
systems for potential use by medical professionals and
patients both, with the end result being personalized
patient treatment. We conclude with a discussion of the
value of our particular approach for such diverse
considerations as coping with misinformation provided
by patients, performing effectively in time-critical
environments where real-time decisions are necessary,
and potential applications facilitating patient
information gathering.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2013:CPR,
author = "Suk Jin Lee and Yuichi Motai and Elisabeth Weiss and
Shumei S. Sun",
title = "Customized prediction of respiratory motion with
clustering from multiple patient interaction",
journal = j-TIST,
volume = "4",
number = "4",
pages = "69:1--69:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508050",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Information processing of radiotherapy systems has
become an important research area for sophisticated
radiation treatment methodology. Geometrically precise
delivery of radiotherapy in the thorax and upper
abdomen is compromised by respiratory motion during
treatment. Accurate prediction of the respiratory
motion would be beneficial for improving tumor
targeting. However, a wide variety of breathing
patterns can make it difficult to predict the breathing
motion with explicit models. We proposed a respiratory
motion predictor, that is, customized prediction with
multiple patient interactions using neural network
(CNN). For the preprocedure of prediction for
individual patient, we construct the clustering based
on breathing patterns of multiple patients using the
feature selection metrics that are composed of a
variety of breathing features. In the intraprocedure,
the proposed CNN used neural networks (NN) for a part
of the prediction and the extended Kalman filter (EKF)
for a part of the correction. The prediction accuracy
of the proposed method was investigated with a variety
of prediction time horizons using normalized root mean
squared error (NRMSE) values in comparison with the
alternate recurrent neural network (RNN). We have also
evaluated the prediction accuracy using the marginal
value that can be used as the reference value to judge
how many signals lie outside the confidence level. The
experimental results showed that the proposed CNN can
outperform RNN with respect to the prediction accuracy
with an improvement of 50\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Baralis:2013:EPH,
author = "Elena Baralis and Tania Cerquitelli and Silvia
Chiusano and Vincenzo D'Elia and Riccardo Molinari and
Davide Susta",
title = "Early prediction of the highest workload in
incremental cardiopulmonary tests",
journal = j-TIST,
volume = "4",
number = "4",
pages = "70:1--70:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508051",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Incremental tests are widely used in cardiopulmonary
exercise testing, both in the clinical domain and in
sport sciences. The highest workload (denoted
W$_{peak}$ ) reached in the test is key information for
assessing the individual body response to the test and
for analyzing possible cardiac failures and planning
rehabilitation, and training sessions. Being physically
very demanding, incremental tests can significantly
increase the body stress on monitored individuals and
may cause cardiopulmonary overload. This article
presents a new approach to cardiopulmonary testing that
addresses these drawbacks. During the test, our
approach analyzes the individual body response to the
exercise and predicts the W$_{peak}$ value that will be
reached in the test and an evaluation of its accuracy.
When the accuracy of the prediction becomes
satisfactory, the test can be prematurely stopped, thus
avoiding its entire execution. To predict W$_{peak}$,
we introduce a new index, the CardioPulmonary
Efficiency Index (CPE), summarizing the cardiopulmonary
response of the individual to the test. Our approach
analyzes the CPE trend during the test, together with
the characteristics of the individual, and predicts
W$_{peak}$. A K-nearest-neighbor-based classifier and
an ANN-based classier are exploited for the prediction.
The experimental evaluation showed that the W$_{peak}$
value can be predicted with a limited error from the
first steps of the test.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2013:SFI,
author = "Yugyung Lee and Saranya Krishnamoorthy and Deendayal
Dinakarpandian",
title = "A semantic framework for intelligent matchmaking for
clinical trial eligibility criteria",
journal = j-TIST,
volume = "4",
number = "4",
pages = "71:1--71:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508052",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An integral step in the discovery of new treatments
for medical conditions is the matching of potential
subjects with appropriate clinical trials. Eligibility
criteria for clinical trials are typically specified as
inclusion and exclusion criteria for each study in
freetext form. While this is sufficient for a human to
guide a recruitment interview, it cannot be reliably
and computationally construed to identify potential
subjects. Standardization of the representation of
eligibility criteria can enhance the efficiency and
accuracy of this process. This article presents a
semantic framework that facilitates intelligent
matchmaking by identifying a minimal set of eligibility
criteria with maximal coverage of clinical trials. In
contrast to existing top-down manual standardization
efforts, a bottom-up data driven approach is presented
to find a canonical nonredundant representation of an
arbitrary collection of clinical trial criteria. The
methodology has been validated with a corpus of 709
clinical trials related to Generalized Anxiety Disorder
containing 2,760 inclusion and 4,871 exclusion
eligibility criteria. This corpus is well represented
by a relatively small number of 126 inclusion clusters
and 175 exclusion clusters, each of which corresponds
to a semantically distinct criterion. Internal and
external validation measures provide an objective
evaluation of the method. An eligibility criteria
ontology has been constructed based on the clustering.
The resulting model has been incorporated into the
development of the MindTrial clinical trial recruiting
system. The prototype for clinical trial recruitment
illustrates the effectiveness of the methodology in
characterizing clinical trials and subjects and
accurate matching between them.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bi:2013:MLA,
author = "Jinbo Bi and Jiangwen Sun and Yu Wu and Howard Tennen
and Stephen Armeli",
title = "A machine learning approach to college drinking
prediction and risk factor identification",
journal = j-TIST,
volume = "4",
number = "4",
pages = "72:1--72:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508053",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Alcohol misuse is one of the most serious public
health problems facing adolescents and young adults in
the United States. National statistics shows that
nearly 90\% of alcohol consumed by youth under 21 years
of age involves binge drinking and 44\% of college
students engage in high-risk drinking activities.
Conventional alcohol intervention programs, which aim
at installing either an alcohol reduction norm or
prohibition against underage drinking, have yielded
little progress in controlling college binge drinking
over the years. Existing alcohol studies are deductive
where data are collected to investigate a
psychological/behavioral hypothesis, and statistical
analysis is applied to the data to confirm the
hypothesis. Due to this confirmatory manner of
analysis, the resulting statistical models are
cohort-specific and typically fail to replicate on a
different sample. This article presents two machine
learning approaches for a secondary analysis of
longitudinal data collected in college alcohol studies
sponsored by the National Institute on Alcohol Abuse
and Alcoholism. Our approach aims to discover
knowledge, from multiwave cohort-sequential daily data,
which may or may not align with the original hypothesis
but quantifies predictive models with higher likelihood
to generalize to new samples. We first propose a
so-called temporally-correlated support vector machine
to construct a classifier as a function of daily moods,
stress, and drinking expectancies to distinguish days
with nighttime binge drinking from days without for
individual students. We then propose a combination of
cluster analysis and feature selection, where cluster
analysis is used to identify drinking patterns based on
averaged daily drinking behavior and feature selection
is used to identify risk factors associated with each
pattern. We evaluate our methods on two cohorts of 530
total college students recruited during the Spring and
Fall semesters, respectively. Cross validation on these
two cohorts and further on 100 random partitions of the
total students demonstrate that our methods improve the
model generalizability in comparison with traditional
multilevel logistic regression. The discovered risk
factors and the interaction of these factors delineated
in our models can set a potential basis and offer
insights to a new design of more effective college
alcohol interventions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Subbu:2013:LMF,
author = "Kalyan Pathapati Subbu and Brandon Gozick and Ram
Dantu",
title = "{LocateMe}: Magnetic-fields-based indoor localization
using smartphones",
journal = j-TIST,
volume = "4",
number = "4",
pages = "73:1--73:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508054",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Fine-grained localization is extremely important to
accurately locate a user indoors. Although innovative
solutions have already been proposed, there is no
solution that is universally accepted, easily
implemented, user centric, and, most importantly, works
in the absence of GSM coverage or WiFi availability.
The advent of sensor rich smartphones has paved a way
to develop a solution that can cater to these
requirements. By employing a smartphone's built-in
magnetic field sensor, magnetic signatures were
collected inside buildings. These signatures displayed
a uniqueness in their patterns due to the presence of
different kinds of pillars, doors, elevators, etc.,
that consist of ferromagnetic materials like steel or
iron. We theoretically analyze the cause of this
uniqueness and then present an indoor localization
solution by classifying signatures based on their
patterns. However, to account for user walking speed
variations so as to provide an application usable to a
variety of users, we follow a dynamic
time-warping-based approach that is known to work on
similar signals irrespective of their variations in the
time axis. Our approach resulted in localization
distances of approximately 2m--6m with accuracies
between 80--100\% implying that it is sufficient to
walk short distances across hallways to be located by
the smartphone. The implementation of the application
on different smartphones yielded response times of less
than five secs, thereby validating the feasibility of
our approach and making it a viable solution.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:RWM,
author = "Bin Chen and Jian Su and Chew Lim Tan",
title = "Random walks down the mention graphs for event
coreference resolution",
journal = j-TIST,
volume = "4",
number = "4",
pages = "74:1--74:??",
month = sep,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2508037.2508055",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Event coreference is an important task in event
extraction and other natural language processing tasks.
Despite its importance, it was merely discussed in
previous studies. In this article, we present a global
coreference resolution system dedicated to various
sophisticated event coreference phenomena. First, seven
resolvers are utilized to resolve different event and
object coreference mention pairs with a new instance
selection strategy and new linguistic features. Second,
a global solution-a modified random walk
partitioning-is employed for the chain formation. Being
the first attempt to apply the random walk model for
coreference resolution, the revised model utilizes a
sampling method, termination criterion, and stopping
probability to greatly improve the effectiveness of
random walk model for event coreference resolution.
Last but not least, the new model facilitates a
convenient way to incorporate sophisticated linguistic
constraints and preferences, the related object mention
graph, as well as pronoun coreference information not
used in previous studies for effective chain formation.
In total, these techniques impose more than 20\%
F-score improvement over the baseline system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Editors:2013:ISS,
author = "Editors",
title = "Introduction to special section on intelligent mobile
knowledge discovery and management systems",
journal = j-TIST,
volume = "5",
number = "1",
pages = "1:1--1:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542183",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ying:2013:MGT,
author = "Josh Jia-Ching Ying and Wang-Chien Lee and Vincent S.
Tseng",
title = "Mining geographic-temporal-semantic patterns in
trajectories for location prediction",
journal = j-TIST,
volume = "5",
number = "1",
pages = "2:1--2:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542184",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, research on location predictions by
mining trajectories of users has attracted a lot of
attention. Existing studies on this topic mostly treat
such predictions as just a type of location
recommendation, that is, they predict the next location
of a user using location recommenders. However, an user
usually visits somewhere for reasons other than
interestingness. In this article, we propose a novel
mining-based location prediction approach called
Geographic-Temporal-Semantic-based Location Prediction
(GTS-LP), which takes into account a user's
geographic-triggered intentions, temporal-triggered
intentions, and semantic-triggered intentions, to
estimate the probability of the user in visiting a
location. The core idea underlying our proposal is the
discovery of trajectory patterns of users, namely GTS
patterns, to capture frequent movements triggered by
the three kinds of intentions. To achieve this goal, we
define a new trajectory pattern to capture the key
properties of the behaviors that are motivated by the
three kinds of intentions from trajectories of users.
In our GTS-LP approach, we propose a series of novel
matching strategies to calculate the similarity between
the current movement of a user and discovered GTS
patterns based on various moving intentions. On the
basis of similitude, we make an online prediction as to
the location the user intends to visit. To the best of
our knowledge, this is the first work on location
prediction based on trajectory pattern mining that
explores the geographic, temporal, and semantic
properties simultaneously. By means of a comprehensive
evaluation using various real trajectory datasets, we
show that our proposed GTS-LP approach delivers
excellent performance and significantly outperforms
existing state-of-the-art location prediction
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2013:FTC,
author = "Lu-An Tang and Yu Zheng and Jing Yuan and Jiawei Han
and Alice Leung and Wen-Chih Peng and Thomas {La
Porta}",
title = "A framework of traveling companion discovery on
trajectory data streams",
journal = j-TIST,
volume = "5",
number = "1",
pages = "3:1--3:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542185",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The advance of mobile technologies leads to huge
volumes of spatio-temporal data collected in the form
of trajectory data streams. In this study, we
investigate the problem of discovering object groups
that travel together (i.e., traveling companions ) from
trajectory data streams. Such technique has broad
applications in the areas of scientific study,
transportation management, and military surveillance.
To discover traveling companions, the monitoring system
should cluster the objects of each snapshot and
intersect the clustering results to retrieve
moving-together objects. Since both clustering and
intersection steps involve high computational overhead,
the key issue of companion discovery is to improve the
efficiency of algorithms. We propose the models of
closed companion candidates and smart intersection to
accelerate data processing. A data structure termed
traveling buddy is designed to facilitate scalable and
flexible companion discovery from trajectory streams.
The traveling buddies are microgroups of objects that
are tightly bound together. By only storing the object
relationships rather than their spatial coordinates,
the buddies can be dynamically maintained along the
trajectory stream with low cost. Based on traveling
buddies, the system can discover companions without
accessing the object details. In addition, we extend
the proposed framework to discover companions on more
complicated scenarios with spatial and temporal
constraints, such as on the road network and
battlefield. The proposed methods are evaluated with
extensive experiments on both real and synthetic
datasets. Experimental results show that our proposed
buddy-based approach is an order of magnitude faster
than the baselines and achieves higher accuracy in
companion discovery.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Doo:2013:MTF,
author = "Myungcheol Doo and Ling Liu",
title = "{Mondrian} tree: a fast index for spatial alarm
processing",
journal = j-TIST,
volume = "5",
number = "1",
pages = "4:1--4:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542186",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With ubiquitous wireless connectivity and
technological advances in mobile devices, we witness
the growing demands and increasing market shares of
mobile intelligent systems and technologies for
real-time decision making and location-based knowledge
discovery. Spatial alarms are considered as one of the
fundamental capabilities for intelligent mobile
location-based systems. Like time-based alarms that
remind us the arrival of a future time point, spatial
alarms remind us the arrival of a future spatial point.
Existing approaches for scaling spatial alarm
processing are focused on computing Alarm-Free Regions
(A fr) and Alarm-Free Period (Afp) such that mobile
objects traveling within an Afr can safely hibernate
the alarm evaluation process for the computed Afp, to
save battery power, until approaching the nearest alarm
of interest. A key technical challenge in scaling
spatial alarm processing is to efficiently compute Afr
and Afp such that mobile objects traveling within an
Afr can safely hibernate the alarm evaluation process
during the computed Afp, while maintaining high
accuracy. In this article we argue that on-demand
computation of Afr is expensive and may not scale well
for dense populations of mobile objects. Instead, we
propose to maintain an index for both spatial alarms
and empty regions (Afr) such that for a given mobile
user's location, we can find relevant spatial alarms
and whether it is in an alarm-free region more
efficiently. We also show that conventional spatial
indexing methods, such as R-tree family, k -d tree,
Quadtree, and Grid, are by design not well suited to
index empty regions. We present Mondrian Tree --- a
region partitioning tree for indexing both spatial
alarms and alarm-free regions. We first introduce the
Mondrian Tree indexing algorithms, including index
construction, search, and maintenance. Then we describe
a suite of Mondrian Tree optimizations to further
enhance the performance of spatial alarm processing.
Our experimental evaluation shows that the Mondrian
Tree index, as an intelligent technology for mobile
systems, outperforms traditional index methods, such as
R-tree, Quadtree, and k -d tree, for spatial alarm
processing.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bonchi:2013:ISI,
author = "Francesco Bonchi and Wray Buntine and Ricard
Gavald{\'a} and Shengbo Guo",
title = "Introduction to the special issue on {Social Web}
mining",
journal = j-TIST,
volume = "5",
number = "1",
pages = "5:1--5:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542187",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{He:2013:DJS,
author = "Yulan He and Chenghua Lin and Wei Gao and Kam-Fai
Wong",
title = "Dynamic joint sentiment-topic model",
journal = j-TIST,
volume = "5",
number = "1",
pages = "6:1--6:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542188",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Social media data are produced continuously by a large
and uncontrolled number of users. The dynamic nature of
such data requires the sentiment and topic analysis
model to be also dynamically updated, capturing the
most recent language use of sentiments and topics in
text. We propose a dynamic Joint Sentiment-Topic model
(dJST) which allows the detection and tracking of views
of current and recurrent interests and shifts in topic
and sentiment. Both topic and sentiment dynamics are
captured by assuming that the current
sentiment-topic-specific word distributions are
generated according to the word distributions at
previous epochs. We study three different ways of
accounting for such dependency information: (1) sliding
window where the current sentiment-topic word
distributions are dependent on the previous
sentiment-topic-specific word distributions in the last
S epochs; (2) skip model where history sentiment topic
word distributions are considered by skipping some
epochs in between; and (3) multiscale model where
previous long- and short- timescale distributions are
taken into consideration. We derive efficient online
inference procedures to sequentially update the model
with newly arrived data and show the effectiveness of
our proposed model on the Mozilla add-on reviews
crawled between 2007 and 2011.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cataldi:2013:PET,
author = "Mario Cataldi and Luigi {Di Caro} and Claudio
Schifanella",
title = "Personalized emerging topic detection based on a term
aging model",
journal = j-TIST,
volume = "5",
number = "1",
pages = "7:1--7:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542189",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Twitter is a popular microblogging service that acts
as a ground-level information news flashes portal where
people with different background, age, and social
condition provide information about what is happening
in front of their eyes. This characteristic makes
Twitter probably the fastest information service in the
world. In this article, we recognize this role of
Twitter and propose a novel, user-aware topic detection
technique that permits to retrieve, in real time, the
most emerging topics of discussion expressed by the
community within the interests of specific users.
First, we analyze the topology of Twitter looking at
how the information spreads over the network, taking
into account the authority/influence of each active
user. Then, we make use of a novel term aging model to
compute the burstiness of each term, and provide a
graph-based method to retrieve the minimal set of terms
that can represent the corresponding topic. Finally,
since any user can have topic preferences inferable
from the shared content, we leverage such knowledge to
highlight the most emerging topics within her foci of
interest. As evaluation we then provide several
experiments together with a user study proving the
validity and reliability of the proposed approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Arias:2013:FTD,
author = "Marta Arias and Argimiro Arratia and Ramon Xuriguera",
title = "Forecasting with {Twitter} data",
journal = j-TIST,
volume = "5",
number = "1",
pages = "8:1--8:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542190",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The dramatic rise in the use of social network
platforms such as Facebook or Twitter has resulted in
the availability of vast and growing user-contributed
repositories of data. Exploiting this data by
extracting useful information from it has become a
great challenge in data mining and knowledge discovery.
A recently popular way of extracting useful information
from social network platforms is to build indicators,
often in the form of a time series, of general public
mood by means of sentiment analysis. Such indicators
have been shown to correlate with a diverse variety of
phenomena. In this article we follow this line of work
and set out to assess, in a rigorous manner, whether a
public sentiment indicator extracted from daily Twitter
messages can indeed improve the forecasting of social,
economic, or commercial indicators. To this end we have
collected and processed a large amount of Twitter posts
from March 2011 to the present date for two very
different domains: stock market and movie box office
revenue. For each of these domains, we build and
evaluate forecasting models for several target time
series both using and ignoring the Twitter-related
data. If Twitter does help, then this should be
reflected in the fact that the predictions of models
that use Twitter-related data are better than the
models that do not use this data. By systematically
varying the models that we use and their parameters,
together with other tuning factors such as lag or the
way in which we build our Twitter sentiment index, we
obtain a large dataset that allows us to test our
hypothesis under different experimental conditions.
Using a novel decision-tree-based technique that we
call summary tree we are able to mine this large
dataset and obtain automatically those configurations
that lead to an improvement in the prediction power of
our forecasting models. As a general result, we have
seen that nonlinear models do take advantage of Twitter
data when forecasting trends in volatility indices,
while linear ones fail systematically when forecasting
any kind of financial time series. In the case of
predicting box office revenue trend, it is support
vector machines that make best use of Twitter data. In
addition, we conduct statistical tests to determine the
relation between our Twitter time series and the
different target time series.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2013:CES,
author = "Kyumin Lee and James Caverlee and Zhiyuan Cheng and
Daniel Z. Sui",
title = "Campaign extraction from social media",
journal = j-TIST,
volume = "5",
number = "1",
pages = "9:1--9:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542191",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this manuscript, we study the problem of detecting
coordinated free text campaigns in large-scale social
media. These campaigns-ranging from coordinated spam
messages to promotional and advertising campaigns to
political astro-turfing-are growing in significance and
reach with the commensurate rise in massive-scale
social systems. Specifically, we propose and evaluate a
content-driven framework for effectively linking free
text posts with common ``talking points'' and
extracting campaigns from large-scale social media.
Three of the salient features of the campaign
extraction framework are: (i) first, we investigate
graph mining techniques for isolating coherent
campaigns from large message-based graphs; (ii) second,
we conduct a comprehensive comparative study of
text-based message correlation in message and user
levels; and (iii) finally, we analyze temporal
behaviors of various campaign types. Through an
experimental study over millions of Twitter messages we
identify five major types of campaigns-namely Spam,
Promotion, Template, News, and Celebrity campaigns-and
we show how these campaigns may be extracted with high
precision and recall.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fire:2013:CEL,
author = "Michael Fire and Lena Tenenboim-Chekina and Rami Puzis
and Ofrit Lesser and Lior Rokach and Yuval Elovici",
title = "Computationally efficient link prediction in a variety
of social networks",
journal = j-TIST,
volume = "5",
number = "1",
pages = "10:1--10:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542192",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online social networking sites have become
increasingly popular over the last few years. As a
result, new interdisciplinary research directions have
emerged in which social network analysis methods are
applied to networks containing hundreds of millions of
users. Unfortunately, links between individuals may be
missing either due to an imperfect acquirement process
or because they are not yet reflected in the online
network (i.e., friends in the real world did not form a
virtual connection). The primary bottleneck in link
prediction techniques is extracting the structural
features required for classifying links. In this
article, we propose a set of simple, easy-to-compute
structural features that can be analyzed to identify
missing links. We show that by using simple structural
features, a machine learning classifier can
successfully identify missing links, even when applied
to a predicament of classifying links between
individuals with at least one common friend. We also
present a method for calculating the amount of data
needed in order to build more accurate classifiers. The
new Friends measure and Same community features we
developed are shown to be good predictors for missing
links. An evaluation experiment was performed on ten
large social networks datasets: Academia.edu, DBLP,
Facebook, Flickr, Flixster, Google+, Gowalla,
TheMarker, Twitter, and YouTube. Our methods can
provide social network site operators with the
capability of helping users to find known, offline
contacts and to discover new friends online. They may
also be used for exposing hidden links in online social
networks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cruz:2013:CDV,
author = "Juan David Cruz and C{\'e}cile Bothorel and
Fran{\c{c}}ois Poulet",
title = "Community detection and visualization in social
networks: Integrating structural and semantic
information",
journal = j-TIST,
volume = "5",
number = "1",
pages = "11:1--11:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542193",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Due to the explosion of social networking and the
information sharing among their users, the interest in
analyzing social networks has increased over the recent
years. Two general interests in this kind of studies
are community detection and visualization. In the first
case, most of the classic algorithms for community
detection use only the structural information to
identify groups, that is, how clusters are formed
according to the topology of the relationships.
However, these methods do not take into account any
semantic information which could guide the clustering
process, and which may add elements to conduct further
analyses. In the second case most of the layout
algorithms for clustered graphs have been designed to
differentiate the groups within the graph, but they are
not designed to analyze the interactions between such
groups. Identifying these interactions gives an insight
into the way different communities exchange messages or
information, and allows the social network researcher
to identify key actors, roles, and paths from one
community to another. This article presents a novel
model to use, in a conjoint way, the semantic
information from the social network and its structural
information to, first, find structurally and
semantically related groups of nodes, and second, a
layout algorithm for clustered graphs which divides the
nodes into two types, one for nodes with edges
connecting other communities and another with nodes
connecting nodes only within their own community. With
this division the visualization tool focuses on the
connections between groups facilitating deep studies of
augmented social networks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cagliero:2013:PTR,
author = "Luca Cagliero and Alessandro Fiori and Luigi
Grimaudo",
title = "Personalized tag recommendation based on generalized
rules",
journal = j-TIST,
volume = "5",
number = "1",
pages = "12:1--12:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542194",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Tag recommendation is focused on recommending useful
tags to a user who is annotating a Web resource. A
relevant research issue is the recommendation of
additional tags to partially annotated resources, which
may be based on either personalized or collective
knowledge. However, since the annotation process is
usually not driven by any controlled vocabulary, the
collections of user-specific and collective annotations
are often very sparse. Indeed, the discovery of the
most significant associations among tags becomes a
challenging task. This article presents a novel
personalized tag recommendation system that discovers
and exploits generalized association rules, that is,
tag correlations holding at different abstraction
levels, to identify additional pertinent tags to
suggest. The use of generalized rules relevantly
improves the effectiveness of traditional rule-based
systems in coping with sparse tag collections, because:
(i) correlations hidden at the level of individual tags
may be anyhow figured out at higher abstraction levels
and (ii) low-level tag associations discovered from
collective data may be exploited to specialize
high-level associations discovered in the user-specific
context. The effectiveness of the proposed system has
been validated against other personalized approaches on
real-life and benchmark collections retrieved from the
popular photo-sharing system Flickr.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Elahi:2013:ALS,
author = "Mehdi Elahi and Francesco Ricci and Neil Rubens",
title = "Active learning strategies for rating elicitation in
collaborative filtering: a system-wide perspective",
journal = j-TIST,
volume = "5",
number = "1",
pages = "13:1--13:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542195",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The accuracy of collaborative-filtering recommender
systems largely depends on three factors: the quality
of the rating prediction algorithm, and the quantity
and quality of available ratings. While research in the
field of recommender systems often concentrates on
improving prediction algorithms, even the best
algorithms will fail if they are fed poor-quality data
during training, that is, garbage in, garbage out.
Active learning aims to remedy this problem by focusing
on obtaining better-quality data that more aptly
reflects a user's preferences. However, traditional
evaluation of active learning strategies has two major
flaws, which have significant negative ramifications on
accurately evaluating the system's performance
(prediction error, precision, and quantity of elicited
ratings). (1) Performance has been evaluated for each
user independently (ignoring system-wide improvements).
(2) Active learning strategies have been evaluated in
isolation from unsolicited user ratings (natural
acquisition). In this article we show that an elicited
rating has effects across the system, so a typical
user-centric evaluation which ignores any changes of
rating prediction of other users also ignores these
cumulative effects, which may be more influential on
the performance of the system as a whole (system
centric). We propose a new evaluation methodology and
use it to evaluate some novel and state-of-the-art
rating elicitation strategies. We found that the
system-wide effectiveness of a rating elicitation
strategy depends on the stage of the rating elicitation
process, and on the evaluation measures (MAE, NDCG, and
Precision). In particular, we show that using some
common user-centric strategies may actually degrade the
overall performance of a system. Finally, we show that
the performance of many common active learning
strategies changes significantly when evaluated
concurrently with the natural acquisition of ratings in
recommender systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{deMeo:2013:AUB,
author = "Pasquale de Meo and Emilio Ferrara and Fabian Abel and
Lora Aroyo and Geert-Jan Houben",
title = "Analyzing user behavior across social sharing
environments",
journal = j-TIST,
volume = "5",
number = "1",
pages = "14:1--14:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2535526",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this work we present an in-depth analysis of the
user behaviors on different Social Sharing systems. We
consider three popular platforms, Flickr, Delicious and
StumbleUpon, and, by combining techniques from social
network analysis with techniques from semantic
analysis, we characterize the tagging behavior as well
as the tendency to create friendship relationships of
the users of these platforms. The aim of our
investigation is to see if (and how) the features and
goals of a given Social Sharing system reflect on the
behavior of its users and, moreover, if there exists a
correlation between the social and tagging behavior of
the users. We report our findings in terms of the
characteristics of user profiles according to three
different dimensions: (i) intensity of user activities,
(ii) tag-based characteristics of user profiles, and
(iii) semantic characteristics of user profiles.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2013:ACL,
author = "Ziqiang Shi and Jiqing Han and Tieran Zheng",
title = "Audio classification with low-rank matrix
representation features",
journal = j-TIST,
volume = "5",
number = "1",
pages = "15:1--15:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542197",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, a novel framework based on trace norm
minimization for audio classification is proposed. In
this framework, both the feature extraction and
classification are obtained by solving corresponding
convex optimization problem with trace norm
regularization. For feature extraction, robust
principle component analysis (robust PCA) via
minimization a combination of the nuclear norm and the
l$_1$ -norm is used to extract low-rank matrix features
which are robust to white noise and gross corruption
for audio signal. These low-rank matrix features are
fed to a linear classifier where the weight and bias
are learned by solving similar trace norm constrained
problems. For this linear classifier, most methods find
the parameters, that is the weight matrix and bias in
batch-mode, which makes it inefficient for large scale
problems. In this article, we propose a parallel online
framework using accelerated proximal gradient method.
This framework has advantages in processing speed and
memory cost. In addition, as a result of the
regularization formulation of matrix classification,
the Lipschitz constant was given explicitly, and hence
the step size estimation of the general proximal
gradient method was omitted, and this part of computing
burden is saved in our approach. Extensive experiments
on real data sets for laugh/non-laugh and
applause/non-applause classification indicate that this
novel framework is effective and noise robust.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Osman:2013:TMA,
author = "Nardine Osman and Carles Sierra and Fiona Mcneill and
Juan Pane and John Debenham",
title = "Trust and matching algorithms for selecting suitable
agents",
journal = j-TIST,
volume = "5",
number = "1",
pages = "16:1--16:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542198",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article addresses the problem of finding suitable
agents to collaborate with for a given interaction in
distributed open systems, such as multiagent and P2P
systems. The agent in question is given the chance to
describe its confidence in its own capabilities.
However, since agents may be malicious, misinformed,
suffer from miscommunication, and so on, one also needs
to calculate how much trusted is that agent. This
article proposes a novel trust model that calculates
the expectation about an agent's future performance in
a given context by assessing both the agent's
willingness and capability through the semantic
comparison of the current context in question with the
agent's performance in past similar experiences. The
proposed mechanism for assessing trust may be applied
to any real world application where past commitments
are recorded and observations are made that assess
these commitments, and the model can then calculate
one's trust in another with respect to a future
commitment by assessing the other's past performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Montali:2013:MBC,
author = "Marco Montali and Fabrizio M. Maggi and Federico
Chesani and Paola Mello and Wil M. P. van der Aalst",
title = "Monitoring business constraints with the event
calculus",
journal = j-TIST,
volume = "5",
number = "1",
pages = "17:1--17:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542199",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Today, large business processes are composed of
smaller, autonomous, interconnected subsystems,
achieving modularity and robustness. Quite often, these
large processes comprise software components as well as
human actors, they face highly dynamic environments and
their subsystems are updated and evolve independently
of each other. Due to their dynamic nature and
complexity, it might be difficult, if not impossible,
to ensure at design-time that such systems will always
exhibit the desired/expected behaviors. This, in turn,
triggers the need for runtime verification and
monitoring facilities. These are needed to check
whether the actual behavior complies with expected
business constraints, internal/external regulations and
desired best practices. In this work, we present
Mobucon EC, a novel monitoring framework that tracks
streams of events and continuously determines the state
of business constraints. In Mobucon EC, business
constraints are defined using the declarative language
Declare. For the purpose of this work, Declare has been
suitably extended to support quantitative time
constraints and non-atomic, durative activities. The
logic-based language Event Calculus (EC) has been
adopted to provide a formal specification and semantics
to Declare constraints, while a light-weight, logic
programming-based EC tool supports dynamically
reasoning about partial, evolving execution traces. To
demonstrate the applicability of our approach, we
describe a case study about maritime safety and
security and provide a synthetic benchmark to evaluate
its scalability.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lu:2013:SBA,
author = "Qiang Lu and Ruoyun Huang and Yixin Chen and You Xu
and Weixiong Zhang and Guoliang Chen",
title = "A {SAT-based} approach to cost-sensitive temporally
expressive planning",
journal = j-TIST,
volume = "5",
number = "1",
pages = "18:1--18:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542200",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Complex features, such as temporal dependencies and
numerical cost constraints, are hallmarks of real-world
planning problems. In this article, we consider the
challenging problem of cost-sensitive temporally
expressive (CSTE) planning, which requires concurrency
of durative actions and optimization of action costs.
We first propose a scheme to translate a CSTE planning
problem to a minimum cost (MinCost) satisfiability
(SAT) problem and to integrate with a relaxed parallel
planning semantics for handling true temporal
expressiveness. Our scheme finds solution plans that
optimize temporal makespan, and also minimize total
action costs at the optimal makespan. We propose two
approaches for solving MinCost SAT. The first is based
on a transformation of a MinCost SAT problem to a
weighted partial Max-SAT (WPMax-SAT), and the second,
called BB-CDCL, is an integration of the
branch-and-bound technique and the conflict driven
clause learning (CDCL) method. We also develop a CSTE
customized variable branching scheme for BB-CDCL which
can significantly improve the search efficiency. Our
experiments on the existing CSTE benchmark domains show
that our planner compares favorably to the
state-of-the-art temporally expressive planners in both
efficiency and quality.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shieh:2013:RTS,
author = "Jyh-Ren Shieh and Ching-Yung Lin and Shun-Xuan Wang
and Ja-Ling Wu",
title = "Relational term-suggestion graphs incorporating
multipartite concept and expertise networks",
journal = j-TIST,
volume = "5",
number = "1",
pages = "19:1--19:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542201",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Term suggestions recommend query terms to a user based
on his initial query. Suggesting adequate terms is a
challenging issue. Most existing commercial search
engines suggest search terms based on the frequency of
prior used terms that match the leading alphabets the
user types. In this article, we present a novel
mechanism to construct semantic term-relation graphs to
suggest relevant search terms in the semantic level. We
built term-relation graphs based on multipartite
networks of existing social media, especially from
Wikipedia. The multipartite linkage networks of
contributor-term, term-category, and term-term are
extracted from Wikipedia to eventually form term
relation graphs. For fusing these multipartite linkage
networks, we propose to incorporate the
contributor-category networks to model the expertise of
the contributors. Based on our experiments, this step
has demonstrated clear enhancement on the accuracy of
the inferred relatedness of the term-semantic graphs.
Experiments on keyword-expanded search based on 200
TREC-5 ad-hoc topics showed obvious advantage of our
algorithms over existing approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2013:EEM,
author = "Tianshi Chen and Yunji Chen and Qi Guo and Zhi-Hua
Zhou and Ling Li and Zhiwei Xu",
title = "Effective and efficient microprocessor design space
exploration using unlabeled design configurations",
journal = j-TIST,
volume = "5",
number = "1",
pages = "20:1--20:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542202",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Ever-increasing design complexity and advances of
technology impose great challenges on the design of
modern microprocessors. One such challenge is to
determine promising microprocessor configurations to
meet specific design constraints, which is called
Design Space Exploration (DSE). In the computer
architecture community, supervised learning techniques
have been applied to DSE to build regression models for
predicting the qualities of design configurations. For
supervised learning, however, considerable simulation
costs are required for attaining the labeled design
configurations. Given limited resources, it is
difficult to achieve high accuracy. In this article,
inspired by recent advances in semisupervised learning
and active learning, we propose the COAL approach which
can exploit unlabeled design configurations to
significantly improve the models. Empirical study
demonstrates that COAL significantly outperforms a
state-of-the-art DSE technique by reducing mean squared
error by 35\% to 95\%, and thus, promising
architectures can be attained more efficiently.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Singh:2013:NBG,
author = "Munindar P. Singh",
title = "Norms as a basis for governing sociotechnical
systems",
journal = j-TIST,
volume = "5",
number = "1",
pages = "21:1--21:??",
month = dec,
year = "2013",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542182.2542203",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 13 07:29:16 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We understand a sociotechnical system as a
multistakeholder cyber-physical system. We introduce
governance as the administration of such a system by
the stakeholders themselves. In this regard, governance
is a peer-to-peer notion and contrasts with traditional
management, which is a top-down hierarchical notion.
Traditionally, there is no computational support for
governance and it is achieved through out-of-band
interactions among system administrators. Not
surprisingly, traditional approaches simply do not
scale up to large sociotechnical systems. We develop an
approach for governance based on a computational
representation of norms in organizations. Our approach
is motivated by the Ocean Observatory Initiative, a
thirty-year \$400 million project, which supports a
variety of resources dealing with monitoring and
studying the world's oceans. These resources include
autonomous underwater vehicles, ocean gliders, buoys,
and other instrumentation as well as more traditional
computational resources. Our approach has the benefit
of directly reflecting stakeholder needs and assuring
stakeholders of the correctness of the resulting
governance decisions while yielding adaptive resource
allocation in the face of changes in both stakeholder
needs and physical circumstances.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{He:2014:ISI,
author = "Qi He and Juanzi Li and Rong Yan and John Yen and
Haizheng Zhang",
title = "Introduction to the {Special Issue on Linking Social
Granularity and Functions}",
journal = j-TIST,
volume = "5",
number = "2",
pages = "22:1--22:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2594452",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2014:IUI,
author = "Jinpeng Wang and Wayne Xin Zhao and Yulan He and
Xiaoming Li",
title = "Infer User Interests via Link Structure
Regularization",
journal = j-TIST,
volume = "5",
number = "2",
pages = "23:1--23:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2499380",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Learning user interests from online social networks
helps to better understand user behaviors and provides
useful guidance to design user-centric applications.
Apart from analyzing users' online content, it is also
important to consider users' social connections in the
social Web. Graph regularization methods have been
widely used in various text mining tasks, which can
leverage the graph structure information extracted from
data. Previously, graph regularization methods operate
under the cluster assumption that nearby nodes are more
similar and nodes on the same structure (typically
referred to as a cluster or a manifold) are likely to
be similar. We argue that learning user interests from
complex, sparse, and dynamic social networks should be
based on the link structure assumption under which node
similarities are evaluated based on the local link
structures instead of explicit links between two nodes.
We propose a regularization framework based on the
relation bipartite graph, which can be constructed from
any type of relations. Using Twitter as our case study,
we evaluate our proposed framework from social networks
built from retweet relations. Both quantitative and
qualitative experiments show that our proposed method
outperforms a few competitive baselines in learning
user interests over a set of predefined topics. It also
gives superior results compared to the baselines on
retweet prediction and topical authority
identification.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Javari:2014:CBC,
author = "Amin Javari and Mahdi Jalili",
title = "Cluster-Based Collaborative Filtering for Sign
Prediction in Social Networks with Positive and
Negative Links",
journal = j-TIST,
volume = "5",
number = "2",
pages = "24:1--24:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2501977",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Social network analysis and mining get
ever-increasingly important in recent years, which is
mainly due to the availability of large datasets and
advances in computing systems. A class of social
networks is those with positive and negative links. In
such networks, a positive link indicates friendship (or
trust), whereas links with a negative sign correspond
to enmity (or distrust). Predicting the sign of the
links in these networks is an important issue and has
many applications, such as friendship recommendation
and identifying malicious nodes in the network. In this
manuscript, we proposed a new method for sign
prediction in networks with positive and negative
links. Our algorithm is based first on clustering the
network into a number of clusters and then applying a
collaborative filtering algorithm. The clusters are
such that the number of intra-cluster negative links
and inter-cluster positive links are minimal, that is,
the clusters are socially balanced as much as possible
(a signed graph is socially balanced if it can be
divided into clusters with all positive links inside
the clusters and all negative links between them). We
then used similarity between the clusters (based on the
links between them) in a collaborative filtering
algorithm. Our experiments on a number of real datasets
showed that the proposed method outperformed previous
methods, including those based on social balance and
status theories and one based on a machine learning
framework (logistic regression in this work).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2014:CCB,
author = "Yi-Cheng Chen and Wen-Yuan Zhu and Wen-Chih Peng and
Wang-Chien Lee and Suh-Yin Lee",
title = "{CIM}: Community-Based Influence Maximization in
Social Networks",
journal = j-TIST,
volume = "5",
number = "2",
pages = "25:1--25:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532549",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Given a social graph, the problem of influence
maximization is to determine a set of nodes that
maximizes the spread of influences. While some recent
research has studied the problem of influence
maximization, these works are generally too time
consuming for practical use in a large-scale social
network. In this article, we develop a new framework,
community-based influence maximization (CIM), to tackle
the influence maximization problem with an emphasis on
the time efficiency issue. Our proposed framework, CIM,
comprises three phases: (i) community detection, (ii)
candidate generation, and (iii) seed selection.
Specifically, phase (i) discovers the community
structure of the network; phase (ii) uses the
information of communities to narrow down the possible
seed candidates; and phase (iii) finalizes the seed
nodes from the candidate set. By exploiting the
properties of the community structures, we are able to
avoid overlapped information and thus efficiently
select the number of seeds to maximize information
spreads. The experimental results on both synthetic and
real datasets show that the proposed CIM algorithm
significantly outperforms the state-of-the-art
algorithms in terms of efficiency and scalability, with
almost no compromise of effectiveness.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2014:SOG,
author = "Jaewon Yang and Jure Leskovec",
title = "Structure and Overlaps of Ground-Truth Communities in
Networks",
journal = j-TIST,
volume = "5",
number = "2",
pages = "26:1--26:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2594454",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One of the main organizing principles in real-world
networks is that of network communities, where sets of
nodes organize into densely linked clusters. Even
though detection of such communities is of great
interest, understanding the structure communities in
large networks remains relatively limited. In
particular, due to the unavailability of labeled
ground-truth data, it was traditionally very hard to
develop accurate models of network community structure.
Here we use six large social, collaboration, and
information networks where nodes explicitly state their
ground-truth community memberships. For example, nodes
in social networks join into explicitly defined
interest based groups, and we use such groups as
explicitly labeled ground-truth communities. We use
such ground-truth communities to study their structural
signatures by analyzing how ground-truth communities
emerge in networks and how they overlap. We observe
some surprising phenomena. First, ground-truth
communities contain high-degree hub nodes that reside
in community overlaps and link to most of the members
of the community. Second, the overlaps of communities
are more densely connected than the non-overlapping
parts of communities. We show that this in contrast to
the conventional wisdom that community overlaps are
more sparsely connected than the non-overlapping parts
themselves. We then show that many existing models of
network communities do not capture dense community
overlaps. This in turn means that most present models
and community detection methods confuse overlaps as
separate communities. In contrast, we present the
community-affiliation graph model (AGM), a conceptual
model of network community structure. We demonstrate
that AGM reliably captures the overall structure of
networks as well as the overlapping and hierarchical
nature of network communities.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gong:2014:JLP,
author = "Neil Zhenqiang Gong and Ameet Talwalkar and Lester
Mackey and Ling Huang and Eui Chul Richard Shin and
Emil Stefanov and Elaine (Runting) Shi and Dawn Song",
title = "Joint Link Prediction and Attribute Inference Using a
Social-Attribute Network",
journal = j-TIST,
volume = "5",
number = "2",
pages = "27:1--27:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2594455",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The effects of social influence and homophily suggest
that both network structure and node-attribute
information should inform the tasks of link prediction
and node-attribute inference. Recently, Yin et al.
[2010a, 2010b] proposed an attribute-augmented social
network model, which we call Social-Attribute Network
(SAN), to integrate network structure and node
attributes to perform both link prediction and
attribute inference. They focused on generalizing the
random walk with a restart algorithm to the SAN
framework and showed improved performance. In this
article, we extend the SAN framework with several
leading supervised and unsupervised link-prediction
algorithms and demonstrate performance improvement for
each algorithm on both link prediction and attribute
inference. Moreover, we make the novel observation that
attribute inference can help inform link prediction,
that is, link-prediction accuracy is further improved
by first inferring missing attributes. We
comprehensively evaluate these algorithms and compare
them with other existing algorithms using a novel,
large-scale Google+ dataset, which we make publicly
available
(http://www.cs.berkeley.edu/~stevgong/gplus.html).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Pool:2014:DDC,
author = "Simon Pool and Francesco Bonchi and Matthijs van
Leeuwen",
title = "Description-Driven Community Detection",
journal = j-TIST,
volume = "5",
number = "2",
pages = "28:1--28:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2517088",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Traditional approaches to community detection, as
studied by physicists, sociologists, and more recently
computer scientists, aim at simply partitioning the
social network graph. However, with the advent of
online social networking sites, richer data has become
available: beyond the link information, each user in
the network is annotated with additional information,
for example, demographics, shopping behavior, or
interests. In this context, it is therefore important
to develop mining methods which can take advantage of
all available information. In the case of community
detection, this means finding good communities (a set
of nodes cohesive in the social graph) which are
associated with good descriptions in terms of user
information (node attributes). Having good descriptions
associated to our models make them understandable by
domain experts and thus more useful in real-world
applications. Another requirement dictated by
real-world applications, is to develop methods that can
use, when available, any domain-specific background
knowledge. In the case of community detection the
background knowledge could be a vague description of
the communities sought in a specific application, or
some prototypical nodes (e.g., good customers in the
past), that represent what the analyst is looking for
(a community of similar users). Towards this goal, in
this article, we define and study the problem of
finding a diverse set of cohesive communities with
concise descriptions. We propose an effective algorithm
that alternates between two phases: a hill-climbing
phase producing (possibly overlapping) communities, and
a description induction phase which uses techniques
from supervised pattern set mining. Our framework has
the nice feature of being able to build well-described
cohesive communities starting from any given
description or seed set of nodes, which makes it very
flexible and easily applicable in real-world
applications. Our experimental evaluation confirms that
the proposed method discovers cohesive communities with
concise descriptions in realistic and large online
social networks such as Delicious, Flickr, and
LastFM.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2014:LPH,
author = "Nan Li and William Cushing and Subbarao Kambhampati
and Sungwook Yoon",
title = "Learning Probabilistic Hierarchical Task Networks as
Probabilistic Context-Free Grammars to Capture User
Preferences",
journal = j-TIST,
volume = "5",
number = "2",
pages = "29:1--29:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2589481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We introduce an algorithm to automatically learn
probabilistic hierarchical task networks (pHTNs) that
capture a user's preferences on plans by observing only
the user's behavior. HTNs are a common choice of
representation for a variety of purposes in planning,
including work on learning in planning. Our
contributions are twofold. First, in contrast with
prior work, which employs HTNs to represent domain
physics or search control knowledge, we use HTNs to
model user preferences. Second, while most prior work
on HTN learning requires additional information (e.g.,
annotated traces or tasks) to assist the learning
process, our system only takes plan traces as input.
Initially, we will assume that users carry out
preferred plans more frequently, and thus the observed
distribution of plans is an accurate representation of
user preference. We then generalize to the situation
where feasibility constraints frequently prevent the
execution of preferred plans. Taking the prevalent
perspective of viewing HTNs as grammars over primitive
actions, we adapt an expectation-maximization (EM)
technique from the discipline of probabilistic grammar
induction to acquire probabilistic context-free
grammars (pCFG) that capture the distribution on plans.
To account for the difference between the distributions
of possible and preferred plans, we subsequently modify
this core EM technique by rescaling its input. We
empirically demonstrate that the proposed approaches
are able to learn HTNs representing user preferences
better than the inside-outside algorithm. Furthermore,
when feasibility constraints are obfuscated, the
algorithm with rescaled input performs better than the
algorithm with the original input.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reches:2014:FEC,
author = "Shulamit Reches and Meir Kalech and Philip Hendrix",
title = "A Framework for Effectively Choosing between
Alternative Candidate Partners",
journal = j-TIST,
volume = "5",
number = "2",
pages = "30:1--30:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2589482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many multi-agent settings require that agents identify
appropriate partners or teammates with whom to work on
tasks. When selecting potential partners, agents may
benefit from obtaining information about the
alternatives, for instance, through gossip (i.e., by
consulting others) or reputation systems. When
information is uncertain and associated with cost,
deciding on the amount of information needed is a hard
optimization problem. This article defines a
statistical model, the Information-Acquisition Source
Utility model (IASU), by which agents, operating in an
uncertain world, can determine (1) which information
sources they should request for information, and (2)
the amount of information to collect about potential
partners from each source. To maximize the expected
gain from the choice, IASU computes the utility of
choosing a partner by estimating the benefit of
additional information. The article presents empirical
studies through a simulation domain as well as a
real-world domain of restaurants. We compare the IASU
model to other relevant models and show that the use of
the IASU model significantly increases agents' overall
utility.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Heath:2014:CST,
author = "Derrall Heath and David Norton and Dan Ventura",
title = "Conveying Semantics through Visual Metaphor",
journal = j-TIST,
volume = "5",
number = "2",
pages = "31:1--31:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2589483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In the field of visual art, metaphor is a way to
communicate meaning to the viewer. We present a
computational system for communicating visual metaphor
that can identify adjectives for describing an image
based on a low-level visual feature representation of
the image. We show that the system can use this
visual-linguistic association to render source images
that convey the meaning of adjectives in a way
consistent with human understanding. Our conclusions
are based on a detailed analysis of how the system's
artifacts cluster, how these clusters correspond to the
semantic relationships of adjectives as documented in
WordNet, and how these clusters correspond to human
opinion.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lian:2014:MCH,
author = "Defu Lian and Xing Xie",
title = "Mining Check-In History for Personalized Location
Naming",
journal = j-TIST,
volume = "5",
number = "2",
pages = "32:1--32:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2490890",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many innovative location-based services have been
established to offer users greater convenience in their
everyday lives. These services usually cannot map
user's physical locations into semantic names
automatically. The semantic names of locations provide
important context for mobile recommendations and
advertisements. In this article, we proposed a novel
location naming approach which can automatically
provide semantic names for users given their locations
and time. In particular, when a user opens a GPS device
and submits a query with her physical location and
time, she will be returned the most appropriate
semantic name. In our approach, we drew an analogy
between location naming and local search, and designed
a local search framework to propose a spatiotemporal
and user preference (STUP) model for location naming.
STUP combined three components, user preference (UP),
spatial preference (SP), and temporal preference (TP),
by leveraging learning-to-rank techniques. We evaluated
STUP on 466,190 check-ins of 5,805 users from Shanghai
and 135,052 check-ins of 1,361 users from Beijing. The
results showed that SP was most effective among three
components and that UP can provide personalized
semantic names, and thus it was a necessity for
location naming. Although TP was not as discriminative
as the others, it can still be beneficial when
integrated with SP and UP. Finally, according to the
experimental results, STUP outperformed the proposed
baselines and returned accurate semantic names for
23.6\% and 26.6\% of the testing queries from Beijing
and Shanghai, respectively.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bian:2014:EUP,
author = "Jiang Bian and Bo Long and Lihong Li and Taesup Moon
and Anlei Dong and Yi Chang",
title = "Exploiting User Preference for Online Learning in
{Web} Content Optimization Systems",
journal = j-TIST,
volume = "5",
number = "2",
pages = "33:1--33:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2493259",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Web portal services have become an important medium to
deliver digital content (e.g. news, advertisements,
etc.) to Web users in a timely fashion. To attract more
users to various content modules on the Web portal, it
is necessary to design a recommender system that can
effectively achieve Web portal content optimization by
automatically estimating content item attractiveness
and relevance to user interests. The state-of-the-art
online learning methodology adapts dedicated pointwise
models to independently estimate the attractiveness
score for each candidate content item. Although such
pointwise models can be easily adapted for online
recommendation, there still remain a few critical
problems. First, this pointwise methodology fails to
use invaluable user preferences between content items.
Moreover, the performance of pointwise models decreases
drastically when facing the problem of sparse learning
samples. To address these problems, we propose
exploring a new dynamic pairwise learning methodology
for Web portal content optimization in which we exploit
dynamic user preferences extracted based on users'
actions on portal services to compute the
attractiveness scores of content items. In this
article, we introduce two specific pairwise learning
algorithms, a straightforward graph-based algorithm and
a formalized Bayesian modeling one. Experiments on
large-scale data from a commercial Web portal
demonstrate the significant improvement of pairwise
methodologies over the baseline pointwise models.
Further analysis illustrates that our new pairwise
learning approaches can benefit personalized
recommendation more than pointwise models, since the
data sparsity is more critical for personalized content
optimization.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hossain:2014:AFS,
author = "M. Shahriar Hossain and Manish Marwah and Amip Shah
and Layne T. Watson and Naren Ramakrishnan",
title = "{AutoLCA}: a Framework for Sustainable Redesign and
Assessment of Products",
journal = j-TIST,
volume = "5",
number = "2",
pages = "34:1--34:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2505270",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With increasing public consciousness regarding
sustainability, companies are ever more eager to
introduce eco-friendly products and services. Assessing
environmental footprints and designing sustainable
products are challenging tasks since they require
analysis of each component of a product through their
life cycle. To achieve sustainable design of products,
companies need to evaluate the environmental impact of
their system, identify the major contributors to the
footprint, and select the design alternative with the
lowest environmental footprint. In this article, we
formulate sustainable design as a series of clustering
and classification problems, and propose a framework
called AutoLCA that simplifies the effort of estimating
the environmental footprint of a product bill of
materials by more than an order of magnitude over
current methods, which are mostly labor intensive. We
apply AutoLCA to real data from a large computer
manufacturer. We conduct a case study on bill of
materials of four different products, perform a
``hotspot'' assessment analysis to identify major
contributors to carbon footprint, and determine design
alternatives that can reduce the carbon footprint from
1\% to 36\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2014:MLC,
author = "Chuan Shi and Xiangnan Kong and Di Fu and Philip S. Yu
and Bin Wu",
title = "Multi-Label Classification Based on Multi-Objective
Optimization",
journal = j-TIST,
volume = "5",
number = "2",
pages = "35:1--35:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2505272",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multi-label classification refers to the task of
predicting potentially multiple labels for a given
instance. Conventional multi-label classification
approaches focus on single objective setting, where the
learning algorithm optimizes over a single performance
criterion (e.g., Ranking Loss ) or a heuristic
function. The basic assumption is that the optimization
over one single objective can improve the overall
performance of multi-label classification and meet the
requirements of various applications. However, in many
real applications, an optimal multi-label classifier
may need to consider the trade-offs among multiple
inconsistent objectives, such as minimizing Hamming
Loss while maximizing Micro F1. In this article, we
study the problem of multi-objective multi-label
classification and propose a novel solution (called
Moml) to optimize over multiple objectives
simultaneously. Note that optimization objectives may
be inconsistent, even conflicting, thus one cannot
identify a single solution that is optimal on all
objectives. Our Moml algorithm finds a set of
non-dominated solutions which are optimal according to
different trade-offs among multiple objectives. So
users can flexibly construct various predictive models
from the solution set, which provides more meaningful
classification results in different application
scenarios. Empirical studies on real-world tasks
demonstrate that the Moml can effectively boost the
overall performance of multi-label classification by
optimizing over multiple objectives simultaneously.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2014:DSM,
author = "Xuning Tang and Christopher C. Yang",
title = "Detecting Social Media Hidden Communities Using
Dynamic Stochastic Blockmodel with Temporal {Dirichlet}
Process",
journal = j-TIST,
volume = "5",
number = "2",
pages = "36:1--36:??",
month = apr,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2517085",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Apr 24 16:09:50 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Detecting evolving hidden communities within dynamic
social networks has attracted significant attention
recently due to its broad applications in e-commerce,
online social media, security intelligence, public
health, and other areas. Many community network
detection techniques employ a two-stage approach to
identify and detect evolutionary relationships between
communities of two adjacent time epochs. These
techniques often identify communities with high
temporal variation, since the two-stage approach
detects communities of each epoch independently without
considering the continuity of communities across two
time epochs. Other techniques require identification of
a predefined number of hidden communities which is not
realistic in many applications. To overcome these
limitations, we propose the Dynamic Stochastic
Blockmodel with Temporal Dirichlet Process, which
enables the detection of hidden communities and tracks
their evolution simultaneously from a network stream.
The number of hidden communities is automatically
determined by a temporal Dirichlet process without
human intervention. We tested our proposed technique on
three different testbeds with results identifying a
high performance level when compared to the baseline
algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2014:ISS,
author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai
Yang",
title = "Introduction to the Special Section on Urban
Computing",
journal = j-TIST,
volume = "5",
number = "3",
pages = "37:1--37:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2642650",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2014:UCC,
author = "Yu Zheng and Licia Capra and Ouri Wolfson and Hai
Yang",
title = "Urban Computing: Concepts, Methodologies, and
Applications",
journal = j-TIST,
volume = "5",
number = "3",
pages = "38:1--38:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629592",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Urbanization's rapid progress has modernized many
people's lives but also engendered big issues, such as
traffic congestion, energy consumption, and pollution.
Urban computing aims to tackle these issues by using
the data that has been generated in cities (e.g.,
traffic flow, human mobility, and geographical data).
Urban computing connects urban sensing, data
management, data analytics, and service providing into
a recurrent process for an unobtrusive and continuous
improvement of people's lives, city operation systems,
and the environment. Urban computing is an
interdisciplinary field where computer sciences meet
conventional city-related fields, like transportation,
civil engineering, environment, economy, ecology, and
sociology in the context of urban spaces. This article
first introduces the concept of urban computing,
discussing its general framework and key challenges
from the perspective of computer sciences. Second, we
classify the applications of urban computing into seven
categories, consisting of urban planning,
transportation, the environment, energy, social,
economy, and public safety and security, presenting
representative scenarios in each category. Third, we
summarize the typical technologies that are needed in
urban computing into four folds, which are about urban
sensing, urban data management, knowledge fusion across
heterogeneous data, and urban data visualization.
Finally, we give an outlook on the future of urban
computing, suggesting a few research topics that are
somehow missing in the community.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Etienne:2014:MBC,
author = "C{\^o}me Etienne and Oukhellou Latifa",
title = "Model-Based Count Series Clustering for Bike Sharing
System Usage Mining: a Case Study with the {V{\'e}lib'}
System of {Paris}",
journal = j-TIST,
volume = "5",
number = "3",
pages = "39:1--39:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2560188",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Today, more and more bicycle sharing systems (BSSs)
are being introduced in big cities. These
transportation systems generate sizable transportation
data, the mining of which can reveal the underlying
urban phenomenon linked to city dynamics. This article
presents a statistical model to automatically analyze
the trip data of a bike sharing system. The proposed
solution partitions (i.e., clusters) the stations
according to their usage profiles. To do so, count
series describing the stations's usage through
departure/arrival counts per hour throughout the day
are built and analyzed. The model for processing these
count series is based on Poisson mixtures and
introduces a station scaling factor that handles the
differences between the stations's global usage.
Differences between weekday and weekend usage are also
taken into account. This model identifies the latent
factors that shape the geography of trips, and the
results may thus offer insights into the relationships
between station neighborhood type (its amenities, its
demographics, etc.) and the generated mobility
patterns. In other words, the proposed method brings to
light the different functions in different areas that
induce specific patterns in BSS data. These potentials
are demonstrated through an in-depth analysis of the
results obtained on the Paris V{\'e}lib' large-scale
bike sharing system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ying:2014:MUC,
author = "Josh Jia-Ching Ying and Wen-Ning Kuo and Vincent S.
Tseng and Eric Hsueh-Chan Lu",
title = "Mining User Check-In Behavior with a Random Walk for
Urban Point-of-Interest Recommendations",
journal = j-TIST,
volume = "5",
number = "3",
pages = "40:1--40:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2523068",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, research into the mining of user
check-in behavior for point-of-interest (POI)
recommendations has attracted a lot of attention.
Existing studies on this topic mainly treat such
recommendations in a traditional manner-that is, they
treat POIs as items and check-ins as ratings. However,
users usually visit a place for reasons other than to
simply say that they have visited. In this article, we
propose an approach referred to as Urban POI-Walk
(UPOI-Walk), which takes into account a user's
social-triggered intentions (SI), preference-triggered
intentions (PreI), and popularity-triggered intentions
(PopI), to estimate the probability of a user
checking-in to a POI. The core idea of UPOI-Walk
involves building a HITS-based random walk on the
normalized check-in network, thus supporting the
prediction of POI properties related to each user's
preferences. To achieve this goal, we define several
user--POI graphs to capture the key properties of the
check-in behavior motivated by user intentions. In our
UPOI-Walk approach, we propose a new kind of random
walk model-Dynamic HITS-based Random Walk-which
comprehensively considers the relevance between POIs
and users from different aspects. On the basis of
similitude, we make an online recommendation as to the
POI the user intends to visit. To the best of our
knowledge, this is the first work on urban POI
recommendations that considers user check-in behavior
motivated by SI, PreI, and PopI in location-based
social network data. Through comprehensive experimental
evaluations on two real datasets, the proposed
UPOI-Walk is shown to deliver excellent performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mcardle:2014:UDF,
author = "Gavin Mcardle and Eoghan Furey and Aonghus Lawlor and
Alexei Pozdnoukhov",
title = "Using Digital Footprints for a City-Scale Traffic
Simulation",
journal = j-TIST,
volume = "5",
number = "3",
pages = "41:1--41:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2517028",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article introduces a microsimulation of urban
traffic flows within a large-scale scenario implemented
for the Greater Dublin region in Ireland.
Traditionally, the data available for traffic
simulations come from a population census and dedicated
road surveys that only partly cover shopping, leisure,
or recreational trips. To account for the latter, the
presented traffic modeling framework exploits the
digital footprints of city inhabitants on services such
as Twitter and Foursquare. We enriched the model with
findings from our previous studies on geographical
layout of communities in a country-wide mobile phone
network to account for socially related journeys. These
datasets were used to calibrate a variant of a
radiation model of spatial choice, which we introduced
in order to drive individuals' decisions on trip
destinations within an assigned daily activity plan. We
observed that given the distribution of population, the
workplace locations, a comprehensive set of urban
facilities, and a list of typical activity sequences of
city dwellers collected within a national travel
survey, the developed microsimulation reproduces not
only the journey statistics such as peak travel periods
but also the traffic volumes at main road segments with
surprising accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Momtazpour:2014:CSI,
author = "Marjan Momtazpour and Patrick Butler and Naren
Ramakrishnan and M. Shahriar Hossain and Mohammad C.
Bozchalui and Ratnesh Sharma",
title = "Charging and Storage Infrastructure Design for
Electric Vehicles",
journal = j-TIST,
volume = "5",
number = "3",
pages = "42:1--42:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2513567",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Ushered by recent developments in various areas of
science and technology, modern energy systems are going
to be an inevitable part of our societies. Smart grids
are one of these modern systems that have attracted
many research activities in recent years. Before
utilizing the next generation of smart grids, we should
have a comprehensive understanding of the
interdependent energy networks and processes.
Next-generation energy systems networks cannot be
effectively designed, analyzed, and controlled in
isolation from the social, economic, sensing, and
control contexts in which they operate. In this
article, we present a novel framework to support
charging and storage infrastructure design for electric
vehicles. We develop coordinated clustering techniques
to work with network models of urban environments to
aid in placement of charging stations for an electrical
vehicle deployment scenario. Furthermore, we evaluate
the network before and after the deployment of charging
stations, to recommend the installation of appropriate
storage units to overcome the extra load imposed on the
network by the charging stations. We demonstrate the
multiple factors that can be simultaneously leveraged
in our framework to achieve practical urban deployment.
Our ultimate goal is to help realize sustainable energy
system management in urban electrical infrastructure by
modeling and analyzing networks of interactions between
electric systems and urban populations.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tan:2014:OOT,
author = "Chang Tan and Qi Liu and Enhong Chen and Hui Xiong and
Xiang Wu",
title = "Object-Oriented Travel Package Recommendation",
journal = j-TIST,
volume = "5",
number = "3",
pages = "43:1--43:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542665",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Providing better travel services for tourists is one
of the important applications in urban computing.
Though many recommender systems have been developed for
enhancing the quality of travel service, most of them
lack a systematic and open framework to dynamically
incorporate multiple types of additional context
information existing in the tourism domain, such as the
travel area, season, and price of travel packages. To
that end, in this article, we propose an open
framework, the Objected-Oriented Recommender System
(ORS), for the developers performing personalized
travel package recommendations to tourists. This
framework has the ability to import all the available
additional context information to the travel package
recommendation process in a cost-effective way.
Specifically, the different types of additional
information are extracted and uniformly represented as
feature--value pairs. Then, we define the Object, which
is the collection of the feature--value pairs. We
propose two models that can be used in the ORS
framework for extracting the implicit relationships
among Objects. The Objected-Oriented Topic Model (OTM)
can extract the topics conditioned on the intrinsic
feature--value pairs of the Objects. The
Objected-Oriented Bayesian Network (OBN) can
effectively infer the cotravel probability of two
tourists by calculating the co-occurrence time of
feature--value pairs belonging to different kinds of
Objects. Based on the relationships mined by OTM or
OBN, the recommendation list is generated by the
collaborative filtering method. Finally, we evaluate
these two models and the ORS framework on real-world
travel package data, and the experimental results show
that the ORS framework is more flexible in terms of
incorporating additional context information, and thus
leads to better performances for travel package
recommendations. Meanwhile, for feature selection in
ORS, we define the feature information entropy, and the
experimental results demonstrate that using features
with lower entropies usually leads to better
recommendation results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gurung:2014:TIP,
author = "Sashi Gurung and Dan Lin and Wei Jiang and Ali Hurson
and Rui Zhang",
title = "Traffic Information Publication with Privacy
Preservation",
journal = j-TIST,
volume = "5",
number = "3",
pages = "44:1--44:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542666",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We are experiencing the expanding use of
location-based services such as AT\&T's TeleNav GPS
Navigator and Intel's Thing Finder. Existing
location-based services have collected a large amount
of location data, which has great potential for
statistical usage in applications like traffic flow
analysis, infrastructure planning, and advertisement
dissemination. The key challenge is how to wisely use
the data without violating each user's location privacy
concerns. In this article, we first identify a new
privacy problem, namely, the inference-route problem,
and then present our anonymization algorithms for
privacy-preserving trajectory publishing. The
experimental results have demonstrated that our
approach outperforms the latest related work in terms
of both efficiency and effectiveness.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hsieh:2014:MRT,
author = "Hsun-Ping Hsieh and Cheng-Te Li and Shou-De Lin",
title = "Measuring and Recommending Time-Sensitive Routes from
Location-Based Data",
journal = j-TIST,
volume = "5",
number = "3",
pages = "45:1--45:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542668",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Location-based services allow users to perform
geospatial recording actions, which facilitates the
mining of the moving activities of human beings. This
article proposes to recommend time-sensitive trip
routes consisting of a sequence of locations with
associated timestamps based on knowledge extracted from
large-scale timestamped location sequence data (e.g.,
check-ins and GPS traces). We argue that a good route
should consider (a) the popularity of places, (b) the
visiting order of places, (c) the proper visiting time
of each place, and (d) the proper transit time from one
place to another. By devising a statistical model, we
integrate these four factors into a route goodness
function that aims to measure the quality of a route.
Equipped with the route goodness, we recommend
time-sensitive routes for two scenarios. The first is
about constructing the route based on the
user-specified source location with the starting time.
The second is about composing the route between the
specified source location and the destination location
given a starting time. To handle these queries, we
propose a search method, Guidance Search, which
consists of a novel heuristic satisfaction function
that guides the search toward the destination location
and a backward checking mechanism to boost the
effectiveness of the constructed route. Experiments on
the Gowalla check-in datasets demonstrate the
effectiveness of our model on detecting real routes and
performing cloze test of routes, comparing with other
baseline methods. We also develop a system TripRouter
as a real-time demo platform.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Joseph:2014:CIB,
author = "Kenneth Joseph and Kathleen M. Carley and Jason I.
Hong",
title = "Check-ins in {``Blau Space''}: Applying {Blau}'s
Macrosociological Theory to Foursquare Check-ins from
New {York} City",
journal = j-TIST,
volume = "5",
number = "3",
pages = "46:1--46:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2566617",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Peter Blau was one of the first to define a latent
social space and utilize it to provide concrete
hypotheses. Blau defines social structure via social
``parameters'' (constraints). Actors that are closer
together (more homogeneous) in this social parameter
space are more likely to interact. One of Blau's most
important hypotheses resulting from this work was that
the consolidation of parameters could lead to isolated
social groups. For example, the consolidation of race
and income might lead to segregation. In the present
work, we use Foursquare data from New York City to
explore evidence of homogeneity along certain social
parameters and consolidation that breeds social
isolation in communities of locations checked in to by
similar users. More specifically, we first test the
extent to which communities detected via Latent
Dirichlet Allocation are homogeneous across a set of
four social constraints-racial homophily, income
homophily, personal interest homophily and physical
space. Using a bootstrapping approach, we find that 14
(of 20) communities are statistically, and all but one
qualitatively, homogeneous along one of these social
constraints, showing the relevance of Blau's latent
space model in venue communities determined via user
check-in behavior. We then consider the extent to which
communities with consolidated parameters, those
homogeneous on more than one parameter, represent
socially isolated populations. We find communities
homogeneous on multiple parameters, including a
homosexual community and a ``hipster'' community, that
show support for Blau's hypothesis that consolidation
breeds social isolation. We consider these results in
the context of mediated communication, in particular in
the context of self-representation on social media.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mahmud:2014:HLI,
author = "Jalal Mahmud and Jeffrey Nichols and Clemens Drews",
title = "Home Location Identification of {Twitter} Users",
journal = j-TIST,
volume = "5",
number = "3",
pages = "47:1--47:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2528548",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present a new algorithm for inferring the home
location of Twitter users at different granularities,
including city, state, time zone, or geographic region,
using the content of users' tweets and their tweeting
behavior. Unlike existing approaches, our algorithm
uses an ensemble of statistical and heuristic
classifiers to predict locations and makes use of a
geographic gazetteer dictionary to identify place-name
entities. We find that a hierarchical classification
approach, where time zone, state, or geographic region
is predicted first and city is predicted next, can
improve prediction accuracy. We have also analyzed
movement variations of Twitter users, built a
classifier to predict whether a user was travelling in
a certain period of time, and use that to further
improve the location detection accuracy. Experimental
evidence suggests that our algorithm works well in
practice and outperforms the best existing algorithms
for predicting the home location of Twitter users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Neviarouskaya:2014:IIT,
author = "Alena Neviarouskaya and Masaki Aono and Helmut
Prendinger and Mitsuru Ishizuka",
title = "Intelligent Interface for Textual Attitude Analysis",
journal = j-TIST,
volume = "5",
number = "3",
pages = "48:1--48:??",
month = sep,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2535912",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:08 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article describes a novel intelligent interface
for attitude sensing in text driven by a robust
computational tool for the analysis of fine-grained
attitudes (emotions, judgments, and appreciations)
expressed in text. The module responsible for textual
attitude analysis was developed using a compositional
linguistic approach based on the attitude-conveying
lexicon, the analysis of syntactic and dependency
relations between words in a sentence, the
compositionality principle applied at various
grammatical levels, the rules elaborated for
semantically distinct verb classes, and a method
considering the hierarchy of concepts. The performance
of this module was evaluated on sentences from personal
stories about life experiences. The developed web-based
interface supports recognition of nine emotions,
positive and negative judgments, and positive and
negative appreciations conveyed in text. It allows
users to adjust parameters, to enable or disable
various functionality components of the algorithm, and
to select the format of text annotation and attitude
statistics visualization.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Song:2014:UGF,
author = "Yicheng Song and Yongdong Zhang and Juan Cao and
Jinhui Tang and Xingyu Gao and Jintao Li",
title = "A Unified Geolocation Framework for {Web} Videos",
journal = j-TIST,
volume = "5",
number = "3",
pages = "49:1--49:??",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2533989",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Jul 18 14:11:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we propose a unified geolocation
framework to automatically determine where on the earth
a web video was shot. We analyze different social,
visual, and textual relationships from a real-world
dataset and find four relationships with apparent
geography clues that can be used for web video
geolocation. Then, the geolocation process is
formulated as an optimization problem that
simultaneously takes the social, visual, and textual
relationships into consideration. The optimization
problem is solved by an iterative procedure, which can
be interpreted as a propagation of the geography
information among the web video social network.
Extensive experiments on a real-world dataset clearly
demonstrate the effectiveness of our proposed
framework, with the geolocation accuracy higher than
state-of-the-art approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhao:2014:PRL,
author = "Yi-Liang Zhao and Liqiang Nie and Xiangyu Wang and
Tat-Seng Chua",
title = "Personalized Recommendations of Locally Interesting
Venues to Tourists via Cross-Region Community
Matching",
journal = j-TIST,
volume = "5",
number = "3",
pages = "50:1--50:??",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532439",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Jul 18 14:11:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "You are in a new city. You are not familiar with the
places and neighborhoods. You want to know all about
the exciting sights, food outlets, and cultural venues
that the locals frequent, in particular those that suit
your personal interests. Even though there exist many
mapping, local search, and travel assistance sites,
they mostly provide popular and famous listings such as
Statue of Liberty and Eiffel Tower, which are
well-known places but may not suit your personal needs
or interests. Therefore, there is a gap between what
tourists want and what dominant tourism resources are
providing. In this work, we seek to provide a solution
to bridge this gap by exploiting the rich
user-generated location contents in location-based
social networks in order to offer tourists the most
relevant and personalized local venue recommendations.
In particular, we first propose a novel Bayesian
approach to extract the social dimensions of people at
different geographical regions to capture their latent
local interests. We next mine the local interest
communities in each geographical region. We then
represent each local community using aggregated
behaviors of community members. Finally, we correlate
communities across different regions and generate venue
recommendations to tourists via cross-region community
matching. We have sampled a representative subset of
check-ins from Foursquare and experimentally verified
the effectiveness of our proposed approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2014:VNF,
author = "Shuaiqiang Wang and Jiankai Sun and Byron J. Gao and
Jun Ma",
title = "{VSRank}: a Novel Framework for Ranking-Based
Collaborative Filtering",
journal = j-TIST,
volume = "5",
number = "3",
pages = "51:1--51:??",
month = jul,
year = "2014",
CODEN = "????",
DOI = "https://doi.org/10.1145/2542048",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Jul 18 14:11:13 MDT 2014",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative filtering (CF) is an effective technique
addressing the information overload problem. CF
approaches generally fall into two categories: rating
based and ranking based. The former makes
recommendations based on historical rating scores of
items and the latter based on their rankings.
Ranking-based CF has demonstrated advantages in
recommendation accuracy, being able to capture the
preference similarity between users even if their
rating scores differ significantly. In this study, we
propose VSRank, a novel framework that seeks accuracy
improvement of ranking-based CF through adaptation of
the vector space model. In VSRank, we consider each
user as a document and his or her pairwise relative
preferences as terms. We then use a novel
degree-specialty weighting scheme resembling TF-IDF to
weight the terms. Extensive experiments on benchmarks
in comparison with the state-of-the-art approaches
demonstrate the promise of our approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Castells:2015:ISI,
author = "Pablo Castells and Jun Wang and Rub{\'e}n Lara and
Dell Zhang",
title = "Introduction to the Special Issue on Diversity and
Discovery in Recommender Systems",
journal = j-TIST,
volume = "5",
number = "4",
pages = "52:1--52:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668113",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ribeiro:2015:MPE,
author = "Marco Tulio Ribeiro and Nivio Ziviani and Edleno
{Silva De Moura} and Itamar Hata and Anisio Lacerda and
Adriano Veloso",
title = "Multiobjective {Pareto}-Efficient Approaches for
Recommender Systems",
journal = j-TIST,
volume = "5",
number = "4",
pages = "53:1--53:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629350",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommender systems are quickly becoming ubiquitous in
applications such as e-commerce, social media channels,
and content providers, among others, acting as an
enabling mechanism designed to overcome the information
overload problem by improving browsing and consumption
experience. A typical task in many recommender systems
is to output a ranked list of items, so that items
placed higher in the rank are more likely to be
interesting to the users. Interestingness measures
include how accurate, novel, and diverse are the
suggested items, and the objective is usually to
produce ranked lists optimizing one of these measures.
Suggesting items that are simultaneously accurate,
novel, and diverse is much more challenging, since this
may lead to a conflicting-objective problem, in which
the attempt to improve a measure further may result in
worsening other measures. In this article, we propose
new approaches for multiobjective recommender systems
based on the concept of Pareto efficiency-a state
achieved when the system is devised in the most
efficient manner in the sense that there is no way to
improve one of the objectives without making any other
objective worse off. Given that existing multiobjective
recommendation algorithms differ in their level of
accuracy, diversity, and novelty, we exploit the
Pareto-efficiency concept in two distinct manners: (i)
the aggregation of ranked lists produced by existing
algorithms into a single one, which we call
Pareto-efficient ranking, and (ii) the weighted
combination of existing algorithms resulting in a
hybrid one, which we call Pareto-efficient
hybridization. Our evaluation involves two real
application scenarios: music recommendation with
implicit feedback (i.e., Last.fm) and movie
recommendation with explicit feedback (i.e.,
MovieLens). We show that the proposed Pareto-efficient
approaches are effective in suggesting items that are
likely to be simultaneously accurate, diverse, and
novel. We discuss scenarios where the system achieves
high levels of diversity and novelty without
compromising its accuracy. Further, comparison against
multiobjective baselines reveals improvements in terms
of accuracy (from 10.4\% to 10.9\%), novelty (from
5.7\% to 7.5\%), and diversity (from 1.6\% to 4.2\%).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Adamopoulos:2015:URS,
author = "Panagiotis Adamopoulos and Alexander Tuzhilin",
title = "On Unexpectedness in Recommender Systems: Or How to
Better Expect the Unexpected",
journal = j-TIST,
volume = "5",
number = "4",
pages = "54:1--54:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2559952",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Although the broad social and business success of
recommender systems has been achieved across several
domains, there is still a long way to go in terms of
user satisfaction. One of the key dimensions for
significant improvement is the concept of
unexpectedness. In this article, we propose a method to
improve user satisfaction by generating unexpected
recommendations based on the utility theory of
economics. In particular, we propose a new concept of
unexpectedness as recommending to users those items
that depart from what they would expect from the system
--- the consideration set of each user. We define and
formalize the concept of unexpectedness and discuss how
it differs from the related notions of novelty,
serendipity, and diversity. In addition, we suggest
several mechanisms for specifying the users'
expectations and propose specific performance metrics
to measure the unexpectedness of recommendation lists.
We also take into consideration the quality of
recommendations using certain utility functions and
present an algorithm for providing users with
unexpected recommendations of high quality that are
hard to discover but fairly match their interests.
Finally, we conduct several experiments on
``real-world'' datasets and compare our recommendation
results with other methods. The proposed approach
outperforms these baseline methods in terms of
unexpectedness and other important metrics, such as
coverage, aggregate diversity and dispersion, while
avoiding any accuracy loss.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kucuktunc:2015:DCR,
author = "Onur K{\"u}{\c{c}}{\"u}ktun{\c{c}} and Erik Saule and
Kamer Kaya and {\"U}mit V. {\c{C}}ataly{\"u}rek",
title = "Diversifying Citation Recommendations",
journal = j-TIST,
volume = "5",
number = "4",
pages = "55:1--55:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668106",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Literature search is one of the most important steps
of academic research. With more than 100,000 papers
published each year just in computer science,
performing a complete literature search becomes a
Herculean task. Some of the existing approaches and
tools for literature search cannot compete with the
characteristics of today's literature, and they suffer
from ambiguity and homonymy. Techniques based on
citation information are more robust to the mentioned
issues. Thus, we recently built a Web service called
the advisor, which provides personalized
recommendations to researchers based on their papers of
interest. Since most recommendation methods may return
redundant results, diversifying the results of the
search process is necessary to increase the amount of
information that one can reach via an automated search.
This article targets the problem of result
diversification in citation-based bibliographic search,
assuming that the citation graph itself is the only
information available and no categories or intents are
known. The contribution of this work is threefold. We
survey various random walk--based diversification
methods and enhance them with the direction awareness
property to allow users to reach either old,
foundational (possibly well-cited and well-known)
research papers or recent (most likely less-known)
ones. Next, we propose a set of novel algorithms based
on vertex selection and query refinement. A set of
experiments with various evaluation criteria shows that
the proposed $ \gamma $-RLM algorithm performs better
than the existing approaches and is suitable for
real-time bibliographic search in practice.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Javari:2015:ANR,
author = "Amin Javari and Mahdi Jalili",
title = "Accurate and Novel Recommendations: an Algorithm Based
on Popularity Forecasting",
journal = j-TIST,
volume = "5",
number = "4",
pages = "56:1--56:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668107",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommender systems are in the center of network
science, and they are becoming increasingly important
in individual businesses for providing efficient,
personalized services and products to users. Previous
research in the field of recommendation systems focused
on improving the precision of the system through
designing more accurate recommendation lists. Recently,
the community has been paying attention to diversity
and novelty of recommendation lists as key
characteristics of modern recommender systems. In many
cases, novelty and precision do not go hand in hand,
and the accuracy--novelty dilemma is one of the
challenging problems in recommender systems, which
needs efforts in making a trade-off between them. In
this work, we propose an algorithm for providing novel
and accurate recommendation to users. We consider the
standard definition of accuracy and an effective
self-information--based measure to assess novelty of
the recommendation list. The proposed algorithm is
based on item popularity, which is defined as the
number of votes received in a certain time interval.
Wavelet transform is used for analyzing popularity time
series and forecasting their trend in future timesteps.
We introduce two filtering algorithms based on the
information extracted from analyzing popularity time
series of the items. The popularity-based filtering
algorithm gives a higher chance to items that are
predicted to be popular in future timesteps. The other
algorithm, denoted as a novelty and population-based
filtering algorithm, is to move toward items with low
popularity in past timesteps that are predicted to
become popular in the future. The introduced filters
can be applied as adds-on to any recommendation
algorithm. In this article, we use the proposed
algorithms to improve the performance of classic
recommenders, including item-based collaborative
filtering and Markov-based recommender systems. The
experiments show that the algorithms could
significantly improve both the accuracy and effective
novelty of the classic recommenders.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shen:2015:ISI,
author = "Dou Shen and Deepak Agarwal",
title = "Introduction to the Special Issue on Online
Advertising",
journal = j-TIST,
volume = "5",
number = "4",
pages = "57:1--57:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668123",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhu:2015:MMU,
author = "Hengshu Zhu and Enhong Chen and Hui Xiong and Kuifei
Yu and Huanhuan Cao and Jilei Tian",
title = "Mining Mobile User Preferences for Personalized
Context-Aware Recommendation",
journal = j-TIST,
volume = "5",
number = "4",
pages = "58:1--58:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532515",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recent advances in mobile devices and their sensing
capabilities have enabled the collection of rich
contextual information and mobile device usage records
through the device logs. These context-rich logs open a
venue for mining the personal preferences of mobile
users under varying contexts and thus enabling the
development of personalized context-aware
recommendation and other related services, such as
mobile online advertising. In this article, we
illustrate how to extract personal context-aware
preferences from the context-rich device logs, or
context logs for short, and exploit these identified
preferences for building personalized context-aware
recommender systems. A critical challenge along this
line is that the context log of each individual user
may not contain sufficient data for mining his or her
context-aware preferences. Therefore, we propose to
first learn common context-aware preferences from the
context logs of many users. Then, the preference of
each user can be represented as a distribution of these
common context-aware preferences. Specifically, we
develop two approaches for mining common context-aware
preferences based on two different assumptions, namely,
context-independent and context-dependent assumptions,
which can fit into different application scenarios.
Finally, extensive experiments on a real-world dataset
show that both approaches are effective and outperform
baselines with respect to mining personal context-aware
preferences for mobile users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ashkan:2015:LQA,
author = "Azin Ashkan and Charles L. A. Clarke",
title = "Location- and Query-Aware Modeling of Browsing and
Click Behavior in Sponsored Search",
journal = j-TIST,
volume = "5",
number = "4",
pages = "59:1--59:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2534398",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An online advertisement's clickthrough rate provides a
fundamental measure of its quality, which is widely
used in ad selection strategies. Unfortunately, ads
placed in contexts where they are rarely viewed-or
where users are unlikely to be interested in commercial
results-may receive few clicks regardless of their
quality. In this article, we model the variability of a
user's browsing behavior for the purpose of click
analysis and prediction in sponsored search. Our model
incorporates several important contextual factors that
influence ad clickthrough rates, including the user's
query and ad placement on search engine result pages.
We formally model these factors with respect to the
list of ads displayed on a result page, the probability
that the user will initiate browsing of this list, and
the persistence of the user in browsing the list. We
incorporate these factors into existing click models by
augmenting them with appropriate query and location
biases. Using expectation maximization, we learn the
parameters of these augmented models from click signals
recorded in the logs of a commercial search engine. To
evaluate the performance of the models and to compare
them with state-of-the-art performance, we apply
standard evaluation metrics, including log-likelihood
and perplexity. Our evaluation results indicate that,
through the incorporation of query and location biases,
significant improvements can be achieved in predicting
browsing and click behavior in sponsored search. In
addition, we explore the extent to which these biases
actually reflect varying behavioral patterns. Our
observations confirm that correlations exist between
the biases and user search behavior.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Qin:2015:SSA,
author = "Tao Qin and Wei Chen and Tie-Yan Liu",
title = "Sponsored Search Auctions: Recent Advances and Future
Directions",
journal = j-TIST,
volume = "5",
number = "4",
pages = "60:1--60:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668108",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Sponsored search has been proven to be a successful
business model, and sponsored search auctions have
become a hot research direction. There have been many
exciting advances in this field, especially in recent
years, while at the same time, there are also many open
problems waiting for us to resolve. In this article, we
provide a comprehensive review of sponsored search
auctions in hopes of helping both industry
practitioners and academic researchers to become
familiar with this field, to know the state of the art,
and to identify future research topics. Specifically,
we organize the article into two parts. In the first
part, we review research works on sponsored search
auctions with basic settings, where fully rational
advertisers without budget constraints, preknown
click-through rates (CTRs) without interdependence, and
exact match between queries and keywords are assumed.
Under these assumptions, we first introduce the
generalized second price (GSP) auction, which is the
most popularly used auction mechanism in the industry.
Then we give the definitions of several well-studied
equilibria and review the latest results on GSP's
efficiency and revenue in these equilibria. In the
second part, we introduce some advanced topics on
sponsored search auctions. In these advanced topics,
one or more assumptions made in the basic settings are
relaxed. For example, the CTR of an ad could be unknown
and dependent on other ads; keywords could be broadly
matched to queries before auctions are executed; and
advertisers are not necessarily fully rational, could
have budget constraints, and may prefer rich bidding
languages. Given that the research on these advanced
topics is still immature, in each section of the second
part, we provide our opinions on how to make further
advances, in addition to describing what has been done
by researchers in the corresponding direction.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chapelle:2015:SSR,
author = "Olivier Chapelle and Eren Manavoglu and Romer
Rosales",
title = "Simple and Scalable Response Prediction for Display
Advertising",
journal = j-TIST,
volume = "5",
number = "4",
pages = "61:1--61:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532128",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Clickthrough and conversation rates estimation are two
core predictions tasks in display advertising. We
present in this article a machine learning framework
based on logistic regression that is specifically
designed to tackle the specifics of display
advertising. The resulting system has the following
characteristics: It is easy to implement and deploy, it
is highly scalable (we have trained it on terabytes of
data), and it provides models with state-of-the-art
accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Balakrishnan:2015:RTB,
author = "Raju Balakrishnan and Rushi P. Bhatt",
title = "Real-Time Bid Optimization for Group-Buying Ads",
journal = j-TIST,
volume = "5",
number = "4",
pages = "62:1--62:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2532441",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Group-buying ads seeking a minimum number of customers
before the deal expiry are increasingly used by
daily-deal providers. Unlike traditional web ads, the
advertiser's profits for group-buying ads depend on the
time to expiry and additional customers needed to
satisfy the minimum group size. Since both these
quantities are time-dependent, optimal bid amounts to
maximize profits change with every impression.
Consequently, traditional static bidding strategies are
far from optimal. Instead, bid values need to be
optimized in real-time to maximize expected bidder
profits. This online optimization of deal profits is
made possible by the advent of ad exchanges offering
real-time (spot) bidding. To this end, we propose a
real-time bidding strategy for group-buying deals based
on the online optimization of bid values. We derive the
expected bidder profit of deals as a function of the
bid amounts and dynamically vary the bids to maximize
profits. Furthermore, to satisfy time constraints of
the online bidding, we present methods of minimizing
computation timings. Subsequently, we derive the
real-time ad selection, admissibility, and real-time
bidding of the traditional ads as the special cases of
the proposed method. We evaluate the proposed bidding,
selection, and admission strategies on a multimillion
click stream of 935 ads. The proposed real-time
bidding, selection, and admissibility show significant
profit increases over the existing strategies. Further
experiments illustrate the robustness of the bidding
and acceptable computation timings.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2015:IAC,
author = "Qingzhong Liu and Zhongxue Chen",
title = "Improved Approaches with Calibrated Neighboring Joint
Density to Steganalysis and Seam-Carved Forgery
Detection in {JPEG} Images",
journal = j-TIST,
volume = "5",
number = "4",
pages = "63:1--63:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2560365",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/cryptography2010.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Steganalysis and forgery detection in image forensics
are generally investigated separately. We have designed
a method targeting the detection of both steganography
and seam-carved forgery in JPEG images. We analyze the
neighboring joint density of the DCT coefficients and
reveal the difference between the untouched image and
the modified version. In realistic detection, the
untouched image and the modified version may not be
obtained at the same time, and different JPEG images
may have different neighboring joint density features.
By exploring the self-calibration under different shift
recompressions, we propose calibrated neighboring joint
density-based approaches with a simple feature set to
distinguish steganograms and tampered images from
untouched ones. Our study shows that this approach has
multiple promising applications in image forensics.
Compared to the state-of-the-art steganalysis
detectors, our approach delivers better or comparable
detection performances with a much smaller feature set
while detecting several JPEG-based steganographic
systems including DCT-embedding-based adaptive
steganography and Yet Another Steganographic Scheme
(YASS). Our approach is also effective in detecting
seam-carved forgery in JPEG images. By integrating
calibrated neighboring density with spatial domain rich
models that were originally designed for steganalysis,
the hybrid approach obtains the best detection accuracy
to discriminate seam-carved forgery from an untouched
image. Our study also offers a promising manner to
explore steganalysis and forgery detection together.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Azaria:2015:SID,
author = "Amos Azaria and Zinovi Rabinovich and Claudia V.
Goldman and Sarit Kraus",
title = "Strategic Information Disclosure to People with
Multiple Alternatives",
journal = j-TIST,
volume = "5",
number = "4",
pages = "64:1--64:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2558397",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we study automated agents that are
designed to encourage humans to take some actions over
others by strategically disclosing key pieces of
information. To this end, we utilize the framework of
persuasion games-a branch of game theory that deals
with asymmetric interactions where one player (Sender)
possesses more information about the world, but it is
only the other player (Receiver) who can take an
action. In particular, we use an extended persuasion
model, where the Sender's information is imperfect and
the Receiver has more than two alternative actions
available. We design a computational algorithm that,
from the Sender's standpoint, calculates the optimal
information disclosure rule. The algorithm is
parameterized by the Receiver's decision model (i.e.,
what choice he will make based on the information
disclosed by the Sender) and can be retuned
accordingly. We then provide an extensive experimental
study of the algorithm's performance in interactions
with human Receivers. First, we consider a fully
rational (in the Bayesian sense) Receiver decision
model and experimentally show the efficacy of the
resulting Sender's solution in a routing domain.
Despite the discrepancy in the Sender's and the
Receiver's utilities from each of the Receiver's
choices, our Sender agent successfully persuaded human
Receivers to select an option more beneficial for the
agent. Dropping the Receiver's rationality assumption,
we introduce a machine learning procedure that
generates a more realistic human Receiver model. We
then show its significant benefit to the Sender
solution by repeating our routing experiment. To
complete our study, we introduce a second
(supply--demand) experimental domain and, by
contrasting it with the routing domain, obtain general
guidelines for a Sender on how to construct a Receiver
model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2015:SPA,
author = "Si Liu and Qiang Chen and Shuicheng Yan and Changsheng
Xu and Hanqing Lu",
title = "{Snap \& Play}: Auto-Generated Personalized
Find-the-Difference Game",
journal = j-TIST,
volume = "5",
number = "4",
pages = "65:1--65:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668109",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, by taking a popular game, the
Find-the-Difference (FiDi) game, as a concrete example,
we explore how state-of-the-art image processing
techniques can assist in developing a personalized,
automatic, and dynamic game. Unlike the traditional
FiDi game, where image pairs (source image and target
image) with five different patches are manually
produced by professional game developers, the proposed
Personalized FiDi (P-FiDi) electronic game can be
played in a fully automatic Snap \& Play mode. Snap
means that players first take photos with their digital
cameras. The newly captured photos are used as source
images and fed into the P-FiDi system to autogenerate
the counterpart target images for users to play. Four
steps are adopted to autogenerate target images:
enhancing the visual quality of source images,
extracting some changeable patches from the source
image, selecting the most suitable combination of
changeable patches and difference styles for the image,
and generating the differences on the target image with
state-of-the-art image processing techniques. In
addition, the P-FiDi game can be easily redesigned for
the im-game advertising. Extensive experiments show
that the P-FiDi electronic game is satisfying in terms
of player experience, seamless advertisement, and
technical feasibility.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reches:2015:CCU,
author = "Shulamit Reches and Meir Kalech",
title = "Choosing a Candidate Using Efficient Allocation of
Biased Information",
journal = j-TIST,
volume = "5",
number = "4",
pages = "66:1--66:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2558327",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article deals with a decision-making problem
concerning an agent who wants to choose a partner from
multiple candidates for long-term collaboration. To
choose the best partner, the agent can rely on prior
information he knows about the candidates. However, to
improve his decision, he can request additional
information from information sources. Nonetheless,
acquiring information from external information sources
about candidates may be biased due to different
personalities of the agent searching for a partner and
the information source. In addition, information may be
costly. Considering the bias and the cost of the
information sources, the optimization problem addressed
in this article is threefold: (1) determining the
necessary amount of additional information, (2)
selecting information sources from which to request the
information, and (3) choosing the candidates on whom to
request the additional information. We propose a
heuristic to solve this optimization problem. The
results of experiments on simulated and real-world
domains demonstrate the efficiency of our algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhuang:2015:CDS,
author = "Jinfeng Zhuang and Tao Mei and Steven C. H. Hoi and
Xian-Sheng Hua and Yongdong Zhang",
title = "Community Discovery from Social Media by Low-Rank
Matrix Recovery",
journal = j-TIST,
volume = "5",
number = "4",
pages = "67:1--67:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668110",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The pervasive usage and reach of social media have
attracted a surge of attention in the multimedia
research community. Community discovery from social
media has therefore become an important yet challenging
issue. However, due to the subjective generating
process, the explicitly observed communities (e.g.,
group-user and user-user relationship) are often noisy
and incomplete in nature. This paper presents a novel
approach to discovering communities from social media,
including the group membership and user friend
structure, by exploring a low-rank matrix recovery
technique. In particular, we take Flickr as one
exemplary social media platform. We first model the
observed indicator matrix of the Flickr community as a
summation of a low-rank true matrix and a sparse error
matrix. We then formulate an optimization problem by
regularizing the true matrix to coincide with the
available rich context and content (i.e., photos and
their associated tags). An iterative algorithm is
developed to recover the true community indicator
matrix. The proposed approach leads to a variety of
social applications, including community visualization,
interest group refinement, friend suggestion, and
influential user identification. The evaluations on a
large-scale testbed, consisting of 4,919 Flickr users,
1,467 interest groups, and over five million photos,
show that our approach opens a new yet effective
perspective to solve social network problems with
sparse learning technique. Despite being focused on
Flickr, our technique can be applied in any other
social media community.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2015:IPI,
author = "Yiyang Yang and Zhiguo Gong and Leong Hou U.",
title = "Identifying Points of Interest Using Heterogeneous
Features",
journal = j-TIST,
volume = "5",
number = "4",
pages = "68:1--68:??",
month = jan,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668111",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Feb 11 12:29:09 MST 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Deducing trip-related information from web-scale
datasets has received large amounts of attention
recently. Identifying points of interest (POIs) in
geo-tagged photos is one of these problems. The problem
can be viewed as a standard clustering problem of
partitioning two-dimensional objects. In this work, we
study spectral clustering, which is the first attempt
for the identification of POIs. However, there is no
unified approach to assigning the subjective clustering
parameters, and these parameters vary immensely in
different metropolitans and locations. To address this
issue, we study a self-tuning technique that can
properly determine the parameters for the clustering
needed. Besides geographical information, web photos
inherently store other rich information. Such
heterogeneous information can be used to enhance the
identification accuracy. Thereby, we study a novel
refinement framework that is based on the tightness and
cohesion degree of the additional information. We
thoroughly demonstrate our findings by web-scale
datasets collected from Flickr.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ji:2015:WLM,
author = "Rongrong Ji and Yue Gao and Wei Liu and Xing Xie and
Qi Tian and Xuelong Li",
title = "When Location Meets Social Multimedia: a Survey on
Vision-Based Recognition and Mining for Geo-Social
Multimedia Analytics",
journal = j-TIST,
volume = "6",
number = "1",
pages = "1:1--1:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2597181",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Coming with the popularity of multimedia sharing
platforms such as Facebook and Flickr, recent years
have witnessed an explosive growth of geographical tags
on social multimedia content. This trend enables a wide
variety of emerging applications, for example, mobile
location search, landmark recognition, scene
reconstruction, and touristic recommendation, which
range from purely research prototype to commercial
systems. In this article, we give a comprehensive
survey on these applications, covering recent advances
in recognition and mining of geographical-aware social
multimedia. We review related work in the past decade
regarding to location recognition, scene summarization,
tourism suggestion, 3D building modeling, mobile visual
search and city navigation. At the end, we further
discuss potential challenges, future topics, as well as
open issues related to geo-social multimedia computing,
recognition, mining, and analytics.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chin:2015:FPS,
author = "Wei-Sheng Chin and Yong Zhuang and Yu-Chin Juan and
Chih-Jen Lin",
title = "A Fast Parallel Stochastic Gradient Method for Matrix
Factorization in Shared Memory Systems",
journal = j-TIST,
volume = "6",
number = "1",
pages = "2:1--2:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668133",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Matrix factorization is known to be an effective
method for recommender systems that are given only the
ratings from users to items. Currently, stochastic
gradient (SG) method is one of the most popular
algorithms for matrix factorization. However, as a
sequential approach, SG is difficult to be parallelized
for handling web-scale problems. In this article, we
develop a fast parallel SG method, FPSG, for shared
memory systems. By dramatically reducing the cache-miss
rate and carefully addressing the load balance of
threads, FPSG is more efficient than state-of-the-art
parallel algorithms for matrix factorization.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Feuz:2015:TLA,
author = "Kyle D. Feuz and Diane J. Cook",
title = "Transfer Learning across Feature-Rich Heterogeneous
Feature Spaces via {Feature-Space Remapping (FSR)}",
journal = j-TIST,
volume = "6",
number = "1",
pages = "3:1--3:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629528",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Transfer learning aims to improve performance on a
target task by utilizing previous knowledge learned
from source tasks. In this paper we introduce a novel
heterogeneous transfer learning technique,
Feature-Space Remapping (FSR), which transfers
knowledge between domains with different feature
spaces. This is accomplished without requiring typical
feature-feature, feature instance, or instance-instance
co-occurrence data. Instead we relate features in
different feature-spaces through the construction of
metafeatures. We show how these techniques can utilize
multiple source datasets to construct an ensemble
learner which further improves performance. We apply
FSR to an activity recognition problem and a document
classification problem. The ensemble technique is able
to outperform all other baselines and even performs
better than a classifier trained using a large amount
of labeled data in the target domain. These problems
are especially difficult because, in addition to having
different feature-spaces, the marginal probability
distributions and the class labels are also different.
This work extends the state of the art in transfer
learning by considering large transfer across
dramatically different spaces.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Patel:2015:DSI,
author = "Dhaval Patel",
title = "On Discovery of Spatiotemporal Influence-Based Moving
Clusters",
journal = j-TIST,
volume = "6",
number = "1",
pages = "4:1--4:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2631926",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A moving object cluster is a set of objects that move
close to each other for a long time interval. Existing
works have utilized object trajectories to discover
moving object clusters efficiently. In this article, we
define a spatiotemporal influence-based moving cluster
that captures spatiotemporal influence spread over a
set of spatial objects. A spatiotemporal
influence-based moving cluster is a sequence of spatial
clusters, where each cluster is a set of nearby
objects, such that each object in a cluster influences
at least one object in the next immediate cluster and
is also influenced by an object from the immediate
preceding cluster. Real-life examples of spatiotemporal
influence-based moving clusters include diffusion of
infectious diseases and spread of innovative ideas. We
study the discovery of spatiotemporal influence-based
moving clusters in a database of spatiotemporal events.
While the search space for discovering all
spatiotemporal influence-based moving clusters is
prohibitively huge, we design a method, STIMer, to
efficiently retrieve the maximal answer. The algorithm
STIMer adopts a top-down recursive refinement method to
generate the maximal spatiotemporal influence-based
moving clusters directly. Empirical studies on the real
data as well as large synthetic data demonstrate the
effectiveness and efficiency of our method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sepehri-Rad:2015:ICW,
author = "Hoda Sepehri-Rad and Denilson Barbosa",
title = "Identifying Controversial {Wikipedia} Articles Using
Editor Collaboration Networks",
journal = j-TIST,
volume = "6",
number = "1",
pages = "5:1--5:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2630075",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Wikipedia is probably the most commonly used knowledge
reference nowadays, and the high quality of its
articles is widely acknowledged. Nevertheless,
disagreement among editors often causes some articles
to become controversial over time. These articles span
thousands of popular topics, including religion,
history, and politics, to name a few, and are manually
tagged as controversial by the editors, which is
clearly suboptimal. Moreover, disagreement, bias, and
conflict are expressed quite differently in Wikipedia
compared to other social media, rendering previous
approaches ineffective. On the other hand, the social
process of editing Wikipedia is partially captured in
the edit history of the articles, opening the door for
novel approaches. This article describes a novel
controversy model that builds on the interaction
history of the editors and not only predicts
controversy but also sheds light on the process that
leads to controversy. The model considers the
collaboration history of pairs of editors to predict
their attitude toward one another. This is done in a
supervised way, where the votes of Wikipedia
administrator elections are used as labels indicating
agreement (i.e., support vote) or disagreement (i.e.,
oppose vote). From each article, a collaboration
network is built, capturing the pairwise attitude among
editors, allowing the accurate detection of
controversy. Extensive experimental results establish
the superiority of this approach compared to previous
work and very competitive baselines on a wide range of
settings.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Changuel:2015:RSU,
author = "Sahar Changuel and Nicolas Labroche and Bernadette
Bouchon-Meunier",
title = "Resources Sequencing Using Automatic
Prerequisite--Outcome Annotation",
journal = j-TIST,
volume = "6",
number = "1",
pages = "6:1--6:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2505349",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The objective of any tutoring system is to provide
resources to learners that are adapted to their current
state of knowledge. With the availability of a large
variety of online content and the disjunctive nature of
results provided by traditional search engines, it
becomes crucial to provide learners with adapted
learning paths that propose a sequence of resources
that match their learning objectives. In an ideal case,
the sequence of documents provided to the learner
should be such that each new document relies on
concepts that have been already defined in previous
documents. Thus, the problem of determining an
effective learning path from a corpus of web documents
depends on the accurate identification of outcome and
prerequisite concepts in these documents and on their
ordering according to this information. Until now, only
a few works have been proposed to distinguish between
prerequisite and outcome concepts, and to the best of
our knowledge, no method has been introduced so far to
benefit from this information to produce a meaningful
learning path. To this aim, this article first
describes a concept annotation method that relies on
machine-learning techniques to predict the class of
each concept-prerequisite or outcome-on the basis of
contextual and local features. Then, this
categorization is exploited to produce an automatic
resource sequencing on the basis of different
representations and scoring functions that transcribe
the precedence relation between learning resources.
Experiments conducted on a real dataset built from
online resources show that our concept annotation
approach outperforms the baseline method and that the
learning paths automatically generated are consistent
with the ground truth provided by the author of the
online content.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ghosh:2015:MTD,
author = "Siddhartha Ghosh and Steve Reece and Alex Rogers and
Stephen Roberts and Areej Malibari and Nicholas R.
Jennings",
title = "Modeling the Thermal Dynamics of Buildings: a
Latent-Force- Model-Based Approach",
journal = j-TIST,
volume = "6",
number = "1",
pages = "7:1--7:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629674",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Minimizing the energy consumed by heating,
ventilation, and air conditioning (HVAC) systems of
residential buildings without impacting occupants'
comfort has been highlighted as an important artificial
intelligence (AI) challenge. Typically, approaches that
seek to address this challenge use a model that
captures the thermal dynamics within a building, also
referred to as a thermal model. Among thermal models,
gray-box models are a popular choice for modeling the
thermal dynamics of buildings. They combine knowledge
of the physical structure of a building with various
data-driven inputs and are accurate estimators of the
state (internal temperature). However, existing
gray-box models require a detailed specification of all
the physical elements that can affect the thermal
dynamics of a building a priori. This limits their
applicability, particularly in residential buildings,
where additional dynamics can be induced by human
activities such as cooking, which contributes
additional heat, or opening of windows, which leads to
additional leakage of heat. Since the incidence of
these additional dynamics is rarely known, their
combined effects cannot readily be accommodated within
existing models. To overcome this limitation and
improve the general applicability of gray-box models,
we introduce a novel model, which we refer to as a
latent force thermal model of the thermal dynamics of a
building, or LFM-TM. Our model is derived from an
existing gray-box thermal model, which is augmented
with an extra term referred to as the learned residual.
This term is capable of modeling the effect of any a
priori unknown additional dynamic, which, if not
captured, appears as a structure in a thermal model's
residual (the error induced by the model). More
importantly, the learned residual can also capture the
effects of physical elements such as a building's
envelope or the lags in a heating system, leading to a
significant reduction in complexity compared to
existing models. To evaluate the performance of LFM-TM,
we apply it to two independent data sources. The first
is an established dataset, referred to as the FlexHouse
data, which was previously used for evaluating the
efficacy of existing gray-box models [Bacher and Madsen
2011]. The second dataset consists of heating data
logged within homes located on the University of
Southampton campus, which were specifically
instrumented to collect data for our thermal modeling
experiments. On both datasets, we show that LFM-TM
outperforms existing models in its ability to
accurately fit the observed data, generate accurate
day-ahead internal temperature predictions, and explain
a large amount of the variability in the future
observations. This, along with the fact that we also
use a corresponding efficient sequential inference
scheme for LFM-TM, makes it an ideal candidate for
model-based predictive control, where having accurate
online predictions of internal temperatures is
essential for high-quality solutions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:SPL,
author = "Zhao Zhang and Cheng-Lin Liu and Ming-Bo Zhao",
title = "A Sparse Projection and Low-Rank Recovery Framework
for Handwriting Representation and Salient Stroke
Feature Extraction",
journal = j-TIST,
volume = "6",
number = "1",
pages = "9:1--9:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2601408",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we consider the problem of
simultaneous low-rank recovery and sparse projection.
More specifically, a new Robust Principal Component
Analysis (RPCA)-based framework called Sparse
Projection and Low-Rank Recovery (SPLRR) is proposed
for handwriting representation and salient stroke
feature extraction. In addition to achieving a low-rank
component encoding principal features and identify
errors or missing values from a given data matrix as
RPCA, SPLRR also learns a similarity-preserving sparse
projection for extracting salient stroke features and
embedding new inputs for classification. These
properties make SPLRR applicable for handwriting
recognition and stroke correction and enable online
computation. A cosine-similarity-style regularization
term is incorporated into the SPLRR formulation for
encoding the similarities of local handwriting
features. The sparse projection and low-rank recovery
are calculated from a convex minimization problem that
can be efficiently solved in polynomial time. Besides,
the supervised extension of SPLRR is also elaborated.
The effectiveness of our SPLRR is examined by extensive
handwritten digital repairing, stroke correction, and
recognition based on benchmark problems. Compared with
other related techniques, SPLRR delivers strong
generalization capability and state-of-the-art
performance for handwriting representation and
recognition.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Stapleton:2015:CST,
author = "Gem Stapleton and Beryl Plimmer and Aidan Delaney and
Peter Rodgers",
title = "Combining Sketching and Traditional Diagram Editing
Tools",
journal = j-TIST,
volume = "6",
number = "1",
pages = "10:1--10:??",
month = mar,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2631925",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Mar 27 18:08:08 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The least cognitively demanding way to create a
diagram is to draw it with a pen. Yet there is also a
need for more formal visualizations, that is, diagrams
created using both traditional keyboard and mouse
interaction. Our objective is to allow the creation of
diagrams using traditional and stylus-based input.
Having two diagram creation interfaces requires that
changes to a diagram should be automatically rendered
in the other visualization. Because sketches are
imprecise, there is always the possibility that
conversion between visualizations results in a lack of
syntactic consistency between the two visualizations.
We propose methods for converting diagrams between
forms, checking them for equivalence, and rectifying
inconsistencies. As a result of our theoretical
contributions, we present an intelligent software
system allowing users to create and edit diagrams in
sketch or formal mode. Our proof-of-concept tool
supports diagrams with connected and spatial syntactic
elements. Two user studies show that this approach is
viable and participants found the software easy to use.
We conclude that supporting such diagram creation is
now possible in practice.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hong:2015:VUR,
author = "Richang Hong and Shuicheng Yan and Zhengyou Zhang",
title = "Visual Understanding with {RGB-D} Sensors: an
Introduction to the Special Issue",
journal = j-TIST,
volume = "6",
number = "2",
pages = "11:1--11:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2732265",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:KDR,
author = "Chongyu Chen and Jianfei Cai and Jianmin Zheng and Tat
Jen Cham and Guangming Shi",
title = "{Kinect} Depth Recovery Using a Color-Guided,
Region-Adaptive, and Depth-Selective Framework",
journal = j-TIST,
volume = "6",
number = "2",
pages = "12:1--12:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700475",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Considering that the existing depth recovery
approaches have different limitations when applied to
Kinect depth data, in this article, we propose to
integrate their effective features including adaptive
support region selection, reliable depth selection, and
color guidance together under an optimization framework
for Kinect depth recovery. In particular, we formulate
our depth recovery as an energy minimization problem,
which solves the depth hole filling and denoising
simultaneously. The energy function consists of a
fidelity term and a regularization term, which are
designed according to the Kinect characteristics. Our
framework inherits and improves the idea of guided
filtering by incorporating structure information and
prior knowledge of the Kinect noise model. Through
analyzing the solution to the optimization framework,
we also derive a local filtering version that provides
an efficient and effective way of improving the
existing filtering techniques. Quantitative evaluations
on our developed synthesized dataset and experiments on
real Kinect data show that the proposed method achieves
superior performance in terms of recovery accuracy and
visual quality.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Figueroa:2015:CAT,
author = "Nadia Figueroa and Haiwei Dong and Abdulmotaleb {El
Saddik}",
title = "A Combined Approach Toward Consistent Reconstructions
of Indoor Spaces Based on {$6$D RGB-D} Odometry and
{KinectFusion}",
journal = j-TIST,
volume = "6",
number = "2",
pages = "14:1--14:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629673",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We propose a 6D RGB-D odometry approach that finds the
relative camera pose between consecutive RGB-D frames
by keypoint extraction and feature matching both on the
RGB and depth image planes. Furthermore, we feed the
estimated pose to the highly accurate KinectFusion
algorithm, which uses a fast ICP (Iterative Closest
Point) to fine-tune the frame-to-frame relative pose
and fuse the depth data into a global implicit surface.
We evaluate our method on a publicly available RGB-D
SLAM benchmark dataset by Sturm et al. The experimental
results show that our proposed reconstruction method
solely based on visual odometry and KinectFusion
outperforms the state-of-the-art RGB-D SLAM system
accuracy. Moreover, our algorithm outputs a
ready-to-use polygon mesh (highly suitable for creating
3D virtual worlds) without any postprocessing steps.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zha:2015:RMF,
author = "Zheng-Jun Zha and Yang Yang and Jinhui Tang and Meng
Wang and Tat-Seng Chua",
title = "Robust Multiview Feature Learning for {RGB-D} Image
Understanding",
journal = j-TIST,
volume = "6",
number = "2",
pages = "15:1--15:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2735521",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The availability of massive RGB-depth (RGB-D) images
poses a compelling need for effective RGB-D content
understanding techniques. RGB-D images provide
synchronized information from multiple views (e.g.,
color and depth) of real-world objects and scenes. This
work proposes learning compact and discriminative
features from the multiple views of RGB-D content
toward effective feature representation for RGB-D image
understanding. In particular, a robust multiview
feature learning approach is developed, which exploits
the intrinsic relations among multiple views. The
feature learning in multiple views is jointly optimized
in an integrated formulation. The joint optimization
essentially exploits the intrinsic relations among the
views, leading to effective features and making the
learning process robust to noises. The feature learning
function is formulated as a robust nonnegative graph
embedding function over multiple graphs in various
views. The graphs characterize the local geometric and
discriminating structure of the multiview data. The
joint sparsity in $ l_1$-norm graph embedding and $
l_{21}$-norm data factorization further enhances the
robustness of feature learning. We derive an efficient
computational solution for the proposed approach and
provide rigorous theoretical proof with regard to its
convergence. We apply the proposed approach to two
RGB-D image understanding tasks: RGB-D object
classification and RGB-D scene categorization. We
conduct extensive experiments on two real-world RGB-D
image datasets. The experimental results have
demonstrated the effectiveness of the proposed
approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:RDI,
author = "Quanshi Zhang and Xuan Song and Xiaowei Shao and
Huijing Zhao and Ryosuke Shibasaki",
title = "From {RGB-D} Images to {RGB} Images: Single Labeling
for Mining Visual Models",
journal = j-TIST,
volume = "6",
number = "2",
pages = "16:1--16:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629701",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Mining object-level knowledge, that is, building a
comprehensive category model base, from a large set of
cluttered scenes presents a considerable challenge to
the field of artificial intelligence. How to initiate
model learning with the least human supervision (i.e.,
manual labeling) and how to encode the structural
knowledge are two elements of this challenge, as they
largely determine the scalability and applicability of
any solution. In this article, we propose a
model-learning method that starts from a single-labeled
object for each category, and mines further model
knowledge from a number of informally captured,
cluttered scenes. However, in these scenes, target
objects are relatively small and have large variations
in texture, scale, and rotation. Thus, to reduce the
model bias normally associated with less supervised
learning methods, we use the robust 3D shape in RGB-D
images to guide our model learning, then apply the
properly trained category models to both object
detection and recognition in more conventional RGB
images. In addition to model training for their own
categories, the knowledge extracted from the RGB-D
images can also be transferred to guide model learning
for a new category, in which only RGB images without
depth information in the new category are provided for
training. Preliminary testing shows that the proposed
method performs as well as fully supervised learning
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2015:ARM,
author = "Meiyu Huang and Yiqiang Chen and Wen Ji and Chunyan
Miao",
title = "Accurate and Robust Moving-Object Segmentation for
Telepresence Systems",
journal = j-TIST,
volume = "6",
number = "2",
pages = "17:1--17:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629480",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Moving-object segmentation is the key issue of
Telepresence systems. With monocular camera--based
segmentation methods, desirable segmentation results
are hard to obtain in challenging scenes with ambiguous
color, illumination changes, and shadows. Approaches
based on depth sensors often cause holes inside the
object and missegmentations on the object boundary due
to inaccurate and unstable estimation of depth data.
This work proposes an adaptive multi-cue decision
fusion method based on Kinect (which integrates a depth
sensor with an RGB camera). First, the algorithm
obtains an initial foreground mask based on the depth
cue. Second, the algorithm introduces a postprocessing
framework to refine the segmentation results, which
consists of two main steps: (1) automatically adjusting
the weight of two weak decisions to identify foreground
holes based on the color and contrast cue separately;
and (2) refining the object boundary by integrating the
motion probability weighted temporal prior, color
likelihood, and smoothness constraint. The extensive
experiments we conducted demonstrate that our method
can segment moving objects accurately and robustly in
various situations in real time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhu:2015:FMF,
author = "Yu Zhu and Wenbin Chen and Guodong Guo",
title = "Fusing Multiple Features for Depth-Based Action
Recognition",
journal = j-TIST,
volume = "6",
number = "2",
pages = "18:1--18:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Human action recognition is a very active research
topic in computer vision and pattern recognition.
Recently, it has shown a great potential for human
action recognition using the three-dimensional (3D)
depth data captured by the emerging RGB-D sensors.
Several features and/or algorithms have been proposed
for depth-based action recognition. A question is
raised: Can we find some complementary features and
combine them to improve the accuracy significantly for
depth-based action recognition? To address the question
and have a better understanding of the problem, we
study the fusion of different features for depth-based
action recognition. Although data fusion has shown
great success in other areas, it has not been well
studied yet on 3D action recognition. Some issues need
to be addressed, for example, whether the fusion is
helpful or not for depth-based action recognition, and
how to do the fusion properly. In this article, we
study different fusion schemes comprehensively, using
diverse features for action characterization in depth
videos. Two different levels of fusion schemes are
investigated, that is, feature level and decision
level. Various methods are explored at each fusion
level. Four different features are considered to
characterize the depth action patterns from different
aspects. The experiments are conducted on four
challenging depth action databases, in order to
evaluate and find the best fusion methods generally.
Our experimental results show that the four different
features investigated in the article can complement
each other, and appropriate fusion methods can improve
the recognition accuracies significantly over each
individual feature. More importantly, our fusion-based
action recognition outperforms the state-of-the-art
approaches on these challenging databases.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Spurlock:2015:EGD,
author = "Scott Spurlock and Richard Souvenir",
title = "An Evaluation of Gamesourced Data for Human Pose
Estimation",
journal = j-TIST,
volume = "6",
number = "2",
pages = "19:1--19:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629465",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Gamesourcing has emerged as an approach for rapidly
acquiring labeled data for learning-based, computer
vision recognition algorithms. In this article, we
present an approach for using RGB-D sensors to acquire
annotated training data for human pose estimation from
2D images. Unlike other gamesourcing approaches, our
method does not require a specific game, but runs
alongside any gesture-based game using RGB-D sensors.
The automatically generated datasets resulting from
this approach contain joint estimates within a few
pixel units of manually labeled data, and a gamesourced
dataset created using a relatively small number of
players, games, and locations performs as well as
large-scale, manually annotated datasets when used as
training data with recent learning-based human pose
estimation methods for 2D images.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sun:2015:LSV,
author = "Chao Sun and Tianzhu Zhang and Changsheng Xu",
title = "Latent Support Vector Machine Modeling for Sign
Language Recognition with {Kinect}",
journal = j-TIST,
volume = "6",
number = "2",
pages = "20:1--20:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Vision-based sign language recognition has attracted
more and more interest from researchers in the computer
vision field. In this article, we propose a novel
algorithm to model and recognize sign language
performed in front of a Microsoft Kinect sensor. Under
the assumption that some frames are expected to be both
discriminative and representative in a sign language
video, we first assign a binary latent variable to each
frame in training videos for indicating its
discriminative capability, then develop a latent
support vector machine model to classify the signs, as
well as localize the discriminative and representative
frames in each video. In addition, we utilize the depth
map together with the color image captured by the
Kinect sensor to obtain a more effective and accurate
feature to enhance the recognition accuracy. To
evaluate our approach, we conducted experiments on both
word-level sign language and sentence-level sign
language. An American Sign Language dataset including
approximately 2,000 word-level sign language phrases
and 2,000 sentence-level sign language phrases was
collected using the Kinect sensor, and each phrase
contains color, depth, and skeleton information.
Experiments on our dataset demonstrate the
effectiveness of the proposed method for sign language
recognition.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2015:RTH,
author = "Ao Tang and Ke Lu and Yufei Wang and Jie Huang and
Houqiang Li",
title = "A Real-Time Hand Posture Recognition System Using Deep
Neural Networks",
journal = j-TIST,
volume = "6",
number = "2",
pages = "21:1--21:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2735952",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Hand posture recognition (HPR) is quite a challenging
task, due to both the difficulty in detecting and
tracking hands with normal cameras and the limitations
of traditional manually selected features. In this
article, we propose a two-stage HPR system for Sign
Language Recognition using a Kinect sensor. In the
first stage, we propose an effective algorithm to
implement hand detection and tracking. The algorithm
incorporates both color and depth information, without
specific requirements on uniform-colored or stable
background. It can handle the situations in which hands
are very close to other parts of the body or hands are
not the nearest objects to the camera and allows for
occlusion of hands caused by faces or other hands. In
the second stage, we apply deep neural networks (DNNs)
to automatically learn features from hand posture
images that are insensitive to movement, scaling, and
rotation. Experiments verify that the proposed system
works quickly and accurately and achieves a recognition
accuracy as high as 98.12\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:RTS,
author = "Liyan Zhang and Fan Liu and Jinhui Tang",
title = "Real-Time System for Driver Fatigue Detection by
{RGB-D} Camera",
journal = j-TIST,
volume = "6",
number = "2",
pages = "22:1--22:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2629482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Drowsy driving is one of the major causes of fatal
traffic accidents. In this article, we propose a
real-time system that utilizes RGB-D cameras to
automatically detect driver fatigue and generate alerts
to drivers. By introducing RGB-D cameras, the depth
data can be obtained, which provides extra evidence to
benefit the task of head detection and head pose
estimation. In this system, two important visual cues
(head pose and eye state) for driver fatigue detection
are extracted and leveraged simultaneously. We first
present a real-time 3D head pose estimation method by
leveraging RGB and depth data. Then we introduce a
novel method to predict eye states employing the WLBP
feature, which is a powerful local image descriptor
that is robust to noise and illumination variations.
Finally, we integrate the results from both head pose
and eye states to generate the overall conclusion. The
combination and collaboration of the two types of
visual cues can reduce the uncertainties and resolve
the ambiguity that a single cue may induce. The
experiments were performed using an inside-car
environment during the day and night, and they fully
demonstrate the effectiveness and robustness of our
system as well as the proposed methods of predicting
head pose and eye states.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kyan:2015:ABD,
author = "Matthew Kyan and Guoyu Sun and Haiyan Li and Ling
Zhong and Paisarn Muneesawang and Nan Dong and Bruce
Elder and Ling Guan",
title = "An Approach to Ballet Dance Training through {MS
Kinect} and Visualization in a {CAVE} Virtual Reality
Environment",
journal = j-TIST,
volume = "6",
number = "2",
pages = "23:1--23:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2735951",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article proposes a novel framework for the
real-time capture, assessment, and visualization of
ballet dance movements as performed by a student in an
instructional, virtual reality (VR) setting. The
acquisition of human movement data is facilitated by
skeletal joint tracking captured using the popular
Microsoft (MS) Kinect camera system, while instruction
and performance evaluation are provided in the form of
3D visualizations and feedback through a CAVE virtual
environment, in which the student is fully immersed.
The proposed framework is based on the unsupervised
parsing of ballet dance movement into a structured
posture space using the spherical self-organizing map
(SSOM). A unique feature descriptor is proposed to more
appropriately reflect the subtleties of ballet dance
movements, which are represented as gesture
trajectories through posture space on the SSOM. This
recognition subsystem is used to identify the category
of movement the student is attempting when prompted (by
a virtual instructor) to perform a particular dance
sequence. The dance sequence is then segmented and
cross-referenced against a library of gestural
components performed by the teacher. This facilitates
alignment and score-based assessment of individual
movements within the context of the dance sequence. An
immersive interface enables the student to review his
or her performance from a number of vantage points,
each providing a unique perspective and spatial context
suggestive of how the student might make improvements
in training. An evaluation of the recognition and
virtual feedback systems is presented.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2015:ESC,
author = "Miaojing Shi and Xinghai Sun and Dacheng Tao and Chao
Xu and George Baciu and Hong Liu",
title = "Exploring Spatial Correlation for Visual Object
Retrieval",
journal = j-TIST,
volume = "6",
number = "2",
pages = "24:1--24:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2641576",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Bag-of-visual-words (BOVW)-based image representation
has received intense attention in recent years and has
improved content-based image retrieval (CBIR)
significantly. BOVW does not consider the spatial
correlation between visual words in natural images and
thus biases the generated visual words toward noise
when the corresponding visual features are not stable.
This article outlines the construction of a visual word
co-occurrence matrix by exploring visual word
co-occurrence extracted from small affine-invariant
regions in a large collection of natural images. Based
on this co-occurrence matrix, we first present a novel
high-order predictor to accelerate the generation of
spatially correlated visual words and a penalty tree
(PTree) to continue generating the words after the
prediction. Subsequently, we propose two methods of
co-occurrence weighting similarity measure for image
ranking: Co-Cosine and Co-TFIDF. These two new schemes
down-weight the contributions of the words that are
less discriminative because of frequent co-occurrences
with other words. We conduct experiments on Oxford and
Paris Building datasets, in which the ImageNet dataset
is used to implement a large-scale evaluation.
Cross-dataset evaluations between the Oxford and Paris
datasets and Oxford and Holidays datasets are also
provided. Thorough experimental results suggest that
our method outperforms the state of the art without
adding much additional cost to the BOVW model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Doherty:2015:PMT,
author = "Jonathan Doherty and Kevin Curran and Paul McKevitt",
title = "Pattern Matching Techniques for Replacing Missing
Sections of Audio Streamed across Wireless Networks",
journal = j-TIST,
volume = "6",
number = "2",
pages = "25:1--25:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2663358",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Streaming media on the Internet can be unreliable.
Services such as audio-on-demand drastically increase
the loads on networks; therefore, new, robust, and
highly efficient coding algorithms are necessary. One
method overlooked to date, which can work alongside
existing audio compression schemes, is that which takes
into account the semantics and natural repetition of
music. Similarity detection within polyphonic audio has
presented problematic challenges within the field of
music information retrieval. One approach to deal with
bursty errors is to use self-similarity to replace
missing segments. Many existing systems exist based on
packet loss and replacement on a network level, but
none attempt repairs of large dropouts of 5 seconds or
more. Music exhibits standard structures that can be
used as a forward error correction (FEC) mechanism. FEC
is an area that addresses the issue of packet loss with
the onus of repair placed as much as possible on the
listener's device. We have developed a
server--client-based framework (SoFI) for automatic
detection and replacement of large packet losses on
wireless networks when receiving time-dependent
streamed audio. Whenever dropouts occur, SoFI swaps
audio presented to the listener between a live stream
and previous sections of the audio stored locally.
Objective and subjective evaluations of SoFI where
subjects were presented with other simulated approaches
to audio repair together with simulations of
replacements including varying lengths of time in the
repair give positive results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hai:2015:ABU,
author = "Zhen Hai and Kuiyu Chang and Gao Cong and Christopher
C. Yang",
title = "An Association-Based Unified Framework for Mining
Features and Opinion Words",
journal = j-TIST,
volume = "6",
number = "2",
pages = "26:1--26:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2663359",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Mining features and opinion words is essential for
fine-grained opinion analysis of customer reviews. It
is observed that semantic dependencies naturally exist
between features and opinion words, even among features
or opinion words themselves. In this article, we employ
a corpus statistics association measure to quantify the
pairwise word dependencies and propose a generalized
association-based unified framework to identify
features, including explicit and implicit features, and
opinion words from reviews. We first extract explicit
features and opinion words via an association-based
bootstrapping method (ABOOT). ABOOT starts with a small
list of annotated feature seeds and then iteratively
recognizes a large number of domain-specific features
and opinion words by discovering the corpus statistics
association between each pair of words on a given
review domain. Two instances of this ABOOT method are
evaluated based on two particular association models,
likelihood ratio tests (LRTs) and latent semantic
analysis (LSA). Next, we introduce a natural extension
to identify implicit features by employing the
recognized known semantic correlations between features
and opinion words. Experimental results illustrate the
benefits of the proposed association-based methods for
identifying features and opinion words versus benchmark
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2015:HMC,
author = "Shanshan Huang and Jun Ma and Peizhe Cheng and
Shuaiqiang Wang",
title = "A {Hybrid Multigroup CoClustering} Recommendation
Framework Based on Information Fusion",
journal = j-TIST,
volume = "6",
number = "2",
pages = "27:1--27:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700465",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative Filtering (CF) is one of the most
successful algorithms in recommender systems. However,
it suffers from data sparsity and scalability problems.
Although many clustering techniques have been
incorporated to alleviate these two problems, most of
them fail to achieve further significant improvement in
recommendation accuracy. First of all, most of them
assume each user or item belongs to a single cluster.
Since usually users can hold multiple interests and
items may belong to multiple categories, it is more
reasonable to assume that users and items can join
multiple clusters (groups), where each cluster is a
subset of like-minded users and items they prefer.
Furthermore, most of the clustering-based CF models
only utilize historical rating information in the
clustering procedure but ignore other data resources in
recommender systems such as the social connections of
users and the correlations between items. In this
article, we propose HMCoC, a Hybrid Multigroup
CoClustering recommendation framework, which can
cluster users and items into multiple groups
simultaneously with different information resources. In
our framework, we first integrate information of
user--item rating records, user social networks, and
item features extracted from the DBpedia knowledge
base. We then use an optimization method to mine
meaningful user--item groups with all the information.
Finally, we apply the conventional CF method in each
cluster to make predictions. By merging the predictions
from each cluster, we generate the top-n
recommendations to the target users for return.
Extensive experimental results demonstrate the superior
performance of our approach in top-n recommendation in
terms of MAP, NDCG, and F1 compared with other
clustering-based CF models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fire:2015:DMO,
author = "Michael Fire and Yuval Elovici",
title = "Data Mining of Online Genealogy Datasets for Revealing
Lifespan Patterns in Human Population",
journal = j-TIST,
volume = "6",
number = "2",
pages = "28:1--28:??",
month = apr,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700464",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Apr 21 11:29:25 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online genealogy datasets contain extensive
information about millions of people and their past and
present family connections. This vast amount of data
can help identify various patterns in the human
population. In this study, we present methods and
algorithms that can assist in identifying variations in
lifespan distributions of the human population in the
past centuries, in detecting social and genetic
features that correlate with the human lifespan, and in
constructing predictive models of human lifespan based
on various features that can easily be extracted from
genealogy datasets. We have evaluated the presented
methods and algorithms on a large online genealogy
dataset with over a million profiles and over 9 million
connections, all of which were collected from the
WikiTree website. Our findings indicate that
significant but small positive correlations exist
between the parents' lifespan and their children's
lifespan. Additionally, we found slightly higher and
significant correlations between the lifespans of
spouses. We also discovered a very small positive and
significant correlation between longevity and
reproductive success in males, and a small and
significant negative correlation between longevity and
reproductive success in females. Moreover, our
predictive models presented results with a Mean
Absolute Error as low as 13.18 in predicting the
lifespans of individuals who outlived the age of 10,
and our classification models presented better than
random classification results in predicting which
people who outlive the age of 50 will also outlive the
age of 80. We believe that this study will be the first
of many studies to utilize the wealth of data on human
populations, existing in online genealogy datasets, to
better understand factors that influence the human
lifespan. Understanding these factors can assist
scientists in providing solutions for successful
aging.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zheng:2015:TDM,
author = "Yu Zheng",
title = "Trajectory Data Mining: an Overview",
journal = j-TIST,
volume = "6",
number = "3",
pages = "29:1--29:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2743025",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The advances in location-acquisition and mobile
computing techniques have generated massive spatial
trajectory data, which represent the mobility of a
diversity of moving objects, such as people, vehicles,
and animals. Many techniques have been proposed for
processing, managing, and mining trajectory data in the
past decade, fostering a broad range of applications.
In this article, we conduct a systematic survey on the
major research into trajectory data mining, providing a
panorama of the field as well as the scope of its
research topics. Following a road map from the
derivation of trajectory data, to trajectory data
preprocessing, to trajectory data management, and to a
variety of mining tasks (such as trajectory pattern
mining, outlier detection, and trajectory
classification), the survey explores the connections,
correlations, and differences among these existing
techniques. This survey also introduces the methods
that transform trajectories into other data formats,
such as graphs, matrices, and tensors, to which more
data mining and machine learning techniques can be
applied. Finally, some public trajectory datasets are
presented. This survey can help shape the field of
trajectory data mining, providing a quick understanding
of this field to the community.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bouguessa:2015:IAO,
author = "Mohamed Bouguessa and Lotfi Ben Romdhane",
title = "Identifying Authorities in Online Communities",
journal = j-TIST,
volume = "6",
number = "3",
pages = "30:1--30:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Several approaches have been proposed for the problem
of identifying authoritative actors in online
communities. However, the majority of existing methods
suffer from one or more of the following limitations:
(1) There is a lack of an automatic mechanism to
formally discriminate between authoritative and
nonauthoritative users. In fact, a common approach to
authoritative user identification is to provide a
ranked list of users expecting authorities to come
first. A major problem of such an approach is the
question of where to stop reading the ranked list of
users. How many users should be chosen as
authoritative? (2) Supervised learning approaches for
authoritative user identification suffer from their
dependency on the training data. The problem here is
that labeled samples are more difficult, expensive, and
time consuming to obtain than unlabeled ones. (3)
Several approaches rely on some user parameters to
estimate an authority score. Detection accuracy of
authoritative users can be seriously affected if
incorrect values are used. In this article, we propose
a parameterless mixture model-based approach that is
capable of addressing the three aforementioned issues
in a single framework. In our approach, we first
represent each user with a feature vector composed of
information related to its social behavior and activity
in an online community. Next, we propose a statistical
framework, based on the multivariate beta mixtures, in
order to model the estimated set of feature vectors.
The probability density function is therefore estimated
and the beta component that corresponds to the most
authoritative users is identified. The suitability of
the proposed approach is illustrated on real data
extracted from the Stack Exchange question-answering
network and Twitter.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lee:2015:WWR,
author = "Kyumin Lee and Jalal Mahmud and Jilin Chen and
Michelle Zhou and Jeffrey Nichols",
title = "Who Will Retweet This? {Detecting} Strangers from
{Twitter} to Retweet Information",
journal = j-TIST,
volume = "6",
number = "3",
pages = "31:1--31:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700466",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "There has been much effort on studying how social
media sites, such as Twitter, help propagate
information in different situations, including
spreading alerts and SOS messages in an emergency.
However, existing work has not addressed how to
actively identify and engage the right strangers at the
right time on social media to help effectively
propagate intended information within a desired time
frame. To address this problem, we have developed three
models: (1) a feature-based model that leverages
people's exhibited social behavior, including the
content of their tweets and social interactions, to
characterize their willingness and readiness to
propagate information on Twitter via the act of
retweeting; (2) a wait-time model based on a user's
previous retweeting wait times to predict his or her
next retweeting time when asked; and (3) a subset
selection model that automatically selects a subset of
people from a set of available people using
probabilities predicted by the feature-based model and
maximizes retweeting rate. Based on these three models,
we build a recommender system that predicts the
likelihood of a stranger to retweet information when
asked, within a specific time window, and recommends
the top-N qualified strangers to engage with. Our
experiments, including live studies in the real world,
demonstrate the effectiveness of our work.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hirschprung:2015:SDD,
author = "Ron Hirschprung and Eran Toch and Oded Maimon",
title = "Simplifying Data Disclosure Configurations in a Cloud
Computing Environment",
journal = j-TIST,
volume = "6",
number = "3",
pages = "32:1--32:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700472",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Cloud computing offers a compelling vision of
computation, enabling an unprecedented level of data
distribution and sharing. Beyond improving the
computing infrastructure, cloud computing enables a
higher level of interoperability between information
systems, simplifying tasks such as sharing documents
between coworkers or enabling collaboration between an
organization and its suppliers. While these abilities
may result in significant benefits to users and
organizations, they also present privacy challenges due
to unwanted exposure of sensitive information. As
information-sharing processes in cloud computing are
complex and domain specific, configuring these
processes can be an overwhelming and burdensome task
for users. This article investigates the feasibility of
configuring sharing processes through a small and
representative set of canonical configuration options.
For this purpose, we present a generic method, named
SCON-UP (Simplified CON-figuration of User
Preferences). SCON-UP simplifies configuration
interfaces by using a clustering algorithm that
analyzes a massive set of sharing preferences and
condenses them into a small number of discrete
disclosure levels. Thus, the user is provided with a
usable configuration model while guaranteeing adequate
privacy control. We describe the algorithm and
empirically evaluate our model using data collected in
two user studies (n = 121 and n = 352). Our results
show that when provided with three canonical
configuration options, on average, 82\% of the
population can be covered by at least one option. We
exemplify the feasibility of discretizing sharing
levels and discuss the tradeoff between coverage and
simplicity in discrete configuration options.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Elbadrawy:2015:USF,
author = "Asmaa Elbadrawy and George Karypis",
title = "User-Specific Feature-Based Similarity Models for
Top-$n$ Recommendation of New Items",
journal = j-TIST,
volume = "6",
number = "3",
pages = "33:1--33:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700495",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommending new items for suitable users is an
important yet challenging problem due to the lack of
preference history for the new items. Noncollaborative
user modeling techniques that rely on the item features
can be used to recommend new items. However, they only
use the past preferences of each user to provide
recommendations for that user. They do not utilize
information from the past preferences of other users,
which can potentially be ignoring useful information.
More recent factor models transfer knowledge across
users using their preference information in order to
provide more accurate recommendations. These methods
learn a low-rank approximation for the preference
matrix, which can lead to loss of information.
Moreover, they might not be able to learn useful
patterns given very sparse datasets. In this work, we
present {{\sc UFSM}, a method for top-$n$
recommendation of new items given binary user
preferences. {\sc UFSM} learns {{\bf U}ser}-specific
{\bf F}eature}-based item-{\bf S}imilarity {\bf
M}odels, and its strength lies in combining two points:
(1) exploiting preference information across all users
to learn multiple global item similarity functions and
(2) learning user-specific weights that determine the
contribution of each global similarity function in
generating recommendations for each user. {\sc UFSM}
can be considered as a sparse high-dimensional factor
model where the previous preferences of each user are
incorporated within his or her latent representation.
This way, {\sc UFSM} combines the merits of item
similarity models that capture local relations among
items and factor models that learn global preference
patterns. A comprehensive set of experiments was
conduced to compare {\sc UFSM} against state-of-the-art
collaborative factor models and noncollaborative user
modeling techniques. Results show that {\sc UFSM}
outperforms other techniques in terms of recommendation
quality. {\sc UFSM} manages to yield better
recommendations even with very sparse datasets. Results
also show that {\sc UFSM} can efficiently handle
high-dimensional as well as low-dimensional item
feature spaces.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:TGO,
author = "Mingjin Zhang and Huibo Wang and Yun Lu and Tao Li and
Yudong Guang and Chang Liu and Erik Edrosa and Hongtai
Li and Naphtali Rishe",
title = "{TerraFly GeoCloud}: an Online Spatial Data Analysis
and Visualization System",
journal = j-TIST,
volume = "6",
number = "3",
pages = "34:1--34:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700494",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the exponential growth of the usage of web map
services, geo-data analysis has become more and more
popular. This article develops an online spatial data
analysis and visualization system, TerraFly GeoCloud,
which helps end-users visualize and analyze spatial
data and share the analysis results. Built on the
TerraFly Geo spatial database, TerraFly GeoCloud is an
extra layer running upon the TerraFly map and can
efficiently support many different visualization
functions and spatial data analysis models.
Furthermore, users can create unique URLs to visualize
and share the analysis results. TerraFly GeoCloud also
enables the MapQL technology to customize map
visualization using SQL-like statements. The system is
available at http://terrafly.fiu.edu/GeoCloud/.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:SCP,
author = "Yi-Cheng Chen and Wen-Chih Peng and Jiun-Long Huang
and Wang-Chien Lee",
title = "Significant Correlation Pattern Mining in Smart
Homes",
journal = j-TIST,
volume = "6",
number = "3",
pages = "35:1--35:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700484",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Owing to the great advent of sensor technology, the
usage data of appliances in a house can be logged and
collected easily today. However, it is a challenge for
the residents to visualize how these appliances are
used. Thus, mining algorithms are much needed to
discover appliance usage patterns. Most previous
studies on usage pattern discovery are mainly focused
on analyzing the patterns of single appliance rather
than mining the usage correlation among appliances. In
this article, a novel algorithm, namely Correlation
Pattern Miner (CoPMiner), is developed to capture the
usage patterns and correlations among appliances
probabilistically. CoPMiner also employs four pruning
techniques and a statistical model to reduce the search
space and filter out insignificant patterns,
respectively. Furthermore, the proposed algorithm is
applied on a real-world dataset to show the
practicability of correlation pattern mining.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guo:2015:ISI,
author = "Bin Guo and Alvin Chin and Zhiwen Yu and Runhe Huang
and Daqing Zhang",
title = "An Introduction to the Special Issue on Participatory
Sensing and Crowd Intelligence",
journal = j-TIST,
volume = "6",
number = "3",
pages = "36:1--36:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2745712",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:SPU,
author = "Fuzheng Zhang and Nicholas Jing Yuan and David Wilkie
and Yu Zheng and Xing Xie",
title = "Sensing the Pulse of Urban Refueling Behavior: a
Perspective from Taxi Mobility",
journal = j-TIST,
volume = "6",
number = "3",
pages = "37:1--37:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2644828",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Urban transportation is an important factor in energy
consumption and pollution, and is of increasing concern
due to its complexity and economic significance. Its
importance will only increase as urbanization continues
around the world. In this article, we explore drivers'
refueling behavior in urban areas. Compared to
questionnaire-based methods of the past, we propose a
complete data-driven system that pushes towards
real-time sensing of individual refueling behavior and
citywide petrol consumption. Our system provides the
following: detection of individual refueling events
(REs) from which refueling preference can be analyzed;
estimates of gas station wait times from which
recommendations can be made; an indication of overall
fuel demand from which macroscale economic decisions
can be made, and a spatial, temporal, and economic view
of urban refueling characteristics. For individual
behavior, we use reported trajectories from a fleet of
GPS-equipped taxicabs to detect gas station visits. For
time spent estimates, to solve the sparsity issue along
time and stations, we propose context-aware tensor
factorization (CATF), a factorization model that
considers a variety of contextual factors (e.g., price,
brand, and weather condition) that affect consumers'
refueling decision. For fuel demand estimates, we apply
a queue model to calculate the overall visits based on
the time spent inside the station. We evaluated our
system on large-scale and real-world datasets, which
contain 4-month trajectories of 32,476 taxicabs, 689
gas stations, and the self-reported refueling details
of 8,326 online users. The results show that our system
can determine REs with an accuracy of more than 90\%,
estimate time spent with less than 2 minutes of error,
and measure overall visits in the same order of
magnitude with the records in the field study.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tangmunarunkit:2015:OGE,
author = "H. Tangmunarunkit and C. K. Hsieh and B. Longstaff and
S. Nolen and J. Jenkins and C. Ketcham and J. Selsky
and F. Alquaddoomi and D. George and J. Kang and Z.
Khalapyan and J. Ooms and N. Ramanathan and D. Estrin",
title = "{Ohmage}: a General and Extensible End-to-End
Participatory Sensing Platform",
journal = j-TIST,
volume = "6",
number = "3",
pages = "38:1--38:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2717318",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Participatory sensing (PS) is a distributed data
collection and analysis approach where individuals,
acting alone or in groups, use their personal mobile
devices to systematically explore interesting aspects
of their lives and communities [Burke et al. 2006].
These mobile devices can be used to capture diverse
spatiotemporal data through both intermittent
self-report and continuous recording from on-board
sensors and applications. Ohmage (http://ohmage.org) is
a modular and extensible open-source, mobile to Web PS
platform that records, stores, analyzes, and visualizes
data from both prompted self-report and continuous data
streams. These data streams are authorable and can
dynamically be deployed in diverse settings. Feedback
from hundreds of behavioral and technology researchers,
focus group participants, and end users has been
integrated into ohmage through an iterative
participatory design process. Ohmage has been used as
an enabling platform in more than 20 independent
projects in many disciplines. We summarize the PS
requirements, challenges and key design objectives
learned through our design process, and ohmage system
architecture to achieve those objectives. The
flexibility, modularity, and extensibility of ohmage in
supporting diverse deployment settings are presented
through three distinct case studies in education,
health, and clinical research.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xiong:2015:EEE,
author = "Haoyi Xiong and Daqing Zhang and Leye Wang and J. Paul
Gibson and Jie Zhu",
title = "{EEMC}: Enabling Energy-Efficient Mobile Crowdsensing
with Anonymous Participants",
journal = j-TIST,
volume = "6",
number = "3",
pages = "39:1--39:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2644827",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Mobile Crowdsensing (MCS) requires users to be
motivated to participate. However, concerns regarding
energy consumption and privacy-among other things-may
compromise their willingness to join such a crowd. Our
preliminary observations and analysis of common MCS
applications have shown that the data transfer in MCS
applications may incur significant energy consumption
due to the 3G connection setup. However, if data are
transferred in parallel with a traditional phone call,
then such transfer can be done almost ``for free'':
with only an insignificant additional amount of energy
required to piggy-back the data-usually incoming task
assignments and outgoing sensor results-on top of the
call. Here, we present an {\em Energy-Efficient Mobile
Crowdsensing\/} (EEMC) framework where task assignments
and sensing results are transferred in parallel with
phone calls. The main objective, and the principal
contribution of this article, is an MCS task assignment
scheme that guarantees that a minimum number of
anonymous participants return sensor results within a
specified time frame, while also minimizing the waste
of energy due to redundant task assignments and
considering privacy concerns of participants.
Evaluations with a large-scale real-world phone call
dataset show that our proposed {EEMC} framework
outperforms the baseline approaches, and it can reduce
overall energy consumption in data transfer by 54--66\%
when compared to the 3G-based solution.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:CSS,
author = "Wangsheng Zhang and Guande Qi and Gang Pan and Hua Lu
and Shijian Li and Zhaohui Wu",
title = "City-Scale Social Event Detection and Evaluation with
Taxi Traces",
journal = j-TIST,
volume = "6",
number = "3",
pages = "40:1--40:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700478",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A social event is an occurrence that involves lots of
people and is accompanied by an obvious rise in human
flow. Analysis of social events has real-world
importance because events bring about impacts on many
aspects of city life. Traditionally, detection and
impact measurement of social events rely on social
investigation, which involves considerable human
effort. Recently, by analyzing messages in social
networks, researchers can also detect and evaluate
country-scale events. Nevertheless, the analysis of
city-scale events has not been explored. In this
article, we use human flow dynamics, which reflect the
social activeness of a region, to detect social events
and measure their impacts. We first extract human flow
dynamics from taxi traces. Second, we propose a method
that can not only discover the happening time and venue
of events from abnormal social activeness, but also
measure the scale of events through changes in such
activeness. Third, we extract traffic congestion
information from traces and use its change during
social events to measure their impact. The results of
experiments validate the effectiveness of both the
event detection and impact measurement methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sang:2015:ASC,
author = "Jitao Sang and Tao Mei and Changsheng Xu",
title = "Activity Sensor: Check-In Usage Mining for Local
Recommendation",
journal = j-TIST,
volume = "6",
number = "3",
pages = "41:1--41:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700468",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "While on the go, people are using their phones as a
personal concierge discovering what is around and
deciding what to do. Mobile phone has become a
recommendation terminal customized for
individuals-capable of recommending activities and
simplifying the accomplishment of related tasks. In
this article, we conduct usage mining on the check-in
data, with summarized statistics identifying the local
recommendation challenges of huge solution space,
sparse available data, and complicated user intent, and
discovered observations to motivate the hierarchical,
contextual, and sequential solution. We present a
point-of-interest (POI) category-transition--based
approach, with a goal of estimating the visiting
probability of a series of successive POIs conditioned
on current user context and sensor context. A mobile
local recommendation demo application is deployed. The
objective and subjective evaluations validate the
effectiveness in providing mobile users both accurate
recommendation and favorable user experience.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:EDQ,
author = "Bo Zhang and Zheng Song and Chi Harold Liu and Jian Ma
and Wendong Wang",
title = "An Event-Driven {QoI}-Aware Participatory Sensing
Framework with Energy and Budget Constraints",
journal = j-TIST,
volume = "6",
number = "3",
pages = "42:1--42:??",
month = may,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2630074",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu May 21 15:49:31 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Participatory sensing systems can be used for
concurrent event monitoring applications, like noise
levels, fire, and pollutant concentrations. However,
they are facing new challenges as to how to accurately
detect the exact boundaries of these events, and
further, to select the most appropriate participants to
collect the sensing data. On the one hand,
participants' handheld smart devices are constrained
with different energy conditions and sensing
capabilities, and they move around with uncontrollable
mobility patterns in their daily life. On the other
hand, these sensing tasks are within time-varying
quality-of-information (QoI) requirements and budget to
afford the users' incentive expectations. Toward this
end, this article proposes an event-driven QoI-aware
participatory sensing framework with energy and budget
constraints. The main method of this framework is event
boundary detection. For the former, a two-step
heuristic solution is proposed where the coarse-grained
detection step finds its approximation and the
fine-grained detection step identifies the exact
location. Participants are selected by explicitly
considering their mobility pattern, required QoI of
multiple tasks, and users' incentive requirements,
under the constraint of an aggregated task budget.
Extensive experimental results, based on a real trace
in Beijing, show the effectiveness and robustness of
our approach, while comparing with existing schemes.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Anantharam:2015:ECT,
author = "Pramod Anantharam and Payam Barnaghi and Krishnaprasad
Thirunarayan and Amit Sheth",
title = "Extracting City Traffic Events from Social Streams",
journal = j-TIST,
volume = "6",
number = "4",
pages = "43:1--43:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2717317",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Cities are composed of complex systems with physical,
cyber, and social components. Current works on
extracting and understanding city events mainly rely on
technology-enabled infrastructure to observe and record
events. In this work, we propose an approach to
leverage citizen observations of various city systems
and services, such as traffic, public transport, water
supply, weather, sewage, and public safety, as a source
of city events. We investigate the feasibility of using
such textual streams for extracting city events from
annotated text. We formalize the problem of annotating
social streams such as microblogs as a sequence
labeling problem. We present a novel training data
creation process for training sequence labeling models.
Our automatic training data creation process utilizes
instance-level domain knowledge (e.g., locations in a
city, possible event terms). We compare this automated
annotation process to a state-of-the-art tool that
needs manually created training data and show that it
has comparable performance in annotation tasks. An
aggregation algorithm is then presented for event
extraction from annotated text. We carry out a
comprehensive evaluation of the event annotation and
event extraction on a real-world dataset consisting of
event reports and tweets collected over 4 months from
the San Francisco Bay Area. The evaluation results are
promising and provide insights into the utility of
social stream for extracting city events.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sawant:2015:AGC,
author = "Anshul Sawant and John P. Dickerson and Mohammad T.
Hajiaghayi and V. S. Subrahmanian",
title = "Automated Generation of Counterterrorism Policies
Using Multiexpert Input",
journal = j-TIST,
volume = "6",
number = "4",
pages = "44:1--44:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2716328",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The use of game theory to model conflict has been
studied by several researchers, spearheaded by
Schelling. Most of these efforts assume a single payoff
matrix that captures players' utilities under different
assumptions about what the players will do. Our
experience in counterterrorism applications is that
experts disagree on these payoffs. We leverage
Shapley's notion of vector equilibria, which formulates
games where there are multiple payoff matrices, but
note that they are very hard to compute in practice. To
effectively enumerate large numbers of equilibria with
payoffs provided by multiple experts, we propose a
novel combination of vector payoffs and well-supported
$ \epsilon $-approximate equilibria. We develop bounds
related to computation of these equilibria for some
special cases and give a quasipolynomial time
approximation scheme (QPTAS) for the general case when
the number of players is small (which is true in many
real-world applications). Leveraging this QPTAS, we
give efficient algorithms to find such equilibria and
experimental results showing that they work well on
simulated data. We then built a policy recommendation
engine based on vector equilibria, called PREVE. We use
PREVE to model the terrorist group Lashkar-e-Taiba
(LeT), responsible for the 2008 Mumbai attacks, as a
five-player game. Specifically, we apply it to three
payoff matrices provided by experts in India--Pakistan
relations, analyze the equilibria generated by PREVE,
and suggest counterterrorism policies that may reduce
attacks by LeT. We briefly discuss these results and
identify their strengths and weaknesses from a policy
point of view.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bai:2015:OPL,
author = "Aijun Bai and Feng Wu and Xiaoping Chen",
title = "Online Planning for Large {Markov} Decision Processes
with Hierarchical Decomposition",
journal = j-TIST,
volume = "6",
number = "4",
pages = "45:1--45:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2717316",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Markov decision processes (MDPs) provide a rich
framework for planning under uncertainty. However,
exactly solving a large MDP is usually intractable due
to the ``curse of dimensionality''- the state space
grows exponentially with the number of state variables.
Online algorithms tackle this problem by avoiding
computing a policy for the entire state space. On the
other hand, since online algorithm has to find a
near-optimal action online in almost real time, the
computation time is often very limited. In the context
of reinforcement learning, MAXQ is a value function
decomposition method that exploits the underlying
structure of the original MDP and decomposes it into a
combination of smaller subproblems arranged over a task
hierarchy. In this article, we present MAXQ-OP-a novel
online planning algorithm for large MDPs that utilizes
MAXQ hierarchical decomposition in online settings.
Compared to traditional online planning algorithms,
MAXQ-OP is able to reach much more deeper states in the
search tree with relatively less computation time by
exploiting MAXQ hierarchical decomposition online. We
empirically evaluate our algorithm in the standard Taxi
domain-a common benchmark for MDPs-to show the
effectiveness of our approach. We have also conducted a
long-term case study in a highly complex simulated
soccer domain and developed a team named WrightEagle
that has won five world champions and five runners-up
in the recent 10 years of RoboCup Soccer Simulation 2D
annual competitions. The results in the RoboCup domain
confirm the scalability of MAXQ-OP to very large
domains.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ye:2015:SSB,
author = "Yanfang Ye and Tao Li and Haiyin Shen",
title = "{Soter}: Smart Bracelets for Children's Safety",
journal = j-TIST,
volume = "6",
number = "4",
pages = "46:1--46:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent years, crimes against children and cases of
missing children have increased at a high rate.
Therefore, there is an urgent need for safety support
systems to prevent crimes against children or for
antiloss, especially when parents are not with their
children, such as to and from school. However, existing
children's tracking systems are not smart enough to
provide the safety supports, as they simply locate the
children's positions without offering any notification
to parents that their children may be in danger. In
addition, there is limited research on children's
tracking and their antiloss. In this article, based on
location histories, we introduce novel notions of
children's life patterns that capture their general
lifestyles and regularities, and develop an intelligent
data mining framework to learn the safe regions and
safe routes of children on the cloud side. When the
children may be in danger, their parents will receive
automatic notifications from the cloud. We also propose
an effective energy-efficient positioning scheme that
leverages the location tracking accuracy of the
children while keeping energy overhead low by using a
hybrid global positioning system and a global system
for mobile communications. To the best of our
knowledge, this is the first attempt in applying data
mining techniques to applications designed for
children's safety. Our proposed techniques have been
incorporated into Soter, a children's safeguard system
that is used to provide cloud service for smart
bracelets produced by Qihoo. The case studies on real
smart bracelet users of Qihoo demonstrate the
effectiveness of our proposed methods and Soter for
children's safety.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2015:PLL,
author = "Yi Wang and Xuemin Zhao and Zhenlong Sun and Hao Yan
and Lifeng Wang and Zhihui Jin and Liubin Wang and Yang
Gao and Ching Law and Jia Zeng",
title = "{Peacock}: Learning Long-Tail Topic Features for
Industrial Applications",
journal = j-TIST,
volume = "6",
number = "4",
pages = "47:1--47:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700497",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Latent Dirichlet allocation (LDA) is a popular topic
modeling technique in academia but less so in industry,
especially in large-scale applications involving search
engine and online advertising systems. A main
underlying reason is that the topic models used have
been too small in scale to be useful; for example, some
of the largest LDA models reported in literature have
up to 10$^3$ topics, which difficultly cover the
long-tail semantic word sets. In this article, we show
that the number of topics is a key factor that can
significantly boost the utility of topic-modeling
systems. In particular, we show that a ``big'' LDA
model with at least 10$^5$ topics inferred from 10$^9$
search queries can achieve a significant improvement on
industrial search engine and online advertising
systems, both of which serve hundreds of millions of
users. We develop a novel distributed system called
Peacock to learn big LDA models from big data. The main
features of Peacock include hierarchical distributed
architecture, real-time prediction, and topic
de-duplication. We empirically demonstrate that the
Peacock system is capable of providing significant
benefits via highly scalable LDA topic models for
several industrial applications.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jumadinova:2015:APM,
author = "Janyl Jumadinova and Prithviraj Dasgupta",
title = "Automated Pricing in a Multiagent Prediction Market
Using a Partially Observable Stochastic Game",
journal = j-TIST,
volume = "6",
number = "4",
pages = "48:1--48:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700488",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Prediction markets offer an efficient market-based
mechanism to aggregate large amounts of dispersed or
distributed information from different people to
predict the possible outcome of future events.
Recently, automated prediction markets where software
trading agents perform market operations such as
trading and updating beliefs on behalf of humans have
been proposed. A challenging aspect in automated
prediction markets is to develop suitable techniques
that can be used by automated trading agents to update
the price at which they should trade securities related
to an event so that they can increase their profit.
This problem is nontrivial, as the decision to trade
and the price at which trading should occur depends on
several dynamic factors, such as incoming information
related to the event for which the security is being
traded, the belief-update mechanism and risk attitude
of the trading agent, and the trading decision and
trading prices of other agents. To address this
problem, we have proposed a new behavior model for
trading agents based on a game-theoretic framework
called partially observable stochastic game with
information (POSGI). We propose a correlated
equilibrium (CE)-based solution strategy for this game
that allows each agent to dynamically choose an action
(to buy or sell or hold) in the prediction market. We
have also performed extensive simulation experiments
using the data obtained from the Intrade prediction
market for four different prediction markets. Our
results show that our POSGI model and CE strategy
produces prices that are strongly correlated with the
prices of the real prediction markets. Results
comparing our CE strategy with five other strategies
commonly used in similar market show that our CE
strategy improves price predictions and provides higher
utilities to the agents compared to other existing
strategies.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fu:2015:ESG,
author = "Hao Fu and Aston Zhang and Xing Xie",
title = "Effective Social Graph Deanonymization Based on Graph
Structure and Descriptive Information",
journal = j-TIST,
volume = "6",
number = "4",
pages = "49:1--49:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700836",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The study of online social networks has attracted
increasing interest. However, concerns are raised for
the privacy risks of user data since they have been
frequently shared among researchers, advertisers, and
application developers. To solve this problem, a number
of anonymization algorithms have been recently
developed for protecting the privacy of social graphs.
In this article, we proposed a graph node similarity
measurement in consideration with both graph structure
and descriptive information, and a deanonymization
algorithm based on the measurement. Using the proposed
algorithm, we evaluated the privacy risks of several
typical anonymization algorithms on social graphs with
thousands of nodes from Microsoft Academic Search,
LiveJournal, and the Enron email dataset, and a social
graph with millions of nodes from Tencent Weibo. Our
results showed that the proposed algorithm was
efficient and effective to deanonymize social graphs
without any initial seed mappings. Based on the
experiments, we also pointed out suggestions on how to
better maintain the data utility while preserving
privacy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:HIR,
author = "Bo-Hao Chen and Shih-Chia Huang and Jian Hui Ye",
title = "Hazy Image Restoration by Bi-Histogram Modification",
journal = j-TIST,
volume = "6",
number = "4",
pages = "50:1--50:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2710024",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Visibility restoration techniques are widely used for
information recovery of hazy images in many computer
vision applications. Estimation of haze density is an
essential task of visibility restoration techniques.
However, conventional visibility restoration techniques
often suffer from either the generation of serious
artifacts or the loss of object information in the
restored images due to uneven haze density, which
usually means that the images contain heavy haze
formation within their background regions and little
haze formation within their foreground regions. This
frequently occurs when the images feature real-world
scenes with a deep depth of field. How to effectively
and accurately estimate the haze density in the
transmission map for these images is the most
challenging aspect of the traditional state-of-the-art
techniques. In response to this problem, this work
proposes a novel visibility restoration approach that
is based on Bi-Histogram modification, and which
integrates a haze density estimation module and a haze
formation removal module for effective and accurate
estimation of haze density in the transmission map. As
our experimental results demonstrate, the proposed
approach achieves superior visibility restoration
efficacy in comparison with the other state-of-the-art
approaches based on both qualitative and quantitative
evaluations. The proposed approach proves effective and
accurate in terms of both background and foreground
restoration of various hazy scenarios.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Combi:2015:IAT,
author = "Carlo Combi and Jiming Liu",
title = "Introduction to the {ACM TIST} Special Issue on
Intelligent Healthcare Informatics",
journal = j-TIST,
volume = "6",
number = "4",
pages = "51:1--51:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2791398",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kim:2015:AAR,
author = "Eunju Kim and Sumi Helal and Chris Nugent and Mark
Beattie",
title = "Analyzing Activity Recognition Uncertainties in Smart
Home Environments",
journal = j-TIST,
volume = "6",
number = "4",
pages = "52:1--52:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2651445",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In spite of the importance of activity recognition
(AR) for intelligent human-computer interaction in
emerging smart space applications, state-of-the-art AR
technology is not ready or adequate for real-world
deployments due to its insufficient accuracy. The
accuracy limitation is directly attributed to
uncertainties stemming from multiple sources in the AR
system. Hence, one of the major goals of AR research is
to improve system accuracy by minimizing or managing
the uncertainties encountered throughout the AR
process. As we cannot manage uncertainties well without
measuring them, we must first quantify their impact.
Nevertheless, such a quantification process is very
challenging given that uncertainties come from diverse
and heterogeneous sources. In this article, we propose
an approach, which can account for multiple uncertainty
sources and assess their impact on AR systems. We
introduce several metrics to quantify the various
uncertainties and their impact. We then conduct a
quantitative impact analysis of uncertainties utilizing
data collected from actual smart spaces that we have
instrumented. The analysis is intended to serve as
groundwork for developing ``diagnostic'' accuracy
measures of AR systems capable of pinpointing the
sources of accuracy loss. This is to be contrasted with
the currently used accuracy measures.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Soto-Mendoza:2015:DPS,
author = "Valeria Soto-Mendoza and J. Antonio
Garc{\'\i}a-Mac{\'\i}as and Edgar Ch{\'a}vez and Ana I.
Mart{\'\i}nez-Garc{\'\i}a and Jes{\'u}s Favela and
Patricia Serrano-Alvarado and Mayth{\'e} R.
Z{\'u}{\~n}iga Rojas",
title = "Design of a Predictive Scheduling System to Improve
Assisted Living Services for Elders",
journal = j-TIST,
volume = "6",
number = "4",
pages = "53:1--53:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2736700",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "As the number of older adults increases, and with it
the demand for dedicated care, geriatric residences
face a shortage of caregivers, who themselves
experience work overload, stress, and burden. We
conducted a long-term field study in three geriatric
residences to understand the work conditions of
caregivers with the aim of developing technologies to
assist them in their work and help them deal with their
burdens. From this study, we obtained relevant
requirements and insights to design, implement, and
evaluate two prototypes for supporting caregivers'
tasks (e.g., electronic recording and automatic
notifications) in order to validate the feasibility of
their implementation in situ and their technical
requirements. The evaluation in situ of the prototypes
was conducted for a period of 4 weeks. The results of
the evaluation, together with the data collected from 6
months of use, motivated the design of a predictive
schedule, which was iteratively improved and evaluated
in participative sessions with caregivers. PRESENCE,
the predictive schedule we propose, triggers real-time
alerts of risky situations (e.g., falls, entering
off-limits areas such as the infirmary or the kitchen)
and informs caregivers of routine tasks that need to be
performed (e.g., medication administration, diaper
change, etc.). Moreover, PRESENCE helps caregivers to
record caring tasks (such as diaper changes or
medication) and well-being assessments (such as the
mood) that are difficult to automate. This facilitates
caregiver's shift handover and can help to train new
caregivers by suggesting routine tasks and by sending
reminders and timely information about residents. It
can be seen as a tool to reduce the workload of
caregivers and medical staff. Instead of trying to
substitute the caregiver with an automatic caring
system, as proposed by others, we propose our
predictive schedule system that blends caregiver
assessments and measurements from sensors. We show the
feasibility of predicting caregiver tasks and a
formative evaluation with caregivers that provides
preliminary evidence of its utility.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Champaign:2015:EPC,
author = "John Champaign and Robin Cohen and Disney Yan Lam",
title = "Empowering Patients and Caregivers to Manage
Healthcare Via Streamlined Presentation of {Web}
Objects Selected by Modeling Learning Benefits Obtained
by Similar Peers",
journal = j-TIST,
volume = "6",
number = "4",
pages = "54:1--54:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700480",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we introduce a framework for
selecting web objects (texts, videos, simulations) from
a large online repository to present to patients and
caregivers, in order to assist in their healthcare.
Motivated by the paradigm of peer-based intelligent
tutoring, we model the learning gains achieved by users
when exposed to specific web objects in order to
recommend those objects most likely to deliver benefit
to new users. We are able to show that this streamlined
presentation leads to effective knowledge gains, both
through a process of simulated learning and through a
user study, for the specific application of caring for
children with autism. The value of our framework for
peer-driven content selection of health information is
emphasized through two additional roles for peers:
attaching commentary to web objects and proposing
subdivided objects for presentation, both of which are
demonstrated to deliver effective learning gains, in
simulations. In all, we are offering an opportunity for
patients to navigate the deep waters of excessive
online information towards effective management of
healthcare, through content selection influenced by
previous peer experiences.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2015:UHC,
author = "Haodong Yang and Christopher C. Yang",
title = "Using Health-Consumer-Contributed Data to Detect
Adverse Drug Reactions by Association Mining with
Temporal Analysis",
journal = j-TIST,
volume = "6",
number = "4",
pages = "55:1--55:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Since adverse drug reactions (ADRs) represent a
significant health problem all over the world, ADR
detection has become an important research topic in
drug safety surveillance. As many potential ADRs cannot
be detected though premarketing review, drug safety
currently depends heavily on postmarketing
surveillance. Particularly, current postmarketing
surveillance in the United States primarily relies on
the FDA Adverse Event Reporting System (FAERS).
However, the effectiveness of such spontaneous
reporting systems for ADR detection is not as good as
expected because of the extremely high underreporting
ratio of ADRs. Moreover, it often takes the FDA years
to complete the whole process of collecting reports,
investigating cases, and releasing alerts. Given the
prosperity of social media, many online health
communities are publicly available for health consumers
to share and discuss any healthcare experience such as
ADRs they are suffering. Such
health-consumer-contributed content is timely and
informative, but this data source still remains
untapped for postmarketing drug safety surveillance. In
this study, we propose to use (1) association mining to
identify the relations between a drug and an ADR and
(2) temporal analysis to detect drug safety signals at
the early stage. We collect data from MedHelp and use
the FDA's alerts and information of drug labeling
revision as the gold standard to evaluate the
effectiveness of our approach. The experiment results
show that health-related social media is a promising
source for ADR detection, and our proposed techniques
are effective to identify early ADR signals.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ullah:2015:ERL,
author = "Md Zia Ullah and Masaki Aono and Md Hanif Seddiqui",
title = "Estimating a Ranked List of Human Genetic Diseases by
Associating Phenotype-Gene with Gene-Disease Bipartite
Graphs",
journal = j-TIST,
volume = "6",
number = "4",
pages = "56:1--56:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700487",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With vast amounts of medical knowledge available on
the Internet, it is becoming increasingly practical to
help doctors in clinical diagnostics by suggesting
plausible diseases predicted by applying data and text
mining technologies. Recently, Genome-Wide Association
Studies ( GWAS ) have proved useful as a method for
exploring phenotypic associations with diseases.
However, since genetic diseases are difficult to
diagnose because of their low prevalence, large number,
and broad diversity of symptoms, genetic disease
patients are often misdiagnosed or experience long
diagnostic delays. In this article, we propose a method
for ranking genetic diseases for a set of clinical
phenotypes. In this regard, we associate a
phenotype-gene bipartite graph ( PGBG ) with a
gene-disease bipartite graph ( GDBG ) by producing a
phenotype-disease bipartite graph ( PDBG ), and we
estimate the candidate weights of diseases. In our
approach, all paths from a phenotype to a disease are
explored by considering causative genes to assign a
weight based on path frequency, and the phenotype is
linked to the disease in a new PDBG. We introduce the
Bidirectionally induced Importance Weight ( BIW )
prediction method to PDBG for approximating the weights
of the edges of diseases with phenotypes by considering
link information from both sides of the bipartite
graph. The performance of our system is compared to
that of other known related systems by estimating
Normalized Discounted Cumulative Gain ( NDCG ), Mean
Average Precision ( MAP ), and Kendall's tau metrics.
Further experiments are conducted with well-known TF $
\cdot $ IDF, BM25, and Jenson-Shannon divergence as
baselines. The result shows that our proposed method
outperforms the known related tool Phenomizer in terms
of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it
performs worse than Phenomizer in terms of Kendall's
tau-b metric at the top-10 ranks. It also turns out
that our proposed method has overall better performance
than the baseline methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Antonelli:2015:MCM,
author = "Dario Antonelli and Elena Baralis and Giulia Bruno and
Luca Cagliero and Tania Cerquitelli and Silvia Chiusano
and Paolo Garza and Naeem A. Mahoto",
title = "{MeTA}: Characterization of Medical Treatments at
Different Abstraction Levels",
journal = j-TIST,
volume = "6",
number = "4",
pages = "57:1--57:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700479",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Physicians and health care organizations always
collect large amounts of data during patient care.
These large and high-dimensional datasets are usually
characterized by an inherent sparseness. Hence,
analyzing these datasets to figure out interesting and
hidden knowledge is a challenging task. This article
proposes a new data mining framework based on
generalized association rules to discover
multiple-level correlations among patient data.
Specifically, correlations among prescribed
examinations, drugs, and patient profiles are
discovered and analyzed at different abstraction
levels. The rule extraction process is driven by a
taxonomy to generalize examinations and drugs into
their corresponding categories. To ease the manual
inspection of the result, a worthwhile subset of rules
(i.e., nonredundant generalized rules) is considered.
Furthermore, rules are classified according to the
involved data features (medical treatments or patient
profiles) and then explored in a top-down fashion: from
the small subset of high-level rules, a drill-down is
performed to target more specific rules. The
experiments, performed on a real diabetic patient
dataset, demonstrate the effectiveness of the proposed
approach in discovering interesting rule groups at
different abstraction levels.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Motai:2015:SCD,
author = "Yuichi Motai and Dingkun Ma and Alen Docef and
Hiroyuki Yoshida",
title = "Smart Colonography for Distributed Medical Databases
with Group Kernel Feature Analysis",
journal = j-TIST,
volume = "6",
number = "4",
pages = "58:1--58:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668136",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Computer-Aided Detection (CAD) of polyps in Computed
Tomographic (CT) colonography is currently very limited
since a single database at each hospital/institution
doesn't provide sufficient data for training the CAD
system's classification algorithm. To address this
limitation, we propose to use multiple databases,
(e.g., big data studies) to create multiple
institution-wide databases using distributed computing
technologies, which we call smart colonography. Smart
colonography may be built by a larger colonography
database networked through the participation of
multiple institutions via distributed computing. The
motivation herein is to create a distributed database
that increases the detection accuracy of CAD diagnosis
by covering many true-positive cases. Colonography data
analysis is mutually accessible to increase the
availability of resources so that the knowledge of
radiologists is enhanced. In this article, we propose a
scalable and efficient algorithm called Group Kernel
Feature Analysis (GKFA), which can be applied to
multiple cancer databases so that the overall
performance of CAD is improved. The key idea behind the
proposed GKFA method is to allow the feature space to
be updated as the training proceeds with more data
being fed from other institutions into the algorithm.
Experimental results show that GKFA achieves very good
classification accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kim:2015:RPR,
author = "Mi-Young Kim and Ying Xu and Osmar R. Zaiane and Randy
Goebel",
title = "Recognition of Patient-Related Named Entities in Noisy
Tele-Health Texts",
journal = j-TIST,
volume = "6",
number = "4",
pages = "59:1--59:??",
month = aug,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2651444",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 13 17:37:43 MDT 2015",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We explore methods for effectively extracting
information from clinical narratives that are captured
in a public health consulting phone service called
HealthLink. Our research investigates the application
of state-of-the-art natural language processing and
machine learning to clinical narratives to extract
information of interest. The currently available data
consist of dialogues constructed by nurses while
consulting patients by phone. Since the data are
interviews transcribed by nurses during phone
conversations, they include a significant volume and
variety of noise. When we extract the patient-related
information from the noisy data, we have to remove or
correct at least two kinds of noise: explicit noise,
which includes spelling errors, unfinished sentences,
omission of sentence delimiters, and variants of terms,
and implicit noise, which includes non-patient
information and patient's untrustworthy information. To
filter explicit noise, we propose our own biomedical
term detection/normalization method: it resolves
misspelling, term variations, and arbitrary
abbreviation of terms by nurses. In detecting temporal
terms, temperature, and other types of named entities
(which show patients' personal information such as age
and sex), we propose a bootstrapping-based pattern
learning process to detect a variety of arbitrary
variations of named entities. To address implicit
noise, we propose a dependency path-based filtering
method. The result of our denoising is the extraction
of normalized patient information, and we visualize the
named entities by constructing a graph that shows the
relations between named entities. The objective of this
knowledge discovery task is to identify associations
between biomedical terms and to clearly expose the
trends of patients' symptoms and concern; the
experimental results show that we achieve reasonable
performance with our noise reduction methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ding:2015:LRN,
author = "Wenkui Ding and Xiubo Geng and Xu-Dong Zhang",
title = "Learning to Rank from Noisy Data",
journal = j-TIST,
volume = "7",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2576230",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Learning to rank, which learns the ranking function
from training data, has become an emerging research
area in information retrieval and machine learning.
Most existing work on learning to rank assumes that the
training data is clean, which is not always true,
however. The ambiguity of query intent, the lack of
domain knowledge, and the vague definition of relevance
levels all make it difficult for common annotators to
give reliable relevance labels to some documents. As a
result, the relevance labels in the training data of
learning to rank usually contain noise. If we ignore
this fact, the performance of learning-to-rank
algorithms will be damaged. In this article, we propose
considering the labeling noise in the process of
learning to rank and using a two-step approach to
extend existing algorithms to handle noisy training
data. In the first step, we estimate the degree of
labeling noise for a training document. To this end, we
assume that the majority of the relevance labels in the
training data are reliable and we use a graphical model
to describe the generative process of a training query,
the feature vectors of its associated documents, and
the relevance labels of these documents. The parameters
in the graphical model are learned by means of maximum
likelihood estimation. Then the conditional probability
of the relevance label given the feature vector of a
document is computed. If the probability is large, we
regard the degree of labeling noise for this document
as small; otherwise, we regard the degree as large. In
the second step, we extend existing learning-to-rank
algorithms by incorporating the estimated degree of
labeling noise into their loss functions. Specifically,
we give larger weights to those training documents with
smaller degrees of labeling noise and smaller weights
to those with larger degrees of labeling noise. As
examples, we demonstrate the extensions for McRank,
RankSVM, RankBoost, and RankNet. Empirical results on
benchmark datasets show that the proposed approach can
effectively distinguish noisy documents from clean
ones, and the extended learning-to-rank algorithms can
achieve better performances than baselines.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2015:LSB,
author = "Fan Liu and Jinhui Tang and Yan Song and Liyan Zhang
and Zhenmin Tang",
title = "Local Structure-Based Sparse Representation for Face
Recognition",
journal = j-TIST,
volume = "7",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2733383",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article presents a simple yet effective face
recognition method, called local structure-based sparse
representation classification (LS\_SRC). Motivated by
the ``divide-and-conquer'' strategy, we first divide
the face into local blocks and classify each local
block, then integrate all the classification results to
make the final decision. To classify each local block,
we further divide each block into several overlapped
local patches and assume that these local patches lie
in a linear subspace. This subspace assumption reflects
the local structure relationship of the overlapped
patches, making sparse representation-based
classification (SRC) feasible even when encountering
the single-sample-per-person (SSPP) problem. To lighten
the computing burden of LS\_SRC, we further propose the
local structure-based collaborative representation
classification (LS\_CRC). Moreover, the performance of
LS\_SRC and LS\_CRC can be further improved by using
the confusion matrix of the classifier. Experimental
results on four public face databases show that our
methods not only generalize well to SSPP problem but
also have strong robustness to occlusion; little pose
variation; and the variations of expression,
illumination, and time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Groves:2015:OAT,
author = "William Groves and Maria Gini",
title = "On Optimizing Airline Ticket Purchase Timing",
journal = j-TIST,
volume = "7",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2733384",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Proper timing of the purchase of airline tickets is
difficult even when historical ticket prices and some
domain knowledge are available. To address this
problem, we introduce an algorithm that optimizes
purchase timing on behalf of customers and provides
performance estimates of its computed action policy.
Given a desired flight route and travel date, the
algorithm uses machine-learning methods on recent
ticket price quotes from many competing airlines to
predict the future expected minimum price of all
available flights. The main novelty of our algorithm
lies in using a systematic feature-selection technique,
which captures time dependencies in the data by using
time-delayed features, and reduces the number of
features by imposing a class hierarchy among the raw
features and pruning the features based on in-situ
performance. Our algorithm achieves much closer to the
optimal purchase policy than other existing decision
theoretic approaches for this domain, and meets or
exceeds the performance of existing feature-selection
methods from the literature. Applications of our
feature-selection process to other domains are also
discussed.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Dong:2015:NMR,
author = "Yongsheng Dong and Dacheng Tao and Xuelong Li",
title = "Nonnegative Multiresolution Representation-Based
Texture Image Classification",
journal = j-TIST,
volume = "7",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2738050",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Effective representation of image texture is important
for an image-classification task. Statistical modelling
in wavelet domains has been widely used to image
texture representation. However, due to the intraclass
complexity and interclass diversity of textures, it is
hard to use a predefined probability distribution
function to fit adaptively all wavelet subband
coefficients of different textures. In this article, we
propose a novel modelling approach, Heterogeneous and
Incrementally Generated Histogram (HIGH), to indirectly
model the wavelet coefficients by use of four local
features in wavelet subbands. By concatenating all the
HIGHs in all wavelet subbands of a texture, we can
construct a nonnegative multiresolution vector (NMV) to
represent a texture image. Considering the NMV's high
dimensionality and nonnegativity, we further propose a
Hessian regularized discriminative nonnegative matrix
factorization to compute a low-dimensional basis of the
linear subspace of NMVs. Finally, we present a texture
classification approach by projecting NMVs on the
low-dimensional basis. Experimental results show that
our proposed texture classification method outperforms
seven representative approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2015:MKM,
author = "Bowei Chen and Jun Wang and Ingemar J. Cox and Mohan
S. Kankanhalli",
title = "Multi-Keyword Multi-Click Advertisement Option
Contracts for Sponsored Search",
journal = j-TIST,
volume = "7",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2743027",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In sponsored search, advertisement (abbreviated ad)
slots are usually sold by a search engine to an
advertiser through an auction mechanism in which
advertisers bid on keywords. In theory, auction
mechanisms have many desirable economic properties.
However, keyword auctions have a number of limitations
including: the uncertainty in payment prices for
advertisers; the volatility in the search engine's
revenue; and the weak loyalty between advertiser and
search engine. In this article, we propose a special ad
option that alleviates these problems. In our proposal,
an advertiser can purchase an option from a search
engine in advance by paying an upfront fee, known as
the option price. The advertiser then has the right,
but no obligation, to purchase among the prespecified
set of keywords at the fixed cost-per-clicks (CPCs) for
a specified number of clicks in a specified period of
time. The proposed option is closely related to a
special exotic option in finance that contains multiple
underlying assets (multi-keyword) and is also
multi-exercisable (multi-click). This novel structure
has many benefits: advertisers can have reduced
uncertainty in advertising; the search engine can
improve the advertisers' loyalty as well as obtain a
stable and increased expected revenue over time. Since
the proposed ad option can be implemented in
conjunction with the existing keyword auctions, the
option price and corresponding fixed CPCs must be set
such that there is no arbitrage between the two
markets. Option pricing methods are discussed and our
experimental results validate the development. Compared
to keyword auctions, a search engine can have an
increased expected revenue by selling an ad option.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Font:2015:AIT,
author = "Frederic Font and Joan Serr{\`a} and Xavier Serra",
title = "Analysis of the Impact of a Tag Recommendation System
in a Real-World Folksonomy",
journal = j-TIST,
volume = "7",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2743026",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative tagging systems have emerged as a
successful solution for annotating contributed
resources to online sharing platforms, facilitating
searching, browsing, and organizing their contents. To
aid users in the annotation process, several tag
recommendation methods have been proposed. It has been
repeatedly hypothesized that these methods should
contribute to improving annotation quality and reducing
the cost of the annotation process. It has been also
hypothesized that these methods should contribute to
the consolidation of the vocabulary of collaborative
tagging systems. However, to date, no empirical and
quantitative result supports these hypotheses. In this
work, we deeply analyze the impact of a tag
recommendation system in the folksonomy of Freesound, a
real-world and large-scale online sound sharing
platform. Our results suggest that tag recommendation
effectively increases vocabulary sharing among users of
the platform. In addition, tag recommendation is shown
to contribute to the convergence of the vocabulary as
well as to a partial increase in the quality of
annotations. However, according to our analysis, the
cost of the annotation process does not seem to be
effectively reduced. Our work is relevant to increase
our understanding about the nature of tag
recommendation systems and points to future directions
for the further development of those systems and their
analysis.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cheng:2015:HBS,
author = "Fan-Chieh Cheng and Bo-Hao Chen and Shih-Chia Huang",
title = "A Hybrid Background Subtraction Method with Background
and Foreground Candidates Detection",
journal = j-TIST,
volume = "7",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2746409",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Background subtraction for motion detection is often
used in video surveillance systems. However,
difficulties in bootstrapping restrict its development.
This article proposes a novel hybrid background
subtraction technique to solve this problem. For
performance improvement of background subtraction, the
proposed technique not only quickly initializes the
background model but also eliminates unnecessary
regions containing only background pixels in the object
detection process. Furthermore, an embodiment based on
the proposed technique is also presented. Experimental
results verify that the proposed technique allows for
reduced execution time as well as improvement of
performance as evaluated by Recall, Precision, F1, and
Similarity metrics when used with state-of-the-art
background subtraction methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Muntean:2015:LPM,
author = "Cristina Ioana Muntean and Franco Maria Nardini and
Fabrizio Silvestri and Ranieri Baraglia",
title = "On Learning Prediction Models for Tourists Paths",
journal = j-TIST,
volume = "7",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2766459",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we tackle the problem of predicting
the ``next'' geographical position of a tourist, given
her history (i.e., the prediction is done accordingly
to the tourist's current trail) by means of supervised
learning techniques, namely Gradient Boosted Regression
Trees and Ranking SVM. The learning is done on the
basis of an object space represented by a 68-dimension
feature vector specifically designed for
tourism-related data. Furthermore, we propose a
thorough comparison of several methods that are
considered state-of-the-art in recommender and trail
prediction systems for tourism, as well as a popularity
baseline. Experiments show that the methods we propose
consistently outperform the baselines and provide
strong evidence of the performance and robustness of
our solutions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2015:WHP,
author = "Yinting Wang and Mingli Song and Dacheng Tao and Yong
Rui and Jiajun Bu and Ah Chung Tsoi and Shaojie Zhuo
and Ping Tan",
title = "{Where2Stand}: a Human Position Recommendation System
for Souvenir Photography",
journal = j-TIST,
volume = "7",
number = "1",
pages = "9:1--9:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2770879",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "People often take photographs at tourist sites and
these pictures usually have two main elements: a person
in the foreground and scenery in the background. This
type of ``souvenir photo'' is one of the most common
photos clicked by tourists. Although algorithms that
aid a user-photographer in taking a well-composed
picture of a scene exist [Ni et al. 2013], few studies
have addressed the issue of properly positioning human
subjects in photographs. In photography, the common
guidelines of composing portrait images exist. However,
these rules usually do not consider the background
scene. Therefore, in this article, we investigate
human-scenery positional relationships and construct a
photographic assistance system to optimize the position
of human subjects in a given background scene, thereby
assisting the user in capturing high-quality souvenir
photos. We collect thousands of well-composed portrait
photographs to learn human-scenery aesthetic
composition rules. In addition, we define a set of
negative rules to exclude undesirable compositions.
Recommendation results are achieved by combining the
first learned positive rule with our proposed negative
rules. We implement the proposed system on an Android
platform in a smartphone. The system demonstrates its
efficacy by producing well-composed souvenir photos.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hennes:2015:MLS,
author = "Daniel Hennes and Steven {De Jong} and Karl Tuyls and
Ya'akov (Kobi) Gal",
title = "Metastrategies in Large-Scale Bargaining Settings",
journal = j-TIST,
volume = "7",
number = "1",
pages = "10:1--10:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2774224",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article presents novel methods for representing
and analyzing a special class of multiagent bargaining
settings that feature multiple players, large action
spaces, and a relationship among players' goals, tasks,
and resources. We show how to reduce these interactions
to a set of bilateral normal-form games in which the
strategy space is significantly smaller than the
original settings while still preserving much of their
structural relationship. The method is demonstrated
using the Colored Trails (CT) framework, which
encompasses a broad family of games and has been used
in many past studies. We define a set of heuristics
(metastrategies) in multiplayer CT games that make
varying assumptions about players' strategies, such as
boundedly rational play and social preferences. We show
how these CT settings can be decomposed into canonical
bilateral games such as the Prisoners' Dilemma, Stag
Hunt, and Ultimatum games in a way that significantly
facilitates their analysis. We demonstrate the
feasibility of this approach in separate CT settings
involving one-shot and repeated bargaining scenarios,
which are subsequently analyzed using evolutionary
game-theoretic techniques. We provide a set of
necessary conditions for CT games for allowing this
decomposition. Our results have significance for
multiagent systems researchers in mapping large
multiplayer CT task settings to smaller, well-known
bilateral normal-form games while preserving some of
the structure of the original setting.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2015:SSI,
author = "Jia-Dong Zhang and Chi-Yin Chow",
title = "Spatiotemporal Sequential Influence Modeling for
Location Recommendations: a Gravity-based Approach",
journal = j-TIST,
volume = "7",
number = "1",
pages = "11:1--11:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2786761",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommending to users personalized locations is an
important feature of Location-Based Social Networks
(LBSNs), which benefits users who wish to explore new
places and businesses to discover potential customers.
In LBSNs, social and geographical influences have been
intensively used in location recommendations. However,
human movement also exhibits spatiotemporal sequential
patterns, but only a few current studies consider the
spatiotemporal sequential influence of locations on
users' check-in behaviors. In this article, we propose
a new gravity model for location recommendations,
called LORE, to exploit the spatiotemporal sequential
influence on location recommendations. First, LORE
extracts sequential patterns from historical check-in
location sequences of all users as a Location-Location
Transition Graph (L$^2$ TG), and utilizes the L$^2$ TG
to predict the probability of a user visiting a new
location through the developed additive Markov chain
that considers the effect of all visited locations in
the check-in history of the user on the new location.
Furthermore, LORE applies our contrived gravity model
to weigh the effect of each visited location on the new
location derived from the personalized attractive force
(i.e., the weight) between the visited location and the
new location. The gravity model effectively integrates
the spatiotemporal, social, and popularity influences
by estimating a power-law distribution based on (i) the
spatial distance and temporal difference between two
consecutive check-in locations of the same user, (ii)
the check-in frequency of social friends, and (iii) the
popularity of locations from all users. Finally, we
conduct a comprehensive performance evaluation for LORE
using three large-scale real-world datasets collected
from Foursquare, Gowalla, and Brightkite. Experimental
results show that LORE achieves significantly superior
location recommendations compared to other
state-of-the-art location recommendation techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guan:2015:DML,
author = "Tao Guan and Yuesong Wang and Liya Duan and Rongrong
Ji",
title = "On-Device Mobile Landmark Recognition Using Binarized
Descriptor with Multifeature Fusion",
journal = j-TIST,
volume = "7",
number = "1",
pages = "12:1--12:??",
month = oct,
year = "2015",
CODEN = "????",
DOI = "https://doi.org/10.1145/2795234",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Along with the exponential growth of high-performance
mobile devices, on-device Mobile Landmark Recognition
(MLR) has recently attracted increasing research
attention. However, the latency and accuracy of
automatic recognition remain as bottlenecks against its
real-world usage. In this article, we introduce a novel
framework that combines interactive image segmentation
with multifeature fusion to achieve improved MLR with
high accuracy. First, we propose an effective vector
binarization method to reduce the memory usage of image
descriptors extracted on-device, which maintains
comparable recognition accuracy to the original
descriptors. Second, we design a location-aware fusion
algorithm that can fuse multiple visual features into a
compact yet discriminative image descriptor to improve
on-device efficiency. Third, a user-friendly
interaction scheme is developed that enables
interactive foreground/background segmentation to
largely improve recognition accuracy. Experimental
results demonstrate the effectiveness of the proposed
algorithms for on-device MLR applications.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2016:EFC,
author = "Kun Zhang and Zhikun Wang and Jiji Zhang and Bernhard
Sch{\"o}lkopf",
title = "On Estimation of Functional Causal Models: General
Results and Application to the Post-Nonlinear Causal
Model",
journal = j-TIST,
volume = "7",
number = "2",
pages = "13:1--13:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700476",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Compared to constraint-based causal discovery, causal
discovery based on functional causal models is able to
identify the whole causal model under appropriate
assumptions [Shimizu et al. 2006; Hoyer et al. 2009;
Zhang and Hyv{\"a}rinen 2009b]. Functional causal
models represent the effect as a function of the direct
causes together with an independent noise term.
Examples include the linear non-Gaussian acyclic model
(LiNGAM), nonlinear additive noise model, and
post-nonlinear (PNL) model. Currently, there are two
ways to estimate the parameters in the models:
dependence minimization and maximum likelihood. In this
article, we show that for any acyclic functional causal
model, minimizing the mutual information between the
hypothetical cause and the noise term is equivalent to
maximizing the data likelihood with a flexible model
for the distribution of the noise term. We then focus
on estimation of the PNL causal model and propose to
estimate it with the warped Gaussian process with the
noise modeled by the mixture of Gaussians. As a
Bayesian nonparametric approach, it outperforms the
previous one based on mutual information minimization
with nonlinear functions represented by multilayer
perceptrons; we also show that unlike the ordinary
regression, estimation results of the PNL causal model
are sensitive to the assumption on the noise
distribution. Experimental results on both synthetic
and real data support our theoretical claims.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:OSC,
author = "Jiuyong Li and Thuc Duy Le and Lin Liu and Jixue Liu
and Zhou Jin and Bingyu Sun and Saisai Ma",
title = "From Observational Studies to Causal Rule Mining",
journal = j-TIST,
volume = "7",
number = "2",
pages = "14:1--14:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2746410",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Randomised controlled trials (RCTs) are the most
effective approach to causal discovery, but in many
circumstances it is impossible to conduct RCTs.
Therefore, observational studies based on passively
observed data are widely accepted as an alternative to
RCTs. However, in observational studies, prior
knowledge is required to generate the hypotheses about
the cause-effect relationships to be tested, and hence
they can only be applied to problems with available
domain knowledge and a handful of variables. In
practice, many datasets are of high dimensionality,
which leaves observational studies out of the
opportunities for causal discovery from such a wealth
of data sources. In another direction, many efficient
data mining methods have been developed to identify
associations among variables in large datasets. The
problem is that causal relationships imply
associations, but the reverse is not always true.
However, we can see the synergy between the two
paradigms here. Specifically, association rule mining
can be used to deal with the high-dimensionality
problem, whereas observational studies can be utilised
to eliminate noncausal associations. In this article,
we propose the concept of causal rules (CRs) and
develop an algorithm for mining CRs in large datasets.
We use the idea of retrospective cohort studies to
detect CRs based on the results of association rule
mining. Experiments with both synthetic and real-world
datasets have demonstrated the effectiveness and
efficiency of CR mining. In comparison with the
commonly used causal discovery methods, the proposed
approach generally is faster and has better or
competitive performance in finding correct or sensible
causes. It is also capable of finding a cause
consisting of multiple variables-a feature that other
causal discovery methods do not possess.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Leiva:2016:GGG,
author = "Luis A. Leiva and Daniel Mart{\'\i}n-Albo and
R{\'e}jean Plamondon",
title = "Gestures {\`a} Go Go: Authoring Synthetic Human-Like
Stroke Gestures Using the Kinematic Theory of Rapid
Movements",
journal = j-TIST,
volume = "7",
number = "2",
pages = "15:1--15:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2799648",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Training a high-quality gesture recognizer requires
providing a large number of examples to enable good
performance on unseen, future data. However, recruiting
participants, data collection, and labeling, etc.,
necessary for achieving this goal are usually time
consuming and expensive. Thus, it is important to
investigate how to empower developers to quickly
collect gesture samples for improving UI usage and user
experience. In response to this need, we introduce
Gestures {\`a} Go Go ( g3), a web service plus an
accompanying web application for bootstrapping stroke
gesture samples based on the kinematic theory of rapid
human movements. The user only has to provide a gesture
example once, and g3 will create a model of that
gesture. Then, by introducing local and global
perturbations to the model parameters, g3 generates
from tens to thousands of synthetic human-like samples.
Through a comprehensive evaluation, we show that
synthesized gestures perform equally similar to
gestures generated by human users. Ultimately, this
work informs our understanding of designing better user
interfaces that are driven by gestures.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Minkov:2016:EEU,
author = "Einat Minkov",
title = "Event Extraction using Structured Learning and Rich
Domain Knowledge: Application across Domains and Data
Sources",
journal = j-TIST,
volume = "7",
number = "2",
pages = "16:1--16:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2801131",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We consider the task of record extraction from text
documents, where the goal is to automatically populate
the fields of target relations, such as scientific
seminars or corporate acquisition events. There are
various inferences involved in the record-extraction
process, including mention detection, unification, and
field assignments. We use structured learning to find
the appropriate field-value assignments. Unlike
previous works, the proposed approach generates
feature-rich models that enable the modeling of domain
semantics and structural coherence at all levels and
across fields. Given labeled examples, such an approach
can, for instance, learn likely event durations and the
fact that start times should come before end times.
While the inference space is large, effective learning
is achieved using a perceptron-style method and simple,
greedy beam decoding. A main focus of this article is
on practical aspects involved in implementing the
proposed framework for real-world applications. We
argue and demonstrate that this approach is favorable
in conditions of data shift, a real-world setting in
which models learned using a limited set of labeled
examples are applied to examples drawn from a different
data distribution. Much of the framework's robustness
is attributed to the modeling of domain knowledge. We
describe design and implementation details for the case
study of seminar event extraction from email
announcements, and discuss design adaptations across
different domains and text genres.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2016:PAT,
author = "Kun Zhang and Jiuyong Li and Elias Bareinboim and
Bernhard Sch{\"o}lkopf and Judea Pearl",
title = "Preface to the {ACM TIST} Special Issue on Causal
Discovery and Inference",
journal = j-TIST,
volume = "7",
number = "2",
pages = "17:1--17:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2840720",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shan:2016:SBS,
author = "Na Shan and Xiaogang Dong and Pingfeng Xu and Jianhua
Guo",
title = "Sharp Bounds on Survivor Average Causal Effects When
the Outcome Is Binary and Truncated by Death",
journal = j-TIST,
volume = "7",
number = "2",
pages = "18:1--18:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700498",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In randomized trials with follow-up, outcomes may be
undefined for individuals who die before the follow-up
is complete. In such settings, Frangakis and Rubin
[2002] proposed the ``principal stratum effect'' or
``Survivor Average Causal Effect'' (SACE), which is a
fair treatment comparison in the subpopulation that
would have survived under either treatment arm. Many of
the existing results for estimating the SACE are
difficult to carry out in practice. In this article,
when the outcome is binary, we apply the symbolic
Balke-Pearl linear programming method to derive simple
formulas for the sharp bounds on the SACE under the
monotonicity assumption commonly used by many
researchers.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2016:SIC,
author = "Hua Chen and Peng Ding and Zhi Geng and Xiao-Hua
Zhou",
title = "Semiparametric Inference of the Complier Average
Causal Effect with Nonignorable Missing Outcomes",
journal = j-TIST,
volume = "7",
number = "2",
pages = "19:1--19:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668135",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Noncompliance and missing data often occur in
randomized trials, which complicate the inference of
causal effects. When both noncompliance and missing
data are present, previous papers proposed moment and
maximum likelihood estimators for binary and normally
distributed continuous outcomes under the latent
ignorable missing data mechanism. However, the latent
ignorable missing data mechanism may be violated in
practice, because the missing data mechanism may depend
directly on the missing outcome itself. Under
noncompliance and an outcome-dependent nonignorable
missing data mechanism, previous studies showed the
identifiability of complier average causal effect for
discrete outcomes. In this article, we study the
semiparametric identifiability and estimation of
complier average causal effect in randomized clinical
trials with both all-or-none noncompliance and
outcome-dependent nonignorable missing continuous
outcomes, and propose a two-step maximum likelihood
estimator in order to eliminate the infinite
dimensional nuisance parameter. Our method does not
need to specify a parametric form for the missing data
mechanism. We also evaluate the finite sample property
of our method via extensive simulation studies and
sensitivity analysis, with an application to a
double-blinded psychiatric clinical trial.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Luo:2016:BDI,
author = "Peng Luo and Zhi Geng",
title = "Bounds on Direct and Indirect Effects of Treatment on
a Continuous Endpoint",
journal = j-TIST,
volume = "7",
number = "2",
pages = "20:1--20:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2668134",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Direct effect of a treatment variable on an endpoint
variable and indirect effect through a mediate variable
are important concepts for understanding a causal
mechanism. However, the randomized assignment of
treatment is not sufficient for identifying the direct
and indirect effects, and extra assumptions and
conditions are required, such as the sequential
ignorability assumption without unobserved confounders
or the sequential potential ignorability assumption.
But these assumptions may not be credible in many
applications. In this article, we consider the bounds
on controlled direct effect, natural direct effect, and
natural indirect effect without these extra
assumptions. Cai et al. [2008] presented the bounds for
the case of a binary endpoint, and we extend their
results to the general case for an arbitrary
endpoint.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2016:CDD,
author = "Furui Liu and Laiwan Chan",
title = "Causal Discovery on Discrete Data with Extensions to
Mixture Model",
journal = j-TIST,
volume = "7",
number = "2",
pages = "21:1--21:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700477",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we deal with the causal discovery
problem on discrete data. First, we present a causal
discovery method for traditional additive noise models
that identifies the causal direction by analyzing the
supports of the conditional distributions. Then, we
present a causal mixture model to address the problem
that the function transforming cause to effect varies
across the observations. We propose a novel method
called Support Analysis (SA) for causal discovery with
the mixture model. Experiments using synthetic and real
data are presented to demonstrate the performance of
our proposed algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Flaxman:2016:GPI,
author = "Seth R. Flaxman and Daniel B. Neill and Alexander J.
Smola",
title = "{Gaussian} Processes for Independence Tests with
Non-iid Data in Causal Inference",
journal = j-TIST,
volume = "7",
number = "2",
pages = "22:1--22:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2806892",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In applied fields, practitioners hoping to apply
causal structure learning or causal orientation
algorithms face an important question: which
independence test is appropriate for my data? In the
case of real-valued iid data, linear dependencies, and
Gaussian error terms, partial correlation is
sufficient. But once any of these assumptions is
modified, the situation becomes more complex.
Kernel-based tests of independence have gained
popularity to deal with nonlinear dependencies in
recent years, but testing for conditional independence
remains a challenging problem. We highlight the
important issue of non-iid observations: when data are
observed in space, time, or on a network, ``nearby''
observations are likely to be similar. This fact biases
estimates of dependence between variables. Inspired by
the success of Gaussian process regression for handling
non-iid observations in a wide variety of areas and by
the usefulness of the Hilbert--Schmidt Independence
Criterion (HSIC), a kernel-based independence test, we
propose a simple framework to address all of these
issues: first, use Gaussian process regression to
control for certain variables and to obtain residuals.
Second, use HSIC to test for independence. We
illustrate this on two classic datasets, one spatial,
the other temporal, that are usually treated as iid. We
show how properly accounting for spatial and temporal
variation can lead to more reasonable causal graphs. We
also show how highly structured data, like images and
text, can be used in a causal inference framework using
a novel structured input/output Gaussian process
formulation. We demonstrate this idea on a dataset of
translated sentences, trying to predict the source
language.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fire:2016:LPC,
author = "Amy Fire and Song-Chun Zhu",
title = "Learning Perceptual Causality from Video",
journal = j-TIST,
volume = "7",
number = "2",
pages = "23:1--23:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2809782",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Perceptual causality is the perception of causal
relationships from observation. Humans, even as
infants, form such models from observation of the world
around them [Saxe and Carey 2006]. For a deeper
understanding, the computer must make similar models
through the analogous form of observation: video. In
this article, we provide a framework for the
unsupervised learning of this perceptual causal
structure from video. Our method takes action and
object status detections as input and uses heuristics
suggested by cognitive science research to produce the
causal links perceived between them. We greedily modify
an initial distribution featuring independence between
potential causes and effects by adding dependencies
that maximize information gain. We compile the learned
causal relationships into a Causal And-Or Graph, a
probabilistic and-or representation of causality that
adds a prior to causality. Validated against human
perception, experiments show that our method correctly
learns causal relations, attributing status changes of
objects to causing actions amid irrelevant actions. Our
method outperforms Hellinger's $ \chi^2$-statistic by
considering hierarchical action selection, and
outperforms the treatment effect by discounting
coincidental relationships.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Demeshko:2016:NCS,
author = "Marina Demeshko and Takashi Washio and Yoshinobu
Kawahara and Yuriy Pepyolyshev",
title = "A Novel Continuous and Structural {VAR} Modeling
Approach and Its Application to Reactor Noise
Analysis",
journal = j-TIST,
volume = "7",
number = "2",
pages = "24:1--24:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2710025",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A vector autoregressive model in discrete time domain
(DVAR) is often used to analyze continuous time,
multivariate, linear Markov systems through their
observed time series data sampled at discrete
timesteps. Based on previous studies, the DVAR model is
supposed to be a noncanonical representation of the
system, that is, it does not correspond to a unique
system bijectively. However, in this article, we
characterize the relations of the DVAR model with its
corresponding Structural Vector AR (SVAR) and
Continuous Time Vector AR (CTVAR) models through a
finite difference method across continuous and discrete
time domain. We further clarify that the DVAR model of
a continuous time, multivariate, linear Markov system
is canonical under a highly generic condition. Our
analysis shows that we can uniquely reproduce its SVAR
and CTVAR models from the DVAR model. Based on these
results, we propose a novel Continuous and Structural
Vector Autoregressive (CSVAR) modeling approach to
derive the SVAR and the CTVAR models from their DVAR
model empirically derived from the observed time series
of continuous time linear Markov systems. We
demonstrate its superior performance through some
numerical experiments on both artificial and real-world
data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hours:2016:CAS,
author = "Hadrien Hours and Ernst Biersack and Patrick Loiseau",
title = "A Causal Approach to the Study of {TCP} Performance",
journal = j-TIST,
volume = "7",
number = "2",
pages = "25:1--25:??",
month = jan,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2770878",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jan 25 06:10:36 MST 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Communication networks are complex systems whose
operation relies on a large number of components that
work together to provide services to end users. As the
quality of these services depends on different
parameters, understanding how each of them impacts the
final performance of a service is a challenging but
important problem. However, intervening on individual
factors to evaluate the impact of the different
parameters is often impractical due to the high cost of
intervention in a network. It is, therefore, desirable
to adopt a formal approach to understand the role of
the different parameters and to predict how a change in
any of these parameters will impact performance. The
approach of causality pioneered by J. Pearl provides a
powerful framework to investigate these questions. Most
of the existing theory is non-parametric and does not
make any assumption on the nature of the system under
study. However, most of the implementations of causal
model inference algorithms and most of the examples of
usage of a causal model to predict intervention rely on
assumptions such linearity, normality, or discrete
data. In this article, we present a methodology to
overcome the challenges of working with real-world data
and extend the application of causality to complex
systems in the area of telecommunication networks, for
which assumptions of normality, linearity and discrete
data do no hold. Specifically, we study the performance
of TCP, which is the prevalent protocol for reliable
end-to-end transfer in the Internet. Analytical models
of the performance of TCP exist, but they take into
account the state of network only and disregard the
impact of the application at the sender and the
receiver, which often influences TCP performance. To
address this point, we take as application the file
transfer protocol (FTP), which uses TCP for reliable
transfer. Studying a well-understood protocol such as
TCP allows us to validate our approach and compare its
results to previous studies. We first present and
evaluate our methodology using TCP traffic obtained via
network emulation, which allows us to experimentally
validate the prediction of an intervention. We then
apply the methodology to real-world TCP traffic sent
over the Internet. Throughout the article, we compare
the causal approach for studying TCP performance to
other approaches such as analytical modeling or
simulation and and show how they can complement each
other.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Belem:2016:BRE,
author = "Fabiano M. Bel{\'e}m and Carolina S. Batista and
Rodrygo L. T. Santos and Jussara M. Almeida and Marcos
A. Gon{\c{c}}alves",
title = "Beyond Relevance: Explicitly Promoting Novelty and
Diversity in Tag Recommendation",
journal = j-TIST,
volume = "7",
number = "3",
pages = "26:1--26:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2801130",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The design and evaluation of tag recommendation
methods has historically focused on maximizing the
relevance of the suggested tags for a given object,
such as a movie or a song. However, relevance by itself
may not be enough to guarantee recommendation
usefulness. Promoting novelty and diversity in tag
recommendation not only increases the chances that the
user will select ``some'' of the recommended tags but
also promotes complementary information (i.e., tags),
which helps to cover multiple aspects or topics related
to the target object. Previous work has addressed the
tag recommendation problem by exploiting at most two of
the following aspects: (1) relevance, (2) explicit
topic diversity, and (3) novelty. In contrast, here we
tackle these three aspects conjointly, by introducing
two new tag recommendation methods that cover all three
aspects of the problem at different levels. Our first
method, called Random Forest with topic-related
attributes, or RF$_t$, extends a relevance-driven tag
recommender based on the Random Forest ( RF )
learning-to-rank method by including new tag attributes
to capture the extent to which a candidate tag is
related to the topics of the target object. This
solution captures topic diversity as well as novelty at
the attribute level while aiming at maximizing
relevance in its objective function. Our second method,
called Explicit Tag Recommendation Diversifier with
Novelty Promotion, or xTReND, reranks the
recommendations provided by any tag recommender to
jointly promote relevance, novelty, and topic
diversity. We use RF$_t$ as a basic recommender applied
before the reranking, thus building a solution that
addresses the problem at both attribute and objective
levels. Furthermore, to enable the use of our solutions
on applications in which category information is
unavailable, we investigate the suitability of using
latent Dirichlet allocation (LDA) to automatically
generate topics for objects. We evaluate all tag
recommendation approaches using real data from five
popular Web 2.0 applications. Our results show that
RF$_t$ greatly outperforms the relevance-driven RF
baseline in diversity while producing gains in
relevance as well. We also find that our new xTReND
reranker obtains considerable gains in both novelty and
relevance when compared to that same baseline while
keeping the same relevance levels. Furthermore,
compared to our previous reranker method, xTReD, which
does not consider novelty, xTReND is also quite
effective, improving the novelty of the recommended
tags while keeping similar relevance and diversity
levels in most datasets and scenarios. Comparing our
two new proposals, we find that xTReND considerably
outperforms RF$_t$ in terms of novelty and diversity
with only small losses (under 4\%) in relevance.
Overall, considering the trade-off among relevance,
novelty, and diversity, our results demonstrate the
superiority of xTReND over the baselines and the
proposed alternative, RF$_t$. Finally, the use of
automatically generated latent topics as an alternative
to manually labeled categories also provides
significant improvements, which greatly enhances the
applicability of our solutions to applications where
the latter is not available.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Paik:2016:PDM,
author = "Jiaul H. Paik",
title = "Parameterized Decay Model for Information Retrieval",
journal = j-TIST,
volume = "7",
number = "3",
pages = "27:1--27:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2800794",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article proposes a term weighting scheme for
measuring query-document similarity that attempts to
explicitly model the dependency between separate
occurrences of a term in a document. The assumption is
that, if a term appears once in a document, it is more
likely to appear again in the same document. Thus, as
the term appears again and again, the information
content of the subsequent occurrences decreases
gradually, since they are more predictable. We
introduce a parameterized decay function to model this
assumption, where the initial contribution of the term
can be determined using any reasonable term
discrimination factor. The effectiveness of the
proposed model is evaluated on a number of recent web
test collections of varying nature. The experimental
results show that the proposed model significantly
outperforms a number of well known retrieval models
including a recently proposed strong Term Frequency and
Inverse Document Frequency (TF-IDF) model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:MCA,
author = "Zhifeng Li and Dihong Gong and Qiang Li and Dacheng
Tao and Xuelong Li",
title = "Mutual Component Analysis for Heterogeneous Face
Recognition",
journal = j-TIST,
volume = "7",
number = "3",
pages = "28:1--28:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2807705",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Heterogeneous face recognition, also known as
cross-modality face recognition or intermodality face
recognition, refers to matching two face images from
alternative image modalities. Since face images from
different image modalities of the same person are
associated with the same face object, there should be
mutual components that reflect those intrinsic face
characteristics that are invariant to the image
modalities. Motivated by this rationality, we propose a
novel approach called Mutual Component Analysis (MCA)
to infer the mutual components for robust heterogeneous
face recognition. In the MCA approach, a generative
model is first proposed to model the process of
generating face images in different modalities, and
then an Expectation Maximization (EM) algorithm is
designed to iteratively learn the model parameters. The
learned generative model is able to infer the mutual
components (which we call the hidden factor, where
hidden means the factor is unreachable and invisible,
and can only be inferred from observations) that are
associated with the person's identity, thus enabling
fast and effective matching for cross-modality face
recognition. To enhance recognition performance, we
propose an MCA-based multiclassifier framework using
multiple local features. Experimental results show that
our new approach significantly outperforms the
state-of-the-art results on two typical application
scenarios: sketch-to-photo and infrared-to-visible face
recognition.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ye:2016:GIL,
author = "Jintao Ye and Zhao Yan Ming and Tat Seng Chua",
title = "Generating Incremental Length Summary Based on
Hierarchical Topic Coverage Maximization",
journal = j-TIST,
volume = "7",
number = "3",
pages = "29:1--29:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2809433",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Document summarization is playing an important role in
coping with information overload on the Web. Many
summarization models have been proposed recently, but
few try to adjust the summary length and sentence order
according to application scenarios. With the popularity
of handheld devices, presenting key information first
in summaries of flexible length is of great convenience
in terms of faster reading and decision-making and
network consumption reduction. Targeting this problem,
we introduce a novel task of generating summaries of
incremental length. In particular, we require that the
summaries should have the ability to automatically
adjust the coverage of general-detailed information
when the summary length varies. We propose a novel
summarization model that incrementally maximizes topic
coverage based on the document's hierarchical topic
model. In addition to the standard Rouge-1 measure, we
define a new evaluation metric based on the similarity
of the summaries' topic coverage distribution in order
to account for sentence order and summary length.
Extensive experiments on Wikipedia pages, DUC 2007, and
general noninverted writing style documents from
multiple sources show the effectiveness of our proposed
approach. Moreover, we carry out a user study on a
mobile application scenario to show the usability of
the produced summary in terms of improving judgment
accuracy and speed, as well as reducing the reading
burden and network traffic.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2016:PCM,
author = "Dingqi Yang and Daqing Zhang and Bingqing Qu",
title = "Participatory Cultural Mapping Based on Collective
Behavior Data in Location-Based Social Networks",
journal = j-TIST,
volume = "7",
number = "3",
pages = "30:1--30:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2814575",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Culture has been recognized as a driving impetus for
human development. It co-evolves with both human belief
and behavior. When studying culture, Cultural Mapping
is a crucial tool to visualize different aspects of
culture (e.g., religions and languages) from the
perspectives of indigenous and local people. Existing
cultural mapping approaches usually rely on large-scale
survey data with respect to human beliefs, such as
moral values. However, such a data collection method
not only incurs a significant cost of both human
resources and time, but also fails to capture human
behavior, which massively reflects cultural
information. In addition, it is practically difficult
to collect large-scale human behavior data.
Fortunately, with the recent boom in Location-Based
Social Networks (LBSNs), a considerable number of users
report their activities in LBSNs in a participatory
manner, which provides us with an unprecedented
opportunity to study large-scale user behavioral data.
In this article, we propose a participatory cultural
mapping approach based on collective behavior in LBSNs.
First, we collect the participatory sensed user
behavioral data from LBSNs. Second, since only local
users are eligible for cultural mapping, we propose a
progressive ``home'' location identification method to
filter out ineligible users. Third, by extracting three
key cultural features from daily activity, mobility,
and linguistic perspectives, respectively, we propose a
cultural clustering method to discover cultural
clusters. Finally, we visualize the cultural clusters
on the world map. Based on a real-world LBSN dataset,
we experimentally validate our approach by conducting
both qualitative and quantitative analysis on the
generated cultural maps. The results show that our
approach can subtly capture cultural features and
generate representative cultural maps that correspond
well with traditional cultural maps based on survey
data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jia:2016:LPT,
author = "Yantao Jia and Yuanzhuo Wang and Xiaolong Jin and
Xueqi Cheng",
title = "Location Prediction: a Temporal-Spatial {Bayesian}
Model",
journal = j-TIST,
volume = "7",
number = "3",
pages = "31:1--31:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2816824",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In social networks, predicting a user's location
mainly depends on those of his/her friends, where the
key lies in how to select his/her most influential
friends. In this article, we analyze the theoretically
maximal accuracy of location prediction based on
friends' locations and compare it with the practical
accuracy obtained by the state-of-the-art location
prediction methods. Upon observing a big gap between
the theoretical and practical accuracy, we propose a
new strategy for selecting influential friends in order
to improve the practical location prediction accuracy.
Specifically, several features are defined to measure
the influence of the friends on a user's location,
based on which we put forth a sequential
random-walk-with-restart procedure to rank the friends
of the user in terms of their influence. By dynamically
selecting the top N most influential friends of the
user per time slice, we develop a temporal-spatial
Bayesian model to characterize the dynamics of friends'
influence for location prediction. Finally, extensive
experimental results on datasets of real social
networks demonstrate that the proposed influential
friend selection method and temporal-spatial Bayesian
model can significantly improve the accuracy of
location prediction.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:VFE,
author = "Xiaoyan Li and Tongliang Liu and Jiankang Deng and
Dacheng Tao",
title = "Video Face Editing Using Temporal-Spatial-Smooth
Warping",
journal = j-TIST,
volume = "7",
number = "3",
pages = "32:1--32:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2819000",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Editing faces in videos is a popular yet challenging
task in computer vision and graphics that encompasses
various applications, including facial attractiveness
enhancement, makeup transfer, face replacement, and
expression manipulation. Directly applying the existing
warping methods to video face editing has the major
problem of temporal incoherence in the synthesized
videos, which cannot be addressed by simply employing
face tracking techniques or manual interventions, as it
is difficult to eliminate the subtly temporal
incoherence of the facial feature point localizations
in a video sequence. In this article, we propose a
temporal-spatial-smooth warping (TSSW) method to
achieve a high temporal coherence for video face
editing. TSSW is based on two observations: (1) the
control lattices are critical for generating warping
surfaces and achieving the temporal coherence between
consecutive video frames, and (2) the temporal
coherence and spatial smoothness of the control
lattices can be simultaneously and effectively
preserved. Based upon these observations, we impose the
temporal coherence constraint on the control lattices
on two consecutive frames, as well as the spatial
smoothness constraint on the control lattice on the
current frame. TSSW calculates the control lattice (in
either the horizontal or vertical direction) by
updating the control lattice (in the corresponding
direction) on its preceding frame, i.e., minimizing a
novel energy function that unifies a data-driven term,
a smoothness term, and feature point constraints. The
contributions of this article are twofold: (1) we
develop TSSW, which is robust to the subtly temporal
incoherence of the facial feature point localizations
and is effective to preserve the temporal coherence and
spatial smoothness of the control lattices for editing
faces in videos, and (2) we present a new unified video
face editing framework that is capable for improving
the performances of facial attractiveness enhancement,
makeup transfer, face replacement, and expression
manipulation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:MNS,
author = "Zechao Li and Jinhui Tang and Xueming Wang and Jing
Liu and Hanqing Lu",
title = "Multimedia News Summarization in Search",
journal = j-TIST,
volume = "7",
number = "3",
pages = "33:1--33:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2822907",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "It is a necessary but challenging task to relieve
users from the proliferative news information and allow
them to quickly and comprehensively master the
information of the whats and hows that are happening in
the world every day. In this article, we develop a
novel approach of multimedia news summarization for
searching results on the Internet, which uncovers the
underlying topics among query-related news information
and threads the news events within each topic to
generate a query-related brief overview. First, the
hierarchical latent Dirichlet allocation (hLDA) model
is introduced to discover the hierarchical topic
structure from query-related news documents, and a new
approach based on the weighted aggregation and max
pooling is proposed to identify one representative news
article for each topic. One representative image is
also selected to visualize each topic as a complement
to the text information. Given the representative
documents selected for each topic, a time-bias maximum
spanning tree (MST) algorithm is proposed to thread
them into a coherent and compact summary of their
parent topic. Finally, we design a friendly interface
to present users with the hierarchical summarization of
their required news information. Extensive experiments
conducted on a large-scale news dataset collected from
multiple news Web sites demonstrate the encouraging
performance of the proposed solution for news
summarization in news retrieval.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hardegger:2016:SUB,
author = "Michael Hardegger and Daniel Roggen and Alberto
Calatroni and Gerhard Tr{\"o}ster",
title = "{S-SMART}: a Unified {Bayesian} Framework for
Simultaneous Semantic Mapping, Activity Recognition,
and Tracking",
journal = j-TIST,
volume = "7",
number = "3",
pages = "34:1--34:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2824286",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The machine recognition of user trajectories and
activities is fundamental to devise context-aware
applications for support and monitoring in daily life.
So far, tracking and activity recognition were mostly
considered as orthogonal problems, which limits the
richness of possible context inference. In this work,
we introduce the novel unified computational and
representational framework S-SMART that simultaneously
models the environment state (semantic mapping),
localizes the user within this map (tracking), and
recognizes interactions with the environment (activity
recognition). Thus, S-SMART identifies which activities
the user executes where (e.g., turning a handle next to
a window ), and reflects the outcome of these actions
by updating the world model (e.g., the window is now
open ). This in turn conditions the future possibility
of executing actions at specific places (e.g., closing
the window is likely to be the next action at this
location). S-SMART works in a self-contained manner and
iteratively builds the semantic map from wearable
sensors only. This enables the seamless deployment to
new environments. We characterize S-SMART in an
experimental dataset with people performing hand
actions as part of their usual routines at home and in
office buildings. The framework combines dead reckoning
from a foot-worn motion sensor with
template-matching-based action recognition, identifying
objects in the environment (windows, doors, water taps,
phones, etc.) and tracking their state (open/closed,
etc.). In real-life recordings with up to 23 action
classes, S-SMART consistently outperforms independent
systems for positioning and activity recognition, and
constructs accurate semantic maps. This environment
representation enables novel applications that build
upon information about the arrangement and state of the
user's surroundings. For example, it may be possible to
remind elderly people of a window that they left open
before leaving the house, or of a plant they did not
water yet, using solely wearable sensors.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Luo:2016:IMD,
author = "Tie Luo and Sajal K. Das and Hwee Pink Tan and Lirong
Xia",
title = "Incentive Mechanism Design for Crowdsourcing: an
All-Pay Auction Approach",
journal = j-TIST,
volume = "7",
number = "3",
pages = "35:1--35:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2837029",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Crowdsourcing can be modeled as a principal-agent
problem in which the principal (crowdsourcer) desires
to solicit a maximal contribution from a group of
agents (participants) while agents are only motivated
to act according to their own respective advantages. To
reconcile this tension, we propose an all-pay auction
approach to incentivize agents to act in the
principal's interest, i.e., maximizing profit, while
allowing agents to reap strictly positive utility. Our
rationale for advocating all-pay auctions is based on
two merits that we identify, namely all-pay auctions
(i) compress the common, two-stage ``bid-contribute''
crowdsourcing process into a single
``bid-cum-contribute'' stage, and (ii) eliminate the
risk of task nonfulfillment. In our proposed approach,
we enhance all-pay auctions with two additional
features: an adaptive prize and a general crowdsourcing
environment. The prize or reward adapts itself as per a
function of the unknown winning agent's contribution,
and the environment or setting generally accommodates
incomplete and asymmetric information, risk-averse (and
risk-neutral) agents, and a stochastic (and
deterministic) population. We analytically derive this
all-pay auction-based mechanism and extensively
evaluate it in comparison to classic and optimized
mechanisms. The results demonstrate that our proposed
approach remarkably outperforms its counterparts in
terms of the principal's profit, agent's utility, and
social welfare.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ibrahim:2016:IEM,
author = "Azhar Mohd Ibrahim and Ibrahim Venkat and K. G.
Subramanian and Ahamad Tajudin Khader and Philippe {De
Wilde}",
title = "Intelligent Evacuation Management Systems: a Review",
journal = j-TIST,
volume = "7",
number = "3",
pages = "36:1--36:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2842630",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Crowd and evacuation management have been active areas
of research and study in the recent past. Various
developments continue to take place in the process of
efficient evacuation of crowds in mass gatherings. This
article is intended to provide a review of intelligent
evacuation management systems covering the aspects of
crowd monitoring, crowd disaster prediction, evacuation
modelling, and evacuation path guidelines. Soft
computing approaches play a vital role in the design
and deployment of intelligent evacuation applications
pertaining to crowd control management. While the
review deals with video and nonvideo based aspects of
crowd monitoring and crowd disaster prediction,
evacuation techniques are reviewed via the theme of
soft computing, along with a brief review on the
evacuation navigation path. We believe that this review
will assist researchers in developing reliable
automated evacuation systems that will help in ensuring
the safety of the evacuees especially during emergency
evacuation scenarios.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ding:2016:CSP,
author = "Changxing Ding and Dacheng Tao",
title = "A Comprehensive Survey on Pose-Invariant Face
Recognition",
journal = j-TIST,
volume = "7",
number = "3",
pages = "37:1--37:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2845089",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The capacity to recognize faces under varied poses is
a fundamental human ability that presents a unique
challenge for computer vision systems. Compared to
frontal face recognition, which has been intensively
studied and has gradually matured in the past few
decades, Pose-Invariant Face Recognition (PIFR) remains
a largely unsolved problem. However, PIFR is crucial to
realizing the full potential of face recognition for
real-world applications, since face recognition is
intrinsically a passive biometric technology for
recognizing uncooperative subjects. In this article, we
discuss the inherent difficulties in PIFR and present a
comprehensive review of established techniques.
Existing PIFR methods can be grouped into four
categories, that is, pose-robust feature extraction
approaches, multiview subspace learning approaches,
face synthesis approaches, and hybrid approaches. The
motivations, strategies, pros/cons, and performance of
representative approaches are described and compared.
Moreover, promising directions for future research are
discussed.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cremonesi:2016:ISI,
author = "Paolo Cremonesi and Alan Said and Domonkos Tikk and
Michelle X. Zhou",
title = "Introduction to the Special Issue on Recommender
System Benchmarking",
journal = j-TIST,
volume = "7",
number = "3",
pages = "38:1--38:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2870627",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2016:RMC,
author = "Le Wu and Qi Liu and Enhong Chen and Nicholas Jing
Yuan and Guangming Guo and Xing Xie",
title = "Relevance Meets Coverage: a Unified Framework to
Generate Diversified Recommendations",
journal = j-TIST,
volume = "7",
number = "3",
pages = "39:1--39:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700496",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Collaborative filtering (CF) models offer users
personalized recommendations by measuring the relevance
between the active user and each individual candidate
item. Following this idea, user-based collaborative
filtering (UCF) usually selects the local popular items
from the like-minded neighbor users. However, these
traditional relevance-based models only consider the
individuals (i.e., each neighbor user and candidate
item) separately during neighbor set selection and
recommendation set generation, thus usually incurring
highly similar recommendations that lack diversity.
While many researchers have recognized the importance
of diversified recommendations, the proposed solutions
either needed additional semantic information of items
or decreased accuracy in this process. In this article,
we describe how to generate both accurate and
diversified recommendations from a new perspective.
Along this line, we first introduce a simple measure of
coverage that quantifies the usefulness of the whole
set, that is, the neighbor userset and the recommended
itemset as a complete entity. Then we propose a
recommendation framework named REC that considers both
traditional relevance-based scores and the new coverage
measure based on UCF. Under REC, we further prove that
the goals of maximizing relevance and coverage measures
simultaneously in both the neighbor set selection step
and the recommendation set generation step are NP-hard.
Luckily, we can solve them effectively and efficiently
by exploiting the inherent submodular property.
Furthermore, we generalize the coverage notion and the
REC framework from both a data perspective and an
algorithm perspective. Finally, extensive experimental
results on three real-world datasets show that the
REC-based recommendation models can naturally generate
more diversified recommendations without decreasing
accuracy compared to some state-of-the-art models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Doerfel:2016:RCR,
author = "Stephan Doerfel and Robert J{\"a}schke and Gerd
Stumme",
title = "The Role of Cores in Recommender Benchmarking for
Social Bookmarking Systems",
journal = j-TIST,
volume = "7",
number = "3",
pages = "40:1--40:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700485",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Social bookmarking systems have established themselves
as an important part in today's Web. In such systems,
tag recommender systems support users during the
posting of a resource by suggesting suitable tags. Tag
recommender algorithms have often been evaluated in
offline benchmarking experiments. Yet, the particular
setup of such experiments has rarely been analyzed. In
particular, since the recommendation quality usually
suffers from difficulties such as the sparsity of the
data or the cold-start problem for new resources or
users, datasets have often been pruned to so-called
cores (specific subsets of the original datasets),
without much consideration of the implications on the
benchmarking results. In this article, we generalize
the notion of a core by introducing the new notion of a
set-core, which is independent of any graph structure,
to overcome a structural drawback in the previous
constructions of cores on tagging data. We show that
problems caused by some types of cores can be
eliminated using set-cores. Further, we present a
thorough analysis of tag recommender benchmarking
setups using cores. To that end, we conduct a
large-scale experiment on four real-world datasets, in
which we analyze the influence of different cores on
the evaluation of recommendation algorithms. We can
show that the results of the comparison of different
recommendation approaches depends on the selection of
core type and level. For the benchmarking of tag
recommender algorithms, our results suggest that the
evaluation must be set up more carefully and should not
be based on one arbitrarily chosen core type and
level.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Dooms:2016:FDB,
author = "Simon Dooms and Alejandro Bellog{\'\i}n and Toon {De
Pessemier} and Luc Martens",
title = "A Framework for Dataset Benchmarking and Its
Application to a New Movie Rating Dataset",
journal = j-TIST,
volume = "7",
number = "3",
pages = "41:1--41:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2751565",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Rating datasets are of paramount importance in
recommender systems research. They serve as input for
recommendation algorithms, as simulation data, or for
evaluation purposes. In the past, public accessible
rating datasets were not abundantly available, leaving
researchers no choice but to work with old and static
datasets like MovieLens and Netflix. More recently,
however, emerging trends as social media and
smartphones are found to provide rich data sources
which can be turned into valuable research datasets.
While dataset availability is growing, a structured way
for introducing and comparing new datasets is currently
still lacking. In this work, we propose a five-step
framework to introduce and benchmark new datasets in
the recommender systems domain. We illustrate our
framework on a new movie rating dataset-called
MovieTweetings-collected from Twitter. Following our
framework, we detail the origin of the dataset, provide
basic descriptive statistics, investigate external
validity, report the results of a number of
reproducible benchmarks, and conclude by discussing
some interesting advantages and appropriate research
use cases.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Moody:2016:NCF,
author = "Jennifer Moody and David H. Glass",
title = "A Novel Classification Framework for Evaluating
Individual and Aggregate Diversity in Top-{$N$}
Recommendations",
journal = j-TIST,
volume = "7",
number = "3",
pages = "42:1--42:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2700491",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The primary goal of a recommender system is to
generate high quality user-centred recommendations.
However, the traditional evaluation methods and metrics
were developed before researchers understood all the
factors that increase user satisfaction. This study is
an introduction to a novel user and item classification
framework. It is proposed that this framework should be
used during user-centred evaluation of recommender
systems and the need for this framework is justified
through experiments. User profiles are constructed and
matched against other users' profiles to formulate
neighbourhoods and generate top-N recommendations. The
recommendations are evaluated to measure the success of
the process. In conjunction with the framework, a new
diversity metric is presented and explained. The
accuracy, coverage, and diversity of top-N
recommendations is illustrated and discussed for groups
of users. It is found that in contradiction to common
assumptions, not all users suffer as expected from the
data sparsity problem. In fact, the group of users that
receive the most accurate recommendations do not belong
to the least sparse area of the dataset.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ben-Shimon:2016:AAR,
author = "David Ben-Shimon and Lior Rokach and Guy Shani and
Bracha Shapira",
title = "Anytime Algorithms for Recommendation Service
Providers",
journal = j-TIST,
volume = "7",
number = "3",
pages = "43:1--43:??",
month = apr,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2835496",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Jun 20 11:24:25 MDT 2016",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recommender systems (RS) can now be found in many
commercial Web sites, often presenting customers with a
short list of additional products that they might
purchase. Many commercial sites do not typically have
the ability and resources to develop their own system
and may outsource the RS to a third party. This had led
to the growth of a recommendation as a service
industry, where companies, referred to as RS providers,
provide recommendation services. These companies must
carefully balance the cost of building recommendation
models and the payment received from the e-business, as
these payments are expected to be low. In such a
setting, restricting the computational time required
for model building is critical for the RS provider to
be profitable. In this article, we propose anytime
algorithms as an attractive method for balancing
computational time and the recommendation model
performance, thus tackling the RS provider problem. In
an anytime setting, an algorithm can be stopped after
any amount of computational time, always ensuring that
a valid, although suboptimal, solution will be
returned. Given sufficient time, however, the algorithm
should converge to an optimal solution. In this
setting, it is important to evaluate the quality of the
returned solution over time, monitoring quality
improvement. This is significantly different from
traditional evaluation methods, which mostly estimate
the performance of the algorithm only after its
convergence is given sufficient time. We show that the
popular item-item top-N recommendation approach can be
brought into the anytime framework by smartly
considering the order by which item pairs are being
evaluated. We experimentally show that the
time-accuracy trade-off can be significantly improved
for this specific problem.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2016:ISI,
author = "Kuan-Ta Chen and Omar Alonso and Martha Larson and
Irwin King",
title = "Introduction to the Special Issue on Crowd in
Intelligent Systems",
journal = j-TIST,
volume = "7",
number = "4",
pages = "44:1--44:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2920522",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Siddharthan:2016:CCR,
author = "Advaith Siddharthan and Christopher Lambin and
Anne-Marie Robinson and Nirwan Sharma and Richard
Comont and Elaine O'Mahony and Chris Mellish and
Ren{\'e} {Van Der Wal}",
title = "Crowdsourcing Without a Crowd: Reliable Online Species
Identification Using {Bayesian} Models to Minimize
Crowd Size",
journal = j-TIST,
volume = "7",
number = "4",
pages = "45:1--45:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2776896",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We present an incremental Bayesian model that resolves
key issues of crowd size and data quality for consensus
labeling. We evaluate our method using data collected
from a real-world citizen science program, BeeWatch,
which invites members of the public in the United
Kingdom to classify (label) photographs of bumblebees
as one of 22 possible species. The biological recording
domain poses two key and hitherto unaddressed
challenges for consensus models of crowdsourcing: (1)
the large number of potential species makes
classification difficult, and (2) this is compounded by
limited crowd availability, stemming from both the
inherent difficulty of the task and the lack of
relevant skills among the general public. We
demonstrate that consensus labels can be reliably found
in such circumstances with very small crowd sizes of
around three to five users (i.e., through group
sourcing). Our incremental Bayesian model, which
minimizes crowd size by re-evaluating the quality of
the consensus label following each species
identification solicited from the crowd, is competitive
with a Bayesian approach that uses a larger but fixed
crowd size and outperforms majority voting. These
results have important ecological applicability:
biological recording programs such as BeeWatch can
sustain themselves when resources such as taxonomic
experts to confirm identifications by photo submitters
are scarce (as is typically the case), and feedback can
be provided to submitters in a timely fashion. More
generally, our model provides benefits to any
crowdsourced consensus labeling task where there is a
cost (financial or otherwise) associated with
soliciting a label.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Semertzidis:2016:CPS,
author = "Theodoros Semertzidis and Jasminko Novak and Michalis
Lazaridis and Mark Melenhorst and Isabel Micheel and
Dimitrios Michalopoulos and Martin B{\"o}ckle and
Michael G. Strintzis and Petros Daras",
title = "A Crowd-Powered System for Fashion Similarity Search",
journal = j-TIST,
volume = "7",
number = "4",
pages = "46:1--46:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897365",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Driven by the needs of customers and industry, online
fashion search and analytics are recently gaining much
attention. As fashion is mostly expressed by visual
content, the analysis of fashion images in online
social networks is a rich source of possible insights
on evolving trends and customer preferences. Although a
plethora of visual content is available, the modeling
of clothes' physics and movement, the implicit
semantics in fashion designs, and the subjectivity of
their interpretation pose difficulties to fully
automated solutions for fashion search and analysis. In
this article, we present the design and evaluation of a
crowd-powered system for fashion similarity search from
Twitter, supporting trend analysis for fashion
professionals. The system enables fashion similarity
search based on specific human-based similarity
criteria. This is achieved by implementing a novel
machine--crowd workflow that supports complex tasks
requiring highly subjective judgments where multiple
true solutions may coexist. We discuss how this leads
to a novel class of crowd-powered systems for which the
output of the crowd is not used to verify the automatic
analysis but is the desired outcome. Finally, we show
how this kind of crowd involvement enables a novel kind
of similarity search and represents a crucial factor
for the acceptance of system results by the end user.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Borish:2016:RLC,
author = "Michael Borish and Benjamin Lok",
title = "Rapid Low-Cost Virtual Human Bootstrapping via the
Crowd",
journal = j-TIST,
volume = "7",
number = "4",
pages = "47:1--47:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897366",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Virtual human interactions provide an important avenue
for training as emergent opportunities arise. In
response to a new training need, we propose a framework
to rapidly create experiential learning opportunities
in the form of a question--answer chat interaction with
virtual humans. This framework takes quickly generated
case documents and breaks down the case into small
tasks that can be crowdsourced by nonexperts. This
framework can serve as a first step to rapidly
bootstrapping new virtual humans. We have applied our
framework to the task of preparing health care students
and professionals to infrequent, but high-stakes,
situations such as infectious diseases, cranial nerve
disorders, and stroke. Our framework was utilized by
medical professionals interested in providing new
training experiences to students and colleagues. Over
the course of two months, these professionals created
seven scenarios on a diverse range of topics that
included Ebola, cancer, and neurological disorders.
These scenarios were developed for multiple target
audiences such as medical students, residents, and
fellows. As a first step, each scenario utilized our
framework and crowdsourced workers to create an initial
corpus over the course of two days. From these seven
cases, we selected two to evaluate the quality of the
resulting virtual-human corpuses. The two scenarios
were compared to preexisting reference scenarios that
have been in curricular use for several years. We found
a reduction in author time commitment of at least 92\%
while creating a character that was at least 75\% as
accurate as its reference counterparts. The commitment
reduction and accuracy achieved by our framework
represents a first step towards rapid development of a
virtual human. Our framework can then be combined with
other creation processes for further virtual-human
development in order to create a mature virtual human.
As part of a virtual-human development process, our
framework can help to rapidly develop new scenarios in
response to emergent training opportunities.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Radanovic:2016:IEC,
author = "Goran Radanovic and Boi Faltings and Radu Jurca",
title = "Incentives for Effort in Crowdsourcing Using the Peer
Truth Serum",
journal = j-TIST,
volume = "7",
number = "4",
pages = "48:1--48:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2856102",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Crowdsourcing is widely proposed as a method to solve
a large variety of judgment tasks, such as classifying
website content, peer grading in online courses, or
collecting real-world data. As the data reported by
workers cannot be verified, there is a tendency to
report random data without actually solving the task.
This can be countered by making the reward for an
answer depend on its consistency with answers given by
other workers, an approach called peer consistency.
However, it is obvious that the best strategy in such
schemes is for all workers to report the same answer
without solving the task. Dasgupta and Ghosh [2013]
show that, in some cases, exerting high effort can be
encouraged in the highest-paying equilibrium. In this
article, we present a general mechanism that implements
this idea and is applicable to most crowdsourcing
settings. Furthermore, we experimentally test the novel
mechanism, and validate its theoretical properties.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{DeBoer:2016:PTA,
author = "Patrick M. {De Boer} and Abraham Bernstein",
title = "{PPLib}: Toward the Automated Generation of Crowd
Computing Programs Using Process Recombination and
Auto-Experimentation",
journal = j-TIST,
volume = "7",
number = "4",
pages = "49:1--49:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897367",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Crowdsourcing is increasingly being adopted to solve
simple tasks such as image labeling and object tagging,
as well as more complex tasks, where crowd workers
collaborate in processes with interdependent steps. For
the whole range of complexity, research has yielded
numerous patterns for coordinating crowd workers in
order to optimize crowd accuracy, efficiency, and cost.
Process designers, however, often don't know which
pattern to apply to a problem at hand when designing
new applications for crowdsourcing. In this article, we
propose to solve this problem by systematically
exploring the design space of complex crowdsourced
tasks via automated recombination and
auto-experimentation for an issue at hand.
Specifically, we propose an approach to finding the
optimal process for a given problem by defining the
deep structure of the problem in terms of its abstract
operators, generating all possible alternatives via the
(re)combination of the abstract deep structure with
concrete implementations from a Process Repository, and
then establishing the best alternative via
auto-experimentation. To evaluate our approach, we
implemented PPLib (pronounced ``People Lib''), a
program library that allows for the automated
recombination of known processes stored in an easily
extensible Process Repository. We evaluated our work by
generating and running a plethora of process candidates
in two scenarios on Amazon's Mechanical Turk followed
by a meta-evaluation, where we looked at the
differences between the two evaluations. Our first
scenario addressed the problem of text translation,
where our automatic recombination produced multiple
processes whose performance almost matched the
benchmark established by an expert translation. In our
second evaluation, we focused on text shortening; we
automatically generated 41 crowd process candidates,
among them variations of the well-established
Find-Fix-Verify process. While Find-Fix-Verify
performed well in this setting, our recombination
engine produced five processes that repeatedly yielded
better results. We close the article by comparing the
two settings where the Recombinator was used, and
empirically show that the individual processes
performed differently in the two settings, which led us
to contend that there is no unifying formula, hence
emphasizing the necessity for recombination.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kim:2016:UCI,
author = "Yubin Kim and Kevyn Collins-Thompson and Jaime
Teevan",
title = "Using the Crowd to Improve Search Result Ranking and
the Search Experience",
journal = j-TIST,
volume = "7",
number = "4",
pages = "50:1--50:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897368",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Despite technological advances, algorithmic search
systems still have difficulty with complex or subtle
information needs. For example, scenarios requiring
deep semantic interpretation are a challenge for
computers. People, on the other hand, are well suited
to solving such problems. As a result, there is an
opportunity for humans and computers to collaborate
during the course of a search in a way that takes
advantage of the unique abilities of each. While search
tools that rely on human intervention will never be
able to respond as quickly as current search engines
do, recent research suggests that there are scenarios
where a search engine could take more time if it
resulted in a much better experience. This article
explores how crowdsourcing can be used at query time to
augment key stages of the search pipeline. We first
explore the use of crowdsourcing to improve search
result ranking. When the crowd is used to replace or
augment traditional retrieval components such as query
expansion and relevance scoring, we find that we can
increase robustness against failure for query expansion
and improve overall precision for results filtering.
However, the gains that we observe are limited and
unlikely to make up for the extra cost and time that
the crowd requires. We then explore ways to incorporate
the crowd into the search process that more drastically
alter the overall experience. We find that using crowd
workers to support rich query understanding and result
processing appears to be a more worthwhile way to make
use of the crowd during search. Our results confirm
that crowdsourcing can positively impact the search
experience but suggest that significant changes to the
search process may be required for crowdsourcing to
fulfill its potential in search systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Katsimerou:2016:CEI,
author = "Christina Katsimerou and Joris Albeda and Alina
Huldtgren and Ingrid Heynderickx and Judith A. Redi",
title = "Crowdsourcing Empathetic Intelligence: The Case of the
Annotation of {EMMA} Database for Emotion and Mood
Recognition",
journal = j-TIST,
volume = "7",
number = "4",
pages = "51:1--51:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897369",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Unobtrusive recognition of the user's mood is an
essential capability for affect-adaptive systems. Mood
is a subtle, long-term affective state, often
misrecognized even by humans. The challenge to train a
machine to recognize it from, for example, a video of
the user, is significant, and already begins with the
lack of ground truth for supervised learning. Existing
affective databases consist mainly of short videos,
annotated in terms of expressed emotions rather than
mood. In very few cases, we encounter perceived mood
annotations, of questionable reliability, however, due
to the subjectivity of mood estimation and the small
number of coders involved. In this work, we introduce a
new database for mood recognition from video. Our
database contains 180 long, acted videos, depicting
typical daily scenarios, and subtle facial and bodily
expressions. The videos cover three visual modalities
(face, body, Kinect data), and are annotated in terms
of emotions (via G-trace) and mood (via the
Self-Assessment Manikin and the AffectButton). To
annotate the database exhaustively, we exploit
crowdsourcing to reach out to an extensive number of
nonexpert coders. We validate the reliability of our
crowdsourced annotations by (1) adopting a number of
criteria to filter out unreliable coders, and (2)
comparing the annotations of a subset of our videos
with those collected in a controlled lab setting.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2016:UCS,
author = "Chen Chen and Pawe{\l} W. Wo{\'z}niak and Andrzej
Romanowski and Mohammad Obaid and Tomasz Jaworski and
Jacek Kucharski and Krzysztof Grudzie{\'n} and
Shengdong Zhao and Morten Fjeld",
title = "Using Crowdsourcing for Scientific Analysis of
Industrial Tomographic Images",
journal = j-TIST,
volume = "7",
number = "4",
pages = "52:1--52:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897370",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 09:59:46 2018",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we present a novel application domain
for human computation, specifically for crowdsourcing,
which can help in understanding particle-tracking
problems. Through an interdisciplinary inquiry, we
built a crowdsourcing system designed to detect tracer
particles in industrial tomographic images, and applied
it to the problem of bulk solid flow in silos. As
images from silo-sensing systems cannot be adequately
analyzed using the currently available computational
methods, human intelligence is required. However,
limited availability of experts, as well as their high
cost, motivates employing additional nonexperts. We
report on the results of a study that assesses the task
completion time and accuracy of employing nonexpert
workers to process large datasets of images in order to
generate data for bulk flow research. We prove the
feasibility of this approach by comparing results from
a user study with data generated from a computational
algorithm. The study shows that the crowd is more
scalable and more economical than an automatic
solution. The system can help analyze and understand
the physics of flow phenomena to better inform the
future design of silos, and is generalized enough to be
applicable to other domains.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{You:2016:CFP,
author = "Linlin You and Gianmario Motta and Kaixu Liu and
Tianyi Ma",
title = "{CITY FEED}: a Pilot System of Citizen-Sourcing for
City Issue Management",
journal = j-TIST,
volume = "7",
number = "4",
pages = "53:1--53:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2873064",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Crowdsourcing implies user collaboration and
engagement, which fosters a renewal of city governance
processes. In this article, we address a subset of
crowdsourcing, named citizen-sourcing, where citizens
interact with authorities collaboratively and actively.
Many systems have experimented citizen-sourcing in city
governance processes; however, their maturity levels
are mixed. In order to focus on the service maturity,
we introduce a city service maturity framework that
contains five levels of service support and two levels
of information integration. As an example, we introduce
CITY FEED, which implements citizen-sourcing in city
issue management process. In order to support such
process, CITY FEED supports all levels of the maturity
framework (publishing, transacting, interacting,
collaborating, and evaluating) and integrates related
information relationally and heterogeneously. In order
to integrate heterogeneous information, it implements a
threefold feed deduplication mechanism based on the
geographic, text semantic, and image similarities of
feeds. Currently, CITY FEED is in a pilot stage.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rao:2016:LHC,
author = "Huaming Rao and Shih-Wen Huang and Wai-Tat Fu",
title = "Leveraging Human Computations to Improve
Schematization of Spatial Relations from Imagery",
journal = j-TIST,
volume = "7",
number = "4",
pages = "54:1--54:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2873065",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The process of generating schematic maps of salient
objects from a set of pictures of an indoor environment
is challenging. It has been an active area of research
as it is crucial to a wide range of context- and
location-aware services, as well as for general scene
understanding. Although many automated systems have
been developed to solve the problem, most of them
either require predefining labels or expensive
equipment, such as RGBD sensors or lasers, to scan the
environment. In this article, we introduce a prototype
system to show how human computations can be utilized
to generate schematic maps from a set of pictures,
without making strong assumptions or demanding extra
devices. The system requires humans (crowd workers from
Amazon Mechanical Turks) to do simple spatial mapping
tasks in various conditions, and their data are
aggregated by filtering and clustering techniques that
allow salient cues to be identified in the pictures and
their spatial relations to be inferred and projected on
a two-dimensional map. In particular, we tested and
demonstrated the effectiveness of two methods that
improved the quality of the generated schematic map:
(1) We encouraged humans to adopt an allocentric
representations of salient objects by guiding them to
perform mental rotations of these objects and (2) we
sensitized human perception by guided arrows
superimposed on the imagery to improve the accuracy of
depth and width estimation. We demonstrated the
feasibility of our system by evaluating the results of
schematic maps generated from indoor pictures taken
from an office building. By calculating Riemannian
shape distances between the generated maps to the
ground truth, we found that the generated schematic
maps captured the spatial relations well. Our results
showed that the combination of human computations and
machine clustering could lead to more-accurate
schematized maps from imagery. We also discuss how our
approach may have important insights on methods that
leverage human computations in other areas.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Moshfeghi:2016:GTA,
author = "Yashar Moshfeghi and Alvaro Francisco Huertas Rosero
and Joemon M. Jose",
title = "A Game-Theory Approach for Effective Crowdsource-Based
Relevance Assessment",
journal = j-TIST,
volume = "7",
number = "4",
pages = "55:1--55:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2873063",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Despite the ever-increasing popularity of
crowdsourcing (CS) in both industry and academia,
procedures that ensure quality in its results are still
elusive. We hypothesise that a CS design based on game
theory can persuade workers to perform their tasks as
quickly as possible with the highest quality. In order
to do so, in this article we propose a CS framework
inspired by the n -person Chicken game. Our aim is to
address the problem of CS quality without compromising
on CS benefits such as low monetary cost and high task
completion speed. With that goal in mind, we study the
effects of knowledge updates as well as incentives for
good workers to continue playing. We define a general
task with the characteristics of relevance assessment
as a case study, because it has been widely explored in
the past with CS due to its potential cost and
complexity. In order to investigate our hypotheses, we
conduct a simulation where we study the effect of the
proposed framework on data accuracy, task completion
time, and total monetary rewards. Based on a
game-theoretical analysis, we study how different types
of individuals would behave under a particular game
scenario. In particular, we simulate a population
comprised of different types of workers with varying
ability to formulate optimal strategies and learn from
their experiences. A simulation of the proposed
framework produced results that support our
hypothesis.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Han:2016:CHA,
author = "Shuguang Han and Peng Dai and Praveen Paritosh and
David Huynh",
title = "Crowdsourcing Human Annotation on {Web} Page
Structure: Infrastructure Design and Behavior-Based
Quality Control",
journal = j-TIST,
volume = "7",
number = "4",
pages = "56:1--56:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2870649",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Parsing the semantic structure of a web page is a key
component of web information extraction. Successful
extraction algorithms usually require large-scale
training and evaluation datasets, which are difficult
to acquire. Recently, crowdsourcing has proven to be an
effective method of collecting large-scale training
data in domains that do not require much domain
knowledge. For more complex domains, researchers have
proposed sophisticated quality control mechanisms to
replicate tasks in parallel or sequential ways and then
aggregate responses from multiple workers. Conventional
annotation integration methods often put more trust in
the workers with high historical performance; thus,
they are called performance-based methods. Recently,
Rzeszotarski and Kittur have demonstrated that
behavioral features are also highly correlated with
annotation quality in several crowdsourcing
applications. In this article, we present a new
crowdsourcing system, called Wernicke, to provide
annotations for web information extraction. Wernicke
collects a wide set of behavioral features and, based
on these features, predicts annotation quality for a
challenging task domain: annotating web page structure.
We evaluate the effectiveness of quality control using
behavioral features through a case study where 32
workers annotate 200 Q\&A web pages from five popular
websites. In doing so, we discover several things: (1)
Many behavioral features are significant predictors for
crowdsourcing quality. (2) The behavioral-feature-based
method outperforms performance-based methods in recall
prediction, while performing equally with precision
prediction. In addition, using behavioral features is
less vulnerable to the cold-start problem, and the
corresponding prediction model is more generalizable
for predicting recall than precision for cross-website
quality analysis. (3) One can effectively combine
workers' behavioral information and historical
performance information to further reduce prediction
errors.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wei:2016:MDC,
author = "Yunchao Wei and Yao Zhao and Zhenfeng Zhu and Shikui
Wei and Yanhui Xiao and Jiashi Feng and Shuicheng Yan",
title = "Modality-Dependent Cross-Media Retrieval",
journal = j-TIST,
volume = "7",
number = "4",
pages = "57:1--57:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2775109",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we investigate the cross-media
retrieval between images and text, that is, using image
to search text (I2T) and using text to search images
(T2I). Existing cross-media retrieval methods usually
learn one couple of projections, by which the original
features of images and text can be projected into a
common latent space to measure the content similarity.
However, using the same projections for the two
different retrieval tasks (I2T and T2I) may lead to a
tradeoff between their respective performances, rather
than their best performances. Different from previous
works, we propose a modality-dependent cross-media
retrieval (MDCR) model, where two couples of
projections are learned for different cross-media
retrieval tasks instead of one couple of projections.
Specifically, by jointly optimizing the correlation
between images and text and the linear regression from
one modal space (image or text) to the semantic space,
two couples of mappings are learned to project images
and text from their original feature spaces into two
common latent subspaces (one for I2T and the other for
T2I). Extensive experiments show the superiority of the
proposed MDCR compared with other methods. In
particular, based on the 4,096-dimensional
convolutional neural network (CNN) visual feature and
100-dimensional Latent Dirichlet Allocation (LDA)
textual feature, the mAP of the proposed method
achieves the mAP score of 41.5\%, which is a new
state-of-the-art performance on the Wikipedia
dataset.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Morris:2016:DNM,
author = "Robert Morris and Matthew Johnson and K. Brent Venable
and James Lindsey",
title = "Designing Noise-Minimal Rotorcraft Approach
Trajectories",
journal = j-TIST,
volume = "7",
number = "4",
pages = "58:1--58:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2838738",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "NASA and the international aviation community are
investing in the development of a commercial
transportation infrastructure that includes the
increased use of rotorcraft, specifically helicopters
and civil tilt rotors. However, there is significant
concern over the impact of noise on the communities
surrounding the transportation facilities. One way to
address the rotorcraft noise problem is by exploiting
powerful search techniques coming from artificial
intelligence to design low-noise flight profiles that
can be then validated though field tests. This article
investigates the use of discrete heuristic search
methods to design low-noise approach trajectories for
rotorcraft. Our work builds on a long research
tradition in trajectory optimization using either
numerical methods or discrete search. Novel features of
our approach include the use of a discrete search space
with a resolution that can be varied, and the coupling
of search with a robust simulator to evaluate
candidates. The article includes a systematic
comparison of different search techniques; in
particular, in the experiments, we are able to do a
trade study that compares complete search algorithms
such as A$^*$ with faster but approximate methods such
as local search.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fang:2016:SST,
author = "Quan Fang and Changsheng Xu and M. Shamim Hossain and
G. Muhammad",
title = "{STCAPLRS}: a Spatial-Temporal Context-Aware
Personalized Location Recommendation System",
journal = j-TIST,
volume = "7",
number = "4",
pages = "59:1--59:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2842631",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Newly emerging location-based social media network
services (LBSMNS) provide valuable resources to
understand users' behaviors based on their location
histories. The location-based behaviors of a user are
generally influenced by both user intrinsic interest
and the location preference, and moreover are
spatial-temporal context dependent. In this article, we
propose a spatial-temporal context-aware personalized
location recommendation system (STCAPLRS), which offers
a particular user a set of location items such as
points of interest or venues (e.g., restaurants and
shopping malls) within a geospatial range by
considering personal interest, local preference, and
spatial-temporal context influence. STCAPLRS can make
accurate recommendation and facilitate people's local
visiting and new location exploration by exploiting the
context information of user behavior, associations
between users and location items, and the location and
content information of location items. Specifically,
STCAPLRS consists of two components: offline modeling
and online recommendation. The core module of the
offline modeling part is a context-aware regression
mixture model that is designed to model the
location-based user behaviors in LBSMNS to learn the
interest of each individual user, the local preference
of each individual location, and the context-aware
influence factors. The online recommendation part takes
a querying user along with the corresponding querying
spatial-temporal context as input and automatically
combines the learned interest of the querying user, the
local preference of the querying location, and the
context-aware influence factor to produce the top- k
recommendations. We evaluate the performance of
STCAPLRS on two real-world datasets: Dianping and
Foursquare. The results demonstrate the superiority of
STCAPLRS in recommending location items for users in
terms of both effectiveness and efficiency. Moreover,
the experimental analysis results also illustrate the
excellent interpretability of STCAPLRS.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xin:2016:EGF,
author = "Bo Xin and Yoshinobu Kawahara and Yizhou Wang and
Lingjing Hu and Wen Gao",
title = "Efficient Generalized Fused Lasso and Its
Applications",
journal = j-TIST,
volume = "7",
number = "4",
pages = "60:1--60:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2847421",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Generalized fused lasso (GFL) penalizes variables with
l$^1$ norms based both on the variables and their
pairwise differences. GFL is useful when applied to
data where prior information is expressed using a graph
over the variables. However, the existing GFL
algorithms incur high computational costs and do not
scale to high-dimensional problems. In this study, we
propose a fast and scalable algorithm for GFL. Based on
the fact that fusion penalty is the Lov{\'a}sz
extension of a cut function, we show that the key
building block of the optimization is equivalent to
recursively solving graph-cut problems. Thus, we use a
parametric flow algorithm to solve GFL in an efficient
manner. Runtime comparisons demonstrate a significant
speedup compared to existing GFL algorithms. Moreover,
the proposed optimization framework is very general; by
designing different cut functions, we also discuss the
extension of GFL to directed graphs. Exploiting the
scalability of the proposed algorithm, we demonstrate
the applications of our algorithm to the diagnosis of
Alzheimer's disease (AD) and video background
subtraction (BS). In the AD problem, we formulated the
diagnosis of AD as a GFL regularized classification.
Our experimental evaluations demonstrated that the
diagnosis performance was promising. We observed that
the selected critical voxels were well structured,
i.e., connected, consistent according to cross
validation, and in agreement with prior pathological
knowledge. In the BS problem, GFL naturally models
arbitrary foregrounds without predefined grouping of
the pixels. Even by applying simple background models,
e.g., a sparse linear combination of former frames, we
achieved state-of-the-art performance on several public
datasets.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Schulz:2016:MTN,
author = "Sarah Schulz and Guy {De Pauw} and Orph{\'e}e {De
Clercq} and Bart Desmet and V{\'e}ronique Hoste and
Walter Daelemans and Lieve Macken",
title = "Multimodular Text Normalization of {Dutch}
User-Generated Content",
journal = j-TIST,
volume = "7",
number = "4",
pages = "61:1--61:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2850422",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "As social media constitutes a valuable source for data
analysis for a wide range of applications, the need for
handling such data arises. However, the nonstandard
language used on social media poses problems for
natural language processing (NLP) tools, as these are
typically trained on standard language material. We
propose a text normalization approach to tackle this
problem. More specifically, we investigate the
usefulness of a multimodular approach to account for
the diversity of normalization issues encountered in
user-generated content (UGC). We consider three
different types of UGC written in Dutch (SNS, SMS, and
tweets) and provide a detailed analysis of the
performance of the different modules and the overall
system. We also apply an extrinsic evaluation by
evaluating the performance of a part-of-speech tagger,
lemmatizer, and named-entity recognizer before and
after normalization.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hegedus:2016:RDL,
author = "Istv{\'a}n Heged{\H{u}}s and {\'A}rp{\'a}d Berta and
Levente Kocsis and Andr{\'a}s A. Bencz{\'u}r and
M{\'a}rk Jelasity",
title = "Robust Decentralized Low-Rank Matrix Decomposition",
journal = j-TIST,
volume = "7",
number = "4",
pages = "62:1--62:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2854157",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Low-rank matrix approximation is an important tool in
data mining with a wide range of applications,
including recommender systems, clustering, and
identifying topics in documents. When the matrix to be
approximated originates from a large distributed
system, such as a network of mobile phones or smart
meters, a challenging problem arises due to the
strongly conflicting yet essential requirements of
efficiency, robustness, and privacy preservation. We
argue that although collecting sensitive data in a
centralized fashion may be efficient, it is not an
option when considering privacy and efficiency at the
same time. Thus, we do not allow any sensitive data to
leave the nodes of the network. The local information
at each node (personal attributes, documents, media
ratings, etc.) defines one row in the matrix. This
means that all computations have to be performed at the
edge of the network. Known parallel methods that
respect the locality constraint, such as synchronized
parallel gradient search or distributed iterative
methods, require synchronized rounds or have inherent
issues with load balancing, and thus they are not
robust to failure. Our distributed stochastic gradient
descent algorithm overcomes these limitations. During
the execution, any sensitive information remains local,
whereas the global features (e.g., the factor model of
movies) converge to the correct value at all nodes. We
present a theoretical derivation and a thorough
experimental evaluation of our algorithm. We
demonstrate that the convergence speed of our method is
competitive while not relying on synchronization and
being robust to extreme and realistic failure
scenarios. To demonstrate the feasibility of our
approach, we present trace-based simulations, real
smartphone user behavior analysis, and tests over real
movie recommender system data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Luo:2016:TUA,
author = "Chen Luo and Jia Zeng and Mingxuan Yuan and Wenyuan
Dai and Qiang Yang",
title = "Telco User Activity Level Prediction with Massive
Mobile Broadband Data",
journal = j-TIST,
volume = "7",
number = "4",
pages = "63:1--63:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2856057",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Telecommunication (telco) operators aim to provide
users with optimized services and bandwidth in a timely
manner. The goal is to increase user experience while
retaining profit. To do this, knowing the changing
behavior patterns of users through their activity
levels in advance can be a great help for operators to
adjust their management strategies and reduce
operational risk. To achieve this goal, the operators
can make use of knowledge discovered from telco's
historical mobile broadband (MBB) records to predict
mobile access activity level at an early stage. In this
article, we report our research in a real-world telco
setting involving more than one million telco users.
Our novel contribution includes representing users as
documents containing a collection of changing
spatiotemporal ``words'' that express user behavior. By
extracting users' space-time access records in MBB
data, we use latent Dirichlet allocation (LDA) to learn
user-specific compact topic features for user activity
level prediction. We propose a scalable online
expectation-maximization (OEM) algorithm that can scale
LDA to massive MBB data, which is significantly faster
than several state-of-the-art online LDA algorithms.
Using these real-world MBB data, we confirm high
performance in user activity level prediction. In
addition, we show that the inferred topics indicate
that future activity level anomalies correlate highly
with early skewed bandwidth supply and demand
relations. Thus, our prediction system can also guide
the telco operators to balance the telecommunication
network in terms of supply-demand relations, saving
deployment costs and energy of cell towers in the
future.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2016:CFI,
author = "Senzhang Wang and Sihong Xie and Xiaoming Zhang and
Zhoujun Li and Philip S. Yu and Yueying He",
title = "Coranking the Future Influence of Multiobjects in
Bibliographic Network Through Mutual Reinforcement",
journal = j-TIST,
volume = "7",
number = "4",
pages = "64:1--64:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897371",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Scientific literature ranking is essential to help
researchers find valuable publications from a large
literature collection. Recently, with the prevalence of
webpage ranking algorithms such as PageRank and HITS,
graph-based algorithms have been widely used to
iteratively rank papers and researchers through the
networks formed by citation and coauthor relationships.
However, existing graph-based ranking algorithms mostly
focus on ranking the current importance of literature.
For researchers who enter an emerging research area,
they might be more interested in new papers and young
researchers that are likely to become influential in
the future, since such papers and researchers are more
helpful in letting them quickly catch up on the most
recent advances and find valuable research directions.
Meanwhile, although some works have been proposed to
rank the prestige of a certain type of objects with the
help of multiple networks formed of multiobjects, there
still lacks a unified framework to rank multiple types
of objects in the bibliographic network simultaneously.
In this article, we propose a unified ranking framework
MRCoRank to corank the future popularity of four types
of objects: papers, authors, terms, and venues through
mutual reinforcement. Specifically, because the
citation data of new publications are sparse and not
efficient to characterize their innovativeness, we make
the first attempt to extract the text features to help
characterize innovative papers and authors. With the
observation that the current trend is more indicative
of the future trend of citation and coauthor
relationships, we then construct time-aware weighted
graphs to quantify the importance of links established
at different times on both citation and coauthor
graphs. By leveraging both the constructed text
features and time-aware graphs, we finally fuse the
rich information in a mutual reinforcement ranking
framework to rank the future importance of multiobjects
simultaneously. We evaluate the proposed model through
extensive experiments on the ArnetMiner dataset
containing more than 1,500,000 papers. Experimental
results verify the effectiveness of MRCoRank in
coranking the future influence of multiobjects in a
bibliographic network.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2016:MLR,
author = "Teng Li and Bin Cheng and Bingbing Ni and Guangchan
Liu and Shuicheng Yan",
title = "Multitask Low-Rank Affinity Graph for Image
Segmentation and Image Annotation",
journal = j-TIST,
volume = "7",
number = "4",
pages = "65:1--65:??",
month = jul,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2856058",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:56 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article investigates a low-rank
representation--based graph, which can used in
graph-based vision tasks including image segmentation
and image annotation. It naturally fuses multiple types
of image features in a framework named multitask
low-rank affinity pursuit. Given the image patches
described with multiple types of features, we aim at
inferring a unified affinity matrix that implicitly
encodes the relations among these patches. This is
achieved by seeking the sparsity-consistent low-rank
affinities from the joint decompositions of multiple
feature matrices into pairs of sparse and low-rank
matrices, the latter of which is expressed as the
production of the image feature matrix and its
corresponding image affinity matrix. The inference
process is formulated as a minimization problem and
solved efficiently with the augmented Lagrange
multiplier method. Considering image patches as
vertices, a graph can be built based on the resulted
affinity matrix. Compared to previous methods, which
are usually based on a single type of feature, the
proposed method seamlessly integrates multiple types of
features to jointly produce the affinity matrix in a
single inference step. The proposed method is applied
to graph-based image segmentation and graph-based image
annotation. Experiments on benchmark datasets well
validate the superiority of using multiple features
over single feature and also the superiority of our
method over conventional methods for feature fusion.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Leskovec:2016:SGP,
author = "Jure Leskovec and Rok Sosic",
title = "{SNAP}: a General-Purpose Network Analysis and
Graph-Mining Library",
journal = j-TIST,
volume = "8",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2898361",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Large networks are becoming a widely used abstraction
for studying complex systems in a broad set of
disciplines, ranging from social-network analysis to
molecular biology and neuroscience. Despite an
increasing need to analyze and manipulate large
networks, only a limited number of tools are available
for this task. Here, we describe the Stanford Network
Analysis Platform (SNAP), a general-purpose,
high-performance system that provides easy-to-use,
high-level operations for analysis and manipulation of
large networks. We present SNAP functionality, describe
its implementational details, and give performance
benchmarks. SNAP has been developed for single
big-memory machines, and it balances the trade-off
between maximum performance, compact in-memory graph
representation, and the ability to handle dynamic
graphs in which nodes and edges are being added or
removed over time. SNAP can process massive networks
with hundreds of millions of nodes and billions of
edges. SNAP offers over 140 different graph algorithms
that can efficiently manipulate large graphs, calculate
structural properties, generate regular and random
graphs, and handle attributes and metadata on nodes and
edges. Besides being able to handle large graphs, an
additional strength of SNAP is that networks and their
attributes are fully dynamic; they can be modified
during the computation at low cost. SNAP is provided as
an open-source library in C++ as well as a module in
Python. We also describe the Stanford Large Network
Dataset, a set of social and information real-world
networks and datasets, which we make publicly
available. The collection is a complementary resource
to our SNAP software and is widely used for development
and benchmarking of graph analytics algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Phan:2016:TAP,
author = "Nhathai Phan and Javid Ebrahimi and David Kil and
Brigitte Piniewski and Dejing Dou",
title = "Topic-Aware Physical Activity Propagation with
Temporal Dynamics in a Health Social Network",
journal = j-TIST,
volume = "8",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2873066",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Modeling physical activity propagation, such as
activity level and intensity, is a key to preventing
obesity from cascading through communities, and to
helping spread wellness and healthy behavior in a
social network. However, there have not been enough
scientific and quantitative studies to elucidate how
social communication may deliver physical activity
interventions. In this work, we introduce a novel model
named Topic-aware Community-level Physical Activity
Propagation with Temporal Dynamics (TCPT) to analyze
physical activity propagation and social influence at
different granularities (i.e., individual level and
community level). Given a social network, the TCPT
model first integrates the correlations between the
content of social communication, social influences, and
temporal dynamics. Then, a hierarchical approach is
utilized to detect a set of communities and their
reciprocal influence strength of physical activities.
The experimental evaluation shows not only the
effectiveness of our approach but also the correlation
of the detected communities with various health outcome
measures. Our promising results pave a way for
knowledge discovery in health social networks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Doucette:2016:MRA,
author = "John A. Doucette and Graham Pinhey and Robin Cohen",
title = "Multiagent Resource Allocation for Dynamic Task
Arrivals with Preemption",
journal = j-TIST,
volume = "8",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2875441",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we present a distributed algorithm
for allocating resources to tasks in multiagent
systems, one that adapts well to dynamic task arrivals
where new work arises at short notice. Our algorithm is
designed to leverage preemption if it is available,
revoking resource allocations to tasks in progress if
new opportunities arise that those resources are better
suited to handle. Our multiagent model assigns a task
agent to each task that must be completed and a proxy
agent to each resource that is available. Preemption
occurs when a task agent approaches a proxy agent with
a sufficiently compelling need that the proxy agent
determines the newcomer derives more benefit from the
proxy agent's resource than the task agent currently
using that resource. Task agents reason about which
resources to request based on a learning of churn and
congestion. We compare to a well-established multiagent
resource allocation framework that permits preemption
under more conservative assumptions and show through
simulation that our model allows for improved
allocations through more permissive preemption. In all,
we offer a novel approach for multiagent resource
allocation that is able to cope well with dynamic task
arrivals.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ganesan:2016:DSC,
author = "Rajesh Ganesan and Sushil Jajodia and Ankit Shah and
Hasan Cam",
title = "Dynamic Scheduling of Cybersecurity Analysts for
Minimizing Risk Using Reinforcement Learning",
journal = j-TIST,
volume = "8",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2882969",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An important component of the cyber-defense mechanism
is the adequate staffing levels of its cybersecurity
analyst workforce and their optimal assignment to
sensors for investigating the dynamic alert traffic.
The ever-increasing cybersecurity threats faced by
today's digital systems require a strong cyber-defense
mechanism that is both reactive in its response to
mitigate the known risk and proactive in being prepared
for handling the unknown risks. In order to be
proactive for handling the unknown risks, the above
workforce must be scheduled dynamically so the system
is adaptive to meet the day-to-day stochastic demands
on its workforce (both size and expertise mix). The
stochastic demands on the workforce stem from the
varying alert generation and their significance rate,
which causes an uncertainty for the cybersecurity
analyst scheduler that is attempting to schedule
analysts for work and allocate sensors to analysts.
Sensor data are analyzed by automatic processing
systems, and alerts are generated. A portion of these
alerts is categorized to be significant, which requires
thorough examination by a cybersecurity analyst. Risk,
in this article, is defined as the percentage of
significant alerts that are not thoroughly analyzed by
analysts. In order to minimize risk, it is imperative
that the cyber-defense system accurately estimates the
future significant alert generation rate and
dynamically schedules its workforce to meet the
stochastic workload demand to analyze them. The article
presents a reinforcement learning-based stochastic
dynamic programming optimization model that
incorporates the above estimates of future alert rates
and responds by dynamically scheduling cybersecurity
analysts to minimize risk (i.e., maximize significant
alert coverage by analysts) and maintain the risk under
a pre-determined upper bound. The article tests the
dynamic optimization model and compares the results to
an integer programming model that optimizes the static
staffing needs based on a daily-average alert
generation rate with no estimation of future alert
rates (static workforce model). Results indicate that
over a finite planning horizon, the learning-based
optimization model, through a dynamic (on-call)
workforce in addition to the static workforce, (a) is
capable of balancing risk between days and reducing
overall risk better than the static model, (b) is
scalable and capable of identifying the quantity and
the right mix of analyst expertise in an organization,
and (c) is able to determine their dynamic (on-call)
schedule and their sensor-to-analyst allocation in
order to maintain risk below a given upper bound.
Several meta-principles are presented, which are
derived from the optimization model, and they further
serve as guiding principles for hiring and scheduling
cybersecurity analysts. Days-off scheduling was
performed to determine analyst weekly work schedules
that met the cybersecurity system's workforce
constraints and requirements.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Belcastro:2016:USD,
author = "Loris Belcastro and Fabrizio Marozzo and Domenico
Talia and Paolo Trunfio",
title = "Using Scalable Data Mining for Predicting Flight
Delays",
journal = j-TIST,
volume = "8",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2888402",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Flight delays are frequent all over the world (about
20\% of airline flights arrive more than 15min late)
and they are estimated to have an annual cost of
billions of dollars. This scenario makes the prediction
of flight delays a primary issue for airlines and
travelers. The main goal of this work is to implement a
predictor of the arrival delay of a scheduled flight
due to weather conditions. The predicted arrival delay
takes into consideration both flight information
(origin airport, destination airport, scheduled
departure and arrival time) and weather conditions at
origin airport and destination airport according to the
flight timetable. Airline flight and weather
observation datasets have been analyzed and mined using
parallel algorithms implemented as MapReduce programs
executed on a Cloud platform. The results show a high
accuracy in predicting delays above a given threshold.
For instance, with a delay threshold of 15min, we
achieve an accuracy of 74.2\% and 71.8\% recall on
delayed flights, while with a threshold of 60min, the
accuracy is 85.8\% and the delay recall is 86.9\%.
Furthermore, the experimental results demonstrate the
predictor scalability that can be achieved performing
data preparation and mining tasks as MapReduce
applications on the Cloud.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2016:RPG,
author = "Tianben Wang and Zhu Wang and Daqing Zhang and Tao Gu
and Hongbo Ni and Jiangbo Jia and Xingshe Zhou and Jing
Lv",
title = "Recognizing {Parkinsonian} Gait Pattern by Exploiting
Fine-Grained Movement Function Features",
journal = j-TIST,
volume = "8",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2890511",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Parkinson's disease (PD) is one of the typical
movement disorder diseases among elderly people, which
has a serious impact on their daily lives. In this
article, we propose a novel computation framework to
recognize gait patterns in patients with PD. The key
idea of our approach is to distinguish gait patterns in
PD patients from healthy individuals by accurately
extracting gait features that capture all three aspects
of movement functions, that is, stability, symmetry,
and harmony. The proposed framework contains three
steps: gait phase discrimination, feature extraction
and selection, and pattern classification. In the first
step, we put forward a sliding window--based method to
discriminate four gait phases from plantar pressure
data. Based on the gait phases, we extract and select
gait features that characterize stability, symmetry,
and harmony of movement functions. Finally, we
recognize PD gait patterns by applying a hybrid
classification model. We evaluate the framework using
an open dataset that contains real plantar pressure
data of 93 PD patients and 72 healthy individuals.
Experimental results demonstrate that our framework
significantly outperforms the four baseline
approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Towne:2016:MSS,
author = "W. Ben Towne and Carolyn P. Ros{\'e} and James D.
Herbsleb",
title = "Measuring Similarity Similarly: {LDA} and Human
Perception",
journal = j-TIST,
volume = "8",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2890510",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Several intelligent technologies designed to improve
navigability in and digestibility of text corpora use
topic modeling such as the state-of-the-art Latent
Dirichlet Allocation (LDA). This model and variants on
it provide lower-dimensional document representations
used in visualizations and in computing similarity
between documents. This article contributes a method
for validating such algorithms against human
perceptions of similarity, especially applicable to
contexts in which the algorithm is intended to support
navigability between similar documents via dynamically
generated hyperlinks. Such validation enables
researchers to ground their methods in context of
intended use instead of relying on assumptions of fit.
In addition to the methodology, this article presents
the results of an evaluation using a corpus of short
documents and the LDA algorithm. We also present some
analysis of potential causes of differences between
cases in which this model matches human perceptions of
similarity more or less well.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jiang:2016:CCS,
author = "Yexi Jiang and Chang-Shing Perng and Anca Sailer and
Ignacio Silva-Lepe and Yang Zhou and Tao Li",
title = "{CSM}: a Cloud Service Marketplace for Complex Service
Acquisition",
journal = j-TIST,
volume = "8",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2894759",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The cloud service marketplace (CSM) is an exploratory
project aiming to provide ``an AppStore for Services.''
It is an intelligent online marketplace that
facilitates service discovery and acquisition for
enterprise customers. Traditional service discovery and
acquisition are time-consuming. In the era of OneClick
Checkout and pay-as-you-go service plans, users expect
services to be purchased online efficiently and
conveniently. However, as services are complex and
different from software apps, the currently prevailing
App Store based on keyword search is inadequate for
services. In CSM, exploring and configuring services
are an iterative process. Customers provide their
requirements in natural language and interact with the
system through questioning and answering. Learning from
the input, the system can incrementally clarify users'
intention, narrow down the candidate services, and
profile the configuration information for the
candidates at the same time. CSM's back end is built
around the Services Knowledge Graph (SKG) and leverages
data mining technologies to enable the semantic
understanding of customers' requirements. To
quantitatively assess the value of CSM, empirical
evaluation on real and synthetic datasets and case
studies are given to demonstrate the efficacy and
effectiveness of the proposed system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{DiNoia:2016:SSP,
author = "Tommaso {Di Noia} and Vito Claudio Ostuni and Paolo
Tomeo and Eugenio {Di Sciascio}",
title = "{SPrank}: Semantic Path-Based Ranking for Top-{$N$}
Recommendations Using Linked Open Data",
journal = j-TIST,
volume = "8",
number = "1",
pages = "9:1--9:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2899005",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In most real-world scenarios, the ultimate goal of
recommender system applications is to suggest a short
ranked list of items, namely top- N recommendations,
that will appeal to the end user. Often, the problem of
computing top- N recommendations is mainly tackled with
a two-step approach. The system focuses first on
predicting the unknown ratings, which are eventually
used to generate a ranked recommendation list.
Actually, the top- N recommendation task can be
directly seen as a ranking problem where the main goal
is not to accurately predict ratings but to directly
find the best-ranked list of items to recommend. In
this article we present SPrank, a novel hybrid
recommendation algorithm able to compute top- N
recommendations exploiting freely available knowledge
in the Web of Data. In particular, we employ DBpedia, a
well-known encyclopedic knowledge base in the Linked
Open Data cloud, to extract semantic path-based
features and to eventually compute top- N
recommendations in a learning-to-rank fashion.
Experiments with three datasets related to different
domains (books, music, and movies) prove the
effectiveness of our approach compared to
state-of-the-art recommendation algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cheng:2016:UPI,
author = "Chen Cheng and Haiqin Yang and Irwin King and Michael
R. Lyu",
title = "A Unified Point-of-Interest Recommendation Framework
in Location-Based Social Networks",
journal = j-TIST,
volume = "8",
number = "1",
pages = "10:1--10:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2901299",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Location-based social networks (LBSNs), such as
Gowalla, Facebook, Foursquare, Brightkite, and so on,
have attracted millions of users to share their social
friendship and their locations via check-ins in the
past few years. Plenty of valuable information is
accumulated based on the check-in behaviors, which
makes it possible to learn users' moving patterns as
well as their preferences. In LBSNs, point-of-interest
(POI) recommendation is one of the most significant
tasks because it can help targeted users explore their
surroundings as well as help third-party developers
provide personalized services. Matrix factorization is
a promising method for this task because it can capture
users' preferences to locations and is widely adopted
in traditional recommender systems such as movie
recommendation. However, the sparsity of the check-in
data makes it difficult to capture users' preferences
accurately. Geographical influence can help alleviate
this problem and have a large impact on the final
recommendation result. By studying users' moving
patterns, we find that users tend to check in around
several centers and different users have different
numbers of centers. Based on this, we propose a
Multi-center Gaussian Model (MGM) to capture this
pattern via modeling the probability of a user's
check-in on a location. Moreover, users are usually
more interested in the top 20 or even top 10
recommended POIs, which makes personalized ranking
important in this task. From previous work, directly
optimizing for pairwise ranking like Bayesian
Personalized Ranking (BPR) achieves better performance
in the top- k recommendation than directly using matrix
matrix factorization that aims to minimize the
point-wise rating error. To consider users'
preferences, geographical influence and personalized
ranking, we propose a unified POI recommendation
framework, which unifies all of them together.
Specifically, we first fuse MGM with matrix
factorization methods and further with BPR using two
different approaches. We conduct experiments on Gowalla
and Foursquare datasets, which are two large-scale
real-world LBSN datasets publicly available online. The
results on both datasets show that our unified POI
recommendation framework can produce better
performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Deng:2016:EKL,
author = "Zhaohong Deng and Yizhang Jiang and Hisao Ishibuchi
and Kup-Sze Choi and Shitong Wang",
title = "Enhanced Knowledge-Leverage-Based {TSK} Fuzzy System
Modeling for Inductive Transfer Learning",
journal = j-TIST,
volume = "8",
number = "1",
pages = "11:1--11:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2903725",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The knowledge-leverage-based Takagi--Sugeno--Kang
fuzzy system (KL-TSK-FS) modeling method has shown
promising performance for fuzzy modeling tasks where
transfer learning is required. However, the
knowledge-leverage mechanism of the KL-TSK-FS can be
further improved. This is because available training
data in the target domain are not utilized for the
learning of antecedents and the knowledge transfer
mechanism from a source domain to the target domain is
still too simple for the learning of consequents when a
Takagi--Sugeno--Kang fuzzy system (TSK-FS) model is
trained in the target domain. The proposed method, that
is, the enhanced KL-TSK-FS (EKL-TSK-FS), has two
knowledge-leverage strategies for enhancing the
parameter learning of the TSK-FS model for the target
domain using available information from the source
domain. One strategy is used for the learning of
antecedent parameters, while the other is for
consequent parameters. It is demonstrated that the
proposed EKL-TSK-FS has higher transfer learning
abilities than the KL-TSK-FS. In addition, the
EKL-TSK-FS has been further extended for the scene of
the multisource domain.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{He:2016:STT,
author = "Tieke He and Hongzhi Yin and Zhenyu Chen and Xiaofang
Zhou and Shazia Sadiq and Bin Luo",
title = "A Spatial-Temporal Topic Model for the Semantic
Annotation of {POIs} in {LBSNs}",
journal = j-TIST,
volume = "8",
number = "1",
pages = "12:1--12:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2905373",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Semantic tags of points of interest (POIs) are a
crucial prerequisite for location search,
recommendation services, and data cleaning. However,
most POIs in location-based social networks (LBSNs) are
either tag-missing or tag-incomplete. This article aims
to develop semantic annotation techniques to
automatically infer tags for POIs. We first analyze two
LBSN datasets and observe that there are two types of
tags, category-related ones and sentimental ones, which
have unique characteristics. Category-related tags are
hierarchical, whereas sentimental ones are
category-aware. All existing related work has adopted
classification methods to predict high-level
category-related tags in the hierarchy, but they cannot
apply to infer either low-level category tags or
sentimental ones. In light of this, we propose a
latent-class probabilistic generative model, namely the
spatial-temporal topic model (STM), to infer personal
interests, the temporal and spatial patterns of
topics/semantics embedded in users' check-in
activities, the interdependence between category-topic
and sentiment-topic, and the correlation between
sentimental tags and rating scores from users' check-in
and rating behaviors. Then, this learned knowledge is
utilized to automatically annotate all POIs with both
category-related and sentimental tags in a unified way.
We conduct extensive experiments to evaluate the
performance of the proposed STM on a real large-scale
dataset. The experimental results show the superiority
of our proposed STM, and we also observe that the real
challenge of inferring category-related tags for POIs
lies in the low-level ones of the hierarchy and that
the challenge of predicting sentimental tags are those
with neutral ratings.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sintsova:2016:DDS,
author = "Valentina Sintsova and Pearl Pu",
title = "Dystemo: Distant Supervision Method for Multi-Category
Emotion Recognition in Tweets",
journal = j-TIST,
volume = "8",
number = "1",
pages = "13:1--13:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2912147",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Emotion recognition in text has become an important
research objective. It involves building classifiers
capable of detecting human emotions for a specific
application, for example, analyzing reactions to
product launches, monitoring emotions at sports events,
or discerning opinions in political debates. Most
successful approaches rely heavily on costly manual
annotation. To alleviate this burden, we propose a
distant supervision method-Dystemo-for automatically
producing emotion classifiers from tweets labeled using
existing or easy-to-produce emotion lexicons. The goal
is to obtain emotion classifiers that work more
accurately for specific applications than available
emotion lexicons. The success of this method depends
mainly on a novel classifier-Balanced Weighted Voting
(BWV)-designed to overcome the imbalance in emotion
distribution in the initial dataset, and on novel
heuristics for detecting neutral tweets. We demonstrate
how Dystemo works using Twitter data about sports
events, a fine-grained 20-category emotion model, and
three different initial emotion lexicons. Through a
series of carefully designed experiments, we confirm
that Dystemo is effective both in extending initial
emotion lexicons of small coverage to find correctly
more emotional tweets and in correcting emotion
lexicons of low accuracy to perform more accurately.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Nanni:2016:DPC,
author = "Mirco Nanni and Roberto Trasarti and Anna Monreale and
Valerio Grossi and Dino Pedreschi",
title = "Driving Profiles Computation and Monitoring for Car
Insurance {CRM}",
journal = j-TIST,
volume = "8",
number = "1",
pages = "14:1--14:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2912148",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Customer segmentation is one of the most traditional
and valued tasks in customer relationship management
(CRM). In this article, we explore the problem in the
context of the car insurance industry, where the
mobility behavior of customers plays a key role:
Different mobility needs, driving habits, and skills
imply also different requirements (level of coverage
provided by the insurance) and risks (of accidents). In
the present work, we describe a methodology to extract
several indicators describing the driving profile of
customers, and we provide a clustering-oriented
instantiation of the segmentation problem based on such
indicators. Then, we consider the availability of a
continuous flow of fresh mobility data sent by the
circulating vehicles, aiming at keeping our segments
constantly up to date. We tackle a major scalability
issue that emerges in this context when the number of
customers is large-namely, the communication
bottleneck-by proposing and implementing a
sophisticated distributed monitoring solution that
reduces communications between vehicles and company
servers to the essential. We validate the framework on
a large database of real mobility data coming from GPS
devices on private cars. Finally, we analyze the
privacy risks that the proposed approach might involve
for the users, providing and evaluating a
countermeasure based on data perturbation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2016:SCW,
author = "Jialei Wang and Peilin Zhao and Steven C. H. Hoi",
title = "Soft Confidence-Weighted Learning",
journal = j-TIST,
volume = "8",
number = "1",
pages = "15:1--15:??",
month = oct,
year = "2016",
CODEN = "????",
DOI = "https://doi.org/10.1145/2932193",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Online learning plays an important role in many big
data mining problems because of its high efficiency and
scalability. In the literature, many online learning
algorithms using gradient information have been applied
to solve online classification problems. Recently, more
effective second-order algorithms have been proposed,
where the correlation between the features is utilized
to improve the learning efficiency. Among them,
Confidence-Weighted (CW) learning algorithms are very
effective, which assume that the classification model
is drawn from a Gaussian distribution, which enables
the model to be effectively updated with the
second-order information of the data stream. Despite
being studied actively, these CW algorithms cannot
handle nonseparable datasets and noisy datasets very
well. In this article, we propose a family of Soft
Confidence-Weighted (SCW) learning algorithms for both
binary classification and multiclass classification
tasks, which is the first family of online
classification algorithms that enjoys four salient
properties simultaneously: (1) large margin training,
(2) confidence weighting, (3) capability to handle
nonseparable data, and (4) adaptive margin. Our
experimental results show that the proposed SCW
algorithms significantly outperform the original CW
algorithm. When comparing with a variety of
state-of-the-art algorithms (including AROW, NAROW, and
NHERD), we found that SCW in general achieves better or
at least comparable predictive performance, but enjoys
considerably better efficiency advantage (i.e., using a
smaller number of updates and lower time cost). To
facilitate future research, we release all the datasets
and source code to the public at
http://libol.stevenhoi.org/.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Papalexakis:2017:TDM,
author = "Evangelos E. Papalexakis and Christos Faloutsos and
Nicholas D. Sidiropoulos",
title = "Tensors for Data Mining and Data Fusion: Models,
Applications, and Scalable Algorithms",
journal = j-TIST,
volume = "8",
number = "2",
pages = "16:1--16:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2915921",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Tensors and tensor decompositions are very powerful
and versatile tools that can model a wide variety of
heterogeneous, multiaspect data. As a result, tensor
decompositions, which extract useful latent information
out of multiaspect data tensors, have witnessed
increasing popularity and adoption by the data mining
community. In this survey, we present some of the most
widely used tensor decompositions, providing the key
insights behind them, and summarizing them from a
practitioner's point of view. We then provide an
overview of a very broad spectrum of applications where
tensors have been instrumental in achieving
state-of-the-art performance, ranging from social
network analysis to brain data analysis, and from web
mining to healthcare. Subsequently, we present recent
algorithmic advances in scaling tensor decompositions
up to today's big data, outlining the existing systems
and summarizing the key ideas behind them. Finally, we
conclude with a list of challenges and open problems
that outline exciting future research directions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Schedl:2017:IIM,
author = "Markus Schedl and Yi-Hsuan Yang and Perfecto
Herrera-Boyer",
title = "Introduction to Intelligent Music Systems and
Applications",
journal = j-TIST,
volume = "8",
number = "2",
pages = "17:1--17:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2991468",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Intelligent technologies have become an essential part
of music systems and applications. This is evidenced by
today's omnipresence of digital online music stores and
streaming services, which rely on music recommenders,
automatic playlist generators, and music browsing
interfaces. A large amount of research leading to
intelligent music applications deals with the
extraction of musical and acoustic information directly
from the audio signal using signal processing
techniques. Other strategies exploit contextual aspects
of music, not present in the signal, for example,
community meta-data and trails of user interaction, as
found, for instance, on social media platforms. In this
editorial, we discuss the notion of ``intelligent music
system'' and give an overview of the papers selected to
this special issue.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Pachet:2017:JOA,
author = "Fran{\c{c}}ois Pachet",
title = "A Joyful Ode to Automatic Orchestration",
journal = j-TIST,
volume = "8",
number = "2",
pages = "18:1--18:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2897738",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Most works in automatic music generation have
addressed so far specific tasks. Such a reductionist
approach has been extremely successful and some of
these tasks have been solved once and for all. However,
few works have addressed the issue of generating
automatically fully fledged music material, of
human-level quality. In this article, we report on a
specific experiment in holistic music generation: the
reorchestration of Beethoven's Ode to Joy, the European
anthem, in seven styles. These reorchestrations were
produced with algorithms developed in the Flow Machines
project and within a short time frame. We stress the
benefits of having had such a challenging and unifying
goal, and the interesting problems and challenges it
raised along the way.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Widmer:2017:GCE,
author = "Gerhard Widmer",
title = "Getting Closer to the Essence of Music: The Con
Espressione Manifesto",
journal = j-TIST,
volume = "8",
number = "2",
pages = "19:1--19:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2899004",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This text offers a personal and very subjective view
on the current situation of Music Information Research
(MIR). Motivated by the desire to build systems with a
somewhat deeper understanding of music than the ones we
currently have, I try to sketch a number of challenges
for the next decade of MIR research, grouped around six
simple truths about music that are probably generally
agreed on but often ignored in everyday research.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Schindler:2017:HMR,
author = "Alexander Schindler and Andreas Rauber",
title = "Harnessing Music-Related Visual Stereotypes for Music
Information Retrieval",
journal = j-TIST,
volume = "8",
number = "2",
pages = "20:1--20:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2926719",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Over decades, music labels have shaped easily
identifiable genres to improve recognition value and
subsequently market sales of new music acts. Referring
to print magazines and later to music television as
important distribution channels, the visual
representation thus played and still plays a
significant role in music marketing. Visual stereotypes
developed over decades that enable us to quickly
identify referenced music only by sight without
listening. Despite the richness of music-related visual
information provided by music videos and album covers
as well as T-shirts, advertisements, and magazines,
research towards harnessing this information to advance
existing or approach new problems of music retrieval or
recommendation is scarce or missing. In this article,
we present our research on visual music computing that
aims to extract stereotypical music-related visual
information from music videos. To provide comprehensive
and reproducible results, we present the Music Video
Dataset, a thoroughly assembled suite of datasets with
dedicated evaluation tasks that are aligned to current
Music Information Retrieval tasks. Based on this
dataset, we provide evaluations of conventional
low-level image processing and affect-related features
to provide an overview of the expressiveness of
fundamental visual properties such as color,
illumination, and contrasts. Further, we introduce a
high-level approach based on visual concept detection
to facilitate visual stereotypes. This approach
decomposes the semantic content of music video frames
into concrete concepts such as vehicles, tools, and so
on, defined in a wide visual vocabulary. Concepts are
detected using convolutional neural networks and their
frequency distributions as semantic descriptions for a
music video. Evaluations showed that these descriptions
show good performance in predicting the music genre of
a video and even outperform audio-content descriptors
on cross-genre thematic tags. Further, highly
significant performance improvements were observed by
augmenting audio-based approaches through the
introduced visual approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Oramas:2017:SMR,
author = "Sergio Oramas and Vito Claudio Ostuni and Tommaso {Di
Noia} and Xavier Serra and Eugenio {Di Sciascio}",
title = "Sound and Music Recommendation with Knowledge Graphs",
journal = j-TIST,
volume = "8",
number = "2",
pages = "21:1--21:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2926718",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The Web has moved, slowly but steadily, from a
collection of documents towards a collection of
structured data. Knowledge graphs have then emerged as
a way of representing the knowledge encoded in such
data as well as a tool to reason on them in order to
extract new and implicit information. Knowledge graphs
are currently used, for example, to explain search
results, to explore knowledge spaces, to semantically
enrich textual documents, or to feed
knowledge-intensive applications such as recommender
systems. In this work, we describe how to create and
exploit a knowledge graph to supply a hybrid
recommendation engine with information that builds on
top of a collections of documents describing musical
and sound items. Tags and textual descriptions are
exploited to extract and link entities to external
graphs such as WordNet and DBpedia, which are in turn
used to semantically enrich the initial data. By means
of the knowledge graph we build, recommendations are
computed using a feature combination hybrid approach.
Two explicit graph feature mappings are formulated to
obtain meaningful item feature representations able to
catch the knowledge embedded in the graph. Those
content features are further combined with additional
collaborative information deriving from implicit user
feedback. An extensive evaluation on historical data is
performed over two different datasets: a dataset of
sounds composed of tags, textual descriptions, and
user's download information gathered from Freesound.org
and a dataset of songs that mixes song textual
descriptions with tags and user's listening habits
extracted from Songfacts.com and Last.fm, respectively.
Results show significant improvements with respect to
state-of-the-art collaborative algorithms in both
datasets. In addition, we show how the semantic
expansion of the initial descriptions helps in
achieving much better recommendation quality in terms
of aggregated diversity and novelty.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rodriguez-Serrano:2017:TDA,
author = "Francisco Jose Rodriguez-Serrano and Julio Jose
Carabias-Orti and Pedro Vera-Candeas and Damian
Martinez-Munoz",
title = "Tempo Driven Audio-to-Score Alignment Using Spectral
Decomposition and Online Dynamic Time Warping",
journal = j-TIST,
volume = "8",
number = "2",
pages = "22:1--22:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2926717",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we present an online score following
framework designed to deal with automatic
accompaniment. The proposed framework is based on
spectral factorization and online Dynamic Time Warping
(DTW) and has two separated stages: preprocessing and
alignment. In the first one, we convert the score into
a reference audio signal using a MIDI synthesizer
software and we analyze the provided information in
order to obtain the spectral patterns (i.e., basis
functions) associated to each score unit. In this work,
a score unit represents the occurrence of concurrent or
isolated notes in the score. These spectral patterns
are learned from the synthetic MIDI signal using a
method based on Non-negative Matrix Factorization (NMF)
with Beta-divergence, where the gains are initialized
as the ground-truth transcription inferred from the
MIDI. On the second stage, a non-iterative signal
decomposition method with fixed spectral patterns per
score unit is used over the magnitude spectrogram of
the input signal resulting in a distortion matrix that
can be interpreted as the cost of the matching for each
score unit at each frame. Finally, the relation between
the performance and the musical score times is obtained
using a strategy based on online DTW, where the optimal
path is biased by the speed of interpretation. Our
system has been evaluated and compared to other
systems, yielding reliable results and performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tian:2017:TMS,
author = "Mi Tian and Mark B. Sandler",
title = "Towards Music Structural Segmentation across Genres:
Features, Structural Hypotheses, and Annotation
Principles",
journal = j-TIST,
volume = "8",
number = "2",
pages = "23:1--23:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2950066",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article faces the problem of how different audio
features and segmentation methods work with different
music genres. A new annotated corpus of Chinese
traditional Jingju music is presented. We incorporate
this dataset with two existing music datasets from the
literature in an integrated retrieval system to
evaluate existing features, structural hypotheses, and
segmentation algorithms outside a Western bias. A
harmonic-percussive source separation technique is
introduced to the feature extraction process and brings
significant improvement to the segmentation. Results
show that different features capture the structural
patterns of different music genres in different ways.
Novelty- or homogeneity-based segmentation algorithms
and timbre features can surpass the investigated
alternatives for the structure analysis of Jingju due
to their lack of harmonic repetition patterns. Findings
indicate that the design of audio features and
segmentation algorithms as well as the consideration of
contextual information related to the music corpora
should be accounted dependently in an effective
segmentation system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sandouk:2017:LCM,
author = "Ubai Sandouk and Ke Chen",
title = "Learning Contextualized Music Semantics from Tags Via
a {Siamese} Neural Network",
journal = j-TIST,
volume = "8",
number = "2",
pages = "24:1--24:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2953886",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Music information retrieval faces a challenge in
modeling contextualized musical concepts formulated by
a set of co-occurring tags. In this article, we
investigate the suitability of our recently proposed
approach based on a Siamese neural network in fighting
off this challenge. By means of tag features and
probabilistic topic models, the network captures
contextualized semantics from tags via unsupervised
learning. This leads to a distributed semantics space
and a potential solution to the out of vocabulary
problem, which has yet to be sufficiently addressed. We
explore the nature of the resultant music-based
semantics and address computational needs. We conduct
experiments on three public music tag
collections-namely, CAL500, MagTag5K and Million Song
Dataset-and compare our approach to a number of
state-of-the-art semantics learning approaches.
Comparative results suggest that this approach
outperforms previous approaches in terms of semantic
priming and music tag completion.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Marrella:2017:IPA,
author = "Andrea Marrella and Massimo Mecella and Sebastian
Sardina",
title = "Intelligent Process Adaptation in the {SmartPM}
System",
journal = j-TIST,
volume = "8",
number = "2",
pages = "25:1--25:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2948071",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The increasing application of process-oriented
approaches in new challenging dynamic domains beyond
business computing (e.g., healthcare, emergency
management, factories of the future, home automation,
etc.) has led to reconsider the level of flexibility
and support required to manage complex
knowledge-intensive processes in such domains. A
knowledge-intensive process is influenced by user
decision making and coupled with contextual data and
knowledge production, and involves performing complex
tasks in the ``physical'' real world to achieve a
common goal. The physical world, however, is not
entirely predictable, and knowledge-intensive processes
must be robust to unexpected conditions and adaptable
to unanticipated exceptions, recognizing that in
real-world environments it is not adequate to assume
that all possible recovery activities can be predefined
for dealing with the exceptions that can ensue. To
tackle this issue, in this paper we present SmartPM, a
model and a prototype Process Management System
featuring a set of techniques providing support for
automated adaptation of knowledge-intensive processes
at runtime. Such techniques are able to automatically
adapt process instances when unanticipated exceptions
occur, without explicitly defining policies to recover
from exceptions and without the intervention of domain
experts at runtime, aiming at reducing error-prone and
costly manual ad-hoc changes, and thus at relieving
users from complex adaptations tasks. To accomplish
this, we make use of well-established techniques and
frameworks from Artificial Intelligence, such as
situation calculus, IndiGolog and classical planning.
The approach, which is backed by a formal model, has
been implemented and validated with a case study based
on real knowledge-intensive processes coming from an
emergency management domain.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ramamohanarao:2017:SSM,
author = "Kotagiri Ramamohanarao and Hairuo Xie and Lars Kulik
and Shanika Karunasekera and Egemen Tanin and Rui Zhang
and Eman Bin Khunayn",
title = "{SMARTS}: Scalable Microscopic Adaptive Road Traffic
Simulator",
journal = j-TIST,
volume = "8",
number = "2",
pages = "26:1--26:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2898363",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Microscopic traffic simulators are important tools for
studying transportation systems as they describe the
evolution of traffic to the highest level of detail. A
major challenge to microscopic simulators is the slow
simulation speed due to the complexity of traffic
models. We have developed the Scalable Microscopic
Adaptive Road Traffic Simulator (SMARTS), a distributed
microscopic traffic simulator that can utilize multiple
independent processes in parallel. SMARTS can perform
fast large-scale simulations. For example, when
simulating 1 million vehicles in an area the size of
Melbourne, the system runs 1.14 times faster than real
time with 30 computing nodes and 0.2s simulation
timestep. SMARTS supports various driver models and
traffic rules, such as the car-following model and
lane-changing model, which can be driver dependent. It
can simulate multiple vehicle types, including bus and
tram. The simulator is equipped with a wide range of
features that help to customize, calibrate, and monitor
simulations. Simulations are accurate and confirm with
real traffic behaviours. For example, it achieves
79.1\% accuracy in predicting traffic on a 10km freeway
90 minutes into the future. The simulator can be used
for predictive traffic advisories as well as traffic
management decisions as simulations complete well ahead
of real time. SMARTS can be easily deployed to
different operating systems as it is developed with the
standard Java libraries.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kim:2017:DFN,
author = "Jungeun Kim and Jae-Gil Lee and Sungsu Lim",
title = "Differential Flattening: a Novel Framework for
Community Detection in Multi-Layer Graphs",
journal = j-TIST,
volume = "8",
number = "2",
pages = "27:1--27:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2898362",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "A multi-layer graph consists of multiple layers of
weighted graphs, where the multiple layers represent
the different aspects of relationships. Considering
multiple aspects (i.e., layers) together is essential
to achieve a comprehensive and consolidated view. In
this article, we propose a novel framework of
differential flattening, which facilitates the analysis
of multi-layer graphs, and apply this framework to
community detection. Differential flattening merges
multiple graphs into a single graph such that the graph
structure with the maximum clustering coefficient is
obtained from the single graph. It has two distinct
features compared with existing approaches. First,
dealing with multiple layers is done independently of a
specific community detection algorithm, whereas
previous approaches rely on a specific algorithm. Thus,
any algorithm for a single graph becomes applicable to
multi-layer graphs. Second, the contribution of each
layer to the single graph is determined automatically
for the maximum clustering coefficient. Since
differential flattening is formulated by an
optimization problem, the optimal solution is easily
obtained by well-known algorithms such as interior
point methods. Extensive experiments were conducted
using the Lancichinetti-Fortunato-Radicchi (LFR)
benchmark networks as well as the DBLP, 20 Newsgroups,
and MIT Reality Mining networks. The results show that
our approach of differential flattening leads to
discovery of higher-quality communities than baseline
approaches and the state-of-the-art algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xie:2017:JSS,
author = "Liping Xie and Dacheng Tao and Haikun Wei",
title = "Joint Structured Sparsity Regularized Multiview
Dimension Reduction for Video-Based Facial Expression
Recognition",
journal = j-TIST,
volume = "8",
number = "2",
pages = "28:1--28:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2956556",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Video-based facial expression recognition (FER) has
recently received increased attention as a result of
its widespread application. Using only one type of
feature to describe facial expression in video
sequences is often inadequate, because the information
available is very complex. With the emergence of
different features to represent different properties of
facial expressions in videos, an appropriate
combination of these features becomes an important, yet
challenging, problem. Considering that the
dimensionality of these features is usually high, we
thus introduce multiview dimension reduction (MVDR)
into video-based FER. In MVDR, it is critical to
explore the relationships between and within different
feature views. To achieve this goal, we propose a novel
framework of MVDR by enforcing joint structured
sparsity at both inter- and intraview levels. In this
way, correlations on and between the feature spaces of
different views tend to be well-exploited. In addition,
a transformation matrix is learned for each view to
discover the patterns contained in the original
features, so that the different views are comparable in
finding a common representation. The model can be not
only performed in an unsupervised manner, but also
easily extended to a semisupervised setting by
incorporating some domain knowledge. An alternating
algorithm is developed for problem optimization, and
each subproblem can be efficiently solved. Experiments
on two challenging video-based FER datasets demonstrate
the effectiveness of the proposed framework.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Song:2017:PSH,
author = "Xuan Song and Quanshi Zhang and Yoshihide Sekimoto and
Ryosuke Shibasaki and Nicholas Jing Yuan and Xing Xie",
title = "Prediction and Simulation of Human Mobility Following
Natural Disasters",
journal = j-TIST,
volume = "8",
number = "2",
pages = "29:1--29:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2970819",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In recent decades, the frequency and intensity of
natural disasters has increased significantly, and this
trend is expected to continue. Therefore, understanding
and predicting human behavior and mobility during a
disaster will play a vital role in planning effective
humanitarian relief, disaster management, and long-term
societal reconstruction. However, such research is very
difficult to perform owing to the uniqueness of various
disasters and the unavailability of reliable and
large-scale human mobility data. In this study, we
collect big and heterogeneous data (e.g., GPS records
of 1.6 million users$^1$ over 3 years, data on
earthquakes that have occurred in Japan over 4 years,
news report data, and transportation network data) to
study human mobility following natural disasters. An
empirical analysis is conducted to explore the basic
laws governing human mobility following disasters, and
an effective human mobility model is developed to
predict and simulate population movements. The
experimental results demonstrate the efficiency of our
model, and they suggest that human mobility following
disasters can be significantly more predictable and be
more easily simulated than previously thought.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Peng:2017:SLM,
author = "Chong Peng and Jie Cheng and Qiang Cheng",
title = "A Supervised Learning Model for High-Dimensional and
Large-Scale Data",
journal = j-TIST,
volume = "8",
number = "2",
pages = "30:1--30:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2972957",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We introduce a new supervised learning model using a
discriminative regression approach. This new model
estimates a regression vector to represent the
similarity between a test example and training examples
while seamlessly integrating the class information in
the similarity estimation. This distinguishes our model
from usual regression models and locally linear
embedding approaches, rendering our method suitable for
supervised learning problems in high-dimensional
settings. Our model is easily extensible to account for
nonlinear relationship and applicable to general data,
including both high- and low-dimensional data. The
objective function of the model is convex, for which
two optimization algorithms are provided. These two
optimization approaches induce two scalable solvers
that are of mathematically provable, linear time
complexity. Experimental results verify the
effectiveness of the proposed method on various kinds
of data. For example, our method shows comparable
performance on low-dimensional data and superior
performance on high-dimensional data to several widely
used classifiers; also, the linear solvers obtain
promising performance on large-scale classification.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2017:IVL,
author = "Yan Liu and Yang Liu and Shenghua Zhong and Songtao
Wu",
title = "Implicit Visual Learning: Image Recognition via
Dissipative Learning Model",
journal = j-TIST,
volume = "8",
number = "2",
pages = "31:1--31:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2974024",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "According to consciousness involvement, human's
learning can be roughly classified into explicit
learning and implicit learning. Contrasting strongly to
explicit learning with clear targets and rules, such as
our school study of mathematics, learning is implicit
when we acquire new information without intending to do
so. Research from psychology indicates that implicit
learning is ubiquitous in our daily life. Moreover,
implicit learning plays an important role in human
visual perception. But in the past 60 years, most of
the well-known machine-learning models aimed to
simulate explicit learning while the work of modeling
implicit learning was relatively limited, especially
for computer vision applications. This article proposes
a novel unsupervised computational model for implicit
visual learning by exploring dissipative system, which
provides a unifying macroscopic theory to connect
biology with physics. We test the proposed Dissipative
Implicit Learning Model (DILM) on various datasets. The
experiments show that DILM not only provides a good
match to human behavior but also improves the explicit
machine-learning performance obviously on image
classification tasks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Barbieri:2017:EMI,
author = "Nicola Barbieri and Francesco Bonchi and Giuseppe
Manco",
title = "Efficient Methods for Influence-Based
Network-Oblivious Community Detection",
journal = j-TIST,
volume = "8",
number = "2",
pages = "32:1--32:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2979682",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We study the problem of detecting social communities
when the social graph is not available but instead we
have access to a log of user activity, that is, a
dataset of tuples ( u, i, t ) recording the fact that
user u ``adopted'' item i at time t. We propose a
stochastic framework that assumes that the adoption of
items is governed by an underlying diffusion process
over the unobserved social network and that such a
diffusion model is based on community-level influence.
That is, we aim at modeling communities through the
lenses of social contagion. By fitting the model
parameters to the user activity log, we learn the
community membership and the level of influence of each
user in each community. The general framework is
instantiated with two different diffusion models, one
with discrete time and one with continuous time, and we
show that the computational complexity of both
approaches is linear in the number of users and in the
size of the propagation log. Experiments on synthetic
data with planted community structure show that our
methods outperform non-trivial baselines. The
effectiveness of the proposed techniques is further
validated on real-word data, on which our methods are
able to detect high-quality communities.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2017:CDT,
author = "Zhonggang Wu and Zhao Lu and Shan-Yuan Ho",
title = "Community Detection with Topological Structure and
Attributes in Information Networks",
journal = j-TIST,
volume = "8",
number = "2",
pages = "33:1--33:??",
month = jan,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2979681",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Apr 3 11:19:57 MDT 2017",
bibsource = "http://portal.acm.org/;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Information networks contain objects connected by
multiple links and described by rich attributes.
Detecting community for these networks is a challenging
research problem, because there is a scarcity of
effective approaches that balance the features of the
network structure and the characteristics of the nodes.
Some methods detect communities by considering
topological structures while ignoring the contributions
of attributes. Other methods have considered both
topological structure and attributes but pay a high
price in time complexity. We establish a new community
detection algorithm which explores both topological
Structure and Attributes using Global structure and
Local neighborhood features (SAGL) which also has low
computational complexity. The first step of SAGL
evaluates the global importance of every node and
calculates the similarity of each node pair by
combining edge strength and node attribute similarity.
The second step of SAGL uses a clustering algorithm
that identifies communities by measuring the similarity
of two nodes, calculated by the distance of their
neighbors. Experimental results on three real-world
datasets show the effectiveness of SAGL, particularly
its fast convergence compared to current
state-of-the-art attributed graph clustering methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ji:2017:MSM,
author = "Rongrong Ji and Wei Liu and Xing Xie and Yiqiang Chen
and Jiebo Luo",
title = "Mobile Social Multimedia Analytics in the Big Data
Era: an Introduction to the Special Issue",
journal = j-TIST,
volume = "8",
number = "3",
pages = "34:1--34:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3040934",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gao:2017:ECM,
author = "Yue Gao and Hanwang Zhang and Xibin Zhao and Shuicheng
Yan",
title = "Event Classification in Microblogs via Social
Tracking",
journal = j-TIST,
volume = "8",
number = "3",
pages = "35:1--35:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2967502",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Social media websites have become important
information sharing platforms. The rapid development of
social media platforms has led to increasingly
large-scale social media data, which has shown
remarkable societal and marketing values. There are
needs to extract important events in live social media
streams. However, microblogs event classification is
challenging due to two facts, i.e., the
short/conversational nature and the incompatible
meanings between the text and the corresponding image
in social posts, and the rapidly evolving contents. In
this article, we propose to conduct event
classification via deep learning and social tracking.
First, we introduce a Multi-modal Multi-instance Deep
Network (M$^2$ DN) for microblogs classification, which
is able to handle the weakly labeled microblogs data
oriented from the incompatible meanings inside
microblogs. Besides predicting each microblogs as
predefined events, we propose to employ social tracking
to extract social-related auxiliary information to
enrich the testing samples. We extract a set of
candidate-relevant microblogs in a short time window by
using social connections, such as related users and
geographical locations. All these selected microblogs
and the testing data are formulated in a Markov Random
Field model. The inference on the Markov Random Field
is conducted to update the classification results of
the testing microblogs. This method is evaluated on the
Brand-Social-Net dataset for classification of 20
events. Experimental results and comparison with the
state of the arts show that the proposed method can
achieve better performance for the event classification
task.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Nie:2017:LUA,
author = "Liqiang Nie and Luming Zhang and Meng Wang and Richang
Hong and Aleksandr Farseev and Tat-Seng Chua",
title = "Learning User Attributes via Mobile Social Multimedia
Analytics",
journal = j-TIST,
volume = "8",
number = "3",
pages = "36:1--36:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2963105",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Learning user attributes from mobile social media is a
fundamental basis for many applications, such as
personalized and targeting services. A large and
growing body of literature has investigated the user
attributes learning problem. However, far too little
attention has been paid to jointly consider the dual
heterogeneities of user attributes learning by
harvesting multiple social media sources. In
particular, user attributes are complementarily and
comprehensively characterized by multiple social media
sources, including footprints from Foursqare, daily
updates from Twitter, professional careers from
Linkedin, and photo posts from Instagram. On the other
hand, attributes are inter-correlated in a complex way
rather than independent to each other, and highly
related attributes may share similar feature sets.
Towards this end, we proposed a unified model to
jointly regularize the source consistency and
graph-constrained relatedness among tasks. As a
byproduct, it is able to learn the attribute-specific
and attribute-sharing features via graph-guided fused
lasso penalty. Besides, we have theoretically
demonstrated its optimization. Extensive evaluations on
a real-world dataset thoroughly demonstrated the
effectiveness of our proposed model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tao:2017:LSC,
author = "Dapeng Tao and Dacheng Tao and Xuelong Li and Xinbo
Gao",
title = "Large Sparse Cone Non-negative Matrix Factorization
for Image Annotation",
journal = j-TIST,
volume = "8",
number = "3",
pages = "37:1--37:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2987379",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Image annotation assigns relevant tags to query images
based on their semantic contents. Since Non-negative
Matrix Factorization (NMF) has the strong ability to
learn parts-based representations, recently, a number
of algorithms based on NMF have been proposed for image
annotation and have achieved good performance. However,
most of the efforts have focused on the representations
of images and annotations. The properties of the
semantic parts have not been well studied. In this
article, we revisit the sparseness-constrained NMF
(sNMF) proposed by Hoyer [2004]. By endowing the
sparseness constraint with a geometric interpretation
and sNMF with theoretical analyses of the
generalization ability, we show that NMF with such a
sparseness constraint has three advantages for image
annotation tasks: (i) The sparseness constraint is more
l$_0$ -norm oriented than the l$_1$ -norm-based
sparseness, which significantly enhances the ability of
NMF to robustly learn semantic parts. (ii) The
sparseness constraint has a large cone interpretation
and thus allows the reconstruction error of NMF to be
smaller, which means that the learned semantic parts
are more powerful to represent images for tagging.
(iii) The learned semantic parts are less correlated,
which increases the discriminative ability for
annotating images. Moreover, we present a new efficient
large sparse cone NMF (LsCNMF) algorithm to optimize
the sNMF problem by employing the Nesterov's optimal
gradient method. We conducted experiments on the PASCAL
VOC07 dataset and demonstrated the effectiveness of
LsCNMF for image annotation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2017:LBP,
author = "Jiaming Zhang and Shuhui Wang and Qingming Huang",
title = "Location-Based Parallel Tag Completion for Geo-Tagged
Social Image Retrieval",
journal = j-TIST,
volume = "8",
number = "3",
pages = "38:1--38:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3001593",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Having benefited from tremendous growth of
user-generated content, social annotated tags get
higher importance in the organization and retrieval of
large-scale image databases on Online Sharing Websites
(OSW). To obtain high-quality tags from existing
community contributed tags with missing information and
noise, tag-based annotation or recommendation methods
have been proposed for performance promotion of tag
prediction. While images from OSW contain rich social
attributes, they have not taken full advantage of rich
social attributes and auxiliary information associated
with social images to construct global information
completion models. In this article, beyond the
image-tag relation, we take full advantage of the
ubiquitous GPS locations and image-user relationship to
enhance the accuracy of tag prediction and improve the
computational efficiency. For GPS locations, we define
the popular geo-locations where people tend to take
more images as Points of Interests (POI), which are
discovered by mean shift approach. For image-user
relationship, we integrate a localized prior
constraint, expecting the completed tag sub-matrix in
each POI to maintain consistency with users' tagging
behaviors. Based on these two key issues, we propose a
unified tag matrix completion framework, which learns
the image-tag relation within each POI. To solve the
optimization problem, an efficient proximal
sub-gradient descent algorithm is designed. The model
optimization can be easily parallelized and distributed
to learn the tag sub-matrix for each POI. Extensive
experimental results reveal that the learned tag
sub-matrix of each POI reflects the major trend of
users' tagging results with respect to different POIs
and users, and the parallel learning process provides
strong support for processing large-scale online image
databases. To fit the response time requirement and
storage limitations of Tag-based Image Retrieval (TBIR)
on mobile devices, we introduce Asymmetric Locality
Sensitive Hashing (ALSH) to reduce the time cost and
meanwhile improve the efficiency of retrieval.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sang:2017:ESM,
author = "Jitao Sang and Quan Fang and Changsheng Xu",
title = "Exploiting Social-Mobile Information for Location
Visualization",
journal = j-TIST,
volume = "8",
number = "3",
pages = "39:1--39:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3001594",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With a smart phone at hand, it becomes easy now to
snap pictures and publish them online with few lines of
texts. The GPS coordinates and User-Generated Content
(UGC) data embedded in the shared photos provide
opportunities to exploit important knowledge to tackle
interesting tasks like geographically organizing photos
and location visualization. In this work, we propose to
organize photos both geographically and semantically,
and investigate the problem of location visualization
from multiple semantic themes. The novel visualization
scheme provides a rich display landscape for
geographical exploration from versatile views. A
two-level solution is presented, where we first
identify the highly photographed places of interest
(POI) and discover their focused themes, and then
aggregate the lower-level POI themes to generate the
higher-level city themes for location visualization. We
have conducted experiments on crawled Flickr and
Instagram data and exhibited the visualization for the
cities of Singapore and Sydney. The experimental
results have validated the proposed method and
demonstrated the potentials of location visualization
from multiple themes.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hu:2017:COM,
author = "Han Hu and Yonggang Wen and Tat-Seng Chua and Xuelong
Li",
title = "Cost-Optimized Microblog Distribution over
Geo-Distributed Data Centers: Insights from Cross-Media
Analysis",
journal = j-TIST,
volume = "8",
number = "3",
pages = "40:1--40:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3014431",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The unprecedent growth of microblog services poses
significant challenges on network traffic and service
latency to the underlay infrastructure (i.e.,
geo-distributed data centers). Furthermore, the dynamic
evolution in microblog status generates a huge workload
on data consistence maintenance. In this article,
motivated by insights of cross-media analysis-based
propagation patterns, we propose a novel cache strategy
for microblog service systems to reduce the inter-data
center traffic and consistence maintenance cost, while
achieving low service latency. Specifically, we first
present a microblog classification method, which
utilizes the external knowledge from correlated
domains, to categorize microblogs. Then we conduct a
large-scale measurement on a representative online
social network system to study the category-based
propagation diversity on region and time scales. These
insights illustrate social common habits on creating
and consuming microblogs and further motivate our
architecture design. Finally, we formulate the content
cache problem as a constrained optimization problem. By
jointly using the Lyapunov optimization framework and
simplex gradient method, we find the optimal online
control strategy. Extensive trace-driven experiments
further demonstrate that our algorithm reduces the
system cost by 24.5\% against traditional approaches
with the same service latency.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xu:2017:DOD,
author = "Jun Xu and Long Xia and Yanyan Lan and Jiafeng Guo and
Xueqi Cheng",
title = "Directly Optimize Diversity Evaluation Measures: a New
Approach to Search Result Diversification",
journal = j-TIST,
volume = "8",
number = "3",
pages = "41:1--41:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2983921",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The queries issued to search engines are often
ambiguous or multifaceted, which requires search
engines to return diverse results that can fulfill as
many different information needs as possible; this is
called search result diversification. Recently, the
relational learning to rank model, which designs a
learnable ranking function following the criterion of
maximal marginal relevance, has shown effectiveness in
search result diversification [Zhu et al. 2014]. The
goodness of a diverse ranking model is usually
evaluated with diversity evaluation measures such as $
\alpha $-NDCG [Clarke et al. 2008], ERR-IA [Chapelle et
al. 2009], and D\#-NDCG [Sakai and Song 2011]. Ideally
the learning algorithm would train a ranking model that
could directly optimize the diversity evaluation
measures with respect to the training data. Existing
relational learning to rank algorithms, however, only
train the ranking models by optimizing loss functions
that loosely relate to the evaluation measures. To deal
with the problem, we propose a general framework for
learning relational ranking models via directly
optimizing any diversity evaluation measure. In
learning, the loss function upper-bounding the basic
loss function defined on a diverse ranking measure is
minimized. We can derive new diverse ranking algorithms
under the framework, and several diverse ranking
algorithms are created based on different upper bounds
over the basic loss function. We conducted comparisons
between the proposed algorithms with conventional
diverse ranking methods using the TREC benchmark
datasets. Experimental results show that the algorithms
derived under the diverse learning to rank framework
always significantly outperform the state-of-the-art
baselines.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Peng:2017:NMF,
author = "Chong Peng and Zhao Kang and Yunhong Hu and Jie Cheng
and Qiang Cheng",
title = "Nonnegative Matrix Factorization with Integrated Graph
and Feature Learning",
journal = j-TIST,
volume = "8",
number = "3",
pages = "42:1--42:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2987378",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Matrix factorization is a useful technique for data
representation in many data mining and machine learning
tasks. Particularly, for data sets with all nonnegative
entries, matrix factorization often requires that
factor matrices be nonnegative, leading to nonnegative
matrix factorization (NMF). One important application
of NMF is for clustering with reduced dimensions of the
data represented in the new feature space. In this
paper, we propose a new graph regularized NMF method
capable of feature learning and apply it to clustering.
Unlike existing NMF methods that treat all features in
the original feature space equally, our method
distinguishes features by incorporating a feature-wise
sparse approximation error matrix in the formulation.
It enables important features to be more closely
approximated by the factor matrices. Meanwhile, the
graph of the data is constructed using cleaner features
in the feature learning process, which integrates
feature learning and manifold learning procedures into
a unified NMF model. This distinctly differs from
applying the existing graph-based NMF models after
feature selection in that, when these two procedures
are independently used, they often fail to align
themselves toward obtaining a compact and most
expressive data representation. Comprehensive
experimental results demonstrate the effectiveness of
the proposed method, which outperforms state-of-the-art
algorithms when applied to clustering.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2017:LKK,
author = "Shichao Zhang and Xuelong Li and Ming Zong and
Xiaofeng Zhu and Debo Cheng",
title = "Learning $k$ for {$k$NN} Classification",
journal = j-TIST,
volume = "8",
number = "3",
pages = "43:1--43:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2990508",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The K Nearest Neighbor (kNN) method has widely been
used in the applications of data mining and machine
learning due to its simple implementation and
distinguished performance. However, setting all test
data with the same k value in the previous kNN methods
has been proven to make these methods impractical in
real applications. This article proposes to learn a
correlation matrix to reconstruct test data points by
training data to assign different k values to different
test data points, referred to as the Correlation Matrix
kNN (CM-kNN for short) classification. Specifically,
the least-squares loss function is employed to minimize
the reconstruction error to reconstruct each test data
point by all training data points. Then, a graph
Laplacian regularizer is advocated to preserve the
local structure of the data in the reconstruction
process. Moreover, an l$_1$ -norm regularizer and an
l$_{2, 1}$ -norm regularizer are applied to learn
different k values for different test data and to
result in low sparsity to remove the redundant/noisy
feature from the reconstruction process, respectively.
Besides for classification tasks, the kNN methods
(including our proposed CM-kNN method) are further
utilized to regression and missing data imputation. We
conducted sets of experiments for illustrating the
efficiency, and experimental results showed that the
proposed method was more accurate and efficient than
existing kNN methods in data-mining applications, such
as classification, regression, and missing data
imputation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hoang:2017:MTB,
author = "Tuan-Anh Hoang and Ee-Peng Lim",
title = "Modeling Topics and Behavior of Microbloggers: an
Integrated Approach",
journal = j-TIST,
volume = "8",
number = "3",
pages = "44:1--44:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2990507",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Microblogging encompasses both user-generated content
and behavior. When modeling microblogging data, one has
to consider personal and background topics, as well as
how these topics generate the observed content and
behavior. In this article, we propose the Generalized
Behavior-Topic (GBT) model for simultaneously modeling
background topics and users' topical interest in
microblogging data. GBT considers multiple topical
communities (or realms) with different background
topical interests while learning the personal topics of
each user and the user's dependence on realms to
generate both content and behavior. This differentiates
GBT from other previous works that consider either one
realm only or content data only. By associating user
behavior with the latent background and personal
topics, GBT helps to model user behavior by the two
types of topics. GBT also distinguishes itself from
other earlier works by modeling multiple types of
behavior together. Our experiments on two Twitter
datasets show that GBT can effectively mine the
representative topics for each realm. We also
demonstrate that GBT significantly outperforms other
state-of-the-art models in modeling content topics and
user profiling.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mirsky:2017:COP,
author = "Reuth Mirsky and Ya'akov (Kobi) Gal and Stuart M.
Shieber",
title = "{CRADLE}: an Online Plan Recognition Algorithm for
Exploratory Domains",
journal = j-TIST,
volume = "8",
number = "3",
pages = "45:1--45:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2996200",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In exploratory domains, agents' behaviors include
switching between activities, extraneous actions, and
mistakes. Such settings are prevalent in real world
applications such as interaction with open-ended
software, collaborative office assistants, and
integrated development environments. Despite the
prevalence of such settings in the real world, there is
scarce work in formalizing the connection between
high-level goals and low-level behavior and inferring
the former from the latter in these settings. We
present a formal grammar for describing users'
activities in such domains. We describe a new top-down
plan recognition algorithm called CRADLE (Cumulative
Recognition of Activities and Decreasing Load of
Explanations) that uses this grammar to recognize
agents' interactions in exploratory domains. We compare
the performance of CRADLE with state-of-the-art plan
recognition algorithms in several experimental settings
consisting of real and simulated data. Our results show
that CRADLE was able to output plans exponentially more
quickly than the state-of-the-art without compromising
its correctness, as determined by domain experts. Our
approach can form the basis of future systems that use
plan recognition to provide real-time support to users
in a growing class of interesting and challenging
domains.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2017:DSM,
author = "Peng Zhang and Qian Yu and Yuexian Hou and Dawei Song
and Jingfei Li and Bin Hu",
title = "A Distribution Separation Method Using Irrelevance
Feedback Data for Information Retrieval",
journal = j-TIST,
volume = "8",
number = "3",
pages = "46:1--46:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2994608",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In many research and application areas, such as
information retrieval and machine learning, we often
encounter dealing with a probability distribution that
is mixed by one distribution that is relevant to our
task in hand and the other that is irrelevant and that
we want to get rid of. Thus, it is an essential problem
to separate the irrelevant distribution from the
mixture distribution. This article is focused on the
application in Information Retrieval, where relevance
feedback is a widely used technique to build a refined
query model based on a set of feedback documents.
However, in practice, the relevance feedback set, even
provided by users explicitly or implicitly, is often a
mixture of relevant and irrelevant documents.
Consequently, the resultant query model (typically a
term distribution) is often a mixture rather than a
true relevance term distribution, leading to a negative
impact on the retrieval performance. To tackle this
problem, we recently proposed a Distribution Separation
Method (DSM), which aims to approximate the true
relevance distribution by separating a seed irrelevance
distribution from the mixture one. While it achieved a
promising performance in an empirical evaluation with
simulated explicit irrelevance feedback data, it has
not been deployed in the scenario where one should
automatically obtain the irrelevance feedback data. In
this article, we propose a substantial extension of the
basic DSM from two perspectives: developing a further
regularization framework and deploying DSM in the
automatic irrelevance feedback scenario. Specifically,
in order to avoid the output distribution of DSM
drifting away from the true relevance distribution when
the quality of seed irrelevant distribution (as the
input to DSM) is not guaranteed, we propose a DSM
regularization framework to constrain the estimation
for the relevance distribution. This regularization
framework includes three algorithms, each corresponding
to a regularization strategy incorporated in the
objective function of DSM. In addition, we exploit DSM
in automatic (i.e., pseudo) irrelevance feedback, by
automatically detecting the seed irrelevant documents
via three different document reranking methods. We have
carried out extensive experiments based on various TREC
datasets, in order to systematically evaluate the
proposed methods. The experimental results demonstrate
the effectiveness of our proposed approaches in
comparison with various strong baselines.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xiong:2017:DDA,
author = "Haoyi Xiong and Jinghe Zhang and Yu Huang and Kevin
Leach and Laura E. Barnes",
title = "{Daehr}: a Discriminant Analysis Framework for
Electronic Health Record Data and an Application to
Early Detection of Mental Health Disorders",
journal = j-TIST,
volume = "8",
number = "3",
pages = "47:1--47:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3007195",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Electronic health records (EHR) provide a rich source
of temporal data that present a unique opportunity to
characterize disease patterns and risk of imminent
disease. While many data-mining tools have been adopted
for EHR-based disease early detection, linear
discriminant analysis (LDA) is one of the most commonly
used statistical methods. However, it is difficult to
train an accurate LDA model for early disease diagnosis
when too few patients are known to have the target
disease. Furthermore, EHR data are heterogeneous with
significant noise. In such cases, the covariance
matrices used in LDA are usually singular and estimated
with a large variance. This article presents Daehr, an
extension of the LDA framework using electronic health
record data to address these issues. Beyond existing
LDA analyzers, we propose Daehr to (1) eliminate the
data noise caused by the manual encoding of EHR data
and (2) lower the variance of parameter (covariance
matrices) estimation for LDA models when only a few
patients' EHR are available for training. To achieve
these two goals, we designed an iterative algorithm to
improve the covariance matrix estimation with embedded
data-noise/parameter-variance reduction for LDA. We
evaluated Daehr extensively using the College Health
Surveillance Network, a large, real-world EHR dataset.
Specifically, our experiments compared the performance
of LDA to three baselines (i.e., LDA and its
derivatives) in identifying college students at high
risk for mental health disorders from 23 U.S.
universities. Experimental results demonstrate Daehr
significantly outperforms the three baselines by
achieving 1.4\%--19.4\% higher accuracy and a
7.5\%--43.5\% higher F1-score.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2017:SSS,
author = "Weiqing Wang and Hongzhi Yin and Ling Chen and Yizhou
Sun and Shazia Sadiq and Xiaofang Zhou",
title = "{ST-SAGE}: a Spatial-Temporal Sparse Additive
Generative Model for Spatial Item Recommendation",
journal = j-TIST,
volume = "8",
number = "3",
pages = "48:1--48:??",
month = apr,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3011019",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the rapid development of location-based social
networks (LBSNs), spatial item recommendation has
become an important mobile application, especially when
users travel away from home. However, this type of
recommendation is very challenging compared to
traditional recommender systems. A user may visit only
a limited number of spatial items, leading to a very
sparse user-item matrix. This matrix becomes even
sparser when the user travels to a distant place, as
most of the items visited by a user are usually located
within a short distance from the user's home. Moreover,
user interests and behavior patterns may vary
dramatically across different time and geographical
regions. In light of this, we propose ST-SAGE, a
spatial-temporal sparse additive generative model for
spatial item recommendation in this article. ST-SAGE
considers both personal interests of the users and the
preferences of the crowd in the target region at the
given time by exploiting both the co-occurrence
patterns and content of spatial items. To further
alleviate the data-sparsity issue, ST-SAGE exploits the
geographical correlation by smoothing the crowd's
preferences over a well-designed spatial index
structure called the spatial pyramid. To speed up the
training process of ST-SAGE, we implement a parallel
version of the model inference algorithm on the
GraphLab framework. We conduct extensive experiments;
the experimental results clearly demonstrate that
ST-SAGE outperforms the state-of-the-art recommender
systems in terms of recommendation effectiveness, model
training efficiency, and online recommendation
efficiency.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ben-Israel:2017:LPM,
author = "Isaac Ben-Israel",
title = "The Letter from {Prof. Maj. Gen. (Ret.) Isaac
Ben-Israel}",
journal = j-TIST,
volume = "8",
number = "4",
pages = "49:1--49:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3057727",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49e",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Harel:2017:CSR,
author = "Yaniv Harel and Irad Ben Gal and Yuval Elovici",
title = "Cyber Security and the Role of Intelligent Systems in
Addressing its Challenges",
journal = j-TIST,
volume = "8",
number = "4",
pages = "49:1--49:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3057729",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Guri:2017:BAG,
author = "Mordechai Guri and Matan Monitz and Yuval Elovici",
title = "Bridging the Air Gap between Isolated Networks and
Mobile Phones in a Practical Cyber-Attack",
journal = j-TIST,
volume = "8",
number = "4",
pages = "50:1--50:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2870641",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Information is the most critical asset of modern
organizations, and accordingly it is one of the
resources most coveted by adversaries. When highly
sensitive data is involved, an organization may resort
to air gap isolation in which there is no networking
connection between the inner network and the external
world. While infiltrating an air-gapped network has
been proven feasible in recent years, data exfiltration
from an air-gapped network is still considered one of
the most challenging phases of an advanced
cyber-attack. In this article, we present
``AirHopper,'' a bifurcated malware that bridges the
air gap between an isolated network and nearby infected
mobile phones using FM signals. While it is known that
software can intentionally create radio emissions from
a video card, this is the first time that mobile phones
serve as the intended receivers of the maliciously
crafted electromagnetic signals. We examine the attack
model and its limitations and discuss implementation
considerations such as modulation methods, signal
collision, and signal reconstruction. We test AirHopper
in an existing workplace at a typical office building
and demonstrate how valuable data such as keylogging
and files can be exfiltrated from physically isolated
computers to mobile phones at a distance of 1--7
meters, with an effective bandwidth of 13--60 bytes per
second.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ovelgonne:2017:URB,
author = "Michael Ovelg{\"o}nne and Tudor Dumitras and B. Aditya
Prakash and V. S. Subrahmanian and Benjamin Wang",
title = "Understanding the Relationship between Human Behavior
and Susceptibility to Cyber Attacks: a Data-Driven
Approach",
journal = j-TIST,
volume = "8",
number = "4",
pages = "51:1--51:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2890509",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Despite growing speculation about the role of human
behavior in cyber-security of machines, concrete
data-driven analysis and evidence have been lacking.
Using Symantec's WINE platform, we conduct a detailed
study of 1.6 million machines over an 8-month period in
order to learn the relationship between user behavior
and cyber attacks against their personal computers. We
classify users into 4 categories (gamers,
professionals, software developers, and others, plus a
fifth category comprising everyone) and identify a
total of 7 features that act as proxies for human
behavior. For each of the 35 possible combinations (5
categories times 7 features), we studied the
relationship between each of these seven features and
one dependent variable, namely the number of attempted
malware attacks detected by Symantec on the machine.
Our results show that there is a strong relationship
between several features and the number of attempted
malware attacks. Had these hosts not been protected by
Symantec's anti-virus product or a similar product,
they would likely have been infected. Surprisingly, our
results show that software developers are more at risk
of engaging in risky cyber-behavior than other
categories.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ganesan:2017:OSC,
author = "Rajesh Ganesan and Sushil Jajodia and Hasan Cam",
title = "Optimal Scheduling of Cybersecurity Analysts for
Minimizing Risk",
journal = j-TIST,
volume = "8",
number = "4",
pages = "52:1--52:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2914795",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Cybersecurity threats are on the rise with evermore
digitization of the information that many day-to-day
systems depend upon. The demand for cybersecurity
analysts outpaces supply, which calls for optimal
management of the analyst resource. Therefore, a key
component of the cybersecurity defense system is the
optimal scheduling of its analysts. Sensor data is
analyzed by automatic processing systems, and alerts
are generated. A portion of these alerts is considered
to be significant, which requires thorough examination
by a cybersecurity analyst. Risk, in this article, is
defined as the percentage of unanalyzed or not
thoroughly analyzed alerts among the significant alerts
by analysts. The article presents a generalized
optimization model for scheduling cybersecurity
analysts to minimize risk (a.k.a., maximize significant
alert coverage by analysts) and maintain risk under a
pre-determined upper bound. The article tests the
optimization model and its scalability on a set of
given sensors with varying analyst experiences, alert
generation rates, system constraints, and system
requirements. Results indicate that the optimization
model is scalable and is capable of identifying both
the right mix of analyst expertise in an organization
and the sensor-to-analyst allocation in order to
maintain risk below a given upper bound. Several
meta-principles are presented, which are derived from
the optimization model, and they further serve as
guiding principles for hiring and scheduling
cybersecurity analysts. The simulation studies
(validation) of the optimization model outputs indicate
that risk varies non-linearly with an analyst/sensor
ratio, and for a given analyst/sensor ratio, the risk
is independent of the number of sensors in the
system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Neria:2017:RSF,
author = "Michal Ben Neria and Nancy-Sarah Yacovzada and Irad
Ben-Gal",
title = "A Risk-Scoring Feedback Model for {Webpages} and {Web}
Users Based on Browsing Behavior",
journal = j-TIST,
volume = "8",
number = "4",
pages = "53:1--53:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2928274",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "It has been claimed that many security breaches are
often caused by vulnerable (na{\"\i}ve) employees
within the organization [Ponemon Institute LLC 2015a].
Thus, the weakest link in security is often not the
technology itself but rather the people who use it
[Schneier 2003]. In this article, we propose a machine
learning scheme for detecting risky webpages and risky
browsing behavior, performed by na{\"\i}ve users in the
organization. The scheme analyzes the interaction
between two modules: one represents na{\"\i}ve users,
while the other represents risky webpages. It
implements a feedback loop between these modules such
that if a webpage is exposed to a lot of traffic from
risky users, its ``risk score'' increases, while in a
similar manner, as the user is exposed to risky
webpages (with a high ``risk score''), his own ``risk
score'' increases. The proposed scheme is tested on a
real-world dataset of HTTP logs provided by a large
American toolbar company. The results suggest that a
feedback learning process involving webpages and users
can improve the scoring accuracy and lead to the
detection of unknown malicious webpages.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kolman:2017:SCG,
author = "Eyal Kolman and Benny Pinkas",
title = "Securely Computing a Ground Speed Model",
journal = j-TIST,
volume = "8",
number = "4",
pages = "54:1--54:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2998550",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Consider a server offering risk assessment services
and potential clients of these services. The risk
assessment model that is run by the server is based on
current and historical data of the clients. However,
the clients might prefer not sharing such sensitive
data with external parties such as the server, and the
server might consider the possession of this data as a
liability rather than an asset. Secure multi-party
computation (MPC) enables one, in principle, to compute
any function while hiding the inputs to the function,
and would thus enable the computation of the risk
assessment model while hiding the client's data from
the server. However, a direct application of a generic
MPC solution to this problem is rather inefficient due
to the large scale of the data and the complexity of
the function. We examine a specific case of risk
assessment-the ground speed model. In this model, the
geographical locations of successive
user-authentication attempts are compared, and a
warning flag is raised if the physical speed required
to move between these locations is greater than some
threshold, and some other conditions, such as
authentication from two related networks, do not hold.
We describe a very efficient secure computation
solution that is tailored for this problem. This
solution demonstrates that a risk model can be applied
over encrypted data with sufficient efficiency to fit
the requirements of commercial systems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kleinmann:2017:ACS,
author = "Amit Kleinmann and Avishai Wool",
title = "Automatic Construction of Statechart-Based Anomaly
Detection Models for Multi-Threaded Industrial Control
Systems",
journal = j-TIST,
volume = "8",
number = "4",
pages = "55:1--55:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3011018",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Traffic of Industrial Control System (ICS) between the
Human Machine Interface (HMI) and the Programmable
Logic Controller (PLC) is known to be highly periodic.
However, it is sometimes multiplexed, due to
asynchronous scheduling. Modeling the network traffic
patterns of multiplexed ICS streams using Deterministic
Finite Automata (DFA) for anomaly detection typically
produces a very large DFA and a high false-alarm rate.
In this article, we introduce a new modeling approach
that addresses this gap. Our Statechart DFA modeling
includes multiple DFAs, one per cyclic pattern,
together with a DFA-selector that de-multiplexes the
incoming traffic into sub-channels and sends them to
their respective DFAs. We demonstrate how to
automatically construct the statechart from a captured
traffic stream. Our unsupervised learning algorithms
first build a Discrete-Time Markov Chain (DTMC) from
the stream. Next, we split the symbols into sets, one
per multiplexed cycle, based on symbol frequencies and
node degrees in the DTMC graph. Then, we create a
sub-graph for each cycle and extract Euler cycles for
each sub-graph. The final statechart is comprised of
one DFA per Euler cycle. The algorithms allow for
non-unique symbols, which appear in more than one
cycle, and also for symbols that appear more than once
in a cycle. We evaluated our solution on traces from a
production ICS using the Siemens S7-0x72 protocol. We
also stress-tested our algorithms on a collection of
synthetically-generated traces that simulated
multiplexed ICS traces with varying levels of symbol
uniqueness and time overlap. The algorithms were able
to split the symbols into sets with 99.6\% accuracy.
The resulting statechart modeled the traces with a
median false-alarm rate of as low as 0.483\%. In all
but the most extreme scenarios, the Statechart model
drastically reduced both the false-alarm rate and the
learned model size in comparison with the naive
single-DFA model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Maltinsky:2017:NNM,
author = "Alex Maltinsky and Ran Giladi and Yuval Shavitt",
title = "On Network Neutrality Measurements",
journal = j-TIST,
volume = "8",
number = "4",
pages = "56:1--56:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3040966",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Network level surveillance, censorship, and various
man-in-the-middle attacks target only specific types of
network traffic (e.g., HTTP, HTTPS, VoIP, or Email).
Therefore, packets of these types will likely receive
``special'' treatment by a transit network or a
man-in-the-middle attacker. A transit Internet Service
Provider (ISP) or an attacker may pass the targeted
traffic through special software or equipment to gather
data or perform an attack. This creates a measurable
difference between the performance of the targeted
traffic versus the general case. In networking terms,
it violates the principle of ``network neutrality,''
which states that all traffic should be treated
equally. Many techniques were designed to detect
network neutrality violations, and some have naturally
suggested using them to detect surveillance and
censorship. In this article, we show that the existing
network neutrality measurement techniques can be easily
detected and therefore circumvented. We then briefly
propose a new approach to overcome the drawbacks of
current measurement techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hirschprung:2017:AOA,
author = "Ron Hirschprung and Eran Toch and Hadas
Schwartz-Chassidim and Tamir Mendel and Oded Maimon",
title = "Analyzing and Optimizing Access Control Choice
Architectures in Online Social Networks",
journal = j-TIST,
volume = "8",
number = "4",
pages = "57:1--57:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3046676",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The way users manage access to their information and
computers has a tremendous effect on the overall
security and privacy of individuals and organizations.
Usually, access management is conducted using a choice
architecture, a behavioral economics concept that
describes the way decisions are framed to users.
Studies have consistently shown that the design of
choice architectures, mainly the selection of default
options, has a strong effect on the final decisions
users make by nudging them toward certain behaviors. In
this article, we propose a method for optimizing access
control choice architectures in online social networks.
We empirically evaluate the methodology on Facebook,
the world's largest online social network, by measuring
how well the default options cover the existing user
choices and preferences and toward which outcome the
choice architecture nudges users. The evaluation
includes two parts: (a) collecting access control
decisions made by 266 users of Facebook for a period of
3 months; and (b) surveying 533 participants who were
asked to express their preferences regarding default
options. We demonstrate how optimal defaults can be
algorithmically identified from users' decisions and
preferences, and we measure how existing defaults
address users' preferences compared with the optimal
ones. We analyze how access control defaults can better
serve existing users, and we discuss how our method can
be used to establish a common measuring tool when
examining the effects of default options.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2017:TID,
author = "Xitong Yang and Jiebo Luo",
title = "Tracking Illicit Drug Dealing and Abuse on {Instagram}
Using Multimodal Analysis",
journal = j-TIST,
volume = "8",
number = "4",
pages = "58:1--58:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3011871",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Illicit drug trade via social media sites, especially
photo-oriented Instagram, has become a severe problem
in recent years. As a result, tracking drug dealing and
abuse on Instagram is of interest to law enforcement
agencies and public health agencies. However,
traditional approaches are based on manual search and
browsing by trained domain experts, which suffers from
the problem of poor scalability and reproducibility. In
this article, we propose a novel approach to detecting
drug abuse and dealing automatically by utilizing
multimodal data on social media. This approach also
enables us to identify drug-related posts and analyze
the behavior patterns of drug-related user accounts. To
better utilize multimodal data on social media,
multimodal analysis methods including multi-task
learning and decision-level fusion are employed in our
framework. We collect three datasets using Instagram
and web search engine for training and testing our
models. Experiment results on expertly labeled data
have demonstrated the effectiveness of our approach, as
well as its scalability and reproducibility over
labor-intensive conventional approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Panagopoulos:2017:AEC,
author = "Athanasios Aris Panagopoulos and Sasan Maleki and Alex
Rogers and Matteo Venanzi and Nicholas R. Jennings",
title = "Advanced Economic Control of Electricity-Based Space
Heating Systems in Domestic Coalitions with Shared
Intermittent Energy Resources",
journal = j-TIST,
volume = "8",
number = "4",
pages = "59:1--59:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3041216",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Over the past few years, Domestic Heating Automation
Systems (DHASs) that optimize the domestic space
heating control process with minimum user input,
utilizing appropriate occupancy prediction technology,
have emerged as commercial products (e.g., the smart
thermostats from Nest and Honeywell). At the same time,
many houses are being equipped with, potentially
grid-connected, Intermittent Energy Resources (IERs),
such as rooftop photovoltaic systems and/or small wind
turbine generators. Now, in many regions of the world,
such houses can sell energy to the grid but at a lower
price than the price of buying it. In this context, and
given the anticipated increase in electrification of
heating, the next generation DHASs need to incorporate
Advanced Economic Control (AEC). Such AEC can exploit
the energy buffer that heating loads provide, in order
to shift the consumption of electricity-based heating
systems to follow the intermittent energy generation of
the house. By so doing, the energy imported from the
grid can be minimized and considerable monetary gains
for the household can be achieved, without affecting
the occupants' schedule. These benefits can be
amplified still further in domestic coalitions, where a
number of houses come together and share their IER
generation to minimize their cumulative grid energy
import. Given the above, in this work we extend a
state-of-the-art DHAS, to propose AdaHeat+, a practical
DHAS, that, for the first time, incorporates AEC. Our
work is applicable to both individual houses and
domestic coalitions and comes complete with an
allocation mechanism to share the coalition gains.
Importantly, we propose an effective heuristic heating
schedule planning approach for collective AEC that (i)
has a complexity that scales in a linear and
parallelizable manner with the coalition size, and (ii)
enables AdaHeat+ to handle the distinct preferences, in
balancing heating cost and thermal discomfort, of the
households. Our approach relies on stochastic IER power
output predictions. In this context, we propose a
simple and effective formulation for the site-specific
calibration of such predictions based on adaptive
Gaussian process modeling. Finally, we demonstrate the
effectiveness of AdaHeat+ through real data evaluation,
to show that collective AEC can improve heating
cost-efficiency by up to 60\%, compared to independent
AEC (and even more when compared to no-AEC).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bistaffa:2017:AGC,
author = "Filippo Bistaffa and Alessandro Farinelli and
Jes{\'u}s Cerquides and Juan Rodr{\'\i}guez-Aguilar and
Sarvapali D. Ramchurn",
title = "Algorithms for Graph-Constrained Coalition Formation
in the Real World",
journal = j-TIST,
volume = "8",
number = "4",
pages = "60:1--60:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3040967",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Coalition formation typically involves the coming
together of multiple, heterogeneous, agents to achieve
both their individual and collective goals. In this
article, we focus on a special case of coalition
formation known as Graph-Constrained Coalition
Formation (GCCF) whereby a network connecting the
agents constrains the formation of coalitions. We focus
on this type of problem given that in many real-world
applications, agents may be connected by a
communication network or only trust certain peers in
their social network. We propose a novel representation
of this problem based on the concept of edge
contraction, which allows us to model the search space
induced by the GCCF problem as a rooted tree. Then, we
propose an anytime solution algorithm (Coalition
Formation for Sparse Synergies (CFSS)), which is
particularly efficient when applied to a general class
of characteristic functions called m + a functions.
Moreover, we show how CFSS can be efficiently
parallelised to solve GCCF using a nonredundant
partition of the search space. We benchmark CFSS on
both synthetic and realistic scenarios, using a
real-world dataset consisting of the energy consumption
of a large number of households in the UK. Our results
show that, in the best case, the serial version of CFSS
is four orders of magnitude faster than the state of
the art, while the parallel version is 9.44 times
faster than the serial version on a 12-core machine.
Moreover, CFSS is the first approach to provide anytime
approximate solutions with quality guarantees for very
large systems of agents (i.e., with more than 2,700
agents).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{An:2017:DDF,
author = "Bo An and Haipeng Chen and Noseong Park and V. S.
Subrahmanian",
title = "Data-Driven Frequency-Based Airline Profit
Maximization",
journal = j-TIST,
volume = "8",
number = "4",
pages = "61:1--61:??",
month = jul,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3041217",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:41 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Although numerous traditional models predict market
share and demand along airline routes, the prediction
of existing models is not precise enough, and to the
best of our knowledge, there is no use of data
mining--based forecasting techniques for improving
airline profitability. We propose the maximizing
airline profits (MAP) architecture designed to help
airlines and make two key contributions in airline
market share and route demand prediction and
prediction-based airline profit optimization. Compared
to past methods used to forecast market share and
demand along airline routes, we introduce a novel
ensemble forecasting (MAP-EF) approach considering two
new classes of features: (i) features derived from
clusters of similar routes and (ii) features based on
equilibrium pricing. We show that MAP-EF achieves much
better Pearson correlation coefficients (greater than
0.95 vs. 0.82 for market share, 0.98 vs. 0.77 for
demand) and R$^2$ -values compared to three
state-of-the-art works for forecasting market share and
demand while showing much lower variance. Using the
results of MAP-EF, we develop MAP--bilevel branch and
bound (MAP-BBB) and MAP-greedy (MAP-G) algorithms to
optimally allocate flight frequencies over multiple
routes to maximize an airline's profit. We also study
two extensions of the profit maximization problem
considering frequency constraints and long-term
profits. Furthermore, we develop algorithms for
computing Nash equilibrium frequencies when there are
multiple strategic airlines. Experimental results show
that airlines can increase profits by a significant
margin. All experiments were conducted with data
aggregated from four sources: the U.S. Bureau of
Transportation Statistics (BTS), the U.S. Bureau of
Economic Analysis (BEA), the National Transportation
Safety Board (NTSB), and the U.S. Census Bureau (CB).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yao:2017:UCM,
author = "Lina Yao and Quan Z. Sheng and Anne H. H. Ngu and Xue
Li and Boualem Benattalah",
title = "Unveiling Correlations via Mining Human-Thing
Interactions in the {Web of Things}",
journal = j-TIST,
volume = "8",
number = "5",
pages = "62:1--62:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3035967",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With recent advances in radio-frequency identification
(RFID), wireless sensor networks, and Web services,
physical things are becoming an integral part of the
emerging ubiquitous Web. Finding correlations among
ubiquitous things is a crucial prerequisite for many
important applications such as things search,
discovery, classification, recommendation, and
composition. This article presents DisCor-T, a novel
graph-based approach for discovering underlying
connections of things via mining the rich content
embodied in the human-thing interactions in terms of
user, temporal, and spatial information. We model this
various information using two graphs, namely a
spatio-temporal graph and a social graph. Then, random
walk with restart (RWR) is applied to find proximities
among things, and a relational graph of things (RGT)
indicating implicit correlations of things is learned.
The correlation analysis lays a solid foundation
contributing to improved effectiveness in things
management and analytics. To demonstrate the utility of
the proposed approach, we develop a flexible
feature-based classification framework on top of RGT
and perform a systematic case study. Our evaluation
exhibits the strength and feasibility of the proposed
approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lagree:2017:YTS,
author = "Paul Lagr{\'e}e and Bogdan Cautis and Hossein Vahabi",
title = "As-You-Type Social Aware Search",
journal = j-TIST,
volume = "8",
number = "5",
pages = "63:1--63:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3035969",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Modern search applications feature real-time
as-you-type query search. In its elementary form, the
problem consists in retrieving a set of k search
results, that is, performing a search with a given
prefix, and showing the top-ranked results. In this
article, we focus on as-you-type keyword search over
social media, that is, data published by users who are
interconnected through a social network. We adopt a
``network-aware'' interpretation for information
relevance, by which information produced by users who
are closer to the user issuing a request is considered
more relevant. This query model raises new challenges
for effectiveness and efficiency in online search, even
when the intent of the user is fully specified, as a
complete query given as input in one keystroke. This is
mainly because it requires a joint exploration of the
social space and traditional IR indexes, such as
inverted lists. We describe a memory-efficient and
incremental prefix-based retrieval algorithm, which
also exhibits an anytime behavior, allowing output of
the most likely answer within any chosen runtime limit.
We evaluate our approach through extensive experiments
for several applications and search scenarios. We
consider searching for posts in microblogging (Twitter
and Tumblr), for businesses (Yelp), as well as for
movies (Amazon) based on reviews. We also conduct a
series of experiments comparing our algorithm with
baselines using state-of-the-art techniques and
measuring the improvements brought by several key
optimizations. They show that our solution is effective
in answering real-time as-you-type searches over social
media.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gao:2017:SOL,
author = "Xingyu Gao and Steven C. H. Hoi and Yongdong Zhang and
Jianshe Zhou and Ji Wan and Zhenyu Chen and Jintao Li
and Jianke Zhu",
title = "Sparse Online Learning of Image Similarity",
journal = j-TIST,
volume = "8",
number = "5",
pages = "64:1--64:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3065950",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Learning image similarity plays a critical role in
real-world multimedia information retrieval
applications, especially in Content-Based Image
Retrieval (CBIR) tasks, in which an accurate retrieval
of visually similar objects largely relies on an
effective image similarity function. Crafting a good
similarity function is very challenging because visual
contents of images are often represented as feature
vectors in high-dimensional spaces, for example, via
bag-of-words (BoW) representations, and traditional
rigid similarity functions, for example, cosine
similarity, are often suboptimal for CBIR tasks. In
this article, we address this fundamental problem, that
is, learning to optimize image similarity with sparse
and high-dimensional representations from large-scale
training data, and propose a novel scheme of Sparse
Online Learning of Image Similarity (SOLIS). In
contrast to many existing image-similarity learning
algorithms that are designed to work with
low-dimensional data, SOLIS is able to learn image
similarity from large-scale image data in sparse and
high-dimensional spaces. Our encouraging results showed
that the proposed new technique achieves highly
competitive accuracy as compared to the
state-of-the-art approaches but enjoys significant
advantages in computational efficiency, model sparsity,
and retrieval scalability, making it more practical for
real-world multimedia retrieval applications.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Pan:2017:TLB,
author = "Weike Pan and Qiang Yang and Yuchao Duan and Ben Tan
and Zhong Ming",
title = "Transfer Learning for Behavior Ranking",
journal = j-TIST,
volume = "8",
number = "5",
pages = "65:1--65:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3057732",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Intelligent recommendation has been well recognized as
one of the major approaches to address the information
overload problem in the big data era. A typical
intelligent recommendation engine usually consists of
three major components, that is, data as the main
input, algorithms for preference learning, and system
for user interaction and high-performance computation.
We observe that the data (e.g., users' behavior) are
usually in different forms, such as examinations (e.g.,
browse and collection) and ratings, where the former
are often much more abundant than the latter. Although
the data are in different representations, they are
both related to users' true preferences and are also
deemed complementary to each other for preference
learning. However, very few ranking or recommendation
algorithms have been developed to exploit such two
types of user behavior. In this article, we focus on
jointly modeling the examination behavior and rating
behavior and develop a novel and efficient
ranking-oriented recommendation algorithm accordingly.
First, we formally define a new recommendation problem
termed behavior ranking, which aims to build a
ranking-oriented model by exploiting both the
examination behavior and rating behavior. Second, we
develop a simple and generic transfer to rank (ToR)
algorithm for behavior ranking, which transfers
knowledge of candidate items from a global preference
learning task to a local preference learning task.
Compared with the previous work on integrating
heterogeneous user behavior, our ToR algorithm is the
first ranking-oriented solution, which can effectively
generate recommendations in a more direct manner than
those regression-oriented methods. Extensive empirical
studies show that our ToR algorithm performs
significantly more accurately than the state-of-the-art
methods in most cases. Furthermore, our ToR algorithm
is very efficient in terms of the time complexity,
which is similar to those for homogeneous user behavior
alone.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Agrawal:2017:HWS,
author = "Rakesh Agrawal and Behzad Golshan and Evangelos E.
Papalexakis",
title = "Homogeneity in {Web} Search Results: Diagnosis and
Mitigation",
journal = j-TIST,
volume = "8",
number = "5",
pages = "66:1--66:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3057731",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Access to diverse perspectives nurtures an informed
citizenry. Google and Bing have emerged as the duopoly
that largely arbitrates which English-language
documents are seen by web searchers. We present our
empirical study over the search results produced by
Google and Bing that shows a large overlap. Thus,
citizens may not gain different perspectives by
simultaneously probing them for the same query.
Fortunately, our study also shows that by mining
Twitter data, one can obtain search results that are
quite distinct from those produced by Google, Bing, and
Bing News. Additionally, the users found those results
to be quite informative. We also present two novel
tools we designed for this study. One uses tensor
analysis to derive low-dimensional compact
representation of search results and study their
behavior over time. The other uses machine learning and
quantifies the similarity of results between two search
engines by framing it as a prediction problem. Although
these tools have different underpinnings, the
analytical results obtained using them corroborate each
other, which reinforces the confidence one can place in
them for finding meaningful insights from big data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hu:2017:VCF,
author = "Zhenhen Hu and Yonggang Wen and Luoqi Liu and Jianguo
Jiang and Richang Hong and Meng Wang and Shuicheng
Yan",
title = "Visual Classification of Furniture Styles",
journal = j-TIST,
volume = "8",
number = "5",
pages = "67:1--67:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3065951",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Furniture style describes the discriminative
appearance characteristics of furniture. It plays an
important role in real-world indoor decoration. In this
article, we explore the furniture style features and
study the problem of furniture style classification.
Differing from traditional object classification,
furniture style classification aims at classifying
different furniture in terms of the ``style'' that
describes its appearance (e.g., American style, Gothic
style, Rococo style, etc.) rather than the ``kind''
that is more related to its functional structure (e.g.,
bed, desk, etc.). To pursue efficient furniture style
features, we construct a novel dataset of furniture
styles that contains 16 common style categories and
implement three strategies with respect to two
categories of classification, that is, handcrafted
classification and learning-based classification.
First, we follow the typical image classification
pipeline to extract the handcrafted features and train
the classifier by support vector machine. Then we use
the convolutional neural network to extract
learning-based features from training images. To obtain
comprehensive furniture style features, we finally
combine the handcrafted image classification pipeline
and the learning-based network. We experimentally
evaluate the performances of handcrafted features and
learning-based features of each strategy, and the
results show the superiority of learning-based features
and also the comprehensiveness of handcrafted
features.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ou:2017:AIV,
author = "Xinyu Ou and Hefei Ling and Han Yu and Ping Li and
Fuhao Zou and Si Liu",
title = "Adult Image and Video Recognition by a Deep
Multicontext Network and Fine-to-Coarse Strategy",
journal = j-TIST,
volume = "8",
number = "5",
pages = "68:1--68:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3057733",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Adult image and video recognition is an important and
challenging problem in the real world. Low-level
feature cues do not produce good enough information,
especially when the dataset is very large and has
various data distributions. This issue raises a serious
problem for conventional approaches. In this article,
we tackle this problem by proposing a deep multicontext
network with fine-to-coarse strategy for adult image
and video recognition. We employ a deep convolution
networks to model fusion features of sensitive objects
in images. Global contexts and local contexts are both
taken into consideration and are jointly modeled in a
unified multicontext deep learning framework. To make
the model more discriminative for diverse target
objects, we investigate a novel hierarchical method,
and a task-specific fine-to-coarse strategy is designed
to make the multicontext modeling more suitable for
adult object recognition. Furthermore, some recently
proposed deep models are investigated. Our approach is
extensively evaluated on four different datasets. One
dataset is used for ablation experiments, whereas
others are used for generalization experiments. Results
show significant and consistent improvements over the
state-of-the-art methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ottens:2017:DUC,
author = "Brammert Ottens and Christos Dimitrakakis and Boi
Faltings",
title = "{DUCT}: an Upper Confidence Bound Approach to
Distributed Constraint Optimization Problems",
journal = j-TIST,
volume = "8",
number = "5",
pages = "69:1--69:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3066156",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We propose a distributed upper confidence bound
approach, DUCT, for solving distributed constraint
optimization problems. We compare four variants of this
approach with a baseline random sampling algorithm, as
well as other complete and incomplete algorithms for
DCOPs. Under general assumptions, we theoretically show
that the solution found by DUCT after T steps is
approximately T$^{-1}$ -close to the optimal.
Experimentally, we show that DUCT matches the optimal
solution found by the well-known DPOP and O-DPOP
algorithms on moderate-size problems, while always
requiring less agent communication. For larger
problems, where DPOP fails, we show that DUCT produces
significantly better solutions than local, incomplete
algorithms. Overall, we believe that DUCT is a
practical, scalable algorithm for complex DCOPs.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Assem:2017:RRC,
author = "Haytham Assem and Teodora Sandra Buda and Declan
O'Sullivan",
title = "{RCMC}: Recognizing Crowd-Mobility Patterns in Cities
Based on Location Based Social Networks Data",
journal = j-TIST,
volume = "8",
number = "5",
pages = "70:1--70:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3086636",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "During the past few years, the analysis of data
generated from Location-Based Social Networks (LBSNs)
have aided in the identification of urban patterns,
understanding activity behaviours in urban areas, as
well as producing novel recommender systems that
facilitate users' choices. Recognizing crowd-mobility
patterns in cities is very important for public safety,
traffic managment, disaster management, and urban
planning. In this article, we propose a framework for
Recognizing the Crowd Mobility Patterns in Cities using
LBSN data. Our proposed framework comprises four main
components: data gathering, recurrent crowd-mobility
patterns extraction, temporal functional regions
detection, and visualization component. More
specifically, we employ a novel approach based on
Non-negative Matrix Factorization and Gaussian Kernel
Density Estimation for extracting the recurrent
crowd-mobility patterns in cities illustrating how
crowd shifts from one area to another during each day
across various time slots. Moreover, the framework
employs a hierarchical clustering-based algorithm for
identifying what we refer to as temporal functional
regions by modeling functional areas taking into
account temporal variation by means of check-ins'
categories. We build the framework using a
spatial-temporal dataset crawled from Twitter for two
entire years (2013 and 2014) for the area of Manhattan
in New York City. We perform a detailed analysis of the
extracted crowd patterns with an exploratory
visualization showing that our proposed approach can
identify clearly obvious mobility patterns that recur
over time and location in the urban scenario. Using
same time interval, we show that correlating the
temporal functional regions with the recognized
recurrent crowd-mobility patterns can yield to a deeper
understanding of city dynamics and the motivation
behind the crowd mobility. We are confident that our
proposed framework not only can help in managing
complex city environments and better allocation of
resources based on the expected crowd mobility and
temporal functional regions but also can have a direct
implication on a variety of applications such as
personalized recommender systems, anomalous event
detection, disaster resilience management systems, and
others.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cui:2017:ACF,
author = "Chaoran Cui and Jialie Shen and Liqiang Nie and
Richang Hong and Jun Ma",
title = "Augmented Collaborative Filtering for Sparseness
Reduction in Personalized {POI} Recommendation",
journal = j-TIST,
volume = "8",
number = "5",
pages = "71:1--71:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3086635",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "As mobile device penetration increases, it has become
pervasive for images to be associated with locations in
the form of geotags. Geotags bridge the gap between the
physical world and the cyberspace, giving rise to new
opportunities to extract further insights into user
preferences and behaviors. In this article, we aim to
exploit geotagged photos from online photo-sharing
sites for the purpose of personalized Point-of-Interest
(POI) recommendation. Owing to the fact that most users
have only very limited travel experiences, data
sparseness poses a formidable challenge to personalized
POI recommendation. To alleviate data sparseness, we
propose to augment current collaborative filtering
algorithms along from multiple perspectives.
Specifically, hybrid preference cues comprising
user-uploaded and user-favored photos are harvested to
study users' tastes. Moreover, heterogeneous high-order
relationship information is jointly captured from user
social networks and POI multimodal contents with
hypergraph models. We also build upon the matrix
factorization algorithm to integrate the disparate
sources of preference and relationship information, and
apply our approach to directly optimize user preference
rankings. Extensive experiments on a large and publicly
accessible dataset well verified the potential of our
approach for addressing data sparseness and offering
quality recommendations to users, especially for those
who have only limited travel experiences.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhao:2017:PLS,
author = "Hongke Zhao and Yong Ge and Qi Liu and Guifeng Wang
and Enhong Chen and Hefu Zhang",
title = "{P2P} Lending Survey: Platforms, Recent Advances and
Prospects",
journal = j-TIST,
volume = "8",
number = "6",
pages = "72:1--72:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078848",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "P2P lending is an emerging Internet-based application
where individuals can directly borrow money from each
other. The past decade has witnessed the rapid
development and prevalence of online P2P lending
platforms, examples of which include Prosper,
LendingClub, and Kiva. Meanwhile, extensive research
has been done that mainly focuses on the studies of
platform mechanisms and transaction data. In this
article, we provide a comprehensive survey on the
research about P2P lending, which, to the best of our
knowledge, is the first focused effort in this field.
Specifically, we first provide a systematic taxonomy
for P2P lending by summarizing different types of
mainstream platforms and comparing their working
mechanisms in detail. Then, we review and organize the
recent advances on P2P lending from various
perspectives (e.g., economics and sociology
perspective, and data-driven perspective). Finally, we
propose our opinions on the prospects of P2P lending
and suggest some future research directions in this
field. Meanwhile, throughout this paper, some analysis
on real-world data collected from Prosper and Kiva are
also conducted.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Feyisetan:2017:SIP,
author = "Oluwaseyi Feyisetan and Elena Simperl",
title = "Social Incentives in Paid Collaborative
Crowdsourcing",
journal = j-TIST,
volume = "8",
number = "6",
pages = "73:1--73:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078852",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Paid microtask crowdsourcing has traditionally been
approached as an individual activity, with units of
work created and completed independently by the members
of the crowd. Other forms of crowdsourcing have,
however, embraced more varied models, which allow for a
greater level of participant interaction and
collaboration. This article studies the feasibility and
uptake of such an approach in the context of paid
microtasks. Specifically, we compare engagement, task
output, and task accuracy in a paired-worker model with
the traditional, single-worker version. Our experiments
indicate that collaboration leads to better accuracy
and more output, which, in turn, translates into lower
costs. We then explore the role of the social flow and
social pressure generated by collaborating partners as
sources of incentives for improved performance. We
utilise a Bayesian method in conjunction with interface
interaction behaviours to detect when one of the
workers in a pair tries to exit the task. Upon this
realisation, the other worker is presented with the
opportunity to contact the exiting partner to stay:
either for personal financial reasons (i.e., they have
not completed enough tasks to qualify for a payment) or
for fun (i.e., they are enjoying the task). The
findings reveal that: (1) these socially motivated
incentives can act as furtherance mechanisms to help
workers attain and exceed their task requirements and
produce better results than baseline collaborations;
(2) microtask crowd workers are empathic (as opposed to
selfish) agents, willing to go the extra mile to help
their partners get paid; and, (3) social furtherance
incentives create a win-win scenario for the requester
and for the workers by helping more workers get paid by
re-engaging them before they drop out.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Khezerlou:2017:TFA,
author = "Amin Vahedian Khezerlou and Xun Zhou and Lufan Li and
Zubair Shafiq and Alex X. Liu and Fan Zhang",
title = "A Traffic Flow Approach to Early Detection of
Gathering Events: Comprehensive Results",
journal = j-TIST,
volume = "8",
number = "6",
pages = "74:1--74:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078850",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Given a spatial field and the traffic flow between
neighboring locations, the early detection of gathering
events ( edge) problem aims to discover and localize a
set of most likely gathering events. It is important
for city planners to identify emerging gathering events
that might cause public safety or sustainability
concerns. However, it is challenging to solve the edge
problem due to numerous candidate gathering footprints
in a spatial field and the nontrivial task of balancing
pattern quality and computational efficiency. Prior
solutions to model the edge problem lack the ability to
describe the dynamic flow of traffic and the potential
gathering destinations because they rely on static or
undirected footprints. In our recent work, we modeled
the footprint of a gathering event as a Gathering Graph
(G-Graph), where the root of the directed acyclic
G-Graph is the potential destination and the directed
edges represent the most likely paths traffic takes to
move toward the destination. We also proposed an
efficient algorithm called SmartEdge to discover the
most likely nonoverlapping G-Graphs in the given
spatial field. However, it is challenging to perform a
systematic performance study of the proposed algorithm,
due to unavailability of the ground truth of gathering
events. In this article, we introduce an event
simulation mechanism, which makes it possible to
conduct a comprehensive performance study of the
SmartEdge algorithm. We measure the quality of the
detected patterns, in a systematic way, in terms of
timeliness and location accuracy. The results show
that, on average, the SmartEdge algorithm is able to
detect patterns within a grid cell away (less than 500
meters) of the simulated events and detect patterns of
the simulated events as early as 10 minutes prior to
the first arrival to the gathering event.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Feng:2017:MHC,
author = "Xiaodong Feng and Sen Wu and Wenjun Zhou",
title = "Multi-Hypergraph Consistent Sparse Coding",
journal = j-TIST,
volume = "8",
number = "6",
pages = "75:1--75:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078846",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Sparse representation has been a powerful technique
for modeling high-dimensional data. As an unsupervised
technique to extract sparse representations, sparse
coding encodes the original data into a new sparse code
space and simultaneously learns a dictionary
representing high-level semantics. Existing methods
have considered local manifold within high-dimensional
data using graph/hypergraph Laplacian regularization,
and more from the manifold could be utilized to improve
the performance. In this article, we propose to further
regulate the sparse coding so that the learned sparse
codes can well reconstruct the hypergraph structure. In
particular, we add a novel hypergraph consistency
regularization term (HC) by minimizing the
reconstruction error of the hypergraph incidence or
weight matrix. Moreover, we extend the HC term to
multi-hypergraph consistent sparse coding (MultiCSC)
and automatically select the optimal manifold structure
under the multi-hypergraph learning framework. We show
that the optimization of MultiCSC can be solved
efficiently, and that several existing sparse coding
methods can fit into the general framework of MultiCSC
as special cases. As a case study, hypergraph incidence
consistent sparse coding is applied to perform
semi-auto image tagging, demonstrating the
effectiveness of hypergraph consistency regulation. We
perform further experiments using MultiCSC for image
clustering, which outperforms a number of baselines.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "75",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Burt:2017:ISI,
author = "Ronald Burt and Jie Tang and Michalis Vazirgiannis and
Shuang Yang",
title = "Introduction to Special Issue on Social Media
Processing ({TIST --- SMP})",
journal = j-TIST,
volume = "8",
number = "6",
pages = "76:1--76:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3110318",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "76",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2017:PMR,
author = "Yang Li and Jing Jiang and Ting Liu and Minghui Qiu
and Xiaofei Sun",
title = "Personalized Microtopic Recommendation on Microblogs",
journal = j-TIST,
volume = "8",
number = "6",
pages = "77:1--77:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2932192",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Microblogging services such as Sina Weibo and Twitter
allow users to create tags explicitly indicated by the
\# symbol. In Sina Weibo, these tags are called
microtopics, and in Twitter, they are called hashtags.
In Sina Weibo, each microtopic has a designate page and
can be directly visited or commented on. Recommending
these microtopics to users based on their interests can
help users efficiently acquire information. However, it
is non-trivial to recommend microtopics to users to
satisfy their information needs. In this article, we
investigate the task of personalized microtopic
recommendation, which exhibits two challenges. First,
users usually do not give explicit ratings to
microtopics. Second, there exists rich information
about users and microtopics, for example, users'
published content and biographical information, but it
is not clear how to best utilize such information. To
address the above two challenges, we propose a joint
probabilistic latent factor model to integrate rich
information into a matrix factorization-based solution
to microtopic recommendation. Our model builds on top
of collaborative filtering, content analysis, and
feature regression. Using two real-world datasets, we
evaluate our model with different kinds of content and
contextual information. Experimental results show that
our model significantly outperforms a few competitive
baseline methods, especially in the circumstance where
users have few adoption behaviors.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "77",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Glenski:2017:RES,
author = "Maria Glenski and Tim Weninger",
title = "Rating Effects on Social News Posts and Comments",
journal = j-TIST,
volume = "8",
number = "6",
pages = "78:1--78:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/2963104",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "At a time when information seekers first turn to
digital sources for news and opinion, it is critical
that we understand the role that social media plays in
human behavior. This is especially true when
information consumers also act as information producers
and editors through their online activity. In order to
better understand the effects that editorial ratings
have on online human behavior, we report the results of
a two large-scale in vivo experiments in social media.
We find that small, random rating manipulations on
social media posts and comments created significant
changes in downstream ratings, resulting in
significantly different final outcomes. We found
positive herding effects for positive treatments on
posts, increasing the final rating by 11.02\% on
average, but not for positive treatments on comments.
Contrary to the results of related work, we found
negative herding effects for negative treatments on
posts and comments, decreasing the final ratings, on
average, of posts by 5.15\% and of comments by 37.4\%.
Compared to the control group, the probability of
reaching a high rating ($ \geq 2000$) for posts is
increased by 24.6\% when posts receive the positive
treatment and for comments it is decreased by 46.6\%
when comments receive the negative treatment.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "78",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2017:ECB,
author = "Chien-Cheng Chen and Kuo-Wei Hsu and Wen-Chih Peng",
title = "Exploring Communication Behaviors of Users to Target
Potential Users in Mobile Social Networks",
journal = j-TIST,
volume = "8",
number = "6",
pages = "79:1--79:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3022472",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In mobile communication services, users can
communicate with each other over different
telecommunication carriers. For telecom operators, how
to acquire and retain users is a significant and
practical task. Note that telecom operators only have
their own customer profiles. For the users from other
telecom operators, their information is sparse. Thus,
given a set of communication logs, the main theme of
our work is to identify the potential users who will
possibly join the target services in the near future.
Since only a limited amount of information is
available, one challenging issue is how to extract
features from the communication logs. In this article,
we propose a Communication-Based Feature Generation
(CBFG) framework that extracts features and builds
models to infer the potential users. Explicitly, we
construct a heterogeneous information network from the
communication logs of users. Then, we extract the
explicit features, which refer to those calling
features of users, from the potential users'
interaction behaviors in the heterogeneous information
network. Moreover, from the calling behaviors of users,
one could extract the possible community structures of
users. Based on the community structures, we further
extract the implicit features of users. In light of
both explicit and implicit features, we propose an
information-gain-based method to select the effective
features. According to the features selected, we
utilize three popular classifiers (i.e., AdaBoost,
Random Forest, and SVM) to build models to target the
potential users. In addition, we have designed a
sampling approach to extract training data for
classifiers. To evaluate our methods, we have conducted
experiments on a real dataset. The results of our
experiments show that the features extracted by our
proposed method can be effective for targeting the
potential users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "79",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2017:UAI,
author = "Chao Huang and Dong Wang and Jun Tao",
title = "An Unsupervised Approach to Inferring the Localness of
People Using Incomplete Geotemporal Online Check-In
Data",
journal = j-TIST,
volume = "8",
number = "6",
pages = "80:1--80:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3022471",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Inferring the localness of people is to classify
people who are local residents in a city from people
who visit the city by analyzing online check-in points
that are contributed by online users. This information
is critical for the urban planning, user profiling, and
localized recommendation systems. Supervised learning
approaches have been developed to infer the location of
people in a city by assuming the availability of
high-quality training datasets with complete
geotemporal information. In this article, we develop an
unsupervised model to accurately identify local people
in a city by using the incomplete online check-in data
that are publicly available. In particular, we develop
an incomplete geotemporal expectation maximization
(IGT-EM) scheme, which incorporates a set of hidden
variables to represent the localness of people and a
set of estimation parameters to represent the
likelihood of venues to attract local and nonlocal
people, respectively. Our solution can accurately
classify local people from nonlocal nones without
requiring any training data. We also implement a
parallel IGT-EM algorithm by leveraging the computing
power of a graphic processing unit (GPU) that consists
of 2,496 cores. In the evaluation, we compare our new
approach with the existing solutions through four
real-world case studies using data from the New York
City, Chicago, Boston, and Washington, DC. The results
show that our approach can identify the local people
and significantly outperform the compared baselines in
estimation accuracy and execution time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "80",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tu:2017:PPI,
author = "Cunchao Tu and Zhiyuan Liu and Huanbo Luan and Maosong
Sun",
title = "{PRISM}: Profession Identification in Social Media",
journal = j-TIST,
volume = "8",
number = "6",
pages = "81:1--81:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3070665",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Profession is an important social attribute of people.
It plays a crucial role in commercial services such as
personalized recommendation and targeted advertising.
In practice, profession information is usually
unavailable due to privacy and other reasons. In this
article, we explore the task of identifying user
professions according to their behaviors in social
media. The task confronts the following challenges that
make it non-trivial: how to incorporate heterogeneous
information of user behaviors, how to effectively
utilize both labeled and unlabeled data, and how to
exploit community structure. To address these
challenges, we present a framework called Profession
Identification in Social Media. It takes advantage of
both personal information and community structure of
users in the following aspects: (1) We present a
cascaded two-level classifier with heterogeneous
personal features to measure the confidence of users
belonging to different professions. (2) We present a
multi-training process to take advantages of both
labeled and unlabeled data to enhance classification
performance. (3) We design a profession identification
method synthetically considering the confidences from
personal features and community structure. We collect a
real-world dataset to conduct experiments, and
experimental results demonstrate the significant
effectiveness of our method compared with other
baseline methods. By applying prediction on large-scale
users, we also analyze characteristics of microblog
users, finding that there are significant diversities
among users of different professions in demographics,
social network structures, and linguistic styles.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "81",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chikhaoui:2017:DCA,
author = "Belkacem Chikhaoui and Mauricio Chiazzaro and Shengrui
Wang and Martin Sotir",
title = "Detecting Communities of Authority and Analyzing Their
Influence in Dynamic Social Networks",
journal = j-TIST,
volume = "8",
number = "6",
pages = "82:1--82:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3070658",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Users in real-world social networks are organized into
communities that differ from each other in terms of
influence, authority, interest, size, etc. This article
addresses the problems of detecting communities of
authority and of estimating the influence of such
communities in dynamic social networks. These are new
issues that have not yet been addressed in the
literature, and they are important in applications such
as marketing and recommender systems. To facilitate the
identification of communities of authority, our
approach first detects communities sharing common
interests, which we call ``meta-communities,'' by
incorporating topic modeling based on users' community
memberships. Then, communities of authority are
extracted with respect to each meta-community, using a
new measure based on the betweenness centrality. To
assess the influence between communities over time, we
propose a new model based on the Granger causality
method. Through extensive experiments on a variety of
social network datasets, we empirically demonstrate the
suitability of our approach for community-of-authority
detection and assessment of the influence between
communities over time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "82",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fu:2017:RSD,
author = "Hao Fu and Xing Xie and Yong Rui and Neil Zhenqiang
Gong and Guangzhong Sun and Enhong Chen",
title = "Robust Spammer Detection in Microblogs: Leveraging
User Carefulness",
journal = j-TIST,
volume = "8",
number = "6",
pages = "83:1--83:??",
month = sep,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3086637",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Microblogging Web sites, such as Twitter and Sina
Weibo, have become popular platforms for socializing
and sharing information in recent years. Spammers have
also discovered this new opportunity to unfairly
overpower normal users with unsolicited content, namely
social spams. Although it is intuitive for everyone to
follow legitimate users, recent studies show that both
legitimate users and spammers follow spammers for
different reasons. Evidence of users seeking spammers
on purpose is also observed. We regard this behavior as
useful information for spammer detection. In this
article, we approach the problem of spammer detection
by leveraging the ``carefulness'' of users, which
indicates how careful a user is when she is about to
follow a potential spammer. We propose a framework to
measure the carefulness and develop a supervised
learning algorithm to estimate it based on known
spammers and legitimate users. We illustrate how the
robustness of the detection algorithms can be improved
with aid of the proposed measure. Evaluation on two
real datasets from Sina Weibo and Twitter with millions
of users are performed, as well as an online test on
Sina Weibo. The results show that our approach indeed
captures the carefulness, and it is effective for
detecting spammers. In addition, we find that our
measure is also beneficial for other applications, such
as link prediction.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "83",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2017:RGR,
author = "Xuelong Li and Guosheng Cui and Yongsheng Dong",
title = "Refined-Graph Regularization-Based Nonnegative Matrix
Factorization",
journal = j-TIST,
volume = "9",
number = "1",
pages = "1:1--1:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3090312",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Nonnegative matrix factorization (NMF) is one of the
most popular data representation methods in the field
of computer vision and pattern recognition.
High-dimension data are usually assumed to be sampled
from the submanifold embedded in the original
high-dimension space. To preserve the locality
geometric structure of the data, $k$-nearest neighbor
($k$-NN) graph is often constructed to encode the
near-neighbor layout structure. However, $k$-NN graph
is based on Euclidean distance, which is sensitive to
noise and outliers. In this article, we propose a
refined-graph regularized nonnegative matrix
factorization by employing a manifold regularized
least-squares regression (MRLSR) method to compute the
refined graph. In particular, each sample is
represented by the whole dataset regularized with $
l_2$-norm and Laplacian regularizer. Then a MRLSR graph
is constructed based on the representative coefficients
of each sample. Moreover, we present two optimization
schemes to generate refined-graphs by employing a
hard-thresholding technique. We further propose two
refined-graph regularized nonnegative matrix
factorization methods and use them to perform image
clustering. Experimental results on several image
datasets reveal that they outperform 11 representative
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2017:MAO,
author = "Zhifeng Li and Dihong Gong and Kai Zhu and Dacheng Tao
and Xuelong Li",
title = "Multifeature Anisotropic Orthogonal {Gaussian} Process
for Automatic Age Estimation",
journal = j-TIST,
volume = "9",
number = "1",
pages = "2:1--2:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3090311",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Automatic age estimation is an important yet
challenging problem. It has many promising applications
in social media. Of the existing age estimation
algorithms, the personalized approaches are among the
most popular ones. However, most person-specific
approaches rely heavily on the availability of training
images across different ages for a single subject,
which is usually difficult to satisfy in practical
application of age estimation. To address this
limitation, we first propose a new model called
Orthogonal Gaussian Process (OGP), which is not
restricted by the number of training samples per
person. In addition, without sacrifice of
discriminative power, OGP is much more computationally
efficient than the standard Gaussian Process. Based on
OGP, we then develop an effective age estimation
approach, namely anisotropic OGP (A-OGP), to further
reduce the estimation error. A-OGP is based on an
anisotropic noise level learning scheme that
contributes to better age estimation performance. To
finally optimize the performance of age estimation, we
propose a multifeature A-OGP fusion framework that uses
multiple features combined with a random sampling
method in the feature space. Extensive experiments on
several public domain face aging datasets (FG-NET,
MORPH Album1, and MORPH Album 2) are conducted to
demonstrate the state-of-the-art estimation accuracy of
our new algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gao:2017:FSV,
author = "Yang Gao and Yuefeng Li and Raymond Y. K. Lau and Yue
Xu and Md Abul Bashar",
title = "Finding Semantically Valid and Relevant Topics by
Association-Based Topic Selection Model",
journal = j-TIST,
volume = "9",
number = "1",
pages = "3:1--3:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3094786",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Topic modelling methods such as Latent Dirichlet
Allocation (LDA) have been successfully applied to
various fields, since these methods can effectively
characterize document collections by using a mixture of
semantically rich topics. So far, many models have been
proposed. However, the existing models typically
outperform on full analysis on the whole collection to
find all topics but difficult to capture coherent and
specifically meaningful topic representations.
Furthermore, it is very challenging to incorporate user
preferences into existing topic modelling methods to
extract relevant topics. To address these problems, we
develop a novel personalized Association-based Topic
Selection (ATS) model, which can identify semantically
valid and relevant topics from a set of raw topics
based on the semantical relatedness between users'
preferences and the structured patterns captured in
topics. The advantage of the proposed ATS model is that
it enables an interactive topic modelling process
driven by users' specific interests. Based on three
benchmark datasets, namely, RCV1, R8, and WT10G under
the context of information filtering (IF) and
information retrieval (IR), our rigorous experiments
show that the proposed ATS model can effectively
identify relevant topics with respect to users'
specific interests, and hence to improve the
performance of IF and IR.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhu:2017:ATS,
author = "Wenwu Zhu and Jean Walrand and Yike Guo and Zhi Wang",
title = "{ACM TIST} Special Issue on Data-Driven Intelligence
for Wireless Networking",
journal = j-TIST,
volume = "9",
number = "1",
pages = "4:1--4:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3104984",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fan:2017:RMA,
author = "Xiaoyi Fan and Wei Gong and Jiangchuan Liu",
title = "{i$^2$ tag}: {RFID} Mobility and Activity
Identification Through Intelligent Profiling",
journal = j-TIST,
volume = "9",
number = "1",
pages = "5:1--5:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3035968",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many radio frequency identification (RFID)
applications, such as virtual shopping cart and
tag-assisted gaming, involve sensing and recognizing
tag mobility. However, existing RFID localization
methods are mostly designed for static or slowly moving
targets (less than 0.3m/sec). More importantly, we
observe that prior methods suffer from serious
performance degradation for detecting real-world moving
tags in typical indoor environments with multipath
interference. In this article, we present i$^2$ tag, an
intelligent mobility-aware activity identification
system for RFID tags in multipath-rich environments
(e.g., indoors). i$^2$ tag employs a supervised
learning framework based on our novel fine-grain
mobility profile, which can quantify different levels
of mobility. Unlike previous methods that mostly rely
on phase measurement, i$^2$ tag takes into account
various measurements, including RSSI variance, packet
loss rate, and our novel relative phase--based
fingerprint. Additionally, we design a multidimensional
dynamic time warping--based algorithm to robustly
detect mobility and the associated activities. We show
that i$^2$ tag is readily deployable using
off-the-shelf RFID devices. A prototype has been
implemented using a ThingMagic reader and
standard-compatible tags. Experimental results
demonstrate its superiority in mobility detection and
activity identification in various indoor
environments.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2017:EEM,
author = "Wei Zhang and Rui Fan and Yonggang Wen and Fang Liu",
title = "Energy-Efficient Mobile Video Streaming: a
Location-Aware Approach",
journal = j-TIST,
volume = "9",
number = "1",
pages = "6:1--6:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3102301",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Video streaming is one of the most widely used mobile
applications today, and it also accounts for a large
fraction of mobile battery usage. Much of the energy
consumption is for wireless data transmission and is
highly correlated to network bandwidth conditions. In
periods of poor connectivity, up to 90\% of mobile
energy can be used for wireless data transfer. In this
article, we study the problem of energy-efficient
mobile video streaming. We make use of the observed
correlation between bandwidth and user location, and
also observe that a user's location is predictable in
many situations, such as when commuting to a known
destination. Based on the user's predicted locations
and bandwidth conditions, we optimize wireless
transmission times to achieve high quality video
playback while minimizing energy use. We propose an
optimal offline algorithm for this problem, which runs
in O ( Tk ) time, where T is the duration of the video
and k is the size of the video buffer. We also propose
LAWS, a Location AWare Streaming algorithm. LAWS learns
from historical location-aware bandwidth conditions and
predicts future bandwidths along a planned route to
make online wireless download decisions. We evaluate
LAWS using real bandwidth traces, and show that LAWS
closely approximates the performance of the optimal
offline algorithm, achieving 90.6\% of the optimal
performance on average, and 97\% in certain cases. LAWS
also outperforms three popular strategies used in
practice by, on average, 69\%, 63\%, and 38\%,
respectively. Lastly, we show that LAWS is able to deal
with noisy data and can attain the stated performance
after sampling bandwidth conditions only five times.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yin:2017:UUI,
author = "Hao Yin and Wei Wang and Xu Zhang and Yongqiang Lyu
and Geyong Min and Dongchao Guo",
title = "{UMCR}: User Interaction-Driven Mobile Content
Retrieval",
journal = j-TIST,
volume = "9",
number = "1",
pages = "7:1--7:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3102292",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Although mobile application ecosystems have
experienced tremendous growth in recent years,
retrieving content of mobile applications that serves a
key to mobile content search engines still faces grand
challenges. Compared to web content retrieval, it is
much more difficult to capture content in mobile
applications due to the diversity of applications and
the lack of Uniform Resource Locator indices. In this
study, we propose and implement a user
interaction-driven mobile content retrieval (UMCR)
system to address such issues, which is the first
mobile content crawler in the current literature. UMCR
is a distributed system that contains many measurement
nodes, each of which combines the user interaction path
traversing (UIPT) and Deep Package Inspection (DPI)
together to obtain mobile content. UIPT determines the
events of user interactions in various applications to
capture the static content such as text and images, in
which a traversal depth termination scheme and an
optional cut-off component are adopted to balance the
content coverage and traversing efficiency. Meanwhile,
the analysis based on DPI is responsible for extracting
the videos as well as digging the infrastructural
information and performance metrics. In addition, a
distributed traversal scheduling method is designed for
UIPT tasks to improve the throughput and scalability in
large-scale content retrieval. Experiments on
retrieving content of 64 real mobile applications
demonstrate that UMCR can handle diverse mobile
applications efficiently. The scheduler can improve
throughput by 3 times compared to the legacy arbitrary
task assignment strategy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2017:TTD,
author = "Xuyu Wang and Chao Yang and Shiwen Mao",
title = "{TensorBeat}: Tensor Decomposition for Monitoring
Multiperson Breathing Beats with Commodity {WiFi}",
journal = j-TIST,
volume = "9",
number = "1",
pages = "8:1--8:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078855",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Breathing signal monitoring can provide important
clues for health problems. Compared to existing
techniques that require wearable devices and special
equipment, a more desirable approach is to provide
contact-free and long-term breathing rate monitoring by
exploiting wireless signals. In this article, we
propose TensorBeat, a system to employ channel state
information (CSI) phase difference data to
intelligently estimate breathing rates for multiple
persons with commodity WiFi devices. The main idea is
to leverage the tensor decomposition technique to
handle the CSI phase difference data. The proposed
TensorBeat scheme first obtains CSI phase difference
data between pairs of antennas at the WiFi receiver to
create CSI tensors. Then canonical polyadic (CP)
decomposition is applied to obtain the desired
breathing signals. A stable signal matching algorithm
is developed to identify the decomposed signal pairs,
and a peak detection method is applied to estimate the
breathing rates for multiple persons. Our experimental
study shows that TensorBeat can achieve high accuracy
under different environments for multiperson breathing
rate monitoring.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ying:2017:EIW,
author = "Xuhang Ying and Jincheng Zhang and Lichao Yan and Yu
Chen and Guanglin Zhang and Minghua Chen and Ranveer
Chandra",
title = "Exploring Indoor White Spaces in Metropolises",
journal = j-TIST,
volume = "9",
number = "1",
pages = "9:1--9:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3059149",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "It is a promising vision to exploit white spaces, that
is, vacant VHF and UHF TV channels, to meet the rapidly
growing demand for wireless data services in both
outdoor and indoor scenarios. While most prior works
have focused on outdoor white space, the indoor story
is largely open for investigation. Motivated by this
observation and discovering that 70\% of the spectrum
demand comes from indoor environment, we carry out a
comprehensive study to explore indoor white spaces. We
first conduct a large-scale measurement study and
compare outdoor and indoor TV spectrum occupancy at 30+
diverse locations in a typical metropolis-Hong Kong.
Our results show that abundant white spaces are
available in different areas in Hong Kong, which
account for more than 50\% and 70\% of the entire TV
spectrum in outdoor and indoor scenarios, respectively.
Although there are substantially more white spaces
indoors than outdoors, there have been very few
solutions for identifying indoor white space. To fill
in this gap, we develop the first data-driven, low-cost
indoor white space identification system for
White-space Indoor Spectrum EnhanceR (WISER), to allow
secondary users to identify white spaces for
communication without sensing the spectrum themselves.
We design the architecture and algorithms to address
the inherent challenges. We build a WISER prototype and
carry out real-world experiments to evaluate its
performance. Our results show that WISER can identify
30\%--40\% more indoor white spaces with negligible
false alarms, as compared to alternative baseline
approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yeh:2017:SIB,
author = "Lo-Yao Yeh and Woei-Jiunn Tsaur and Hsin-Han Huang",
title = "Secure {IoT}-Based, Incentive-Aware Emergency
Personnel Dispatching Scheme with Weighted Fine-Grained
Access Control",
journal = j-TIST,
volume = "9",
number = "1",
pages = "10:1--10:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3063716",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Emergency response times following a traffic accident
are extremely crucial in reducing the number of
traffic-related deaths. Existing emergency vehicle
dispatching systems rely heavily on manual assignments.
Although some technology-assisted emergency systems
engage in emergency message dissemination and path
planning, efficient emergency response is one of the
main factors that can decrease traffic-related deaths.
Obviously, effective emergency response often plays a
far more important role in a successful rescue. In this
article, we propose a secure IoT-based and
incentive-aware emergency personnel dispatching scheme
(EPDS) with weighted fine-grained access control. Our
EPDS can recruit available medical personnel
on-the-fly, such as physicians driving in the vicinity
of the accident scene. An appropriate incentive, such
as paid leave, can be offered to encourage medical
personnel to join rescue missions. Furthermore,
IoT-based devices are installed in vehicles or wearable
on drivers to gather biometric signals from the driver,
which can be used to decide precisely which divisions
or physicians are needed to administer the appropriate
remedy. Additionally, our scheme can cryptographically
authorize the assigned rescue vehicle to control
traffic to increase rescue efficacy. Our scheme also
takes advantage of adjacent roadside units to organize
the appropriate rescue personnel without requiring
long-distance communication with a trusted traffic
authority. Proof of security is provided and extensive
analyses, including qualitative and quantitative
analyses and simulations, show that the proposed scheme
can significantly improve rescue response time and
effectiveness. To the best of our knowledge, this is
the first work to make use of medical personnel that
are close by in emergency rescue missions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shen:2017:DDD,
author = "Jiaxing Shen and Jiannong Cao and Xuefeng Liu and
Chisheng Zhang",
title = "{DMAD}: Data-Driven Measuring of {Wi-Fi} Access Point
Deployment in Urban Spaces",
journal = j-TIST,
volume = "9",
number = "1",
pages = "11:1--11:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3065949",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Wireless networks offer many advantages over wired
local area networks such as scalability and mobility.
Strategically deployed wireless networks can achieve
multiple objectives like traffic offloading, network
coverage, and indoor localization. To this end, various
mathematical models and optimization algorithms have
been proposed to find optimal deployments of access
points (APs). However, wireless signals can be blocked
by the human body, especially in crowded urban spaces.
As a result, the real coverage of an on-site AP
deployment may shrink to some degree and lead to
unexpected dead spots (areas without wireless
coverage). Dead spots are undesirable, since they
degrade the user experience in network service
continuity, on one hand, and, on the other hand
paralyze some applications and services like tracking
and monitoring when users are in these areas.
Nevertheless, it is nontrivial for existing methods to
analyze the impact of human beings on wireless
coverage. Site surveys are too time consuming and labor
intensive to conduct. It is also infeasible for
simulation methods to predict the number of on-site
people. In this article, we propose DMAD, a Data-driven
Measuring of Wi-Fi Access point Deployment, which not
only estimates potential dead spots of an on-site AP
deployment but also quantifies their severity, using
simple Wi-Fi data collected from the on-site deployment
and shop profiles from the Internet. DMAD first
classifies static devices and mobile devices with a
decision-tree classifier. Then it locates mobile
devices to grid-level locations based on shop
popularities, wireless signal, and visit duration.
Last, DMAD estimates the probability of dead spots for
each grid during different time slots and derives their
severity considering the probability and the number of
potential users. The analysis of Wi-Fi data from static
devices indicates that the Pearson Correlation
Coefficient of wireless coverage status and the number
of on-site people is over 0.7, which confirms that
human beings may have a significant impact on wireless
coverage. We also conduct extensive experiments in a
large shopping mall in Shenzhen. The evaluation results
demonstrate that DMAD can find around 70\% of dead
spots with a precision of over 70\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2017:TPU,
author = "Yanqiu Wu and Tehila Minkus and Keith W. Ross",
title = "Taking the Pulse of {US} College Campuses with
Location-Based Anonymous Mobile Apps",
journal = j-TIST,
volume = "9",
number = "1",
pages = "12:1--12:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078843",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We deploy GPS hacking in conjunction with
location-based mobile apps to passively survey users in
targeted geographical regions. Specifically, we
investigate surveying students at different college
campuses with Yik Yak, an anonymous mobile app that is
popular on US college campuses. In addition to being
campus centric, Yik Yak's anonymity allows students to
express themselves candidly without self-censorship. We
collect nearly 1.6 million Yik Yak messages (``yaks'')
from a diverse set of 45 college campuses in the United
States. We use natural language processing to determine
the sentiment (positive, negative, or neutral) of all
of the yaks. We employ supervised machine learning to
predict the gender of the authors of the yaks and then
analyze how sentiment differs among the two genders on
college campuses. We also use supervised machine
learning to classify all the yaks into nine topics and
then investigate which topics are most popular
throughout the US and how topic popularity varies on
the different campuses. The results in this article
provide significant insight into how campus culture and
student's thinking varies among US colleges and
universities.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2017:EPE,
author = "Ruide Zhang and Ning Zhang and Changlai Du and Wenjing
Lou and Y. Thomas Hou and Yuichi Kawamoto",
title = "From Electromyogram to Password: Exploring the Privacy
Impact of Wearables in Augmented Reality",
journal = j-TIST,
volume = "9",
number = "1",
pages = "13:1--13:??",
month = oct,
year = "2017",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078844",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Dec 23 10:12:42 MST 2017",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the increasing popularity of augmented reality
(AR) services, providing seamless human-computer
interactions in the AR setting has received notable
attention in the industry. Gesture control devices have
recently emerged to be the next great gadgets for AR
due to their unique ability to enable computer
interaction with day-to-day gestures. While these AR
devices are bringing revolutions to our interaction
with the cyber world, it is also important to consider
potential privacy leakages from these always-on
wearable devices. Specifically, the coarse access
control on current AR systems could lead to possible
abuse of sensor data. Although the always-on gesture
sensors are frequently quoted as a privacy concern,
there has not been any study on information leakage of
these devices. In this article, we present our study on
side-channel information leakage of the most popular
gesture control device, Myo. Using signals recorded
from the electromyography (EMG) sensor and
accelerometers on Myo, we can recover sensitive
information such as passwords typed on a keyboard and
PIN sequence entered through a touchscreen. EMG signal
records subtle electric currents of muscle
contractions. We design novel algorithms based on
dynamic cumulative sum and wavelet transform to
determine the exact time of finger movements.
Furthermore, we adopt the Hudgins feature set in a
support vector machine to classify recorded signal
segments into individual fingers or numbers. We also
apply coordinate transformation techniques to recover
fine-grained spatial information with low-fidelity
outputs from the sensor in keystroke recovery. We
evaluated the information leakage using data collected
from a group of volunteers. Our results show that there
is severe privacy leakage from these commodity wearable
sensors. Our system recovers complex passwords
constructed with lowercase letters, uppercase letters,
numbers, and symbols with a mean success rate of
91\%.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Goodwin:2018:KRI,
author = "Travis R. Goodwin and Sanda M. Harabagiu",
title = "Knowledge Representations and Inference Techniques for
Medical Question Answering",
journal = j-TIST,
volume = "9",
number = "2",
pages = "14:1--14:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3106745",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Answering medical questions related to complex medical
cases, as required in modern Clinical Decision Support
(CDS) systems, imposes (1) access to vast medical
knowledge and (2) sophisticated inference techniques.
In this article, we examine the representation and role
of combining medical knowledge automatically derived
from (a) clinical practice and (b) research findings
for inferring answers to medical questions. Knowledge
from medical practice was distilled from a vast
Electronic Medical Record (EMR) system, while research
knowledge was processed from biomedical articles
available in PubMed Central. The knowledge
automatically acquired from the EMR system took into
account the clinical picture and therapy recognized
from each medical record to generate a probabilistic
Markov network denoted as a Clinical Picture and
Therapy Graph (CPTG). Moreover, we represented the
background of medical questions available from the
description of each complex medical case as a medical
knowledge sketch. We considered three possible
representations of medical knowledge sketches that were
used by four different probabilistic inference methods
to pinpoint the answers from the CPTG. In addition,
several answer-informed relevance models were developed
to provide a ranked list of biomedical articles
containing the answers. Evaluations on the TREC-CDS
data show which of the medical knowledge
representations and inference methods perform
optimally. The experiments indicate an improvement of
biomedical article ranking by 49\% over
state-of-the-art results.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sun:2018:SVA,
author = "Guodao Sun and Tan Tang and Tai-Quan Peng and Ronghua
Liang and Yingcai Wu",
title = "{SocialWave}: Visual Analysis of Spatio-temporal
Diffusion of Information on Social Media",
journal = j-TIST,
volume = "9",
number = "2",
pages = "15:1--15:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3106775",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Rapid advancement of social media tremendously
facilitates and accelerates the information diffusion
among users around the world. How and to what extent
will the information on social media achieve widespread
diffusion across the world? How can we quantify the
interaction between users from different geolocations
in the diffusion process? How will the spatial patterns
of information diffusion change over time? To address
these questions, a dynamic social gravity model (SGM)
is proposed to quantify the dynamic spatial interaction
behavior among social media users in information
diffusion. The dynamic SGM includes three factors that
are theoretically significant to the spatial diffusion
of information: geographic distance, cultural
proximity, and linguistic similarity. Temporal
dimension is also taken into account to help detect
recency effect, and ground-truth data is integrated
into the model to help measure the diffusion power.
Furthermore, SocialWave, a visual analytic system, is
developed to support both spatial and temporal
investigative tasks. SocialWave provides a temporal
visualization that allows users to quickly identify the
overall temporal diffusion patterns, which reflect the
spatial characteristics of the diffusion network. When
a meaningful temporal pattern is identified, SocialWave
utilizes a new occlusion-free spatial visualization,
which integrates a node-link diagram into a circular
cartogram for further analysis. Moreover, we propose a
set of rich user interactions that enable in-depth,
multi-faceted analysis of the diffusion on social
media. The effectiveness and efficiency of the
mathematical model and visualization system are
evaluated with two datasets on social media, namely,
Ebola Epidemics and Ferguson Unrest.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhuang:2018:SRL,
author = "Fuzhen Zhuang and Xiaohu Cheng and Ping Luo and Sinno
Jialin Pan and Qing He",
title = "Supervised Representation Learning with Double
Encoding-Layer Autoencoder for Transfer Learning",
journal = j-TIST,
volume = "9",
number = "2",
pages = "16:1--16:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3108257",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Transfer learning has gained a lot of attention and
interest in the past decade. One crucial research issue
in transfer learning is how to find a good
representation for instances of different domains such
that the divergence between domains can be reduced with
the new representation. Recently, deep learning has
been proposed to learn more robust or higher-level
features for transfer learning. In this article, we
adapt the autoencoder technique to transfer learning
and propose a supervised representation learning method
based on double encoding-layer autoencoder. The
proposed framework consists of two encoding layers: one
for embedding and the other one for label encoding. In
the embedding layer, the distribution distance of the
embedded instances between the source and target
domains is minimized in terms of KL-Divergence. In the
label encoding layer, label information of the source
domain is encoded using a softmax regression model.
Moreover, to empirically explore why the proposed
framework can work well for transfer learning, we
propose a new effective measure based on autoencoder to
compute the distribution distance between different
domains. Experimental results show that the proposed
new measure can better reflect the degree of transfer
difficulty and has stronger correlation with the
performance from supervised learning algorithms (e.g.,
Logistic Regression), compared with previous ones, such
as KL-Divergence and Maximum Mean Discrepancy.
Therefore, in our model, we have incorporated two
distribution distance measures to minimize the
difference between source and target domains in the
embedding representations. Extensive experiments
conducted on three real-world image datasets and one
text data demonstrate the effectiveness of our proposed
method compared with several state-of-the-art baseline
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ranganath:2018:UIR,
author = "Suhas Ranganath and Xia Hu and Jiliang Tang and Suhang
Wang and Huan Liu",
title = "Understanding and Identifying Rhetorical Questions in
Social Media",
journal = j-TIST,
volume = "9",
number = "2",
pages = "17:1--17:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3108364",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Social media provides a platform for seeking
information from a large user base. Information seeking
in social media, however, occurs simultaneously with
users expressing their viewpoints by making statements.
Rhetorical questions have the form of a question but
serve the function of a statement and are an important
tool employed by users to express their viewpoints.
Therefore, rhetorical questions might mislead platforms
assisting information seeking in social media. It
becomes difficult to identify rhetorical questions as
they are not syntactically different from other
questions. In this article, we develop a framework to
identify rhetorical questions by modeling some
motivations of the users to post them. We focus on two
motivations of the users drawing from linguistic
theories to implicitly convey a message and to modify
the strength of a statement previously made. We develop
a quantitative framework from these motivations to
identify rhetorical questions in social media. We
evaluate the framework using two datasets of questions
posted on a social media platform Twitter and
demonstrate its effectiveness in identifying rhetorical
questions. This is the first framework, to the best of
our knowledge, to model the possible motivations for
posting rhetorical questions to identify them on social
media platforms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2018:IDC,
author = "Ou Wu and Xue Mao and Weiming Hu",
title = "Iteratively Divide-and-Conquer Learning for Nonlinear
Classification and Ranking",
journal = j-TIST,
volume = "9",
number = "2",
pages = "18:1--18:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3122802",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Nonlinear classifiers (i.e., kernel support vector
machines (SVMs)) are effective for nonlinear data
classification. However, nonlinear classifiers are
usually prohibitively expensive when dealing with large
nonlinear data. Ensembles of linear classifiers have
been proposed to address this inefficiency, which is
called the ensemble linear classifiers for nonlinear
data problem. In this article, a new iterative learning
approach is introduced that involves two steps at each
iteration: partitioning the data into clusters
according to Gaussian mixture models with local
consistency and then training basic classifiers (i.e.,
linear SVMs) for each cluster. The two
divide-and-conquer steps are combined into a graphical
model. Meanwhile, with training, each classifier is
regarded as a task; clustered multitask learning is
employed to capture the relatedness among different
tasks and avoid overfitting in each task. In addition,
two novel extensions are introduced based on the
proposed approach. First, the approach is extended for
quality-aware web data classification. In this problem,
the types of web data vary in terms of information
quality. The ignorance of the variations of information
quality of web data leads to poor classification
models. The proposed approach can effectively integrate
quality-aware factors into web data classification.
Second, the approach is extended for listwise learning
to rank to construct an ensemble of linear ranking
models, whereas most existing listwise ranking methods
construct a solely linear ranking model. Experimental
results on benchmark datasets show that our approach
outperforms state-of-the-art algorithms. During
prediction for nonlinear classification, it also
obtains comparable classification performance to kernel
SVMs, with much higher efficiency.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2018:SCA,
author = "Yexun Zhang and Wenbin Cai and Wenquan Wang and Ya
Zhang",
title = "Stopping Criterion for Active Learning with Model
Stability",
journal = j-TIST,
volume = "9",
number = "2",
pages = "19:1--19:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3125645",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Active learning selectively labels the most
informative instances, aiming to reduce the cost of
data annotation. While much effort has been devoted to
active sampling functions, relatively limited attention
has been paid to when the learning process should stop.
In this article, we focus on the stopping criterion of
active learning and propose a model stability--based
criterion, that is, when a model does not change with
inclusion of additional training instances. The
challenge lies in how to measure the model change
without labeling additional instances and training new
models. Inspired by the stochastic gradient update
rule, we use the gradient of the loss function at each
candidate example to measure its effect on model
change. We propose to stop active learning when the
model change brought by any of the remaining unlabeled
examples is lower than a given threshold. We apply the
proposed stopping criterion to two popular classifiers:
logistic regression (LR) and support vector machines
(SVMs). In addition, we theoretically analyze the
stability and generalization ability of the model
obtained by our stopping criterion. Substantial
experiments on various UCI benchmark datasets and
ImageNet datasets have demonstrated that the proposed
approach is highly effective.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:SCE,
author = "Leye Wang and Daqing Zhang and Dingqi Yang and Animesh
Pathak and Chao Chen and Xiao Han and Haoyi Xiong and
Yasha Wang",
title = "{SPACE-TA}: Cost-Effective Task Allocation Exploiting
Intradata and Interdata Correlations in Sparse
Crowdsensing",
journal = j-TIST,
volume = "9",
number = "2",
pages = "20:1--20:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3131671",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Data quality and budget are two primary concerns in
urban-scale mobile crowdsensing. Traditional research
on mobile crowdsensing mainly takes sensing coverage
ratio as the data quality metric rather than the
overall sensed data error in the target-sensing area.
In this article, we propose to leverage spatiotemporal
correlations among the sensed data in the
target-sensing area to significantly reduce the number
of sensing task assignments. In particular, we exploit
both intradata correlations within the same type of
sensed data and interdata correlations among different
types of sensed data in the sensing task. We propose a
novel crowdsensing task allocation framework called
SPACE-TA (SPArse Cost-Effective Task Allocation),
combining compressive sensing, statistical analysis,
active learning, and transfer learning, to dynamically
select a small set of subareas for sensing in each
timeslot (cycle), while inferring the data of unsensed
subareas under a probabilistic data quality guarantee.
Evaluations on real-life temperature, humidity, air
quality, and traffic monitoring datasets verify the
effectiveness of SPACE-TA. In the
temperature-monitoring task leveraging intradata
correlations, SPACE-TA requires data from only 15.5\%
of the subareas while keeping the inference error below
0.25${}^\circ $C in 95\% of the cycles, reducing the
number of sensed subareas by 18.0\% to 26.5\% compared
to baselines. When multiple tasks run simultaneously,
for example, for temperature and humidity monitoring,
SPACE-TA can further reduce $ \approx $10\% of the
sensed subareas by exploiting interdata correlations.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Katz:2018:VEC,
author = "Gilad Katz and Cornelia Caragea and Asaf Shabtai",
title = "Vertical Ensemble Co-Training for Text
Classification",
journal = j-TIST,
volume = "9",
number = "2",
pages = "21:1--21:??",
month = jan,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3137114",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "High-quality, labeled data is essential for
successfully applying machine learning methods to
real-world text classification problems. However, in
many cases, the amount of labeled data is very small
compared to that of the unlabeled, and labeling
additional samples could be expensive and time
consuming. Co-training algorithms, which make use of
unlabeled data to improve classification, have proven
to be very effective in such cases. Generally,
co-training algorithms work by using two classifiers,
trained on two different views of the data, to label
large amounts of unlabeled data. Doing so can help
minimize the human effort required for labeling new
data, as well as improve classification performance. In
this article, we propose an ensemble-based co-training
approach that uses an ensemble of classifiers from
different training iterations to improve labeling
accuracy. This approach, which we call vertical
ensemble, incurs almost no additional computational
cost. Experiments conducted on six textual datasets
show a significant improvement of over 45\% in AUC
compared with the original co-training algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2018:RTH,
author = "Desheng Zhang and Tian He and Fan Zhang",
title = "Real-Time Human Mobility Modeling with Multi-View
Learning",
journal = j-TIST,
volume = "9",
number = "3",
pages = "22:1--22:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3092692",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Real-time human mobility modeling is essential to
various urban applications. To model such human
mobility, numerous data-driven techniques have been
proposed. However, existing techniques are mostly
driven by data from a single view, for example, a
transportation view or a cellphone view, which leads to
over-fitting of these single-view models. To address
this issue, we propose a human mobility modeling
technique based on a generic multi-view learning
framework called coMobile. In coMobile, we first
improve the performance of single-view models based on
tensor decomposition with correlated contexts, and then
we integrate these improved single-view models together
for multi-view learning to iteratively obtain mutually
reinforced knowledge for real-time human mobility at
urban scale. We implement coMobile based on an
extremely large dataset in the Chinese city Shenzhen,
including data about taxi, bus, and subway passengers
along with cellphone users, capturing more than 27
thousand vehicles and 10 million urban residents. The
evaluation results show that our approach outperforms a
single-view model by 51\% on average. More importantly,
we design a novel application where urban taxis are
dispatched based on unaccounted mobility demand
inferred by coMobile.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{An:2018:ATS,
author = "Bo An and Nick Jennings and Zhenhui Jessie Li",
title = "{ACM TIST} Special Issue on Urban Intelligence",
journal = j-TIST,
volume = "9",
number = "3",
pages = "23:1--23:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3154942",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Varakantham:2018:RSS,
author = "Pradeep Varakantham and Akshat Kumar and Hoong Chuin
Lau and William Yeoh",
title = "Risk-Sensitive Stochastic Orienteering Problems for
Trip Optimization in Urban Environments",
journal = j-TIST,
volume = "9",
number = "3",
pages = "24:1--24:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3080575",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Orienteering Problems (OPs) are used to model many
routing and trip planning problems. OPs are a variant
of the well-known traveling salesman problem where the
goal is to compute the highest reward path that
includes a subset of vertices and has an overall travel
time less than a specified deadline. However, the
applicability of OPs is limited due to the assumption
of deterministic and static travel times. To that end,
Campbell et al. extended OPs to Stochastic OPs (SOPs)
to represent uncertain travel times (Campbell et al.
2011). In this article, we make the following key
contributions: (1) We extend SOPs to Dynamic SOPs
(DSOPs), which allow for time-dependent travel times;
(2) we introduce a new objective criterion for SOPs and
DSOPs to represent a percentile measure of risk; (3) we
provide non-linear optimization formulations along with
their linear equivalents for solving the risk-sensitive
SOPs and DSOPs; (4) we provide a local search mechanism
for solving the risk-sensitive SOPs and DSOPs; and (5)
we provide results on existing benchmark problems and a
real-world theme park trip planning problem.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cao:2018:MBA,
author = "Zhiguang Cao and Hongliang Guo and Jie Zhang",
title = "A Multiagent-Based Approach for Vehicle Routing by
Considering Both Arriving on Time and Total Travel
Time",
journal = j-TIST,
volume = "9",
number = "3",
pages = "25:1--25:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078847",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Arriving on time and total travel time are two
important properties for vehicle routing. Existing
route guidance approaches always consider them
independently, because they may conflict with each
other. In this article, we develop a semi-decentralized
multiagent-based vehicle routing approach where vehicle
agents follow the local route guidance by
infrastructure agents at each intersection, and
infrastructure agents perform the route guidance by
solving a route assignment problem. It integrates the
two properties by expressing them as two objective
terms of the route assignment problem. Regarding
arriving on time, it is formulated based on the
probability tail model, which aims to maximize the
probability of reaching destination before deadline.
Regarding total travel time, it is formulated as a
weighted quadratic term, which aims to minimize the
expected travel time from the current location to the
destination based on the potential route assignment.
The weight for total travel time is designed to be
comparatively large if the deadline is loose.
Additionally, we improve the proposed approach in two
aspects, including travel time prediction and
computational efficiency. Experimental results on real
road networks justify its ability to increase the
average probability of arriving on time, reduce total
travel time, and enhance the overall routing
performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cheng:2018:SUM,
author = "Shih-Fen Cheng and Cen Chen and Thivya Kandappu and
Hoong Chuin Lau and Archan Misra and Nikita Jaiman and
Randy Tandriansyah and Desmond Koh",
title = "Scalable Urban Mobile Crowdsourcing: Handling
Uncertainty in Worker Movement",
journal = j-TIST,
volume = "9",
number = "3",
pages = "26:1--26:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078842",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we investigate effective ways of
utilizing crowdworkers in providing various urban
services. The task recommendation platform that we
design can match tasks to crowdworkers based on
workers' historical trajectories and time budget
limits, thus making recommendations personal and
efficient. One major challenge we manage to address is
the handling of crowdworker's trajectory uncertainties.
In this article, we explicitly allow multiple routine
routes to be probabilistically associated with each
worker. We formulate this problem as an integer linear
program whose goal is to maximize the expected total
utility achieved by all workers. We further exploit the
separable structures of the formulation and apply the
Lagrangian relaxation technique to scale up
computation. Numerical experiments have been performed
over the instances generated using the realistic public
transit dataset in Singapore. The results show that we
can find significantly better solutions than the
deterministic formulation, and in most cases we can
find solutions that are very close to the theoretical
performance limit. To demonstrate the practicality of
our approach, we deployed our recommendation engine to
a campus-scale field trial, and we demonstrate that
workers receiving our recommendations incur fewer
detours and complete more tasks, and are more efficient
against workers relying on their own planning (25\%
more for top workers who receive recommendations). This
is achieved despite having highly uncertain worker
trajectories. We also demonstrate how to further
improve the robustness of the system by using a simple
multi-coverage mechanism.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kaminka:2018:SUP,
author = "Gal A. Kaminka and Natalie Fridman",
title = "Simulating Urban Pedestrian Crowds of Different
Cultures",
journal = j-TIST,
volume = "9",
number = "3",
pages = "27:1--27:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3102302",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Models of crowd dynamics are critically important for
urban planning and management. They support analysis,
facilitate qualitative and quantitative predictions,
and synthesize behaviors for simulations. One promising
approach to crowd modeling relies on micro-level
agent-based simulations, where the interactions of
simulated individual agents in the crowd result in
macro-level crowd dynamics which are the object of
study. This article reports on an agent-based model of
urban pedestrian crowds, where culture is explicitly
modeled. We extend an established agent-based social
agent model, inspired by social psychology, to account
for individual cultural attributes discussed in social
science literature. We then embed the model in a
simulation of pedestrians and explore the resulting
macro-level crowd behaviors, such as pedestrian flow,
lane changes rate, and so on. We validate the model by
quantitatively comparing the simulation results to the
pedestrian dynamics in movies of human crowds in five
different countries: Iraq, Israel, England, Canada, and
France. We conclude that the model can faithfully
replicate urban pedestrians in different cultures.
Encouraged by these results, we explore simulations of
mixed-culture pedestrian crowds.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Auffenberg:2018:CBA,
author = "Frederik Auffenberg and Stephen Snow and Sebastian
Stein and Alex Rogers",
title = "A Comfort-Based Approach to Smart Heating and Air
Conditioning",
journal = j-TIST,
volume = "9",
number = "3",
pages = "28:1--28:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3057730",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we address the interrelated
challenges of predicting user comfort and using this to
reduce energy consumption in smart heating,
ventilation, and air conditioning (HVAC) systems. At
present, such systems use simple models of user comfort
when deciding on a set-point temperature. Being built
using broad population statistics, these models
generally fail to represent individual users'
preferences, resulting in poor estimates of the users'
preferred temperatures. To address this issue, we
propose the Bayesian Comfort Model (BCM). This
personalised thermal comfort model uses a Bayesian
network to learn from a user's feedback, allowing it to
adapt to the users' individual preferences over time.
We further propose an alternative to the ASHRAE 7-point
scale used to assess user comfort. Using this model, we
create an optimal HVAC control algorithm that minimizes
energy consumption while preserving user comfort.
Through an empirical evaluation based on the ASHRAE
RP-884 dataset and data collected in a separate
deployment by us, we show that our model is
consistently 13.2\% to 25.8\% more accurate than
current models and how using our alternative comfort
scale can increase our model's accuracy. Through
simulations we show that using this model, our HVAC
control algorithm can reduce energy consumption by
7.3\% to 13.5\% while decreasing user discomfort by
24.8\% simultaneously.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:STP,
author = "Pengfei Wang and Guannan Liu and Yanjie Fu and
Yuanchun Zhou and Jianhui Li",
title = "Spotting Trip Purposes from Taxi Trajectories: a
General Probabilistic Model",
journal = j-TIST,
volume = "9",
number = "3",
pages = "29:1--29:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078849",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "What is the purpose of a trip? What are the unique
human mobility patterns and spatial contexts in or near
the pickup points and delivery points of trajectories
for a specific trip purpose? Many prior studies have
modeled human mobility patterns in urban regions;
however, these analytics mainly focus on interpreting
the semantic meanings of geographic topics at an
aggregate level. Given the lack of information about
human activities at pick-up and dropoff points, it is
challenging to convert the prior studies into effective
tools for inferring trip purposes. To address this
challenge, in this article, we study large-scale taxi
trajectories from an unsupervised perspective in light
of the following observations. First, the POI
configurations of origin and destination regions
closely relate to the urban functionality of these
regions and further indicate various human activities.
Second, with respect to the functionality of
neighborhood environments, trip purposes can be
discerned from the transitions between regions with
different functionality at particular time periods.
Along these lines, we develop a general probabilistic
framework for spotting trip purposes from massive taxi
GPS trajectories. Specifically, we first augment the
origin and destination regions of trajectories by
attaching neighborhood POIs. Then, we introduce a
latent factor, POI Topic, to represent the mixed
functionality of the regions, such that each origin or
destination point in the city can be modeled as a
mixture over POI Topics. In addition, considering the
transitions from origins to destinations at specific
time periods, the trip time is generated
collaboratively from the pairwise POI Topics at both
ends of the O-D pairs, constituting POI Links, and
hence the trip purpose can be explained semantically by
the POI Links. Finally, we present extensive
experiments with the real-world data of New York City
to demonstrate the effectiveness of our proposed method
for spotting trip purposes, and moreover, the model is
validated to perform well in predicting the
destinations and trip time among all the baseline
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2018:PAT,
author = "Jie Liu and Bin Liu and Yanchi Liu and Huipeng Chen
and Lina Feng and Hui Xiong and Yalou Huang",
title = "Personalized Air Travel Prediction: a Multi-factor
Perspective",
journal = j-TIST,
volume = "9",
number = "3",
pages = "30:1--30:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078845",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Human mobility analysis is one of the most important
research problems in the field of urban computing.
Existing research mainly focuses on the intra-city
ground travel behavior modeling, while the inter-city
air travel behavior modeling has been largely ignored.
Actually, the inter-city travel analysis can be of
equivalent importance and complementary to the
intra-city travel analysis. Understanding massive
passenger-air-travel behavior delivers intelligence for
airlines' precision marketing and related socioeconomic
activities, such as airport planning, emergency
management, local transportation planning, and
tourism-related businesses. Moreover, it provides
opportunities to study the characteristics of cities
and the mutual relationships between them. However,
modeling and predicting air traveler behavior is
challenging due to the complex factors of the market
situation and individual characteristics of customers
(e.g., airlines' market share, customer membership, and
travelers' intrinsic interests on destinations). To
this end, in this article, we present a systematic
study on the personalized air travel prediction
problem, namely where a customer will fly to and which
airline carrier to fly with, by leveraging real-world
anonymized Passenger Name Record (PNR) data.
Specifically, we first propose a relational travel
topic model, which combines the merits of latent factor
model with a neighborhood-based method, to uncover the
personal travel preferences of aviation customers and
the latent travel topics of air routes and airline
carriers simultaneously. Then we present a multi-factor
travel prediction framework, which fuses complex
factors of the market situation and individual
characteristics of customers, to predict airline
customers' personalized travel demands. Experimental
results on two real-world PNR datasets demonstrate the
effectiveness of our approach on both travel topic
discovery and customer travel prediction.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Pellungrini:2018:DMA,
author = "Roberto Pellungrini and Luca Pappalardo and Francesca
Pratesi and Anna Monreale",
title = "A Data Mining Approach to Assess Privacy Risk in Human
Mobility Data",
journal = j-TIST,
volume = "9",
number = "3",
pages = "31:1--31:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3106774",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Human mobility data are an important proxy to
understand human mobility dynamics, develop analytical
services, and design mathematical models for simulation
and what-if analysis. Unfortunately mobility data are
very sensitive since they may enable the
re-identification of individuals in a database.
Existing frameworks for privacy risk assessment provide
data providers with tools to control and mitigate
privacy risks, but they suffer two main shortcomings:
(i) they have a high computational complexity; (ii) the
privacy risk must be recomputed every time new data
records become available and for every selection of
individuals, geographic areas, or time windows. In this
article, we propose a fast and flexible approach to
estimate privacy risk in human mobility data. The idea
is to train classifiers to capture the relation between
individual mobility patterns and the level of privacy
risk of individuals. We show the effectiveness of our
approach by an extensive experiment on real-world GPS
data in two urban areas and investigate the relations
between human mobility patterns and the privacy risk of
individuals.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2018:UOG,
author = "Yingjie Zhang and Beibei Li and Jason Hong",
title = "Using Online Geotagged and Crowdsourced Data to
Understand Human Offline Behavior in the City: an
Economic Perspective",
journal = j-TIST,
volume = "9",
number = "3",
pages = "32:1--32:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078851",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The pervasiveness of mobile technologies today has
facilitated the creation of massive online crowdsourced
and geotagged data from individual users at different
locations in a city. Such ubiquitous user-generated
data allow us to study the social and behavioral
trajectories of individuals across both digital and
physical environments. This information, combined with
traditional economic and behavioral indicators in the
city (e.g., store purchases, restaurant visits,
parking), can help us better understand human behavior
and interactions with cities. In this study, we take an
economic perspective and focus on understanding human
economic behavior in the city by examining the
performance of local businesses based on the values
learned from crowsourced and geotagged data.
Specifically, we extract multiple traffic and human
mobility features from publicly available data source
geomapping and geo-social-tagging techniques and
examine the effects of both static and dynamic features
on booking volume of local restaurants. Our study is
instantiated on a unique dataset of restaurant bookings
from OpenTable for 3,187 restaurants in New York City
from November 2013 to March 2014. Our results suggest
that foot traffic can increase local popularity and
business performance, while mobility and traffic from
automobiles may hurt local businesses, especially the
well-established chains and high-end restaurants. We
also find that, on average, one or more street closure
(caused by events or construction projects) nearby
leads to a 4.7\% decrease in the probability of a
restaurant being fully booked during the dinner peak.
Our study demonstrates the potential to best make use
of the large volumes and diverse sources of
crowdsourced and geotagged user-generated data to
create matrices to predict local economic demand in a
manner that is fast, cheap, accurate, and meaningful.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Dong:2018:SBU,
author = "Xiaowen Dong and Yoshihiko Suhara and Bur{\c{c}}in
Bozkaya and Vivek K. Singh and Bruno Lepri and Alex
`Sandy' Pentland",
title = "Social Bridges in Urban Purchase Behavior",
journal = j-TIST,
volume = "9",
number = "3",
pages = "33:1--33:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3149409",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The understanding and modeling of human purchase
behavior in city environment can have important
implications in the study of urban economy and in the
design and organization of cities. In this article, we
study human purchase behavior at the community level
and argue that people who live in different communities
but work at close-by locations could act as ``social
bridges'' between the respective communities and that
they are correlated with similarity in community
purchase behavior. We provide empirical evidence by
studying millions of credit card transaction records
for tens of thousands of individuals in a city
environment during a period of three months. More
specifically, we show that the number of social bridges
between communities is a much stronger indicator of
similarity in their purchase behavior than
traditionally considered factors such as income and
sociodemographic variables. Our findings also suggest
that such an effect varies across different merchant
categories, that the presence of female customers in
social bridges is a stronger indicator compared to that
of their male counterparts, and that there seems to be
a geographical constraint for this effect, all of which
may have implications in the studies of urban economy
and data-driven urban planning.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2018:GER,
author = "Chao Zhang and Dongming Lei and Quan Yuan and Honglei
Zhuang and Lance Kaplan and Shaowen Wang and Jiawei
Han",
title = "{GeoBurst+}: Effective and Real-Time Local Event
Detection in Geo-Tagged Tweet Streams",
journal = j-TIST,
volume = "9",
number = "3",
pages = "34:1--34:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3066166",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The real-time discovery of local events (e.g.,
protests, disasters) has been widely recognized as a
fundamental socioeconomic task. Recent studies have
demonstrated that the geo-tagged tweet stream serves as
an unprecedentedly valuable source for local event
detection. Nevertheless, how to effectively extract
local events from massive geo-tagged tweet streams in
real time remains challenging. To bridge the gap, we
propose a method for effective and real-time local
event detection from geo-tagged tweet streams. Our
method, named GeoBurst+, first leverages a novel
cross-modal authority measure to identify several
pivots in the query window. Such pivots reveal
different geo-topical activities and naturally attract
similar tweets to form candidate events. GeoBurst+
further summarizes the continuous stream and compares
the candidates against the historical summaries to
pinpoint truly interesting local events. Better still,
as the query window shifts, GeoBurst+ is capable of
updating the event list with little time cost, thus
achieving continuous monitoring of the stream. We used
crowdsourcing to evaluate GeoBurst+ on two
million-scale datasets and found it significantly more
effective than existing methods while being orders of
magnitude faster.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Muralidhar:2018:III,
author = "Nikhil Muralidhar and Chen Wang and Nathan Self and
Marjan Momtazpour and Kiyoshi Nakayama and Ratnesh
Sharma and Naren Ramakrishnan",
title = "{\tt illiad}: {InteLLigent} Invariant and Anomaly
Detection in Cyber-Physical Systems",
journal = j-TIST,
volume = "9",
number = "3",
pages = "35:1--35:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3066167",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Cyber-physical systems (CPSs) are today ubiquitous in
urban environments. Such systems now serve as the
backbone to numerous critical infrastructure
applications, from smart grids to IoT installations.
Scalable and seamless operation of such CPSs requires
sophisticated tools for monitoring the time series
progression of the system, dynamically tracking
relationships, and issuing alerts about anomalies to
operators. We present an online monitoring system (
illiad ) that models the state of the CPS as a function
of its relationships between constituent components,
using a combination of model-based and data-driven
strategies. In addition to accurate inference for state
estimation and anomaly tracking, illiad also exploits
the underlying network structure of the CPS (wired or
wireless) for state estimation purposes. We demonstrate
the application of illiad to two diverse settings: a
wireless sensor motes application and an IEEE 33-bus
microgrid.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2018:EUB,
author = "Liangda Li and Hongyuan Zha",
title = "Energy Usage Behavior Modeling in Energy
Disaggregation via {Hawkes} Processes",
journal = j-TIST,
volume = "9",
number = "3",
pages = "36:1--36:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3108413",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Energy disaggregation, the task of taking a whole home
electricity signal and decomposing it into its
component appliances, has been proved to be essential
in energy conservation research. One powerful cue for
breaking down the entire household's energy consumption
is user's daily energy usage behavior, which has so far
received little attention: existing works on energy
disaggregation mostly ignored the relationship between
the energy usages of various appliances by householders
across different time slots. The major challenge in
modeling such a relationship in that, with ambiguous
appliance usage membership of householders, we find it
difficult to appropriately model the influence between
appliances, since such influence is determined by human
behaviors in energy usage. To address this problem, we
propose to model the influence between householders'
energy usage behaviors directly through a novel
probabilistic model, which combines topic models with
the Hawkes processes. The proposed model simultaneously
disaggregates the whole home electricity signal into
each component appliance and infers the appliance usage
membership of household members and enables those two
tasks to mutually benefit each other. Experimental
results on both synthetic data and four real-world data
sets demonstrate the effectiveness of our model, which
outperforms state-of-the-art approaches in not only
decomposing the entire consumed energy to each
appliance in houses but also the inference of household
structures. We further analyze the inferred
appliance-householder assignment and the corresponding
influence within the appliance usage of each
householder and across different householders, which
provides insight into appealing human behavior patterns
in appliance usage.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tran:2018:RTF,
author = "Luan Tran and Hien To and Liyue Fan and Cyrus
Shahabi",
title = "A Real-Time Framework for Task Assignment in
Hyperlocal Spatial Crowdsourcing",
journal = j-TIST,
volume = "9",
number = "3",
pages = "37:1--37:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3078853",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:53 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Spatial Crowdsourcing (SC) is a novel platform that
engages individuals in the act of collecting various
types of spatial data. This method of data collection
can significantly reduce cost and turnover time and is
particularly useful in urban environmental sensing,
where traditional means fail to provide fine-grained
field data. In this study, we introduce hyperlocal
spatial crowdsourcing, where all workers who are
located within the spatiotemporal vicinity of a task
are eligible to perform the task (e.g., reporting the
precipitation level at their area and time). In this
setting, there is often a budget constraint, either for
every time period or for the entire campaign, on the
number of workers to activate to perform tasks. The
challenge is thus to maximize the number of assigned
tasks under the budget constraint despite the dynamic
arrivals of workers and tasks. We introduce a taxonomy
of several problem variants, such as
budget-per-time-period vs. budget-per-campaign and
binary-utility vs. distance-based-utility. We study the
hardness of the task assignment problem in the offline
setting and propose online heuristics which exploit the
spatial and temporal knowledge acquired over time. Our
experiments are conducted with spatial crowdsourcing
workloads generated by the SCAWG tool, and extensive
results show the effectiveness and efficiency of our
proposed solutions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2018:RCS,
author = "Dingwen Zhang and Huazhu Fu and Junwei Han and Ali
Borji and Xuelong Li",
title = "A Review of Co-Saliency Detection Algorithms:
Fundamentals, Applications, and Challenges",
journal = j-TIST,
volume = "9",
number = "4",
pages = "38:1--38:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3158674",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Co-saliency detection is a newly emerging and rapidly
growing research area in the computer vision community.
As a novel branch of visual saliency, co-saliency
detection refers to the discovery of common and salient
foregrounds from two or more relevant images, and it
can be widely used in many computer vision tasks. The
existing co-saliency detection algorithms mainly
consist of three components: extracting effective
features to represent the image regions, exploring the
informative cues or factors to characterize
co-saliency, and designing effective computational
frameworks to formulate co-saliency. Although numerous
methods have been developed, the literature is still
lacking a deep review and evaluation of co-saliency
detection techniques. In this article, we aim at
providing a comprehensive review of the fundamentals,
challenges, and applications of co-saliency detection.
Specifically, we provide an overview of some related
computer vision works, review the history of
co-saliency detection, summarize and categorize the
major algorithms in this research area, discuss some
open issues in this area, present the potential
applications of co-saliency detection, and finally
point out some unsolved challenges and promising future
works. We expect this review to be beneficial to both
fresh and senior researchers in this field and to give
insights to researchers in other related areas
regarding the utility of co-saliency detection
algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:VME,
author = "Bingsheng Wang and Zhiqian Chen and Arnold P.
Boedihardjo and Chang-Tien Lu",
title = "Virtual Metering: an Efficient Water Disaggregation
Algorithm via Nonintrusive Load Monitoring",
journal = j-TIST,
volume = "9",
number = "4",
pages = "39:1--39:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3141770",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The scarcity of potable water is a critical challenge
in many regions around the world. Previous studies have
shown that knowledge of device-level water usage can
lead to significant conservation. Although there is
considerable interest in determining discriminative
features via sparse coding for water disaggregation to
separate whole-house consumption into its component
appliances, existing methods lack a mechanism for
fitting coefficient distributions and are thus unable
to accurately discriminate parallel devices'
consumption. This article proposes a Bayesian
discriminative sparse coding model, referred to as
Virtual Metering (VM), for this disaggregation task.
Mixture-of-Gammas is employed for the prior
distribution of coefficients, contributing two
benefits: (i) guaranteeing the coefficients' sparseness
and non-negativity, and (ii) capturing the distribution
of active coefficients. The resulting method
effectively adapts the bases to aggregated consumption
to facilitate discriminative learning in the proposed
model, and devices' shape features are formalized and
incorporated into Bayesian sparse coding to direct the
learning of basis functions. Compact Gibbs Sampling
(CGS) is developed to accelerate the inference process
by utilizing the sparse structure of coefficients. The
empirical results obtained from applying the new model
to large-scale real and synthetic datasets revealed
that VM significantly outperformed the benchmark
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Fu:2018:MLM,
author = "Yanjie Fu and Junming Liu and Xiaolin Li and Hui
Xiong",
title = "A Multi-Label Multi-View Learning Framework for In-App
Service Usage Analysis",
journal = j-TIST,
volume = "9",
number = "4",
pages = "40:1--40:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3151937",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The service usage analysis, aiming at identifying
customers' messaging behaviors based on encrypted App
traffic flows, has become a challenging and emergent
task for service providers. Prior literature usually
starts from segmenting a traffic sequence into
single-usage subsequences, and then classify the
subsequences into different usage types. However, they
could suffer from inaccurate traffic segmentations and
mixed-usage subsequences. To address this challenge, we
exploit a multi-label multi-view learning strategy and
develop an enhanced framework for in-App usage
analytics. Specifically, we first devise an enhanced
traffic segmentation method to reduce mixed-usage
subsequences. Besides, we develop a multi-label
multi-view logistic classification method, which
comprises two alignments. The first alignment is to
make use of the classification consistency between
packet-length view and time-delay view of traffic
subsequences and improve classification accuracy. The
second alignment is to combine the classification of
single-usage subsequence and the post-classification of
mixed-usage subsequences into a unified multi-label
logistic classification problem. Finally, we present
extensive experiments with real-world datasets to
demonstrate the effectiveness of our approach. We find
that the proposed multi-label multi-view framework can
help overcome the pain of mixed-usage subsequences and
can be generalized to latent activity analysis in
sequential data, beyond in-App usage analytics.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:CAB,
author = "Pengwei Wang and Lei Ji and Jun Yan and Dejing Dou and
Nisansa {De Silva} and Yong Zhang and Lianwen Jin",
title = "Concept and Attention-Based {CNN} for Question
Retrieval in Multi-View Learning",
journal = j-TIST,
volume = "9",
number = "4",
pages = "41:1--41:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3151957",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Question retrieval, which aims to find similar
versions of a given question, is playing a pivotal role
in various question answering (QA) systems. This task
is quite challenging, mainly in regard to five aspects:
synonymy, polysemy, word order, question length, and
data sparsity. In this article, we propose a unified
framework to simultaneously handle these five problems.
We use the word combined with corresponding concept
information to handle the synonymy problem and the
polysemous problem. Concept embedding and word
embedding are learned at the same time from both the
context-dependent and context-independent views. To
handle the word-order problem, we propose a high-level
feature-embedded convolutional semantic model to learn
question embedding by inputting concept embedding and
word embedding. Due to the fact that the lengths of
some questions are long, we propose a value-based
convolutional attentional method to enhance the
proposed high-level feature-embedded convolutional
semantic model in learning the key parts of the
question and the answer. The proposed high-level
feature-embedded convolutional semantic model nicely
represents the hierarchical structures of word
information and concept information in sentences with
their layer-by-layer convolution and pooling. Finally,
to resolve data sparsity, we propose using the
multi-view learning method to train the attention-based
convolutional semantic model on question-answer pairs.
To the best of our knowledge, we are the first to
propose simultaneously handling the above five problems
in question retrieval using one framework. Experiments
on three real question-answering datasets show that the
proposed framework significantly outperforms the
state-of-the-art solutions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Khan:2018:NIC,
author = "Naimul Mefraz Khan and Riadh Ksantini and Ling Guan",
title = "A Novel Image-Centric Approach Toward Direct Volume
Rendering",
journal = j-TIST,
volume = "9",
number = "4",
pages = "42:1--42:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3152875",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Transfer function (TF) generation is a fundamental
problem in direct volume rendering (DVR). A TF maps
voxels to color and opacity values to reveal inner
structures. Existing TF tools are complex and
unintuitive for the users who are more likely to be
medical professionals than computer scientists. In this
article, we propose a novel image-centric method for TF
generation where instead of complex tools, the user
directly manipulates volume data to generate DVR. The
user's work is further simplified by presenting only
the most informative volume slices for selection. Based
on the selected parts, the voxels are classified using
our novel sparse nonparametric support vector machine
classifier, which combines both local and near-global
distributional information of the training data. The
voxel classes are mapped to aesthetically pleasing and
distinguishable color and opacity values using harmonic
colors. Experimental results on several benchmark
datasets and a detailed user survey show the
effectiveness of the proposed method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2018:QBP,
author = "Michael Xuelin Huang and Jiajia Li and Grace Ngai and
Hong Va Leong",
title = "Quick Bootstrapping of a Personalized Gaze Model from
Real-Use Interactions",
journal = j-TIST,
volume = "9",
number = "4",
pages = "43:1--43:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3156682",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Understanding human visual attention is essential for
understanding human cognition, which in turn benefits
human--computer interaction. Recent work has
demonstrated a Personalized, Auto-Calibrating
Eye-tracking (PACE) system, which makes it possible to
achieve accurate gaze estimation using only an
off-the-shelf webcam by identifying and collecting data
implicitly from user interaction events. However, this
method is constrained by the need for large amounts of
well-annotated data. We thus present fast-PACE, an
adaptation to PACE that exploits knowledge from
existing data from different users to accelerate the
learning speed of the personalized model. The result is
an adaptive, data-driven approach that continuously
``learns'' its user and recalibrates, adapts, and
improves with additional usage by a user. Experimental
evaluations of fast-PACE demonstrate its competitive
accuracy in iris localization, validity of alignment
identification between gaze and interactions, and
effectiveness of gaze transfer. In general, fast-PACE
achieves an initial visual error of 3.98 degrees and
then steadily improves to 2.52 degrees given
incremental interaction-informed data. Our performance
is comparable to state-of-the-art, but without the need
for explicit training or calibration. Our technique
addresses the data quality and quantity problems. It
therefore has the potential to enable comprehensive
gaze-aware applications in the wild.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Kulev:2018:BAI,
author = "Igor Kulev and Pearl Pu and Boi Faltings",
title = "A {Bayesian} Approach to Intervention-Based
Clustering",
journal = j-TIST,
volume = "9",
number = "4",
pages = "44:1--44:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3156683",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "An important task for intelligent healthcare systems
is to predict the effect of a new intervention on
individuals. This is especially true for medical
treatments. For example, consider patients who do not
respond well to a new drug or have adversary reactions.
Predicting the likelihood of positive or negative
response before trying the drug on the patient can
potentially save his or her life. We are therefore
interested in identifying distinctive subpopulations
that respond differently to a given intervention. For
this purpose, we have developed a novel technique,
Intervention-based Clustering, based on a Bayesian
mixture model. Compared to the baseline techniques, the
novelty of our approach lies in its ability to model
complex decision boundaries by using soft clustering,
thus predicting the effect for individuals more
accurately. It can also incorporate prior knowledge,
making the method useful even for smaller datasets. We
demonstrate how our method works by applying it to both
simulated and real data. Results of our evaluation show
that our model has strong predictive power and is
capable of producing high-quality clusters compared to
the baseline methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lu:2018:SPA,
author = "Jing Lu and Doyen Sahoo and Peilin Zhao and Steven C.
H. Hoi",
title = "Sparse Passive-Aggressive Learning for Bounded Online
Kernel Methods",
journal = j-TIST,
volume = "9",
number = "4",
pages = "45:1--45:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3156684",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One critical deficiency of traditional online kernel
learning methods is their unbounded and growing number
of support vectors in the online learning process,
making them inefficient and non-scalable for
large-scale applications. Recent studies on scalable
online kernel learning have attempted to overcome this
shortcoming, e.g., by imposing a constant budget on the
number of support vectors. Although they attempt to
bound the number of support vectors at each online
learning iteration, most of them fail to bound the
number of support vectors for the final output
hypothesis, which is often obtained by averaging the
series of hypotheses over all the iterations. In this
article, we propose a novel framework for bounded
online kernel methods, named ``Sparse
Passive-Aggressive (SPA)'' learning, which is able to
yield a final output kernel-based hypothesis with a
bounded number of support vectors. Unlike the common
budget maintenance strategy used by many existing
budget online kernel learning approaches, the idea of
our approach is to attain the bounded number of support
vectors using an efficient stochastic sampling strategy
that samples an incoming training example as a new
support vector with a probability proportional to its
loss suffered. We theoretically prove that SPA achieves
an optimal mistake bound in expectation, and we
empirically show that it outperforms various budget
online kernel learning algorithms. Finally, in addition
to general online kernel learning tasks, we also apply
SPA to derive bounded online multiple-kernel learning
algorithms, which can significantly improve the
scalability of traditional Online Multiple-Kernel
Classification (OMKC) algorithms while achieving
satisfactory learning accuracy as compared with the
existing unbounded OMKC algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Reyes:2018:ESP,
author = "Oscar Reyes and Sebasti{\'a}n Ventura",
title = "Evolutionary Strategy to Perform Batch-Mode Active
Learning on Multi-Label Data",
journal = j-TIST,
volume = "9",
number = "4",
pages = "46:1--46:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3161606",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multi-label learning has become an important area of
research owing to the increasing number of real-world
problems that contain multi-label data. Data labeling
is an expensive process that requires expert handling.
The annotation of multi-label data is laborious since a
human expert needs to consider the presence/absence of
each possible label. Consequently, numerous modern
multi-label problems may involve a small number of
labeled examples and plentiful unlabeled examples
simultaneously. Active learning methods allow us to
induce better classifiers by selecting the most useful
unlabeled data, thus considerably reducing the labeling
effort and the cost of training an accurate model.
Batch-mode active learning methods focus on selecting a
set of unlabeled examples in each iteration in such a
way that the selected examples are informative and as
diverse as possible. This article presents a strategy
to perform batch-mode active learning on multi-label
data. The batch-mode active learning is formulated as a
multi-objective problem, and it is solved by means of
an evolutionary algorithm. Extensive experiments were
conducted in a large collection of datasets, and the
experimental results confirmed the effectiveness of our
proposal for better batch-mode multi-label active
learning.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2018:MQC,
author = "Qin Chen and Qinmin Hu and Jimmy Xiangji Huang and
Liang He",
title = "Modeling Queries with Contextual Snippets for
Information Retrieval",
journal = j-TIST,
volume = "9",
number = "4",
pages = "47:1--47:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3161607",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Query expansion under the pseudo-relevance feedback
(PRF) framework has been extensively studied in
information retrieval. However, most expansion methods
are mainly based on the statistics of single terms,
which can generate plenty of irrelevant query terms and
decrease retrieval performance. To alleviate this
problem, we propose an approach that adapts the
PRF-based contextual snippets into a context-aware
topic model to enhance query representations.
Specifically, instead of selecting a series of
independent terms, we make full use of the query
contextual information and focus on the snippets with
the length of n in the PRF documents. Furthermore, we
propose a context-aware topic (CAT) model to mine the
topic distributions of the query-relevant snippets,
namely, fine contextual snippets. In contrast to the
traditional topic models that infer the topics from the
whole corpus, we establish a bridge between the
snippets and the corresponding PRF documents, which can
be used for modeling the topics more precisely and
efficiently. Finally, the topic distributions of the
fine snippets are used for context-aware and
topic-sensitive query representations. To evaluate the
performance of our approach, we integrate the obtained
queries into a topic-based hybrid retrieval model and
conduct extensive experiments on various TREC
collections. The experimental results show that our
query-modeling approach is more effective in boosting
retrieval performance compared with the
state-of-the-art methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2018:FCD,
author = "Qi Liu and Runze Wu and Enhong Chen and Guandong Xu
and Yu Su and Zhigang Chen and Guoping Hu",
title = "Fuzzy Cognitive Diagnosis for Modelling Examinee
Performance",
journal = j-TIST,
volume = "9",
number = "4",
pages = "48:1--48:??",
month = feb,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3168361",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Mar 22 10:01:54 MDT 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Recent decades have witnessed the rapid growth of
educational data mining (EDM), which aims at
automatically extracting valuable information from
large repositories of data generated by or related to
people's learning activities in educational settings.
One of the key EDM tasks is cognitive modelling with
examination data, and cognitive modelling tries to
profile examinees by discovering their latent knowledge
state and cognitive level (e.g. the proficiency of
specific skills). However, to the best of our
knowledge, the problem of extracting information from
both objective and subjective examination problems to
achieve more precise and interpretable cognitive
analysis remains underexplored. To this end, we propose
a fuzzy cognitive diagnosis framework (FuzzyCDF) for
examinees' cognitive modelling with both objective and
subjective problems. Specifically, to handle the
partially correct responses on subjective problems, we
first fuzzify the skill proficiency of examinees. Then
we combine fuzzy set theory and educational hypotheses
to model the examinees' mastery on the problems based
on their skill proficiency. Finally, we simulate the
generation of examination score on each problem by
considering slip and guess factors. In this way, the
whole diagnosis framework is built. For further
comprehensive verification, we apply our FuzzyCDF to
three classical cognitive assessment tasks, i.e.,
predicting examinee performance, slip and guess
detection, and cognitive diagnosis visualization.
Extensive experiments on three real-world datasets for
these assessment tasks prove that FuzzyCDF can reveal
the knowledge states and cognitive level of the
examinees effectively and interpretatively.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2018:DLE,
author = "Zixing Zhang and J{\"u}rgen Geiger and Jouni
Pohjalainen and Amr El-Desoky Mousa and Wenyu Jin and
Bj{\"o}rn Schuller",
title = "Deep Learning for Environmentally Robust Speech
Recognition: an Overview of Recent Developments",
journal = j-TIST,
volume = "9",
number = "5",
pages = "49:1--49:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3178115",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Eliminating the negative effect of non-stationary
environmental noise is a long-standing research topic
for automatic speech recognition but still remains an
important challenge. Data-driven supervised approaches,
especially the ones based on deep neural networks, have
recently emerged as potential alternatives to
traditional unsupervised approaches and with sufficient
training, can alleviate the shortcomings of the
unsupervised methods in various real-life acoustic
environments. In this light, we review recently
developed, representative deep learning approaches for
tackling non-stationary additive and convolutional
degradation of speech with the aim of providing
guidelines for those involved in the development of
environmentally robust speech recognition systems. We
separately discuss single- and multi-channel techniques
developed for the front-end and back-end of speech
recognition systems, as well as joint front-end and
back-end training frameworks. In the meanwhile, we
discuss the pros and cons of these approaches and
provide their experimental results on benchmark
databases. We expect that this overview can facilitate
the development of the robustness of speech recognition
systems in acoustic noisy environments.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2018:MSM,
author = "Qiang Liu and Feng Yu and Shu Wu and Liang Wang",
title = "Mining Significant Microblogs for Misinformation
Identification: an Attention-Based Approach",
journal = j-TIST,
volume = "9",
number = "5",
pages = "50:1--50:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3173458",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "With the rapid growth of social media, massive
misinformation is also spreading widely on social
media, e.g., Weibo and Twitter, and brings negative
effects to human life. Today, automatic misinformation
identification has drawn attention from academic and
industrial communities. Whereas an event on social
media usually consists of multiple microblogs, current
methods are mainly constructed based on global
statistical features. However, information on social
media is full of noise, which should be alleviated.
Moreover, most of the microblogs about an event have
little contribution to the identification of
misinformation, where useful information can be easily
overwhelmed by useless information. Thus, it is
important to mine significant microblogs for
constructing a reliable misinformation identification
method. In this article, we propose an attention-based
approach for identification of misinformation (AIM).
Based on the attention mechanism, AIM can select
microblogs with the largest attention values for
misinformation identification. The attention mechanism
in AIM contains two parts: content attention and
dynamic attention. Content attention is the
calculated-based textual features of each microblog.
Dynamic attention is related to the time interval
between the posting time of a microblog and the
beginning of the event. To evaluate AIM, we conduct a
series of experiments on the Weibo and Twitter
datasets, and the experimental results show that the
proposed AIM model outperforms the state-of-the-art
methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shah:2018:DOL,
author = "Ankit Shah and Rajesh Ganesan and Sushil Jajodia and
Hasan Cam",
title = "Dynamic Optimization of the Level of Operational
Effectiveness of a {CSOC} Under Adverse Conditions",
journal = j-TIST,
volume = "9",
number = "5",
pages = "51:1--51:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3173457",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The analysts at a cybersecurity operations center
(CSOC) analyze the alerts that are generated by
intrusion detection systems (IDSs). Under normal
operating conditions, sufficient numbers of analysts
are available to analyze the alert workload. For the
purpose of this article, this means that the
cybersecurity analysts in each shift can fully
investigate each and every alert that is generated by
the IDSs in a reasonable amount of time and perform
their normal tasks in a shift. Normal tasks include
analysis time, time to attend training programs, report
writing time, personal break time, and time to update
the signatures on new patterns in alerts as detected by
the IDS. There are several disruptive factors that
occur randomly and can adversely impact the normal
operating condition of a CSOC, such as (1) higher alert
generation rates from a few IDSs, (2) new alert
patterns that decrease the throughput of the alert
analysis process, and (3) analyst absenteeism. The
impact of the preceding factors is that the alerts wait
for a long duration before being analyzed, which
impacts the level of operational effectiveness (LOE) of
the CSOC. To return the CSOC to normal operating
conditions, the manager of a CSOC can take several
actions, such as increasing the alert analysis time
spent by analysts in a shift by canceling a training
program, spending some of his own time to assist the
analysts in alert investigation, and calling upon the
on-call analyst workforce to boost the service rate of
alerts. However, additional resources are limited in
quantity over a 14-day work cycle, and the CSOC manager
must determine when and how much action to take in the
face of uncertainty, which arises from both the
intensity and the random occurrences of the disruptive
factors. The preceding decision by the CSOC manager is
nontrivial and is often made in an ad hoc manner using
prior experiences. This work develops a reinforcement
learning (RL) model for optimizing the LOE throughout
the entire 14-day work cycle of a CSOC in the face of
uncertainties due to disruptive events. Results
indicate that the RL model is able to assist the CSOC
manager with a decision support tool to make better
decisions than current practices in determining when
and how much resource to allocate when the LOE of a
CSOC deviates from the normal operating condition.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jian:2018:EMI,
author = "Ling Jian and Jundong Li and Huan Liu",
title = "Exploiting Multilabel Information for Noise-Resilient
Feature Selection",
journal = j-TIST,
volume = "9",
number = "5",
pages = "52:1--52:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3158675",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In a conventional supervised learning paradigm, each
data instance is associated with one single class
label. Multilabel learning differs in the way that data
instances may belong to multiple concepts
simultaneously, which naturally appear in a variety of
high impact domains, ranging from bioinformatics and
information retrieval to multimedia analysis. It
targets leveraging the multiple label information of
data instances to build a predictive learning model
that can classify unlabeled instances into one or
multiple predefined target classes. In multilabel
learning, even though each instance is associated with
a rich set of class labels, the label information could
be noisy and incomplete as the labeling process is both
time consuming and labor expensive, leading to
potential missing annotations or even erroneous
annotations. The existence of noisy and missing labels
could negatively affect the performance of underlying
learning algorithms. More often than not, multilabeled
data often has noisy, irrelevant, and redundant
features of high dimensionality. The existence of these
uninformative features may also deteriorate the
predictive power of the learning model due to the curse
of dimensionality. Feature selection, as an effective
dimensionality reduction technique, has shown to be
powerful in preparing high-dimensional data for
numerous data mining and machine-learning tasks.
However, a vast majority of existing multilabel feature
selection algorithms either boil down to solving
multiple single-labeled feature selection problems or
directly make use of the imperfect labels to guide the
selection of representative features. As a result, they
may not be able to obtain discriminative features
shared across multiple labels. In this article, to
bridge the gap between a rich source of multilabel
information and its blemish in practical usage, we
propose a novel noise-resilient multilabel informed
feature selection framework (MIFS) by exploiting the
correlations among different labels. In particular, to
reduce the negative effects of imperfect label
information in obtaining label correlations, we
decompose the multilabel information of data instances
into a low-dimensional space and then employ the
reduced label representation to guide the feature
selection phase via a joint sparse regression
framework. Empirical studies on both synthetic and
real-world datasets demonstrate the effectiveness and
efficiency of the proposed MIFS framework.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shen:2018:MDH,
author = "Xiaobo Shen and Fumin Shen and Li Liu and Yun-Hao Yuan
and Weiwei Liu and Quan-Sen Sun",
title = "Multiview Discrete Hashing for Scalable Multimedia
Search",
journal = j-TIST,
volume = "9",
number = "5",
pages = "53:1--53:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3178119",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Hashing techniques have recently gained increasing
research interest in multimedia studies. Most existing
hashing methods only employ single features for hash
code learning. Multiview data with each view
corresponding to a type of feature generally provides
more comprehensive information. How to efficiently
integrate multiple views for learning compact hash
codes still remains challenging. In this article, we
propose a novel unsupervised hashing method, dubbed
multiview discrete hashing (MvDH), by effectively
exploring multiview data. Specifically, MvDH performs
matrix factorization to generate the hash codes as the
latent representations shared by multiple views, during
which spectral clustering is performed simultaneously.
The joint learning of hash codes and cluster labels
enables that MvDH can generate more discriminative hash
codes, which are optimal for classification. An
efficient alternating algorithm is developed to solve
the proposed optimization problem with guaranteed
convergence and low computational complexity. The
binary codes are optimized via the discrete cyclic
coordinate descent (DCC) method to reduce the
quantization errors. Extensive experimental results on
three large-scale benchmark datasets demonstrate the
superiority of the proposed method over several
state-of-the-art methods in terms of both accuracy and
scalability.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2018:AEB,
author = "Chen Li and William K. Cheung and Jiming Liu and
Joseph K. Ng",
title = "Automatic Extraction of Behavioral Patterns for
Elderly Mobility and Daily Routine Analysis",
journal = j-TIST,
volume = "9",
number = "5",
pages = "54:1--54:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3178116",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The elderly living in smart homes can have their daily
movement recorded and analyzed. As different elders can
have their own living habits, a methodology that can
automatically identify their daily activities and
discover their daily routines will be useful for better
elderly care and support. In this article, we focus on
automatic detection of behavioral patterns from the
trajectory data of an individual for activity
identification as well as daily routine discovery. The
underlying challenges lie in the need to consider
longer-range dependency of the sensor triggering events
and spatiotemporal variations of the behavioral
patterns exhibited by humans. We propose to represent
the trajectory data using a behavior-aware flow graph
that is a probabilistic finite state automaton with its
nodes and edges attributed with some local
behavior-aware features. We identify the underlying
subflows as the behavioral patterns using the kernel k
-means algorithm. Given the identified activities, we
propose a novel nominal matrix factorization method
under a Bayesian framework with Lasso to extract highly
interpretable daily routines. For empirical evaluation,
the proposed methodology has been compared with a
number of existing methods based on both synthetic and
publicly available real smart home datasets with
promising results obtained. We also discuss how the
proposed unsupervised methodology can be used to
support exploratory behavior analysis for elderly
care.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:IHU,
author = "Jun-Zhe Wang and Jiun-Long Huang",
title = "On Incremental High Utility Sequential Pattern
Mining",
journal = j-TIST,
volume = "9",
number = "5",
pages = "55:1--55:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3178114",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "High utility sequential pattern (HUSP) mining is an
emerging topic in pattern mining, and only a few
algorithms have been proposed to address it. In
practice, most sequence databases usually grow over
time, and it is inefficient for existing algorithms to
mine HUSPs from scratch when databases grow with a
small portion of updates. In view of this, we propose
the IncUSP-Miner$^+$ algorithm to mine HUSPs
incrementally. Specifically, to avoid redundant
re-computations, we propose a tighter upper bound of
the utility of a sequence, called Tight Sequence
Utility (TSU), and then we design a novel data
structure, called the candidate pattern tree, to buffer
the sequences whose TSU values are greater than or
equal to the minimum utility threshold in the original
database. Accordingly, to avoid keeping a huge amount
of utility information for each sequence, a set of
concise utility information is designed to be stored in
each tree node. To improve the mining efficiency,
several strategies are proposed to reduce the amount of
computation for utility update and the scopes of
database scans. Moreover, several strategies are also
proposed to properly adjust the candidate pattern tree
for the support of multiple database updates.
Experimental results on some real and synthetic
datasets show that IncUSP-Miner$^+$ is able to
efficiently mine HUSPs incrementally.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zdesar:2018:OVP,
author = "Andrej Zdesar and Igor Skrjanc",
title = "Optimum Velocity Profile of Multiple
{Bernstein--B{\'e}zier} Curves Subject to Constraints
for Mobile Robots",
journal = j-TIST,
volume = "9",
number = "5",
pages = "56:1--56:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3183891",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article deals with trajectory planning that is
suitable for nonholonomic differentially driven wheeled
mobile robots. The path is approximated with a spline
that consists of multiple Bernstein-B{\'e}zier curves
that are merged together in a way that continuous
curvature of the spline is achieved. The article
presents the approach for optimization of velocity
profile of Bernstein-B{\'e}zier spline subject to
velocity and acceleration constraints. For the purpose
of optimization, velocity and turning points are
introduced. Based on these singularity points, local
segments are defined where local velocity profiles are
optimized independently of each other. From the locally
optimum velocity profiles, the global optimum velocity
profile is determined. Since each local velocity
profile can be evaluated independently, the algorithm
is suitable for concurrent implementation and
modification of one part of the curve does not require
recalculation of all local velocity profiles. These
properties enable efficient implementation of the
optimization algorithm. The optimization algorithm is
also suitable for the splines that consist of
Bernstein-B{\'e}zier curves that have substantially
different lengths. The proposed optimization approach
was experimentally evaluated and validated in
simulation environment and on real mobile robots.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Peng:2018:ICD,
author = "Chong Peng and Zhao Kang and Shuting Cai and Qiang
Cheng",
title = "Integrate and Conquer: Double-Sided Two-Dimensional
$k$-Means Via Integrating of Projection and Manifold
Construction",
journal = j-TIST,
volume = "9",
number = "5",
pages = "57:1--57:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200488",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In this article, we introduce a novel, general
methodology, called integrate and conquer, for
simultaneously accomplishing the tasks of feature
extraction, manifold construction, and clustering,
which is taken to be superior to building a clustering
method as a single task. When the proposed novel
methodology is used on two-dimensional (2D) data, it
naturally induces a new clustering method highly
effective on 2D data. Existing clustering algorithms
usually need to convert 2D data to vectors in a
preprocessing step, which, unfortunately, severely
damages 2D spatial information and omits inherent
structures and correlations in the original data. The
induced new clustering method can overcome the
matrix-vectorization-related issues to enhance the
clustering performance on 2D matrices. More
specifically, the proposed methodology mutually
enhances three tasks of finding subspaces, learning
manifolds, and constructing data representation in a
seamlessly integrated fashion. When used on 2D data, we
seek two projection matrices with optimal numbers of
directions to project the data into low-rank,
noise-mitigated, and the most expressive subspaces, in
which manifolds are adaptively updated according to the
projections, and new data representation is built with
respect to the projected data by accounting for
nonlinearity via adaptive manifolds. Consequently, the
learned subspaces and manifolds are clean and
intrinsic, and the new data representation is
discriminative and robust. Extensive experiments have
been conducted and the results confirm the
effectiveness of the proposed methodology and
algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Huang:2018:CFR,
author = "Dingjiang Huang and Shunchang Yu and Bin Li and Steven
C. H. Hoi and Shuigeng Zhou",
title = "Combination Forecasting Reversion Strategy for Online
Portfolio Selection",
journal = j-TIST,
volume = "9",
number = "5",
pages = "58:1--58:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200692",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Machine learning and artificial intelligence
techniques have been applied to construct online
portfolio selection strategies recently. A popular and
state-of-the-art family of strategies is to explore the
reversion phenomenon through online learning algorithms
and statistical prediction models. Despite gaining
promising results on some benchmark datasets, these
strategies often adopt a single model based on a
selection criterion (e.g., breakdown point) for
predicting future price. However, such model selection
is often unstable and may cause unnecessarily high
variability in the final estimation, leading to poor
prediction performance in real datasets and thus
non-optimal portfolios. To overcome the drawbacks, in
this article, we propose to exploit the reversion
phenomenon by using combination forecasting estimators
and design a novel online portfolio selection strategy,
named Combination Forecasting Reversion (CFR), which
outputs optimal portfolios based on the improved
reversion estimator. We further present two efficient
CFR implementations based on online Newton step (ONS)
and online gradient descent (OGD) algorithms,
respectively, and theoretically analyze their regret
bounds, which guarantee that the online CFR model
performs as well as the best CFR model in hindsight. We
evaluate the proposed algorithms on various real
markets with extensive experiments. Empirical results
show that CFR can effectively overcome the drawbacks of
existing reversion strategies and achieve the
state-of-the-art performance.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rossi:2018:IVG,
author = "Ryan A. Rossi and Nesreen K. Ahmed and Rong Zhou and
Hoda Eldardiry",
title = "Interactive Visual Graph Mining and Learning",
journal = j-TIST,
volume = "9",
number = "5",
pages = "59:1--59:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200764",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article presents a platform for interactive graph
mining and relational machine learning called GraphVis.
The platform combines interactive visual
representations with state-of-the-art graph mining and
relational machine learning techniques to aid in
revealing important insights quickly as well as
learning an appropriate and highly predictive model for
a particular task (e.g., classification, link
prediction, discovering the roles of nodes, and finding
influential nodes). Visual representations and
interaction techniques and tools are developed for
simple, fast, and intuitive real-time interactive
exploration, mining, and modeling of graph data. In
particular, we propose techniques for interactive
relational learning (e.g., node/link classification),
interactive link prediction and weighting, role
discovery and community detection, higher-order network
analysis (via graphlets, network motifs), among others.
GraphVis also allows for the refinement and tuning of
graph mining and relational learning methods for
specific application domains and constraints via an
end-to-end interactive visual analytic pipeline that
learns, infers, and provides rapid interactive
visualization with immediate feedback at each
change/prediction in real-time. Other key aspects
include interactive filtering, querying, ranking,
manipulating, exporting, as well as tools for dynamic
network analysis and visualization, interactive graph
generators (including new block model approaches), and
a variety of multi-level network analysis techniques.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Peng:2018:EPH,
author = "Xuefeng Peng and Li-Kai Chi and Jiebo Luo",
title = "The Effect of Pets on Happiness: a Large-Scale
Multi-Factor Analysis Using Social Multimedia",
journal = j-TIST,
volume = "9",
number = "5",
pages = "60:1--60:??",
month = jul,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200751",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "From reducing stress and loneliness, to boosting
productivity and overall well-being, pets are believed
to play a significant role in people's daily lives.
Many traditional studies have identified that frequent
interactions with pets could make individuals become
healthier and more optimistic, and ultimately enjoy a
happier life. However, most of those studies are not
only restricted in scale, but also may carry biases by
using subjective self-reports, interviews, and
questionnaires as the major approaches. In this
article, we leverage large-scale data collected from
social media and the state-of-the-art deep learning
technologies to study this phenomenon in depth and
breadth. Our study includes five major steps: (1)
collecting timeline posts from around 20,000 Instagram
users; (2) using face detection and recognition on 2
million photos to infer users' demographics,
relationship status, and whether having children, (3)
analyzing a user's degree of happiness based on images
and captions via smiling classification and textual
sentiment analysis; (4) applying transfer learning
techniques to retrain the final layer of the Inception
v3 model for pet classification; and (5) analyzing the
effects of pets on happiness in terms of multiple
factors of user demographics. Our main results have
demonstrated the efficacy of our proposed method with
many new insights. We believe this method is also
applicable to other domains as a scalable, efficient,
and effective methodology for modeling and analyzing
social behaviors and psychological well-being. In
addition, to facilitate the research involving human
faces, we also release our dataset of 700K analyzed
faces.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:TTP,
author = "Weiqing Wang and Hongzhi Yin and Xingzhong Du and Quoc
Viet Hung Nguyen and Xiaofang Zhou",
title = "{TPM}: a Temporal Personalized Model for Spatial Item
Recommendation",
journal = j-TIST,
volume = "9",
number = "6",
pages = "61:1--61:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3230706",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3230706",
abstract = "With the rapid development of location-based social
networks (LBSNs), spatial item recommendation has
become an important way of helping users discover
interesting locations to increase their engagement with
location-based services. The availability of spatial,
temporal, and social information in LBSNs offers an
unprecedented opportunity to enhance the spatial item
recommendation. Many previous works studied spatial and
social influences on spatial item recommendation in
LBSNs. Due to the strong correlations between a user's
check-in time and the corresponding check-in location,
which include the sequential influence and temporal
cyclic effect, it is essential for spatial item
recommender system to exploit the temporal effect to
improve the recommendation accuracy. Leveraging
temporal information in spatial item recommendation is,
however, very challenging, considering (1) when
integrating sequential influences, users' check-in data
in LBSNs has a low sampling rate in both space and
time, which renders existing location prediction
techniques on GPS trajectories ineffective, and the
prediction space is extremely large, with millions of
distinct locations as the next prediction target, which
impedes the application of classical Markov chain
models; (2) there are various temporal cyclic patterns
(i.e., daily, weekly, and monthly) in LBSNs, but
existing work is limited to one specific pattern; and
(3) there is no existing framework that unifies users'
personal interests, temporal cyclic patterns, and the
sequential influence of recently visited locations in a
principled manner. In light of the above challenges, we
propose a Temporal Personalized Model ( TPM ), which
introduces a novel latent variable topic-region to
model and fuse sequential influence, cyclic patterns
with personal interests in the latent and exponential
space. The advantages of modeling the temporal effect
at the topic-region level include a significantly
reduced prediction space, an effective alleviation of
data sparsity, and a direct expression of the semantic
meaning of users' spatial activities. Moreover, we
introduce two methods to model the effect of various
cyclic patterns. The first method is a time indexing
scheme that encodes the effect of various cyclic
patterns into a binary code. However, the indexing
scheme faces the data sparsity problem in each time
slice. To deal with this data sparsity problem, the
second method slices the time according to each cyclic
pattern separately and explores these patterns in a
joint additive model. Furthermore, we design an
asymmetric Locality Sensitive Hashing (ALSH) technique
to speed up the online top- k recommendation process by
extending the traditional LSH. We evaluate the
performance of TPM on two real datasets and one
large-scale synthetic dataset. The performance of TPM
in recommending cold-start items is also evaluated. The
results demonstrate a significant improvement in TPM's
ability to recommend spatial items, in terms of both
effectiveness and efficiency, compared with the
state-of-the-art methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Lucchese:2018:XCL,
author = "Claudio Lucchese and Franco Maria Nardini and
Salvatore Orlando and Raffaele Perego and Fabrizio
Silvestri and Salvatore Trani",
title = "{X-CLEaVER}: Learning Ranking Ensembles by Growing and
Pruning Trees",
journal = j-TIST,
volume = "9",
number = "6",
pages = "62:1--62:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3205453",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3205453",
abstract = "Learning-to-Rank (LtR) solutions are commonly used in
large-scale information retrieval systems such as Web
search engines, which have to return highly relevant
documents in response to user query within fractions of
seconds. The most effective LtR algorithms adopt a
gradient boosting approach to build additive ensembles
of weighted regression trees. Since the required
ranking effectiveness is achieved with very large
ensembles, the impact on response time and query
throughput of these solutions is not negligible. In
this article, we propose X-CLE aVER, an iterative
meta-algorithm able to build more efficient and
effective ranking ensembles. X-CLEaVER interleaves the
iterations of a given gradient boosting learning
algorithm with pruning and re-weighting phases. First,
redundant trees are removed from the given ensemble,
then the weights of the remaining trees are fine-tuned
by optimizing the desired ranking quality metric. We
propose and analyze several pruning strategies and we
assess their benefits showing that interleaving pruning
and re-weighting phases during learning is more
effective than applying a single post-learning
optimization step. Experiments conducted using two
publicly available LtR datasets show that X-CLEaVER can
be successfully exploited on top of several LtR
algorithms as it is effective in optimizing the
effectiveness of the learnt ensembles, thus obtaining
more compact forests that hence are much more efficient
at scoring time.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:LUC,
author = "Pengyang Wang and Yanjie Fu and Jiawei Zhang and
Xiaolin Li and Dan Lin",
title = "Learning Urban Community Structures: a Collective
Embedding Perspective with Periodic Spatial-temporal
Mobility Graphs",
journal = j-TIST,
volume = "9",
number = "6",
pages = "63:1--63:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3209686",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Learning urban community structures refers to the
efforts of quantifying, summarizing, and representing
an urban community's (i) static structures, e.g.,
Point-Of-Interests (POIs) buildings and corresponding
geographic allocations, and (ii) dynamic structures,
e.g., human mobility patterns among POIs. By learning
the community structures, we can better quantitatively
represent urban communities and understand their
evolutions in the development of cities. This can help
us boost commercial activities, enhance public
security, foster social interactions, and, ultimately,
yield livable, sustainable, and viable environments.
However, due to the complex nature of urban systems, it
is traditionally challenging to learn the structures of
urban communities. To address this problem, in this
article, we propose a collective embedding framework to
learn the community structure from multiple periodic
spatial-temporal graphs of human mobility.
Specifically, we first exploit a probabilistic
propagation-based approach to create a set of mobility
graphs from periodic human mobility records. In these
mobility graphs, the static POIs are regarded as
vertexes, the dynamic mobility connectivities between
POI pairs are regarded as edges, and the edge weights
periodically evolve over time. A collective deep
auto-encoder method is then developed to
collaboratively learn the embeddings of POIs from
multiple spatial-temporal mobility graphs. In addition,
we develop a Unsupervised Graph based Weighted
Aggregation method to align and aggregate the POI
embeddings into the representation of the community
structures. We apply the proposed embedding framework
to two applications (i.e., spotting vibrant communities
and predicting housing price return rates) to evaluate
the performance of our proposed method. Extensive
experimental results on real-world urban communities
and human mobility data demonstrate the effectiveness
of the proposed collective embedding framework.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Anaissi:2018:AOO,
author = "Ali Anaissi and Nguyen Lu Dang Khoa and Thierry
Rakotoarivelo and Mehrisadat Makki Alamdari and Yang
Wang",
title = "Adaptive Online One-Class Support Vector Machines with
Applications in Structural Health Monitoring",
journal = j-TIST,
volume = "9",
number = "6",
pages = "64:1--64:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3230708",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "One-class support vector machine (OCSVM) has been
widely used in the area of structural health
monitoring, where only data from one class (i.e.,
healthy) are available. Incremental learning of OCSVM
is critical for online applications in which huge data
streams continuously arrive and the healthy data
distribution may vary over time. This article proposes
a novel adaptive self-advised online OCSVM that
incrementally tunes the kernel parameter and decides
whether a model update is required or not. As opposed
to existing methods, this novel online algorithm does
not rely on any fixed threshold, but it uses the slack
variables in the OCSVM to determine which new data
points should be included in the training set and
trigger a model update. The algorithm also
incrementally tunes the kernel parameter of OCSVM
automatically based on the spatial locations of the
edge and interior samples in the training data with
respect to the constructed hyperplane of OCSVM. This
new online OCSVM algorithm was extensively evaluated
using synthetic data and real data from case studies in
structural health monitoring. The results showed that
the proposed method significantly improved the
classification error rates, was able to assimilate the
changes in the positive data distribution over time,
and maintained a high damage detection accuracy in all
case studies.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2018:DOS,
author = "Xuelong Li and Guosheng Cui and Yongsheng Dong",
title = "Discriminative and Orthogonal Subspace
Constraints-Based Nonnegative Matrix Factorization",
journal = j-TIST,
volume = "9",
number = "6",
pages = "65:1--65:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3229051",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3229051",
abstract = "Nonnegative matrix factorization (NMF) is one widely
used feature extraction technology in the tasks of
image clustering and image classification. For the
former task, various unsupervised NMF methods based on
the data distribution structure information have been
proposed. While for the latter task, the label
information of the dataset is one very important
guiding. However, most previous proposed supervised NMF
methods emphasis on imposing the discriminant
constraints on the coefficient matrix. When dealing
with new coming samples, the transpose or the
pseudoinverse of the basis matrix is used to project
these samples to the low dimension space. In this way,
the label influence to the basis matrix is indirect.
Although, there are also some methods trying to
constrain the basis matrix in NMF framework, either
they only restrict within-class samples or impose
improper constraint on the basis matrix. To address
these problems, in this article a novel NMF framework
named discriminative and orthogonal subspace
constraints-based nonnegative matrix factorization
(DOSNMF) is proposed. In DOSNMF, the discriminative
constraints are imposed on the projected subspace
instead of the directly learned representation. In this
manner, the discriminative information is directly
connected with the projected subspace. At the same
time, an orthogonal term is incorporated in DOSNMF to
adjust the orthogonality of the learned basis matrix,
which can ensure the orthogonality of the learned
subspace and improve the sparseness of the basis matrix
at the same time. This framework can be implemented in
two ways. The first way is based on the manifold
learning theory. In this way, two graphs, i.e., the
intrinsic graph and the penalty graph, are constructed
to capture the intra-class structure and the
inter-class distinctness. With this design, both the
manifold structure information and the discriminative
information of the dataset are utilized. For
convenience, we name this method as the name of the
framework, i.e., DOSNMF. The second way is based on the
Fisher's criterion, we name it Fisher's criterion-based
DOSNMF (FDOSNMF). The objective functions of DOSNMF and
FDOSNMF can be easily optimized using multiplicative
update (MU) rules. The new methods are tested on five
datasets and compared with several supervised and
unsupervised variants of NMF. The experimental results
reveal the effectiveness of the proposed methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2018:HPC,
author = "Xiaobai Liu and Qian Xu and Yadong Mu and Jiadi Yang
and Liang Lin and Shuicheng Yan",
title = "High-Precision Camera Localization in Scenes with
Repetitive Patterns",
journal = j-TIST,
volume = "9",
number = "6",
pages = "66:1--66:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3226111",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "This article presents a high-precision multi-modal
approach for localizing moving cameras with monocular
videos, which has wide potentials in many intelligent
applications, including robotics, autonomous vehicles,
and so on. Existing visual odometry methods often
suffer from symmetric or repetitive scene patterns,
e.g., windows on buildings or parking stalls. To
address this issue, we introduce a robust camera
localization method that contributes in two aspects.
First, we formulate feature tracking, the critical step
of visual odometry, as a hierarchical min-cost network
flow optimization task, and we regularize the formula
with flow constraints, cross-scale consistencies, and
motion heuristics. The proposed regularized formula is
capable of adaptively selecting distinctive features or
feature combinations, which is more effective than
traditional methods that detect and group repetitive
patterns in a separate step. Second, we develop a joint
formula for integrating dense visual odometry and
sparse GPS readings in a common reference coordinate.
The fusion process is guided with high-order statistics
knowledge to suppress the impacts of noises, clusters,
and model drifting. We evaluate the proposed camera
localization method on both public video datasets and a
newly created dataset that includes scenes full of
repetitive patterns. Results with comparisons show that
our method can achieve comparable performance to
state-of-the-art methods and is particularly effective
for addressing repetitive pattern issues.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2018:CDR,
author = "Cheng-Te Li and Chia-Tai Hsu and Man-Kwan Shan",
title = "A Cross-Domain Recommendation Mechanism for Cold-Start
Users Based on Partial Least Squares Regression",
journal = j-TIST,
volume = "9",
number = "6",
pages = "67:1--67:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3231601",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3231601",
abstract = "Recommender systems are common in e-commerce platforms
in recent years. Recommender systems are able to help
users find preferential items among a large amount of
products so that users' time is saved and sellers'
profits are increased. Cross-domain recommender systems
aim to recommend items based on users' different tastes
across domains. While recommender systems usually
suffer from the user cold-start problem that leads to
unsatisfying recommendation performance, cross-domain
recommendation can remedy such a problem. This article
proposes a novel cross-domain recommendation model
based on regression analysis, partial least squares
regression (PLSR). The proposed recommendation models,
PLSR-CrossRec and PLSR-Latent, are able to purely use
source-domain ratings to predict the ratings for
cold-start users who never rated items in the target
domains. Experiments conducted on the Epinions dataset
with ten various domains' rating records demonstrate
that PLSR-Latent can outperform several matrix
factorization-based competing methods under a variety
of cross-domain settings. The time efficiency of
PLSR-Latent is also satisfactory.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2018:CUS,
author = "Longqi Yang and Chen Fang and Hailin Jin and Matthew
D. Hoffman and Deborah Estrin",
title = "Characterizing User Skills from Application Usage
Traces with Hierarchical Attention Recurrent Networks",
journal = j-TIST,
volume = "9",
number = "6",
pages = "68:1--68:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3232231",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3232231",
abstract = "Predicting users' proficiencies is a critical
component of AI-powered personal assistants. This
article introduces a novel approach for the prediction
based on users' diverse, noisy, and passively generated
application usage histories. We propose a novel
bi-directional recurrent neural network with
hierarchical attention mechanism to extract sequential
patterns and distinguish informative traces from noise.
Our model is able to attend to the most discriminative
actions and sessions to make more accurate and directly
interpretable predictions while requiring 50$ \times $
less training data than the state-of-the-art sequential
learning approach. We evaluate our model with two large
scale datasets collected from 68K Photoshop users: a
digital design skill dataset where the user skill is
determined by the quality of the end products and a
software skill dataset where users self-disclose their
software usage skill levels. The empirical results
demonstrate our model's superior performance compared
to existing user representation learning techniques
that leverage action frequencies and sequential
patterns. In addition, we qualitatively illustrate the
model's significant interpretative power. The proposed
approach is broadly relevant to applications that
generate user time-series analytics.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2018:RFI,
author = "Suhang Wang and Charu Aggarwal and Huan Liu",
title = "Random-Forest-Inspired Neural Networks",
journal = j-TIST,
volume = "9",
number = "6",
pages = "69:1--69:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3232230",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3232230",
abstract = "Neural networks have become very popular in recent
years, because of the astonishing success of deep
learning in various domains such as image and speech
recognition. In many of these domains, specific
architectures of neural networks, such as convolutional
networks, seem to fit the particular structure of the
problem domain very well and can therefore perform in
an astonishingly effective way. However, the success of
neural networks is not universal across all domains.
Indeed, for learning problems without any special
structure, or in cases where the data are somewhat
limited, neural networks are known not to perform well
with respect to traditional machine-learning methods
such as random forests. In this article, we show that a
carefully designed neural network with random forest
structure can have better generalization ability. In
fact, this architecture is more powerful than random
forests, because the back-propagation algorithm reduces
to a more powerful and generalized way of constructing
a decision tree. Furthermore, the approach is efficient
to train and requires a small constant factor of the
number of training examples. This efficiency allows the
training of multiple neural networks to improve the
generalization accuracy. Experimental results on
real-world benchmark datasets demonstrate the
effectiveness of the proposed enhancements for
classification and regression.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Du:2018:SMS,
author = "Bowen Du and Yifeng Cui and Yanjie Fu and Runxing
Zhong and Hui Xiong",
title = "{SmartTransfer}: Modeling the Spatiotemporal Dynamics
of Passenger Transfers for Crowdedness-Aware Route
Recommendations",
journal = j-TIST,
volume = "9",
number = "6",
pages = "70:1--70:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3232229",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3232229",
abstract = "In urban transportation systems, transfer stations
refer to hubs connecting a variety of bus and subway
lines and, thus, are the most important nodes in
transportation networks. The pervasive availability of
large-scale travel traces of passengers, collected from
automated fare collection (AFC) systems, has provided
unprecedented opportunities for understanding citywide
transfer patterns, which can benefit smart
transportation, such as smart route recommendation to
avoid crowded lines, and dynamic bus scheduling to
enhance transportation efficiency. To this end, in this
article, we provide a systematic study of the
measurement, patterns, and modeling of spatiotemporal
dynamics of passenger transfers. Along this line, we
develop a data-driven analytical system for modeling
the transfer volumes of each transfer station. More
specifically, we first identify and quantify the
discriminative patterns of spatiotemporal dynamics of
passenger transfers by utilizing heterogeneous sources
of transfer related data for each station. Also, we
develop a multi-task spatiotemporal learning model for
predicting the transfer volumes of a specific station
at a specific time period. Moreover, we further
leverage the predictive model of passenger transfers to
provide crowdedness-aware route recommendations.
Finally, we conduct the extensive evaluations with a
variety of real-world data. Experimental results
demonstrate the effectiveness of our proposed modeling
method and its applications for smart transportation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2018:FST,
author = "Wenhe Liu and Xiaojun Chang and Yan Yan and Yi Yang
and Alexander G. Hauptmann",
title = "Few-Shot Text and Image Classification via Analogical
Transfer Learning",
journal = j-TIST,
volume = "9",
number = "6",
pages = "71:1--71:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3230709",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3230709",
abstract = "Learning from very few samples is a challenge for
machine learning tasks, such as text and image
classification. Performance of such task can be
enhanced via transfer of helpful knowledge from related
domains, which is referred to as transfer learning. In
previous transfer learning works, instance transfer
learning algorithms mostly focus on selecting the
source domain instances similar to the target domain
instances for transfer. However, the selected instances
usually do not directly contribute to the learning
performance in the target domain. Hypothesis transfer
learning algorithms focus on the model/parameter level
transfer. They treat the source hypotheses as
well-trained and transfer their knowledge in terms of
parameters to learn the target hypothesis. Such
algorithms directly optimize the target hypothesis by
the observable performance improvements. However, they
fail to consider the problem that instances that
contribute to the source hypotheses may be harmful for
the target hypothesis, as instance transfer learning
analyzed. To relieve the aforementioned problems, we
propose a novel transfer learning algorithm, which
follows an analogical strategy. Particularly, the
proposed algorithm first learns a revised source
hypothesis with only instances contributing to the
target hypothesis. Then, the proposed algorithm
transfers both the revised source hypothesis and the
target hypothesis (only trained with a few samples) to
learn an analogical hypothesis. We denote our algorithm
as Analogical Transfer Learning. Extensive experiments
on one synthetic dataset and three real-world benchmark
datasets demonstrate the superior performance of the
proposed algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chin:2018:EAN,
author = "Wei-Sheng Chin and Bo-Wen Yuan and Meng-Yuan Yang and
Chih-Jen Lin",
title = "An Efficient Alternating {Newton} Method for Learning
Factorization Machines",
journal = j-TIST,
volume = "9",
number = "6",
pages = "72:1--72:??",
month = nov,
year = "2018",
CODEN = "????",
DOI = "https://doi.org/10.1145/3230710",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Nov 15 16:23:08 MST 2018",
bibsource = "https://www.math.utah.edu/pub/tex/bib/multithreading.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3230710",
abstract = "To date, factorization machines (FMs) have emerged as
a powerful model in many applications. In this work, we
study the training of FM with the logistic loss for
binary classification, which is a nonlinear extension
of the linear model with the logistic loss (i.e.,
logistic regression). For the training of large-scale
logistic regression, Newton methods have been shown to
be an effective approach, but it is difficult to apply
such methods to FM because of the nonconvexity. We
consider a modification of FM that is multiblock convex
and propose an alternating minimization algorithm based
on Newton methods. Some novel optimization techniques
are introduced to reduce the running time. Our
experiments demonstrate that the proposed algorithm is
more efficient than stochastic gradient algorithms and
coordinate descent methods. The parallelism of our
method is also investigated for the acceleration in
multithreading environments.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cao:2019:ATS,
author = "Nan Cao and Steffen Koch and David Gotz / Yingcai Wu",
title = "{ACM TIST} Special Issue on Visual Analytics",
journal = j-TIST,
volume = "10",
number = "1",
pages = "1:1--1:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3277019",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3277019",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2019:RVR,
author = "Wei Chen and Jing Xia and Xumeng Wang and Yi Wang and
Jun Chen and Liang Chang",
title = "{RelationLines}: Visual Reasoning of Egocentric
Relations from Heterogeneous Urban Data",
journal = j-TIST,
volume = "10",
number = "1",
pages = "2:1--2:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200766",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3200766",
abstract = "The increased accessibility of urban sensor data and
the popularity of social network applications is
enabling the discovery of crowd mobility and personal
communication patterns. However, studying the
egocentric relationships of an individual can be very
challenging because available data may refer to direct
contacts, such as phone calls between individuals, or
indirect contacts, such as paired location presence. In
this article, we develop methods to integrate three
facets extracted from heterogeneous urban data
(timelines, calls, and locations) through a progressive
visual reasoning and inspection scheme. Our approach
uses a detect-and-filter scheme such that, prior to
visual refinement and analysis, a coarse detection is
performed to extract the target individual and
construct the timeline of the target. It then detects
spatio-temporal co-occurrences or call-based contacts
to develop the egocentric network of the individual.
The filtering stage is enhanced with a line-based
visual reasoning interface that facilitates a flexible
and comprehensive investigation of egocentric
relationships and connections in terms of time, space,
and social networks. The integrated system,
RelationLines, is demonstrated using a dataset that
contains taxi GPS data, cell-base mobility data, mobile
calling data, microblog data, and point-of-interest
(POI) data from a city with millions of citizens. We
examine the effectiveness and efficiency of our system
with three case studies and user review.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xu:2019:TSV,
author = "Mingliang Xu and Hua Wang and Shili Chu and Yong Gan
and Xiaoheng Jiang and Yafei Li and Bing Zhou",
title = "Traffic Simulation and Visual Verification in Smog",
journal = j-TIST,
volume = "10",
number = "1",
pages = "3:1--3:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200491",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3200491",
abstract = "Smog causes low visibility on the road and it can
impact the safety of traffic. Modeling traffic in smog
will have a significant impact on realistic traffic
simulations. Most existing traffic models assume that
drivers have optimal vision in the simulations, making
these simulations are not suitable for modeling smog
weather conditions. In this article, we introduce the
Smog Full Velocity Difference Model (SMOG-FVDM) for a
realistic simulation of traffic in smog weather
conditions. In this model, we present a stadia model
for drivers in smog conditions. We introduce it into a
car-following traffic model using both psychological
force and body force concepts, and then we introduce
the SMOG-FVDM. Considering that there are lots of
parameters in the SMOG-FVDM, we design a visual
verification system based on SMOG-FVDM to arrive at an
adequate solution which can show visual simulation
results under different road scenarios and different
degrees of smog by reconciling the parameters.
Experimental results show that our model can give a
realistic and efficient traffic simulation of smog
weather conditions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Xie:2019:VAH,
author = "Cong Xie and Wen Zhong and Wei Xu and Klaus Mueller",
title = "Visual Analytics of Heterogeneous Data Using
Hypergraph Learning",
journal = j-TIST,
volume = "10",
number = "1",
pages = "4:1--4:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200765",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3200765",
abstract = "For real-world learning tasks (e.g., classification),
graph-based models are commonly used to fuse the
information distributed in diverse data sources, which
can be heterogeneous, redundant, and incomplete. These
models represent the relations in different datasets as
pairwise links. However, these links cannot deal with
high-order relations which connect multiple objects
(e.g., in public health datasets, more than two patient
groups admitted by the same hospital in 2014). In this
article, we propose a visual analytics approach for the
classification on heterogeneous datasets using the
hypergraph model. The hypergraph is an extension to
traditional graphs in which a hyperedge connects
multiple vertices instead of just two. We model various
high-order relations in heterogeneous datasets as
hyperedges and fuse different datasets with a unified
hypergraph structure. We use the hypergraph learning
algorithm for predicting missing labels in the
datasets. To allow users to inject their domain
knowledge into the model-learning process, we augment
the traditional learning algorithm in a number of ways.
In addition, we also propose a set of visualizations
which enable the user to construct the hypergraph
structure and the parameters of the learning model
interactively during the analysis. We demonstrate the
capability of our approach via two real-world cases.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Vogogias:2019:BVS,
author = "Athanasios Vogogias and Jessie Kennedy and Daniel
Archambault and Benjamin Bach and V. Anne Smith and
Hannah Currant",
title = "{BayesPiles}: Visualisation Support for {Bayesian}
Network Structure Learning",
journal = j-TIST,
volume = "10",
number = "1",
pages = "5:1--5:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3230623",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3230623",
abstract = "We address the problem of exploring, combining, and
comparing large collections of scored, directed
networks for understanding inferred Bayesian networks
used in biology. In this field, heuristic algorithms
explore the space of possible network solutions,
sampling this space based on algorithm parameters and a
network score that encodes the statistical fit to the
data. The goal of the analyst is to guide the heuristic
search and decide how to determine a final consensus
network structure, usually by selecting the top-scoring
network or constructing the consensus network from a
collection of high-scoring networks. BayesPiles, our
visualisation tool, helps with understanding the
structure of the solution space and supporting the
construction of a final consensus network that is
representative of the underlying dataset. BayesPiles
builds upon and extends MultiPiles to meet our domain
requirements. We developed BayesPiles in conjunction
with computational biologists who have used this tool
on datasets used in their research. The biologists
found our solution provides them with new insights and
helps them achieve results that are representative of
the underlying data.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2019:DVT,
author = "Dongyu Liu and Weiwei Cui and Kai Jin and Yuxiao Guo
and Huamin Qu",
title = "{DeepTracker}: Visualizing the Training Process of
Convolutional Neural Networks",
journal = j-TIST,
volume = "10",
number = "1",
pages = "6:1--6:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200489",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3200489",
abstract = "Deep Convolutional Neural Networks (CNNs) have
achieved remarkable success in various fields. However,
training an excellent CNN is practically a
trial-and-error process that consumes a tremendous
amount of time and computer resources. To accelerate
the training process and reduce the number of trials,
experts need to understand what has occurred in the
training process and why the resulting CNN behaves as
it does. However, current popular training platforms,
such as TensorFlow, only provide very little and
general information, such as training/validation
errors, which is far from enough to serve this purpose.
To bridge this gap and help domain experts with their
training tasks in a practical environment, we propose a
visual analytics system, DeepTracker, to facilitate the
exploration of the rich dynamics of CNN training
processes and to identify the unusual patterns that are
hidden behind the huge amount of information in
training log. Specifically, we combine a hierarchical
index mechanism and a set of hierarchical small
multiples to help experts explore the entire training
log from different levels of detail. We also introduce
a novel cube-style visualization to reveal the complex
correlations among multiple types of heterogeneous
training data, including neuron weights, validation
images, and training iterations. Three case studies are
conducted to demonstrate how DeepTracker provides its
users with valuable knowledge in an industry-level CNN
training process; namely, in our case, training
ResNet-50 on the ImageNet dataset. We show that our
method can be easily applied to other state-of-the-art
``very deep'' CNN models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jin:2019:LFE,
author = "Hai Jin and Yuanfeng Lian and Jing Hua",
title = "Learning Facial Expressions with {$3$D} Mesh
Convolutional Neural Network",
journal = j-TIST,
volume = "10",
number = "1",
pages = "7:1--7:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200572",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3200572",
abstract = "Making machines understand human expressions enables
various useful applications in human-machine
interaction. In this article, we present a novel facial
expression recognition approach with 3D Mesh
Convolutional Neural Networks (3DMCNN) and a visual
analytics-guided 3DMCNN design and optimization scheme.
From an RGBD camera, we first reconstruct a 3D face
model of a subject with facial expressions and then
compute the geometric properties of the surface.
Instead of using regular Convolutional Neural Networks
(CNNs) to learn intensities of the facial images, we
convolve the geometric properties on the surface of the
3D model using 3DMCNN. We design a geodesic
distance-based convolution method to overcome the
difficulties raised from the irregular sampling of the
face surface mesh. We further present interactive
visual analytics for the purpose of designing and
modifying the networks to analyze the learned features
and cluster similar nodes in 3DMCNN. By removing
low-activity nodes in the network, the performance of
the network is greatly improved. We compare our method
with the regular CNN-based method by interactively
visualizing each layer of the networks and analyze the
effectiveness of our method by studying representative
cases. Testing on public datasets, our method achieves
a higher recognition accuracy than traditional
image-based CNN and other 3D CNNs. The proposed
framework, including 3DMCNN and interactive visual
analytics of the CNN, can be extended to other
applications.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2019:RVA,
author = "Chen Zhang and Hao Wang",
title = "{ResumeVis}: a Visual Analytics System to Discover
Semantic Information in Semi-structured Resume Data",
journal = j-TIST,
volume = "10",
number = "1",
pages = "8:1--8:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3230707",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3230707",
abstract = "Massive public resume data emerging on the internet
indicates individual-related characteristics in terms
of profile and career experiences. Resume Analysis (RA)
provides opportunities for many applications, such as
recruitment trend predict, talent seeking and
evaluation. Existing RA studies either largely rely on
the knowledge of domain experts, or leverage classic
statistical or data mining models to identify and
filter explicit attributes based on pre-defined rules.
However, they fail to discover the latent semantic
information from semi-structured resume text, i.e.,
individual career progress trajectory and
social-relations, which are otherwise vital to
comprehensive understanding of people's career evolving
patterns. Besides, when dealing with large numbers of
resumes, how to properly visualize such semantic
information to reduce the information load and to
support better human cognition is also challenging. To
tackle these issues, we propose a visual analytics
system called ResumeVis to mine and visualize resume
data. First, a text mining-based approach is presented
to extract semantic information. Then, a set of
visualizations are devised to represent the semantic
information in multiple perspectives. Through
interactive exploration on ResumeVis performed by
domain experts, the following tasks can be
accomplished: to trace individual career evolving
trajectory; to mine latent social-relations among
individuals; and to hold the full picture of massive
resumes' collective mobility. Case studies with over
2,500 government officer resumes demonstrate the
effectiveness of our system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Du:2019:VIR,
author = "Fan Du and Catherine Plaisant and Neil Spring and Ben
Shneiderman",
title = "Visual Interfaces for Recommendation Systems: Finding
Similar and Dissimilar Peers",
journal = j-TIST,
volume = "10",
number = "1",
pages = "9:1--9:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200490",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3200490",
abstract = "Recommendation applications can guide users in making
important life choices by referring to the activities
of similar peers. For example, students making academic
plans may learn from the data of similar students,
while patients and their physicians may explore data
from similar patients to select the best treatment.
Selecting an appropriate peer group has a strong impact
on the value of the guidance that can result from
analyzing the peer group data. In this article, we
describe a visual interface that helps users review the
similarity and differences between a seed record and a
group of similar records and refine the selection. We
introduce the LikeMeDonuts, Ranking Glyph, and History
Heatmap visualizations. The interface was refined
through three rounds of formative usability evaluation
with 12 target users, and its usefulness was evaluated
by a case study with a student review manager using
real student data. We describe three analytic workflows
observed during use and summarize how users' input
shaped the final design.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liang:2019:CTB,
author = "Haoran Liang and Ming Jiang and Ronghua Liang and Qi
Zhao",
title = "{CapVis}: Toward Better Understanding of Visual-Verbal
Saliency Consistency",
journal = j-TIST,
volume = "10",
number = "1",
pages = "10:1--10:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3200767",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3200767",
abstract = "When looking at an image, humans shift their attention
toward interesting regions, making sequences of eye
fixations. When describing an image, they also come up
with simple sentences that highlight the key elements
in the scene. What is the correlation between where
people look and what they describe in an image? To
investigate this problem intuitively, we develop a
visual analytics system, CapVis, to look into visual
attention and image captioning, two types of subjective
annotations that are relatively task-free and natural.
Using these annotations, we propose a word-weighting
scheme to extract visual and verbal saliency ranks to
compare against each other. In our approach, a number
of low-level and semantic-level features relevant to
visual-verbal saliency consistency are proposed and
visualized for a better understanding of image content.
Our method also shows the different ways that a human
and a computational model look at and describe images,
which provides reliable information for a captioning
model. Experiment also shows that the visualized
feature can be integrated into a computational model to
effectively predict the consistency between the two
modalities on an image dataset with both types of
annotations.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Chen:2019:DMI,
author = "Siming Chen and Shuai Chen and Zhenhuang Wang and Jie
Liang and Yadong Wu and Xiaoru Yuan",
title = "{D-Map+}: Interactive Visual Analysis and Exploration
of Ego-centric and Event-centric Information Diffusion
Patterns in Social Media",
journal = j-TIST,
volume = "10",
number = "1",
pages = "11:1--11:??",
month = jan,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3183347",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3183347",
abstract = "Information diffusion analysis is important in social
media. In this work, we present a coherent ego-centric
and event-centric model to investigate diffusion
patterns and user behaviors. Applying the model, we
propose Diffusion Map+ (D-Maps+), a novel visualization
method to support exploration and analysis of user
behaviors and diffusion patterns through a map
metaphor. For ego-centric analysis, users who
participated in reposting (i.e., resending a message
initially posted by others) one central user's posts
(i.e., a series of original tweets) are collected.
Event-centric analysis focuses on multiple central
users discussing a specific event, with all the people
participating and reposting messages about it. Social
media users are mapped to a hexagonal grid based on
their behavior similarities and in the chronological
order of repostings. With the additional interactions
and linkings, D-Map+ is capable of providing visual
profiling of influential users, describing their social
behaviors and analyzing the evolution of significant
events in social media. A comprehensive visual analysis
system is developed to support interactive exploration
with D-Map+. We evaluate our work with real-world
social media data and find interesting patterns among
users and events. We also perform evaluations including
user studies and expert feedback to certify the
capabilities of our method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2019:FML,
author = "Qiang Yang and Yang Liu and Tianjian Chen and Yongxin
Tong",
title = "Federated Machine Learning: Concept and Applications",
journal = j-TIST,
volume = "10",
number = "2",
pages = "12:1--12:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3298981",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3298981",
abstract = "Today's artificial intelligence still faces two major
challenges. One is that, in most industries, data
exists in the form of isolated islands. The other is
the strengthening of data privacy and security. We
propose a possible solution to these challenges: secure
federated learning. Beyond the federated-learning
framework first proposed by Google in 2016, we
introduce a comprehensive secure federated-learning
framework, which includes horizontal federated
learning, vertical federated learning, and federated
transfer learning. We provide definitions,
architectures, and applications for the
federated-learning framework, and provide a
comprehensive survey of existing works on this subject.
In addition, we propose building data networks among
organizations based on federated mechanisms as an
effective solution to allowing knowledge to be shared
without compromising user privacy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2019:SZS,
author = "Wei Wang and Vincent W. Zheng and Han Yu and Chunyan
Miao",
title = "A Survey of Zero-Shot Learning: Settings, Methods, and
Applications",
journal = j-TIST,
volume = "10",
number = "2",
pages = "13:1--13:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3293318",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3293318",
abstract = "Most machine-learning methods focus on classifying
instances whose classes have already been seen in
training. In practice, many applications require
classifying instances whose classes have not been seen
previously. Zero-shot learning is a powerful and
promising learning paradigm, in which the classes
covered by training instances and the classes we aim to
classify are disjoint. In this paper, we provide a
comprehensive survey of zero-shot learning. First of
all, we provide an overview of zero-shot learning.
According to the data utilized in model optimization,
we classify zero-shot learning into three learning
settings. Second, we describe different semantic spaces
adopted in existing zero-shot learning works. Third, we
categorize existing zero-shot learning methods and
introduce representative methods under each category.
Fourth, we discuss different applications of zero-shot
learning. Finally, we highlight promising future
research directions of zero-shot learning.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mirsky:2019:GPR,
author = "Reuth Mirsky and Kobi Gal and Roni Stern and Meir
Kalech",
title = "Goal and Plan Recognition Design for Plan Libraries",
journal = j-TIST,
volume = "10",
number = "2",
pages = "14:1--14:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3234464",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3234464",
abstract = "This article provides new techniques for optimizing
domain design for goal and plan recognition using plan
libraries. We define two new problems: Goal Recognition
Design for Plan Libraries (GRD-PL) and Plan Recognition
Design (PRD). Solving the GRD-PL helps to infer which
goal the agent is trying to achieve, while solving PRD
can help to infer how the agent is going to achieve its
goal. For each problem, we define a worst-case
distinctiveness measure that is an upper bound on the
number of observations that are necessary to
unambiguously recognize the agent's goal or plan. This
article studies the relationship between these
measures, showing that the worst-case distinctiveness
of GRD-PL is a lower bound of the worst-case plan
distinctiveness of PRD and that they are equal under
certain conditions. We provide two complete algorithms
for minimizing the worst-case distinctiveness of plan
libraries without reducing the agent's ability to
complete its goals: One is a brute-force search over
all possible plans and one is a constraint-based search
that identifies plans that are most difficult to
distinguish in the domain. These algorithms are
evaluated in three hierarchical plan recognition
settings from the literature. We were able to reduce
the worst-case distinctiveness of the domains using our
approach, in some cases reaching 100\% improvement
within a predesignated time window. Our iterative
algorithm outperforms the brute-force approach by an
order of magnitude in terms of runtime.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Skibski:2019:ECS,
author = "Oskar Skibski and Talal Rahwan and Tomasz P. Michalak
and Michael Wooldridge",
title = "Enumerating Connected Subgraphs and Computing the
{Myerson} and {Shapley} Values in Graph-Restricted
Games",
journal = j-TIST,
volume = "10",
number = "2",
pages = "15:1--15:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3235026",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3235026",
abstract = "At the heart of multi-agent systems is the ability to
cooperate to improve the performance of individual
agents and/or the system as a whole. While a widespread
assumption in the literature is that such cooperation
is essentially unrestricted, in many realistic settings
this assumption does not hold. A highly influential
approach for modelling such scenarios are
graph-restricted games introduced by Myerson [36]. In
this approach, agents are represented by nodes in a
graph, edges represent communication channels, and a
group can generate an arbitrary value only if there
exists a direct or indirect communication channel
between every pair of agents within the group. Two
fundamental solution-concepts that were proposed for
such games are the Myerson value and the Shapley value.
While an algorithm has been developed to compute the
Shapley value in arbitrary graph-restricted games, no
such general-purpose algorithm has been developed for
the Myerson value to date. With this in mind, we set
out to develop for such games a general-purpose
algorithm to compute the Myerson value, and a more
efficient algorithm to compute the Shapley value. Since
the computation of either value involves enumerating
all connected induced subgraphs of the game's
underlying graph, we start by developing an algorithm
dedicated to this enumeration, and then we show
empirically that it is faster than the state of the art
in the literature. Finally, we present a sample
application of both algorithms, in which we test the
Myerson value and the Shapley value as advanced
measures of node centrality in networks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2019:UEO,
author = "Jason Shuo Zhang and Qin Lv",
title = "Understanding Event Organization at Scale in
Event-Based Social Networks",
journal = j-TIST,
volume = "10",
number = "2",
pages = "16:1--16:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3243227",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3243227",
abstract = "Understanding real-world event participation behavior
has been a subject of active research and can offer
valuable insights for event-related recommendation and
advertisement. The emergence of event-based social
networks (EBSNs), which attracts online users to
host/attend offline events, has enabled exciting new
research in this domain. However, most existing works
focus on understanding or predicting individual users'
event participation behavior or recommending events to
individual users. Few studies have addressed the
problem of event popularity from the event organizer's
point of view. In this work, we study the latent
factors for determining event popularity using
large-scale datasets collected from the popular
Meetup.com EBSN in five major cities around the world.
We analyze and model four contextual factors: spatial
factor using location convenience, quality, popularity
density, and competitiveness; group factor using group
member entropy and loyalty; temporal factor using
temporal preference and weekly event patterns; and
semantic factor using readability, sentiment, part of
speech, and text novelty. In addition, we have
developed a group-based social influence propagation
network to model group-specific influences on events.
By combining the COntextual features and Social
Influence NEtwork, our integrated prediction framework
COSINE can capture the diverse influential factors of
event participation and can be used by event organizers
to predict/improve the popularity of their events.
Detailed evaluations demonstrate that our COSINE
framework achieves high accuracy for event popularity
prediction in all five cities with diverse cultures and
user event behaviors.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Hsieh:2019:IOS,
author = "Hsun-Ping Hsieh and Cheng-Te Li",
title = "Inferring Online Social Ties from Offline Geographical
Activities",
journal = j-TIST,
volume = "10",
number = "2",
pages = "17:1--17:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3293319",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3293319",
abstract = "As mobile devices are becoming ubiquitous nowadays,
the geographical activities and interactions of human
beings can be easily recorded and accessed. Each mobile
individual can belong to an online social network.
Unfortunately, the underlying online social
relationships are hidden and only available to service
providers. Acquiring the social network of mobile users
would enrich lots of mobile applications, such as
friend recommendation and energy-saving mobile database
management. In this work, we propose to infer online
social ties using purely offline geographical
activities of users, such as check-in records and
spatial meeting events. To tackle the problem, we
devise a novel inference framework, O2O-I nf, which
consists of two components, Feature Modeling and Link
Inference. Feature modeling is to characterize both
direct and indirect geographical interactions between
nodes from co-location and graph features. Link
inference aims to infer the social ties based on a
small set of observed social links, and the idea is
that pairs of nodes sharing similar geographical
behaviors have the same tendency of linkage (i.e.,
either being friends or non-friends). Experiments
conducted on a Gowalla location-based social network
and a Meetup event-based social network exhibit a
satisfying performance in comparison to
state-of-the-art prediction methods under the settings
of offline-to-online network inference and geo-link
prediction.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yu:2019:RHR,
author = "Zeng Yu and Tianrui Li and Ning Yu and Yi Pan and
Hongmei Chen and Bing Liu",
title = "Reconstruction of Hidden Representation for Robust
Feature Extraction",
journal = j-TIST,
volume = "10",
number = "2",
pages = "18:1--18:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3284174",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3284174",
abstract = "This article aims to develop a new and robust approach
to feature representation. Motivated by the success of
Auto-Encoders, we first theoretically analyze and
summarize the general properties of all algorithms that
are based on traditional Auto-Encoders: (1) The
reconstruction error of the input cannot be lower than
a lower bound, which can be viewed as a guiding
principle for reconstructing the input. Additionally,
when the input is corrupted with noises, the
reconstruction error of the corrupted input also cannot
be lower than a lower bound. (2) The reconstruction of
a hidden representation achieving its ideal situation
is the necessary condition for the reconstruction of
the input to reach the ideal state. (3) Minimizing the
Frobenius norm of the Jacobian matrix of the hidden
representation has a deficiency and may result in a
much worse local optimum value. We believe that
minimizing the reconstruction error of the hidden
representation is more robust than minimizing the
Frobenius norm of the Jacobian matrix of the hidden
representation. Based on the above analysis, we propose
a new model termed Double Denoising Auto-Encoders
(DDAEs), which uses corruption and reconstruction on
both the input and the hidden representation. We
demonstrate that the proposed model is highly flexible
and extensible and has a potentially better capability
to learn invariant and robust feature representations.
We also show that our model is more robust than
Denoising Auto-Encoders (DAEs) for dealing with noises
or inessential features. Furthermore, we detail how to
train DDAEs with two different pretraining methods by
optimizing the objective function in a combined and
separate manner, respectively. Comparative experiments
illustrate that the proposed model is significantly
better for representation learning than the
state-of-the-art models.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wang:2019:SBT,
author = "Hongjian Wang and Xianfeng Tang and Yu-Hsuan Kuo and
Daniel Kifer and Zhenhui Li",
title = "A Simple Baseline for Travel Time Estimation using
Large-scale Trip Data",
journal = j-TIST,
volume = "10",
number = "2",
pages = "19:1--19:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3293317",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3293317",
abstract = "The increased availability of large-scale trajectory
data provides rich information for the study of urban
dynamics. For example, New York City Taxi 8 Limousine
Commission regularly releases source/destination
information of taxi trips, where 173 million taxi trips
released for Year 2013 [29]. Such a big dataset
provides us potential new perspectives to address the
traditional traffic problems. In this article, we study
the travel time estimation problem. Instead of
following the traditional route-based travel time
estimation, we propose to simply use a large amount of
taxi trips without using the intermediate trajectory
points to estimate the travel time between source and
destination. Our experiments show very promising
results. The proposed big-data-driven approach
significantly outperforms both state-of-the-art
route-based method and online map services. Our study
indicates that novel simple approaches could be
empowered by big data and these approaches could serve
as new baselines for some traditional computational
problems.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Deng:2019:DMS,
author = "Cheng Deng and Zhao Li and Xinbo Gao and Dacheng Tao",
title = "Deep Multi-scale Discriminative Networks for Double
{JPEG} Compression Forensics",
journal = j-TIST,
volume = "10",
number = "2",
pages = "20:1--20:??",
month = feb,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3301274",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3301274",
abstract = "As JPEG is the most widely used image format, the
importance of tampering detection for JPEG images in
blind forensics is self-evident. In this area,
extracting effective statistical characteristics from a
JPEG image for classification remains a challenge.
Effective features are designed manually in traditional
methods, suggesting that extensive labor-consuming
research and derivation is required. In this article,
we propose a novel image tampering detection method
based on deep multi-scale discriminative networks
(MSD-Nets). The multi-scale module is designed to
automatically extract multiple features from the
discrete cosine transform (DCT) coefficient histograms
of the JPEG image. This module can capture the
characteristic information in different scale spaces.
In addition, a discriminative module is also utilized
to improve the detection effect of the networks in
those difficult situations when the first compression
quality ( QF 1) is higher than the second one ( QF 2).
A special network in this module is designed to
distinguish the small statistical difference between
authentic and tampered regions in these cases. Finally,
a probability map can be obtained and the specific
tampering area is located using the last classification
results. Extensive experiments demonstrate the
superiority of our proposed method in both quantitative
and qualitative metrics when compared with
state-of-the-art approaches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Sharma:2019:CFN,
author = "Karishma Sharma and Feng Qian and He Jiang and Natali
Ruchansky and Ming Zhang and Yan Liu",
title = "Combating Fake News: a Survey on Identification and
Mitigation Techniques",
journal = j-TIST,
volume = "10",
number = "3",
pages = "21:1--21:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3305260",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3305260",
abstract = "The proliferation of fake news on social media has
opened up new directions of research for timely
identification and containment of fake news and
mitigation of its widespread impact on public opinion.
While much of the earlier research was focused on
identification of fake news based on its contents or by
exploiting users' engagements with the news on social
media, there has been a rising interest in proactive
intervention strategies to counter the spread of
misinformation and its impact on society. In this
survey, we describe the modern-day problem of fake news
and, in particular, highlight the technical challenges
associated with it. We discuss existing methods and
techniques applicable to both identification and
mitigation, with a focus on the significant advances in
each method and their advantages and limitations. In
addition, research has often been limited by the
quality of existing datasets and their specific
application contexts. To alleviate this problem, we
comprehensively compile and summarize characteristic
features of available datasets. Furthermore, we outline
new directions of research to facilitate future
development of effective and interdisciplinary
solutions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2019:CSD,
author = "Zun Li and Congyan Lang and Jiashi Feng and Yidong Li
and Tao Wang and Songhe Feng",
title = "Co-saliency Detection with Graph Matching",
journal = j-TIST,
volume = "10",
number = "3",
pages = "22:1--22:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3313874",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3313874",
abstract = "Recently, co-saliency detection, which aims to
automatically discover common and salient objects
appeared in several relevant images, has attracted
increased interest in the computer vision community. In
this article, we present a novel graph-matching based
model for co-saliency detection in image pairs. A
solution of graph matching is proposed to integrate the
visual appearance, saliency coherence, and spatial
structural continuity for detecting co-saliency
collaboratively. Since the saliency and the visual
similarity have been seamlessly integrated, such a
joint inference schema is able to produce more accurate
and reliable results. More concretely, the proposed
model first computes the intra-saliency for each image
by aggregating multiple saliency cues. The common and
salient regions across multiple images are thus
discovered via a graph matching procedure. Then, a
graph reconstruction scheme is proposed to refine the
intra-saliency iteratively. Compared to existing
co-saliency detection methods that only utilize visual
appearance cues, our proposed model can effectively
exploit both visual appearance and structure
information to better guide co-saliency detection.
Extensive experiments on several challenging image pair
databases demonstrate that our model outperforms
state-of-the-art baselines significantly.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Likhyani:2019:LSI,
author = "Ankita Likhyani and Srikanta Bedathur and Deepak P.",
title = "Location-Specific Influence Quantification in
Location-Based Social Networks",
journal = j-TIST,
volume = "10",
number = "3",
pages = "23:1--23:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3300199",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3300199",
abstract = "Location-based social networks (LBSNs) such as
Foursquare offer a platform for users to share and be
aware of each other's physical movements. As a result
of such a sharing of check-in information with each
other, users can be influenced to visit (or check-in)
at the locations visited by their friends. Quantifying
such influences in these LBSNs is useful in various
settings such as location promotion, personalized
recommendations, mobility pattern prediction, and so
forth. In this article, we develop a model to quantify
the influence specific to a location between a pair of
users. Specifically, we develop a framework called
LoCaTe, that combines (a) a user mobility model based
on kernel density estimates; (b) a model of the
semantics of the location using topic models; and (c) a
user correlation model that uses an exponential
distribution. We further develop LoCaTe+, an advanced
model within the same framework where user correlation
is quantified using a Mutually Exciting Hawkes Process.
We show the applicability of LoCaTe and LoCaTe+ for
location promotion and location recommendation tasks
using LBSNs. Our models are validated using a long-term
crawl of Foursquare data collected between January 2015
and February 2016, as well as other publicly available
LBSN datasets. Our experiments demonstrate the efficacy
of the LoCaTe framework in capturing location-specific
influence between users. We also show that our models
improve over state-of-the-art models for the task of
location promotion as well as location
recommendation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yao:2019:PAP,
author = "Huaxiu Yao and Defu Lian and Yi Cao and Yifan Wu and
Tao Zhou",
title = "Predicting Academic Performance for College Students:
a Campus Behavior Perspective",
journal = j-TIST,
volume = "10",
number = "3",
pages = "24:1--24:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3299087",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3299087",
abstract = "Detecting abnormal behaviors of students in time and
providing personalized intervention and guidance at the
early stage is important in educational management.
Academic performance prediction is an important
building block to enabling this pre-intervention and
guidance. Most of the previous studies are based on
questionnaire surveys and self-reports, which suffer
from small sample size and social desirability bias. In
this article, we collect longitudinal behavioral data
from the smart cards of 6,597 students and propose
three major types of discriminative behavioral factors,
diligence, orderliness, and sleep patterns. Empirical
analysis demonstrates these behavioral factors are
strongly correlated with academic performance.
Furthermore, motivated by the social influence theory,
we analyze the correlation between each student's
academic performance with his/her behaviorally similar
students'. Statistical tests indicate this correlation
is significant. Based on these factors, we further
build a multi-task predictive framework based on a
learning-to-rank algorithm for academic performance
prediction. This framework captures inter-semester
correlation, inter-major correlation, and integrates
student similarity to predict students' academic
performance. The experiments on a large-scale
real-world dataset show the effectiveness of our
methods for predicting academic performance and the
effectiveness of proposed behavioral factors.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Yang:2019:MAC,
author = "Bailin Yang and Luhong Zhang and Frederick W. B. Li
and Xiaoheng Jiang and Zhigang Deng and Meng Wang and
Mingliang Xu",
title = "Motion-Aware Compression and Transmission of Mesh
Animation Sequences",
journal = j-TIST,
volume = "10",
number = "3",
pages = "25:1--25:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3300198",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3300198",
abstract = "With the increasing demand in using 3D mesh data over
networks, supporting effective compression and
efficient transmission of meshes has caught lots of
attention in recent years. This article introduces a
novel compression method for 3D mesh animation
sequences, supporting user-defined and progressive
transmissions over networks. Our motion-aware approach
starts with clustering animation frames based on their
motion similarities, dividing a mesh animation sequence
into fragments of varying lengths. This is done by a
novel temporal clustering algorithm, which measures
motion similarity based on the curvature and torsion of
a space curve formed by corresponding vertices along a
series of animation frames. We further segment each
cluster based on mesh vertex coherence, representing
topological proximity within an object under certain
motion. To produce a compact representation, we perform
intra-cluster compression based on Graph Fourier
Transform (GFT) and Set Partitioning In Hierarchical
Trees (SPIHT) coding. Optimized compression results can
be achieved by applying GFT due to the proximity in
vertex position and motion. We adapt SPIHT to support
progressive transmission and design a mechanism to
transmit mesh animation sequences with user-defined
quality. Experimental results show that our method can
obtain a high compression ratio while maintaining a low
reconstruction error.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Wu:2019:OHT,
author = "Hanrui Wu and Yuguang Yan and Yuzhong Ye and Huaqing
Min and Michael K. Ng and Qingyao Wu",
title = "Online Heterogeneous Transfer Learning by Knowledge
Transition",
journal = j-TIST,
volume = "10",
number = "3",
pages = "26:1--26:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3309537",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3309537",
abstract = "In this article, we study the problem of online
heterogeneous transfer learning, where the objective is
to make predictions for a target data sequence arriving
in an online fashion, and some offline labeled
instances from a heterogeneous source domain are
provided as auxiliary data. The feature spaces of the
source and target domains are completely different,
thus the source data cannot be used directly to assist
the learning task in the target domain. To address this
issue, we take advantage of unlabeled co-occurrence
instances as intermediate supplementary data to connect
the source and target domains, and perform knowledge
transition from the source domain into the target
domain. We propose a novel online heterogeneous
transfer learning algorithm called Online Heterogeneous
Knowledge Transition (OHKT) for this purpose. In OHKT,
we first seek to generate pseudo labels for the
co-occurrence data based on the labeled source data,
and then develop an online learning algorithm to
classify the target sequence by leveraging the
co-occurrence data with pseudo labels. Experimental
results on real-world data sets demonstrate the
effectiveness and efficiency of the proposed
algorithm.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Shi:2019:CBV,
author = "Neng Shi and Yubo Tao",
title = "{CNNs} Based Viewpoint Estimation for Volume
Visualization",
journal = j-TIST,
volume = "10",
number = "3",
pages = "27:1--27:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3309993",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3309993",
abstract = "Viewpoint estimation from 2D rendered images is
helpful in understanding how users select viewpoints
for volume visualization and guiding users to select
better viewpoints based on previous visualizations. In
this article, we propose a viewpoint estimation method
based on Convolutional Neural Networks (CNNs) for
volume visualization. We first design an
overfit-resistant image rendering pipeline to generate
the training images with accurate viewpoint
annotations, and then train a category-specific
viewpoint classification network to estimate the
viewpoint for the given rendered image. Our method can
achieve good performance on images rendered with
different transfer functions and rendering parameters
in several categories. We apply our model to recover
the viewpoints of the rendered images in publications,
and show how experts look at volumes. We also introduce
a CNN feature-based image similarity measure for
similarity voting based viewpoint selection, which can
suggest semantically meaningful optimal viewpoints for
different volumes and transfer functions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Mikhail:2019:SBN,
author = "Joseph W. Mikhail and John M. Fossaceca and Ronald
Iammartino",
title = "A Semi-Boosted Nested Model With Sensitivity-Based
Weighted Binarization for Multi-Domain Network
Intrusion Detection",
journal = j-TIST,
volume = "10",
number = "3",
pages = "28:1--28:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3313778",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3313778",
abstract = "Effective network intrusion detection techniques are
required to thwart evolving cybersecurity threats.
Historically, traditional enterprise networks have been
researched extensively in this regard. However, the
cyber threat landscape has grown to include wireless
networks. In this article, the authors present a novel
model that can be trained on completely different
feature sets and applied to two distinct intrusion
detection applications: traditional enterprise networks
and 802.11 wireless networks. This is the first method
that demonstrates superior performance in both
aforementioned applications. The model is based on a
one-versus-all binary framework comprising multiple
nested sub-ensembles. To provide good generalization
ability, each sub-ensemble contains a collection of
sub-learners, and only a portion of the sub-learners
implement boosting. A class weight based on the
sensitivity metric (true-positive rate), learned from
the training data only, is assigned to the
sub-ensembles of each class. The use of pruning to
remove sub-learners that do not contribute to or have
an adverse effect on overall system performance is
investigated as well. The results demonstrate that the
proposed system can achieve exceptional performance in
applications to both traditional enterprise intrusion
detection and 802.11 wireless intrusion detection.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gou:2019:LMR,
author = "Jianping Gou and Wenmo Qiu and Zhang Yi and Yong Xu
and Qirong Mao and Yongzhao Zhan",
title = "A Local Mean Representation-based {$K$}-Nearest
Neighbor Classifier",
journal = j-TIST,
volume = "10",
number = "3",
pages = "29:1--29:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3319532",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3319532",
abstract = "K -nearest neighbor classification method (KNN), as
one of the top 10 algorithms in data mining, is a very
simple and yet effective nonparametric technique for
pattern recognition. However, due to the selective
sensitiveness of the neighborhood size k, the simple
majority vote, and the conventional metric measure, the
KNN-based classification performance can be easily
degraded, especially in the small training sample size
cases. In this article, to further improve the
classification performance and overcome the main issues
in the KNN-based classification, we propose a local
mean representation-based k -nearest neighbor
classifier (LMRKNN). In the LMRKNN, the categorical k
-nearest neighbors of a query sample are first chosen
to calculate the corresponding categorical k -local
mean vectors, and then the query sample is represented
by the linear combination of the categorical k -local
mean vectors; finally, the class-specific
representation-based distances between the query sample
and the categorical k -local mean vectors are adopted
to determine the class of the query sample. Extensive
experiments on many UCI and KEEL datasets and three
popular face databases are carried out by comparing
LMRKNN to the state-of-art KNN-based methods. The
experimental results demonstrate that the proposed
LMRKNN outperforms the related competitive KNN-based
methods with more robustness and effectiveness.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhuo:2019:RMA,
author = "Hankz Hankui Zhuo",
title = "Recognizing Multi-Agent Plans When Action Models and
Team Plans Are Both Incomplete",
journal = j-TIST,
volume = "10",
number = "3",
pages = "30:1--30:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3319403",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3319403",
abstract = "Multi-Agent Plan Recognition (MAPR) aims to recognize
team structures (which are composed of team plans) from
the observed team traces (action sequences) of a set of
intelligent agents. In this article, we introduce the
problem formulation of MAPR based on partially observed
team traces, and present a weighted MAX-SAT-based
framework to recognize multi-agent plans from partially
observed team traces with the help of two types of
auxiliary knowledge to help recognize multi-agent
plans, i.e., a library of incomplete team plans and a
set of incomplete action models. Our framework
functions with two phases. We first build a set of hard
constraints that encode the correctness property of the
team plans, and a set of soft constraints that encode
the optimal utility property of team plans based on the
input team trace, incomplete team plans, and incomplete
action models. After that, we solve all of the
constraints using a weighted MAX-SAT solver and convert
the solution to a set of team plans that best explain
the structure of the observed team trace. We
empirically exhibit both effectiveness and efficiency
of our framework in benchmark domains from
International Planning Competition (IPC).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Golpayegani:2019:USD,
author = "Fatemeh Golpayegani and Ivana Dusparic and Siobhan
Clarke",
title = "Using Social Dependence to Enable Neighbourly
Behaviour in Open Multi-Agent Systems",
journal = j-TIST,
volume = "10",
number = "3",
pages = "31:1--31:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3319402",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3319402",
abstract = "Agents frequently collaborate to achieve a shared goal
or to accomplish a task that they cannot do alone.
However, collaboration is difficult in open multi-agent
systems where agents share constrained resources to
achieve both individual and shared goals. In current
approaches to collaboration, agents are organised into
disjoint groups and social reasoning is used to capture
their capabilities when selecting a qualified set of
collaborators. These approaches are not useful when
agents are in multiple, overlapping groups; depend on
each other when using shared resources; have multiple
goals to achieve simultaneously; and have to share the
overall costs and benefits. In this article, agents use
social reasoning to enhance their understanding of
other agents' goals and their dependencies, and
self-adaptive techniques to adapt their level of
self-interest in a collaborative process, with a view
to contributing to lowering shared costs or increasing
shared benefits. This model aims at improving the
extent to which agents' goals are met while improving
shared resource usage efficiency. For example, in a
public transport system where each mode of transport
has limited capacity, commuters will be enabled to make
choices that avoid over-capacity in different modes, or
in a smart energy grid with limited capacity, users can
make choices as to when they increase their demand. The
model simultaneously helps avoid overloading a shared
resource while allowing users to achieve their own
goals. The proposed model is evaluated in an open
multi-agent system with 100 agents operating in
multiple overlapping groups and sharing multiple
constrained resources. The impact of agents' varying
levels of social dependencies, mobility, and their
groups' density on their individual and shared goal
achievement is analysed.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhu:2019:EVC,
author = "Chunbiao Zhu and Wenhao Zhang and Thomas H. Li and
Shan Liu and Ge Li",
title = "Exploiting the Value of the Center-dark Channel Prior
for Salient Object Detection",
journal = j-TIST,
volume = "10",
number = "3",
pages = "32:1--32:??",
month = may,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3319368",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:44 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3319368",
abstract = "Saliency detection aims to detect the most attractive
objects in images and is widely used as a foundation
for various applications. In this article, we propose a
novel salient object detection algorithm for RGB-D
images using center-dark channel priors. First, we
generate an initial saliency map based on a color
saliency map and a depth saliency map of a given RGB-D
image. Then, we generate a center-dark channel map
based on center saliency and dark channel priors.
Finally, we fuse the initial saliency map with the
center dark channel map to generate the final saliency
map. Extensive evaluations over four benchmark datasets
demonstrate that our proposed method performs favorably
against most of the state-of-the-art approaches.
Besides, we further discuss the application of the
proposed algorithm in small target detection and
demonstrate the universal value of center-dark channel
priors in the field of object detection.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Bian:2019:TDC,
author = "Jiang Bian and Dayong Tian and Yuanyan Tang and
Dacheng Tao",
title = "Trajectory Data Classification: a Review",
journal = j-TIST,
volume = "10",
number = "4",
pages = "33:1--33:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3330138",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3330138",
abstract = "This article comprehensively surveys the development
of trajectory data classification. Considering the
critical role of trajectory data classification in
modern intelligent systems for surveillance security,
abnormal behavior detection, crowd behavior analysis,
and traffic control, trajectory data classification has
attracted growing attention. According to the
availability of manual labels, which is critical to the
classification performances, the methods can be
classified into three categories, i.e., unsupervised,
semi-supervised, and supervised. Furthermore,
classification methods are divided into some
sub-categories according to what extracted features are
used. We provide a holistic understanding and deep
insight into three types of trajectory data
classification methods and present some promising
future directions.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Verenich:2019:SCB,
author = "Ilya Verenich and Marlon Dumas and Marcello {La Rosa}
and Fabrizio Maria Maggi and Irene Teinemaa",
title = "Survey and Cross-benchmark Comparison of Remaining
Time Prediction Methods in Business Process
Monitoring",
journal = j-TIST,
volume = "10",
number = "4",
pages = "34:1--34:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3331449",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3331449",
abstract = "Predictive business process monitoring methods exploit
historical process execution logs to generate
predictions about running instances (called cases) of a
business process, such as the prediction of the
outcome, next activity, or remaining cycle time of a
given process case. These insights could be used to
support operational managers in taking remedial actions
as business processes unfold, e.g., shifting resources
from one case onto another to ensure the latter is
completed on time. A number of methods to tackle the
remaining cycle time prediction problem have been
proposed in the literature. However, due to differences
in their experimental setup, choice of datasets,
evaluation measures, and baselines, the relative merits
of each method remain unclear. This article presents a
systematic literature review and taxonomy of methods
for remaining time prediction in the context of
business processes, as well as a cross-benchmark
comparison of 16 such methods based on 17 real-life
datasets originating from different industry domains.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Gong:2019:MMC,
author = "Chen Gong and Jian Yang and Dacheng Tao",
title = "Multi-Modal Curriculum Learning over Graphs",
journal = j-TIST,
volume = "10",
number = "4",
pages = "35:1--35:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3322122",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3322122",
abstract = "Curriculum Learning (CL) is a recently proposed
learning paradigm that aims to achieve satisfactory
performance by properly organizing the learning
sequence from simple curriculum examples to more
difficult ones. Up to now, few works have been done to
explore CL for the data with graph structure.
Therefore, this article proposes a novel CL algorithm
that can be utilized to guide the Label Propagation
(LP) over graphs, of which the target is to ``learn''
the labels of unlabeled examples on the graphs.
Specifically, we assume that different unlabeled
examples have different levels of difficulty for
propagation, and their label learning should follow a
simple-to-difficult sequence with the updated
curricula. Furthermore, considering that the practical
data are often characterized by multiple modalities,
every modality in our method is associated with a
``teacher'' that not only evaluates the difficulties of
examples from its own viewpoint, but also cooperates
with other teachers to generate the overall simplest
curriculum examples for propagation. By taking the
curriculums suggested by the teachers as a whole, the
common preference (i.e., commonality) of teachers on
selecting the simplest examples can be discovered by a
row-sparse matrix, and their distinct opinions (i.e.,
individuality) are captured by a sparse noise matrix.
As a result, an accurate curriculum sequence can be
established and the propagation quality can thus be
improved. Theoretically, we prove that the propagation
risk bound is closely related to the examples'
difficulty information, and empirically, we show that
our method can generate higher accuracy than the
state-of-the-art CL approach and LP algorithms on
various multi-modal tasks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ao:2019:LSF,
author = "Xiang Ao and Haoran Shi and Jin Wang and Luo Zuo and
Hongwei Li and Qing He",
title = "Large-Scale Frequent Episode Mining from Complex Event
Sequences with Hierarchies",
journal = j-TIST,
volume = "10",
number = "4",
pages = "36:1--36:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3326163",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3326163",
abstract = "Frequent Episode Mining (FEM), which aims at mining
frequent sub-sequences from a single long event
sequence, is one of the essential building blocks for
the sequence mining research field. Existing studies
about FEM suffer from unsatisfied scalability when
faced with complex sequences as it is an NP-complete
problem for testing whether an episode occurs in a
sequence. In this article, we propose a scalable,
distributed framework to support FEM on ``big'' event
sequences. As a rule of thumb, ``big'' illustrates an
event sequence is either very long or with masses of
simultaneous events. Meanwhile, the events in this
article are arranged in a predefined hierarchy. It
derives some abstractive events that can form episodes
that may not directly appear in the input sequence.
Specifically, we devise an event-centered and
hierarchy-aware partitioning strategy to allocate
events from different levels of the hierarchy into
local processes. We then present an efficient
special-purpose algorithm to improve the local mining
performance. We also extend our framework to support
maximal and closed episode mining in the context of
event hierarchy, and to the best of our knowledge, we
are the first attempt to define and discover
hierarchy-aware maximal and closed episodes. We
implement the proposed framework on Apache Spark and
conduct experiments on both synthetic and real-world
datasets. Experimental results demonstrate the
efficiency and scalability of the proposed approach and
show that we can find practical patterns when taking
event hierarchies into account.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cong:2019:EUG,
author = "Phan Thanh Cong and Nguyen Thanh Tam and Hongzhi Yin
and Bolong Zheng and Bela Stantic and Nguyen Quoc Viet
Hung",
title = "Efficient User Guidance for Validating Participatory
Sensing Data",
journal = j-TIST,
volume = "10",
number = "4",
pages = "37:1--37:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3326164",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3326164",
abstract = "Participatory sensing has become a new data collection
paradigm that leverages the wisdom of the crowd for big
data applications without spending cost to buy
dedicated sensors. It collects data from human sensors
by using their own devices such as cell phone
accelerometers, cameras, and GPS devices. This benefit
comes with a drawback: human sensors are arbitrary and
inherently uncertain due to the lack of quality
guarantee. Moreover, participatory sensing data are
time series that exhibit not only highly irregular
dependencies on time but also high variance between
sensors. To overcome these limitations, we formulate
the problem of validating uncertain time series
collected by participatory sensors. In this article, we
approach the problem by an iterative validation process
on top of a probabilistic time series model. First, we
generate a series of probability distributions from raw
data by tailoring a state-of-the-art dynamical model,
namely Generalised Auto Regressive Conditional
Heteroskedasticity (GARCH), for our joint time series
setting. Second, we design a feedback process that
consists of an adaptive aggregation model to unify the
joint probabilistic time series and an efficient user
guidance model to validate aggregated data with minimal
effort. Through extensive experimentation, we
demonstrate the efficiency and effectiveness of our
approach on both real data and synthetic data.
Highlights from our experiences include the fast
running time of a probabilistic model, the robustness
of an aggregation model to outliers, and the
significant effort saving of a guidance model.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Cui:2019:STA,
author = "Wanqiu Cui and Junping Du and Dawei Wang and Xunpu
Yuan and Feifei Kou and Liyan Zhou and Nan Zhou",
title = "Short Text Analysis Based on Dual Semantic Extension
and Deep Hashing in Microblog",
journal = j-TIST,
volume = "10",
number = "4",
pages = "38:1--38:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3326166",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3326166",
abstract = "Short text analysis is a challenging task as far as
the sparsity and limitation of semantics. The semantic
extension approach learns the meaning of a short text
by introducing external knowledge. However, for the
randomness of short text descriptions in microblogs,
traditional extension methods cannot accurately mine
the semantics suitable for the microblog theme.
Therefore, we use the prominent and refined hashtag
information in microblogs as well as complex social
relationships to provide implicit guidance for semantic
extension of short text. Specifically, we design a deep
hash model based on social and conceptual semantic
extension, which consists of dual semantic extension
and deep hashing representation. In the extension
method, the short text is first conceptualized to
achieve the construction of hashtag graph under
conceptual space. Then, the associated hashtags are
generated by correlation calculation based on the
integration of social relationships and concepts to
extend the short text. In the deep hash model, we use
the semantic hashing model to encode the abundant
semantic features and form a compact and meaningful
binary encoding. Finally, extensive experiments
demonstrate that our method can learn and represent the
short texts well by using more meaningful semantic
signal. It can effectively enhance and guide the
semantic analysis and understanding of short text in
microblogs.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{He:2019:STA,
author = "Suining He and Kang G. Shin",
title = "Spatio-temporal Adaptive Pricing for Balancing
Mobility-on-Demand Networks",
journal = j-TIST,
volume = "10",
number = "4",
pages = "39:1--39:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3331450",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3331450",
abstract = "Pricing in mobility-on-demand (MOD) networks, such as
Uber, Lyft, and connected taxicabs, is done adaptively
by leveraging the price responsiveness of drivers
(supplies) and passengers (demands) to achieve such
goals as maximizing drivers' incomes, improving riders'
experience, and sustaining platform operation. Existing
pricing policies only respond to short-term demand
fluctuations without accurate trip forecast and spatial
demand-supply balancing, thus mismatching drivers to
riders and resulting in loss of profit. We propose
CAPrice, a novel adaptive pricing scheme for urban MOD
networks. It uses a new spatio-temporal deep capsule
network (STCapsNet) that accurately predicts ride
demands and driver supplies with vectorized neuron
capsules while accounting for comprehensive
spatio-temporal and external factors. Given accurate
perception of zone-to-zone traffic flows in a city,
CAPrice formulates a joint optimization problem by
considering spatial equilibrium to balance the
platform, providing drivers and riders/passengers with
proactive pricing ``signals.'' We have conducted an
extensive experimental evaluation upon over 4.0$ \times
$ 10$^8$ MOD trips (Uber, Didi Chuxing, and connected
taxicabs) in New York City, Beijing, and Chengdu,
validating the accuracy, effectiveness, and
profitability (often 20\% ride prediction accuracy and
30\% profit improvements over the state-of-the-arts) of
CAPrice in managing urban MOD networks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tonge:2019:PAT,
author = "Ashwini Tonge and Cornelia Caragea",
title = "Privacy-aware Tag Recommendation for Accurate Image
Privacy Prediction",
journal = j-TIST,
volume = "10",
number = "4",
pages = "40:1--40:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3335054",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3335054",
abstract = "Online images' tags are very important for indexing,
sharing, and searching of images, as well as surfacing
images with private or sensitive content, which needs
to be protected. Social media sites such as Flickr
generate these metadata from user-contributed tags.
However, as the tags are at the sole discretion of
users, these tags tend to be noisy and incomplete. In
this article, we present a privacy-aware approach to
automatic image tagging, which aims at improving the
quality of user annotations, while also preserving the
images' original privacy sharing patterns. Precisely,
we recommend potential tags for each target image by
mining privacy-aware tags from the most similar images
of the target image, which are obtained from a large
collection. Experimental results show that, although
the user-input tags compose noise, our privacy-aware
approach is able to predict accurate tags that can
improve the performance of a downstream application on
image privacy prediction and outperforms an existing
privacy-oblivious approach to image tagging. The
results also show that, even for images that do not
have any user tags, our proposed approach can recommend
accurate tags. Crowd-sourcing the predicted tags
exhibits the quality of our privacy-aware recommended
tags. Our code, features, and the dataset used in
experiments are available at:
https://github.com/ashwinitonge/privacy-aware-tag-rec.git.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhao:2019:PRG,
author = "Guoshuai Zhao and Hao Fu and Ruihua Song and Tetsuya
Sakai and Zhongxia Chen and Xing Xie and Xueming Qian",
title = "Personalized Reason Generation for Explainable Song
Recommendation",
journal = j-TIST,
volume = "10",
number = "4",
pages = "41:1--41:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3337967",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3337967",
abstract = "Personalized recommendation has received a lot of
attention as a highly practical research topic.
However, existing recommender systems provide the
recommendations with a generic statement such as
``Customers who bought this item also bought
\ldots{}''. Explainable recommendation, which makes a
user aware of why such items are recommended, is in
demand. The goal of our research is to make the users
feel as if they are receiving recommendations from
their friends. To this end, we formulate a new
challenging problem called personalized reason
generation for explainable recommendation for songs in
conversation applications and propose a solution that
generates a natural language explanation of the reason
for recommending a song to that particular user. For
example, if the user is a student, our method can
generate an output such as ``Campus radio plays this
song at noon every day, and I think it sounds
wonderful,'' which the student may find easy to relate
to. In the offline experiments, through manual
assessments, the gain of our method is statistically
significant on the relevance to songs and
personalization to users comparing with baselines.
Large-scale online experiments show that our method
outperforms manually selected reasons by 8.2\% in terms
of click-through rate. Evaluation results indicate that
our generated reasons are relevant to songs and
personalized to users, and they attract users to click
the recommendations.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Thukral:2019:DER,
author = "Deepak Thukral and Adesh Pandey and Rishabh Gupta and
Vikram Goyal and Tanmoy Chakraborty",
title = "{DiffQue}: Estimating Relative Difficulty of Questions
in Community Question Answering Services",
journal = j-TIST,
volume = "10",
number = "4",
pages = "42:1--42:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3337799",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3337799",
abstract = "Automatic estimation of relative difficulty of a pair
of questions is an important and challenging problem in
community question answering (CQA) services. There are
limited studies that addressed this problem. Past
studies mostly leveraged expertise of users answering
the questions and barely considered other properties of
CQA services such as metadata of users and posts,
temporal information, and textual content. In this
article, we propose DiffQue, a novel system that maps
this problem to a network-aided edge directionality
prediction problem. DiffQue starts by constructing a
novel network structure that captures different notions
of difficulties among a pair of questions. It then
measures the relative difficulty of two questions by
predicting the direction of a (virtual) edge connecting
these two questions in the network. It leverages
features extracted from the network structure, metadata
of users/posts, and textual description of questions
and answers. Experiments on datasets obtained from two
CQA sites (further divided into four datasets) with
human annotated ground-truth show that DiffQue
outperforms four state-of-the-art methods by a
significant margin (28.77\% higher F$_1$ score and
28.72\% higher AUC than the best baseline). As opposed
to the other baselines, (i) DiffQue appropriately
responds to the training noise, (ii) DiffQue is capable
of adapting multiple domains (CQA datasets), and (iii)
DiffQue can efficiently handle the ``cold start''
problem that may arise due to the lack of information
for newly posted questions or newly arrived users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Jiang:2019:SEL,
author = "Zhe Jiang and Arpan Man Sainju and Yan Li and Shashi
Shekhar and Joseph Knight",
title = "Spatial Ensemble Learning for Heterogeneous Geographic
Data with Class Ambiguity",
journal = j-TIST,
volume = "10",
number = "4",
pages = "43:1--43:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3337798",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3337798",
abstract = "Class ambiguity refers to the phenomenon whereby
similar features correspond to different classes at
different locations. Given heterogeneous geographic
data with class ambiguity, the spatial ensemble
learning (SEL) problem aims to find a decomposition of
the geographic area into disjoint zones such that class
ambiguity is minimized and a local classifier can be
learned in each zone. The problem is important for
applications such as land cover mapping from
heterogeneous earth observation data with spectral
confusion. However, the problem is challenging due to
its high computational cost. Related work in ensemble
learning either assumes an identical sample
distribution (e.g., bagging, boosting, random forest)
or decomposes multi-modular input data in the feature
vector space (e.g., mixture of experts, multimodal
ensemble) and thus cannot effectively minimize class
ambiguity. In contrast, we propose a spatial ensemble
framework that explicitly partitions input data in
geographic space. Our approach first preprocesses data
into homogeneous spatial patches and uses a greedy
heuristic to allocate pairs of patches with high class
ambiguity into different zones. We further extend our
spatial ensemble learning framework with spatial
dependency between nearby zones based on the spatial
autocorrelation effect. Both theoretical analysis and
experimental evaluations on two real world wetland
mapping datasets show the feasibility of the proposed
approach.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Banerjee:2019:AAR,
author = "Suvadeep Banerjee and Abhijit Chatterjee",
title = "{ALERA}: Accelerated Reinforcement Learning Driven
Adaptation to Electro-Mechanical Degradation in
Nonlinear Control Systems Using Encoded State Space
Error Signatures",
journal = j-TIST,
volume = "10",
number = "4",
pages = "44:1--44:??",
month = aug,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3338123",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3338123",
abstract = "The successful deployment of autonomous real-time
systems is contingent on their ability to recover from
performance degradation of sensors, actuators, and
other electro-mechanical subsystems with low latency.
In this article, we introduce ALERA, a novel framework
for real-time control law adaptation in nonlinear
control systems assisted by system state encodings that
generate an error signal when the code properties are
violated in the presence of failures. The fundamental
contributions of this methodology are twofold-first, we
show that the time-domain error signal contains
perturbed system parameters' diagnostic information
that can be used for quick control law adaptation to
failure conditions and second, this quick adaptation is
performed via reinforcement learning algorithms that
relearn the control law of the perturbed system from a
starting condition dictated by the diagnostic
information, thus achieving significantly faster
recovery. The fast (up to 80X faster than traditional
reinforcement learning paradigms) performance recovery
enabled by ALERA is demonstrated on an inverted
pendulum balancing problem, a brake-by-wire system, and
a self-balancing robot.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Strobl:2019:ECF,
author = "Eric V. Strobl and Peter L. Spirtes and Shyam
Visweswaran",
title = "Estimating and Controlling the False Discovery Rate of
the {PC} Algorithm Using Edge-specific {$P$}-Values",
journal = j-TIST,
volume = "10",
number = "5",
pages = "46:1--46:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3351342",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Many causal discovery algorithms infer graphical
structure from observational data. The PC algorithm in
particular estimates a completed partially directed
acyclic graph (CPDAG), or an acyclic graph containing
directed edges identifiable with conditional
independence testing. However, few groups have
investigated strategies for estimating and controlling
the false discovery rate (FDR) of the edges in the
CPDAG. In this article, we introduce PC with p-values
(PC-p), a fast algorithm that robustly computes
edge-specific p-values and then estimates and controls
the FDR across the edges. PC-p specifically uses the
p-values returned by many conditional independence (CI)
tests to upper bound the p-values of more complex
edge-specific hypothesis tests. The algorithm then
estimates and controls the FDR using the bounded
p-values and the Benjamini-Yekutieli FDR procedure.
Modifications to the original PC algorithm also help
PC-p accurately compute the upper bounds despite
non-zero Type II error rates. Experiments show that
PC-p yields more accurate FDR estimation and control
across the edges in a variety of CPDAGs compared to
alternative methods.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Herd:2019:DCR,
author = "Benjamin C. Herd and Simon Miles",
title = "Detecting Causal Relationships in Simulation Models
Using Intervention-based Counterfactual Analysis",
journal = j-TIST,
volume = "10",
number = "5",
pages = "47:1--47:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3322123",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Central to explanatory simulation models is their
capability to not just show that but also why
particular things happen. Explanation is closely
related with the detection of causal relationships and
is, in a simulation context, typically done by means of
controlled experiments. However, for complex simulation
models, conventional ``blackbox'' experiments may be
too coarse-grained to cope with spurious relationships.
We present an intervention-based causal analysis
methodology that exploits the manipulability of
computational models, and detects and circumvents
spurious effects. The core of the methodology is a
formal model that maps basic causal assumptions to
causal observations and allows for the identification
of combinations of assumptions that have a negative
impact on observability. First, experiments indicate
that the methodology can successfully deal with
notoriously tricky situations involving asymmetric and
symmetric overdetermination and detect fine-grained
causal relationships between events in the simulation.
As illustrated in the article, the methodology can be
easily integrated into an existing simulation
environment.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Rahmadi:2019:SSS,
author = "Ridho Rahmadi and Perry Groot and Tom Heskes",
title = "Stable Specification Search in Structural Equation
Models with Latent Variables",
journal = j-TIST,
volume = "10",
number = "5",
pages = "48:1--48:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3341557",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "In our previous study, we introduced stable
specification search for cross-sectional data (S3C). It
is an exploratory causal method that combines the
concept of stability selection and multi-objective
optimization to search for stable and parsimonious
causal structures across the entire range of model
complexities. S3C, however, is designed to model causal
relations among observed variables. In this study, we
extended S3C to S3C-Latent, to model linear causal
relations between latent variables that are measured
through observed proxies. We evaluated S3C-Latent on
simulated data and compared the results to those of
PC-MIMBuild, an extension of the PC algorithm, the
state-of-the-art causal discovery method. The
comparison shows that S3C-Latent achieved better
performance. We also applied S3C-Latent to real-world
data of children with attention deficit/hyperactivity
disorder and data about measuring mental abilities
among pupils. The results are consistent with those of
previous studies.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Liu:2019:LLA,
author = "Yue Liu and Zheng Cai and Chunchen Liu and Zhi Geng",
title = "Local Learning Approaches for Finding Effects of a
Specified Cause and Their Causal Paths",
journal = j-TIST,
volume = "10",
number = "5",
pages = "49:1--49:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3313147",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Causal networks are used to describe and to discover
causal relationships among variables and data
generating mechanisms. There have been many approaches
for learning a global causal network of all observed
variables. In many applications, we may be interested
in finding what are the effects of a specified cause
variable and what are the causal paths from the cause
variable to its effects. Instead of learning a global
causal network, we propose several local learning
approaches for finding all effects (or descendants) of
the specified cause variable and the causal paths from
the cause variable to some effect variable of interest.
We discuss the identifiability of the effects and the
causal paths from observed data and prior knowledge.
For the case that the causal paths are not
identifiable, our approaches try to find a path set
that contains the causal paths of interest.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2019:MCI,
author = "Hao Zhang and Shuigeng Zhou and Jihong Guan and Jun
(Luke) Huan",
title = "Measuring Conditional Independence by Independent
Residuals for Causal Discovery",
journal = j-TIST,
volume = "10",
number = "5",
pages = "50:1--50:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3325708",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "We investigate the relationship between conditional
independence (CI) x \vdbar y | Z and the independence
of two residuals x -E( x | Z )\vdbar y -E( y | Z ),
where x and y are two random variables and Z is a set
of random variables. We show that if x, y, and Z are
generated by following linear structural equation
models and all external influences follow joint
Gaussian distribution, then x \vdbar y | Z if and only
if x -E( x | Z )\vdbar y -E( y | Z ). That is, the test
of x \vdbar y | Z can be relaxed to a simpler
unconditional independence test of x -E( x | Z )\vdbar
y -E( y | Z ). Furthermore, testing x -E( x | Z )\vdbar
y -E( y | Z ) can be simplified by testing x -E( x | Z
)\vdbar y or y -E( y | Z )\vdbar x. On the other side,
if all these external influences follow non-Gaussian
distributions and the model satisfies structural
faithfulness condition, then we have x \vdbar y | Z
{$<$}={$>$} x -E( x | Z )\vdbar y -E( y | Z ). We apply
the results above to the causal discovery problem,
where the causal directions are generally determined by
a set of V -structures and their consistent
propagations, so CI test-based methods can return a set
of Markov equivalence classes. We show that in the
linear non-Gaussian context, in many cases x -E( x | Z
)\vdbar z or y -E( y | Z )\vdbar z ( \forall z \in Z
and Z is a minimal d -separator) is satisfied when x E(
x | Z )\vdbar y -E( y | Z ), which implies z causes x
(or y ) if z directly connects to x (or y ). Therefore,
we conclude that CIs have useful information for
distinguishing Markov equivalence classes. In summary,
comparing with the existing discretization-based and
kernel-based CI testing methods, the proposed method
provides a simpler way to measure CI, which needs only
one unconditional independence test and two regression
operations. When being applied to causal discovery, it
can find more causal relationships, which is
extensively validated by experiments.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
remark = "The symbol denoted \vdbar is a horizontal bar on the
baseline, with two vertical bars extending upward; I
cannot find it in TeX math font listings, or in Unicode
5.0.",
}
@Article{Heckerman:2019:TAH,
author = "David Heckerman",
title = "Toward Accounting for Hidden Common Causes When
Inferring Cause and Effect from Observational Data",
journal = j-TIST,
volume = "10",
number = "5",
pages = "51:1--51:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3309720",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Hidden common causes make it difficult to infer causal
relationships from observational data. Here, we begin
an investigation into a new method to account for a
hidden common cause that infers its presence from the
data. As with other approaches that can account for
common causes, this approach is successful only in some
cases. We describe such a case taken from the field of
genomics, wherein one tries to identify which genomic
markers causally influence a trait of interest.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ling:2019:BBM,
author = "Zhaolong Ling and Kui Yu and Hao Wang and Lin Liu and
Wei Ding and Xindong Wu",
title = "{BAMB}: a Balanced {Markov} Blanket Discovery Approach
to Feature Selection",
journal = j-TIST,
volume = "10",
number = "5",
pages = "52:1--52:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3335676",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The discovery of Markov blanket (MB) for feature
selection has attracted much attention in recent years,
since the MB of the class attribute is the optimal
feature subset for feature selection. However, almost
all existing MB discovery algorithms focus on either
improving computational efficiency or boosting learning
accuracy, instead of both. In this article, we propose
a novel MB discovery algorithm for balancing efficiency
and accuracy, called BAlanced Markov Blanket (BAMB)
discovery. To achieve this goal, given a class
attribute of interest, BAMB finds candidate PC (parents
and children) and spouses and removes false positives
from the candidate MB set in one go. Specifically, once
a feature is successfully added to the current PC set,
BAMB finds the spouses with regard to this feature,
then uses the updated PC and the spouse set to remove
false positives from the current MB set. This makes the
PC and spouses of the target as small as possible and
thus achieves a trade-off between computational
efficiency and learning accuracy. In the experiments,
we first compare BAMB with 8 state-of-the-art MB
discovery algorithms on 7 benchmark Bayesian networks,
then we use 10 real-world datasets and compare BAMB
with 12 feature selection algorithms, including 8
state-of-the-art MB discovery algorithms and 4 other
well-established feature selection methods. On
prediction accuracy, BAMB outperforms 12 feature
selection algorithms compared. On computational
efficiency, BAMB is close to the IAMB algorithm while
it is much faster than the remaining seven MB discovery
algorithms.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2019:MVF,
author = "Yongshan Zhang and Jia Wu and Chuan Zhou and Zhihua
Cai and Jian Yang and Philip S. Yu",
title = "Multi-View Fusion with Extreme Learning Machine for
Clustering",
journal = j-TIST,
volume = "10",
number = "5",
pages = "53:1--53:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3340268",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Unlabeled, multi-view data presents a considerable
challenge in many real-world data analysis tasks. These
data are worth exploring because they often contain
complementary information that improves the quality of
the analysis results. Clustering with multi-view data
is a particularly challenging problem as revealing the
complex data structures between many feature spaces
demands discriminative features that are specific to
the task and, when too few of these features are
present, performance suffers. Extreme learning machines
(ELMs) are an emerging form of learning model that have
shown an outstanding representation ability and
superior performance in a range of different learning
tasks. Motivated by the promise of this advancement, we
have developed a novel multi-view fusion clustering
framework based on an ELM, called MVEC. MVEC learns the
embeddings from each view of the data via the ELM
network, then constructs a single unified embedding
according to the correlations and dependencies between
each embedding and automatically weighting the
contribution of each. This process exposes the
underlying clustering structures embedded within
multi-view data with a high degree of accuracy. A
simple yet efficient solution is also provided to solve
the optimization problem within MVEC. Experiments and
comparisons on eight different benchmarks from
different domains confirm MVEC's clustering accuracy.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Law:2019:TLA,
author = "Stephen Law and Brooks Paige and Chris Russell",
title = "Take a Look Around: Using Street View and Satellite
Images to Estimate House Prices",
journal = j-TIST,
volume = "10",
number = "5",
pages = "54:1--54:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3342240",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "When an individual purchases a home, they
simultaneously purchase its structural features, its
accessibility to work, and the neighborhood amenities.
Some amenities, such as air quality, are measurable
while others, such as the prestige or the visual
impression of a neighborhood, are difficult to
quantify. Despite the well-known impacts intangible
housing features have on house prices, limited
attention has been given to systematically quantifying
these difficult to measure amenities. Two issues have
led to this neglect. Not only do few quantitative
methods exist that can measure the urban environment,
but that the collection of such data is both costly and
subjective. We show that street image and satellite
image data can capture these urban qualities and
improve the estimation of house prices. We propose a
pipeline that uses a deep neural network model to
automatically extract visual features from images to
estimate house prices in London, UK. We make use of
traditional housing features such as age, size, and
accessibility as well as visual features from Google
Street View images and Bing aerial images in estimating
the house price model. We find encouraging results
where learning to characterize the urban quality of a
neighborhood improves house price prediction, even when
generalizing to previously unseen London boroughs. We
explore the use of non-linear vs. linear methods to
fuse these cues with conventional models of house
pricing, and show how the interpretability of linear
models allows us to directly extract proxy variables
for visual desirability of neighborhoods that are both
of interest in their own right, and could be used as
inputs to other econometric methods. This is
particularly valuable as once the network has been
trained with the training data, it can be applied
elsewhere, allowing us to generate vivid dense maps of
the visual appeal of London streets.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhang:2019:DDF,
author = "Ya-Lin Zhang and Jun Zhou and Wenhao Zheng and Ji Feng
and Longfei Li and Ziqi Liu and Ming Li and Zhiqiang
Zhang and Chaochao Chen and Xiaolong Li and Yuan (Alan)
Qi and Zhi-Hua Zhou",
title = "Distributed Deep Forest and its Application to
Automatic Detection of Cash-Out Fraud",
journal = j-TIST,
volume = "10",
number = "5",
pages = "55:1--55:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3342241",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Internet companies are facing the need for handling
large-scale machine learning applications on a daily
basis and distributed implementation of machine
learning algorithms which can handle extra-large-scale
tasks with great performance is widely needed. Deep
forest is a recently proposed deep learning framework
which uses tree ensembles as its building blocks and it
has achieved highly competitive results on various
domains of tasks. However, it has not been tested on
extremely large-scale tasks. In this work, based on our
parameter server system, we developed the distributed
version of deep forest. To meet the need for real-world
tasks, many improvements are introduced to the original
deep forest model, including MART (Multiple Additive
Regression Tree) as base learners for efficiency and
effectiveness consideration, the cost-based method for
handling prevalent class-imbalanced data, MART based
feature selection for high dimension data, and
different evaluation metrics for automatically
determining the cascade level. We tested the deep
forest model on an extra-large-scale task, i.e.,
automatic detection of cash-out fraud, with more than
100 million training samples. Experimental results
showed that the deep forest model has the best
performance according to the evaluation metrics from
different perspectives even with very little effort for
parameter tuning. This model can block fraud
transactions in a large amount of money each day. Even
compared with the best-deployed model, the deep forest
model can additionally bring a significant decrease in
economic loss each day.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Braytee:2019:CML,
author = "Ali Braytee and Wei Liu and Ali Anaissi and Paul J.
Kennedy",
title = "Correlated Multi-label Classification with Incomplete
Label Space and Class Imbalance",
journal = j-TIST,
volume = "10",
number = "5",
pages = "56:1--56:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3342512",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Multi-label classification is defined as the problem
of identifying the multiple labels or categories of new
observations based on labeled training data.
Multi-labeled data has several challenges, including
class imbalance, label correlation, incomplete
multi-label matrices, and noisy and irrelevant
features. In this article, we propose an integrated
multi-label classification approach with incomplete
label space and class imbalance (ML-CIB) for
simultaneously training the multi-label classification
model and addressing the aforementioned challenges. The
model learns a new label matrix and captures new label
correlations, because it is difficult to find a
complete label vector for each instance in real-world
data. We also propose a label regularization to handle
the imbalanced multi-labeled issue in the new label,
and l$_1$ regularization norm is incorporated in the
objective function to select the relevant sparse
features. A multi-label feature selection (ML-CIB-FS)
method is presented as a variant of the proposed ML-CIB
to show the efficacy of the proposed method in
selecting the relevant features. ML-CIB is formulated
as a constrained objective function. We use the
accelerated proximal gradient method to solve the
proposed optimisation problem. Last, extensive
experiments are conducted on 19 regular-scale and
large-scale imbalanced multi-labeled datasets. The
promising results show that our method significantly
outperforms the state-of-the-art.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zamani:2019:AAT,
author = "Hamed Zamani and Markus Schedl and Paul Lamere and
Ching-Wei Chen",
title = "An Analysis of Approaches Taken in the {ACM RecSys
Challenge 2018} for Automatic Music Playlist
Continuation",
journal = j-TIST,
volume = "10",
number = "5",
pages = "57:1--57:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3344257",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The ACM Recommender Systems Challenge 2018 focused on
the task of automatic music playlist continuation,
which is a form of the more general task of sequential
recommendation. Given a playlist of arbitrary length
with some additional meta-data, the task was to
recommend up to 500 tracks that fit the target
characteristics of the original playlist. For the
RecSys Challenge, Spotify released a dataset of one
million user-generated playlists. Participants could
compete in two tracks, i.e., main and creative tracks.
Participants in the main track were only allowed to use
the provided training set, however, in the creative
track, the use of external public sources was
permitted. In total, 113 teams submitted 1,228 runs to
the main track; 33 teams submitted 239 runs to the
creative track. The highest performing team in the main
track achieved an R-precision of 0.2241, an NDCG of
0.3946, and an average number of recommended songs
clicks of 1.784. In the creative track, an R-precision
of 0.2233, an NDCG of 0.3939, and a click rate of 1.785
was obtained by the best team. This article provides an
overview of the challenge, including motivation, task
definition, dataset description, and evaluation. We
further report and analyze the results obtained by the
top-performing teams in each track and explore the
approaches taken by the winners. We finally summarize
our key findings, discuss generalizability of
approaches and results to domains other than music, and
list the open avenues and possible future directions in
the area of automatic playlist continuation.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Corno:2019:RRI,
author = "Fulvio Corno and Luigi {De Russis} and Alberto Monge
Roffarello",
title = "{RecRules}: Recommending {IF--THEN} Rules for End-User
Development",
journal = j-TIST,
volume = "10",
number = "5",
pages = "58:1--58:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3344211",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "Nowadays, end users can personalize their smart
devices and web applications by defining or reusing
IF-THEN rules through dedicated End-User Development
(EUD) tools. Despite apparent simplicity, such tools
present their own set of issues. The emerging and
increasing complexity of the Internet of Things, for
example, is barely taken into account, and the number
of possible combinations between triggers and actions
of different smart devices and web applications is
continuously growing. Such a large design space makes
end-user personalization a complex task for
non-programmers, and motivates the need of assisting
users in easily discovering and managing rules and
functionality, e.g., through recommendation techniques.
In this article, we tackle the emerging problem of
recommending IF-THEN rules to end users by presenting
RecRules, a hybrid and semantic recommendation system.
Through a mixed content and collaborative approach, the
goal of RecRules is to recommend by functionality: it
suggests rules based on their final purposes, thus
overcoming details like manufacturers and brands. The
algorithm uses a semantic reasoning process to enrich
rules with semantic information, with the aim of
uncovering hidden connections between rules in terms of
shared functionality. Then, it builds a collaborative
semantic graph, and it exploits different types of
path-based features to train a learning to rank
algorithm and compute top-N recommendations. We
evaluate RecRules through different experiments on real
user data extracted from IFTTT, one of the most popular
EUD tools. Results are promising: they show the
effectiveness of our approach with respect to other
state-of-the-art algorithms and open the way for a new
class of recommender systems for EUD that take into
account the actual functionality needed by end users.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Pappalardo:2019:PDD,
author = "Luca Pappalardo and Paolo Cintia and Paolo Ferragina
and Emanuele Massucco and Dino Pedreschi and Fosca
Giannotti",
title = "{PlayeRank}: Data-driven Performance Evaluation and
Player Ranking in Soccer via a Machine Learning
Approach",
journal = j-TIST,
volume = "10",
number = "5",
pages = "59:1--59:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3343172",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The problem of evaluating the performance of soccer
players is attracting the interest of many companies
and the scientific community, thanks to the
availability of massive data capturing all the events
generated during a match (e.g., tackles, passes, shots,
etc.). Unfortunately, there is no consolidated and
widely accepted metric for measuring performance
quality in all of its facets. In this article, we
design and implement PlayeRank, a data-driven framework
that offers a principled multi-dimensional and
role-aware evaluation of the performance of soccer
players. We build our framework by deploying a massive
dataset of soccer-logs and consisting of millions of
match events pertaining to four seasons of 18 prominent
soccer competitions. By comparing PlayeRank to known
algorithms for performance evaluation in soccer, and by
exploiting a dataset of players' evaluations made by
professional soccer scouts, we show that PlayeRank
significantly outperforms the competitors. We also
explore the ratings produced by PlayeRank and discover
interesting patterns about the nature of excellent
performances and what distinguishes the top players
from the others. At the end, we explore some
applications of PlayeRank-i.e. searching players and
player versatility-showing its flexibility and
efficiency, which makes it worth to be used in the
design of a scalable platform for soccer analytics.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Ning:2019:DRL,
author = "Zhaolong Ning and Peiran Dong and Xiaojie Wang and
Joel J. P. C. Rodrigues and Feng Xia",
title = "Deep Reinforcement Learning for Vehicular Edge
Computing: an Intelligent Offloading System",
journal = j-TIST,
volume = "10",
number = "6",
pages = "60:1--60:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3317572",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The development of smart vehicles brings drivers and
passengers a comfortable and safe environment. Various
emerging applications are promising to enrich users'
traveling experiences and daily life. However, how to
execute computing-intensive applications on
resource-constrained vehicles still faces huge
challenges. In this article, we construct an
intelligent offloading system for vehicular edge
computing by leveraging deep reinforcement learning.
First, both the communication and computation states
are modelled by finite Markov chains. Moreover, the
task scheduling and resource allocation strategy is
formulated as a joint optimization problem to maximize
users' Quality of Experience (QoE). Due to its
complexity, the original problem is further divided
into two sub-optimization problems. A two-sided
matching scheme and a deep reinforcement learning
approach are developed to schedule offloading requests
and allocate network resources, respectively.
Performance evaluations illustrate the effectiveness
and superiority of our constructed system.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tariq:2019:EES,
author = "Umair Ullah Tariq and Haider Ali and Lu Liu and John
Panneerselvam and Xiaojun Zhai",
title = "Energy-efficient Static Task Scheduling on {VFI}-based
{NoC--HMPSoCs} for Intelligent Edge Devices in
Cyber-physical Systems",
journal = j-TIST,
volume = "10",
number = "6",
pages = "66:1--66:??",
month = oct,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3336121",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 22 11:55:45 MDT 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "The interlinked processing units in modern
Cyber-Physical Systems (CPS) creates a large network of
connected computing embedded systems. Network-on-Chip
(NoC)-based Multiprocessor System-on-Chip (MPSoC)
architecture is becoming a de facto computing platform
for real-time applications due to its higher
performance and Quality-of-Service (QoS). The number of
processors has increased significantly on the
multiprocessor systems in CPS; therefore, Voltage
Frequency Island (VFI) has been recently adopted for
effective energy management mechanism in the
large-scale multiprocessor chip designs. In this
article, we investigated energy-efficient and
contention-aware static scheduling for tasks with
precedence and deadline constraints on intelligent edge
devices deploying heterogeneous VFI-based NoC-MPSoCs
(VFI-NoC-HMPSoC) with DVFS-enabled processors. Unlike
the existing population-based optimization algorithms,
we proposed a novel population-based algorithm called
ARSH-FATI that can dynamically switch between
explorative and exploitative search modes at run-time.
Our static scheduler ARHS-FATI collectively performs
task mapping, scheduling, and voltage scaling.
Consequently, its performance is superior to the
existing state-of-the-art approach proposed for
homogeneous VFI-based NoC-MPSoCs. We also developed a
communication contention-aware Earliest Edge Consistent
Deadline First (EECDF) scheduling algorithm and
gradient descent--inspired voltage scaling algorithm
called Energy Gradient Decent (EGD). We introduced a
notion of Energy Gradient (EG) that guides EGD in its
search for island voltage settings and minimize the
total energy consumption. We conducted the experiments
on eight real benchmarks adopted from Embedded Systems
Synthesis Benchmarks (E3S). Our static scheduling
approach ARSH-FATI outperformed state-of-the-art
technique and achieved an average energy-efficiency of
$ \approx $24\% and $ \approx $30\% over CA-TMES-Search
and CA-TMES-Quick, respectively.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhou:2019:LCN,
author = "Junhao Zhou and Hong-Ning Dai and Hao Wang",
title = "Lightweight Convolution Neural Networks for Mobile
Edge Computing in Transportation Cyber Physical
Systems",
journal = j-TIST,
volume = "10",
number = "6",
pages = "67:1--67:??",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3339308",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Dec 16 07:23:45 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3339308",
abstract = "Cloud computing extends Transportation Cyber-Physical
Systems (T-CPS) with provision of enhanced computing
and storage capability via offloading computing tasks
to remote cloud servers. However, cloud computing
cannot fulfill the requirements such as low latency and
context awareness in T-CPS. The appearance of Mobile
Edge Computing (MEC) can overcome the limitations of
cloud computing via offloading the computing tasks at
edge servers in approximation to users, consequently
reducing the latency and improving the context
awareness. Although MEC has the potential in improving
T-CPS, it is incapable of processing
computational-intensive tasks such as deep learning
algorithms due to the intrinsic storage and
computing-capability constraints. Therefore, we design
and develop a lightweight deep learning model to
support MEC applications in T-CPS. In particular, we
put forth a stacked convolutional neural network (CNN)
consisting of factorization convolutional layers
alternating with compression layers (namely,
lightweight CNN-FC). Extensive experimental results
show that our proposed lightweight CNN-FC can greatly
decrease the number of unnecessary parameters, thereby
reducing the model size while maintaining the high
accuracy in contrast to conventional CNN models. In
addition, we also evaluate the performance of our
proposed model via conducting experiments at a
realistic MEC platform. Specifically, experimental
results at this MEC platform show that our model can
maintain the high accuracy while preserving the
portable model size.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Tang:2019:EPP,
author = "Wenjuan Tang and Ju Ren and Kuan Zhang and Deyu Zhang
and Yaoxue Zhang and Xuemin (Sherman) Shen",
title = "Efficient and Privacy-preserving Fog-assisted Health
Data Sharing Scheme",
journal = j-TIST,
volume = "10",
number = "6",
pages = "68:1--68:??",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3341104",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Dec 16 07:23:45 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3341104",
abstract = "Pervasive data collected from e-healthcare devices
possess significant medical value through data sharing
with professional healthcare service providers.
However, health data sharing poses several security
issues, such as access control and privacy leakage, as
well as faces critical challenges to obtain efficient
data analysis and services. In this article, we propose
an efficient and privacy-preserving fog-assisted health
data sharing (PFHDS) scheme for e-healthcare systems.
Specifically, we integrate the fog node to classify the
shared data into different categories according to
disease risks for efficient health data analysis.
Meanwhile, we design an enhanced attribute-based
encryption method through combination of a personal
access policy on patients and a professional access
policy on the fog node for effective medical service
provision. Furthermore, we achieve significant
encryption consumption reduction for patients by
offloading a portion of the computation and storage
burden from patients to the fog node. Security
discussions show that PFHDS realizes data
confidentiality and fine-grained access control with
collusion resistance. Performance evaluations
demonstrate cost-efficient encryption computation,
storage and energy consumption.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Li:2019:SDS,
author = "Jin Li and Tong Li and Zheli Liu and Xiaofeng Chen",
title = "Secure Deduplication System with Active Key Update and
Its Application in {IoT}",
journal = j-TIST,
volume = "10",
number = "6",
pages = "69:1--69:??",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3356468",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Dec 16 07:23:45 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3356468",
abstract = "The rich cloud services in the Internet of Things
create certain needs for edge computing, in which
devices should be able to handle storage tasks
securely, reliably, and efficiently. When processing
the storage requests from edge devices, each cloud
server is supposed to eliminate duplicate copies of
repeating data to reduce the amount of storage space
and save on bandwidth. To protect data confidentiality
while supporting deduplication, some
convergent-encryption-based techniques have been
proposed to encrypt the data before uploading. However,
all these works cannot meet two requirements while
preventing brute-force attacks: (i) power-constrained
edge nodes should update encryption keys efficiently
when an edge node is abandoned; and (ii) the access
privacy of edge nodes should be guaranteed. In this
article, we propose a novel encryption scheme for
secure chunk-level deduplication. Based on this scheme,
we present two constructions of the secure
deduplication system that support an efficient key
update protocol. The key update protocol does not
involve any edge node in computational tasks, so that
the deduplication system can adopt an active key update
strategy. Moreover, one of our constructions, which is
called advance construction, can provide access privacy
assurances for edge nodes. The security analysis is
given in terms of the proposed threat model. The
experimental analysis demonstrates that the proposed
deduplication system is practical.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhao:2019:VAA,
author = "Ying Zhao and Lei Wang and Shijie Li and Fangfang Zhou
and Xiaoru Lin and Qiang Lu and Lei Ren",
title = "A Visual Analysis Approach for Understanding
Durability Test Data of Automotive Products",
journal = j-TIST,
volume = "10",
number = "6",
pages = "70:1--70:??",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3345640",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Dec 16 07:23:45 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
abstract = "People face data-rich manufacturing environments in
Industry 4.0. As an important technology for explaining
and understanding complex data, visual analytics has
been increasingly introduced into industrial data
analysis scenarios. With the durability test of
automotive starters as background, this study proposes
a visual analysis approach for understanding
large-scale and long-term durability test data. Guided
by detailed scenario and requirement analyses, we first
propose a migration-adapted clustering algorithm that
utilizes a segmentation strategy and a group of
matching-updating operations to achieve an efficient
and accurate clustering analysis of the data for
starting mode identification and abnormal test
detection. We then design and implement a visual
analysis system that provides a set of user-friendly
visual designs and lightweight interactions to help
people gain data insights into the test process
overview, test data patterns, and durability
performance dynamics. Finally, we conduct a
quantitative algorithm evaluation, case study, and user
interview by using real-world starter durability test
datasets. The results demonstrate the effectiveness of
the approach and its possible inspiration for the
durability test data analysis of other similar
industrial products.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Leyli-Abadi:2019:MJN,
author = "Milad Leyli-Abadi and Allou sam{\'e} and Latifa
Oukhellou and Nicolas Cheifetz and Pierre Mandel and
C{\'e}dric F{\'e}liers and Olivier Chesneau",
title = "Mixture of Joint Nonhomogeneous {Markov} Chains to
Cluster and Model Water Consumption Behavior
Sequences",
journal = j-TIST,
volume = "10",
number = "6",
pages = "71:1--71:??",
month = dec,
year = "2019",
CODEN = "????",
DOI = "https://doi.org/10.1145/3347452",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Dec 16 07:23:45 MST 2019",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/ft_gateway.cfm?id=3347452",
abstract = "The emergence of smart meters has fostered the
collection of massive data that support a better
understanding of consumer behaviors and better
management of water resources and networks. The main
focus of this article is to analyze consumption
behavior over time; thus, we first identify the main
weekly consumption patterns. This approach allows each
meter to be represented by a categorical series, where
each category corresponds to a weekly consumption
behavior. By considering the resulting consumption
behavior sequences, we propose a new methodology based
on a mixture of nonhomogeneous Markov models to cluster
these categorical time series. Using this method, the
meters are described by the Markovian dynamics of their
cluster. The latent variable that controls cluster
membership is estimated alongside the parameters of the
Markov model using a novel classification expectation
maximization algorithm. A specific entropy measure is
formulated to evaluate the quality of the estimated
partition by considering the joint Markovian dynamics.
The proposed clustering model can also be used to
predict future consumption behaviors within each
cluster. Numerical experiments using real water
consumption data provided by a water utility in France
and gathered over 19 months are conducted to evaluate
the performance of the proposed approach in terms of
both clustering and prediction. The results demonstrate
the effectiveness of the proposed method.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "http://portal.acm.org/citation.cfm?id=J1318",
}
@Article{Zhou:2020:FPT,
author = "Binbin Zhou and Sha Zhao and Longbiao Chen and Shijian
Li and Zhaohui Wu and Gang Pan",
title = "Forecasting Price Trend of Bulk Commodities Leveraging
Cross-domain Open Data Fusion",
journal = j-TIST,
volume = "11",
number = "1",
pages = "1:1--1:26",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3354287",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3354287",
abstract = "Forecasting price trend of bulk commodities is
important in international trade, not only for markets
participants to schedule production and marketing plans
but also for government administrators to adjust
policies. Previous studies cannot support \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xie:2020:DIS,
author = "Yiqun Xie and Xun Zhou and Shashi Shekhar",
title = "Discovering Interesting Subpaths with Statistical
Significance from Spatiotemporal Datasets",
journal = j-TIST,
volume = "11",
number = "1",
pages = "2:1--2:24",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3354189",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3354189",
abstract = "Given a path in a spatial or temporal framework, we
aim to find all contiguous subpaths that are both
interesting (e.g., abrupt changes) and statistically
significant (i.e., persistent trends rather than local
fluctuations). Discovering interesting \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lopes:2020:GBR,
author = "Ramon Lopes and Renato Assun{\c{c}}{\~a}o and Rodrygo
L. T. Santos",
title = "Graph-based Recommendation Meets {Bayes} and
Similarity Measures",
journal = j-TIST,
volume = "11",
number = "1",
pages = "3:1--3:26",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3356882",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3356882",
abstract = "Graph-based approaches provide an effective
memory-based alternative to latent factor models for
collaborative recommendation. Modern approaches rely on
either sampling short walks or enumerating short paths
starting from the target user in a user-item bipartite
graph. While the effectiveness of random walk sampling
heavily depends on the underlying path sampling
strategy, path enumeration is sensitive to the strategy
adopted for scoring each individual path. In this
article, we demonstrate how both strategies can be
improved through Bayesian reasoning. In particular, we
propose to improve random walk sampling by exploiting
distributional aspects of items' ratings on the sampled
paths. Likewise, we extend existing path enumeration
approaches to leverage categorical ratings and to scale
the score of each path proportionally to the affinity
of pairs of users and pairs of items on the path.
Experiments on several publicly available datasets
demonstrate the effectiveness of our proposed
approaches compared to state-of-the-art graph-based
recommenders.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Nelke:2020:MCB,
author = "Sofia Amador Nelke and Steven Okamoto and Roie Zivan",
title = "Market Clearing-based Dynamic Multi-agent Task
Allocation",
journal = j-TIST,
volume = "11",
number = "1",
pages = "4:1--4:25",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3356467",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3356467",
abstract = "Realistic multi-agent team applications often feature
dynamic environments with soft deadlines that penalize
late execution of tasks. This puts a premium on quickly
allocating tasks to agents. However, when such problems
include temporal and spatial \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Waniek:2020:SAA,
author = "Marcin Waniek and Tomasz P. Michalak and Aamena
Alshamsi",
title = "Strategic Attack \& Defense in Security Diffusion
Games",
journal = j-TIST,
volume = "11",
number = "1",
pages = "5:1--5:35",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3357605",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3357605",
abstract = "Security games model the confrontation between a
defender protecting a set of targets and an attacker
who tries to capture them. A variant of these games
assumes security interdependence between targets,
facilitating contagion of an attack. So far, only
stochastic spread of an attack has been considered. In
this work, we introduce a version of security games,
where the attacker strategically drives the entire
spread of attack and where interconnections between
nodes affect their susceptibility to be captured. We
find that the strategies effective in the settings
without contagion or with stochastic contagion are no
longer feasible when spread of attack is strategic.
While in the former settings it was possible to
efficiently find optimal strategies of the attacker,
doing so in the latter setting turns out to be an
NP-complete problem for an arbitrary network. However,
for some simpler network structures, such as cliques,
stars, and trees, we show that it is possible to
efficiently find optimal strategies of both players.
For arbitrary networks, we study and compare the
efficiency of various heuristic strategies. As opposed
to previous works with no or stochastic contagion, we
find that centrality-based defense is often effective
when spread of attack is strategic, particularly for
centrality measures based on the Shapley value.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2020:TLD,
author = "Jindong Wang and Yiqiang Chen and Wenjie Feng and Han
Yu and Meiyu Huang and Qiang Yang",
title = "Transfer Learning with Dynamic Distribution
Adaptation",
journal = j-TIST,
volume = "11",
number = "1",
pages = "6:1--6:25",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3360309",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3360309",
abstract = "Transfer learning aims to learn robust classifiers for
the target domain by leveraging knowledge from a source
domain. Since the source and the target domains are
usually from different distributions, existing methods
mainly focus on adapting the cross-. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Horne:2020:RFN,
author = "Benjamin D. Horne and Jeppe N{\o}rregaard and Sibel
Adali",
title = "Robust Fake News Detection Over Time and Attack",
journal = j-TIST,
volume = "11",
number = "1",
pages = "7:1--7:23",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3363818",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3363818",
abstract = "In this study, we examine the impact of time on
state-of-the-art news veracity classifiers. We show
that, as time progresses, classification performance
for both unreliable and hyper-partisan news
classification slowly degrade. While this degradation
does happen, it happens slower than expected,
illustrating that hand-crafted, content-based features,
such as style of writing, are fairly robust to changes
in the news cycle. We show that this small degradation
can be mitigated using online learning. Last, we
examine the impact of adversarial content manipulation
by malicious news producers. Specifically, we test
three types of attack based on changes in the input
space and data availability. We show that static models
are susceptible to content manipulation attacks, but
online models can recover from such attacks.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Pan:2020:DDH,
author = "Menghai Pan and Weixiao Huang and Yanhua Li and Xun
Zhou and Zhenming Liu and Rui Song and Hui Lu and
Zhihong Tian and Jun Luo",
title = "{DHPA}: Dynamic Human Preference Analytics Framework:
a Case Study on Taxi Drivers' Learning Curve Analysis",
journal = j-TIST,
volume = "11",
number = "1",
pages = "8:1--8:19",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3360312",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3360312",
abstract = "Many real-world human behaviors can be modeled and
characterized as sequential decision-making processes,
such as a taxi driver's choices of working regions and
times. Each driver possesses unique preferences on the
sequential choices over time and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2020:FMH,
author = "Meng Wang and Hui Li and Jiangtao Cui and Sourav S.
Bhowmick and Ping Liu",
title = "{FROST}: Movement History-Conscious Facility
Relocation",
journal = j-TIST,
volume = "11",
number = "1",
pages = "9:1--9:26",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3361740",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3361740",
abstract = "The facility relocation (FR) problem, which aims to
optimize the placement of facilities to accommodate the
changes of users' locations, has a broad spectrum of
applications. Despite the significant progress made by
existing solutions to the FR problem, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fu:2020:TER,
author = "Tao-Yang Fu and Wang-Chien Lee",
title = "{Trembr}: Exploring Road Networks for Trajectory
Representation Learning",
journal = j-TIST,
volume = "11",
number = "1",
pages = "10:1--10:25",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3361741",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3361741",
abstract = "In this article, we propose a novel representation
learning framework, namely TRajectory EMBedding via
Road networks (Trembr), to learn trajectory embeddings
(low-dimensional feature vectors) for use in a variety
of trajectory applications. The novelty \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Beigi:2020:SSG,
author = "Ghazaleh Beigi and Jiliang Tang and Huan Liu",
title = "Social Science-guided Feature Engineering: a Novel
Approach to Signed Link Analysis",
journal = j-TIST,
volume = "11",
number = "1",
pages = "11:1--11:27",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3364222",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3364222",
abstract = "Many real-world relations can be represented by signed
networks with positive links (e.g., friendships and
trust) and negative links (e.g., foes and distrust).
Link prediction helps advance tasks in social network
analysis such as recommendation \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Luo:2020:ECN,
author = "Ping Luo and Kai Shu and Junjie Wu and Li Wan and Yong
Tan",
title = "Exploring Correlation Network for Cheating Detection",
journal = j-TIST,
volume = "11",
number = "1",
pages = "12:1--12:23",
month = feb,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3364221",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Feb 15 07:31:36 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3364221",
abstract = "The correlation network, typically formed by computing
pairwise correlations between variables, has recently
become a competitive paradigm to discover insights in
various application domains, such as climate
prediction, financial marketing, and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2020:WTE,
author = "Shuo Zhang and Krisztian Balog",
title = "{Web} Table Extraction, Retrieval, and Augmentation: a
Survey",
journal = j-TIST,
volume = "11",
number = "2",
pages = "13:1--13:35",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3372117",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3372117",
abstract = "Tables are powerful and popular tools for organizing
and manipulating data. A vast number of tables can be
found on the Web, which represent a valuable knowledge
resource. The objective of this survey is to synthesize
and present two decades of research \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhu:2020:FMM,
author = "Lei Zhu and Xu Lu and Zhiyong Cheng and Jingjing Li
and Huaxiang Zhang",
title = "Flexible Multi-modal Hashing for Scalable Multimedia
Retrieval",
journal = j-TIST,
volume = "11",
number = "2",
pages = "14:1--14:20",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3365841",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3365841",
abstract = "Multi-modal hashing methods could support efficient
multimedia retrieval by combining multi-modal features
for binary hash learning at the both offline training
and online query stages. However, existing multi-modal
methods cannot binarize the queries, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shmueli:2020:MSM,
author = "Erez Shmueli and Tamir Tassa",
title = "Mediated Secure Multi-Party Protocols for
Collaborative Filtering",
journal = j-TIST,
volume = "11",
number = "2",
pages = "15:1--15:25",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3375402",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3375402",
abstract = "Recommender systems have become extremely common in
recent years and are utilized in a variety of domains
such as movies, music, news, products, restaurants, and
so on. While a typical recommender system bases its
recommendations solely on users' \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Oliveira:2020:RAE,
author = "Samuel E. L. Oliveira and Victor Diniz and Anisio
Lacerda and Luiz Merschmanm and Gisele L. Pappa",
title = "Is Rank Aggregation Effective in Recommender Systems?
{An} Experimental Analysis",
journal = j-TIST,
volume = "11",
number = "2",
pages = "16:1--16:26",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3365375",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3365375",
abstract = "Recommender Systems are tools designed to help users
find relevant information from the myriad of content
available online. They work by actively suggesting
items that are relevant to users according to their
historical preferences or observed actions. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ye:2020:XLA,
author = "Juan Ye and Simon Dobson and Franco Zambonelli",
title = "{XLearn}: Learning Activity Labels across
Heterogeneous Datasets",
journal = j-TIST,
volume = "11",
number = "2",
pages = "17:1--17:28",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3368272",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3368272",
abstract = "Sensor-driven systems often need to map sensed data
into meaningfully labelled activities to classify the
phenomena being observed. A motivating and challenging
example comes from human activity recognition in which
smart home and other datasets are \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhuo:2020:DUP,
author = "Hankz Hankui Zhuo and Yantian Zha and Subbarao
Kambhampati and Xin Tian",
title = "Discovering Underlying Plans Based on Shallow Models",
journal = j-TIST,
volume = "11",
number = "2",
pages = "18:1--18:30",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3368270",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3368270",
abstract = "Plan recognition aims to discover target plans (i.e.,
sequences of actions) behind observed actions, with
history plan libraries or action models in hand.
Previous approaches either discover plans by maximally
``matching'' observed actions to plan \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2020:NMC,
author = "Chien-Chih Wang and Kent Loong Tan and Chih-Jen Lin",
title = "{Newton} Methods for Convolutional Neural Networks",
journal = j-TIST,
volume = "11",
number = "2",
pages = "19:1--19:30",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3368271",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3368271",
abstract = "Deep learning involves a difficult non-convex
optimization problem, which is often solved by
stochastic gradient (SG) methods. While SG is usually
effective, it may not be robust in some situations.
Recently, Newton methods have been investigated as an
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Huang:2020:SIS,
author = "Shih-Chia Huang and Da-Wei Jaw and Bo-Hao Chen and
Sy-Yen Kuo",
title = "Single Image Snow Removal Using Sparse Representation
and Particle Swarm Optimizer",
journal = j-TIST,
volume = "11",
number = "2",
pages = "20:1--20:15",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3372116",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3372116",
abstract = "Images are often corrupted by natural obscuration
(e.g., snow, rain, and haze) during acquisition in bad
weather conditions. The removal of snowflakes from only
a single image is a challenging task due to situational
variety and has been investigated \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2020:PBU,
author = "Wenhe Liu and Xiaojun Chang and Ling Chen and Dinh
Phung and Xiaoqin Zhang and Yi Yang and Alexander G.
Hauptmann",
title = "Pair-based Uncertainty and Diversity Promoting Early
Active Learning for Person Re-identification",
journal = j-TIST,
volume = "11",
number = "2",
pages = "21:1--21:15",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3372121",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3372121",
abstract = "The effective training of supervised Person
Re-identification (Re-ID) models requires sufficient
pairwise labeled data. However, when there is limited
annotation resource, it is difficult to collect
pairwise labeled data. We consider a challenging and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2020:TRF,
author = "Lei Chen and Zhiang Wu and Jie Cao and Guixiang Zhu
and Yong Ge",
title = "Travel Recommendation via Fusing Multi-Auxiliary
Information into Matrix Factorization",
journal = j-TIST,
volume = "11",
number = "2",
pages = "22:1--22:24",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3372118",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3372118",
abstract = "As an e-commerce feature, the personalized
recommendation is invariably highly-valued by both
consumers and merchants. The e-tourism has become one
of the hottest industries with the adoption of
recommendation systems. Several lines of evidence have
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Pereira:2020:USO,
author = "Ramon Fraga Pereira and Nir Oren and Felipe
Meneguzzi",
title = "Using Sub-Optimal Plan Detection to Identify
Commitment Abandonment in Discrete Environments",
journal = j-TIST,
volume = "11",
number = "2",
pages = "23:1--23:26",
month = mar,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3372119",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 3 09:15:47 MST 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3372119",
abstract = "Assessing whether an agent has abandoned a goal or is
actively pursuing it is important when multiple agents
are trying to achieve joint goals, or when agents
commit to achieving goals for each other. Making such a
determination for a single goal by \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2020:AAD,
author = "Wei Emma Zhang and Quan Z. Sheng and Ahoud Alhazmi and
Chenliang Li",
title = "Adversarial Attacks on Deep-learning Models in Natural
Language Processing: a Survey",
journal = j-TIST,
volume = "11",
number = "3",
pages = "24:1--24:41",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3374217",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3374217",
abstract = "With the development of high computational devices,
deep neural networks (DNNs), in recent years, have
gained significant popularity in many Artificial
Intelligence (AI) applications. However, previous
efforts have shown that DNNs are vulnerable to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2020:UGA,
author = "Zhuang Liu and Keli Xiao and Bo Jin and Kaiyu Huang
and Degen Huang and Yunxia Zhang",
title = "Unified Generative Adversarial Networks for
Multiple-Choice Oriented Machine Comprehension",
journal = j-TIST,
volume = "11",
number = "3",
pages = "25:1--25:20",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3372120",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3372120",
abstract = "In this article, we address the multiple-choice
machine comprehension (MC) problem in natural language
processing. Existing approaches for MC are usually
designed for general cases; however, we specially
develop a novel method for solving the
multiple-\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shah:2020:TCP,
author = "Ankit Shah and Arunesh Sinha and Rajesh Ganesan and
Sushil Jajodia and Hasan Cam",
title = "Two Can Play That Game: an Adversarial Evaluation of a
Cyber-Alert Inspection System",
journal = j-TIST,
volume = "11",
number = "3",
pages = "32:1--32:20",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3377554",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3377554",
abstract = "Cyber-security is an important societal concern.
Cyber-attacks have increased in numbers as well as in
the extent of damage caused in every attack. Large
organizations operate a Cyber Security Operation Center
(CSOC), which forms the first line of cyber-\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lin:2020:CDM,
author = "Adi Lin and Jie Lu and Junyu Xuan and Fujin Zhu and
Guangquan Zhang",
title = "A Causal {Dirichlet} Mixture Model for Causal
Inference from Observational Data",
journal = j-TIST,
volume = "11",
number = "3",
pages = "33:1--33:29",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3379500",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3379500",
abstract = "Estimating causal effects by making causal inferences
from observational data is common practice in
scientific studies, business decision-making, and daily
life. In today's data-driven world, causal inference
has become a key part of the evaluation \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lyu:2020:HPL,
author = "Gengyu Lyu and Songhe Feng and Yidong Li and Yi Jin
and Guojun Dai and Congyan Lang",
title = "{HERA}: Partial Label Learning by Combining
Heterogeneous Loss with Sparse and Low-Rank
Regularization",
journal = j-TIST,
volume = "11",
number = "3",
pages = "34:1--34:19",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3379501",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3379501",
abstract = "Partial label learning (PLL) aims to learn from the
data where each training instance is associated with a
set of candidate labels, among which only one is
correct. Most existing methods deal with this type of
problem by either treating each candidate \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Horvath:2020:CBA,
author = "G{\'a}bor Horv{\'a}th and Edith Kov{\'a}cs and Roland
Molontay and Szabolcs Nov{\'a}czki",
title = "Copula-Based Anomaly Scoring and Localization for
Large-Scale, High-Dimensional Continuous Data",
journal = j-TIST,
volume = "11",
number = "3",
pages = "26:1--26:26",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3372274",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3372274",
abstract = "The anomaly detection method presented by this article
has a special feature: it not only indicates whether or
not an observation is anomalous but also tells what
exactly makes an anomalous observation unusual. Hence,
it provides support to localize the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jin:2020:MNP,
author = "Di Jin and Bingyi Li and Pengfei Jiao and Dongxiao He
and Hongyu Shan and Weixiong Zhang",
title = "Modeling with Node Popularities for Autonomous
Overlapping Community Detection",
journal = j-TIST,
volume = "11",
number = "3",
pages = "27:1--27:23",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3373760",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3373760",
abstract = "Overlapping community detection has triggered recent
research in network analysis. One of the promising
techniques for finding overlapping communities is the
popular stochastic models, which, unfortunately, have
some common drawbacks. They do not \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhao:2020:UDR,
author = "Yawei Zhao and Qian Zhao and Xingxing Zhang and En Zhu
and Xinwang Liu and Jianping Yin",
title = "Understand Dynamic Regret with Switching Cost for
Online Decision Making",
journal = j-TIST,
volume = "11",
number = "3",
pages = "28:1--28:21",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3375788",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3375788",
abstract = "As a metric to measure the performance of an online
method, dynamic regret with switching cost has drawn
much attention for online decision making problems.
Although the sublinear regret has been provided in much
previous research, we still have little \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2020:DNC,
author = "Xueliang Liu and Xun Yang and Meng Wang and Richang
Hong",
title = "Deep Neighborhood Component Analysis for Visual
Similarity Modeling",
journal = j-TIST,
volume = "11",
number = "3",
pages = "29:1--29:15",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3375787",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3375787",
abstract = "Learning effective visual similarity is an essential
problem in multimedia research. Despite the promising
progress made in recent years, most existing approaches
learn visual features and similarities in two separate
stages, which inevitably limits \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Brock:2020:LTD,
author = "Heike Brock and Felix Law and Kazuhiro Nakadai and
Yuji Nagashima",
title = "Learning Three-dimensional Skeleton Data from Sign
Language Video",
journal = j-TIST,
volume = "11",
number = "3",
pages = "30:1--30:24",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3377552",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3377552",
abstract = "Data for sign language research is often difficult and
costly to acquire. We therefore present a novel
pipeline able to generate motion three-dimensional (3D)
skeleton data from single-camera sign language videos
only. First, three recurrent neural \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2020:WUA,
author = "Lei Zhang and Yixiang Zhang and Xiaolong Zheng",
title = "{WiSign}: Ubiquitous {American Sign Language}
Recognition Using Commercial {Wi-Fi} Devices",
journal = j-TIST,
volume = "11",
number = "3",
pages = "31:1--31:24",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3377553",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3377553",
abstract = "In this article, we propose WiSign that recognizes the
continuous sentences of American Sign Language (ASL)
with existing WiFi infrastructure. Instead of
identifying the individual ASL words from the manually
segmented ASL sentence in existing works, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Arora:2020:ADC,
author = "Udit Arora and Hridoy Sankar Dutta and Brihi Joshi and
Aditya Chetan and Tanmoy Chakraborty",
title = "Analyzing and Detecting Collusive Users Involved in
Blackmarket Retweeting Activities",
journal = j-TIST,
volume = "11",
number = "3",
pages = "35:1--35:24",
month = may,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3380537",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue May 19 09:21:48 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3380537",
abstract = "With the rise in popularity of social media platforms
like Twitter, having higher influence on these
platforms has a greater value attached to it, since it
has the power to influence many decisions in the form
of brand promotions and shaping opinions. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yao:2020:VOS,
author = "Rui Yao and Guosheng Lin and Shixiong Xia and Jiaqi
Zhao and Yong Zhou",
title = "Video Object Segmentation and Tracking: a Survey",
journal = j-TIST,
volume = "11",
number = "4",
pages = "36:1--36:47",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3391743",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3391743",
abstract = "Object segmentation and object tracking are
fundamental research areas in the computer vision
community. These two topics are difficult to handle
some common challenges, such as occlusion, deformation,
motion blur, scale variation, and more. The former
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2020:KAA,
author = "Yingying Zhang and Quan Fang and Shengsheng Qian and
Changsheng Xu",
title = "Knowledge-aware Attentive {Wasserstein} Adversarial
Dialogue Response Generation",
journal = j-TIST,
volume = "11",
number = "4",
pages = "37:1--37:20",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3384675",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3384675",
abstract = "Natural language generation has become a fundamental
task in dialogue systems. RNN-based natural response
generation methods encode the dialogue context and
decode it into a response. However, they tend to
generate dull and simple responses. In this \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Feygin:2020:BBI,
author = "Sidney A. Feygin and Jessica R. Lazarus and Edward H.
Forscher and Valentine Golfier-Vetterli and Jonathan W.
Lee and Abhishek Gupta and Rashid A. Waraich and Colin
J. R. Sheppard and Alexandre M. Bayen",
title = "{BISTRO}: {Berkeley Integrated System for
Transportation Optimization}",
journal = j-TIST,
volume = "11",
number = "4",
pages = "38:1--38:27",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3384344",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3384344",
abstract = "The current trend toward urbanization and adoption of
flexible and innovative mobility technologies will have
complex and difficult-to-predict effects on urban
transportation systems. Comprehensive methodological
frameworks that account for the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2020:SRM,
author = "Hui Liu and Haiou Wang and Yan Wu and Lei Xing",
title = "Superpixel Region Merging Based on Deep Network for
Medical Image Segmentation",
journal = j-TIST,
volume = "11",
number = "4",
pages = "39:1--39:22",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3386090",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3386090",
abstract = "Automatic and accurate semantic segmentation of
pathological structures in medical images is
challenging because of noisy disturbance, deformable
shapes of pathology, and low contrast between soft
tissues. Classical superpixel-based classification
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Singhal:2020:CBM,
author = "Divya Singhal and Abhinav Gupta and Anurag Tripathi
and Ravi Kothari",
title = "{CNN}-based Multiple Manipulation Detector Using
Frequency Domain Features of Image Residuals",
journal = j-TIST,
volume = "11",
number = "4",
pages = "40:1--40:26",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3388634",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3388634",
abstract = "Increasingly sophisticated image editing tools make it
easy to modify images. Often these modifications are
elaborate, convincing, and undetectable by even careful
human inspection. These considerations have prompted
the development of forensic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2020:CPC,
author = "Lin Li and Weike Pan and Zhong Ming",
title = "{CoFi}-points: Collaborative Filtering via Pointwise
Preference Learning on User\slash Item-Set",
journal = j-TIST,
volume = "11",
number = "4",
pages = "41:1--41:24",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3389127",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3389127",
abstract = "With the explosive growth of web resources, an
increasingly important task in recommender systems is
to provide high-quality personalized services by
learning users' preferences from historically observed
information. As an effective preference learning
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ma:2020:ABR,
author = "Jing Ma and Wei Gao and Shafiq Joty and Kam-Fai Wong",
title = "An Attention-based Rumor Detection Model with
Tree-structured Recursive Neural Networks",
journal = j-TIST,
volume = "11",
number = "4",
pages = "42:1--42:28",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3391250",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3391250",
abstract = "Rumor spread in social media severely jeopardizes the
credibility of online content. Thus, automatic
debunking of rumors is of great importance to keep
social media a healthy environment. While facing a
dubious claim, people often dispute its \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2020:MHU,
author = "Jun-Zhe Wang and Yi-Cheng Chen and Wen-Yueh Shih and
Lin Yang and Yu-Shao Liu and Jiun-Long Huang",
title = "Mining High-utility Temporal Patterns on Time
Interval-based Data",
journal = j-TIST,
volume = "11",
number = "4",
pages = "43:1--43:31",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3391230",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3391230",
abstract = "In this article, we propose a novel temporal pattern
mining problem, named high-utility temporal pattern
mining, to fulfill the needs of various applications.
Different from classical temporal pattern mining aimed
at discovering frequent temporal \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2020:DAC,
author = "Hanrui Wu and Yuguang Yan and Michael K. Ng and
Qingyao Wu",
title = "Domain-attention Conditional {Wasserstein} Distance
for Multi-source Domain Adaptation",
journal = j-TIST,
volume = "11",
number = "4",
pages = "44:1--44:19",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3391229",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3391229",
abstract = "Multi-source domain adaptation has received
considerable attention due to its effectiveness of
leveraging the knowledge from multiple related sources
with different distributions to enhance the learning
performance. One of the fundamental challenges in
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Kim:2020:GCC,
author = "Jungeun Kim and Jae-Gil Lee and Byung Suk Lee and
Jiajun Liu",
title = "Geosocial Co-Clustering: a Novel Framework for
Geosocial Community Detection",
journal = j-TIST,
volume = "11",
number = "4",
pages = "45:1--45:26",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3391708",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3391708",
abstract = "As location-based services using mobile devices have
become globally popular these days, social network
analysis (especially, community detection) increasingly
benefits from combining social relationships with
geographic preferences. In this regard, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Huang:2020:TDE,
author = "Yapei Huang and Yun Tian and Zhijie Liu and Xiaowei
Jin and Yanan Liu and Shifeng Zhao and Daxin Tian",
title = "A Traffic Density Estimation Model Based on
Crowdsourcing Privacy Protection",
journal = j-TIST,
volume = "11",
number = "4",
pages = "46:1--46:18",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3391707",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3391707",
abstract = "Acquiring traffic condition information is of great
significance in transportation guidance, urban
planning, and route recommendation. To date, traffic
density data are generally acquired by road sound
analysis, video data analysis, or in-vehicle \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2020:EET,
author = "Min Wang and Congyan Lang and Liqian Liang and Songhe
Feng and Tao Wang and Yutong Gao",
title = "End-to-End Text-to-Image Synthesis with Spatial
Constrains",
journal = j-TIST,
volume = "11",
number = "4",
pages = "47:1--47:19",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3391709",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3391709",
abstract = "Although the performance of automatically generating
high-resolution realistic images from text descriptions
has been significantly boosted, many challenging issues
in image synthesis have not been fully investigated,
due to shapes variations, viewpoint \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2020:ULT,
author = "Guang Wang and Fan Zhang and Huijun Sun and Yang Wang
and Desheng Zhang",
title = "Understanding the Long-Term Evolution of Electric Taxi
Networks: a Longitudinal Measurement Study on Mobility
and Charging Patterns",
journal = j-TIST,
volume = "11",
number = "4",
pages = "48:1--48:27",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3393671",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3393671",
abstract = "Due to the ever-growing concerns over air pollution
and energy security, more and more cities have started
to replace their conventional taxi fleets with electric
ones. Even though environmentally friendly, the rapid
promotion of electric taxis raises \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2020:DMB,
author = "Xiang Zhang and Lina Yao and Chaoran Huang and Tao Gu
and Zheng Yang and Yunhao Liu",
title = "{DeepKey}: a Multimodal Biometric Authentication
System via Deep Decoding Gaits and Brainwaves",
journal = j-TIST,
volume = "11",
number = "4",
pages = "49:1--49:24",
month = jul,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3393619",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Jul 8 17:19:20 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/abs/10.1145/3393619",
abstract = "Biometric authentication involves various technologies
to identify individuals by exploiting their unique,
measurable physiological and behavioral
characteristics. However, traditional biometric
authentication systems (e.g., face recognition, iris,
\ldots{}).",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wilson:2020:SUD,
author = "Garrett Wilson and Diane J. Cook",
title = "A Survey of Unsupervised Deep Domain Adaptation",
journal = j-TIST,
volume = "11",
number = "5",
pages = "51:1--51:46",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3400066",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3400066",
abstract = "Deep learning has produced state-of-the-art results
for a variety of tasks. While such approaches for
supervised learning have performed well, they assume
that training and testing data are drawn from the same
distribution, which may not always be the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2020:PPP,
author = "Chaochao Chen and Jun Zhou and Bingzhe Wu and Wenjing
Fang and Li Wang and Yuan Qi and Xiaolin Zheng",
title = "Practical Privacy Preserving {POI} Recommendation",
journal = j-TIST,
volume = "11",
number = "5",
pages = "52:1--52:20",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3394138",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3394138",
abstract = "Point-of-Interest (POI) recommendation has been
extensively studied and successfully applied in
industry recently. However, most existing approaches
build centralized models on the basis of collecting
users' data. Both private data and models are held
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Muralidhar:2020:CRT,
author = "Nikhil Muralidhar and Anika Tabassum and Liangzhe Chen
and Supriya Chinthavali and Naren Ramakrishnan and B.
Aditya Prakash",
title = "{Cut-n-Reveal}: Time Series Segmentations with
Explanations",
journal = j-TIST,
volume = "11",
number = "5",
pages = "53:1--53:26",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3394118",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3394118",
abstract = "Recent hurricane events have caused unprecedented
amounts of damage on critical infrastructure systems
and have severely threatened our public safety and
economic health. The most observable (and severe)
impact of these hurricanes is the loss of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Huang:2020:MTL,
author = "Jizhou Huang and Haifeng Wang and Wei Zhang and Ting
Liu",
title = "Multi-Task Learning for Entity Recommendation and
Document Ranking in {Web} Search",
journal = j-TIST,
volume = "11",
number = "5",
pages = "54:1--54:24",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3396501",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3396501",
abstract = "Entity recommendation, providing users with an
improved search experience by proactively recommending
related entities to a given query, has become an
indispensable feature of today's Web search engine.
Existing studies typically only consider the query
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Albaqsami:2020:AHM,
author = "Ahmad Albaqsami and Maryam S. Hosseini and Masoomeh
Jasemi and Nader Bagherzadeh",
title = "Adaptive {HTF-MPR}: an Adaptive Heterogeneous
{TensorFlow} Mapper Utilizing {Bayesian} Optimization
and Genetic Algorithms",
journal = j-TIST,
volume = "11",
number = "5",
pages = "55:1--55:25",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3396949",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3396949",
abstract = "Deep neural networks are widely used in many
artificial intelligence applications. They have
demonstrated state-of-the-art accuracy on many
artificial intelligence tasks. For this high accuracy
to occur, deep neural networks require the right
parameter \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2020:SDA,
author = "Rui Liu and Runze Liu and Andrea Pugliese and V. S.
Subrahmanian",
title = "{STARS}: Defending against Sockpuppet-Based Targeted
Attacks on Reviewing Systems",
journal = j-TIST,
volume = "11",
number = "5",
pages = "56:1--56:25",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3397463",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3397463",
abstract = "Customers of virtually all online marketplaces rely
upon reviews in order to select the product or service
they wish to buy. These marketplaces in turn deploy
review fraud detection systems so that the integrity of
reviews is preserved. A well-known \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2020:DCN,
author = "Yuxiang Zhou and Lejian Liao and Yang Gao and Heyan
Huang and Xiaochi Wei",
title = "A Discriminative Convolutional Neural Network with
Context-aware Attention",
journal = j-TIST,
volume = "11",
number = "5",
pages = "57:1--57:21",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3397464",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3397464",
abstract = "Feature representation and feature extraction are two
crucial procedures in text mining. Convolutional Neural
Networks (CNN) have shown overwhelming success for
text-mining tasks, since they are capable of
efficiently extracting n -gram features from \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dai:2020:STM,
author = "Chenglong Dai and Dechang Pi and Stefanie I. Becker",
title = "Shapelet-transformed Multi-channel {EEG} Channel
Selection",
journal = j-TIST,
volume = "11",
number = "5",
pages = "58:1--58:27",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3397850",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3397850",
abstract = "This article proposes an approach to select EEG
channels based on EEG shapelet transformation, aiming
to reduce the setup time and inconvenience for subjects
and to improve the applicable performance of
Brain-Computer Interfaces (BCIs). In detail, the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yadamjav:2020:QRC,
author = "Munkh-Erdene Yadamjav and Zhifeng Bao and Baihua Zheng
and Farhana M. Choudhury and Hanan Samet",
title = "Querying Recurrent Convoys over Trajectory Data",
journal = j-TIST,
volume = "11",
number = "5",
pages = "59:1--59:24",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3400730",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3400730",
abstract = "Moving objects equipped with location-positioning
devices continuously generate a large amount of
spatio-temporal trajectory data. An interesting finding
over a trajectory stream is a group of objects that are
travelling together for a certain period of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tu:2020:LGI,
author = "Xiaoguang Tu and Zheng Ma and Jian Zhao and Guodong Du
and Mei Xie and Jiashi Feng",
title = "Learning Generalizable and Identity-Discriminative
Representations for Face Anti-Spoofing",
journal = j-TIST,
volume = "11",
number = "5",
pages = "60:1--60:19",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3402446",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/cryptography2020.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3402446",
abstract = "Face anti-spoofing aims to detect presentation attack
to face recognition--based authentication systems. It
has drawn growing attention due to the high security
demand. The widely adopted CNN-based methods usually
well recognize the spoofing faces when \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tian:2020:MGD,
author = "Qing Tian and Wenqiang Zhang and Meng Cao and Liping
Wang and Songcan Chen and Hujun Yin",
title = "Moment-Guided Discriminative Manifold Correlation
Learning on Ordinal Data",
journal = j-TIST,
volume = "11",
number = "5",
pages = "61:1--61:18",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3402445",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3402445",
abstract = "Canonical correlation analysis (CCA) is a typical and
useful learning paradigm in big data analysis for
capturing correlation across multiple views of the same
objects. When dealing with data with additional ordinal
information, traditional CCA suffers \ldots{}.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yuan:2020:DTS,
author = "Kun Yuan and Guannan Liu and Junjie Wu and Hui Xiong",
title = "Dancing with {Trump} in the Stock Market: a Deep
Information Echoing Model",
journal = j-TIST,
volume = "11",
number = "5",
pages = "62:1--62:22",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3403578",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3403578",
abstract = "It is always deemed crucial to identify the key
factors that could have significant impact on the stock
market trend. Recently, an interesting phenomenon has
emerged that some of President Trump's posts in Twitter
can surge into a dominant role on the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Maddalena:2020:MPI,
author = "Eddy Maddalena and Luis-Daniel Ib{\'a}{\~n}ez and
Elena Simperl",
title = "Mapping Points of Interest Through Street View Imagery
and Paid Crowdsourcing",
journal = j-TIST,
volume = "11",
number = "5",
pages = "63:1--63:28",
month = sep,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3403931",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Mon Sep 7 06:54:29 MDT 2020",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3403931",
abstract = "We present the Virtual City Explorer (VCE), an online
crowdsourcing platform for the collection of rich
geotagged information in urban environments. Compared
to other volunteered geographic information approaches,
which are constrained by the number and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xia:2020:DPP,
author = "Tong Xia and Yong Li and Jie Feng and Depeng Jin and
Qing Zhang and Hengliang Luo and Qingmin Liao",
title = "{DeepApp}: Predicting Personalized Smartphone App
Usage via Context-Aware Multi-Task Learning",
journal = j-TIST,
volume = "11",
number = "6",
pages = "64:1--64:12",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3408325",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3408325",
abstract = "Smartphone mobile application (App) usage prediction,
i.e., which Apps will be used next, is beneficial for
user experience improvement. Through an in-depth
analysis on a real-world dataset, we find that App
usage is highly spatio-temporally correlated \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zheng:2020:CAD,
author = "Zimu Zheng and Jie Pu and Linghui Liu and Dan Wang and
Xiangming Mei and Sen Zhang and Quanyu Dai",
title = "Contextual Anomaly Detection in Solder Paste
Inspection with Multi-Task Learning",
journal = j-TIST,
volume = "11",
number = "6",
pages = "65:1--65:17",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3383261",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3383261",
abstract = "In this article, we study solder paste inspection
(SPI), an important stage that is used in the
semiconductor manufacturing industry, where abnormal
boards should be detected. A highly accurate SPI can
substantially reduce human expert involvement, as
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Levy:2020:SLN,
author = "Sharon Levy and Wenhan Xiong and Elizabeth Belding and
William Yang Wang",
title = "{SafeRoute}: Learning to Navigate Streets Safely in an
Urban Environment",
journal = j-TIST,
volume = "11",
number = "6",
pages = "66:1--66:17",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3402818",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3402818",
abstract = "Recent studies show that 85\% of women have changed
their traveled routes to avoid harassment and assault.
Despite this, current mapping tools do not empower
users with information to take charge of their personal
safety. We propose SafeRoute, a novel \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jan:2020:MEB,
author = "Zohaib Md. Jan and Brijesh Verma",
title = "Multiple Elimination of Base Classifiers in Ensemble
Learning Using Accuracy and Diversity Comparisons",
journal = j-TIST,
volume = "11",
number = "6",
pages = "67:1--67:17",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3405790",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3405790",
abstract = "When generating ensemble classifiers, selecting the
best set of classifiers from the base classifier pool
is considered a combinatorial problem and an efficient
classifier selection methodology must be utilized.
Different researchers have used different \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tama:2020:EID,
author = "Bayu Adhi Tama and Marco Comuzzi and Jonghyeon Ko",
title = "An Empirical Investigation of Different Classifiers,
Encoding, and Ensemble Schemes for Next Event
Prediction Using Business Process Event Logs",
journal = j-TIST,
volume = "11",
number = "6",
pages = "68:1--68:34",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3406541",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3406541",
abstract = "There is a growing need for empirical benchmarks that
support researchers and practitioners in selecting the
best machine learning technique for given prediction
tasks. In this article, we consider the next event
prediction task in business process \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Banerjee:2020:BRB,
author = "Debopriyo Banerjee and Krothapalli Sreenivasa Rao and
Shamik Sural and Niloy Ganguly",
title = "{BOXREC}: Recommending a {Box} of Preferred Outfits in
Online Shopping",
journal = j-TIST,
volume = "11",
number = "6",
pages = "69:1--69:28",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3408890",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3408890",
abstract = "Fashionable outfits are generally created by expert
fashionistas, who use their creativity and in-depth
understanding of fashion to make attractive outfits.
Over the past few years, automation of outfit
composition has gained much attention from the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2020:LUR,
author = "Pan Li and Alexander Tuzhilin",
title = "Latent Unexpected Recommendations",
journal = j-TIST,
volume = "11",
number = "6",
pages = "70:1--70:25",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3404855",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3404855",
abstract = "Unexpected recommender system constitutes an important
tool to tackle the problem of filter bubbles and user
boredom, which aims at providing unexpected and
satisfying recommendations to target users at the same
time. Previous unexpected recommendation \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Eiras-Franco:2020:FDN,
author = "Carlos Eiras-Franco and David Mart{\'\i}nez-Rego and
Leslie Kanthan and C{\'e}sar Pi{\~n}eiro and Antonio
Bahamonde and Bertha Guijarro-Berdi{\~n}as and Amparo
Alonso-Betanzos",
title = "Fast Distributed $k$ {NN} Graph Construction Using
Auto-tuned Locality-sensitive Hashing",
journal = j-TIST,
volume = "11",
number = "6",
pages = "71:1--71:18",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3408889",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/hash.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3408889",
abstract = "The k -nearest-neighbors ( k NN) graph is a popular
and powerful data structure that is used in various
areas of Data Science, but the high computational cost
of obtaining it hinders its use on large datasets.
Approximate solutions have been described in \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2020:JNM,
author = "Junwei Li and Le Wu and Richang Hong and Kun Zhang and
Yong Ge and Yan Li",
title = "A Joint Neural Model for User Behavior Prediction on
Social Networking Platforms",
journal = j-TIST,
volume = "11",
number = "6",
pages = "72:1--72:25",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3406540",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3406540",
abstract = "Social networking services provide platforms for users
to perform two kinds of behaviors: consumption behavior
(e.g., recommending items of interest) and social link
behavior (e.g., recommending potential social links).
Accurately modeling and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mash:2020:HCC,
author = "Moshe Mash and Roy Fairstein and Yoram Bachrach and
Kobi Gal and Yair Zick",
title = "Human-computer Coalition Formation in Weighted Voting
Games",
journal = j-TIST,
volume = "11",
number = "6",
pages = "73:1--73:20",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3408294",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3408294",
abstract = "This article proposes a negotiation game, based on the
weighted voting paradigm in cooperative game theory,
where agents need to form coalitions and agree on how
to share the gains. Despite the prevalence of weighted
voting in the real world, there has \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fang:2020:AEC,
author = "Xiu Susie Fang and Quan Z. Sheng and Xianzhi Wang and
Wei Emma Zhang and Anne H. H. Ngu and Jian Yang",
title = "From Appearance to Essence: Comparing Truth Discovery
Methods without Using Ground Truth",
journal = j-TIST,
volume = "11",
number = "6",
pages = "74:1--74:24",
month = nov,
year = "2020",
CODEN = "????",
DOI = "https://doi.org/10.1145/3411749",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:28 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3411749",
abstract = "Truth discovery has been widely studied in recent
years as a fundamental means for resolving the
conflicts in multi-source data. Although many truth
discovery methods have been proposed based on different
considerations and intuitions, investigations
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Adamczak:2021:SBH,
author = "Jens Adamczak and Yashar Deldjoo and Farshad
Bakhshandegan Moghaddam and Peter Knees and Gerard-Paul
Leyson and Philipp Monreal",
title = "Session-based Hotel Recommendations Dataset: As part
of the {ACM Recommender System Challenge 2019}",
journal = j-TIST,
volume = "12",
number = "1",
pages = "1:1--1:20",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3412379",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3412379",
abstract = "In 2019, the Recommender Systems Challenge [17] dealt
for the first time with a real-world task from the area
of e-tourism, namely the recommendation of hotels in
booking sessions. In this context, we present the
release of a new dataset that we believe \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2021:IFT,
author = "Di Jiang and Yongxin Tong and Yuanfeng Song and
Xueyang Wu and Weiwei Zhao and Jinhua Peng and
Rongzhong Lian and Qian Xu and Qiang Yang",
title = "Industrial Federated Topic Modeling",
journal = j-TIST,
volume = "12",
number = "1",
pages = "2:1--2:22",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3418283",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3418283",
abstract = "Probabilistic topic modeling has been applied in a
variety of industrial applications. Training a
high-quality model usually requires a massive amount of
data to provide comprehensive co-occurrence information
for the model to learn. However, industrial \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Duan:2021:NMT,
author = "Mingxing Duan and Kenli Li and Keqin Li and Qi Tian",
title = "A Novel Multi-task Tensor Correlation Neural Network
for Facial Attribute Prediction",
journal = j-TIST,
volume = "12",
number = "1",
pages = "3:1--3:22",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3418285",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3418285",
abstract = "Multi-task learning plays an important role in face
multi-attribute prediction. At present, most researches
excavate the shared information between attributes by
sharing all convolutional layers. However, it is not
appropriate to treat the low-level and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yan:2021:SWR,
author = "Caixia Yan and Xiaojun Chang and Minnan Luo and
Qinghua Zheng and Xiaoqin Zhang and Zhihui Li and
Feiping Nie",
title = "Self-weighted Robust {LDA} for Multiclass
Classification with Edge Classes",
journal = j-TIST,
volume = "12",
number = "1",
pages = "4:1--4:19",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3418284",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3418284",
abstract = "Linear discriminant analysis (LDA) is a popular
technique to learn the most discriminative features for
multi-class classification. A vast majority of existing
LDA algorithms are prone to be dominated by the class
with very large deviation from the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xuan:2021:BNU,
author = "Junyu Xuan and Jie Lu and Guangquan Zhang",
title = "{Bayesian} Nonparametric Unsupervised Concept Drift
Detection for Data Stream Mining",
journal = j-TIST,
volume = "12",
number = "1",
pages = "5:1--5:22",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3420034",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3420034",
abstract = "Online data stream mining is of great significance in
practice because of its ubiquity in many real-world
scenarios, especially in the big data era. Traditional
data mining algorithms cannot be directly applied to
data streams due to (1) the possible \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bouguessa:2021:BBN,
author = "Mohamed Bouguessa and Khaled Nouri",
title = "{BiNeTClus}: Bipartite Network Community Detection
Based on Transactional Clustering",
journal = j-TIST,
volume = "12",
number = "1",
pages = "6:1--6:26",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3423067",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3423067",
abstract = "We investigate the problem of community detection in
bipartite networks that are characterized by the
presence of two types of nodes such that connections
exist only between nodes of different types. While some
approaches have been proposed to identify \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Alim:2021:CSC,
author = "Adil Alim and Jin-Hee Cho and Feng Chen",
title = "{CSL+}: Scalable Collective Subjective Logic under
Multidimensional Uncertainty",
journal = j-TIST,
volume = "12",
number = "1",
pages = "7:1--7:26",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3426193",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3426193",
abstract = "Using unreliable information sources generating
conflicting evidence may lead to a large uncertainty,
which significantly hurts the decision making process.
Recently, many approaches have been taken to integrate
conflicting data from multiple sources \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lai:2021:DEF,
author = "Chih-Te Lai and Cheng-Te Li and Shou-De Lin",
title = "Deep Energy Factorization Model for Demographic
Prediction",
journal = j-TIST,
volume = "12",
number = "1",
pages = "8:1--8:16",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3426240",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3426240",
abstract = "Demographic information is important for various
commercial and academic proposes, but in reality, few
of these data are accessible for analysis and research.
To solve this problem, several studies predict
demographic attributes from users' behavioral
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2021:DLT,
author = "Shuo Liu and Mingliang Gao and Vijay John and Zheng
Liu and Erik Blasch",
title = "Deep Learning Thermal Image Translation for Night
Vision Perception",
journal = j-TIST,
volume = "12",
number = "1",
pages = "9:1--9:18",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3426239",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3426239",
abstract = "Context enhancement is critical for the environmental
perception in night vision applications, especially for
the dark night situation without sufficient
illumination. In this article, we propose a thermal
image translation method, which can translate
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2021:TRL,
author = "Wendi Wu and Yawei Zhao and En Zhu and Xinwang Liu and
Xingxing Zhang and Lailong Luo and Shixiong Wang and
Jianping Yin",
title = "A Theoretical Revisit to Linear Convergence for Saddle
Point Problems",
journal = j-TIST,
volume = "12",
number = "1",
pages = "10:1--10:17",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3420035",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3420035",
abstract = "Recently, convex-concave bilinear Saddle Point
Problems (SPP) is widely used in lasso problems,
Support Vector Machines, game theory, and so on.
Previous researches have proposed many methods to solve
SPP, and present their convergence rate \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2021:RLR,
author = "Meng-Xiang Wang and Wang-Chien Lee and Tao-Yang Fu and
Ge Yu",
title = "On Representation Learning for Road Networks",
journal = j-TIST,
volume = "12",
number = "1",
pages = "11:1--11:27",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3424346",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3424346",
abstract = "Informative representation of road networks is
essential to a wide variety of applications on
intelligent transportation systems. In this article, we
design a new learning framework, called Representation
Learning for Road Networks (RLRN), which \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2021:UMB,
author = "Yiyi Zhou and Rongrong Ji and Jinsong Su and Jiaquan
Yao",
title = "Uncovering Media Bias via Social Network Learning",
journal = j-TIST,
volume = "12",
number = "1",
pages = "12:1--12:12",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3422181",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3422181",
abstract = "It is known that media outlets, such as CNN and FOX,
have intrinsic political bias that is reflected in
their news reports. The computational prediction of
such bias has broad application prospects. However, the
prediction is difficult via directly \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2021:PAR,
author = "Guang Wang and Zhihan Fang and Xiaoyang Xie and Shuai
Wang and Huijun Sun and Fan Zhang and Yunhuai Liu and
Desheng Zhang",
title = "Pricing-aware Real-time Charging Scheduling and
Charging Station Expansion for Large-scale Electric
Buses",
journal = j-TIST,
volume = "12",
number = "1",
pages = "13:1--13:26",
month = feb,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3428080",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Feb 23 10:41:29 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3428080",
abstract = "We are witnessing a rapid growth of electrified
vehicles due to the ever-increasing concerns on urban
air quality and energy security. Compared to other
types of electric vehicles, electric buses have not yet
been prevailingly adopted worldwide due to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Guo:2021:CTG,
author = "Bin Guo and Hao Wang and Yasan Ding and Wei Wu and
Shaoyang Hao and Yueqi Sun and Zhiwen Yu",
title = "Conditional Text Generation for Harmonious
Human-Machine Interaction",
journal = j-TIST,
volume = "12",
number = "2",
pages = "14:1--14:50",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3439816",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3439816",
abstract = "In recent years, with the development of deep
learning, text-generation technology has undergone
great changes and provided many kinds of services for
human beings, such as restaurant reservation and daily
communication. The automatically generated text
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Firdaus:2021:AAR,
author = "Mauajama Firdaus and Nidhi Thakur and Asif Ekbal",
title = "Aspect-Aware Response Generation for Multimodal
Dialogue System",
journal = j-TIST,
volume = "12",
number = "2",
pages = "15:1--15:33",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3430752",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3430752",
abstract = "Multimodality in dialogue systems has opened up new
frontiers for the creation of robust conversational
agents. Any multimodal system aims at bridging the gap
between language and vision by leveraging diverse and
often complementary information from \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Baek:2021:RMR,
author = "Yoonji Baek and Unil Yun and Heonho Kim and Hyoju Nam
and Hyunsoo Kim and Jerry Chun-Wei Lin and Bay Vo and
Witold Pedrycz",
title = "{RHUPS}: Mining Recent High Utility Patterns with
Sliding Window-based Arrival Time Control over Data
Streams",
journal = j-TIST,
volume = "12",
number = "2",
pages = "16:1--16:27",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3430767",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3430767",
abstract = "Databases that deal with the real world have various
characteristics. New data is continuously inserted over
time without limiting the length of the database, and a
variety of information about the items constituting the
database is contained. Recently \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Winter:2021:CBS,
author = "Felix Winter and Nysret Musliu",
title = "Constraint-based Scheduling for Paint Shops in the
Automotive Supply Industry",
journal = j-TIST,
volume = "12",
number = "2",
pages = "17:1--17:25",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3430710",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3430710",
abstract = "Factories in the automotive supply industry paint a
large number of items requested by car manufacturing
companies on a daily basis. As these factories face
numerous constraints and optimization objectives,
finding a good schedule becomes a challenging
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dahmen:2021:ISA,
author = "Jessamyn Dahmen and Diane J. Cook",
title = "Indirectly Supervised Anomaly Detection of Clinically
Meaningful Health Events from Smart Home Data",
journal = j-TIST,
volume = "12",
number = "2",
pages = "18:1--18:18",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3439870",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3439870",
abstract = "Anomaly detection techniques can extract a wealth of
information about unusual events. Unfortunately, these
methods yield an abundance of findings that are not of
interest, obscuring relevant anomalies. In this work,
we improve upon traditional anomaly \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mansoury:2021:FBP,
author = "Masoud Mansoury and Robin Burke and Bamshad Mobasher",
title = "Flatter Is Better: Percentile Transformations for
Recommender Systems",
journal = j-TIST,
volume = "12",
number = "2",
pages = "19:1--19:16",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3437910",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3437910",
abstract = "It is well known that explicit user ratings in
recommender systems are biased toward high ratings and
that users differ significantly in their usage of the
rating scale. Implementers usually compensate for these
issues through rating normalization or \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Cui:2021:DIR,
author = "Zeyu Cui and Feng Yu and Shu Wu and Qiang Liu and
Liang Wang",
title = "Disentangled Item Representation for Recommender
Systems",
journal = j-TIST,
volume = "12",
number = "2",
pages = "20:1--20:20",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3445811",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3445811",
abstract = "Item representations in recommendation systems are
expected to reveal the properties of items.
Collaborative recommender methods usually represent an
item as one single latent vector. Nowadays the
e-commercial platforms provide various kinds of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ali:2021:PAN,
author = "Sarwan Ali and Muhammad Haroon Shakeel and Imdadullah
Khan and Safiullah Faizullah and Muhammad Asad Khan",
title = "Predicting Attributes of Nodes Using Network
Structure",
journal = j-TIST,
volume = "12",
number = "2",
pages = "21:1--21:23",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3442390",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3442390",
abstract = "In many graphs such as social networks, nodes have
associated attributes representing their behavior.
Predicting node attributes in such graphs is an
important task with applications in many domains like
recommendation systems, privacy preservation, and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mao:2021:FGB,
author = "Jiali Mao and Jiaye Liu and Cheqing Jin and Aoying
Zhou",
title = "Feature Grouping-based Trajectory Outlier Detection
over Distributed Streams",
journal = j-TIST,
volume = "12",
number = "2",
pages = "22:1--22:23",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3444753",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3444753",
abstract = "Owing to a wide variety of deployment of GPS -enabled
devices, tremendous amounts of trajectories have been
generated in distributed stream manner. It opens up new
opportunities to track and analyze the moving behaviors
of the entities. In this work, we \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2021:CMT,
author = "Zijian Li and Ruichu Cai and Hong Wei Ng and Marianne
Winslett and Tom Z. J. Fu and Boyan Xu and Xiaoyan Yang
and Zhenjie Zhang",
title = "Causal Mechanism Transfer Network for Time Series
Domain Adaptation in Mechanical Systems",
journal = j-TIST,
volume = "12",
number = "2",
pages = "23:1--23:21",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3445033",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3445033",
abstract = "Data-driven models are becoming essential parts in
modern mechanical systems, commonly used to capture the
behavior of various equipment and varying environmental
characteristics. Despite the advantages of these
data-driven models on excellent \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bashar:2021:ALE,
author = "Md Abul Bashar and Richi Nayak",
title = "Active Learning for Effectively Fine-Tuning Transfer
Learning to Downstream Task",
journal = j-TIST,
volume = "12",
number = "2",
pages = "24:1--24:24",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3446343",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3446343",
abstract = "Language model (LM) has become a common method of
transfer learning in Natural Language Processing (NLP)
tasks when working with small labeled datasets. An LM
is pretrained using an easily available large
unlabelled text corpus and is fine-tuned with
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2021:DPB,
author = "Jianguo Chen and Kenli Li and Keqin Li and Philip S.
Yu and Zeng Zeng",
title = "Dynamic Planning of Bicycle Stations in Dockless
Public Bicycle-sharing System Using Gated Graph Neural
Network",
journal = j-TIST,
volume = "12",
number = "2",
pages = "25:1--25:22",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3446342",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3446342",
abstract = "Benefiting from convenient cycling and flexible
parking locations, the Dockless Public Bicycle-sharing
(DL-PBS) network becomes increasingly popular in many
countries. However, redundant and low-utility stations
waste public urban space and maintenance \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2021:AEA,
author = "Qianli Zhou and Tianrui Hui and Rong Wang and Haimiao
Hu and Si Liu",
title = "Attentive Excitation and Aggregation for Bilingual
Referring Image Segmentation",
journal = j-TIST,
volume = "12",
number = "2",
pages = "26:1--26:17",
month = mar,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3446345",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed Mar 17 08:23:18 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3446345",
abstract = "The goal of referring image segmentation is to
identify the object matched with an input natural
language expression. Previous methods only support
English descriptions, whereas Chinese is also broadly
used around the world, which limits the potential
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Cui:2021:MMV,
author = "Wanqiu Cui and Junping Du and Dawei Wang and Feifei
Kou and Zhe Xue",
title = "{MVGAN}: Multi-View Graph Attention Network for Social
Event Detection",
journal = j-TIST,
volume = "12",
number = "3",
pages = "27:1--27:24",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3447270",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3447270",
abstract = "Social networks are critical sources for event
detection thanks to the characteristics of publicity
and dissemination. Unfortunately, the randomness and
semantic sparsity of the social network text bring
significant challenges to the event detection task.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2021:MTA,
author = "Yan Liu and Bin Guo and Daqing Zhang and Djamal
Zeghlache and Jingmin Chen and Sizhe Zhang and Dan Zhou
and Xinlei Shi and Zhiwen Yu",
title = "{MetaStore}: a Task-adaptative Meta-learning Model for
Optimal Store Placement with Multi-city Knowledge
Transfer",
journal = j-TIST,
volume = "12",
number = "3",
pages = "28:1--28:23",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3447271",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3447271",
abstract = "Optimal store placement aims to identify the optimal
location for a new brick-and-mortar store that can
maximize its sale by analyzing and mining users'
preferences from large-scale urban data. In recent
years, the expansion of chain enterprises in new
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bhatia:2021:ISG,
author = "Munish Bhatia",
title = "Intelligent System of Game-Theory-Based Decision
Making in Smart Sports Industry",
journal = j-TIST,
volume = "12",
number = "3",
pages = "29:1--29:23",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3447986",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3447986",
abstract = "Internet of Things (IoT) technology backed by
Artificial Intelligence (AI) techniques has been
increasingly utilized for the realization of the
Industry 4.0 vision. Conspicuously, this work provides
a novel notion of the smart sports industry for
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2021:GCE,
author = "Di Jiang and Conghui Tan and Jinhua Peng and Chaotao
Chen and Xueyang Wu and Weiwei Zhao and Yuanfeng Song
and Yongxin Tong and Chang Liu and Qian Xu and Qiang
Yang and Li Deng",
title = "A {GDPR}-compliant Ecosystem for Speech Recognition
with Transfer, Federated, and Evolutionary Learning",
journal = j-TIST,
volume = "12",
number = "3",
pages = "30:1--30:19",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3447687",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3447687",
abstract = "Automatic Speech Recognition (ASR) is playing a vital
role in a wide range of real-world applications.
However, Commercial ASR solutions are typically
``one-size-fits-all'' products and clients are
inevitably faced with the risk of severe performance
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Elmadany:2021:IAR,
author = "Nour Eldin Elmadany and Yifeng He and Ling Guan",
title = "Improving Action Recognition via Temporal and
Complementary Learning",
journal = j-TIST,
volume = "12",
number = "3",
pages = "31:1--31:24",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3447686",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3447686",
abstract = "In this article, we study the problem of video-based
action recognition. We improve the action recognition
performance by finding an effective temporal and
appearance representation. For capturing the temporal
representation, we introduce two temporal \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shi:2021:GGT,
author = "Yukai Shi and Sen Zhang and Chenxing Zhou and Xiaodan
Liang and Xiaojun Yang and Liang Lin",
title = "{GTAE}: Graph Transformer-Based Auto-Encoders for
Linguistic-Constrained Text Style Transfer",
journal = j-TIST,
volume = "12",
number = "3",
pages = "32:1--32:16",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3448733",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3448733",
abstract = "Non-parallel text style transfer has attracted
increasing research interests in recent years. Despite
successes in transferring the style based on the
encoder-decoder framework, current approaches still
lack the ability to preserve the content and even
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yin:2021:IFR,
author = "Chunyong Yin and Haoqi Cuan and Yuhang Zhu and Zhichao
Yin",
title = "Improved Fake Reviews Detection Model Based on
Vertical Ensemble Tri-Training and Active Learning",
journal = j-TIST,
volume = "12",
number = "3",
pages = "33:1--33:19",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3450285",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3450285",
abstract = "People's increasingly frequent online activity has
generated a large number of reviews, whereas fake
reviews can mislead users and harm their personal
interests. In addition, it is not feasible to label
reviews on a large scale because of the high cost of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gupta:2021:VQB,
author = "Amulya Gupta and Zhu Zhang",
title = "Vector-Quantization-Based Topic Modeling",
journal = j-TIST,
volume = "12",
number = "3",
pages = "34:1--34:30",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3450946",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3450946",
abstract = "With the purpose of learning and utilizing explicit
and dense topic embeddings, we propose three variations
of novel vector-quantization-based topic models
(VQ-TMs): (1) Hard VQ-TM, (2) Soft VQ-TM, and (3)
Multi-View Soft VQ-TM. The model family \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2021:PPC,
author = "Shilei Li and Meng Li and Jiongming Su and Shaofei
Chen and Zhimin Yuan and Qing Ye",
title = "{PP-PG}: Combining Parameter Perturbation with Policy
Gradient Methods for Effective and Efficient
Explorations in Deep Reinforcement Learning",
journal = j-TIST,
volume = "12",
number = "3",
pages = "35:1--35:21",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3452008",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3452008",
abstract = "Efficient and stable exploration remains a key
challenge for deep reinforcement learning (DRL)
operating in high-dimensional action and state spaces.
Recently, a more promising approach by combining the
exploration in the action space with the exploration
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hong:2021:SRI,
author = "Thanh Phuoc Hong and Ling Guan",
title = "A Scale and Rotational Invariant Key-point Detector
based on Sparse Coding",
journal = j-TIST,
volume = "12",
number = "3",
pages = "36:1--36:19",
month = jul,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3452009",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jul 22 08:10:42 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3452009",
abstract = "Most popular hand-crafted key-point detectors such as
Harris corner, SIFT, SURF aim to detect corners, blobs,
junctions, or other human-defined structures in images.
Though being robust with some geometric
transformations, unintended scenarios or non-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xia:2021:CSK,
author = "Zhenchang Xia and Jia Wu and Libing Wu and Yanjiao
Chen and Jian Yang and Philip S. Yu",
title = "A Comprehensive Survey of the Key Technologies and
Challenges Surrounding Vehicular Ad Hoc Networks",
journal = j-TIST,
volume = "12",
number = "4",
pages = "37:1--37:30",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3451984",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3451984",
abstract = "Vehicular ad hoc networks (VANETs) and the services
they support are an essential part of intelligent
transportation. Through physical technologies,
applications, protocols, and standards, they help to
ensure traffic moves efficiently and vehicles operate
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tian:2021:CIG,
author = "Jiajie Tian and Qihao Tang and Rui Li and Zhu Teng and
Baopeng Zhang and Jianping Fan",
title = "A Camera Identity-guided Distribution Consistency
Method for Unsupervised Multi-target Domain Person
Re-identification",
journal = j-TIST,
volume = "12",
number = "4",
pages = "38:1--38:18",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3454130",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3454130",
abstract = "Unsupervised domain adaptation (UDA) for person
re-identification (re-ID) is a challenging task due to
large variations in human classes, illuminations,
camera views, and so on. Currently, existing UDA
methods focus on two-domain adaptation and are
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2021:LMU,
author = "Huandong Wang and Yong Li and Gang Wang and Depeng
Jin",
title = "Linking Multiple User Identities of Multiple Services
from Massive Mobility Traces",
journal = j-TIST,
volume = "12",
number = "4",
pages = "39:1--39:28",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3439817",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3439817",
abstract = "Understanding the linkability of online user
identifiers (IDs) is critical to both service providers
(for business intelligence) and individual users (for
assessing privacy risks). Existing methods are designed
to match IDs across two services but face \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shen:2021:MMK,
author = "Xiangjun Shen and Kou Lu and Sumet Mehta and Jianming
Zhang and Weifeng Liu and Jianping Fan and Zhengjun
Zha",
title = "{MKEL}: Multiple Kernel Ensemble Learning via Unified
Ensemble Loss for Image Classification",
journal = j-TIST,
volume = "12",
number = "4",
pages = "40:1--40:21",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3457217",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3457217",
abstract = "In this article, a novel ensemble model, called
Multiple Kernel Ensemble Learning (MKEL), is developed
by introducing a unified ensemble loss. Different from
the previous multiple kernel learning (MKL) methods,
which attempt to seek a linear combination \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sun:2021:VIV,
author = "Guodao Sun and Hao Wu and Lin Zhu and Chaoqing Xu and
Haoran Liang and Binwei Xu and Ronghua Liang",
title = "{VSumVis}: Interactive Visual Understanding and
Diagnosis of Video Summarization Model",
journal = j-TIST,
volume = "12",
number = "4",
pages = "41:1--41:28",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3458928",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3458928",
abstract = "With the rapid development of mobile Internet, the
popularity of video capture devices has brought a surge
in multimedia video resources. Utilizing machine
learning methods combined with well-designed features,
we could automatically obtain video \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2021:MCB,
author = "Daheng Wang and Qingkai Zeng and Nitesh V. Chawla and
Meng Jiang",
title = "Modeling Complementarity in Behavior Data with
Multi-Type Itemset Embedding",
journal = j-TIST,
volume = "12",
number = "4",
pages = "42:1--42:25",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3458724",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3458724",
abstract = "People are looking for complementary contexts, such as
team members of complementary skills for project team
building and/or reading materials of complementary
knowledge for effective student learning, to make their
behaviors more likely to be successful. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Huang:2021:SHF,
author = "Anbu Huang and Yang Liu and Tianjian Chen and Yongkai
Zhou and Quan Sun and Hongfeng Chai and Qiang Yang",
title = "{StarFL}: Hybrid Federated Learning Architecture for
Smart Urban Computing",
journal = j-TIST,
volume = "12",
number = "4",
pages = "43:1--43:23",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3467956",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3467956",
abstract = "From facial recognition to autonomous driving,
Artificial Intelligence (AI) will transform the way we
live and work over the next couple of decades. Existing
AI approaches for urban computing suffer from various
challenges, including dealing with \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Abhadiomhen:2021:MCS,
author = "Stanley Ebhohimhen Abhadiomhen and Zhiyang Wang and
Xiangjun Shen and Jianping Fan",
title = "Multiview Common Subspace Clustering via Coupled Low
Rank Representation",
journal = j-TIST,
volume = "12",
number = "4",
pages = "44:1--44:25",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3465056",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3465056",
abstract = "Multi-view subspace clustering (MVSC) finds a shared
structure in latent low-dimensional subspaces of
multi-view data to enhance clustering performance.
Nonetheless, we observe that most existing MVSC methods
neglect the diversity in multi-view data by \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Aydogan:2021:NVB,
author = "Reyhan Aydogan and {\"O}zg{\"u}r Kafali and Furkan
Arslan and Catholijn M. Jonker and Munindar P. Singh",
title = "Nova: Value-based Negotiation of Norms",
journal = j-TIST,
volume = "12",
number = "4",
pages = "45:1--45:29",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3465054",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3465054",
abstract = "Specifying a normative multiagent system (nMAS) is
challenging, because different agents often have
conflicting requirements. Whereas existing approaches
can resolve clear-cut conflicts, tradeoffs might occur
in practice among alternative nMAS \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bozzano:2021:CAB,
author = "Marco Bozzano and Alessandro Cimatti and Marco
Roveri",
title = "A Comprehensive Approach to On-board Autonomy
Verification and Validation",
journal = j-TIST,
volume = "12",
number = "4",
pages = "46:1--46:29",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3472715",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3472715",
abstract = "Deep space missions are characterized by severely
constrained communication links. To meet the needs of
future missions and increase their scientific return,
future space systems will require an increased level of
autonomy on-board. In this work, we \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tajeuna:2021:MCC,
author = "Etienne Gael Tajeuna and Mohamed Bouguessa and
Shengrui Wang",
title = "Mining Customers' Changeable Electricity Consumption
for Effective Load Forecasting",
journal = j-TIST,
volume = "12",
number = "4",
pages = "47:1--47:26",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466684",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466684",
abstract = "Most existing approaches for electricity load
forecasting perform the task based on overall
electricity consumption. However, using such a global
methodology can affect load forecasting accuracy, as it
does not consider the possibility that customers'
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fu:2021:MCE,
author = "Teng Fu and Guido Zampieri and David Hodgson and
Claudio Angione and Yifeng Zeng",
title = "Modeling Customer Experience in a Contact Center
through Process Log Mining",
journal = j-TIST,
volume = "12",
number = "4",
pages = "48:1--48:21",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3468269",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3468269",
abstract = "The use of data mining and modeling methods in service
industry is a promising avenue for optimizing current
processes in a targeted manner, ultimately reducing
costs and improving customer experience. However, the
introduction of such tools in already \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Khezerlou:2021:DPU,
author = "Amin Vahedian Khezerlou and Xun Zhou and Xinyi Li and
W. Nick Street and Yanhua Li",
title = "{DILSA+}: Predicting Urban Dispersal Events through
Deep Survival Analysis with Enhanced Urban Features",
journal = j-TIST,
volume = "12",
number = "4",
pages = "49:1--49:25",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3469085",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3469085",
abstract = "Urban dispersal events occur when an unexpectedly
large number of people leave an area in a relatively
short period of time. It is beneficial for the city
authorities, such as law enforcement and city
management, to have an advance knowledge of such
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hou:2021:TTL,
author = "Chenyu Hou and Bin Cao and Sijie Ruan and Jing Fan",
title = "{TLDS}: a Transfer-Learning-Based Delivery Station
Location Selection Pipeline",
journal = j-TIST,
volume = "12",
number = "4",
pages = "50:1--50:24",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3469084",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3469084",
abstract = "Delivery stations play important roles in logistics
systems. Well-designed delivery station planning can
improve delivery efficiency significantly. However,
existing delivery station locations are decided by
experts, which requires much preliminary \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhao:2021:PCL,
author = "Qi Zhao and Chuqiao Chen and Guangcan Liu and Qingshan
Liu and Shengyong Chen",
title = "Parallel Connected {LSTM} for Matrix Sequence
Prediction with Elusive Correlations",
journal = j-TIST,
volume = "12",
number = "4",
pages = "51:1--51:16",
month = aug,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3469437",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 28 07:23:27 MDT 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3469437",
abstract = "This article is about a challenging problem called
matrix sequence prediction, which is motivated from the
application of taxi order prediction. Remarkably, the
problem differs greatly from previous sequence
prediction tasks in the sense that the time-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yuan:2021:MGC,
author = "Changsen Yuan and Heyan Huang and Chong Feng",
title = "Multi-Graph Cooperative Learning Towards Distant
Supervised Relation Extraction",
journal = j-TIST,
volume = "12",
number = "5",
pages = "52:1--52:21",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466560",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466560",
abstract = "The Graph Convolutional Network (GCN) is a universal
relation extraction method that can predict relations
of entity pairs by capturing sentences' syntactic
features. However, existing GCN methods often use
dependency parsing to generate graph matrices
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chaudhari:2021:ASA,
author = "Sneha Chaudhari and Varun Mithal and Gungor Polatkan
and Rohan Ramanath",
title = "An Attentive Survey of Attention Models",
journal = j-TIST,
volume = "12",
number = "5",
pages = "53:1--53:32",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3465055",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3465055",
abstract = "Attention Model has now become an important concept in
neural networks that has been researched within diverse
application domains. This survey provides a structured
and comprehensive overview of the developments in
modeling attention. In particular, we \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2021:BSB,
author = "Qiong Wu and Adam Hare and Sirui Wang and Yuwei Tu and
Zhenming Liu and Christopher G. Brinton and Yanhua Li",
title = "{BATS}: a Spectral Biclustering Approach to Single
Document Topic Modeling and Segmentation",
journal = j-TIST,
volume = "12",
number = "5",
pages = "54:1--54:29",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3468268",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3468268",
abstract = "Existing topic modeling and text segmentation
methodologies generally require large datasets for
training, limiting their capabilities when only small
collections of text are available. In this work, we
reexamine the inter-related problems of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhu:2021:CLG,
author = "Yisheng Zhu and Hu Han and Guangcan Liu and Qingshan
Liu",
title = "Collaborative Local-Global Learning for Temporal
Action Proposal",
journal = j-TIST,
volume = "12",
number = "5",
pages = "55:1--55:14",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466181",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466181",
abstract = "Temporal action proposal generation is an essential
and challenging task in video understanding, which aims
to locate the temporal intervals that likely contain
the actions of interest. Although great progress has
been made, the problem is still far from \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2021:QAE,
author = "Congliang Chen and Li Shen and Haozhi Huang and Wei
Liu",
title = "Quantized {Adam} with Error Feedback",
journal = j-TIST,
volume = "12",
number = "5",
pages = "56:1--56:26",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3470890",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3470890",
abstract = "In this article, we present a distributed variant of
an adaptive stochastic gradient method for training
deep neural networks in the parameter-server model. To
reduce the communication cost among the workers and
server, we incorporate two types of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Guo:2021:DDH,
author = "Jinjin Guo and Zhiguo Gong and Longbing Cao",
title = "{dhCM}: Dynamic and Hierarchical Event Categorization
and Discovery for Social Media Stream",
journal = j-TIST,
volume = "12",
number = "5",
pages = "57:1--57:25",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3470888",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3470888",
abstract = "The online event discovery in social media based
documents is useful, such as for disaster recognition
and intervention. However, the diverse events
incrementally identified from social media streams
remain accumulated, ad hoc, and unstructured. They
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Cheng:2021:SNF,
author = "Yuan Cheng and Yuchao Yang and Hai-Bao Chen and Ngai
Wong and Hao Yu",
title = "{S3-Net}: a Fast Scene Understanding Network by
Single-Shot Segmentation for Autonomous Driving",
journal = j-TIST,
volume = "12",
number = "5",
pages = "58:1--58:19",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3470660",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3470660",
abstract = "Real-time segmentation and understanding of driving
scenes are crucial in autonomous driving. Traditional
pixel-wise approaches extract scene information by
segmenting all pixels in a frame, and hence are
inefficient and slow. Proposal-wise approaches
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hu:2021:IID,
author = "Chuanbo Hu and Minglei Yin and Bin Liu and Xin Li and
Yanfang Ye",
title = "Identifying Illicit Drug Dealers on {Instagram} with
Large-scale Multimodal Data Fusion",
journal = j-TIST,
volume = "12",
number = "5",
pages = "59:1--59:23",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3472713",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3472713",
abstract = "Illicit drug trafficking via social media sites such
as Instagram have become a severe problem, thus drawing
a great deal of attention from law enforcement and
public health agencies. How to identify illicit drug
dealers from social media data has \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2021:FGS,
author = "Min Wang and Congyan Lang and Liqian Liang and Songhe
Feng and Tao Wang and Yutong Gao",
title = "Fine-Grained Semantic Image Synthesis with
Object-Attention Generative Adversarial Network",
journal = j-TIST,
volume = "12",
number = "5",
pages = "60:1--60:18",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3470008",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3470008",
abstract = "Semantic image synthesis is a new rising and
challenging vision problem accompanied by the recent
promising advances in generative adversarial networks.
The existing semantic image synthesis methods only
consider the global information provided by the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ji:2021:LGE,
author = "Shengwei Ji and Chenyang Bu and Lei Li and Xindong
Wu",
title = "Local Graph Edge Partitioning",
journal = j-TIST,
volume = "12",
number = "5",
pages = "61:1--61:25",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466685",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466685",
abstract = "Graph edge partitioning, which is essential for the
efficiency of distributed graph computation systems,
divides a graph into several balanced partitions within
a given size to minimize the number of vertices to be
cut. Existing graph partitioning models \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xie:2021:SDS,
author = "Yiqun Xie and Xiaowei Jia and Shashi Shekhar and Han
Bao and Xun Zhou",
title = "Significant {DBSCAN+}: Statistically Robust
Density-based Clustering",
journal = j-TIST,
volume = "12",
number = "5",
pages = "62:1--62:26",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3474842",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3474842",
abstract = "Cluster detection is important and widely used in a
variety of applications, including public health,
public safety, transportation, and so on. Given a
collection of data points, we aim to detect
density-connected spatial clusters with varying
geometric \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2021:NED,
author = "Xingjian Li and Haoyi Xiong and Zeyu Chen and Jun Huan
and Cheng-Zhong Xu and Dejing Dou",
title = "{``In-Network Ensemble''}: Deep Ensemble Learning with
Diversified Knowledge Distillation",
journal = j-TIST,
volume = "12",
number = "5",
pages = "63:1--63:19",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3473464",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3473464",
abstract = "Ensemble learning is a widely used technique to train
deep convolutional neural networks (CNNs) for improved
robustness and accuracy. While existing algorithms
usually first train multiple diversified networks and
then assemble these networks as an \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dutta:2021:DAC,
author = "Hridoy Sankar Dutta and Mayank Jobanputra and Himani
Negi and Tanmoy Chakraborty",
title = "Detecting and Analyzing Collusive Entities on
{YouTube}",
journal = j-TIST,
volume = "12",
number = "5",
pages = "64:1--64:28",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3477300",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3477300",
abstract = "YouTube sells advertisements on the posted videos,
which in turn enables the content creators to monetize
their videos. As an unintended consequence, this has
proliferated various illegal activities such as
artificial boosting of views, likes, comments,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2021:CSG,
author = "Yu Wang and Yuelin Wang and Kai Dang and Jie Liu and
Zhuo Liu",
title = "A Comprehensive Survey of Grammatical Error
Correction",
journal = j-TIST,
volume = "12",
number = "5",
pages = "65:1--65:51",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3474840",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/spell.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3474840",
abstract = "Grammatical error correction (GEC) is an important
application aspect of natural language processing
techniques, and GEC system is a kind of very important
intelligent system that has long been explored both in
academic and industrial communities. The past decade
has witnessed significant progress achieved in GEC for
the sake of increasing popularity of machine learning
and deep learning. However, there is not a survey that
untangles the large amount of research works and
progress in this field. We present the first survey in
GEC for a comprehensive retrospective of the literature
in this area. We first give the definition of GEC task
and introduce the public datasets and data annotation
schema. After that, we discuss six kinds of basic
approaches, six commonly applied performance boosting
techniques for GEC systems, and three data augmentation
methods. Since GEC is typically viewed as a sister task
of Machine Translation (MT), we put more emphasis on
the statistical machine translation (SMT)-based
approaches and neural machine translation (NMT)-based
approaches for the sake of their importance. Similarly,
some performance-boosting techniques are adapted from
MT and are successfully combined with GEC systems for
enhancement on the final performance. More importantly,
after the introduction of the evaluation in GEC, we
make an in-depth analysis based on empirical results in
aspects of GEC approaches and GEC systems for a clearer
pattern of progress in GEC, where error type analysis
and system recapitulation are clearly presented.
Finally, we discuss five prospective directions for
future GEC researches.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Koutroulis:2021:KCC,
author = "Georgios Koutroulis and Leo Botler and Belgin Mutlu
and Konrad Diwold and Kay R{\"o}mer and Roman Kern",
title = "{KOMPOS}: Connecting Causal Knots in Large Nonlinear
Time Series with Non-Parametric Regression Splines",
journal = j-TIST,
volume = "12",
number = "5",
pages = "66:1--66:27",
month = oct,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3480971",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:08 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3480971",
abstract = "Recovering causality from copious time series data
beyond mere correlations has been an important
contributing factor in numerous scientific fields. Most
existing works assume linearity in the data that may
not comply with many real-world scenarios. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2021:ATS,
author = "Senzhang Wang and Junbo Zhang and Yanjie Fu and Yong
Li",
title = "{ACM TIST} Special Issue on Deep Learning for
Spatio-Temporal Data: {Part 1}",
journal = j-TIST,
volume = "12",
number = "6",
pages = "67:1--67:3",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495188",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495188",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Huang:2021:THG,
author = "Ling Huang and Xing-Xing Liu and Shu-Qiang Huang and
Chang-Dong Wang and Wei Tu and Jia-Meng Xie and Shuai
Tang and Wendi Xie",
title = "Temporal Hierarchical Graph Attention Network for
Traffic Prediction",
journal = j-TIST,
volume = "12",
number = "6",
pages = "68:1--68:21",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3446430",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3446430",
abstract = "As a critical task in intelligent traffic systems,
traffic prediction has received a large amount of
attention in the past few decades. The early efforts
mainly model traffic prediction as the time-series
mining problem, in which the spatial dependence
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2021:PEL,
author = "Haoyi Zhou and Hao Peng and Jieqi Peng and Shuai Zhang
and Jianxin Li",
title = "{POLLA}: Enhancing the Local Structure Awareness in
Long Sequence Spatial-temporal Modeling",
journal = j-TIST,
volume = "12",
number = "6",
pages = "69:1--69:24",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3447987",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3447987",
abstract = "The spatial-temporal modeling on long sequences is of
great importance in many real-world applications.
Recent studies have shown the potential of applying the
self-attention mechanism to improve capturing the
complex spatial-temporal dependencies. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Qiao:2021:DCN,
author = "Shaojie Qiao and Nan Han and Jianbin Huang and Kun Yue
and Rui Mao and Hongping Shu and Qiang He and Xindong
Wu",
title = "A Dynamic Convolutional Neural Network Based
Shared-Bike Demand Forecasting Model",
journal = j-TIST,
volume = "12",
number = "6",
pages = "70:1--70:24",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3447988",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3447988",
abstract = "Bike-sharing systems are becoming popular and generate
a large volume of trajectory data. In a bike-sharing
system, users can borrow and return bikes at different
stations. In particular, a bike-sharing system will be
affected by weather, the time period, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dharejo:2021:TGT,
author = "Fayaz Ali Dharejo and Farah Deeba and Yuanchun Zhou
and Bhagwan Das and Munsif Ali Jatoi and Muhammad
Zawish and Yi Du and Xuezhi Wang",
title = "{TWIST-GAN}: Towards Wavelet Transform and Transferred
{GAN} for Spatio-Temporal Single Image Super
Resolution",
journal = j-TIST,
volume = "12",
number = "6",
pages = "71:1--71:20",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3456726",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3456726",
abstract = "Single Image Super-resolution (SISR) produces
high-resolution images with fine spatial resolutions
from a remotely sensed image with low spatial
resolution. Recently, deep learning and generative
adversarial networks (GANs) have made breakthroughs for
the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{He:2021:TNF,
author = "Yifan He and Zhao Li and Lei Fu and Anhui Wang and
Peng Zhang and Shuigeng Zhou and Ji Zhang and Ting Yu",
title = "{TARA-Net}: a Fusion Network for Detecting Takeaway
Rider Accidents",
journal = j-TIST,
volume = "12",
number = "6",
pages = "72:1--72:19",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3457218",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3457218",
abstract = "In the emerging business of food delivery, rider
traffic accidents raise financial cost and social
traffic burden. Although there has been much effort on
traffic accident forecasting using temporal-spatial
prediction models, none of the existing work \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dai:2021:PPT,
author = "Tianlun Dai and Bohan Li and Ziqiang Yu and Xiangrong
Tong and Meng Chen and Gang Chen",
title = "{PARP}: a Parallel Traffic Condition Driven Route
Planning Model on Dynamic Road Networks",
journal = j-TIST,
volume = "12",
number = "6",
pages = "73:1--73:24",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3459099",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3459099",
abstract = "The problem of route planning on road network is
essential to many Location-Based Services (LBSs). Road
networks are dynamic in the sense that the weights of
the edges in the corresponding graph constantly change
over time, representing evolving traffic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Guo:2021:ROE,
author = "Pengzhan Guo and Keli Xiao and Zeyang Ye and Wei Zhu",
title = "Route Optimization via Environment-Aware Deep Network
and Reinforcement Learning",
journal = j-TIST,
volume = "12",
number = "6",
pages = "74:1--74:21",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3461645",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3461645",
abstract = "Vehicle mobility optimization in urban areas is a
long-standing problem in smart city and spatial data
analysis. Given the complex urban scenario and
unpredictable social events, our work focuses on
developing a mobile sequential recommendation system to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2021:TTA,
author = "Jiajie Xu and Saijun Xu and Rui Zhou and Chengfei Liu
and An Liu and Lei Zhao",
title = "{TAML}: a Traffic-aware Multi-task Learning Model for
Estimating Travel Time",
journal = j-TIST,
volume = "12",
number = "6",
pages = "75:1--75:14",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466686",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466686",
abstract = "Travel time estimation has been recognized as an
important research topic that can find broad
applications. Existing approaches aim to explore
mobility patterns via trajectory embedding for travel
time estimation. Though state-of-the-art methods
utilize \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "75",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gupta:2021:SVA,
author = "Jayant Gupta and Carl Molnar and Yiqun Xie and Joe
Knight and Shashi Shekhar",
title = "Spatial Variability Aware Deep Neural Networks
{(SVANN)}: a General Approach",
journal = j-TIST,
volume = "12",
number = "6",
pages = "76:1--76:21",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466688",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466688",
abstract = "Spatial variability is a prominent feature of various
geographic phenomena such as climatic zones, USDA plant
hardiness zones, and terrestrial habitat types (e.g.,
forest, grasslands, wetlands, and deserts). However,
current deep learning methods follow a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "76",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tedjopurnomo:2021:STS,
author = "David Alexander Tedjopurnomo and Xiucheng Li and
Zhifeng Bao and Gao Cong and Farhana Choudhury and A.
K. Qin",
title = "Similar Trajectory Search with Spatio-Temporal Deep
Representation Learning",
journal = j-TIST,
volume = "12",
number = "6",
pages = "77:1--77:26",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466687",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466687",
abstract = "Similar trajectory search is a crucial task that
facilitates many downstream spatial data analytic
applications. Despite its importance, many of the
current literature focus solely on the trajectory's
spatial similarity while neglecting the temporal
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "77",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tao:2021:PHM,
author = "Shuo Tao and Jingang Jiang and Defu Lian and Kai Zheng
and Enhong Chen",
title = "Predicting Human Mobility with
Reinforcement-Learning-Based Long-Term Periodicity
Modeling",
journal = j-TIST,
volume = "12",
number = "6",
pages = "78:1--78:23",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3469860",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3469860",
abstract = "Mobility prediction plays an important role in a wide
range of location-based applications and services.
However, there are three problems in the existing
literature: (1) explicit high-order interactions of
spatio-temporal features are not systemically
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "78",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Strohmeier:2021:CFI,
author = "Martin Strohmeier and Matthew Smith and Vincent
Lenders and Ivan Martinovic",
title = "{Classi-Fly}: Inferring Aircraft Categories from Open
Data",
journal = j-TIST,
volume = "12",
number = "6",
pages = "79:1--79:23",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3480969",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3480969",
abstract = "In recent years, air traffic communication data has
become easy to access, enabling novel research in many
fields. Exploiting this new data source, a wide range
of applications have emerged, from weather forecasting
to stock market prediction, or the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "79",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Qiao:2021:CDC,
author = "Jie Qiao and Ruichu Cai and Kun Zhang and Zhenjie
Zhang and Zhifeng Hao",
title = "Causal Discovery with Confounding Cascade Nonlinear
Additive Noise Models",
journal = j-TIST,
volume = "12",
number = "6",
pages = "80:1--80:28",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3482879",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3482879",
abstract = "Identification of causal direction between a
causal-effect pair from observed data has recently
attracted much attention. Various methods based on
functional causal models have been proposed to solve
this problem, by assuming the causal process satisfies
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "80",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Notaro:2021:SAM,
author = "Paolo Notaro and Jorge Cardoso and Michael Gerndt",
title = "A Survey of {AIOps} Methods for Failure Management",
journal = j-TIST,
volume = "12",
number = "6",
pages = "81:1--81:45",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3483424",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3483424",
abstract = "Modern society is increasingly moving toward complex
and distributed computing systems. The increase in
scale and complexity of these systems challenges O\&M
teams that perform daily monitoring and repair
operations, in contrast with the increasing demand
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "81",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhao:2021:MSF,
author = "Jiaqi Zhao and Yong Zhou and Boyu Shi and Jingsong
Yang and Di Zhang and Rui Yao",
title = "Multi-Stage Fusion and Multi-Source Attention Network
for Multi-Modal Remote Sensing Image Segmentation",
journal = j-TIST,
volume = "12",
number = "6",
pages = "82:1--82:20",
month = dec,
year = "2021",
CODEN = "????",
DOI = "https://doi.org/10.1145/3484440",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Dec 24 06:30:09 MST 2021",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3484440",
abstract = "With the rapid development of sensor technology, lots
of remote sensing data have been collected. It
effectively obtains good semantic segmentation
performance by extracting feature maps based on
multi-modal remote sensing images since extra modal
data \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "82",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zheng:2022:ISIa,
author = "Kai Zheng and Yong Li and Cyrus Shahabi and Hongzhi
Yin",
title = "Introduction to the Special Issue on Intelligent
Trajectory Analytics: {Part I}",
journal = j-TIST,
volume = "13",
number = "1",
pages = "1:1--1:2",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495230",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495230",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2022:PMP,
author = "Yuandong Wang and Hongzhi Yin and Tong Chen and
Chunyang Liu and Ben Wang and Tianyu Wo and Jie Xu",
title = "Passenger Mobility Prediction via Representation
Learning for Dynamic Directed and Weighted Graphs",
journal = j-TIST,
volume = "13",
number = "1",
pages = "2:1--2:25",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3446344",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3446344",
abstract = "In recent years, ride-hailing services have been
increasingly prevalent, as they provide huge
convenience for passengers. As a fundamental problem,
the timely prediction of passenger demands in different
regions is vital for effective traffic flow control
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2022:IBD,
author = "Wen-Cheng Chen and Wan-Lun Tsai and Huan-Hua Chang and
Min-Chun Hu and Wei-Ta Chu",
title = "Instant Basketball Defensive Trajectory Generation",
journal = j-TIST,
volume = "13",
number = "1",
pages = "3:1--3:20",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3460619",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3460619",
abstract = "Tactic learning in virtual reality (VR) has been
proven to be effective for basketball training. Endowed
with the ability of generating virtual defenders in
real time according to the movement of virtual
offenders controlled by the user, a VR basketball
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2022:CTL,
author = "Fan Zhou and Pengyu Wang and Xovee Xu and Wenxin Tai
and Goce Trajcevski",
title = "Contrastive Trajectory Learning for Tour
Recommendation",
journal = j-TIST,
volume = "13",
number = "1",
pages = "4:1--4:25",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3462331",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3462331",
abstract = "The main objective of Personalized Tour Recommendation
(PTR) is to generate a sequence of point-of-interest
(POIs) for a particular tourist, according to the
user-specific constraints such as duration time, start
and end points, the number of attractions \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2022:OAL,
author = "Meng Chen and Qingjie Liu and Weiming Huang and Teng
Zhang and Yixuan Zuo and Xiaohui Yu",
title = "Origin-Aware Location Prediction Based on Historical
Vehicle Trajectories",
journal = j-TIST,
volume = "13",
number = "1",
pages = "5:1--5:18",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3462675",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3462675",
abstract = "Next location prediction is of great importance for
many location-based applications and provides essential
intelligence to various businesses. In previous
studies, a common approach to next location prediction
is to learn the sequential transitions with \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Loffler:2022:DSM,
author = "Christoffer L{\"o}ffler and Luca Reeb and Daniel
Dzibela and Robert Marzilger and Nicolas Witt and
Bj{\"o}rn M. Eskofier and Christopher Mutschler",
title = "Deep {Siamese} Metric Learning: a Highly Scalable
Approach to Searching Unordered Sets of Trajectories",
journal = j-TIST,
volume = "13",
number = "1",
pages = "6:1--6:23",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3465057",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3465057",
abstract = "This work proposes metric learning for fast
similarity-based scene retrieval of unstructured
ensembles of trajectory data from large databases. We
present a novel representation learning approach using
Siamese Metric Learning that approximates a distance
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sun:2022:PFL,
author = "Heli Sun and Xianglan Guo and Zhou Yang and Xuguang
Chu and Xinwang Liu and Liang He",
title = "Predicting Future Locations with Semantic
Trajectories",
journal = j-TIST,
volume = "13",
number = "1",
pages = "7:1--7:20",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3465060",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3465060",
abstract = "Location prediction has attracted much attention due
to its important role in many location-based services,
including taxi services, route navigation, traffic
planning, and location-based advertisements.
Traditional methods only use spatial-temporal
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Luo:2022:LTS,
author = "Hui Luo and Zhifeng Bao and Gao Cong and J. Shane
Culpepper and Nguyen Lu Dang Khoa",
title = "Let Trajectories Speak Out the Traffic Bottlenecks",
journal = j-TIST,
volume = "13",
number = "1",
pages = "8:1--8:21",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3465058",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3465058",
abstract = "Traffic bottlenecks are a set of road segments that
have an unacceptable level of traffic caused by a poor
balance between road capacity and traffic volume. A
huge volume of trajectory data which captures realtime
traffic conditions in road networks \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Niu:2022:ERT,
author = "Hongting Niu and Hengshu Zhu and Ying Sun and Xinjiang
Lu and Jing Sun and Zhiyuan Zhao and Hui Xiong and Bo
Lang",
title = "Exploring the Risky Travel Area and Behavior of
Car-hailing Service",
journal = j-TIST,
volume = "13",
number = "1",
pages = "9:1--9:22",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3465059",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3465059",
abstract = "Recent years have witnessed the rapid development of
car-hailing services, which provide a convenient
approach for connecting passengers and local drivers
using their personal vehicles. At the same time, the
concern on passenger safety has gradually \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhu:2022:SPC,
author = "Yanliang Zhu and Dongchun Ren and Yi Xu and Deheng
Qian and Mingyu Fan and Xin Li and Huaxia Xia",
title = "Simultaneous Past and Current Social Interaction-aware
Trajectory Prediction for Multiple Intelligent Agents
in Dynamic Scenes",
journal = j-TIST,
volume = "13",
number = "1",
pages = "10:1--10:16",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3466182",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3466182",
abstract = "Trajectory prediction of multiple agents in a crowded
scene is an essential component in many applications,
including intelligent monitoring, autonomous robotics,
and self-driving cars. Accurate agent trajectory
prediction remains a significant challenge \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bi:2022:UBN,
author = "Xin Bi and Chao Zhang and Fangtong Wang and Zhixun Liu
and Xiangguo Zhao and Ye Yuan and Guoren Wang",
title = "An Uncertainty-based Neural Network for Explainable
Trajectory Segmentation",
journal = j-TIST,
volume = "13",
number = "1",
pages = "11:1--11:18",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3467978",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3467978",
abstract = "As a variant task of time-series segmentation,
trajectory segmentation is a key task in the
applications of transportation pattern recognition and
traffic analysis. However, segmenting trajectory is
faced with challenges of implicit patterns and sparse
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Waniek:2022:HMC,
author = "Marcin Waniek and Tomasz P. Michalak and Michael
Wooldridge and Talal Rahwan",
title = "How Members of Covert Networks Conceal the Identities
of Their Leaders",
journal = j-TIST,
volume = "13",
number = "1",
pages = "12:1--12:29",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3490462",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3490462",
abstract = "Centrality measures are the most commonly advocated
social network analysis tools for identifying leaders
of covert organizations. While the literature has
predominantly focused on studying the effectiveness of
existing centrality measures or developing \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Huang:2022:SAF,
author = "Shih-Chia Huang and Quoc-Viet Hoang and Da-Wei Jaw",
title = "Self-Adaptive Feature Transformation Networks for
Object Detection in low luminance Images",
journal = j-TIST,
volume = "13",
number = "1",
pages = "13:1--13:11",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3480973",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3480973",
abstract = "Despite the recent improvement of object detection
techniques, many of them fail to detect objects in
low-luminance images. The blurry and dimmed nature of
low-luminance images results in the extraction of vague
features and failure to detect objects. In \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wen:2022:MWP,
author = "Yu-Ting Wen and Hui-Kuo Yang and Wen-Chih Peng",
title = "Mining Willing-to-Pay Behavior Patterns from Payment
Datasets",
journal = j-TIST,
volume = "13",
number = "1",
pages = "14:1--14:19",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3485848",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3485848",
abstract = "The customer base is the most valuable resource to
E-commerce companies. A comprehensive understanding of
customers' preferences and behavior is crucial to
developing good marketing strategies, in order to
achieve optimal customer lifetime values (CLVs).
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2022:GNN,
author = "Yu Zhou and Haixia Zheng and Xin Huang and Shufeng Hao
and Dengao Li and Jumin Zhao",
title = "Graph Neural Networks: Taxonomy, Advances, and
Trends",
journal = j-TIST,
volume = "13",
number = "1",
pages = "15:1--15:54",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495161",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495161",
abstract = "Graph neural networks provide a powerful toolkit for
embedding real-world graphs into low-dimensional spaces
according to specific tasks. Up to now, there have been
several surveys on this topic. However, they usually
lay emphasis on different angles so \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2022:FFA,
author = "Cheng-Te Li and Cheng Hsu and Yang Zhang",
title = "{FairSR}: Fairness-aware Sequential Recommendation
through Multi-Task Learning with Preference Graph
Embeddings",
journal = j-TIST,
volume = "13",
number = "1",
pages = "16:1--16:21",
month = feb,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495163",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Feb 17 07:52:04 MST 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495163",
abstract = "Sequential recommendation (SR) learns from the
temporal dynamics of user-item interactions to predict
the next ones. Fairness-aware recommendation mitigates
a variety of algorithmic biases in the learning of user
preferences. This article aims at bringing \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2022:ISI,
author = "Senzhang Wang and Junbo Zhang and Yanjie Fu and Yong
Li",
title = "Introduction to the Special Issue on Deep Learning for
Spatio-Temporal Data:{Part 2}",
journal = j-TIST,
volume = "13",
number = "2",
pages = "17:1--17:4",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510023",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510023",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Saxena:2022:MST,
author = "Divya Saxena and Jiannong Cao",
title = "Multimodal Spatio-Temporal Prediction with Stochastic
Adversarial Networks",
journal = j-TIST,
volume = "13",
number = "2",
pages = "18:1--18:23",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3458025",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3458025",
abstract = "Spatio-temporal (ST) data is a collection of multiple
time series data with different spatial locations and
is inherently stochastic and unpredictable. An accurate
prediction over such data is an important building
block for several urban applications, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2022:DST,
author = "He Li and Xuejiao Li and Liangcai Su and Duo Jin and
Jianbin Huang and Deshuang Huang",
title = "Deep Spatio-temporal Adaptive {$3$D} Convolutional
Neural Networks for Traffic Flow Prediction",
journal = j-TIST,
volume = "13",
number = "2",
pages = "19:1--19:21",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510829",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510829",
abstract = "Traffic flow prediction is the upstream problem of
path planning, intelligent transportation system, and
other tasks. Many studies have been carried out on the
traffic flow prediction of the spatio-temporal network,
but the effects of spatio-temporal \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lu:2022:GSN,
author = "Zhilong Lu and Weifeng Lv and Zhipu Xie and Bowen Du
and Guixi Xiong and Leilei Sun and Haiquan Wang",
title = "Graph Sequence Neural Network with an Attention
Mechanism for Traffic Speed Prediction",
journal = j-TIST,
volume = "13",
number = "2",
pages = "20:1--20:24",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3470889",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3470889",
abstract = "Recent years have witnessed the emerging success of
Graph Neural Networks (GNNs) for modeling graphical
data. A GNN can model the spatial dependencies of nodes
in a graph based on message passing through node
aggregation. However, in many application \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2022:PCC,
author = "Renhe Jiang and Zekun Cai and Zhaonan Wang and Chuang
Yang and Zipei Fan and Quanjun Chen and Xuan Song and
Ryosuke Shibasaki",
title = "Predicting Citywide Crowd Dynamics at Big Events: a
Deep Learning System",
journal = j-TIST,
volume = "13",
number = "2",
pages = "21:1--21:24",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3472300",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3472300",
abstract = "Event crowd management has been a significant research
topic with high social impact. When some big events
happen such as an earthquake, typhoon, and national
festival, crowd management becomes the first priority
for governments (e.g., police) and public \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gao:2022:GAN,
author = "Nan Gao and Hao Xue and Wei Shao and Sichen Zhao and
Kyle Kai Qin and Arian Prabowo and Mohammad Saiedur
Rahaman and Flora D. Salim",
title = "Generative Adversarial Networks for Spatio-temporal
Data: a Survey",
journal = j-TIST,
volume = "13",
number = "2",
pages = "22:1--22:25",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3474838",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3474838",
abstract = "Generative Adversarial Networks (GANs) have shown
remarkable success in producing realistic-looking
images in the computer vision area. Recently, GAN-based
techniques are shown to be promising for
spatio-temporal-based applications such as trajectory
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2022:UTD,
author = "Yingxue Zhang and Yanhua Li and Xun Zhou and Jun Luo
and Zhi-Li Zhang",
title = "Urban Traffic Dynamics Prediction --- a Continuous
Spatial-temporal Meta-learning Approach",
journal = j-TIST,
volume = "13",
number = "2",
pages = "23:1--23:19",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3474837",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3474837",
abstract = "Urban traffic status (e.g., traffic speed and volume)
is highly dynamic in nature, namely, varying across
space and evolving over time. Thus, predicting such
traffic dynamics is of great importance to urban
development and transportation management. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wen:2022:DMC,
author = "Haomin Wen and Youfang Lin and Huaiyu Wan and Shengnan
Guo and Fan Wu and Lixia Wu and Chao Song and Yinghui
Xu",
title = "{DeepRoute+}: Modeling Couriers' Spatial-temporal
Behaviors and Decision Preferences for Package Pick-up
Route Prediction",
journal = j-TIST,
volume = "13",
number = "2",
pages = "24:1--24:23",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3481006",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3481006",
abstract = "Over 10 billion packages are picked up every day in
China. A fundamental task raised in the emerging
intelligent logistics systems is the couriers' package
pick-up route prediction, which is beneficial for
package dispatching, arrival-time estimation and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2022:WSS,
author = "Zhe Jiang and Wenchong He and Marcus Stephen Kirby and
Arpan Man Sainju and Shaowen Wang and Lawrence V.
Stanislawski and Ethan J. Shavers and E. Lynn Usery",
title = "Weakly Supervised Spatial Deep Learning for {Earth}
Image Segmentation Based on Imperfect Polyline Labels",
journal = j-TIST,
volume = "13",
number = "2",
pages = "25:1--25:20",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3480970",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3480970",
abstract = "In recent years, deep learning has achieved tremendous
success in image segmentation for computer vision
applications. The performance of these models heavily
relies on the availability of large-scale high-quality
training labels (e.g., PASCAL VOC 2012). \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{He:2022:EIS,
author = "Wenchong He and Arpan Man Sainju and Zhe Jiang and Da
Yan and Yang Zhou",
title = "{Earth} Imagery Segmentation on Terrain Surface with
Limited Training Labels: a Semi-supervised Approach
based on Physics-Guided Graph Co-Training",
journal = j-TIST,
volume = "13",
number = "2",
pages = "26:1--26:22",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3481043",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3481043",
abstract = "Given earth imagery with spectral features on a
terrain surface, this paper studies surface
segmentation based on both explanatory features and
surface topology. The problem is important in many
spatial and spatiotemporal applications such as flood
extent \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bao:2022:CGE,
author = "Han Bao and Xun Zhou and Yiqun Xie and Yingxue Zhang
and Yanhua Li",
title = "{COVID-GAN+}: Estimating Human Mobility Responses to
{COVID-19} through Spatio-temporal Generative
Adversarial Networks with Enhanced Features",
journal = j-TIST,
volume = "13",
number = "2",
pages = "27:1--27:23",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3481617",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3481617",
abstract = "Estimating human mobility responses to the large-scale
spreading of the COVID-19 pandemic is crucial, since
its significance guides policymakers to give
Non-pharmaceutical Interventions, such as closure or
reopening of businesses. It is challenging to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lu:2022:MMC,
author = "Bin Lu and Xiaoying Gan and Haiming Jin and Luoyi Fu
and Xinbing Wang and Haisong Zhang",
title = "Make More Connections: Urban Traffic Flow Forecasting
with Spatiotemporal Adaptive Gated Graph Convolution
Network",
journal = j-TIST,
volume = "13",
number = "2",
pages = "28:1--28:25",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3488902",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3488902",
abstract = "Urban traffic flow forecasting is a critical issue in
intelligent transportation systems. Due to the
complexity and uncertainty of urban road conditions,
how to capture the dynamic spatiotemporal correlation
and make accurate predictions is very \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2022:DDT,
author = "Liang Wang and Zhiwen Yu and Bin Guo and Dingqi Yang
and Lianbo Ma and Zhidan Liu and Fei Xiong",
title = "Data-driven Targeted Advertising Recommendation System
for Outdoor Billboard",
journal = j-TIST,
volume = "13",
number = "2",
pages = "29:1--29:23",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495159",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495159",
abstract = "In this article, we propose and study a novel
data-driven framework for Targeted Outdoor Advertising
Recommendation (TOAR) with a special consideration of
user profiles and advertisement topics. Given an
advertisement query and a set of outdoor billboards
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{He:2022:BAB,
author = "Yulin He and Xuan Ye and Joshua Zhexue Huang and
Philippe Fournier-Viger",
title = "{Bayesian} Attribute Bagging-Based Extreme Learning
Machine for High-Dimensional Classification and
Regression",
journal = j-TIST,
volume = "13",
number = "2",
pages = "30:1--30:26",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495164",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495164",
abstract = "This article presents a Bayesian attribute
bagging-based extreme learning machine (BAB-ELM) to
handle high-dimensional classification and regression
problems. First, the decision-making degree (DMD) of a
condition attribute is calculated based on the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2022:STC,
author = "Qian Li and Hao Peng and Jianxin Li and Congying Xia
and Renyu Yang and Lichao Sun and Philip S. Yu and
Lifang He",
title = "A Survey on Text Classification: From Traditional to
Deep Learning",
journal = j-TIST,
volume = "13",
number = "2",
pages = "31:1--31:41",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495162",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495162",
abstract = "Text classification is the most fundamental and
essential task in natural language processing. The last
decade has seen a surge of research in this area due to
the unprecedented success of deep learning. Numerous
methods, datasets, and evaluation metrics \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gao:2022:LCH,
author = "Guangliang Gao and Zhifeng Bao and Jie Cao and A. K.
Qin and Timos Sellis",
title = "Location-Centered House Price Prediction: a Multi-Task
Learning Approach",
journal = j-TIST,
volume = "13",
number = "2",
pages = "32:1--32:25",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501806",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501806",
abstract = "Accurate house prediction is of great significance to
various real estate stakeholders such as house owners,
buyers, and investors. We propose a location-centered
prediction framework that differs from existing work in
terms of data profiling and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liang:2022:CFS,
author = "Weichao Liang and Zhiang Wu and Zhe Li and Yong Ge",
title = "{CrimeTensor}: Fine-Scale Crime Prediction via Tensor
Learning with Spatiotemporal Consistency",
journal = j-TIST,
volume = "13",
number = "2",
pages = "33:1--33:24",
month = apr,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501807",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Fri Apr 22 08:41:23 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501807",
abstract = "Crime poses a major threat to human life and property,
which has been recognized as one of the most crucial
problems in our society. Predicting the number of crime
incidents in each region of a city before they happen
is of great importance to fight \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zheng:2022:ISIb,
author = "Kai Zheng and Yong Li and Cyrus Shahabi and Hongzhi
Yin",
title = "Introduction to the Special Issue on Intelligent
Trajectory Analytics: {Part II}",
journal = j-TIST,
volume = "13",
number = "3",
pages = "34:1--34:2",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510021",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510021",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Deng:2022:EES,
author = "Liwei Deng and Hao Sun and Rui Sun and Yan Zhao and
Han Su",
title = "Efficient and Effective Similar Subtrajectory Search:
a Spatial-aware Comprehension Approach",
journal = j-TIST,
volume = "13",
number = "3",
pages = "35:1--35:22",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3456723",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3456723",
abstract = "Although many applications take subtrajectories as
basic units for analysis, there is little research on
the similar subtrajectory search problem aiming to
return a portion of a trajectory (i.e., subtrajectory),
which is the most similar to a query \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sharma:2022:ATG,
author = "Arun Sharma and Shashi Shekhar",
title = "Analyzing Trajectory Gaps to Find Possible Rendezvous
Region",
journal = j-TIST,
volume = "13",
number = "3",
pages = "36:1--36:23",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3467977",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3467977",
abstract = "Given trajectory data with gaps, we investigate
methods to identify possible rendezvous regions. The
problem has societal applications such as improving
maritime safety and regulatory enforcement. The
challenges come from two aspects. First, gaps in
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zheng:2022:SDA,
author = "Bolong Zheng and Lingfeng Ming and Qi Hu and Zhipeng
L{\"u} and Guanfeng Liu and Xiaofang Zhou",
title = "Supply-Demand-aware Deep Reinforcement Learning for
Dynamic Fleet Management",
journal = j-TIST,
volume = "13",
number = "3",
pages = "37:1--37:19",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3467979",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3467979",
abstract = "Online ride-hailing platforms have reduced
significantly the amounts of the time that taxis are
idle and that passengers spend on waiting. As a key
component of these platforms, the fleet management
problem can be naturally modeled as a Markov Decision
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2022:MCA,
author = "Senzhang Wang and Meiyue Zhang and Hao Miao and
Zhaohui Peng and Philip S. Yu",
title = "Multivariate Correlation-aware Spatio-temporal Graph
Convolutional Networks for Multi-scale Traffic
Prediction",
journal = j-TIST,
volume = "13",
number = "3",
pages = "38:1--38:22",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3469087",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3469087",
abstract = "Traffic flow prediction based on vehicle trajectories
collected from the installed GPS devices is critically
important to Intelligent Transportation Systems (ITS).
One limitation of existing traffic prediction models is
that they mostly focus on \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2022:IAS,
author = "Yifan Zhang and Jinghuai Zhang and Jindi Zhang and
Jianping Wang and Kejie Lu and Jeff Hong",
title = "Integrating Algorithmic Sampling-Based Motion Planning
with Learning in Autonomous Driving",
journal = j-TIST,
volume = "13",
number = "3",
pages = "39:1--39:27",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3469086",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3469086",
abstract = "Sampling-based motion planning (SBMP) is a major
algorithmic trajectory planning approach in autonomous
driving given its high efficiency and outstanding
performance in practice. However, driving safety still
calls for further refinement of SBMP. In this
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fang:2022:GFC,
author = "Chenglong Fang and Feng Wang and Bin Yao and Jianqiu
Xu",
title = "{GPSClean}: a Framework for Cleaning and Repairing
{GPS} Data",
journal = j-TIST,
volume = "13",
number = "3",
pages = "40:1--40:22",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3469088",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3469088",
abstract = "The rise of GPS-equipped mobile devices has led to the
emergence of big trajectory data. The collected raw
data usually contain errors and anomalies information
caused by device failure, sensor error, and environment
influence. Low-quality data fails to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Huang:2022:DRL,
author = "Jianbin Huang and Longji Huang and Meijuan Liu and He
Li and Qinglin Tan and Xiaoke Ma and Jiangtao Cui and
De-Shuang Huang",
title = "Deep Reinforcement Learning-based Trajectory Pricing
on Ride-hailing Platforms",
journal = j-TIST,
volume = "13",
number = "3",
pages = "41:1--41:19",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3474841",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3474841",
abstract = "Dynamic pricing plays an important role in solving the
problems such as traffic load reduction, congestion
control, and revenue improvement. Efficient dynamic
pricing strategies can increase capacity utilization,
total revenue of service providers, and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yao:2022:PPT,
author = "Lin Yao and Zhenyu Chen and Haibo Hu and Guowei Wu and
Bin Wu",
title = "Privacy Preservation for Trajectory Publication Based
on Differential Privacy",
journal = j-TIST,
volume = "13",
number = "3",
pages = "42:1--42:21",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3474839",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3474839",
abstract = "With the proliferation of location-aware devices,
trajectory data have been used widely in real-life
applications. However, trajectory data are often
associated with sensitive labels, such as users'
purchase transactions and planned activities. As such,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Han:2022:ATP,
author = "Nan Han and Shaojie Qiao and Kun Yue and Jianbin Huang
and Qiang He and Tingting Tang and Faliang Huang and
Chunlin He and Chang-An Yuan",
title = "Algorithms for Trajectory Points Clustering in
Location-based Social Networks",
journal = j-TIST,
volume = "13",
number = "3",
pages = "43:1--43:29",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3480972",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3480972",
abstract = "Recent advances in localization techniques have
fundamentally enhanced social networking services,
allowing users to share their locations and
location-related contents. This has further increased
the popularity of location-based social networks
(LBSNs) \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zheng:2022:UAP,
author = "Zhirun Zheng and Zhetao Li and Jie Li and Hongbo Jiang
and Tong Li and Bin Guo",
title = "Utility-aware and Privacy-preserving Trajectory
Synthesis Model that Resists Social Relationship
Privacy Attacks",
journal = j-TIST,
volume = "13",
number = "3",
pages = "44:1--44:28",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3495160",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3495160",
abstract = "For academic research and business intelligence,
trajectory data has been widely collected and analyzed.
Releasing trajectory data to a third party may lead to
serious privacy leakage, which has spawned considerable
researches on trajectory privacy \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Kim:2022:NNE,
author = "Cheolhyeong Kim and Haeseong Moon and Hyung Ju Hwang",
title = "{NEAR}: Neighborhood Edge {AggregatoR} for Graph
Classification",
journal = j-TIST,
volume = "13",
number = "3",
pages = "45:1--45:17",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3506714",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3506714",
abstract = "Learning graph-structured data with graph neural
networks (GNNs) has been recently emerging as an
important field because of its wide applicability in
bioinformatics, chemoinformatics, social network
analysis, and data mining. Recent GNN algorithms are
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wei:2022:WSV,
author = "Lili Wei and Congyan Lang and Liqian Liang and Songhe
Feng and Tao Wang and Shidi Chen",
title = "Weakly Supervised Video Object Segmentation via
Dual-attention Cross-branch Fusion",
journal = j-TIST,
volume = "13",
number = "3",
pages = "46:1--46:20",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3506716",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3506716",
abstract = "Recently, concerning the challenge of collecting
large-scale explicitly annotated videos, weakly
supervised video object segmentation (WSVOS) using
video tags has attracted much attention. Existing WSVOS
approaches follow a general pipeline including two
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yan:2022:CFM,
author = "Runze Yan and Xinwen Liu and Janine Dutcher and
Michael Tumminia and Daniella Villalba and Sheldon
Cohen and David Creswell and Kasey Creswell and
Jennifer Mankoff and Anind Dey and Afsaneh Doryab",
title = "A Computational Framework for Modeling Biobehavioral
Rhythms from Mobile and Wearable Data Streams",
journal = j-TIST,
volume = "13",
number = "3",
pages = "47:1--47:27",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510029",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510029",
abstract = "This paper presents a computational framework for
modeling biobehavioral rhythms --- the repeating cycles
of physiological, psychological, social, and
environmental events --- from mobile and wearable data
streams. The framework incorporates four main
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hu:2022:WCK,
author = "Yang Hu and Adriane Chapman and Guihua Wen and Dame
Wendy Hall",
title = "What Can Knowledge Bring to Machine Learning? --- a
Survey of Low-shot Learning for Structured Data",
journal = j-TIST,
volume = "13",
number = "3",
pages = "48:1--48:45",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510030",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510030",
abstract = "Supervised machine learning has several drawbacks that
make it difficult to use in many situations. Drawbacks
include heavy reliance on massive training data,
limited generalizability, and poor expressiveness of
high-level semantics. Low-shot Learning \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lin:2022:TTP,
author = "Fandel Lin and Hsun-Ping Hsieh",
title = "Traveling Transporter Problem: Arranging a New
Circular Route in a Public Transportation System Based
on Heterogeneous Non-Monotonic Urban Data",
journal = j-TIST,
volume = "13",
number = "3",
pages = "49:1--49:25",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510034",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510034",
abstract = "Hybrid computational intelligent systems that
synergize learning-based inference models and route
planning strategies have thrived in recent years. In
this article, we focus on the non-monotonicity
originated from heterogeneous urban data, as well as
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gupta:2022:DML,
author = "Vinayak Gupta and Srikanta Bedathur",
title = "Doing More with Less: Overcoming Data Scarcity for
{POI} Recommendation via Cross-Region Transfer",
journal = j-TIST,
volume = "13",
number = "3",
pages = "50:1--50:24",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3511711",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3511711",
abstract = "Variability in social app usage across regions results
in a high skew of the quantity and the quality of
check-in data collected, which in turn is a challenge
for effective location recommender systems. In this
article, we present Axolotl (Automated cross.
\ldots{})",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Choudhary:2022:SSS,
author = "Nurendra Choudhary and Charu C. Aggarwal and Karthik
Subbian and Chandan K. Reddy",
title = "Self-supervised Short-text Modeling through Auxiliary
Context Generation",
journal = j-TIST,
volume = "13",
number = "3",
pages = "51:1--51:21",
month = jun,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3511712",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Wed May 25 07:55:15 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3511712",
abstract = "Short text is ambiguous and often relies predominantly
on the domain and context at hand in order to attain
semantic relevance. Existing classification models
perform poorly on short text due to data sparsity and
inadequate context. Auxiliary context, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2022:ISI,
author = "Qiang Yang and Yongxin Tong and Yang Liu and Yangqiu
Song and Hao Peng and Boi Faltings",
title = "Introduction to the Special Issue on the Federated
Learning: Algorithms, Systems, and Applications: {Part
1}",
journal = j-TIST,
volume = "13",
number = "4",
pages = "52:1--52:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3514223",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3514223",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2022:TSP,
author = "Jun Zhou and Longfei Zheng and Chaochao Chen and Yan
Wang and Xiaolin Zheng and Bingzhe Wu and Cen Chen and
Li Wang and Jianwei Yin",
title = "Toward Scalable and Privacy-preserving Deep Neural
Network via Algorithmic-Cryptographic Co-design",
journal = j-TIST,
volume = "13",
number = "4",
pages = "53:1--53:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501809",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501809",
abstract = "Deep Neural Networks (DNNs) have achieved remarkable
progress in various real-world applications, especially
when abundant training data are provided. However, data
isolation has become a serious problem currently.
Existing works build privacy-preserving \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Antunes:2022:FLH,
author = "Rodolfo Stoffel Antunes and Cristiano Andr{\'e} da
Costa and Arne K{\"u}derle and Imrana Abdullahi Yari
and Bj{\"o}rn Eskofier",
title = "Federated Learning for Healthcare: Systematic Review
and Architecture Proposal",
journal = j-TIST,
volume = "13",
number = "4",
pages = "54:1--54:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501813",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501813",
abstract = "The use of machine learning (ML) with electronic
health records (EHR) is growing in popularity as a
means to extract knowledge that can improve the
decision-making process in healthcare. Such methods
require training of high-quality learning models based
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2022:FSR,
author = "Zhiwei Liu and Liangwei Yang and Ziwei Fan and Hao
Peng and Philip S. Yu",
title = "Federated Social Recommendation with Graph Neural
Network",
journal = j-TIST,
volume = "13",
number = "4",
pages = "55:1--55:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501815",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501815",
abstract = "Recommender systems have become prosperous nowadays,
designed to predict users' potential interests in items
by learning embeddings. Recent developments of the
Graph Neural Networks (GNNs) also provide recommender
systems (RSs) with powerful backbones to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2022:FDG,
author = "Meng Jiang and Taeho Jung and Ryan Karl and Tong
Zhao",
title = "Federated Dynamic Graph Neural Networks with Secure
Aggregation for Video-based Distributed Surveillance",
journal = j-TIST,
volume = "13",
number = "4",
pages = "56:1--56:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501808",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501808",
abstract = "Distributed surveillance systems have the ability to
detect, track, and snapshot objects moving around in a
certain space. The systems generate video data from
multiple personal devices or street cameras.
Intelligent video-analysis models are needed to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hu:2022:DAF,
author = "Ziheng Hu and Hongtao Xie and Lingyun Yu and Xingyu
Gao and Zhihua Shang and Yongdong Zhang",
title = "Dynamic-Aware Federated Learning for Face Forgery
Video Detection",
journal = j-TIST,
volume = "13",
number = "4",
pages = "57:1--57:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501814",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501814",
abstract = "The spread of face forgery videos is a serious threat
to information credibility, calling for effective
detection algorithms to identify them. Most existing
methods have assumed a shared or centralized training
set. However, in practice, data may be \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ren:2022:IAV,
author = "Zhenghang Ren and Liu Yang and Kai Chen",
title = "Improving Availability of Vertical Federated Learning:
Relaxing Inference on Non-overlapping Data",
journal = j-TIST,
volume = "13",
number = "4",
pages = "58:1--58:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501817",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501817",
abstract = "Vertical Federated Learning (VFL) enables multiple
parties to collaboratively train a machine learning
model over vertically distributed datasets without data
privacy leakage. However, there is a limitation of the
current VFL solutions: current VFL models \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chai:2022:EFM,
author = "Di Chai and Leye Wang and Kai Chen and Qiang Yang",
title = "Efficient Federated Matrix Factorization Against
Inference Attacks",
journal = j-TIST,
volume = "13",
number = "4",
pages = "59:1--59:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501812",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501812",
abstract = "Recommender systems typically require the revelation
of users' ratings to the recommender server, which will
subsequently use these ratings to provide personalized
services. However, such revelations make users
vulnerable to a broader set of inference \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2022:GSE,
author = "Zelei Liu and Yuanyuan Chen and Han Yu and Yang Liu
and Lizhen Cui",
title = "{GTG-Shapley}: Efficient and Accurate Participant
Contribution Evaluation in Federated Learning",
journal = j-TIST,
volume = "13",
number = "4",
pages = "60:1--60:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501811",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501811",
abstract = "Federated Learning (FL) bridges the gap between
collaborative machine learning and preserving data
privacy. To sustain the long-term operation of an FL
ecosystem, it is important to attract high-quality data
owners with appropriate incentive schemes. As
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Che:2022:FMV,
author = "Sicong Che and Zhaoming Kong and Hao Peng and Lichao
Sun and Alex Leow and Yong Chen and Lifang He",
title = "Federated Multi-view Learning for Private Medical Data
Integration and Analysis",
journal = j-TIST,
volume = "13",
number = "4",
pages = "61:1--61:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501816",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501816",
abstract = "Along with the rapid expansion of information
technology and digitalization of health data, there is
an increasing concern on maintaining data privacy while
garnering the benefits in the medical field. Two
critical challenges are identified: First, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2022:FFN,
author = "Chuhan Wu and Fangzhao Wu and Lingjuan Lyu and
Yongfeng Huang and Xing Xie",
title = "{FedCTR}: Federated Native Ad {CTR} Prediction with
Cross-platform User Behavior Data",
journal = j-TIST,
volume = "13",
number = "4",
pages = "62:1--62:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3506715",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3506715",
abstract = "Native ad is a popular type of online advertisement
that has similar forms with the native content
displayed on websites. Native ad click-through rate
(CTR) prediction is useful for improving user
experience and platform revenue. However, it is
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hu:2022:OBS,
author = "Sixu Hu and Yuan Li and Xu Liu and Qinbin Li and
Zhaomin Wu and Bingsheng He",
title = "The {OARF} Benchmark Suite: Characterization and
Implications for Federated Learning Systems",
journal = j-TIST,
volume = "13",
number = "4",
pages = "63:1--63:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510540",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510540",
abstract = "This article presents and characterizes an Open
Application Repository for Federated Learning (OARF), a
benchmark suite for federated machine learning systems.
Previously available benchmarks for federated learning
(FL) have focused mainly on synthetic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Kang:2022:FSS,
author = "Yan Kang and Yang Liu and Xinle Liang",
title = "{FedCVT}: Semi-supervised Vertical Federated Learning
with Cross-view Training",
journal = j-TIST,
volume = "13",
number = "4",
pages = "64:1--64:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510031",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510031",
abstract = "Federated learning allows multiple parties to build
machine learning models collaboratively without
exposing data. In particular, vertical federated
learning (VFL) enables participating parties to build a
joint machine learning model based upon \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ren:2022:GGR,
author = "Hanchi Ren and Jingjing Deng and Xianghua Xie",
title = "{GRNN}: Generative Regression Neural Network --- a
Data Leakage Attack for Federated Learning",
journal = j-TIST,
volume = "13",
number = "4",
pages = "65:1--65:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510032",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510032",
abstract = "Data privacy has become an increasingly important
issue in Machine Learning (ML), where many approaches
have been developed to tackle this challenge, e.g.,
cryptography (Homomorphic Encryption (HE), Differential
Privacy (DP)) and collaborative training \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tian:2022:FWF,
author = "Yuanyishu Tian and Yao Wan and Lingjuan Lyu and
Dezhong Yao and Hai Jin and Lichao Sun",
title = "{FedBERT}: When Federated Learning Meets
Pre-training",
journal = j-TIST,
volume = "13",
number = "4",
pages = "66:1--66:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510033",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510033",
abstract = "The fast growth of pre-trained models (PTMs) has
brought natural language processing to a new era, which
has become a dominant technique for various natural
language processing (NLP) applications. Every user can
download the weights of PTMs, then fine-. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mao:2022:CEF,
author = "Yuzhu Mao and Zihao Zhao and Guangfeng Yan and Yang
Liu and Tian Lan and Linqi Song and Wenbo Ding",
title = "Communication-Efficient Federated Learning with
Adaptive Quantization",
journal = j-TIST,
volume = "13",
number = "4",
pages = "67:1--67:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3510587",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3510587",
abstract = "Federated learning (FL) has attracted tremendous
attentions in recent years due to its
privacy-preserving measures and great potential in some
distributed but privacy-sensitive applications, such as
finance and health. However, high communication
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Guo:2022:FLP,
author = "Xu Guo and Han Yu and Boyang Li and Hao Wang and
Pengwei Xing and Siwei Feng and Zaiqing Nie and Chunyan
Miao",
title = "Federated Learning for Personalized Humor
Recognition",
journal = j-TIST,
volume = "13",
number = "4",
pages = "68:1--68:??",
month = aug,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3511710",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:17 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3511710",
abstract = "Computational understanding of humor is an important
topic under creative language understanding and
modeling. It can play a key role in complex human-AI
interactions. The challenge here is that human
perception of humorous content is highly subjective.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2022:PFL,
author = "Qiang Yang and Yongxin Tong and Yang Liu and Yangqiu
Song and Hao Peng and Boi Faltings",
title = "Preface to Federated Learning: Algorithms, Systems,
and Applications: {Part 2}",
journal = j-TIST,
volume = "13",
number = "5",
pages = "69:1--69:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3536420",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3536420",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2022:PPA,
author = "Xiaolong Xu and Wentao Liu and Yulan Zhang and Xuyun
Zhang and Wanchun Dou and Lianyong Qi and Md Zakirul
Alam Bhuiyan",
title = "{PSDF}: Privacy-aware {IoV} Service Deployment with
Federated Learning in Cloud-Edge Computing",
journal = j-TIST,
volume = "13",
number = "5",
pages = "70:1--70:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501810",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501810",
abstract = "Through the collaboration of cloud and edge,
cloud-edge computing allows the edge that approximates
end-users undertakes those non-computationally
intensive service processing of the cloud, reducing the
communication overhead and satisfying the low
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhong:2022:FHF,
author = "Zhengyi Zhong and Weidong Bao and Ji Wang and Xiaomin
Zhu and Xiongtao Zhang",
title = "{FLEE}: a Hierarchical Federated Learning Framework
for Distributed Deep Neural Network over Cloud, Edge,
and End Device",
journal = j-TIST,
volume = "13",
number = "5",
pages = "71:1--71:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3514501",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3514501",
abstract = "With the development of smart devices, the computing
capabilities of portable end devices such as mobile
phones have been greatly enhanced. Meanwhile,
traditional cloud computing faces great challenges
caused by privacy-leakage and time-delay problems,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dang:2022:FLE,
author = "Trung Kien Dang and Xiang Lan and Jianshu Weng and
Mengling Feng",
title = "Federated Learning for Electronic Health Records",
journal = j-TIST,
volume = "13",
number = "5",
pages = "72:1--72:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3514500",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3514500",
abstract = "In data-driven medical research, multi-center studies
have long been preferred over single-center ones due to
a single institute sometimes not having enough data to
obtain sufficient statistical power for certain
hypothesis testings as well as predictive \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2022:AWR,
author = "Shenghui Li and Edith Ngai and Fanghua Ye and Thiemo
Voigt",
title = "Auto-weighted Robust Federated Learning with Corrupted
Data Sources",
journal = j-TIST,
volume = "13",
number = "5",
pages = "73:1--73:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3517821",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3517821",
abstract = "Federated learning provides a communication-efficient
and privacy-preserving training process by enabling
learning statistical models with massive participants
without accessing their local data. Standard federated
learning techniques that naively \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2022:SFL,
author = "Xue Jiang and Xuebing Zhou and Jens Grossklags",
title = "{SignDS-FL}: Local Differentially Private Federated
Learning with Sign-based Dimension Selection",
journal = j-TIST,
volume = "13",
number = "5",
pages = "74:1--74:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3517820",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3517820",
abstract = "Federated Learning (FL) [ 31 ] is a decentralized
learning mechanism that has attracted increasing
attention due to its achievements in computational
efficiency and privacy preservation. However, recent
research highlights that the original FL framework may
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zeng:2022:CCB,
author = "Bixiao Zeng and Xiaodong Yang and Yiqiang Chen and
Hanchao Yu and Yingwei Zhang",
title = "{CLC}: a Consensus-based Label Correction Approach in
Federated Learning",
journal = j-TIST,
volume = "13",
number = "5",
pages = "75:1--75:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3519311",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3519311",
abstract = "Federated learning (FL) is a novel distributed
learning framework where multiple participants
collaboratively train a global model without sharing
any raw data to preserve privacy. However, data quality
may vary among the participants, the most typical of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "75",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2022:DAP,
author = "Chien-Lun Chen and Sara Babakniya and Marco Paolieri
and Leana Golubchik",
title = "Defending against Poisoning Backdoor Attacks on
Federated Meta-learning",
journal = j-TIST,
volume = "13",
number = "5",
pages = "76:1--76:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3523062",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3523062",
abstract = "Federated learning allows multiple users to
collaboratively train a shared classification model
while preserving data privacy. This approach, where
model updates are aggregated by a central server, was
shown to be vulnerable to poisoning backdoor attacks:
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "76",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xie:2022:ELF,
author = "Lunchen Xie and Jiaqi Liu and Songtao Lu and Tsung-Hui
Chang and Qingjiang Shi",
title = "An Efficient Learning Framework for Federated
{XGBoost} Using Secret Sharing and Distributed
Optimization",
journal = j-TIST,
volume = "13",
number = "5",
pages = "77:1--77:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3523061",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3523061",
abstract = "XGBoost is one of the most widely used machine
learning models in the industry due to its superior
learning accuracy and efficiency. Targeting at data
isolation issues in the big data problems, it is
crucial to deploy a secure and efficient federated
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "77",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Stripelis:2022:SSF,
author = "Dimitris Stripelis and Paul M. Thompson and Jos{\'e}
Luis Ambite",
title = "Semi-Synchronous Federated Learning for
Energy-Efficient Training and Accelerated Convergence
in Cross-Silo Settings",
journal = j-TIST,
volume = "13",
number = "5",
pages = "78:1--78:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3524885",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3524885",
abstract = "There are situations where data relevant to machine
learning problems are distributed across multiple
locations that cannot share the data due to regulatory,
competitiveness, or privacy reasons. Machine learning
approaches that require data to be copied \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "78",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Damaskinos:2022:FOF,
author = "Georgios Damaskinos and Rachid Guerraoui and
Anne-Marie Kermarrec and Vlad Nitu and Rhicheek Patra
and Fran{\c{c}}ois Taiani",
title = "{FLeet}: Online Federated Learning via Staleness
Awareness and Performance Prediction",
journal = j-TIST,
volume = "13",
number = "5",
pages = "79:1--79:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3527621",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3527621",
abstract = "Federated learning (FL) is very appealing for its
privacy benefits: essentially, a global model is
trained with updates computed on mobile devices while
keeping the data of users local. Standard FL
infrastructures are however designed to have no energy
or \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "79",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2022:FMT,
author = "Yijing Liu and Dongming Han and Jianwei Zhang and
Haiyang Zhu and Mingliang Xu and Wei Chen",
title = "Federated Multi-task Graph Learning",
journal = j-TIST,
volume = "13",
number = "5",
pages = "80:1--80:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3527622",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3527622",
abstract = "Distributed processing and analysis of large-scale
graph data remain challenging because of the high-level
discrepancy among graphs. This study investigates a
novel subproblem: the distributed multi-task learning
on the graph, which jointly learns \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "80",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2022:CFP,
author = "Fuxian Li and Jie Feng and Huan Yan and Depeng Jin and
Yong Li",
title = "Crowd Flow Prediction for Irregular Regions with
Semantic Graph Attention Network",
journal = j-TIST,
volume = "13",
number = "5",
pages = "81:1--81:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3501805",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3501805",
abstract = "It is essential to predict crowd flow precisely in a
city, which is practically partitioned into irregular
regions based on road networks and functionality.
However, prior works mainly focus on grid-based crowd
flow prediction, where a city is divided \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "81",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2022:GBS,
author = "Zongwei Wang and Min Gao and Jundong Li and Junwei
Zhang and Jiang Zhong",
title = "Gray-Box Shilling Attack: an Adversarial Learning
Approach",
journal = j-TIST,
volume = "13",
number = "5",
pages = "82:1--82:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3512352",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3512352",
abstract = "Recommender systems are essential components of many
information services, which aim to find relevant items
that match user preferences. Several studies have shown
that shilling attacks can significantly weaken the
robustness of recommender systems by \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "82",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Di:2022:FSR,
author = "Kai Di and Yifeng Zhou and Fuhan Yan and Jiuchuan
Jiang and Shaofu Yang and Yichuan Jiang",
title = "A Foraging Strategy with Risk Response for Individual
Robots in Adversarial Environments",
journal = j-TIST,
volume = "13",
number = "5",
pages = "83:1--83:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3514499",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3514499",
abstract = "As an essential problem in robotics, foraging means
that robots collect objects from a given environment
and return them to a specified location. On many
occasions, robots are required to perform foraging
tasks in adversarial environments, such as \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "83",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Rai:2022:RSC,
author = "Sawan Rai and Ramesh Chandra Belwal and Atul Gupta",
title = "A Review on Source Code Documentation",
journal = j-TIST,
volume = "13",
number = "5",
pages = "84:1--84:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3519312",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3519312",
abstract = "Context: Coding is an incremental activity where a
developer may need to understand a code before making
suitable changes in the code. Code documentation is
considered one of the best practices in software
development but requires significant efforts from
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "84",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2022:IIN,
author = "Xiaoyu Chen and Yingyan Zeng and Sungku Kang and Ran
Jin",
title = "{INN}: an Interpretable Neural Network for {AI}
Incubation in Manufacturing",
journal = j-TIST,
volume = "13",
number = "5",
pages = "85:1--85:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3519313",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3519313",
abstract = "Both artificial intelligence (AI) and domain knowledge
from human experts play an important role in
manufacturing decision making. Smart manufacturing
emphasizes a fully automated data-driven
decision-making; however, the AI incubation process
involves \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "85",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2022:DPG,
author = "Ke Li and Bin Guo and Jiaqi Liu and Jiangtao Wang and
Haoyang Ren and Fei Yi and Zhiwen Yu",
title = "Dynamic Probabilistic Graphical Model for Progressive
Fake News Detection on Social Media Platform",
journal = j-TIST,
volume = "13",
number = "5",
pages = "86:1--86:??",
month = oct,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3523060",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Oct 29 07:22:19 MDT 2022",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3523060",
abstract = "Recently, fake news has been readily spread by massive
amounts of users in social media, and automatic fake
news detection has become necessary. The existing works
need to prepare the overall data to perform detection,
losing important information about \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "86",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Verma:2022:IDB,
author = "Rohit Verma and Sugandh Pargal and Debasree Das and
Tanusree Parbat and Sai Shankar Kambalapalli and Bivas
Mitra and Sandip Chakraborty",
title = "Impact of Driving Behavior on {Commuter}'s Comfort
During Cab Rides: Towards a New Perspective of Driver
Rating",
journal = j-TIST,
volume = "13",
number = "6",
pages = "87:1--87:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3523063",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3523063",
abstract = "Commuter comfort in cab rides affects driver rating as
well as the reputation of ride-hailing firms like
Uber/Lyft. Existing research has revealed that commuter
comfort not only varies at a personalized level but
also is perceived differently on different \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "87",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2022:IPI,
author = "Lin Zhang and Lixin Fan and Yong Luo and Ling-Yu
Duan",
title = "Intrinsic Performance Influence-based Participant
Contribution Estimation for Horizontal Federated
Learning",
journal = j-TIST,
volume = "13",
number = "6",
pages = "88:1--88:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3523059",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3523059",
abstract = "The rapid development of modern artificial
intelligence technique is mainly attributed to
sufficient and high-quality data. However, in the data
collection, personal privacy is at risk of being
leaked. This issue can be addressed by federated
learning, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "88",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ren:2022:DHC,
author = "Siyuan Ren and Bin Guo and Longbing Cao and Ke Li and
Jiaqi Liu and Zhiwen Yu",
title = "{DeepExpress}: Heterogeneous and Coupled Sequence
Modeling for Express Delivery Prediction",
journal = j-TIST,
volume = "13",
number = "6",
pages = "89:1--89:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3526087",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3526087",
abstract = "The prediction of express delivery sequence, i.e.,
modeling and estimating the volumes of daily incoming
and outgoing parcels for delivery, is critical for
online business, logistics, and positive customer
experience, and specifically for resource \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "89",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zheng:2022:JOE,
author = "Qian Zheng and Yueming Wang and Zhenfang Hu and Xiaobo
Zhang and Zhaohui Wu and Gang Pan",
title = "Jointly Optimizing Expressional and Residual Models
for {$3$D} Facial Expression Removal",
journal = j-TIST,
volume = "13",
number = "6",
pages = "90:1--90:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3533312",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3533312",
abstract = "This article proposes a facial expression removal
method to recover a 3D neutral face from a single 3D
expressional or non-neutral face. We treat a 3D
non-neutral face as the sum of its neutral one and the
residual. This can be satisfied if the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "90",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hammedi:2022:TLO,
author = "Wided Hammedi and Sidi Mohammed Senouci and Philippe
Brunet and Metzli Ramirez-Martinez",
title = "Two-Level Optimization to Reduce Waiting Time at Locks
in Inland Waterway Transportation",
journal = j-TIST,
volume = "13",
number = "6",
pages = "91:1--91:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3527822",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3527822",
abstract = "Inland vessels often have to cross numerous locks
before reaching their final destination, which leads to
a significant delay and sometimes represents as much as
half of the total travel time. The delay affects
shipment costs and can affect other parts of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "91",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Elhamod:2022:CPL,
author = "Mohannad Elhamod and Jie Bu and Christopher Singh and
Matthew Redell and Abantika Ghosh and Viktor Podolskiy
and Wei-Cheng Lee and Anuj Karpatne",
title = "{CoPhy-PGNN}: Learning Physics-guided Neural Networks
with Competing Loss Functions for Solving Eigenvalue
Problems",
journal = j-TIST,
volume = "13",
number = "6",
pages = "92:1--92:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3530911",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3530911",
abstract = "Physics-guided Neural Networks (PGNNs) represent an
emerging class of neural networks that are trained
using physics-guided (PG) loss functions (capturing
violations in network outputs with known physics),
along with the supervision contained in data.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "92",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ding:2022:MME,
author = "Yasan Ding and Bin Guo and Yan Liu and Yunji Liang and
Haocheng Shen and Zhiwen Yu",
title = "{MetaDetector}: Meta Event Knowledge Transfer for Fake
News Detection",
journal = j-TIST,
volume = "13",
number = "6",
pages = "93:1--93:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3532851",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3532851",
abstract = "The blooming of fake news on social networks has
devastating impacts on society, the economy, and public
security. Although numerous studies are conducted for
the automatic detection of fake news, the majority tend
to utilize deep neural networks to learn \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "93",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2022:CST,
author = "Yao Zhang and Wenping Fan and Qichen Hao and Xinya Wu
and Min-Ling Zhang",
title = "{CAFE} and {SOUP}: Toward Adaptive {VDI} Workload
Prediction",
journal = j-TIST,
volume = "13",
number = "6",
pages = "94:1--94:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3529536",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3529536",
abstract = "For Virtual Desktop Infrastructure (VDI) system,
effective resource management is rather important where
turning off spare virtual machines would help save
running cost while maintaining sufficient virtual
machines is essential to secure satisfactory user
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "94",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dong:2022:HAR,
author = "Junyi Dong and Qingze Huo and Silvia Ferrari",
title = "A Holistic Approach for Role Inference and Action
Anticipation in Human Teams",
journal = j-TIST,
volume = "13",
number = "6",
pages = "95:1--95:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3531230",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3531230",
abstract = "The ability to anticipate human actions is critical to
many cyber-physical systems, such as robots and
autonomous vehicles. Computer vision and sensing
algorithms to date have focused on extracting and
predicting visual features that are explicit in the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "95",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hografer:2022:SEP,
author = "Marius Hogr{\"a}fer and Marco Angelini and Giuseppe
Santucci and Hans-J{\"o}rg Schulz",
title = "Steering-by-example for Progressive Visual Analytics",
journal = j-TIST,
volume = "13",
number = "6",
pages = "96:1--96:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3531229",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3531229",
abstract = "Progressive visual analytics allows users to interact
with early, partial results of long-running
computations on large datasets. In this context,
computational steering is often brought up as a means
to prioritize the progressive computation. This is
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "96",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2022:RLV,
author = "Xian Wu and Chao Huang and Pablo Robles-Granda and
Nitesh V. Chawla",
title = "Representation Learning on Variable Length and
Incomplete Wearable-Sensory Time Series",
journal = j-TIST,
volume = "13",
number = "6",
pages = "97:1--97:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3531228",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3531228",
abstract = "The prevalence of wearable sensors (e.g., smart
wristband) is creating unprecedented opportunities to
not only inform health and wellness states of
individuals, but also assess and infer personal
attributes, including demographic and personality
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "97",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ceh-Varela:2022:PEA,
author = "Edgar Ceh-Varela and Huiping Cao and Hady W. Lauw",
title = "Performance Evaluation of Aggregation-based Group
Recommender Systems for Ephemeral Groups",
journal = j-TIST,
volume = "13",
number = "6",
pages = "98:1--98:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3542804",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3542804",
abstract = "Recommender Systems ( RecSys ) provide suggestions in
many decision-making processes. Given that groups of
people can perform many real-world activities (e.g., a
group of people attending a conference looking for a
place to dine), the need for \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "98",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Das:2022:CSF,
author = "Anirban Das and Timothy Castiglia and Shiqiang Wang
and Stacy Patterson",
title = "Cross-Silo Federated Learning for Multi-Tier Networks
with Vertical and Horizontal Data Partitioning",
journal = j-TIST,
volume = "13",
number = "6",
pages = "99:1--99:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3543433",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3543433",
abstract = "We consider federated learning in tiered communication
networks. Our network model consists of a set of silos,
each holding a vertical partition of the data. Each
silo contains a hub and a set of clients, with the
silo's vertical data shard partitioned \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "99",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Navia-Vazquez:2022:BDS,
author = "A. Navia-V{\'a}zquez and R. D{\'\i}az-Morales and M.
Fern{\'a}ndez-D{\'\i}az",
title = "Budget Distributed Support Vector Machine for Non-{ID}
Federated Learning Scenarios",
journal = j-TIST,
volume = "13",
number = "6",
pages = "100:1--100:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3539734",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3539734",
abstract = "In recent years, there has been remarkable growth in
Federated Learning (FL) approaches because they have
proven to be very effective in training large Machine
Learning (ML) models and also serve to preserve data
confidentiality, as recommended by the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "100",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hu:2022:DET,
author = "Yue Hu and Ao Qu and Dan Work",
title = "Detecting Extreme Traffic Events Via a Context
Augmented Graph Autoencoder",
journal = j-TIST,
volume = "13",
number = "6",
pages = "101:1--101:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3539735",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3539735",
abstract = "Accurate and timely detection of large events on urban
transportation networks enables informed mobility
management. This work tackles the problem of extreme
event detection on large-scale transportation networks
using origin-destination mobility data, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "101",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tassa:2022:PPC,
author = "Tamir Tassa and Alon {Ben Horin}",
title = "Privacy-preserving Collaborative Filtering by
Distributed Mediation",
journal = j-TIST,
volume = "13",
number = "6",
pages = "102:1--102:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3542950",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3542950",
abstract = "Recommender systems have become very influential in
our everyday decision making, e.g., helping us choose a
movie from a content platform, or offering us suitable
products on e-commerce websites. While most vendors who
utilize recommender systems rely \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "102",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gupta:2022:MCT,
author = "Vinayak Gupta and Srikanta Bedathur and Sourangshu
Bhattacharya and Abir De",
title = "Modeling Continuous Time Sequences with Intermittent
Observations using Marked Temporal Point Processes",
journal = j-TIST,
volume = "13",
number = "6",
pages = "103:1--103:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3545118",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3545118",
abstract = "A large fraction of data generated via human
activities such as online purchases, health records,
spatial mobility, etc. can be represented as a sequence
of events over a continuous-time. Learning deep
learning models over these continuous-time event
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "103",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ou:2022:AAE,
author = "Jinxiang Ou and Yunheng Shen and Feng Wang and Qiao
Liu and Xuegong Zhang and Hairong Lv",
title = "{AggEnhance}: Aggregation Enhancement by Class
Interior Points in Federated Learning with Non-{IID}
Data",
journal = j-TIST,
volume = "13",
number = "6",
pages = "104:1--104:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3544495",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3544495",
abstract = "Federated learning (FL) is a privacy-preserving
paradigm for multi-institutional collaborations, where
the aggregation is an essential procedure after
training on the local datasets. Conventional
aggregation algorithms often apply a weighted averaging
of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "104",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Costa:2022:SCN,
author = "Miguel Costa and Diogo Costa and Tiago Gomes and
Sandro Pinto",
title = "Shifting Capsule Networks from the Cloud to the Deep
Edge",
journal = j-TIST,
volume = "13",
number = "6",
pages = "105:1--105:??",
month = dec,
year = "2022",
CODEN = "????",
DOI = "https://doi.org/10.1145/3544562",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:22 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3544562",
abstract = "Capsule networks (CapsNets) are an emerging trend in
image processing. In contrast to a convolutional neural
network, CapsNets are not vulnerable to object
deformation, as the relative spatial information of the
objects is preserved across the network. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "105",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2023:NFL,
author = "Xiaojin Zhang and Hanlin Gu and Lixin Fan and Kai Chen
and Qiang Yang",
title = "No Free Lunch Theorem for Security and Utility in
Federated Learning",
journal = j-TIST,
volume = "14",
number = "1",
pages = "1:1--1:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3563219",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3563219",
abstract = "In a federated learning scenario where multiple
parties jointly learn a model from their respective
data, there exist two conflicting goals for the choice
of appropriate algorithms. On one hand, private and
sensitive training data must be kept secure as
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shao:2023:IIT,
author = "Erzhuo Shao and Zhenyu Han and Yulai Xie and Yang
Zhang and Lu Geng and Yong Li",
title = "Interior Individual Trajectory Simulation with
Population Distribution Constraint",
journal = j-TIST,
volume = "14",
number = "1",
pages = "2:1--2:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3529108",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3529108",
abstract = "Individual trajectory generation plays an important
role in simulation tasks, reconstructing fine-grained
mobility behaviors that can be used to evaluate
epidemic risks, congestion risks, or commercial profit.
Previous research works adopt the Newton's \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Danzinger:2023:SAI,
author = "Philipp Danzinger and Tobias Geibinger and David
Janneau and Florian Mischek and Nysret Musliu and
Christian Poschalko",
title = "A System for Automated Industrial Test Laboratory
Scheduling",
journal = j-TIST,
volume = "14",
number = "1",
pages = "3:1--3:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3546871",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3546871",
abstract = "Automated scheduling solutions are tremendously
important for the efficient operation of industrial
laboratories. The Test Laboratory Scheduling Problem
(TLSP) is an extension of the well-known Resource
Constrained Project Scheduling Problem (RCPSP) and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2023:TAC,
author = "Haochen Liu and Yiqi Wang and Wenqi Fan and Xiaorui
Liu and Yaxin Li and Shaili Jain and Yunhao Liu and
Anil Jain and Jiliang Tang",
title = "Trustworthy {AI}: a Computational Perspective",
journal = j-TIST,
volume = "14",
number = "1",
pages = "4:1--4:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3546872",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3546872",
abstract = "In the past few decades, artificial intelligence (AI)
technology has experienced swift developments, changing
everyone's daily life and profoundly altering the
course of human society. The intention behind
developing AI was and is to benefit humans by
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:ALC,
author = "Pan Li and Brian Brost and Alexander Tuzhilin",
title = "Adversarial Learning for Cross Domain
Recommendations",
journal = j-TIST,
volume = "14",
number = "1",
pages = "5:1--5:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3548776",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3548776",
abstract = "Existing cross domain recommender systems typically
assume homogeneous user preferences across multiple
domains to capture similarities of user-item
interactions and to provide cross domain
recommendations accordingly. Meanwhile, the
heterogeneity of user \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2023:IDM,
author = "Shaohan Chen and Chuanhou Gao and Ping Zhang",
title = "Incorporation of Data-Mined Knowledge into Black-Box
{SVM} for Interpretability",
journal = j-TIST,
volume = "14",
number = "1",
pages = "6:1--6:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3548775",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3548775",
abstract = "The lack of interpretability often makes black-box
models challenging to be applied in many practical
domains. For this reason, the current work, from the
black-box model input port, proposes to incorporate
data-mined knowledge into the black-box soft-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2023:HSI,
author = "Wei-Yao Wang and Teng-Fong Chan and Wen-Chih Peng and
Hui-Kuo Yang and Chih-Chuan Wang and Yao-Chung Fan",
title = "How Is the Stroke? {Inferring} Shot Influence in
Badminton Matches via Long Short-term Dependencies",
journal = j-TIST,
volume = "14",
number = "1",
pages = "7:1--7:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3551391",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3551391",
abstract = "Identifying significant shots in a rally is important
for evaluating players' performance in badminton
matches. While there are several studies that have
quantified player performance in other sports,
analyzing badminton data has remained untouched. In
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hao:2023:HMA,
author = "Qianyue Hao and Fengli Xu and Lin Chen and Pan Hui and
Yong Li",
title = "Hierarchical Multi-agent Model for Reinforced Medical
Resource Allocation with Imperfect Information",
journal = j-TIST,
volume = "14",
number = "1",
pages = "8:1--8:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3552436",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3552436",
abstract = "With the advent of the COVID-19 pandemic, the shortage
in medical resources became increasingly more evident.
Therefore, efficient strategies for medical resource
allocation are urgently needed. However, conventional
rule-based methods employed by public \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Qin:2023:DGA,
author = "Xin Qin and Jindong Wang and Yiqiang Chen and Wang Lu
and Xinlong Jiang",
title = "Domain Generalization for Activity Recognition via
Adaptive Feature Fusion",
journal = j-TIST,
volume = "14",
number = "1",
pages = "9:1--9:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3552434",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3552434",
abstract = "Human activity recognition requires the efforts to
build a generalizable model using the training datasets
with the hope to achieve good performance in test
datasets. However, in real applications, the training
and testing datasets may have totally \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Freitas:2023:DLE,
author = "Lucas Freitas and Valter Martins and Marilton de
Aguiar and Lisane de Brisolara and Paulo Ferreira",
title = "Deep Learning Embedded into Smart Traps for Fruit
Insect Pests Detection",
journal = j-TIST,
volume = "14",
number = "1",
pages = "10:1--10:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3552435",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3552435",
abstract = "This article presents a novel approach to identify two
species of fruit insect pests as part of a network of
intelligent traps designed to monitor the population of
these insects in a plantation. The proposed approach
uses a simple Digital Image \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2023:SSD,
author = "Wenlu Yang and Hongjun Wang and Yinghui Zhang and
Zehao Liu and Tianrui Li",
title = "Self-supervised Discriminative Representation Learning
by Fuzzy Autoencoder",
journal = j-TIST,
volume = "14",
number = "1",
pages = "11:1--11:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3555777",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3555777",
abstract = "Representation learning based on autoencoders has
received great concern for its potential ability to
capture valuable latent information. Conventional
autoencoders pursue minimal reconstruction error, but
in most machine learning tasks such as \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2023:QOR,
author = "Jianqiu Xu and Hua Lu and Zhifeng Bao",
title = "A Query Optimizer for Range Queries over
Multi-Attribute Trajectories",
journal = j-TIST,
volume = "14",
number = "1",
pages = "12:1--12:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3555811",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3555811",
abstract = "A multi-attribute trajectory consists of a
spatio-temporal trajectory and a set of descriptive
attributes. Such data enrich the representation of
traditional spatio-temporal trajectories to have
comprehensive knowledge of moving objects. Range query
is a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2023:DOL,
author = "Wendi Wu and Zongren Li and Yawei Zhao and Chen Yu and
Peilin Zhao and Ji Liu and Kunlun He",
title = "Decentralized Online Learning: Take Benefits from
Others' Data without Sharing Your Own to Track Global
Trend",
journal = j-TIST,
volume = "14",
number = "1",
pages = "13:1--13:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3559765",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3559765",
abstract = "Decentralized online learning (online learning in
decentralized networks) has been attracting more and
more attention, since it is believed that decentralized
online learning can help data providers cooperatively
better solve their online problems without \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{He:2023:FFA,
author = "Mingkai He and Jing Lin and Jinwei Luo and Weike Pan
and Zhong Ming",
title = "{FLAG}: a Feedback-aware Local and Global Model for
Heterogeneous Sequential Recommendation",
journal = j-TIST,
volume = "14",
number = "1",
pages = "14:1--14:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3557046",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3557046",
abstract = "Heterogeneous sequential recommendation that models
sequences of items associated with more than one type
of feedback such as examinations and purchases is an
emerging topic in the research community, which is also
an important problem in many real-world \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lyu:2023:RLL,
author = "Gengyu Lyu and Songhe Feng and Wei Liu and Shuoyan Liu
and Congyan Lang",
title = "Redundant Label Learning via Subspace Representation
and Global Disambiguation",
journal = j-TIST,
volume = "14",
number = "1",
pages = "15:1--15:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3558547",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3558547",
abstract = "Redundant Label Learning (RLL) aims at inducing a
robust model from training data, where each example is
associated with a set of candidate labels, among which
some of them are incorrect. Most existing approaches
deal with such problem by disambiguating \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Barros:2023:NSS,
author = "Pedro Barros and Fabiane Queiroz and Fl{\'a}vio
Figueiredo and Jefersson A. {Dos Santos} and Heitor
Ramos",
title = "A New Similarity Space Tailored for Supervised Deep
Metric Learning",
journal = j-TIST,
volume = "14",
number = "1",
pages = "16:1--16:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3559766",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3559766",
abstract = "We propose a novel deep metric learning method.
Differently from many works in this area, we define a
novel latent space obtained through an autoencoder. The
new space, namely S-space, is divided into different
regions describing positions where pairs of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sun:2023:MBR,
author = "Wei Sun and Shaoxiong Ji and Erik Cambria and Pekka
Marttinen",
title = "Multitask Balanced and Recalibrated Network for
Medical Code Prediction",
journal = j-TIST,
volume = "14",
number = "1",
pages = "17:1--17:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3563041",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3563041",
abstract = "Human coders assign standardized medical codes to
clinical documents generated during patients'
hospitalization, which is error prone and labor
intensive. Automated medical coding approaches have
been developed using machine learning methods, such as
deep \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shi:2023:MIL,
author = "Lei Shi and Yuankai Luo and Shuai Ma and Hanghang Tong
and Zhetao Li and Xiatian Zhang and Zhiguang Shan",
title = "Mobility Inference on Long-Tailed Sparse Trajectory",
journal = j-TIST,
volume = "14",
number = "1",
pages = "18:1--18:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3563457",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3563457",
abstract = "Analyzing the urban trajectory in cities has become an
important topic in data mining. How can we model the
human mobility consisting of stay and travel states
from the raw trajectory data? How can we infer these
mobility states from a single user's \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Leiva:2023:DUS,
author = "Luis A. Leiva and Asutosh Hota and Antti Oulasvirta",
title = "Describing {UI} Screenshots in Natural Language",
journal = j-TIST,
volume = "14",
number = "1",
pages = "19:1--19:??",
month = feb,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3564702",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Mar 11 08:47:24 MST 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3564702",
abstract = "Being able to describe any user interface (UI)
screenshot in natural language can promote
understanding of the main purpose of the UI, yet
currently it cannot be accomplished with
state-of-the-art captioning systems. We introduce XUI,
a novel method \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:SDH,
author = "Jia Li and Dandan Song and Zhijing Wu",
title = "A Semantically Driven Hybrid Network for Unsupervised
Entity Alignment",
journal = j-TIST,
volume = "14",
number = "2",
pages = "20:1--20:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3567829",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3567829",
abstract = "The major challenge in the task of entity alignment
(EA) lies in the heterogeneity of the knowledge graph.
The traditional solution to EA is to first map entities
to the same space via knowledge embedding and then
calculate the similarity between entities \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:RBE,
author = "Lei Li and Yongfeng Zhang and Li Chen",
title = "On the Relationship between Explanation and
Recommendation: Learning to Rank Explanations for
Improved Performance",
journal = j-TIST,
volume = "14",
number = "2",
pages = "21:1--21:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3569423",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3569423",
abstract = "Explaining to users why some items are recommended is
critical, as it can help users to make better
decisions, increase their satisfaction, and gain their
trust in recommender systems (RS). However, existing
explainable RS usually consider explanation as
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2023:SCL,
author = "Yanzhao Wu and Ling Liu",
title = "Selecting and Composing Learning Rate Policies for
Deep Neural Networks",
journal = j-TIST,
volume = "14",
number = "2",
pages = "22:1--22:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3570508",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3570508",
abstract = "The choice of learning rate (LR) functions and
policies has evolved from a simple fixed LR to the
decaying LR and the cyclic LR, aiming to improve the
accuracy and reduce the training time of Deep Neural
Networks (DNNs). This article presents a systematic
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gupta:2023:NTM,
author = "Amulya Gupta and Zhu Zhang",
title = "Neural Topic Modeling via Discrete Variational
Inference",
journal = j-TIST,
volume = "14",
number = "2",
pages = "23:1--23:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3570509",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3570509",
abstract = "Topic models extract commonly occurring latent topics
from textual data. Statistical models such as Latent
Dirichlet Allocation do not produce dense topic
embeddings readily integratable into neural
architectures, whereas earlier neural topic models are
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shen:2023:CST,
author = "Ziyu Shen and Binghui Liu and Qing Zhou and Zheng Liu
and Bin Xia and Yun Li",
title = "Cost-sensitive Tensor-based Dual-stage Attention
{LSTM} with Feature Selection for Data Center Server
Power Forecasting",
journal = j-TIST,
volume = "14",
number = "2",
pages = "24:1--24:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3569422",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3569422",
abstract = "Power forecasting has a guiding effect on power-aware
scheduling strategies to reduce unnecessary power
consumption in data centers. Many metrics related to
power consumption can be collected in physical servers,
such as the status of CPU, memory, and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lyu:2023:PKC,
author = "Gengyu Lyu and Songhe Feng and Shaokai Wang and Zhen
Yang",
title = "Prior Knowledge Constrained Adaptive Graph Framework
for Partial Label Learning",
journal = j-TIST,
volume = "14",
number = "2",
pages = "25:1--25:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3569421",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3569421",
abstract = "Partial label learning (PLL) aims to learn a robust
multi-class classifier from the ambiguous data, where
each instance is given with several candidate labels,
among which only one label is real. Most existing
methods usually cope with such problem by \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sarkar:2023:AHM,
author = "Souvika Sarkar and Biddut Sarker Bijoy and Syeda
Jannatus Saba and Dongji Feng and Yash Mahajan and
Mohammad Ruhul Amin and Sheikh Rabiul Islam and Shubhra
Kanti Karmaker (``Santu'')",
title = "Ad-Hoc Monitoring of {COVID-19} Global Research Trends
for Well-Informed Policy Making",
journal = j-TIST,
volume = "14",
number = "2",
pages = "26:1--26:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3576901",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3576901",
abstract = "The COVID-19 pandemic has affected millions of people
worldwide with severe health, economic, social, and
political implications. Healthcare Policy Makers (HPMs)
and medical experts are at the core of responding to
this continuously evolving pandemic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2023:OOS,
author = "Donglin Zhang and Xiao-Jun Wu and Guoqing Chen",
title = "{ONION}: Online Semantic Autoencoder Hashing for
Cross-Modal Retrieval",
journal = j-TIST,
volume = "14",
number = "2",
pages = "27:1--27:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3572032",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3572032",
abstract = "Cross-modal hashing (CMH) has recently received
increasing attention with the merit of speed and
storage in performing large-scale cross-media
similarity search. However, most existing cross-media
approaches utilize the batch-based mode to update hash
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ren:2023:GLA,
author = "Jing Ren and Feng Xia and Ivan Lee and Azadeh Noori
Hoshyar and Charu Aggarwal",
title = "Graph Learning for Anomaly Analytics: Algorithms,
Applications, and Challenges",
journal = j-TIST,
volume = "14",
number = "2",
pages = "28:1--28:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3570906",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3570906",
abstract = "Anomaly analytics is a popular and vital task in
various research contexts that has been studied for
several decades. At the same time, deep learning has
shown its capacity in solving many graph-based tasks,
like node classification, link prediction, and
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Kang:2023:HET,
author = "Jian Kang and Dan Lin",
title = "Highly Efficient Traffic Planning for Autonomous
Vehicles to Cross Intersections Without a Stop",
journal = j-TIST,
volume = "14",
number = "2",
pages = "29:1--29:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3572034",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3572034",
abstract = "Waiting in a long queue at traffic lights not only
wastes valuable time but also pollutes the environment.
With the advances in autonomous vehicles and 5G
networks, the previous jamming scenarios at
intersections may be turned into non-stop weaving
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tian:2023:SFU,
author = "Qing Tian and Shun Peng and Tinghuai Ma",
title = "Source-free Unsupervised Domain Adaptation with
Trusted Pseudo Samples",
journal = j-TIST,
volume = "14",
number = "2",
pages = "30:1--30:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3570510",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3570510",
abstract = "Source-free unsupervised domain adaptation (SFUDA)
aims to accomplish the task of adaptation to the target
domain by utilizing pre-trained source domain model and
unlabeled target domain samples, without directly
accessing any source domain data. Although \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2023:GFF,
author = "Hao Wu and Jianyang Gu and Xiaojin Fan and He Li and
Lidong Xie and Jian Zhao",
title = "{$3$D}-Guided Frontal Face Generation for
Pose-Invariant Recognition",
journal = j-TIST,
volume = "14",
number = "2",
pages = "31:1--31:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3572035",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3572035",
abstract = "Although deep learning techniques have achieved
extraordinary accuracy in recognizing human faces, the
pose variances of images captured in real-world
scenarios still hinder reliable model appliance. To
mitigate this gap, we propose to recognize faces via
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yao:2023:CCD,
author = "Jing Yao and Zheng Liu and Junhan Yang and Zhicheng
Dou and Xing Xie and Ji-Rong Wen",
title = "{CDSM}: Cascaded Deep Semantic Matching on Textual
Graphs Leveraging Ad-hoc Neighbor Selection",
journal = j-TIST,
volume = "14",
number = "2",
pages = "32:1--32:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3573204",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3573204",
abstract = "Deep semantic matching aims at discriminating the
relationship between documents based on deep neural
networks. In recent years, it becomes increasingly
popular to organize documents with a graph structure,
then leverage both the intrinsic document \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2023:DRL,
author = "Meng Xu and Jianping Wang",
title = "Deep Reinforcement Learning for Parameter Tuning of
Robot Visual Servoing",
journal = j-TIST,
volume = "14",
number = "2",
pages = "33:1--33:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579829",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579829",
abstract = "Robot visual servoing controls the motion of a robot
through real-time visual observations. Kinematics is a
key approach to achieving visual servoing. One key
challenge of kinematics-based visual servoing is that
it requires time-varying parameter \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tian:2023:DLD,
author = "Jieru Tian and Yongxin Wang and Zhenduo Chen and Xin
Luo and Xinshun Xu",
title = "Diagnose Like Doctors: Weakly Supervised Fine-Grained
Classification of Breast Cancer",
journal = j-TIST,
volume = "14",
number = "2",
pages = "34:1--34:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3572033",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3572033",
abstract = "Breast cancer is the most common type of cancers in
women. Therefore, how to accurately and timely diagnose
it becomes very important. Some computer-aided
diagnosis models based on pathological images have been
proposed for this task. However, there are \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2023:ESV,
author = "Guozhen Zhang and Jinhui Yi and Jian Yuan and Yong Li
and Depeng Jin",
title = "{DAS}: Efficient Street View Image Sampling for Urban
Prediction",
journal = j-TIST,
volume = "14",
number = "2",
pages = "35:1--35:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3576902",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3576902",
abstract = "Street view data is one of the most common data
sources for urban prediction tasks, such as estimating
socioeconomic status, sensing physical urban changes,
and identifying urban villages. Typical research in
this field consists of two steps: acquiring a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yu:2023:STG,
author = "Shuo Yu and Feng Xia and Shihao Li and Mingliang Hou
and Quan Z. Sheng",
title = "Spatio-temporal Graph Learning for Epidemic
Prediction",
journal = j-TIST,
volume = "14",
number = "2",
pages = "36:1--36:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579815",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579815",
abstract = "The COVID-19 pandemic has posed great challenges to
public health services, government agencies, and
policymakers, raising huge social conflicts between
public health and economic resilience. Policies such as
reopening or closure of business activities \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fang:2023:RAG,
author = "Yujie Fang and Xin Li and Rui Ye and Xiaoyan Tan and
Peiyao Zhao and Mingzhong Wang",
title = "Relation-aware Graph Convolutional Networks for
Multi-relational Network Alignment",
journal = j-TIST,
volume = "14",
number = "2",
pages = "37:1--37:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579827",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579827",
abstract = "The alignment of multiple multi-relational networks,
such as knowledge graphs, is vital for many AI
applications. In comparison with existing GCNs which
cannot fully utilize relational information of multiple
types, we propose a relation-aware graph \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yin:2023:CBA,
author = "Chunyong Yin and Shuangshuang Chen and Zhichao Yin",
title = "Clustering-based Active Learning Classification
towards Data Stream",
journal = j-TIST,
volume = "14",
number = "2",
pages = "38:1--38:??",
month = apr,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579830",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Mar 21 06:21:38 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579830",
abstract = "Many practical applications, such as social media and
monitoring system, will constantly generate streaming
data, which has problems of instability, lack of labels
and multiclass imbalance. In order to solve these
problems, a cluster-based active learning \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zaji:2023:OBD,
author = "Amirhossein Zaji and Zheng Liu and Takashi Bando and
Lihua Zhao",
title = "Ontology-Based Driving Simulation for Traffic Lights
Optimization",
journal = j-TIST,
volume = "14",
number = "3",
pages = "39:1--39:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579839",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579839",
abstract = "Traffic lights optimization is one of the principal
components to lessen the traffic flow and travel time
in an urban area. The present article seeks to
introduce a novel procedure to design the traffic
lights in a city using evolutionary-based \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Cai:2023:FLG,
author = "Yaoming Cai and Zijia Zhang and Pedram Ghamisi and
Zhihua Cai and Xiaobo Liu and Yao Ding",
title = "Fully Linear Graph Convolutional Networks for
Semi-Supervised and Unsupervised Classification",
journal = j-TIST,
volume = "14",
number = "3",
pages = "40:1--40:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579828",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579828",
abstract = "This article presents FLGC, a simple yet effective
fully linear graph convolutional network for
semi-supervised and unsupervised learning. Instead of
using gradient descent, we train FLGC based on
computing a global optimal closed-form solution with a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Manfredi:2023:TST,
author = "Gilda Manfredi and Nicola Capece and Ugo Erra and
Monica Gruosso",
title = "{TreeSketchNet}: From Sketch to {$3$D} Tree Parameters
Generation",
journal = j-TIST,
volume = "14",
number = "3",
pages = "41:1--41:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579831",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579831",
abstract = "Three-dimensional (3D) modeling of non-linear objects
from stylized sketches is a challenge even for computer
graphics experts. The extrapolation of object
parameters from a stylized sketch is a very complex and
cumbersome task. In the present study, we \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2023:CVP,
author = "Wenshan Wang and Su Yang and Weishan Zhang",
title = "Customer Volume Prediction Using Fusion of
Shared-private Dynamic Weighting over Multiple
Modalities",
journal = j-TIST,
volume = "14",
number = "3",
pages = "42:1--42:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579826",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579826",
abstract = "Customer volume prediction is crucial for a variety of
urban applications, such as store location selection.
So far, the key challenge lies in how to fuse multiple
modalities from different data sources, on account of
the massive amount of data accessible,. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2023:REK,
author = "Lu Jiang and Kunpeng Liu and Yibin Wang and Dongjie
Wang and Pengyang Wang and Yanjie Fu and Minghao Yin",
title = "Reinforced Explainable Knowledge Concept
Recommendation in {MOOCs}",
journal = j-TIST,
volume = "14",
number = "3",
pages = "43:1--43:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3579991",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3579991",
abstract = "In this article, we study knowledge concept
recommendation in Massive Open Online Courses (MOOCs)
in an explainable manner. Knowledge concepts, composing
course units (e.g., videos) in MOOCs, refer to topics
and skills that students are expected to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sun:2023:RLQ,
author = "Shuo Sun and Rundong Wang and Bo An",
title = "Reinforcement Learning for Quantitative Trading",
journal = j-TIST,
volume = "14",
number = "3",
pages = "44:1--44:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3582560",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3582560",
abstract = "Quantitative trading (QT), which refers to the usage
of mathematical models and data-driven techniques in
analyzing the financial market, has been a popular
topic in both academia and financial industry since
1970s. In the last decade, reinforcement \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dai:2023:SAT,
author = "Zeyu Dai and Shengcai Liu and Qing Li and Ke Tang",
title = "Saliency Attack: Towards Imperceptible Black-box
Adversarial Attack",
journal = j-TIST,
volume = "14",
number = "3",
pages = "45:1--45:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3582563",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3582563",
abstract = "Deep neural networks are vulnerable to adversarial
examples, even in the black-box setting where the
attacker is only accessible to the model output. Recent
studies have devised effective black-box attacks with
high query efficiency. However, such \ldots{}.",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:RLE,
author = "Xing Li and Wei Wei and Ruizhi Zhang and Zhenyu Shi
and Zhiming Zheng and Xiangnan Feng",
title = "Representation Learning of Enhanced Graphs Using
Random Walk Graph Convolutional Network",
journal = j-TIST,
volume = "14",
number = "3",
pages = "46:1--46:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3582841",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3582841",
abstract = "Nowadays, graph structure data has played a key role
in machine learning because of its simple topological
structure, and therefore, the graph representation
learning methods have attracted great attention. And it
turns out that the low-dimensional \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Cai:2023:RDR,
author = "Mingjian Cai and Xiangjun Shen and Stanley Ebhohimhen
Abhadiomhen and Yingfeng Cai and Sirui Tian",
title = "Robust Dimensionality Reduction via Low-rank
{Laplacian} Graph Learning",
journal = j-TIST,
volume = "14",
number = "3",
pages = "47:1--47:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3582698",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3582698",
abstract = "Manifold learning is a widely used technique for
dimensionality reduction as it can reveal the intrinsic
geometric structure of data. However, its performance
decreases drastically when data samples are
contaminated by heavy noise or occlusions, which
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yin:2023:HRD,
author = "Chunyong Yin and Sun Zhang and Qingkui Zeng",
title = "Hybrid Representation and Decision Fusion towards
Visual-textual Sentiment",
journal = j-TIST,
volume = "14",
number = "3",
pages = "48:1--48:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3583076",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3583076",
abstract = "The rising use of online media has changed social
customs of the public. Users have become gradually
accustomed to sharing daily experiences and publishing
personal opinions on social networks. Social data
carrying with emotions and attitudes have \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2023:MWB,
author = "En Xu and Zhiwen Yu and Zhuo Sun and Bin Guo and Lina
Yao",
title = "Modeling Within-Basket Auxiliary Item Recommendation
with Matchability and Ubiquity",
journal = j-TIST,
volume = "14",
number = "3",
pages = "49:1--49:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3574157",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3574157",
abstract = "Within-basket recommendation is to recommend suitable
items for the current basket with some already known
items. The within-basket auxiliary item recommendation
( WBAIR ) is to recommend auxiliary items based on the
primary items in the basket. Such a task \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wen:2023:EWC,
author = "Haomin Wen and Youfang Lin and Fan Wu and Huaiyu Wan
and Zhongxiang Sun and Tianyue Cai and Hongyu Liu and
Shengnan Guo and Jianbin Zheng and Chao Song and Lixia
Wu",
title = "Enough Waiting for the Couriers: Learning to Estimate
Package Pick-up Arrival Time from Couriers'
Spatial-Temporal Behaviors",
journal = j-TIST,
volume = "14",
number = "3",
pages = "50:1--50:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3582561",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3582561",
abstract = "In intelligent logistics systems, predicting the
Estimated Time of Pick-up Arrival (ETPA) of packages is
a crucial task, which aims to predict the courier's
arrival time to all the unpicked-up packages at any
time. Accurate prediction of ETPA can help \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mei:2023:FRT,
author = "Jianbiao Mei and Mengmeng Wang and Yu Yang and Yanjun
Li and Yong Liu",
title = "Fast Real-Time Video Object Segmentation with a
Tangled Memory Network",
journal = j-TIST,
volume = "14",
number = "3",
pages = "51:1--51:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3585076",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3585076",
abstract = "In this article, we present a fast real-time tangled
memory network that segments the objects effectively
and efficiently for semi-supervised video object
segmentation (VOS). We propose a tangled reference
encoder and a memory bank organization mechanism
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2023:TBE,
author = "Hu Wang and Hui Li and Meng Wang and Jiangtao Cui",
title = "Toward Balancing the Efficiency and Effectiveness in
$k$-Facility Relocation Problem",
journal = j-TIST,
volume = "14",
number = "3",
pages = "52:1--52:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3587039",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3587039",
abstract = "Facility Relocation (FR), which is an effort to
reallocate the placement of facilities to adapt to the
changes of urban planning, has remarkable impact on
many areas. Existing solutions fail to guarantee the
result quality on relocating k {$>$} 1 facilities.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Luo:2023:HLR,
author = "Qilun Luo and Ming Yang and Wen Li and Mingqing Xiao",
title = "Hyper-{Laplacian} Regularized Multi-View Clustering
with Exclusive {L21} Regularization and Tensor
Log-Determinant Minimization Approach",
journal = j-TIST,
volume = "14",
number = "3",
pages = "53:1--53:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3587034",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3587034",
abstract = "Multi-view clustering aims to capture the multiple
views inherent information by identifying the data
clustering that reflects distinct features of datasets.
Since there is a consensus in literature that different
views of a dataset share a common latent \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Preti:2023:MAM,
author = "Giulia Preti and Gianmarco {De Francisci Morales} and
Matteo Riondato",
title = "{MaNIACS}: Approximate Mining of Frequent Subgraph
Patterns through Sampling",
journal = j-TIST,
volume = "14",
number = "3",
pages = "54:1--54:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3587254",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3587254",
abstract = "We present MaNIACS, a sampling-based randomized
algorithm for computing high-quality approximations of
the collection of the subgraph patterns that are
frequent in a single, large, vertex-labeled graph,
according to the Minimum Node Image-based (MNI)
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gao:2023:DIT,
author = "Lei Gao and Ling Guan",
title = "A Discriminant Information Theoretic Learning
Framework for Multi-modal Feature Representation",
journal = j-TIST,
volume = "14",
number = "3",
pages = "55:1--55:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3587253",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3587253",
abstract = "As sensory and computing technology advances,
multi-modal features have been playing a central role
in ubiquitously representing patterns and phenomena for
effective information analysis and recognition. As a
result, multi-modal feature representation is
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gupta:2023:ERV,
author = "Trasha Gupta and Rajni Jindal and Indu Sreedevi",
title = "Empirical Review of Various Thermography-based
Computer-aided Diagnostic Systems for Multiple
Diseases",
journal = j-TIST,
volume = "14",
number = "3",
pages = "56:1--56:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3583778",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3583778",
abstract = "The lifestyle led by today's generation and its
negligence towards health is highly susceptible to
various diseases. Developing countries are at a higher
risk of mortality due to late-stage presentation,
inaccessible diagnosis, and high-cost treatment.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2023:RPL,
author = "Wun-Ting Yang and Chiao-Ting Chen and Chuan-Yun Sang
and Szu-Hao Huang",
title = "Reinforced {PU}-learning with Hybrid Negative Sampling
Strategies for Recommendation",
journal = j-TIST,
volume = "14",
number = "3",
pages = "57:1--57:??",
month = jun,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3582562",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Jun 1 14:12:36 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3582562",
abstract = "The data of recommendation systems typically only
contain the purchased item as positive data and other
un-purchased items as unlabeled data. To train a good
recommendation model, in addition to the known positive
information, we also need high-quality \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xiang:2023:TQE,
author = "Tao Xiang and Hangcheng Liu and Shangwei Guo and Yan
Gan and Wenjian He and Xiaofeng Liao",
title = "Towards Query-Efficient Black-{Box} Attacks: a
Universal Dual Transferability-Based Framework",
journal = j-TIST,
volume = "14",
number = "4",
pages = "58:1--58:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3583777",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3583777",
abstract = "Adversarial attacks have threatened the application of
deep neural networks in security-sensitive scenarios.
Most existing black-box attacks fool the target model
by interacting with it many times and producing global
perturbations. However, all pixels \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lin:2023:CCD,
author = "Zhuoyi Lin and Lei Feng and Xingzhi Guo and Yu Zhang
and Rui Yin and Chee Keong Kwoh and Chi Xu",
title = "{COMET}: Convolutional Dimension Interaction for
Collaborative Filtering",
journal = j-TIST,
volume = "14",
number = "4",
pages = "59:1--59:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3588576",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3588576",
abstract = "Representation learning-based recommendation models
play a dominant role among recommendation techniques.
However, most of the existing methods assume both
historical interactions and embedding dimensions are
independent of each other, and thus \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2023:UUK,
author = "Yu Liu and Jingtao Ding and Yanjie Fu and Yong Li",
title = "{UrbanKG}: an Urban Knowledge Graph System",
journal = j-TIST,
volume = "14",
number = "4",
pages = "60:1--60:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3588577",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3588577",
abstract = "Every day, our living city produces a tremendous
amount of spatial-temporal data, involved with multiple
sources from the individual scale to the city scale.
Undoubtedly, such massive urban data can be explored
for a better city and better life, as what \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:LRS,
author = "Tong Li and Yanxin Xi and Huandong Wang and Yong Li
and Sasu Tarkoma and Pan Hui",
title = "Learning Representations of Satellite Imagery by
Leveraging Point-of-Interests",
journal = j-TIST,
volume = "14",
number = "4",
pages = "61:1--61:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3589344",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3589344",
abstract = "Satellite imagery depicts the Earth's surface remotely
and provides comprehensive information for many
applications, such as land use monitoring and urban
planning. Existing studies on unsupervised
representation learning for satellite images only take
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Baradaaji:2023:JLS,
author = "A. Baradaaji and F. Dornaika",
title = "Joint Latent Space and Label Inference Estimation with
Adaptive Fused Data and Label Graphs",
journal = j-TIST,
volume = "14",
number = "4",
pages = "62:1--62:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3590172",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3590172",
abstract = "Recently, structured computing has become an
interesting topic in the world of artificial
intelligence, especially in the field of machine
learning, as most researchers focus on the development
of graph-based semi-supervised learning models. In this
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gao:2023:CYF,
author = "Yujia Gao and Pengfei Wang and Liang Liu and Chi Zhang
and Huadong Ma",
title = "Configure Your Federation: Hierarchical
Attention-enhanced Meta-Learning Network for
Personalized Federated Learning",
journal = j-TIST,
volume = "14",
number = "4",
pages = "63:1--63:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3591362",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3591362",
abstract = "Federated learning, as a distributed machine learning
framework, enables clients to conduct model training
without transmitting their data to the server, which is
used to solve the dilemma of data silos and data
privacy. It can work well on clients having \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jin:2023:DGC,
author = "Guangyin Jin and Huan Yan and Fuxian Li and Yong Li
and Jincai Huang",
title = "Dual Graph Convolution Architecture Search for Travel
Time Estimation",
journal = j-TIST,
volume = "14",
number = "4",
pages = "64:1--64:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3591361",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3591361",
abstract = "Travel time estimation (TTE) is a crucial task in
intelligent transportation systems, which has been
widely used in navigation and route planning. In recent
years, several deep learning frameworks have been
proposed to capture the dynamic features of road
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2023:NAL,
author = "Jinghui Zhang and Dingyang Lv and Qiangsheng Dai and
Fa Xin and Fang Dong",
title = "Noise-aware Local Model Training Mechanism for
Federated Learning",
journal = j-TIST,
volume = "14",
number = "4",
pages = "65:1--65:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3591363",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3591363",
abstract = "As a new paradigm in training intelligent models,
federated learning is widely used to train a global
model without requiring local data to be uploaded from
end devices. However, there are often mislabeled
samples (i.e., noisy samples) in the dataset,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2023:RFS,
author = "Tianying Liu and Lu Zhang and Yang Wang and Jihong
Guan and Yanwei Fu and Jiajia Zhao and Shuigeng Zhou",
title = "Recent Few-shot Object Detection Algorithms: a Survey
with Performance Comparison",
journal = j-TIST,
volume = "14",
number = "4",
pages = "66:1--66:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3593588",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3593588",
abstract = "The generic object detection (GOD) task has been
successfully tackled by recent deep neural networks,
trained by an avalanche of annotated training samples
from some common classes. However, it is still
non-trivial to generalize these object detectors to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2023:CLM,
author = "Lingling Xu and Haoran Xie and Zongxi Li and Fu Lee
Wang and Weiming Wang and Qing Li",
title = "Contrastive Learning Models for Sentence
Representations",
journal = j-TIST,
volume = "14",
number = "4",
pages = "67:1--67:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3593590",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3593590",
abstract = "Sentence representation learning is a crucial task in
natural language processing, as the quality of learned
representations directly influences downstream tasks,
such as sentence classification and sentiment analysis.
Transformer-based pretrained \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Kljucaric:2023:DLI,
author = "Luke Kljucaric and Alan D. George",
title = "Deep Learning Inferencing with High-performance
Hardware Accelerators",
journal = j-TIST,
volume = "14",
number = "4",
pages = "68:1--68:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3594221",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3594221",
abstract = "As computer architectures continue to integrate
application-specific hardware, it is critical to
understand the relative performance of devices for
maximum app acceleration. The goal of benchmarking
suites, such as MLPerf for analyzing machine learning
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Shi:2023:SLI,
author = "Yanhang Shi and Xue Li and Siguang Chen",
title = "Skin Lesion Intelligent Diagnosis in Edge Computing
Networks: an {FCL} Approach",
journal = j-TIST,
volume = "14",
number = "4",
pages = "69:1--69:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3595186",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3595186",
abstract = "In recent years, automatic skin lesion diagnosis
methods based on artificial intelligence have achieved
great success. However, the lack of labeled data,
visual similarity between skin diseases, and
restriction on private data sharing remain the major
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sun:2023:MUM,
author = "Li Sun and Zhongbao Zhang and Gen Li and Pengxin Ji
and Sen Su and Philip S. Yu",
title = "{MC$^2$}: Unsupervised Multiple Social Network
Alignment",
journal = j-TIST,
volume = "14",
number = "4",
pages = "70:1--70:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3596514",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3596514",
abstract = "Social network alignment, identifying social accounts
of the same individual across different social
networks, shows fundamental importance in a wide
spectrum of applications, such as link prediction and
information diffusion. Individuals more often than
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:YHY,
author = "Tong Li and Yong Li and Mingyang Zhang and Sasu
Tarkoma and Pan Hui",
title = "You Are How You Use Apps: User Profiling Based on
Spatiotemporal App Usage Behavior",
journal = j-TIST,
volume = "14",
number = "4",
pages = "71:1--71:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3597212",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3597212",
abstract = "Mobile apps have become an indispensable part of
people's daily lives. Users determine what apps to use
and when and where to use them based on their tastes,
interests, and personal demands, depending on their
personality traits. This article aims to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gunarathna:2023:RTR,
author = "Udesh Gunarathna and Hairuo Xie and Egemen Tanin and
Shanika Karunasekera and Renata Borovica-Gajic",
title = "Real-time Road Network Optimization with Coordinated
Reinforcement Learning",
journal = j-TIST,
volume = "14",
number = "4",
pages = "72:1--72:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3603379",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3603379",
abstract = "Dynamic road network optimization has been used for
improving traffic flow in an infrequent and localized
manner. The development of intelligent systems and
technology provides an opportunity to improve the
frequency and scale of dynamic road network \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2023:HHG,
author = "Hanchen Yang and Wengen Li and Siyun Hou and Jihong
Guan and Shuigeng Zhou",
title = "{HiGRN}: a Hierarchical Graph Recurrent Network for
Global Sea Surface Temperature Prediction",
journal = j-TIST,
volume = "14",
number = "4",
pages = "73:1--73:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3597937",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3597937",
abstract = "Sea surface temperature (SST) is one critical
parameter of global climate change, and accurate SST
prediction is important to various applications, e.g.,
weather forecasting, fishing directions, and disaster
warnings. The global ocean system is unified \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Choi:2023:GNR,
author = "Jeongwhan Choi and Noseong Park",
title = "Graph Neural Rough Differential Equations for Traffic
Forecasting",
journal = j-TIST,
volume = "14",
number = "4",
pages = "74:1--74:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3604808",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3604808",
abstract = "Traffic forecasting is one of the most popular
spatio-temporal tasks in the field of machine learning.
A prevalent approach in the field is to combine graph
convolutional networks and recurrent neural networks
for the spatio-temporal processing. There has
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Maggi:2023:DAD,
author = "Fabrizio Maria Maggi and Andrea Marrella and Fabio
Patrizi and Vasyl Skydanienko",
title = "Data-Aware Declarative Process Mining with {SAT}",
journal = j-TIST,
volume = "14",
number = "4",
pages = "75:1--75:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3600106",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3600106",
abstract = "Process Mining is a family of techniques for analyzing
business process execution data recorded in event logs.
Process models can be obtained as output of automated
process discovery techniques or can be used as input of
techniques for conformance \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "75",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Guo:2023:FCP,
author = "Kun Guo and Wenzhong Guo and Enjie Ye and Yutong Fang
and Jiachen Zheng and Ximeng Liu and Kai Chen",
title = "Federated Clique Percolation for Privacy-preserving
Overlapping Community Detection",
journal = j-TIST,
volume = "14",
number = "4",
pages = "76:1--76:??",
month = aug,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3604807",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Aug 19 07:08:56 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3604807",
abstract = "Community structure is a typical characteristic of
complex networks. Finding communities in complex
networks has many important applications, such as the
advertisement and recommendation based on social
networks and the discovery of new protein molecules
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "76",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:JER,
author = "Qibin Li and Nianmin Yao and Nai Zhou and Jian Zhao
and Yanan Zhang",
title = "A Joint Entity and Relation Extraction Model based on
Efficient Sampling and Explicit Interaction",
journal = j-TIST,
volume = "14",
number = "5",
pages = "77:1--77:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3604811",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3604811",
abstract = "Joint entity and relation extraction (RE) construct a
framework for unifying entity recognition and
relationship extraction, and the approach can exploit
the dependencies between the two tasks to improve the
performance of the task. However, the existing
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "77",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2023:CFS,
author = "Shuai Yang and Xianjie Guo and Kui Yu and Xiaoling
Huang and Tingting Jiang and Jin He and Lichuan Gu",
title = "Causal Feature Selection in the Presence of Sample
Selection Bias",
journal = j-TIST,
volume = "14",
number = "5",
pages = "78:1--78:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3604809",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3604809",
abstract = "Almost all existing causal feature selection methods
are proposed without considering the problem of sample
selection bias. However, in practice, as data-gathering
process cannot be fully controlled, sample selection
bias often occurs, leading to spurious \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "78",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2023:DCT,
author = "Mudan Wang and Yuan Yuan and Huan Yan and Hongjie Sui
and Fan Zuo and Yue Liu and Yong Li and Depeng Jin",
title = "Discovering Causes of Traffic Congestion via Deep
Transfer Clustering",
journal = j-TIST,
volume = "14",
number = "5",
pages = "79:1--79:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3604810",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3604810",
abstract = "Traffic congestion incurs long delay in travel time,
which seriously affects our daily travel experiences.
Exploring why traffic congestion occurs is
significantly important to effectively address the
problem of traffic congestion and improve user
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "79",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mullner:2023:RNR,
author = "Peter M{\"u}llner and Elisabeth Lex and Markus Schedl
and Dominik Kowald",
title = "{ReuseKNN}: Neighborhood Reuse for Differentially
Private {KNN-Based} Recommendations",
journal = j-TIST,
volume = "14",
number = "5",
pages = "80:1--80:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3608481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3608481",
abstract = "User-based KNN recommender systems ( UserKNN ) utilize
the rating data of a target user's k nearest neighbors
in the recommendation process. This, however, increases
the privacy risk of the neighbors, since the
recommendations could expose the neighbors' \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "80",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lu:2023:RLA,
author = "Sidi Lu and Xin Yuan and Aggelos K. Katsaggelos and
Weisong Shi",
title = "Reinforcement Learning for Adaptive Video Compressive
Sensing",
journal = j-TIST,
volume = "14",
number = "5",
pages = "81:1--81:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3608479",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3608479",
abstract = "We apply reinforcement learning to video compressive
sensing to adapt the compression ratio. Specifically,
video snapshot compressive imaging (SCI), which
captures high-speed video using a low-speed camera is
considered in this work, in which multiple ( B )
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "81",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhu:2023:UGR,
author = "Yanqiao Zhu and Yichen Xu and Feng Yu and Qiang Liu
and Shu Wu",
title = "Unsupervised Graph Representation Learning with
Cluster-aware Self-training and Refining",
journal = j-TIST,
volume = "14",
number = "5",
pages = "82:1--82:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3608480",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3608480",
abstract = "Unsupervised graph representation learning aims to
learn low-dimensional node embeddings without
supervision while preserving graph topological
structures and node attributive features. Previous
Graph Neural Networks (GNN) require a large number of
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "82",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Goethals:2023:PIC,
author = "Sofie Goethals and Kenneth S{\"o}rensen and David
Martens",
title = "The Privacy Issue of Counterfactual Explanations:
Explanation Linkage Attacks",
journal = j-TIST,
volume = "14",
number = "5",
pages = "83:1--83:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3608482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3608482",
abstract = "Black-box machine learning models are used in an
increasing number of high-stakes domains, and this
creates a growing need for Explainable AI (XAI).
However, the use of XAI in machine learning introduces
privacy risks, which currently remain largely
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "83",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Han:2023:DSA,
author = "Zhenyu Han and Siran Ma and Changzheng Gao and Erzhuo
Shao and Yulai Xie and Yang Zhang and Lu Geng and Yong
Li",
title = "Disease Simulation in Airport Scenario Based on
Individual Mobility Model",
journal = j-TIST,
volume = "14",
number = "5",
pages = "84:1--84:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3593589",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3593589",
abstract = "As the rapid-spreading disease COVID-19 occupies the
world, most governments adopt strict control policies
to alleviate the impact of the virus. These policies
successfully reduced the prevalence and delayed the
epidemic peak, while they are also \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "84",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yu:2023:ODI,
author = "Guangsheng Yu and Xu Wang and Caijun Sun and Ping Yu
and Wei Ni and Ren Ping Liu",
title = "Obfuscating the Dataset: Impacts and Applications",
journal = j-TIST,
volume = "14",
number = "5",
pages = "85:1--85:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3597936",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3597936",
abstract = "Obfuscating a dataset by adding random noises to
protect the privacy of sensitive samples in the
training dataset is crucial to prevent data leakage to
untrusted parties when dataset sharing is essential. We
conduct comprehensive experiments to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "85",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Maddalena:2023:QEP,
author = "Eddy Maddalena and Luis-Daniel Ib{\'a}{\~n}ez and Neal
Reeves and Elena Simperl",
title = "{Qrowdsmith}: Enhancing Paid Microtask Crowdsourcing
with Gamification and Furtherance Incentives",
journal = j-TIST,
volume = "14",
number = "5",
pages = "86:1--86:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3604940",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3604940",
abstract = "Microtask crowdsourcing platforms are social
intelligence systems in which volunteers, called
crowdworkers, complete small, repetitive tasks in
return for a small fee. Beyond payments, task
requesters are considering non-monetary incentives such
as points,. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "86",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhadan:2023:MAR,
author = "Anastasia Zhadan and Alexander Allahverdyan and Ivan
Kondratov and Vikenty Mikheev and Ovanes Petrosian and
Aleksei Romanovskii and Vitaliy Kharin",
title = "Multi-agent Reinforcement Learning-based Adaptive
Heterogeneous {DAG} Scheduling",
journal = j-TIST,
volume = "14",
number = "5",
pages = "87:1--87:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3610300",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3610300",
abstract = "Static scheduling of computational workflow
represented by a directed acyclic graph (DAG) is an
important problem in many areas of computer science.
The main idea and novelty of the proposed algorithm is
an adaptive heuristic or graph metric that uses a
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "87",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2023:DDD,
author = "Kuan-Chun Chen and Cheng-Te Li and Kuo-Jung Lee",
title = "{DDNAS}: Discretized Differentiable Neural
Architecture Search for Text Classification",
journal = j-TIST,
volume = "14",
number = "5",
pages = "88:1--88:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3610299",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3610299",
abstract = "Neural Architecture Search (NAS) has shown promising
capability in learning text representation. However,
existing text-based NAS neither performs a learnable
fusion of neural operations to optimize the
architecture nor encodes the latent hierarchical
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "88",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mahesar:2023:ASD,
author = "Quratul-Ain Mahesar and Simon Parsons",
title = "Argument Schemes and a Dialogue System for Explainable
Planning",
journal = j-TIST,
volume = "14",
number = "5",
pages = "89:1--89:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3610301",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3610301",
abstract = "Artificial Intelligence (AI) is being increasingly
deployed in practical applications. However, there is a
major concern whether AI systems will be trusted by
humans. To establish trust in AI systems, there is a
need for users to understand the reasoning \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "89",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Deng:2023:RLP,
author = "Bangchao Deng and Dingqi Yang and Bingqing Qu and
Benjamin Fankhauser and Philippe Cudre-Mauroux",
title = "Robust Location Prediction over Sparse Spatiotemporal
Trajectory Data: Flashback to the Right Moment!",
journal = j-TIST,
volume = "14",
number = "5",
pages = "90:1--90:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3616541",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3616541",
abstract = "As a fundamental problem in human mobility modeling,
location prediction forecasts a user's next location
based on historical user mobility trajectories.
Recurrent neural networks (RNNs) have been widely used
to capture sequential patterns of user visited
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "90",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dominguez-Martin:2023:NAF,
author = "Javier Dom{\'\i}nguez-Mart{\'\i}n and Mar{\'\i}a J.
G{\'o}mez-Silva and Arturo {De la Escalera}",
title = "Neural Architectures for Feature Embedding in Person
Re-Identification: a Comparative View",
journal = j-TIST,
volume = "14",
number = "5",
pages = "91:1--91:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3610298",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3610298",
abstract = "Solving Person Re-Identification (Re-Id) through Deep
Convolutional Neural Networks is a daunting challenge
due to the small size and variety of the training data,
especially in Single-Shot Re-Id, where only two images
per person are available. The lack \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "91",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhao:2023:FCG,
author = "Tianxiang Zhao and Dongsheng Luo and Xiang Zhang and
Suhang Wang",
title = "Faithful and Consistent Graph Neural Network
Explanations with Rationale Alignment",
journal = j-TIST,
volume = "14",
number = "5",
pages = "92:1--92:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3616542",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3616542",
abstract = "Uncovering rationales behind predictions of graph
neural networks (GNNs) has received increasing
attention over recent years. Instance-level GNN
explanation aims to discover critical input elements,
such as nodes or edges, that the target GNN relies upon
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "92",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhu:2023:LSA,
author = "Yupeng Zhu and Yanxiang Chen and Zuxing Zhao and
Xueliang Liu and Jinlin Guo",
title = "Local Self-attention-based Hybrid Multiple Instance
Learning for Partial Spoof Speech Detection",
journal = j-TIST,
volume = "14",
number = "5",
pages = "93:1--93:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3616540",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3616540",
abstract = "The development of speech synthesis technology has
increased the attention toward the threat of spoofed
speech. Although various high-performance spoofing
countermeasures have been proposed in recent years, a
particular scenario is overlooked: partially \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "93",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Moscato:2023:FSN,
author = "Vincenzo Moscato and Marco Postiglione and Giancarlo
Sperl{\'\i}",
title = "Few-shot Named Entity Recognition: Definition,
Taxonomy and Research Directions",
journal = j-TIST,
volume = "14",
number = "5",
pages = "94:1--94:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3609483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3609483",
abstract = "Recent years have seen an exponential growth (+98\% in
2022 w.r.t. the previous year) of the number of
research articles in the few-shot learning field, which
aims at training machine learning models with extremely
limited available data. The research \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "94",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:FRF,
author = "Yunqi Li and Hanxiong Chen and Shuyuan Xu and
Yingqiang Ge and Juntao Tan and Shuchang Liu and
Yongfeng Zhang",
title = "Fairness in Recommendation: Foundations, Methods, and
Applications",
journal = j-TIST,
volume = "14",
number = "5",
pages = "95:1--95:??",
month = oct,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3610302",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Oct 17 05:58:14 MDT 2023",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3610302",
abstract = "As one of the most pervasive applications of machine
learning, recommender systems are playing an important
role on assisting human decision-making. The
satisfaction of users and the interests of platforms
are closely related to the quality of the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "95",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Teng:2023:IHF,
author = "Shang-Hua Teng",
title = "``{Intelligent} Heuristics Are the Future of
Computing''",
journal = j-TIST,
volume = "14",
number = "6",
pages = "96:1--96:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3627708",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3627708",
abstract = "Back in 1988, the partial game trees explored by
computer chess programs were among the largest search
structures in real-world computing. Because the game
tree is too large to be fully evaluated, chess programs
must make heuristic strategic decisions \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "96",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Rokh:2023:CSM,
author = "Babak Rokh and Ali Azarpeyvand and Alireza
Khanteymoori",
title = "A Comprehensive Survey on Model Quantization for Deep
Neural Networks in Image Classification",
journal = j-TIST,
volume = "14",
number = "6",
pages = "97:1--97:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3623402",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3623402",
abstract = "Recent advancements in machine learning achieved by
Deep Neural Networks (DNNs) have been significant.
While demonstrating high accuracy, DNNs are associated
with a huge number of parameters and computations,
which leads to high memory usage and energy \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "97",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2023:TPU,
author = "Xiaojin Zhang and Yan Kang and Kai Chen and Lixin Fan
and Qiang Yang",
title = "Trading Off Privacy, Utility, and Efficiency in
Federated Learning",
journal = j-TIST,
volume = "14",
number = "6",
pages = "98:1--98:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3595185",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3595185",
abstract = "Federated learning (FL) enables participating parties
to collaboratively build a global model with boosted
utility without disclosing private data information.
Appropriate protection mechanisms have to be adopted to
fulfill the opposing requirements in \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "98",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2023:SHA,
author = "Bolei Chen and Yongzheng Cui and Ping Zhong and Wang
Yang and Yixiong Liang and Jianxin Wang",
title = "{STExplorer}: a Hierarchical Autonomous Exploration
Strategy with Spatio-temporal Awareness for Aerial
Robots",
journal = j-TIST,
volume = "14",
number = "6",
pages = "99:1--99:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3595184",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3595184",
abstract = "The autonomous exploration task we consider requires
Unmanned Aerial Vehicles (UAVs) to actively navigate
through unknown environments with the goal of fully
perceiving and mapping the environments. Some existing
exploration strategies suffer from rough \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "99",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2023:AAN,
author = "Yihao Zhang and Chu Zhao and Weiwen Liao and Wei Zhou
and Meng Yuan",
title = "Asymmetrical Attention Networks Fused Autoencoder for
Debiased Recommendation",
journal = j-TIST,
volume = "14",
number = "6",
pages = "100:1--100:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3596498",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3596498",
abstract = "Popularity bias is a massive challenge for
autoencoder-based models, which decreases the level of
personalization and hurts the fairness of
recommendations. User reviews reflect their preferences
and help mitigate bias or unfairness in the
recommendation. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "100",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2023:UGI,
author = "Yingwen Wu and Sizhe Chen and Kun Fang and Xiaolin
Huang",
title = "Unifying Gradients to Improve Real-World Robustness
for Deep Networks",
journal = j-TIST,
volume = "14",
number = "6",
pages = "101:1--101:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3617895",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3617895",
abstract = "The wide application of deep neural networks (DNNs)
demands an increasing amount of attention to their
real-world robustness, i.e., whether a DNN resists
black-box adversarial attacks, among which score-based
query attacks (SQAs) are the most threatening
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "101",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yao:2023:AGA,
author = "Rui Yao and Ying Chen and Yong Zhou and Fuyuan Hu and
Jiaqi Zhao and Bing Liu and Zhiwen Shao",
title = "Attention-guided Adversarial Attack for Video Object
Segmentation",
journal = j-TIST,
volume = "14",
number = "6",
pages = "102:1--102:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3617067",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3617067",
abstract = "Video Object Segmentation (VOS) methods have made many
breakthroughs with the help of the continuous
development and advancement of deep learning. However,
the deep learning model is vulnerable to malicious
adversarial attacks, which mislead the model to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "102",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lin:2023:MAU,
author = "Rui Lin and Jing Fan and Haifeng Wu",
title = "Multi-aspect Understanding with Cooperative Graph
Attention Networks for Medical Dialogue Information
Extraction",
journal = j-TIST,
volume = "14",
number = "6",
pages = "103:1--103:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3620675",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3620675",
abstract = "Medical dialogue information extraction is an
important but challenging task for Electronic Medical
Records. Existing medical information extraction
methods ignore the crucial information of sentence and
multi-level dependency in dialogue, which limits
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "103",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Kasthuriarachchy:2023:MST,
author = "Buddhika Kasthuriarachchy and Madhu Chetty and Adrian
Shatte and Darren Walls",
title = "Meaning-Sensitive Text Data Augmentation with
Intelligent Masking",
journal = j-TIST,
volume = "14",
number = "6",
pages = "104:1--104:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3623403",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3623403",
abstract = "With the recent popularity of applying large-scale
deep neural network-based models for natural language
processing (NLP), attention to develop methods for text
data augmentation is at its peak, since the limited
size of training data tends to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "104",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2023:LPP,
author = "Chu-Chen Li and Cheng-Te Li and Shou-De Lin",
title = "Learning Privacy-Preserving Embeddings for Image Data
to Be Published",
journal = j-TIST,
volume = "14",
number = "6",
pages = "105:1--105:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3623404",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3623404",
abstract = "Deep learning shows superiority in learning feature
representations that offer promising performance in
various application domains. Recent advances have shown
that privacy attributes of users and patients (e.g.,
identity, gender, and race) can be \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "105",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{u:2023:GBA,
author = "Shuaiyi L(y)u and Kai Wang and Yuliang Wei and Hongri
Liu and Qilin Fan and Bailing Wang",
title = "{GNN}-based Advanced Feature Integration for {ICS}
Anomaly Detection",
journal = j-TIST,
volume = "14",
number = "6",
pages = "106:1--106:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3620676",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3620676",
abstract = "Recent adversaries targeting the Industrial Control
Systems (ICSs) have started exploiting their
sophisticated inherent contextual semantics such as the
data associativity among heterogeneous field devices.
In light of the subtlety rendered in these \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "106",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2023:DWP,
author = "Meng Xu and Yechao She and Yang Jin and Jianping
Wang",
title = "Dynamic Weights and Prior Reward in Policy Fusion for
Compound Agent Learning",
journal = j-TIST,
volume = "14",
number = "6",
pages = "107:1--107:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3623405",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3623405",
abstract = "In Deep Reinforcement Learning (DRL) domain, a
compound learning task is often decomposed into several
sub-tasks in a divide-and-conquer manner, each trained
separately and then fused concurrently to achieve the
original task, referred to as policy \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "107",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tran:2023:MNB,
author = "Nhu-Thuat Tran and Hady W. Lauw",
title = "Memory Network-Based Interpreter of User Preferences
in Content-Aware Recommender Systems",
journal = j-TIST,
volume = "14",
number = "6",
pages = "108:1--108:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625239",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625239",
abstract = "This article introduces a novel architecture for two
objectives recommendation and interpretability in a
unified model. We leverage textual content as a source
of interpretability in content-aware recommender
systems. The goal is to characterize user \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "108",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2023:LEC,
author = "Jianhang Zhou and Guancheng Wang and Shaoning Zeng and
Bob Zhang",
title = "Learning with {Euler} Collaborative Representation for
Robust Pattern Analysis",
journal = j-TIST,
volume = "14",
number = "6",
pages = "109:1--109:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625235",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625235",
abstract = "The Collaborative Representation (CR) framework has
provided various effective and efficient solutions to
pattern analysis. By leveraging between discriminative
coefficient coding ($l_2$ regularization) and the best
reconstruction quality (collaboration), \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "109",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Reyes:2023:PCD,
author = "{\'O}scar Reyes and Eduardo P{\'e}rez",
title = "Performing Cancer Diagnosis via an Isoform Expression
Ranking-based {LSTM} Model",
journal = j-TIST,
volume = "14",
number = "6",
pages = "110:1--110:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625237",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625237",
abstract = "The known set of genetic factors involved in the
development of several types of cancer has considerably
been expanded, thus easing to devise and implement
better therapeutic strategies. The automatic diagnosis
of cancer, however, remains as a complex \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "110",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Cheong:2023:AIC,
author = "Chin Wang Cheong and Kejing Yin and William K. Cheung
and Benjamin C. M. Fung and Jonathan Poon",
title = "Adaptive Integration of Categorical and
Multi-relational Ontologies with {EHR} Data for Medical
Concept Embedding",
journal = j-TIST,
volume = "14",
number = "6",
pages = "111:1--111:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625224",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625224",
abstract = "Representation learning has been applied to Electronic
Health Records (EHR) for medical concept embedding and
the downstream predictive analytics tasks with
promising results. Medical ontologies can also be
integrated to guide the learning so the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "111",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sun:2023:WYN,
author = "Heli Sun and Chen Cao and Xuguang Chu and Tingting Hu
and Junzhi Lu and Liang He and Zhi Wang and Hui He and
Hui Xiong",
title = "What Your Next Check-in Might Look Like: Next Check-in
Behavior Prediction",
journal = j-TIST,
volume = "14",
number = "6",
pages = "112:1--112:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625234",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625234",
abstract = "In recent years, the next-POI recommendation has
become a trending research topic in the field of
trajectory data mining. For protection of user privacy,
users' complete GPS trajectories are difficult to
obtain. The check-in information posted by users on
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "112",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Qu:2023:AAD,
author = "Ao Qu and Yihong Tang and Wei Ma",
title = "Adversarial Attacks on Deep Reinforcement
Learning-based Traffic Signal Control Systems with
Colluding Vehicles",
journal = j-TIST,
volume = "14",
number = "6",
pages = "113:1--113:??",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625236",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625236",
abstract = "The rapid advancements of Internet of Things (IoT) and
Artificial Intelligence (AI) have catalyzed the
development of adaptive traffic control systems (ATCS)
for smart cities. In particular, deep reinforcement
learning (DRL) models produce state-of-the-. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "113",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2023:SAR,
author = "Ronghui Xu and Weiming Huang and Jun Zhao and Meng
Chen and Liqiang Nie",
title = "A Spatial and Adversarial Representation Learning
Approach for Land Use Classification with {POIs}",
journal = j-TIST,
volume = "14",
number = "6",
pages = "114:1--114:25",
month = dec,
year = "2023",
CODEN = "????",
DOI = "https://doi.org/10.1145/3627824",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Tue Jun 4 05:57:07 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3627824",
abstract = "Points-of-interests (POIs) have been proven to be
indicative for sensing urban land use in numerous
studies. However, recent progress mainly relies on
spatial co-occurrence patterns among POI categories,
which falls short in utilizing the rich semantic
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "114",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Makhdomi:2024:TGF,
author = "Aqsa Ashraf Makhdomi and Iqra Altaf Gillani",
title = "Towards a Greener and Fairer Transportation System: a
Survey of Route Recommendation Techniques",
journal = j-TIST,
volume = "15",
number = "1",
pages = "1:1--1:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3627825",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3627825",
abstract = "In recent years, ride-hailing services have emerged as
a popular means of transportation for the residents of
urban areas. There is an inequality in the
spatio-temporal distribution of demand and supply,
which requires the proper recommendation of routes
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "1",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lu:2024:ESR,
author = "Wei-Qing Lu and Hai-Miao Hu and Jinzuo Yu and Shifeng
Zhang and Hanzi Wang",
title = "Explicit State Representation Guided Video-based
Pedestrian Attribute Recognition",
journal = j-TIST,
volume = "15",
number = "1",
pages = "2:1--2:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3626240",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3626240",
abstract = "The pedestrian attribute recognition aims to generate
a structured description of pedestrians, which serves
an important role in surveillance. Current works
usually assume that the images and the specific
pedestrian states, including pedestrian occlusion
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "2",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2024:HPT,
author = "Kun Wu and Chengxiang Yin and Zhengping Che and Bo
Jiang and Jian Tang and Zheng Guan and Gangyi Ding",
title = "Human Pose Transfer with Augmented Disentangled
Feature Consistency",
journal = j-TIST,
volume = "15",
number = "1",
pages = "3:1--3:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3626241",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3626241",
abstract = "Deep generative models have made great progress in
synthesizing images with arbitrary human poses and
transferring the poses of one person to others. Though
many different methods have been proposed to generate
images with high visual fidelity, the main \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "3",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bougie:2024:IIL,
author = "Nicolas Bougie and Takashi Onishi and Yoshimasa
Tsuruoka",
title = "Interpretable Imitation Learning with Symbolic
Rewards",
journal = j-TIST,
volume = "15",
number = "1",
pages = "4:1--4:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3627822",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3627822",
abstract = "Sample inefficiency of deep reinforcement learning
methods is a major obstacle for their use in real-world
tasks as they naturally feature sparse rewards. In
fact, this from-scratch approach is often impractical
in environments where extreme negative \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "4",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2024:WSF,
author = "Wenyuan Yang and Shuo Shao and Yue Yang and Xiyao Liu
and Ximeng Liu and Zhihua Xia and Gerald Schaefer and
Hui Fang",
title = "Watermarking in Secure Federated Learning: a
Verification Framework Based on Client-Side
Backdooring",
journal = j-TIST,
volume = "15",
number = "1",
pages = "5:1--5:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3630636",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3630636",
abstract = "Federated learning (FL) allows multiple participants
to collaboratively build deep learning (DL) models
without directly sharing data. Consequently, the issue
of copyright protection in FL becomes important since
unreliable participants may gain access to \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "5",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Dornaika:2024:OSM,
author = "F. Dornaika",
title = "One-step Multi-view Clustering with Consensus Graph
and Data Representation Convolution",
journal = j-TIST,
volume = "15",
number = "1",
pages = "6:1--6:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3630634",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3630634",
abstract = "Multi-view clustering aims to partition unlabeled
patterns into disjoint clusters using consistent and
complementary information derived from features of
patterns in multiple views. Downstream methods perform
this clustering sequentially: estimation of \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "6",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zeng:2024:EAL,
author = "Yingyan Zeng and Xiaoyu Chen and Ran Jin",
title = "Ensemble Active Learning by Contextual Bandits for
{AI} Incubation in Manufacturing",
journal = j-TIST,
volume = "15",
number = "1",
pages = "7:1--7:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3627821",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3627821",
abstract = "An Industrial Cyber-physical System (ICPS) provides a
digital foundation for data-driven decision-making by
artificial intelligence (AI) models. However, the poor
data quality (e.g., inconsistent distribution,
imbalanced classes) of high-speed, large-. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "7",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Belkhouja:2024:DDT,
author = "Taha Belkhouja and Yan Yan and Janardhan Rao Doppa",
title = "Out-of-distribution Detection in Time-series Domain: a
Novel Seasonal Ratio Scoring Approach",
journal = j-TIST,
volume = "15",
number = "1",
pages = "8:1--8:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3630633",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3630633",
abstract = "Safe deployment of time-series classifiers for
real-world applications relies on the ability to detect
the data that is not generated from the same
distribution as training data. This task is referred to
as out-of-distribution (OOD) detection. We consider
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "8",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Nguyen:2024:IGE,
author = "Thanh Toan Nguyen and Thanh Tam Nguyen and Thanh Hung
Nguyen and Hongzhi Yin and Thanh Thi Nguyen and Jun Jo
and Quoc Viet Hung Nguyen",
title = "Isomorphic Graph Embedding for Progressive Maximal
Frequent Subgraph Mining",
journal = j-TIST,
volume = "15",
number = "1",
pages = "9:1--9:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3630635",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3630635",
abstract = "Maximal frequent subgraph mining (MFSM) is the task of
mining only maximal frequent subgraphs, i.e., subgraphs
that are not a part of other frequent subgraphs.
Although many intelligent systems require MFSM, MFSM is
challenging compared to frequent \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "9",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lv:2024:SOC,
author = "Junwei Lv and Yuqi Chu and Jun Hu and Peipei Li and
Xuegang Hu",
title = "Second-order Confidence Network for Early
Classification of Time Series",
journal = j-TIST,
volume = "15",
number = "1",
pages = "10:1--10:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3631531",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3631531",
abstract = "Time series data are ubiquitous in a variety of
disciplines. Early classification of time series, which
aims to predict the class label of a time series as
early and accurately as possible, is a significant but
challenging task in many time-sensitive \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "10",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Saad:2024:QLI,
author = "Yossef Saad and Joachim Meyer",
title = "Quantifying Levels of Influence and Causal
Responsibility in Dynamic Decision Making Events",
journal = j-TIST,
volume = "15",
number = "1",
pages = "11:1--11:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3631611",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3631611",
abstract = "Intelligent systems support human operators'
decision-making processes, many of which are dynamic
and involve temporal changes in the decision-related
parameters. As we increasingly depend on automation, it
becomes imperative to understand and quantify
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "11",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gao:2024:IRM,
author = "Qiang Gao and Hongzhu Fu and Kunpeng Zhang and Goce
Trajcevski and Xu Teng and Fan Zhou",
title = "Inferring Real Mobility in Presence of Fake Check-ins
Data",
journal = j-TIST,
volume = "15",
number = "1",
pages = "12:1--12:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3604941",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3604941",
abstract = "Understanding human mobility has become an important
aspect of location-based services in tasks such as
personalized recommendation and individual moving
pattern recognition, enabled by the large volumes of
data from geo-tagged social media (GTSM). Prior
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "12",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tang:2024:EGN,
author = "Hao Tang and Cheng Wang and Jianguo Zheng and Changjun
Jiang",
title = "Enabling Graph Neural Networks for Semi-Supervised
Risk Prediction in Online Credit Loan Services",
journal = j-TIST,
volume = "15",
number = "1",
pages = "13:1--13:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3623401",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3623401",
abstract = "Graph neural networks (GNNs) are playing exciting
roles in the application scenarios where features are
hidden in information associations. Fraud prediction of
online credit loan services (OCLSs) is such a typical
scenario. But it has another rather \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "13",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ma:2024:ESI,
author = "Shuo Ma and Yingwei Zhang and Yiqiang Chen and Tao Xie
and Shuchao Song and Ziyu Jia",
title = "Exploring Structure Incentive Domain Adversarial
Learning for Generalizable Sleep Stage Classification",
journal = j-TIST,
volume = "15",
number = "1",
pages = "14:1--14:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625238",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625238",
abstract = "Sleep stage classification is crucial for sleep state
monitoring and health interventions. In accordance with
the standards prescribed by the American Academy of
Sleep Medicine, a sleep episode follows a specific
structure comprising five distinctive \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "14",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2024:HPD,
author = "Yanzhao Wu and Ka-Ho Chow and Wenqi Wei and Ling Liu",
title = "Hierarchical Pruning of Deep Ensembles with Focal
Diversity",
journal = j-TIST,
volume = "15",
number = "1",
pages = "15:1--15:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3633286",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3633286",
abstract = "Deep neural network ensembles combine the wisdom of
multiple deep neural networks to improve the
generalizability and robustness over individual
networks. It has gained increasing popularity to study
and apply deep ensemble techniques in the deep learning
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "15",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2024:VRT,
author = "Yunchao Wang and Guodao Sun and Zihao Zhu and Tong Li
and Ling Chen and Ronghua Liang",
title = "{E$^2$Storyline}: Visualizing the Relationship with
Triplet Entities and Event Discovery",
journal = j-TIST,
volume = "15",
number = "1",
pages = "16:1--16:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3633519",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3633519",
abstract = "The narrative progression of events, evolving into a
cohesive story, relies on the entity-entity
relationships. Among the plethora of visualization
techniques, storyline visualization has gained
significant recognition for its effectiveness in
offering an \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "16",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2024:RTF,
author = "Shengyu Chen and Tianshu Bao and Peyman Givi and Can
Zheng and Xiaowei Jia",
title = "Reconstructing Turbulent Flows Using Spatio-temporal
Physical Dynamics",
journal = j-TIST,
volume = "15",
number = "1",
pages = "17:1--17:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3637491",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3637491",
abstract = "Accurate simulation of turbulent flows is of crucial
importance in many branches of science and engineering.
Direct numerical simulation (DNS) provides the highest
fidelity means of capturing all intricate physics of
turbulent transport. However, the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "17",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Terroso-Saenz:2024:NAP,
author = "Fernando Terroso-Saenz and Juan Morales-Garc{\'\i}a
and Andres Mu{\~n}oz",
title = "Nationwide Air Pollution Forecasting with
Heterogeneous Graph Neural Networks",
journal = j-TIST,
volume = "15",
number = "1",
pages = "18:1--18:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3637492",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3637492",
abstract = "Nowadays, air pollution is one of the most relevant
environmental problems in most urban settings. Due to
the utility in operational terms of anticipating
certain pollution levels, several predictors based on
Graph Neural Networks (GNN) have been proposed
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "18",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Khoshraftar:2024:SGR,
author = "Shima Khoshraftar and Aijun An",
title = "A Survey on Graph Representation Learning Methods",
journal = j-TIST,
volume = "15",
number = "1",
pages = "19:1--19:??",
month = feb,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3633518",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:38 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3633518",
abstract = "Graph representation learning has been a very active
research area in recent years. The goal of graph
representation learning is to generate graph
representation vectors that capture the structure and
features of large graphs accurately. This is \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "19",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhao:2024:ELL,
author = "Haiyan Zhao and Hanjie Chen and Fan Yang and Ninghao
Liu and Huiqi Deng and Hengyi Cai and Shuaiqiang Wang
and Dawei Yin and Mengnan Du",
title = "Explainability for Large Language Models: a Survey",
journal = j-TIST,
volume = "15",
number = "2",
pages = "20:1--20:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3639372",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3639372",
abstract = "Large language models (LLMs) have demonstrated
impressive capabilities in natural language processing.
However, their internal mechanisms are still unclear
and this lack of transparency poses unwanted risks for
downstream applications. Therefore, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "20",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2024:DDU,
author = "Yunke Zhang and Tong Li and Yuan Yuan and Fengli Xu
and Fan Yang and Funing Sun and Yong Li",
title = "Demand-driven Urban Facility Visit Prediction",
journal = j-TIST,
volume = "15",
number = "2",
pages = "21:1--21:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625233",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625233",
abstract = "Predicting citizens' visiting behaviors to urban
facilities is instrumental for city governors and
planners to detect inequalities in urban opportunities
and optimize the distribution of facilities and
resources. Previous works predict facility visits
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "21",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Tsouvalas:2024:LCL,
author = "Vasileios Tsouvalas and Aaqib Saeed and Tanir Ozcelebi
and Nirvana Meratnia",
title = "Labeling Chaos to Learning Harmony: Federated Learning
with Noisy Labels",
journal = j-TIST,
volume = "15",
number = "2",
pages = "22:1--22:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3626242",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3626242",
abstract = "Federated Learning (FL) is a distributed machine
learning paradigm that enables learning models from
decentralized private datasets where the labeling
effort is entrusted to the clients. While most existing
FL approaches assume high-quality labels are \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "22",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2024:DSN,
author = "Wei Chen and Hongjun Wang and Yinghui Zhang and Ping
Deng and Zhipeng Luo and Tianrui Li",
title = "{$T$}-Distributed Stochastic Neighbor Embedding for
Co-Representation Learning",
journal = j-TIST,
volume = "15",
number = "2",
pages = "23:1--23:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3627823",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3627823",
abstract = "Co-clustering is the simultaneous clustering of the
samples and attributes of a data matrix that provides
deeper insight into data than traditional clustering.
However, there is a lack of representation learning
algorithms that serve this mechanism of co-. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "23",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lee:2024:TFF,
author = "Sangwon Lee and Junho Hong and Ling Liu and Wonik
Choi",
title = "{TS-Fastformer}: Fast Transformer for Time-series
Forecasting",
journal = j-TIST,
volume = "15",
number = "2",
pages = "24:1--24:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3630637",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3630637",
abstract = "Many real-world applications require precise and fast
time-series forecasting. Recent trends in time-series
forecasting models are shifting from LSTM-based models
to Transformer-based models. However, the
Transformer-based model has a limited ability to
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "24",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lee:2024:EPC,
author = "Eunji Lee and Sihyeon Kim and Sundong Kim and Soyeon
Jung and Heeja Kim and Meeyoung Cha",
title = "Explainable Product Classification for Customs",
journal = j-TIST,
volume = "15",
number = "2",
pages = "25:1--25:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3635158",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3635158",
abstract = "The task of assigning internationally accepted
commodity codes (aka HS codes) to traded goods is a
critical function of customs offices. Like court
decisions made by judges, this task follows the
doctrine of precedent and can be nontrivial even for
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "25",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Neacsu:2024:EBA,
author = "Ana Neacsu and Jean-Christophe Pesquet and Corneliu
Burileanu",
title = "{EMG}-Based Automatic Gesture Recognition Using
{Lipschitz}-Regularized Neural Networks",
journal = j-TIST,
volume = "15",
number = "2",
pages = "26:1--26:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3635159",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3635159",
abstract = "This article introduces a novel approach for building
a robust Automatic Gesture Recognition system based on
Surface Electromyographic (sEMG) signals, acquired at
the forearm level. Our main contribution is to propose
new constrained learning strategies \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "26",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Pessach:2024:FDP,
author = "Dana Pessach and Tamir Tassa and Erez Shmueli",
title = "Fairness-Driven Private Collaborative Machine
Learning",
journal = j-TIST,
volume = "15",
number = "2",
pages = "27:1--27:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3639368",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3639368",
abstract = "The performance of machine learning algorithms can be
considerably improved when trained over larger
datasets. In many domains, such as medicine and
finance, larger datasets can be obtained if several
parties, each having access to limited amounts of
data,. \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "27",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2024:EDK,
author = "Zhiyuan Wu and Sheng Sun and Yuwei Wang and Min Liu
and Quyang Pan and Junbo Zhang and Zeju Li and
Qingxiang Liu",
title = "Exploring the Distributed Knowledge Congruence in
Proxy-data-free Federated Distillation",
journal = j-TIST,
volume = "15",
number = "2",
pages = "28:1--28:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3639369",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3639369",
abstract = "Federated learning (FL) is a privacy-preserving
machine learning paradigm in which the server
periodically aggregates local model parameters from cli
ents without assembling their private data. Constrained
communication and personalization requirements
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "28",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yuan:2024:GDA,
author = "Yuan Yuan and Jingtao Ding and Huandong Wang and
Depeng Jin",
title = "Generating Daily Activities with Need Dynamics",
journal = j-TIST,
volume = "15",
number = "2",
pages = "29:1--29:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3637493",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3637493",
abstract = "Daily activity data recording individuals' various
activities in daily life are widely used in many
applications such as activity scheduling, activity
recommendation, and policymaking. Though with high
value, its accessibility is limited due to high
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "29",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Xu:2024:SCC,
author = "Meng Xu and Xinhong Chen and Yechao She and Yang Jin
and Guanyi Zhao and Jianping Wang",
title = "Strengthening Cooperative Consensus in Multi-Robot
Confrontation",
journal = j-TIST,
volume = "15",
number = "2",
pages = "30:1--30:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3639371",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3639371",
abstract = "Multi-agent reinforcement learning (MARL) has proven
effective in training multi-robot confrontation, such
as StarCraft and robot soccer games. However, the
current joint action policies utilized in MARL have
been unsuccessful in recognizing and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "30",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Han:2024:RND,
author = "Jin Han and Yun-Feng Ren and Alessandro Brighente and
Mauro Conti",
title = "{RANGO}: a Novel Deep Learning Approach to Detect
Drones Disguising from Video Surveillance Systems",
journal = j-TIST,
volume = "15",
number = "2",
pages = "31:1--31:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3641282",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3641282",
abstract = "Video surveillance systems provide means to detect the
presence of potentially malicious drones in the
surroundings of critical infrastructures. In
particular, these systems collect images and feed them
to a deep-learning classifier able to detect the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "31",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zong:2024:RLS,
author = "Zefang Zong and Xia Tong and Meng Zheng and Yong Li",
title = "Reinforcement Learning for Solving Multiple Vehicle
Routing Problem with Time Window",
journal = j-TIST,
volume = "15",
number = "2",
pages = "32:1--32:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3625232",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3625232",
abstract = "Vehicle routing problem with time window (VRPTW) is of
great importance for a wide spectrum of services and
real-life applications, such as online take-out and
car-hailing platforms. A promising method should
generate high-qualified solutions within \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "32",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2024:EKG,
author = "Jhih-Chen Liu and Chiao-Ting Chen and Chi Lee and
Szu-Hao Huang",
title = "Evolving Knowledge Graph Representation Learning with
Multiple Attention Strategies for Citation
Recommendation System",
journal = j-TIST,
volume = "15",
number = "2",
pages = "33:1--33:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3635273",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3635273",
abstract = "The growing number of publications in the field of
artificial intelligence highlights the need for
researchers to enhance their efficiency in searching
for relevant articles. Most paper recommendation models
either rely on simplistic citation \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "33",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jiang:2024:VVN,
author = "Fenyu Jiang and Huandong Wang and Yong Li",
title = "{VesNet}: a Vessel Network for Jointly Learning Route
Pattern and Future Trajectory",
journal = j-TIST,
volume = "15",
number = "2",
pages = "34:1--34:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3639370",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3639370",
abstract = "Vessel trajectory prediction is the key to maritime
applications such as traffic surveillance, collision
avoidance, anomaly detection, and so on. Making
predictions more precisely requires a better
understanding of the moving trend for a particular
vessel \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "34",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2024:CCF,
author = "Chiao-Ting Chen and Chi Lee and Szu-Hao Huang and
Wen-Chih Peng",
title = "Credit Card Fraud Detection via Intelligent Sampling
and Self-supervised Learning",
journal = j-TIST,
volume = "15",
number = "2",
pages = "35:1--35:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3641283",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3641283",
abstract = "The significant increase in credit card transactions
can be attributed to the rapid growth of online
shopping and digital payments, particularly during the
COVID-19 pandemic. To safeguard cardholders, e-commerce
companies, and financial institutions, the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "35",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Job:2024:OTS,
author = "Simi Job and Xiaohui Tao and Lin Li and Haoran Xie and
Taotao Cai and Jianming Yong and Qing Li",
title = "Optimal Treatment Strategies for Critical Patients
with Deep Reinforcement Learning",
journal = j-TIST,
volume = "15",
number = "2",
pages = "36:1--36:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643856",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643856",
abstract = "Personalized clinical decision support systems are
increasingly being adopted due to the emergence of
data-driven technologies, with this approach now
gaining recognition in critical care. The task of
incorporating diverse patient conditions and treatment
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "36",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Hao:2024:SSB,
author = "Mai Hao and Ming Cai and Minghui Fang and Linlin You",
title = "{SiG}: a {Siamese}-Based Graph Convolutional Network
to Align Knowledge in Autonomous Transportation
Systems",
journal = j-TIST,
volume = "15",
number = "2",
pages = "37:1--37:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643861",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643861",
abstract = "Domain knowledge is gradually renovating its
attributes to exhibit distinct features in autonomy,
propelled by the shift of modern transportation systems
(TS) toward autonomous TS (ATS) comprising three
progressive generations. The knowledge graph (KG)
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "37",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ang:2024:TIM,
author = "Gary Ang and Ee-Peng Lim",
title = "Temporal Implicit Multimodal Networks for Investment
and Risk Management",
journal = j-TIST,
volume = "15",
number = "2",
pages = "38:1--38:??",
month = apr,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643855",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:40 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643855",
abstract = "Many deep learning works on financial time-series
forecasting focus on predicting future prices/returns
of individual assets with numerical price-related
information for trading, and hence propose models
designed for univariate, single-task, and/or \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "38",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chang:2024:SEL,
author = "Yupeng Chang and Xu Wang and Jindong Wang and Yuan Wu
and Linyi Yang and Kaijie Zhu and Hao Chen and Xiaoyuan
Yi and Cunxiang Wang and Yidong Wang and Wei Ye and Yue
Zhang and Yi Chang and Philip S. Yu and Qiang Yang and
Xing Xie",
title = "A Survey on Evaluation of Large Language Models",
journal = j-TIST,
volume = "15",
number = "3",
pages = "39:1--39:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3641289",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3641289",
abstract = "Large language models (LLMs) are gaining increasing
popularity in both academia and industry, owing to
their unprecedented performance in various
applications. As LLMs continue to play a vital role in
both research and daily use, their evaluation becomes
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "39",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Molho:2024:DLS,
author = "Dylan Molho and Jiayuan Ding and Wenzhuo Tang and
Zhaoheng Li and Hongzhi Wen and Yixin Wang and Julian
Venegas and Wei Jin and Renming Liu and Runze Su and
Patrick Danaher and Robert Yang and Yu Leo Lei and
Yuying Xie and Jiliang Tang",
title = "Deep Learning in Single-cell Analysis",
journal = j-TIST,
volume = "15",
number = "3",
pages = "40:1--40:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3641284",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3641284",
abstract = "Single-cell technologies are revolutionizing the
entire field of biology. The large volumes of data
generated by single-cell technologies are high
dimensional, sparse, and heterogeneous and have
complicated dependency structures, making analyses
using \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "40",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ge:2024:MNM,
author = "Xuri Ge and Joemon M. Jose and Songpei Xu and Xiao Liu
and Hu Han",
title = "{MGRR-Net}: Multi-level Graph Relational Reasoning
Network for Facial Action Unit Detection",
journal = j-TIST,
volume = "15",
number = "3",
pages = "41:1--41:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643863",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643863",
abstract = "The Facial Action Coding System (FACS) encodes the
action units (AUs) in facial images, which has
attracted extensive research attention due to its wide
use in facial expression analysis. Many methods that
perform well on automatic facial action unit (AU)
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "41",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yang:2024:BSN,
author = "Qin Yang and Ramviyas Parasuraman",
title = "{Bayesian} Strategy Networks Based Soft Actor-Critic
Learning",
journal = j-TIST,
volume = "15",
number = "3",
pages = "42:1--42:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643862",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643862",
abstract = "A strategy refers to the rules that the agent chooses
the available actions to achieve goals. Adopting
reasonable strategies is challenging but crucial for an
intelligent agent with limited resources working in
hazardous, unstructured, and dynamic \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "42",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Samarakoon:2024:IRR,
author = "S. M. Bhagya P. Samarakoon and M. A. Viraj J.
Muthugala and Mohan Rajesh Elara",
title = "Internal Rehearsals for a Reconfigurable Robot to
Improve Area Coverage Performance",
journal = j-TIST,
volume = "15",
number = "3",
pages = "43:1--43:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643854",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643854",
abstract = "Reconfigurable robots are deployed for applications
demanding area coverage, such as cleaning and
inspections. Reconfiguration per context, considering
beyond a small set of predefined shapes, is crucial for
area coverage performance. However, the \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "43",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Kim:2024:GRG,
author = "Bum Jun Kim and Hyeyeon Choi and Hyeonah Jang and Sang
Woo Kim",
title = "Guidelines for the Regularization of Gammas in Batch
Normalization for Deep Residual Networks",
journal = j-TIST,
volume = "15",
number = "3",
pages = "44:1--44:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643860",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643860",
abstract = "L$_2$ regularization for weights in neural networks is
widely used as a standard training trick. In addition
to weights, the use of batch normalization involves an
additional trainable parameter $\gamma$, which acts as
a scaling factor. However, L$_2$ regularization
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "44",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{He:2024:MDS,
author = "Weidong He and Zhi Li and Hao Wang and Tong Xu and
Zhefeng Wang and Baoxing Huai and Nicholas Jing Yuan
and Enhong Chen",
title = "Multimodal Dialogue Systems via Capturing
Context-aware Dependencies and Ordinal Information of
Semantic Elements",
journal = j-TIST,
volume = "15",
number = "3",
pages = "45:1--45:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3645099",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3645099",
abstract = "The topic of multimodal conversation systems has
recently garnered significant attention across various
industries, including travel and retail, among others.
While pioneering works in this field have shown
promising performance, they often focus solely
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "45",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gherardini:2024:CCA,
author = "Luca Gherardini and Varun Ravi Varma and Karol
Capa{\l}a and Roger Woods and Jose Sousa",
title = "{CACTUS}: a Comprehensive Abstraction and
Classification Tool for Uncovering Structures",
journal = j-TIST,
volume = "15",
number = "3",
pages = "46:1--46:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3649459",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3649459",
abstract = "The availability of large datasets is providing the
impetus for driving many current artificial intelligent
developments. However, specific challenges arise in
developing solutions that exploit small datasets,
mainly due to practical and cost-effective \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "46",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Vukadin:2024:AAB,
author = "Davor Vukadin and Petar Afri{\'c} and Marin Sili{\'c}
and Goran Delac",
title = "Advancing Attribution-Based Neural Network
Explainability through Relative Absolute Magnitude
Layer-Wise Relevance Propagation and Multi-Component
Evaluation",
journal = j-TIST,
volume = "15",
number = "3",
pages = "47:1--47:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3649458",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3649458",
abstract = "Recent advancement in deep-neural network performance
led to the development of new state-of-the-art
approaches in numerous areas. However, the black-box
nature of neural networks often prohibits their use in
areas where model explainability and model \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "47",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liang:2024:LCM,
author = "Yunji Liang and Nengzhen Chen and Zhiwen Yu and Lei
Tang and Hongkai Yu and Bin Guo and Daniel Dajun Zeng",
title = "Learning Cross-modality Interaction for Robust Depth
Perception of Autonomous Driving",
journal = j-TIST,
volume = "15",
number = "3",
pages = "48:1--48:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650039",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650039",
abstract = "As one of the fundamental tasks of autonomous driving,
depth perception aims to perceive physical objects in
three dimensions and to judge their distances away from
the ego vehicle. Although great efforts have been made
for depth perception, LiDAR-based \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "48",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gupta:2024:TTA,
author = "Vinayak Gupta and Srikanta Bedathur",
title = "Tapestry of Time and Actions: Modeling Human Activity
Sequences Using Temporal Point Process Flows",
journal = j-TIST,
volume = "15",
number = "3",
pages = "49:1--49:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650045",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650045",
abstract = "Human beings always engage in a vast range of
activities and tasks that demonstrate their ability to
adapt to different scenarios. These activities can
range from the simplest daily routines, like walking
and sitting, to multi-level complex endeavors such
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "49",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yi:2024:DCM,
author = "Jing Yi and Zhenzhong Chen",
title = "Deconfounded Cross-modal Matching for Content-based
Micro-video Background Music Recommendation",
journal = j-TIST,
volume = "15",
number = "3",
pages = "50:1--50:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650042",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650042",
abstract = "Object-oriented micro-video background music
recommendation is a complicated task where the matching
degree between videos and background music is a major
issue. However, music selections in user-generated
content (UGC) are prone to selection bias caused
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "50",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fu:2024:MMH,
author = "Chaofan Fu and Pengyang Yu and Yanwei Yu and Chao
Huang and Zhongying Zhao and Junyu Dong",
title = "{MHGCN+}: Multiplex Heterogeneous Graph Convolutional
Network",
journal = j-TIST,
volume = "15",
number = "3",
pages = "51:1--51:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650046",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650046",
abstract = "Heterogeneous graph convolutional networks have gained
great popularity in tackling various network analytical
tasks on heterogeneous graph data, ranging from link
prediction to node classification. However, most
existing works ignore the relation \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "51",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2024:GTF,
author = "Xiaojin Zhang and Lixin Fan and Siwei Wang and Wenjie
Li and Kai Chen and Qiang Yang",
title = "A Game-theoretic Framework for Privacy-preserving
Federated Learning",
journal = j-TIST,
volume = "15",
number = "3",
pages = "52:1--52:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3656049",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3656049",
abstract = "In federated learning, benign participants aim to
optimize a global model collaboratively. However, the
risk of privacy leakage cannot be ignored in the
presence of semi-honest adversaries. Existing research
has focused either on designing protection \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "52",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhong:2024:SSB,
author = "Shenghai Zhong and Shu Guo and Jing Liu and Hongren
Huang and Lihong Wang and Jianxin Li and Chen Li and
Yiming Hei",
title = "Self-supervised Bipartite Graph Representation
Learning: a {Dirichlet} Max-margin Matrix Factorization
Approach",
journal = j-TIST,
volume = "15",
number = "3",
pages = "53:1--53:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3645098",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3645098",
abstract = "Bipartite graph representation learning aims to obtain
node embeddings by compressing sparse vectorized
representations of interactions between two types of
nodes, e.g., users and items. Incorporating structural
attributes among homogeneous nodes, such as \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "53",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zeng:2024:EPM,
author = "Jinwei Zeng and Guozhen Zhang and Jian Yuan and Yong
Li and Depeng Jin",
title = "Empowering Predictive Modeling by {GAN-based} Causal
Information Learning",
journal = j-TIST,
volume = "15",
number = "3",
pages = "54:1--54:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3652610",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3652610",
abstract = "Generally speaking, we can easily specify many causal
relationships in the prediction tasks of ubiquitous
computing, such as human activity prediction, mobility
prediction, and health prediction. However, most of the
existing methods in these fields \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "54",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhang:2024:MLF,
author = "Xiaojin Zhang and Yan Kang and Lixin Fan and Kai Chen
and Qiang Yang",
title = "A Meta-Learning Framework for Tuning Parameters of
Protection Mechanisms in Trustworthy Federated
Learning",
journal = j-TIST,
volume = "15",
number = "3",
pages = "55:1--55:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3652612",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3652612",
abstract = "Trustworthy federated learning typically leverages
protection mechanisms to guarantee privacy. However,
protection mechanisms inevitably introduce utility loss
or efficiency reduction while protecting data privacy.
Therefore, protection mechanisms and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "55",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lewis:2024:EFG,
author = "Cody Lewis and Vijay Varadharajan and Nasimul Noman
and Uday Tupakula",
title = "Ensuring Fairness and Gradient Privacy in Personalized
Heterogeneous Federated Learning",
journal = j-TIST,
volume = "15",
number = "3",
pages = "56:1--56:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3652613",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3652613",
abstract = "With the increasing tension between conflicting
requirements of the availability of large amounts of
data for effective machine learning-based analysis, and
for ensuring their privacy, the paradigm of federated
learning has emerged, a distributed machine \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "56",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bano:2024:FFC,
author = "Saira Bano and Nicola Tonellotto and Pietro
Cassar{\`a} and Alberto Gotta",
title = "{FedCMD}: a Federated Cross-modal Knowledge
Distillation for Drivers' Emotion Recognition",
journal = j-TIST,
volume = "15",
number = "3",
pages = "57:1--57:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650040",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650040",
abstract = "Emotion recognition has attracted a lot of interest in
recent years in various application areas such as
healthcare and autonomous driving. Existing approaches
to emotion recognition are based on visual, speech, or
psychophysiological signals. However, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "57",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2024:PAT,
author = "Rongchang Li and Tianyang Xu and Xiao-Jun Wu and
Zhongwei Shen and Josef Kittler",
title = "Perceiving Actions via Temporal Video Frame Pairs",
journal = j-TIST,
volume = "15",
number = "3",
pages = "58:1--58:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3652611",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3652611",
abstract = "Video action recognition aims at classifying the
action category in given videos. In general,
semantic-relevant video frame pairs reflect significant
action patterns such as object appearance variation and
abstract temporal concepts like speed, rhythm,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "58",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2024:SBG,
author = "Pengyu Wang and Xuechen Luo and Wenxin Tai and Kunpeng
Zhang and Goce Trajcevsky and Fan Zhou",
title = "Score-based Graph Learning for Urban Flow Prediction",
journal = j-TIST,
volume = "15",
number = "3",
pages = "59:1--59:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3655629",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3655629",
abstract = "Accurate urban flow prediction (UFP) is crucial for a
range of smart city applications such as traffic
management, urban planning, and risk assessment. To
capture the intrinsic characteristics of urban flow,
recent efforts have utilized spatial and \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "59",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{DeSmet:2024:HCA,
author = "Chance DeSmet and Diane Cook",
title = "{HydraGAN}: a Cooperative Agent Model for
Multi-Objective Data Generation",
journal = j-TIST,
volume = "15",
number = "3",
pages = "60:1--60:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3653982",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3653982",
abstract = "Generative adversarial networks have become a de facto
approach to generate synthetic data points that
resemble their real counterparts. We tackle the
situation where the realism of individual samples is
not the sole criterion for synthetic data \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "60",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhou:2024:QBR,
author = "Cangqi Zhou and Hui Chen and Jing Zhang and Qianmu Li
and Dianming Hu",
title = "Quintuple-based Representation Learning for Bipartite
Heterogeneous Networks",
journal = j-TIST,
volume = "15",
number = "3",
pages = "61:1--61:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3653978",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3653978",
abstract = "Recent years have seen rapid progress in network
representation learning, which removes the need for
burdensome feature engineering and facilitates
downstream network-based tasks. In reality, networks
often exhibit heterogeneity, which means there may
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "61",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sekulic:2024:AUL,
author = "Ivan Sekuli{\'c} and Mohammad Alinannejadi and Fabio
Crestani",
title = "Analysing Utterances in {LLM-Based} User Simulation
for Conversational Search",
journal = j-TIST,
volume = "15",
number = "3",
pages = "62:1--62:??",
month = jun,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650041",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:41 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650041",
abstract = "Clarifying underlying user information needs by asking
clarifying questions is an important feature of modern
conversational search systems. However, evaluation of
such systems through answering prompted clarifying
questions requires significant human \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "62",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Miao:2024:FMC,
author = "Runxuan Miao and Erdem Koyuncu",
title = "Federated Momentum Contrastive Clustering",
journal = j-TIST,
volume = "15",
number = "4",
pages = "63:1--63:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3653981",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3653981",
abstract = "Self-supervised representation learning and deep
clustering are mutually beneficial to learn
high-quality representations and cluster data
simultaneously in centralized settings. However, it is
not always feasible to gather large amounts of data at
a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "63",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Al-Bazzaz:2024:EFM,
author = "Hussein Al-Bazzaz and Muhammad Azam and Manar Amayri
and Nizar Bouguila",
title = "Explainable finite mixture of mixtures of bounded
asymmetric generalized {Gaussian} and Uniform
distributions learning for energy demand management",
journal = j-TIST,
volume = "15",
number = "4",
pages = "64:1--64:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3653980",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3653980",
abstract = "We introduce a mixture of mixtures of bounded
asymmetric generalized Gaussian and uniform
distributions. Based on this framework, we propose
model-based classification and model-based clustering
algorithms. We develop an objective function for the
minimum \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "64",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Piao:2024:DEL,
author = "Hai Yin Piao and Shengqi Yang and Hechang Chen and
Junnan Li and Jin Yu and Xuanqi Peng and Xin Yang and
Zhen Yang and Zhixiao Sun and Yi Chang",
title = "Discovering Expert-Level Air Combat Knowledge via Deep
Excitatory-Inhibitory Factorized Reinforcement
Learning",
journal = j-TIST,
volume = "15",
number = "4",
pages = "65:1--65:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3653979",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3653979",
abstract = "Artificial Intelligence (AI) has achieved a wide range
of successes in autonomous air combat decision-making
recently. Previous research demonstrated that
AI-enabled air combat approaches could even acquire
beyond human-level capabilities. However, there
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "65",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chen:2024:RSA,
author = "Xu Chen",
title = "Robust Structure-Aware Graph-based Semi-Supervised
Learning: Batch and Recursive Processing",
journal = j-TIST,
volume = "15",
number = "4",
pages = "66:1--66:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3653986",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3653986",
abstract = "Graph-based semi-supervised learning plays an
important role in large scale image classification
tasks. However, the problem becomes very challenging in
the presence of noisy labels and outliers. Moreover,
traditional robust semi-supervised learning \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "66",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Jian:2024:CGC,
author = "Meng Jian and Yulong Bai and Xusong Fu and Jingjing
Guo and Ge Shi and Lifang Wu",
title = "Counterfactual Graph Convolutional Learning for
Personalized Recommendation",
journal = j-TIST,
volume = "15",
number = "4",
pages = "67:1--67:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3655632",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3655632",
abstract = "Recently, recommender systems have witnessed the fast
evolution of Internet services. However, it suffers
hugely from inherent bias and sparsity issues in
interactions. The conventional uniform embedding
learning policies fail to utilize the imbalanced
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "67",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhu:2024:DCR,
author = "Yaochen Zhu and Jing Yi and Jiayi Xie and Zhenzhong
Chen",
title = "Deep Causal Reasoning for Recommendations",
journal = j-TIST,
volume = "15",
number = "4",
pages = "68:1--68:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3653985",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3653985",
abstract = "Traditional recommender systems aim to estimate a
user's rating to an item based on observed ratings from
the population. As with all observational studies,
hidden confounders, which are factors that affect both
item exposures and user ratings, lead to a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "68",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ting:2024:EEW,
author = "Lo Pang-Yun Ting and Rong Chao and Chai-Shi Chang and
Kun-Ta Chuang",
title = "An Explore-Exploit Workload-Bounded Strategy for Rare
Event Detection in Massive Energy Sensor Time Series",
journal = j-TIST,
volume = "15",
number = "4",
pages = "69:1--69:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3657641",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3657641",
abstract = "With the rise of Internet-of-Things devices, the
analysis of sensor-generated energy time series data
has become increasingly important. This is especially
crucial for detecting rare events like unusual
electricity usage or water leakages in residential
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "69",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhao:2024:CCG,
author = "Zhuo Zhao and Guangyou Zhou and Zhiwen Xie and Lingfei
Wu and Jimmy Xiangji Huang",
title = "{CGKPN}: Cross-Graph Knowledge Propagation Network
with Adaptive Connection for Reasoning-Based Machine
Reading Comprehension",
journal = j-TIST,
volume = "15",
number = "4",
pages = "70:1--70:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3658673",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3658673",
abstract = "The task of machine reading comprehension (MRC) is to
enable machine to read and understand a piece of text
and then answer the corresponding question correctly.
This task requires machine to not only be able to
perform semantic understanding but also \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "70",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Pai:2024:IDD,
author = "Yu-Tung Pai and Nien-En Sun and Cheng-Te Li and
Shou-de Lin",
title = "Incremental Data Drifting: Evaluation Metrics, Data
Generation, and Approach Comparison",
journal = j-TIST,
volume = "15",
number = "4",
pages = "71:1--71:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3655630",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3655630",
abstract = "Incremental data drifting is a common problem when
employing a machine-learning model in industrial
applications. The underlying data distribution evolves
gradually, e.g., users change their buying preferences
on an E-commerce website over time. The \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "71",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yao:2024:SIR,
author = "Lina Yao and Julian McAuley and Xianzhi Wang and
Dietmar Jannach",
title = "Special Issue on Responsible Recommender Systems {Part
1}",
journal = j-TIST,
volume = "15",
number = "4",
pages = "72:1--72:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3663528",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3663528",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "72",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ahn:2024:BPU,
author = "Yongsu Ahn and Yu-Ru Lin",
title = "Break Out of a Pigeonhole: a Unified Framework for
Examining Miscalibration, Bias, and Stereotype in
Recommender Systems",
journal = j-TIST,
volume = "15",
number = "4",
pages = "73:1--73:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650044",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650044",
abstract = "Despite the benefits of personalizing items and
information tailored to users' needs, it has been found
that recommender systems tend to introduce biases that
favor popular items or certain categories of items and
dominant user groups. In this study, we \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "73",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Coppolillo:2024:BQS,
author = "Erica Coppolillo and Marco Minici and Ettore Ritacco
and Luciano Caroprese and Francesco Pisani and Giuseppe
Manco",
title = "Balanced Quality Score: Measuring Popularity Debiasing
in Recommendation",
journal = j-TIST,
volume = "15",
number = "4",
pages = "74:1--74:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3650043",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3650043",
abstract = "Popularity bias is the tendency of recommender systems
to further suggest popular items while disregarding
niche ones, hence giving no chance for items with low
popularity to emerge. Although the literature is rich
in debiasing techniques, it still lacks \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "74",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Duran:2024:ODU,
author = "Paula G. Duran and Pere Gilabert and Santi Segu{\'\i}
and Jordi Vitri{\`a}",
title = "Overcoming Diverse Undesired Effects in Recommender
Systems: a Deontological Approach",
journal = j-TIST,
volume = "15",
number = "4",
pages = "75:1--75:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643857",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643857",
abstract = "In today's digital landscape, recommender systems have
gained ubiquity as a means of directing users toward
personalized products, services, and content. However,
despite their widespread adoption and a long track of
research, these systems are not immune \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "75",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Liu:2024:PPP,
author = "Yuwen Liu and Xiaokang Zhou and Huaizhen Kou and Yawu
Zhao and Xiaolong Xu and Xuyun Zhang and Lianyong Qi",
title = "Privacy-preserving Point-of-interest Recommendation
based on Simplified Graph Convolutional Network for
Geological Traveling",
journal = j-TIST,
volume = "15",
number = "4",
pages = "76:1--76:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3620677",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3620677",
abstract = "The provision of privacy-preserving recommendations
for geological tourist attractions is an important
research area. The historical check-in data collected
from location-based social networks (LBSNs) can be
utilized to mine their preferences, thereby \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "76",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2024:DFR,
author = "Zhitao Li and Zhaohao Lin and Feng Liang and Weike Pan
and Qiang Yang and Zhong Ming",
title = "Decentralized Federated Recommendation with
Privacy-aware Structured Client-level Graph",
journal = j-TIST,
volume = "15",
number = "4",
pages = "77:1--77:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3641287",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3641287",
abstract = "Recommendation models are deployed in a variety of
commercial applications to provide personalized
services for users. However, most of them rely on the
users' original rating records that are often collected
by a centralized server for model training, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "77",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ali:2024:RRS,
author = "Waqar Ali and Rajesh Kumar and Xiangmin Zhou and Jie
Shao",
title = "Responsible Recommendation Services with Blockchain
Empowered Asynchronous Federated Learning",
journal = j-TIST,
volume = "15",
number = "4",
pages = "78:1--78:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3633520",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3633520",
abstract = "Privacy and trust are highly demanding in practical
recommendation engines. Although Federated Learning
(FL) has significantly addressed privacy concerns,
commercial operators are still worried about several
technical challenges while bringing FL into \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "78",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gao:2024:NBB,
author = "Tieliang Gao and Li Duan and Lufeng Feng and Wei Ni
and Quan Z. Sheng",
title = "A Novel Blockchain-based Responsible Recommendation
System for Service Process Creation and
Recommendation",
journal = j-TIST,
volume = "15",
number = "4",
pages = "79:1--79:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643858",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/bitcoin.bib;
https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643858",
abstract = "Service composition platforms play a crucial role in
creating personalized service processes. Challenges,
including the risk of tampering with service data
during service invocation and the potential single
point of failure in centralized service \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "79",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2024:FQR,
author = "Nan Li and Bo Kang and Jefrey Lijffijt and Tijl {De
Bie}",
title = "{FEIR}: Quantifying and Reducing Envy and Inferiority
for Fair Recommendation of Limited Resources",
journal = j-TIST,
volume = "15",
number = "4",
pages = "80:1--80:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643891",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643891",
abstract = "Recommendation in settings such as e-recruitment and
online dating involves distributing limited
opportunities, which differs from recommending
practically unlimited goods such as in e-commerce or
music recommendation. This setting calls for novel
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "80",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Li:2024:BHE,
author = "Ming Li and Lin Li and Xiaohui Tao and Zhongwei Xie
and Qing Xie and Jingling Yuan",
title = "Boosting Healthiness Exposure in Category-Constrained
Meal Recommendation Using Nutritional Standards",
journal = j-TIST,
volume = "15",
number = "4",
pages = "81:1--81:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3643859",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3643859",
abstract = "Food computing, a newly emerging topic, is closely
linked to human life through computational
methodologies. Meal recommendation, a food-related
study about human health, aims to provide users a meal
with courses constrained from specific categories
(e.g.,. \ldots{})",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "81",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ma:2024:PFR,
author = "Jianghong Ma and Huiyue Sun and Dezhao Yang and Haijun
Zhang",
title = "Personalized Fashion Recommendations for Diverse Body
Shapes with Contrastive Multimodal Cross-Attention
Network",
journal = j-TIST,
volume = "15",
number = "4",
pages = "82:1--82:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3637217",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3637217",
abstract = "Fashion recommendation has become a prominent focus in
the realm of online shopping, with various tasks being
explored to enhance the customer experience. Recent
research has particularly emphasized fashion
recommendation based on body shapes, yet a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "82",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Elahi:2024:KGE,
author = "Ehsan Elahi and Sajid Anwar and Babar Shah and Zahid
Halim and Abrar Ullah and Imad Rida and Muhammad
Waqas",
title = "Knowledge Graph Enhanced Contextualized
Attention-Based Network for Responsible User-Specific
Recommendation",
journal = j-TIST,
volume = "15",
number = "4",
pages = "83:1--83:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3641288",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3641288",
abstract = "With ever-increasing dataset size and data storage
capacity, there is a strong need to build systems that
can effectively utilize these vast datasets to extract
valuable information. Large datasets often exhibit
sparsity and pose cold start problems, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "83",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2024:TRS,
author = "Shoujin Wang and Xiuzhen Zhang and Yan Wang and
Francesco Ricci",
title = "Trustworthy Recommender Systems",
journal = j-TIST,
volume = "15",
number = "4",
pages = "84:1--84:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3627826",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3627826",
abstract = "Recommender systems (RSs) aim at helping users to
effectively retrieve items of their interests from a
large catalogue. For a quite long time, researchers and
practitioners have been focusing on developing accurate
RSs. Recent years have witnessed an \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "84",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yu:2024:MMS,
author = "Dongjin Yu and Xingliang Wang and Yu Xiong and Xudong
Shen and Runze Wu and Dongjing Wang and Zhene Zou and
Guandong Xu",
title = "{MHANER}: a Multi-source Heterogeneous Graph Attention
Network for Explainable Recommendation in Online
Games",
journal = j-TIST,
volume = "15",
number = "4",
pages = "85:1--85:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3626243",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3626243",
abstract = "Recommender system helps address information overload
problem and satisfy consumers' personalized requirement
in many applications such as e-commerce, social
networks, and in-game store. However, existing
approaches mainly focus on improving the accuracy
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "85",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ren:2024:EKG,
author = "Xuhui Ren and Tong Chen and Quoc Viet Hung Nguyen and
Lizhen Cui and Zi Huang and Hongzhi Yin",
title = "Explicit Knowledge Graph Reasoning for Conversational
Recommendation",
journal = j-TIST,
volume = "15",
number = "4",
pages = "86:1--86:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3637216",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3637216",
abstract = "Traditional recommender systems estimate user
preference on items purely based on historical
interaction records, thus failing to capture
fine-grained yet dynamic user interests and letting
users receive recommendation only passively. Recent
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "86",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Lu:2024:ACD,
author = "Kezhi Lu and Qian Zhang and Danny Hughes and Guangquan
Zhang and Jie Lu",
title = "{AMT-CDR}: a Deep Adversarial Multi-Channel Transfer
Network for Cross-Domain Recommendation",
journal = j-TIST,
volume = "15",
number = "4",
pages = "87:1--87:??",
month = aug,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3641286",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Thu Aug 29 08:03:44 MDT 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3641286",
abstract = "Recommender systems are one of the most successful
applications of using AI for providing personalized
e-services to customers. However, data sparsity is
presenting enormous challenges that are hindering the
further development of advanced recommender \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "87",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Gilman:2024:ADC,
author = "Ekaterina Gilman and Francesca Bugiotti and Ahmed
Khalid and Hassan Mehmood and Panos Kostakos and Lauri
Tuovinen and Johanna Ylipulli and Xiang Su and Denzil
Ferreira",
title = "Addressing Data Challenges to Drive the Transformation
of Smart Cities",
journal = j-TIST,
volume = "15",
number = "5",
pages = "88:1--88:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3663482",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3663482",
abstract = "Cities serve as vital hubs of economic activity and
knowledge generation and dissemination. As such, cities
bear a significant responsibility to uphold
environmental protection measures while promoting the
welfare and living comfort of their residents.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "88",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sharma:2024:NMD,
author = "Mandar Sharma and Ajay Kumar Gogineni and Naren
Ramakrishnan",
title = "Neural Methods for Data-to-text Generation",
journal = j-TIST,
volume = "15",
number = "5",
pages = "89:1--89:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3660639",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3660639",
abstract = "The neural boom that has sparked natural language
processing (NLP) research throughout the last decade
has similarly led to significant innovations in
data-to-text (D2T) generation. This survey offers a
consolidated view into the neural D2T paradigm with
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "89",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Diffallah:2024:TSF,
author = "Zhor Diffallah and Hadjer Ykhlef and Hafida Bouarfa",
title = "Teacher--Student Framework for Polyphonic
Semi-supervised Sound Event Detection: Survey and
Empirical Analysis",
journal = j-TIST,
volume = "15",
number = "5",
pages = "90:1--90:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3660641",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3660641",
abstract = "Polyphonic sound event detection refers to the task of
automatically identifying sound events occurring
simultaneously in an auditory scene. Due to the
inherent complexity and variability of real-world
auditory scenes, building robust detectors for
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "90",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fan:2024:BRL,
author = "Lizhou Fan and Lingyao Li and Zihui Ma and Sanggyu Lee
and Huizi Yu and Libby Hemphill",
title = "A Bibliometric Review of Large Language Models
Research from 2017 to 2023",
journal = j-TIST,
volume = "15",
number = "5",
pages = "91:1--91:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3664930",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3664930",
abstract = "Large language models (LLMs), such as OpenAI's
Generative Pre-trained Transformer (GPT), are a class
of language models that have demonstrated outstanding
performance across a range of natural language
processing (NLP) tasks. LLMs have become a highly
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "91",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Choudhary:2024:BTM,
author = "Monika Choudhary and Satyendra Singh Chouhan and
Santosh Singh Rathore",
title = "Beyond Text: Multimodal Credibility Assessment
Approaches for Online User-Generated Content",
journal = j-TIST,
volume = "15",
number = "5",
pages = "92:1--92:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3673236",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3673236",
abstract = "User-generated content (UGC) is increasingly becoming
prevalent on various digital platforms. The content
generated on social media, review forums, and
question-answer platforms impacts a larger audience and
influences their political, social, and other
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "92",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Layne:2024:ARA,
author = "Janet Layne and Qudrat E. Alahy Ratul and Edoardo
Serra and Sushil Jajodia",
title = "Analyzing Robustness of Automatic Scientific Claim
Verification Tools against Adversarial Rephrasing
Attacks",
journal = j-TIST,
volume = "15",
number = "5",
pages = "93:1--93:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3663481",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3663481",
abstract = "The coronavirus pandemic has fostered an explosion of
misinformation about the disease, including the risk
and effectiveness of vaccination. AI tools for
automatic Scientific Claim Verification (SCV) can be
crucial to defeat misinformation campaigns \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "93",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Mahajan:2024:PPD,
author = "Yash Mahajan and Jin-Hee Cho and Ing-Ray Chen",
title = "Privacy-Preserving and Diversity-Aware Trust-based
Team Formation in Online Social Networks",
journal = j-TIST,
volume = "15",
number = "5",
pages = "94:1--94:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3670411",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3670411",
abstract = "As online social networks (OSNs) become more
prevalent, a new paradigm for problem-solving through
crowd-sourcing has emerged. By leveraging the OSN
platforms, users can post a problem to be solved and
then form a team to collaborate and solve the
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "94",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Bi:2024:MAA,
author = "Haoyang Bi and Qi Liu and Han Wu and Weidong He and
Zhenya Huang and Yu Yin and Haiping Ma and Yu Su and
Shijin Wang and Enhong Chen",
title = "Model-Agnostic Adaptive Testing for Intelligent
Education Systems via Meta-learned Gradient
Embeddings",
journal = j-TIST,
volume = "15",
number = "5",
pages = "95:1--95:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3660642",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3660642",
abstract = "The field of education has undergone a significant
revolution with the advent of intelligent systems and
technology, which aim to personalize the learning
experience, catering to the unique needs and abilities
of individual learners. In this pursuit, a \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "95",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Yu:2024:FGC,
author = "Fudan Yu and Guozhen Zhang and Haotian Wang and Depeng
Jin and Yong Li",
title = "Fine-grained {Courier} Delivery Behavior Recovery with
a Digital Twin Based Iterative Calibration Framework",
journal = j-TIST,
volume = "15",
number = "5",
pages = "96:1--96:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3663484",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3663484",
abstract = "Recovering the fine-grained working process of
couriers is becoming one of the essential problems for
improving the express delivery systems because knowing
the detailed process of how couriers accomplish their
daily work facilitates the analyzing, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "96",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wu:2024:DDI,
author = "Xuansheng Wu and Hanqin Wan and Qiaoyu Tan and Wenlin
Yao and Ninghao Liu",
title = "{DIRECT}: Dual Interpretable Recommendation with
Multi-aspect Word Attribution",
journal = j-TIST,
volume = "15",
number = "5",
pages = "97:1--97:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3663483",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3663483",
abstract = "Recommending products to users with intuitive
explanations helps improve the system in transparency,
persuasiveness, and satisfaction. Existing
interpretation techniques include post hoc methods and
interpretable modeling. The former category could
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "97",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Luo:2024:PEP,
author = "Sichun Luo and Yuanzhang Xiao and Xinyi Zhang and Yang
Liu and Wenbo Ding and Linqi Song",
title = "{PerFedRec++}: Enhancing Personalized Federated
Recommendation with Self-Supervised Pre-Training",
journal = j-TIST,
volume = "15",
number = "5",
pages = "98:1--98:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3664927",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3664927",
abstract = "Federated recommendation systems employ federated
learning techniques to safeguard user privacy by
transmitting model parameters instead of raw user data
between user devices and the central server.
Nevertheless, the current federated recommender system
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "98",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Wang:2024:DDN,
author = "Dexian Wang and Tianrui Li and Ping Deng and Zhipeng
Luo and Pengfei Zhang and Keyu Liu and Wei Huang",
title = "{DNSRF}: Deep Network-based {Semi-NMF} Representation
Framework",
journal = j-TIST,
volume = "15",
number = "5",
pages = "99:1--99:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3670408",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3670408",
abstract = "Representation learning is an important topic in
machine learning, pattern recognition, and data mining
research. Among many representation learning
approaches, semi-nonnegative matrix factorization
(SNMF) is a frequently-used one. However, a typical
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "99",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Levy:2024:RTA,
author = "Moshe Levy and Guy Amit and Yuval Elovici and Yisroel
Mirsky",
title = "Ranking the Transferability of Adversarial Examples",
journal = j-TIST,
volume = "15",
number = "5",
pages = "100:1--100:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3670409",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3670409",
abstract = "Adversarial transferability in blackbox scenarios
presents a unique challenge: while attackers can employ
surrogate models to craft adversarial examples, they
lack assurance on whether these examples will
successfully compromise the target model. Until
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "100",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Cai:2024:MRB,
author = "Miaomiao Cai and Min Hou and Lei Chen and Le Wu and
Haoyue Bai and Yong Li and Meng Wang",
title = "Mitigating Recommendation Biases via Group-Alignment
and Global-Uniformity in Representation Learning",
journal = j-TIST,
volume = "15",
number = "5",
pages = "101:1--101:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3664931",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3664931",
abstract = "Collaborative Filtering (CF) plays a crucial role in
modern recommender systems, leveraging historical
user-item interactions to provide personalized
suggestions. However, CF-based methods often encounter
biases due to imbalances in training data. This
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "101",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Baghersalimi:2024:MMS,
author = "Saleh Baghersalimi and Alireza Amirshahi and Farnaz
Forooghifar and Tomas Teijeiro and Amir Aminifar and
David Atienza",
title = "{M2SKD}: Multi-to-Single Knowledge Distillation of
Real-Time Epileptic Seizure Detection for Low-Power
Wearable Systems",
journal = j-TIST,
volume = "15",
number = "5",
pages = "102:1--102:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3675402",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3675402",
abstract = "Integrating low-power wearable systems into routine
health monitoring is an ongoing challenge. Recent
advances in the computation capabilities of wearables
make it possible to target complex scenarios by
exploiting multiple biosignals and using high-.
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "102",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Breve:2024:HPL,
author = "Bernardo Breve and Gaetano Cimino and Vincenzo
Deufemia",
title = "Hybrid Prompt Learning for Generating Justifications
of Security Risks in Automation Rules",
journal = j-TIST,
volume = "15",
number = "5",
pages = "103:1--103:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3675401",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3675401",
abstract = "Trigger-action platforms (TAPs) enable users without
programming experience to personalize the behavior of
Internet of Things applications and services through
IF-THEN rules. Unfortunately, the arbitrary connection
of smart devices and online services, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "103",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Fu:2024:DOD,
author = "Shucun Fu and Fang Dong and Dian Shen and Runze Chen
and Jiangshan Hao",
title = "{DESIGN}: Online Device Selection and Edge Association
for Federated Synergy Learning-enabled {AIoT}",
journal = j-TIST,
volume = "15",
number = "5",
pages = "104:1--104:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3673237",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3673237",
abstract = "The artificial intelligence of things (AIoT) is an
emerging technology that enables numerous AIoT devices
to participate in big data analytics and machine
learning (ML) model training, providing various
customized intelligent services for industry \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "104",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Makhdomi:2024:FER,
author = "Aqsa Ashraf Makhdomi and Iqra Altaf Gillani",
title = "Fair and Efficient Ridesharing: a Dynamic
Programming-based Relocation Approach",
journal = j-TIST,
volume = "15",
number = "5",
pages = "105:1--105:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3675403",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3675403",
abstract = "Recommending routes by their probability of having a
rider has long been the goal of conventional route
recommendation systems. While this maximizes the
platform-specific criteria of efficiency, it results in
sub-optimal outcomes with the disparity among
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "105",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sharma:2024:PBA,
author = "Arun Sharma and Subhankar Ghosh and Shashi Shekhar",
title = "Physics-Based Abnormal Trajectory Gap Detection",
journal = j-TIST,
volume = "15",
number = "5",
pages = "106:1--106:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3673235",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3673235",
abstract = "Given trajectories with gaps (i.e., missing data), we
investigate algorithms to identify abnormal gaps in
trajectories which occur when a given moving object did
not report its location, but other moving objects in
the same geographic region periodically \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "106",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Sghaier:2024:LNB,
author = "Oussama Sghaier and Manar Amayri and Nizar Bouguila",
title = "{Libby--Novick} Beta-{Liouville} Distribution for
Enhanced Anomaly Detection in Proportional Data",
journal = j-TIST,
volume = "15",
number = "5",
pages = "107:1--107:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3675405",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3675405",
abstract = "We consider the problem of anomaly detection in
proportional data by investigating the Libby-Novick
Beta-Liouville distribution, a novel distribution
merging the salient characteristics of Liouville and
Libby-Novick Beta distributions. Its main benefit,
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "107",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Zhuang:2024:MEN,
author = "Yan Zhuang and Junyan Zhang and Ruogu Lu and Kunlun He
and Xiuxing Li",
title = "{MedNER}: Enhanced Named Entity Recognition in Medical
Corpus via Optimized Balanced and Deep Active
Learning",
journal = j-TIST,
volume = "15",
number = "5",
pages = "108:1--108:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3678178",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3678178",
abstract = "Ever-growing electronic medical corpora provide
unprecedented opportunities for researchers to analyze
patient conditions and drug effects. Meanwhile, severe
challenges emerged in the large-scale electronic
medical records process phase. Primarily, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "108",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Ting:2024:OST,
author = "Lo Pang-Yun Ting and Huan-Yang Wang and Jhe-Yun Jhang
and Kun-Ta Chuang",
title = "Online Spatial-Temporal {EV} Charging Scheduling with
Incentive Promotion",
journal = j-TIST,
volume = "15",
number = "5",
pages = "109:1--109:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3678180",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3678180",
abstract = "The growing adoption of electric vehicles (EVs) has
resulted in an increased demand for public EV charging
infrastructure. Currently, the collaboration between
these stations has become vital for efficient charging
scheduling and cost reduction. However, \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "109",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{LaQuatra:2024:SST,
author = "Moreno {La Quatra} and Giuseppe Gallipoli and Luca
Cagliero",
title = "Self-supervised Text Style Transfer Using
Cycle-Consistent Adversarial Networks",
journal = j-TIST,
volume = "15",
number = "5",
pages = "110:1--110:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3678179",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3678179",
abstract = "Text Style Transfer (TST) is a relevant branch of
natural language processing that aims to control the
style attributes of a piece of text while preserving
its original content. To address TST in the absence of
parallel data, Cycle-consistent Generative \ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "110",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
@Article{Chi:2024:WSZ,
author = "Te-Yu Chi and Jyh-Shing Roger Jang",
title = "{WC-SBERT}: Zero-Shot Topic Classification Using
{SBERT} and Light Self-Training on {Wikipedia}
Categories",
journal = j-TIST,
volume = "15",
number = "5",
pages = "111:1--111:??",
month = oct,
year = "2024",
CODEN = "????",
DOI = "https://doi.org/10.1145/3678183",
ISSN = "2157-6904 (print), 2157-6912 (electronic)",
ISSN-L = "2157-6904",
bibdate = "Sat Nov 9 16:17:42 MST 2024",
bibsource = "https://www.math.utah.edu/pub/tex/bib/tist.bib",
URL = "https://dl.acm.org/doi/10.1145/3678183",
abstract = "In natural language processing (NLP), zero-shot topic
classification requires machines to understand the
contextual meanings of texts in a downstream task
without using the corresponding labeled texts for
training, which is highly desirable for various
\ldots{}",
acknowledgement = ack-nhfb,
ajournal = "ACM Trans. Intell. Syst. Technol.",
articleno = "111",
fjournal = "ACM Transactions on Intelligent Systems and Technology
(TIST)",
journal-URL = "https://dl.acm.org/loi/tist",
}
