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%%% -*-BibTeX-*-
%%% ====================================================================
%%% BibTeX-file{
%%%     author          = "Nelson H. F. Beebe",
%%%     version         = "1.35",
%%%     date            = "25 September 2024",
%%%     time            = "11:24:47 MDT",
%%%     filename        = "tiis.bib",
%%%     address         = "University of Utah
%%%                        Department of Mathematics, 110 LCB
%%%                        155 S 1400 E RM 233
%%%                        Salt Lake City, UT 84112-0090
%%%                        USA",
%%%     telephone       = "+1 801 581 5254",
%%%     FAX             = "+1 801 581 4148",
%%%     URL             = "https://www.math.utah.edu/~beebe",
%%%     checksum        = "53367 15495 83072 787186",
%%%     email           = "beebe at math.utah.edu, beebe at acm.org,
%%%                        beebe at computer.org (Internet)",
%%%     codetable       = "ISO/ASCII",
%%%     keywords        = "bibliography; BibTeX; ACM Transactions on
%%%                        Interactive Intelligent Systems (TIIS)",
%%%     license         = "public domain",
%%%     supported       = "yes",
%%%     docstring       = "This is a COMPLETE BibTeX bibliography for
%%%                        ACM Transactions on Interactive Intelligent
%%%                        Systems (TIIS) (CODEN ????, ISSN 2160-6455
%%%                        (print), 2160-6463 (electronic)), covering
%%%                        all journal issues from 2011 -- date.
%%%
%%%                        At version 1.35, the COMPLETE journal
%%%                        coverage looked like this:
%%%
%%%                             2011 (   6)    2016 (  36)    2021 (  32)
%%%                             2012 (  24)    2017 (  19)    2022 (  36)
%%%                             2013 (  20)    2018 (  32)    2023 (  30)
%%%                             2014 (  20)    2019 (  24)    2024 (  24)
%%%                             2015 (  24)    2020 (  34)
%%%
%%%                             Article:        361
%%%
%%%                             Total entries:  361
%%%
%%%                        The journal Web site can be found at:
%%%
%%%                            http://tiis.acm.org/
%%%                            http://www.acm.org/tiis/
%%%
%%%                        The journal table of contents page is at:
%%%
%%%                            http://dl.acm.org/pub.cfm?id=J1341
%%%                            http://portal.acm.org/browse_dl.cfm?idx=J1341
%%%
%%%                        Qualified subscribers can retrieve the full
%%%                        text of recent articles in PDF form.
%%%
%%%                        The initial draft was extracted from the ACM
%%%                        Web pages.
%%%
%%%                        ACM copyrights explicitly permit abstracting
%%%                        with credit, so article abstracts, keywords,
%%%                        and subject classifications have been
%%%                        included in this bibliography wherever
%%%                        available.  Article reviews have been
%%%                        omitted, until their copyright status has
%%%                        been clarified.
%%%
%%%                        bibsource keys in the bibliography entries
%%%                        below indicate the entry originally came
%%%                        from the computer science bibliography
%%%                        archive, even though it has likely since
%%%                        been corrected and updated.
%%%
%%%                        URL keys in the bibliography point to
%%%                        World Wide Web locations of additional
%%%                        information about the entry.
%%%
%%%                        BibTeX citation tags are uniformly chosen
%%%                        as name:year:abbrev, where name is the
%%%                        family name of the first author or editor,
%%%                        year is a 4-digit number, and abbrev is a
%%%                        3-letter condensation of important title
%%%                        words. Citation tags were automatically
%%%                        generated by software developed for the
%%%                        BibNet Project.
%%%
%%%                        In this bibliography, entries are sorted in
%%%                        publication order, using ``bibsort -byvolume.''
%%%
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%%%                        checksum as the first value, followed by the
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%%% ====================================================================
%%% Acknowledgement abbreviations:
@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/|"}

%%% ====================================================================
%%% Journal abbreviations:
@String{j-TIIS                  = "ACM Transactions on Interactive Intelligent
                                  Systems (TIIS)"}

%%% ====================================================================
%%% Bibliography entries:
@Article{Jameson:2011:ITI,
  author =       "Anthony Jameson and John Riedl",
  title =        "Introduction to the {Transactions on Interactive
                 Intelligent Systems}",
  journal =      j-TIIS,
  volume =       "1",
  number =       "1",
  pages =        "1:1--1:??",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2030365.2030366",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Nov 3 17:51:10 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kulesza:2011:WOE,
  author =       "Todd Kulesza and Simone Stumpf and Weng-Keen Wong and
                 Margaret M. Burnett and Stephen Perona and Andrew Ko
                 and Ian Oberst",
  title =        "Why-oriented end-user debugging of naive {Bayes} text
                 classification",
  journal =      j-TIIS,
  volume =       "1",
  number =       "1",
  pages =        "2:1--2:??",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2030365.2030367",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Nov 3 17:51:10 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hoi:2011:AMK,
  author =       "Steven C. H. Hoi and Rong Jin",
  title =        "Active multiple kernel learning for interactive {$3$D}
                 object retrieval systems",
  journal =      j-TIIS,
  volume =       "1",
  number =       "1",
  pages =        "3:1--3:??",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2030365.2030368",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Nov 3 17:51:10 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hammond:2011:RSM,
  author =       "Tracy Hammond and Brandon Paulson",
  title =        "Recognizing sketched multistroke primitives",
  journal =      j-TIIS,
  volume =       "1",
  number =       "1",
  pages =        "4:1--4:??",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2030365.2030369",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Nov 3 17:51:10 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Okita:2011:MAA,
  author =       "Sandra Y. Okita and Victor Ng-Thow-Hing and Ravi K.
                 Sarvadevabhatla",
  title =        "Multimodal approach to affective human-robot
                 interaction design with children",
  journal =      j-TIIS,
  volume =       "1",
  number =       "1",
  pages =        "5:1--5:??",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2030365.2030370",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Nov 3 17:51:10 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Gibet:2011:SSD,
  author =       "Sylvie Gibet and Nicolas Courty and Kyle Duarte and
                 Thibaut Le Naour",
  title =        "The {SignCom} system for data-driven animation of
                 interactive virtual signers: Methodology and
                 Evaluation",
  journal =      j-TIIS,
  volume =       "1",
  number =       "1",
  pages =        "6:1--6:??",
  month =        oct,
  year =         "2011",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2030365.2030371",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Nov 3 17:51:10 MDT 2011",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Castellano:2012:ISI,
  author =       "Ginevra Castellano and Laurel D. Riek and Christopher
                 Peters and Kostas Karpouzis and Jean-Claude Martin and
                 Louis-Philippe Morency",
  title =        "Introduction to the special issue on affective
                 interaction in natural environments",
  journal =      j-TIIS,
  volume =       "2",
  number =       "1",
  pages =        "1:1--1:??",
  month =        mar,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2133366.2133367",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Mar 16 12:34:07 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Affect-sensitive systems such as social robots and
                 virtual agents are increasingly being investigated in
                 real-world settings. In order to work effectively in
                 natural environments, these systems require the ability
                 to infer the affective and mental states of humans and
                 to provide appropriate timely output that helps to
                 sustain long-term interactions. This special issue,
                 which appears in two parts, includes articles on the
                 design of socio-emotional behaviors and expressions in
                 robots and virtual agents and on computational
                 approaches for the automatic recognition of social
                 signals and affective states.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Beck:2012:EBL,
  author =       "Aryel Beck and Brett Stevens and Kim A. Bard and Lola
                 Ca{\~n}amero",
  title =        "Emotional body language displayed by artificial
                 agents",
  journal =      j-TIIS,
  volume =       "2",
  number =       "1",
  pages =        "2:1--2:??",
  month =        mar,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2133366.2133368",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Mar 16 12:34:07 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Complex and natural social interaction between
                 artificial agents (computer-generated or robotic) and
                 humans necessitates the display of rich emotions in
                 order to be believable, socially relevant, and
                 accepted, and to generate the natural emotional
                 responses that humans show in the context of social
                 interaction, such as engagement or empathy. Whereas
                 some robots use faces to display (simplified) emotional
                 expressions, for other robots such as Nao, body
                 language is the best medium available given their
                 inability to convey facial expressions. Displaying
                 emotional body language that can be interpreted whilst
                 interacting with the robot should significantly improve
                 naturalness. This research investigates the creation of
                 an affect space for the generation of emotional body
                 language to be displayed by humanoid robots. To do so,
                 three experiments investigating how emotional body
                 language displayed by agents is interpreted were
                 conducted. The first experiment compared the
                 interpretation of emotional body language displayed by
                 humans and agents. The results showed that emotional
                 body language displayed by an agent or a human is
                 interpreted in a similar way in terms of recognition.
                 Following these results, emotional key poses were
                 extracted from an actor's performances and implemented
                 in a Nao robot. The interpretation of these key poses
                 was validated in a second study where it was found that
                 participants were better than chance at interpreting
                 the key poses displayed. Finally, an affect space was
                 generated by blending key poses and validated in a
                 third study. Overall, these experiments confirmed that
                 body language is an appropriate medium for robots to
                 display emotions and suggest that an affect space for
                 body expressions can be used to improve the
                 expressiveness of humanoid robots.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hiolle:2012:ECB,
  author =       "Antoine Hiolle and Lola Ca{\~n}amero and Marina
                 Davila-Ross and Kim A. Bard",
  title =        "Eliciting caregiving behavior in dyadic human-robot
                 attachment-like interactions",
  journal =      j-TIIS,
  volume =       "2",
  number =       "1",
  pages =        "3:1--3:??",
  month =        mar,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2133366.2133369",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Mar 16 12:34:07 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We present here the design and applications of an
                 arousal-based model controlling the behavior of a Sony
                 AIBO robot during the exploration of a novel
                 environment: a children's play mat. When the robot
                 experiences too many new perceptions, the increase of
                 arousal triggers calls for attention towards its human
                 caregiver. The caregiver can choose to either calm the
                 robot down by providing it with comfort, or to leave
                 the robot coping with the situation on its own. When
                 the arousal of the robot has decreased, the robot moves
                 on to further explore the play mat. We gathered results
                 from two experiments using this arousal-driven control
                 architecture. In the first setting, we show that such a
                 robotic architecture allows the human caregiver to
                 influence greatly the learning outcomes of the
                 exploration episode, with some similarities to a
                 primary caregiver during early childhood. In a second
                 experiment, we tested how human adults behaved in a
                 similar setup with two different robots: one `needy',
                 often demanding attention, and one more independent,
                 requesting far less care or assistance. Our results
                 show that human adults recognise each profile of the
                 robot for what they have been designed, and behave
                 accordingly to what would be expected, caring more for
                 the needy robot than for the other. Additionally, the
                 subjects exhibited a preference and more positive
                 affect whilst interacting and rating the robot we
                 designed as needy. This experiment leads us to the
                 conclusion that our architecture and setup succeeded in
                 eliciting positive and caregiving behavior from adults
                 of different age groups and technological background.
                 Finally, the consistency and reactivity of the robot
                 during this dyadic interaction appeared crucial for the
                 enjoyment and engagement of the human partner.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Scherer:2012:SLN,
  author =       "Stefan Scherer and Michael Glodek and Friedhelm
                 Schwenker and Nick Campbell and G{\"u}nther Palm",
  title =        "Spotting laughter in natural multiparty conversations:
                 a comparison of automatic online and offline approaches
                 using audiovisual data",
  journal =      j-TIIS,
  volume =       "2",
  number =       "1",
  pages =        "4:1--4:??",
  month =        mar,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2133366.2133370",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Mar 16 12:34:07 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "It is essential for the advancement of human-centered
                 multimodal interfaces to be able to infer the current
                 user's state or communication state. In order to enable
                 a system to do that, the recognition and interpretation
                 of multimodal social signals (i.e., paralinguistic and
                 nonverbal behavior) in real-time applications is
                 required. Since we believe that laughs are one of the
                 most important and widely understood social nonverbal
                 signals indicating affect and discourse quality, we
                 focus in this work on the detection of laughter in
                 natural multiparty discourses. The conversations are
                 recorded in a natural environment without any specific
                 constraint on the discourses using unobtrusive
                 recording devices. This setup ensures natural and
                 unbiased behavior, which is one of the main foci of
                 this work. To compare results of methods, namely
                 Gaussian Mixture Model (GMM) supervectors as input to a
                 Support Vector Machine (SVM), so-called Echo State
                 Networks (ESN), and a Hidden Markov Model (HMM)
                 approach, are utilized in online and offline detection
                 experiments. The SVM approach proves very accurate in
                 the offline classification task, but is outperformed by
                 the ESN and HMM approach in the online detection (F 1
                 scores: GMM SVM 0.45, ESN 0.63, HMM 0.72). Further, we
                 were able to utilize the proposed HMM approach in a
                 cross-corpus experiment without any retraining with
                 respectable generalization capability (F 1 score:
                 0.49). The results and possible reasons for these
                 outcomes are shown and discussed in the article. The
                 proposed methods may be directly utilized in practical
                 tasks such as the labeling or the online detection of
                 laughter in conversational data and affect-aware
                 applications.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Song:2012:CBH,
  author =       "Yale Song and David Demirdjian and Randall Davis",
  title =        "Continuous body and hand gesture recognition for
                 natural human-computer interaction",
  journal =      j-TIIS,
  volume =       "2",
  number =       "1",
  pages =        "5:1--5:??",
  month =        mar,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2133366.2133371",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Mar 16 12:34:07 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Intelligent gesture recognition systems open a new era
                 of natural human-computer interaction: Gesturing is
                 instinctive and a skill we all have, so it requires
                 little or no thought, leaving the focus on the task
                 itself, as it should be, not on the interaction
                 modality. We present a new approach to gesture
                 recognition that attends to both body and hands, and
                 interprets gestures continuously from an unsegmented
                 and unbounded input stream. This article describes the
                 whole procedure of continuous body and hand gesture
                 recognition, from the signal acquisition to processing,
                 to the interpretation of the processed signals. Our
                 system takes a vision-based approach, tracking body and
                 hands using a single stereo camera. Body postures are
                 reconstructed in 3D space using a generative
                 model-based approach with a particle filter, combining
                 both static and dynamic attributes of motion as the
                 input feature to make tracking robust to
                 self-occlusion. The reconstructed body postures guide
                 searching for hands. Hand shapes are classified into
                 one of several canonical hand shapes using an
                 appearance-based approach with a multiclass support
                 vector machine. Finally, the extracted body and hand
                 features are combined and used as the input feature for
                 gesture recognition. We consider our task as an online
                 sequence labeling and segmentation problem. A
                 latent-dynamic conditional random field is used with a
                 temporal sliding window to perform the task
                 continuously. We augment this with a novel technique
                 called multilayered filtering, which performs filtering
                 both on the input layer and the prediction layer.
                 Filtering on the input layer allows capturing
                 long-range temporal dependencies and reducing input
                 signal noise; filtering on the prediction layer allows
                 taking weighted votes of multiple overlapping
                 prediction results as well as reducing estimation
                 noise. We tested our system in a scenario of real-world
                 gestural interaction using the NATOPS dataset, an
                 official vocabulary of aircraft handling gestures. Our
                 experimental results show that: (1) the use of both
                 static and dynamic attributes of motion in body
                 tracking allows statistically significant improvement
                 of the recognition performance over using static
                 attributes of motion alone; and (2) the multilayered
                 filtering statistically significantly improves
                 recognition performance over the nonfiltering method.
                 We also show that, on a set of twenty-four NATOPS
                 gestures, our system achieves a recognition accuracy of
                 75.37\%.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Eyben:2012:MAC,
  author =       "Florian Eyben and Martin W{\"o}llmer and Bj{\"o}rn
                 Schuller",
  title =        "A multitask approach to continuous five-dimensional
                 affect sensing in natural speech",
  journal =      j-TIIS,
  volume =       "2",
  number =       "1",
  pages =        "6:1--6:??",
  month =        mar,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2133366.2133372",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Mar 16 12:34:07 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Automatic affect recognition is important for the
                 ability of future technical systems to interact with us
                 socially in an intelligent way by understanding our
                 current affective state. In recent years there has been
                 a shift in the field of affect recognition from `in the
                 lab' experiments with acted data to `in the wild'
                 experiments with spontaneous and naturalistic data. Two
                 major issues thereby are the proper segmentation of the
                 input and adequate description and modeling of
                 affective states. The first issue is crucial for
                 responsive, real-time systems such as virtual agents
                 and robots, where the latency of the analysis must be
                 as small as possible. To address this issue we
                 introduce a novel method of incremental segmentation to
                 be used in combination with supra-segmental modeling.
                 For modeling of continuous affective states we use Long
                 Short-Term Memory Recurrent Neural Networks, with which
                 we can show an improvement in performance over standard
                 recurrent neural networks and feed-forward neural
                 networks as well as Support Vector Regression. For
                 experiments we use the SEMAINE database, which contains
                 recordings of spontaneous and natural human to
                 Wizard-of-Oz conversations. The recordings are
                 annotated continuously in time and magnitude with
                 FeelTrace for five affective dimensions, namely
                 activation, expectation, intensity, power/dominance,
                 and valence. To exploit dependencies between the five
                 affective dimensions we investigate multitask learning
                 of all five dimensions augmented with inter-rater
                 standard deviation. We can show improvements for
                 multitask over single-task modeling. Correlation
                 coefficients of up to 0.81 are obtained for the
                 activation dimension and up to 0.58 for the valence
                 dimension. The performance for the remaining dimensions
                 were found to be in between that for activation and
                 valence.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Yazdani:2012:ARB,
  author =       "Ashkan Yazdani and Jong-Seok Lee and Jean-Marc Vesin
                 and Touradj Ebrahimi",
  title =        "Affect recognition based on physiological changes
                 during the watching of music videos",
  journal =      j-TIIS,
  volume =       "2",
  number =       "1",
  pages =        "7:1--7:??",
  month =        mar,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2133366.2133373",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Mar 16 12:34:07 MDT 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Assessing emotional states of users evoked during
                 their multimedia consumption has received a great deal
                 of attention with recent advances in multimedia content
                 distribution technologies and increasing interest in
                 personalized content delivery. Physiological signals
                 such as the electroencephalogram (EEG) and peripheral
                 physiological signals have been less considered for
                 emotion recognition in comparison to other modalities
                 such as facial expression and speech, although they
                 have a potential interest as alternative or
                 supplementary channels. This article presents our work
                 on: (1) constructing a dataset containing EEG and
                 peripheral physiological signals acquired during
                 presentation of music video clips, which is made
                 publicly available, and (2) conducting binary
                 classification of induced positive/negative valence,
                 high/low arousal, and like/dislike by using the
                 aforementioned signals. The procedure for the dataset
                 acquisition, including stimuli selection, signal
                 acquisition, self-assessment, and signal processing is
                 described in detail. Especially, we propose a novel
                 asymmetry index based on relative wavelet entropy for
                 measuring the asymmetry in the energy distribution of
                 EEG signals, which is used for EEG feature extraction.
                 Then, the classification systems based on EEG and
                 peripheral physiological signals are presented.
                 Single-trial and single-run classification results
                 indicate that, on average, the performance of the
                 EEG-based classification outperforms that of the
                 peripheral physiological signals. However, the
                 peripheral physiological signals can be considered as a
                 good alternative to EEG signals in the case of
                 assessing a user's preference for a given music video
                 clip (like/dislike) since they have a comparable
                 performance to EEG signals while being more easily
                 measured.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Park:2012:CFM,
  author =       "Souneil Park and Seungwoo Kang and Sangyoung Chung and
                 Junehwa Song",
  title =        "A Computational Framework for Media Bias Mitigation",
  journal =      j-TIIS,
  volume =       "2",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jun,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2209310.2209311",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:39 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Bias in the news media is an inherent flaw of the news
                 production process. The bias often causes a sharp
                 increase in political polarization and in the cost of
                 conflict on social issues such as the Iraq war. This
                 article presents NewsCube, a novel Internet news
                 service which aims to mitigate the effect of media
                 bias. NewsCube automatically creates and promptly
                 provides readers with multiple classified views on a
                 news event. As such, it helps readers understand the
                 event from a plurality of views and to formulate their
                 own, more balanced, viewpoints. The media bias problem
                 has been studied extensively in mass communications and
                 social science. This article reviews related mass
                 communication and journalism studies and provides a
                 structured view of the media bias problem and its
                 solution. We propose media bias mitigation as a
                 practical solution and demonstrate it through NewsCube.
                 We evaluate and discuss the effectiveness of NewsCube
                 through various performance studies.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Berkovsky:2012:IIF,
  author =       "Shlomo Berkovsky and Jill Freyne and Harri
                 Oinas-Kukkonen",
  title =        "Influencing Individually: Fusing Personalization and
                 Persuasion",
  journal =      j-TIIS,
  volume =       "2",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jun,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2209310.2209312",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:39 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Personalized technologies aim to enhance user
                 experience by taking into account users' interests,
                 preferences, and other relevant information. Persuasive
                 technologies aim to modify user attitudes, intentions,
                 or behavior through computer-human dialogue and social
                 influence. While both personalized and persuasive
                 technologies influence user interaction and behavior,
                 we posit that this influence could be significantly
                 increased if the two technologies were combined to
                 create personalized and persuasive systems. For
                 example, the persuasive power of a one-size-fits-all
                 persuasive intervention could be enhanced by
                 considering the users being influenced and their
                 susceptibility to the persuasion being offered.
                 Likewise, personalized technologies could cash in on
                 increased success, in terms of user satisfaction,
                 revenue, and user experience, if their services used
                 persuasive techniques. Hence, the coupling of
                 personalization and persuasion has the potential to
                 enhance the impact of both technologies. This new,
                 developing area clearly offers mutual benefits to both
                 research areas, as we illustrate in this special
                 issue.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kaptein:2012:APS,
  author =       "Maurits Kaptein and Boris {De Ruyter} and Panos
                 Markopoulos and Emile Aarts",
  title =        "Adaptive Persuasive Systems: a Study of Tailored
                 Persuasive Text Messages to Reduce Snacking",
  journal =      j-TIIS,
  volume =       "2",
  number =       "2",
  pages =        "10:1--10:??",
  month =        jun,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2209310.2209313",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:39 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article describes the use of personalized short
                 text messages (SMS) to reduce snacking. First, we
                 describe the development and validation ( N = 215) of a
                 questionnaire to measure individual susceptibility to
                 different social influence strategies. To evaluate the
                 external validity of this Susceptibility to Persuasion
                 Scale (STPS) we set up a two week text-messaging
                 intervention that used text messages implementing
                 social influence strategies as prompts to reduce
                 snacking behavior. In this experiment ( N = 73) we show
                 that messages that are personalized (tailored) to the
                 individual based on their scores on the STPS, lead to a
                 higher decrease in snacking consumption than randomized
                 messages or messages that are not tailored
                 (contra-tailored) to the individual. We discuss the
                 importance of this finding for the design of persuasive
                 systems and detail how designers can use tailoring at
                 the level of social influence strategies to increase
                 the effects of their persuasive technologies.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Cremonesi:2012:IPP,
  author =       "Paolo Cremonesi and Franca Garzotto and Roberto
                 Turrin",
  title =        "Investigating the Persuasion Potential of Recommender
                 Systems from a Quality Perspective: an Empirical
                 Study",
  journal =      j-TIIS,
  volume =       "2",
  number =       "2",
  pages =        "11:1--11:??",
  month =        jun,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2209310.2209314",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:39 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Recommender Systems (RSs) help users search large
                 amounts of digital contents and services by allowing
                 them to identify the items that are likely to be more
                 attractive or useful. RSs play an important persuasion
                 role, as they can potentially augment the users' trust
                 towards in an application and orient their decisions or
                 actions towards specific directions. This article
                 explores the persuasiveness of RSs, presenting two vast
                 empirical studies that address a number of research
                 questions. First, we investigate if a design property
                 of RSs, defined by the statistically measured quality
                 of algorithms, is a reliable predictor of their
                 potential for persuasion. This factor is measured in
                 terms of perceived quality, defined by the overall
                 satisfaction, as well as by how users judge the
                 accuracy and novelty of recommendations. For our
                 purposes, we designed an empirical study involving 210
                 subjects and implemented seven full-sized versions of a
                 commercial RS, each one using the same interface and
                 dataset (a subset of Netflix), but each with a
                 different recommender algorithm. In each experimental
                 configuration we computed the statistical quality
                 (recall and F-measures) and collected data regarding
                 the quality perceived by 30 users. The results show us
                 that algorithmic attributes are less crucial than we
                 might expect in determining the user's perception of an
                 RS's quality, and suggest that the user's judgment and
                 attitude towards a recommender are likely to be more
                 affected by factors related to the user experience.
                 Second, we explore the persuasiveness of RSs in the
                 context of large interactive TV services. We report a
                 study aimed at assessing whether measurable persuasion
                 effects (e.g., changes of shopping behavior) can be
                 achieved through the introduction of a recommender. Our
                 data, collected for more than one year, allow us to
                 conclude that, (1) the adoption of an RS can affect
                 both the lift factor and the conversion rate,
                 determining an increased volume of sales and
                 influencing the user's decision to actually buy one of
                 the recommended products, (2) the introduction of an RS
                 tends to diversify purchases and orient users towards
                 less obvious choices (the long tail), and (3) the
                 perceived novelty of recommendations is likely to be
                 more influential than their perceived accuracy.
                 Overall, the results of these studies improve our
                 understanding of the persuasion phenomena induced by
                 RSs, and have implications that can be of interest to
                 academic scholars, designers, and adopters of this
                 class of systems.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Andrews:2012:SPP,
  author =       "Pierre Y. Andrews",
  title =        "System Personality and Persuasion in Human-Computer
                 Dialogue",
  journal =      j-TIIS,
  volume =       "2",
  number =       "2",
  pages =        "12:1--12:??",
  month =        jun,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2209310.2209315",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:39 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The human-computer dialogue research field has been
                 studying interaction with computers since the early
                 stage of Artificial Intelligence, however, research has
                 often focused on very practical tasks to be completed
                 with the dialogues. A new trend in the field tries to
                 implement persuasive techniques with automated
                 interactive agents; unlike booking a train ticket, for
                 example, such dialogues require the system to show more
                 anthropomorphic qualities. The influences of such
                 qualities in the effectiveness of persuasive dialogue
                 is only starting to be studied. In this article we
                 focus on one important perceived trait of the system:
                 personality, and explore how it influences the
                 persuasiveness of a dialogue system. We introduce a new
                 persuasive dialogue system and combine it with a state
                 of the art personality utterance generator. By doing
                 so, we can control the system's extraversion
                 personality trait and observe its influence on the
                 user's perception of the dialogue and its output. In
                 particular, we observe that the user's extraversion
                 influences their perception of the dialogue and its
                 persuasiveness, and that the perceived personality of
                 the system can affect its trustworthiness and
                 persuasiveness. We believe that theses observations
                 will help to set up guidelines to tailor dialogue
                 systems to the user's interaction expectations and
                 improve the persuasive interventions.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Vig:2012:TGE,
  author =       "Jesse Vig and Shilad Sen and John Riedl",
  title =        "The Tag Genome: Encoding Community Knowledge to
                 Support Novel Interaction",
  journal =      j-TIIS,
  volume =       "2",
  number =       "3",
  pages =        "13:1--13:??",
  month =        sep,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2362394.2362395",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:40 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article introduces the tag genome, a data
                 structure that extends the traditional tagging model to
                 provide enhanced forms of user interaction. Just as a
                 biological genome encodes an organism based on a
                 sequence of genes, the tag genome encodes an item in an
                 information space based on its relationship to a common
                 set of tags. We present a machine learning approach for
                 computing the tag genome, and we evaluate several
                 learning models on a ground truth dataset provided by
                 users. We describe an application of the tag genome
                 called Movie Tuner which enables users to navigate from
                 one item to nearby items along dimensions represented
                 by tags. We present the results of a 7-week field trial
                 of 2,531 users of Movie Tuner and a survey evaluating
                 users' subjective experience. Finally, we outline the
                 broader space of applications of the tag genome.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Lieberman:2012:ISI,
  author =       "Henry Lieberman and Catherine Havasi",
  title =        "Introduction to the {Special Issue on Common Sense for
                 Interactive Systems}",
  journal =      j-TIIS,
  volume =       "2",
  number =       "3",
  pages =        "14:1--14:??",
  month =        sep,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2362394.2362396",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:40 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This editorial introduction describes the aims and
                 scope of the special issue on Common Sense for
                 Interactive Systems of the ACM Transactions on
                 Interactive Intelligent Systems. It explains why the
                 common sense knowledge problem is crucial for both
                 artificial intelligence and human-computer interaction,
                 and it shows how the four articles selected for this
                 issue fit into the theme.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Gil:2012:CCK,
  author =       "Yolanda Gil and Varun Ratnakar and Timothy Chklovski
                 and Paul Groth and Denny Vrandecic",
  title =        "Capturing Common Knowledge about Tasks: Intelligent
                 Assistance for To-Do Lists",
  journal =      j-TIIS,
  volume =       "2",
  number =       "3",
  pages =        "15:1--15:??",
  month =        sep,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2362394.2362397",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:40 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Although to-do lists are a ubiquitous form of personal
                 task management, there has been no work on intelligent
                 assistance to automate, elaborate, or coordinate a
                 user's to-dos. Our research focuses on three aspects of
                 intelligent assistance for to-dos. We investigated the
                 use of intelligent agents to automate to-dos in an
                 office setting. We collected a large corpus from users
                 and developed a paraphrase-based approach to matching
                 agent capabilities with to-dos. We also investigated
                 to-dos for personal tasks and the kinds of assistance
                 that can be offered to users by elaborating on them on
                 the basis of substep knowledge extracted from the Web.
                 Finally, we explored coordination of user tasks with
                 other users through a to-do management application
                 deployed in a popular social networking site. We
                 discuss the emergence of Social Task Networks, which
                 link users` tasks to their social network as well as to
                 relevant resources on the Web. We show the benefits of
                 using common sense knowledge to interpret and elaborate
                 to-dos. Conversely, we also show that to-do lists are a
                 valuable way to create repositories of common sense
                 knowledge about tasks.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Swanson:2012:SAU,
  author =       "Reid Swanson and Andrew S. Gordon",
  title =        "Say Anything: Using Textual Case-Based Reasoning to
                 Enable Open-Domain Interactive Storytelling",
  journal =      j-TIIS,
  volume =       "2",
  number =       "3",
  pages =        "16:1--16:??",
  month =        sep,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2362394.2362398",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:40 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We describe Say Anything, a new interactive
                 storytelling system that collaboratively writes textual
                 narratives with human users. Unlike previous attempts,
                 this interactive storytelling system places no
                 restrictions on the content or direction of the user's
                 contribution to the emerging storyline. In response to
                 these contributions, the computer continues the
                 storyline with narration that is both coherent and
                 entertaining. This capacity for open-domain interactive
                 storytelling is enabled by an extremely large
                 repository of nonfiction personal stories, which is
                 used as a knowledge base in a case-based reasoning
                 architecture. In this article, we describe the three
                 main components of our case-based reasoning approach: a
                 million-item corpus of personal stories mined from
                 internet weblogs, a case retrieval strategy that is
                 optimized for narrative coherence, and an adaptation
                 strategy that ensures that repurposed sentences from
                 the case base are appropriate for the user's emerging
                 fiction. We describe a series of evaluations of the
                 system's ability to produce coherent and entertaining
                 stories, and we compare these narratives with
                 single-author stories posted to internet weblogs.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kuo:2012:PRM,
  author =       "Yen-Ling Kuo and Jane Yung-Jen Hsu",
  title =        "Planning for Reasoning with Multiple Common Sense
                 Knowledge Bases",
  journal =      j-TIIS,
  volume =       "2",
  number =       "3",
  pages =        "17:1--17:??",
  month =        sep,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2362394.2362399",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:40 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Intelligent user interfaces require common sense
                 knowledge to bridge the gap between the functionality
                 of applications and the user's goals. While current
                 reasoning methods have been used to provide contextual
                 information for interface agents, the quality of their
                 reasoning results is limited by the coverage of their
                 underlying knowledge bases. This article presents
                 reasoning composition, a planning-based approach to
                 integrating reasoning methods from multiple common
                 sense knowledge bases to answer queries. The reasoning
                 results of one reasoning method are passed to other
                 reasoning methods to form a reasoning chain to the
                 target context of a query. By leveraging different weak
                 reasoning methods, we are able to find answers to
                 queries that cannot be directly answered by querying a
                 single common sense knowledge base. By conducting
                 experiments on ConceptNet and WordNet, we compare the
                 reasoning results of reasoning composition, directly
                 querying merged knowledge bases, and spreading
                 activation. The results show an 11.03\% improvement in
                 coverage over directly querying merged knowledge bases
                 and a 49.7\% improvement in accuracy over spreading
                 activation. Two case studies are presented, showing how
                 reasoning composition can improve performance of
                 retrieval in a video editing system and a dialogue
                 assistant.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Dinakar:2012:CSR,
  author =       "Karthik Dinakar and Birago Jones and Catherine Havasi
                 and Henry Lieberman and Rosalind Picard",
  title =        "Common Sense Reasoning for Detection, Prevention, and
                 Mitigation of Cyberbullying",
  journal =      j-TIIS,
  volume =       "2",
  number =       "3",
  pages =        "18:1--18:??",
  month =        sep,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2362394.2362400",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Nov 6 19:14:40 MST 2012",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Cyberbullying (harassment on social networks) is
                 widely recognized as a serious social problem,
                 especially for adolescents. It is as much a threat to
                 the viability of online social networks for youth today
                 as spam once was to email in the early days of the
                 Internet. Current work to tackle this problem has
                 involved social and psychological studies on its
                 prevalence as well as its negative effects on
                 adolescents. While true solutions rest on teaching
                 youth to have healthy personal relationships, few have
                 considered innovative design of social network software
                 as a tool for mitigating this problem. Mitigating
                 cyberbullying involves two key components: robust
                 techniques for effective detection and reflective user
                 interfaces that encourage users to reflect upon their
                 behavior and their choices. Spam filters have been
                 successful by applying statistical approaches like
                 Bayesian networks and hidden Markov models. They can,
                 like Google's GMail, aggregate human spam judgments
                 because spam is sent nearly identically to many people.
                 Bullying is more personalized, varied, and contextual.
                 In this work, we present an approach for bullying
                 detection based on state-of-the-art natural language
                 processing and a common sense knowledge base, which
                 permits recognition over a broad spectrum of topics in
                 everyday life. We analyze a more narrow range of
                 particular subject matter associated with bullying
                 (e.g. appearance, intelligence, racial and ethnic
                 slurs, social acceptance, and rejection), and construct
                 BullySpace, a common sense knowledge base that encodes
                 particular knowledge about bullying situations. We then
                 perform joint reasoning with common sense knowledge
                 about a wide range of everyday life topics. We analyze
                 messages using our novel AnalogySpace common sense
                 reasoning technique. We also take into account social
                 network analysis and other factors. We evaluate the
                 model on real-world instances that have been reported
                 by users on Formspring, a social networking website
                 that is popular with teenagers. On the intervention
                 side, we explore a set of reflective user-interaction
                 paradigms with the goal of promoting empathy among
                 social network participants. We propose an ``air
                 traffic control''-like dashboard, which alerts
                 moderators to large-scale outbreaks that appear to be
                 escalating or spreading and helps them prioritize the
                 current deluge of user complaints. For potential
                 victims, we provide educational material that informs
                 them about how to cope with the situation, and connects
                 them with emotional support from others. A user
                 evaluation shows that in-context, targeted, and dynamic
                 help during cyberbullying situations fosters end-user
                 reflection that promotes better coping strategies.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Jameson:2012:ISI,
  author =       "Anthony Jameson and John Riedl",
  title =        "Introduction to the special issue on highlights of the
                 decade in interactive intelligent systems",
  journal =      j-TIIS,
  volume =       "2",
  number =       "4",
  pages =        "19:1--19:??",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2395123.2395124",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:15 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This editorial introduction explains the motivation
                 and origin of the TiiS special issue on Highlights of
                 the Decade in Interactive Intelligent Systems and shows
                 how its five articles exemplify the types of research
                 contribution that TiiS aims to encourage and publish.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hoey:2012:PSD,
  author =       "Jesse Hoey and Craig Boutilier and Pascal Poupart and
                 Patrick Olivier and Andrew Monk and Alex Mihailidis",
  title =        "People, sensors, decisions: Customizable and adaptive
                 technologies for assistance in healthcare",
  journal =      j-TIIS,
  volume =       "2",
  number =       "4",
  pages =        "20:1--20:??",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2395123.2395125",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:15 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The ratio of healthcare professionals to care
                 recipients is dropping at an alarming rate,
                 particularly for the older population. It is estimated
                 that the number of persons with Alzheimer's disease,
                 for example, will top 100 million worldwide by the year
                 2050 [Alzheimer's Disease International 2009]. It will
                 become harder and harder to provide needed health
                 services to this population of older adults. Further,
                 patients are becoming more aware and involved in their
                 own healthcare decisions. This is creating a void in
                 which technology has an increasingly important role to
                 play as a tool to connect providers with recipients.
                 Examples of interactive technologies range from
                 telecare for remote regions to computer games promoting
                 fitness in the home. Currently, such technologies are
                 developed for specific applications and are difficult
                 to modify to suit individual user needs. The future
                 potential economic and social impact of technology in
                 the healthcare field therefore lies in our ability to
                 make intelligent devices that are customizable by
                 healthcare professionals and their clients, that are
                 adaptive to users over time, and that generalize across
                 tasks and environments. A wide application area for
                 technology in healthcare is for assistance and
                 monitoring in the home. As the population ages, it
                 becomes increasingly dependent on chronic healthcare,
                 such as assistance for tasks of everyday life (washing,
                 cooking, dressing), medication taking, nutrition, and
                 fitness. This article will present a summary of work
                 over the past decade on the development of intelligent
                 systems that provide assistance to persons with
                 cognitive disabilities. These systems are unique in
                 that they are all built using a common framework, a
                 decision-theoretic model for general-purpose assistance
                 in the home. In this article, we will show how this
                 type of general model can be applied to a range of
                 assistance tasks, including prompting for activities of
                 daily living, assistance for art therapists, and stroke
                 rehabilitation. This model is a Partially Observable
                 Markov Decision Process (POMDP) that can be customized
                 by end-users, that can integrate complex sensor
                 information, and that can adapt over time. These three
                 characteristics of the POMDP model will allow for
                 increasing uptake and long-term efficiency and
                 robustness of technology for assistance.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Carberry:2012:AMA,
  author =       "Sandra Carberry and Stephanie Elzer Schwartz and
                 Kathleen Mccoy and Seniz Demir and Peng Wu and Charles
                 Greenbacker and Daniel Chester and Edward Schwartz and
                 David Oliver and Priscilla Moraes",
  title =        "Access to multimodal articles for individuals with
                 sight impairments",
  journal =      j-TIIS,
  volume =       "2",
  number =       "4",
  pages =        "21:1--21:??",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2395123.2395126",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:15 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Although intelligent interactive systems have been the
                 focus of many research efforts, very few have addressed
                 systems for individuals with disabilities. This article
                 presents our methodology for an intelligent interactive
                 system that provides individuals with sight impairments
                 with access to the content of information graphics
                 (such as bar charts and line graphs) in popular media.
                 The article describes the methodology underlying the
                 system's intelligent behavior, its interface for
                 interacting with users, examples processed by the
                 implemented system, and evaluation studies both of the
                 methodology and the effectiveness of the overall
                 system. This research advances universal access to
                 electronic documents.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chen:2012:MBI,
  author =       "Fang Chen and Natalie Ruiz and Eric Choi and Julien
                 Epps and M. Asif Khawaja and Ronnie Taib and Bo Yin and
                 Yang Wang",
  title =        "Multimodal behavior and interaction as indicators of
                 cognitive load",
  journal =      j-TIIS,
  volume =       "2",
  number =       "4",
  pages =        "22:1--22:??",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2395123.2395127",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:15 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "High cognitive load arises from complex time and
                 safety-critical tasks, for example, mapping out flight
                 paths, monitoring traffic, or even managing nuclear
                 reactors, causing stress, errors, and lowered
                 performance. Over the last five years, our research has
                 focused on using the multimodal interaction paradigm to
                 detect fluctuations in cognitive load in user behavior
                 during system interaction. Cognitive load variations
                 have been found to impact interactive behavior: by
                 monitoring variations in specific modal input features
                 executed in tasks of varying complexity, we gain an
                 understanding of the communicative changes that occur
                 when cognitive load is high. So far, we have identified
                 specific changes in: speech, namely acoustic, prosodic,
                 and linguistic changes; interactive gesture; and
                 digital pen input, both interactive and freeform. As
                 ground-truth measurements, galvanic skin response,
                 subjective, and performance ratings have been used to
                 verify task complexity. The data suggest that it is
                 feasible to use features extracted from behavioral
                 changes in multiple modal inputs as indices of
                 cognitive load. The speech-based indicators of load,
                 based on data collected from user studies in a variety
                 of domains, have shown considerable promise. Scenarios
                 include single-user and team-based tasks; think-aloud
                 and interactive speech; and single-word, reading, and
                 conversational speech, among others. Pen-based
                 cognitive load indices have also been tested with some
                 success, specifically with pen-gesture, handwriting,
                 and freeform pen input, including diagraming. After
                 examining some of the properties of these measurements,
                 we present a multimodal fusion model, which is
                 illustrated with quantitative examples from a case
                 study. The feasibility of employing user input and
                 behavior patterns as indices of cognitive load is
                 supported by experimental evidence. Moreover,
                 symptomatic cues of cognitive load derived from user
                 behavior such as acoustic speech signals, transcribed
                 text, digital pen trajectories of handwriting, and
                 shapes pen, can be supported by well-established
                 theoretical frameworks, including O'Donnell and
                 Eggemeier's workload measurement [1986] Sweller's
                 Cognitive Load Theory [Chandler and Sweller 1991], and
                 Baddeley's model of modal working memory [1992] as well
                 as McKinstry et al.'s [2008] and Rosenbaum's [2005]
                 action dynamics work. The benefit of using this
                 approach to determine the user's cognitive load in real
                 time is that the data can be collected implicitly that
                 is, during day-to-day use of intelligent interactive
                 systems, thus overcomes problems of intrusiveness and
                 increases applicability in real-world environments,
                 while adapting information selection and presentation
                 in a dynamic computer interface with reference to
                 load.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Dmello:2012:AAA,
  author =       "Sidney D'mello and Art Graesser",
  title =        "{AutoTutor} and {Affective Autotutor}: Learning by
                 talking with cognitively and emotionally intelligent
                 computers that talk back",
  journal =      j-TIIS,
  volume =       "2",
  number =       "4",
  pages =        "23:1--23:??",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2395123.2395128",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:15 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We present AutoTutor and Affective AutoTutor as
                 examples of innovative 21$^{st}$ century interactive
                 intelligent systems that promote learning and
                 engagement. AutoTutor is an intelligent tutoring system
                 that helps students compose explanations of difficult
                 concepts in Newtonian physics and enhances computer
                 literacy and critical thinking by interacting with them
                 in natural language with adaptive dialog moves similar
                 to those of human tutors. AutoTutor constructs a
                 cognitive model of students' knowledge levels by
                 analyzing the text of their typed or spoken responses
                 to its questions. The model is used to dynamically
                 tailor the interaction toward individual students'
                 zones of proximal development. Affective AutoTutor
                 takes the individualized instruction and human-like
                 interactivity to a new level by automatically detecting
                 and responding to students' emotional states in
                 addition to their cognitive states. Over 20 controlled
                 experiments comparing AutoTutor with ecological and
                 experimental controls such reading a textbook have
                 consistently yielded learning improvements of
                 approximately one letter grade after brief
                 30--60-minute interactions. Furthermore, Affective
                 AutoTutor shows even more dramatic improvements in
                 learning than the original AutoTutor system,
                 particularly for struggling students with low domain
                 knowledge. In addition to providing a detailed
                 description of the implementation and evaluation of
                 AutoTutor and Affective AutoTutor, we also discuss new
                 and exciting technologies motivated by AutoTutor such
                 as AutoTutor-Lite, Operation ARIES, GuruTutor,
                 DeepTutor, MetaTutor, and AutoMentor. We conclude this
                 article with our vision for future work on interactive
                 and engaging intelligent tutoring systems.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kay:2012:CPS,
  author =       "Judy Kay and Bob Kummerfeld",
  title =        "Creating personalized systems that people can
                 scrutinize and control: Drivers, principles and
                 experience",
  journal =      j-TIIS,
  volume =       "2",
  number =       "4",
  pages =        "24:1--24:??",
  month =        dec,
  year =         "2012",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2395123.2395129",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:15 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Widespread personalized computing systems play an
                 already important and fast-growing role in diverse
                 contexts, such as location-based services,
                 recommenders, commercial Web-based services, and
                 teaching systems. The personalization in these systems
                 is driven by information about the user, a user model.
                 Moreover, as computers become both ubiquitous and
                 pervasive, personalization operates across the many
                 devices and information stores that constitute the
                 user's personal digital ecosystem. This enables
                 personalization, and the user models driving it, to
                 play an increasing role in people's everyday lives.
                 This makes it critical to establish ways to address key
                 problems of personalization related to privacy,
                 invisibility of personalization, errors in user models,
                 wasted user models, and the broad issue of enabling
                 people to control their user models and associated
                 personalization. We offer scrutable user models as a
                 foundation for tackling these problems. This article
                 argues the importance of scrutable user modeling and
                 personalization, illustrating key elements in case
                 studies from our work. We then identify the broad roles
                 for scrutable user models. The article describes how to
                 tackle the technical and interface challenges of
                 designing and building scrutable user modeling systems,
                 presenting design principles and showing how they were
                 established over our twenty years of work on the
                 Personis software framework. Our contributions are the
                 set of principles for scrutable personalization linked
                 to our experience from creating and evaluating
                 frameworks and associated applications built upon them.
                 These constitute a general approach to tackling
                 problems of personalization by enabling users to
                 scrutinize their user models as a basis for
                 understanding and controlling personalization.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Giunchiglia:2013:ISS,
  author =       "Fausto Giunchiglia and David Robertson",
  title =        "Introduction to the special section on
                 {Internet}-scale human problem solving",
  journal =      j-TIIS,
  volume =       "3",
  number =       "1",
  pages =        "1:1--1:??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:17 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This editorial introduction first outlines some of the
                 research challenges raised by the emerging forms of
                 internet-scale human problem solving. It then explains
                 how the two articles in this special section can serve
                 as illuminating complementary case studies, providing
                 concrete examples embedded in general conceptual
                 frameworks.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Yu:2013:ISI,
  author =       "Lixiu Yu and Jeffrey V. Nickerson",
  title =        "An {Internet}-scale idea generation system",
  journal =      j-TIIS,
  volume =       "3",
  number =       "1",
  pages =        "2:1--2:??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:17 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "A method of organizing the crowd to generate ideas is
                 described. It integrates crowds using evolutionary
                 algorithms. The method increases the creativity of
                 ideas across generations, and it works better than
                 greenfield idea generation. Specifically, a design
                 space of internet-scale idea generation systems is
                 defined, and one instance is tested: a crowd idea
                 generation system that uses combination to improve
                 previous designs. The key process of the system is the
                 following: A crowd generates designs, then another
                 crowd combines the designs of the previous crowd. In an
                 experiment with 540 participants, the combined designs
                 were compared to the initial designs and to the designs
                 produced by a greenfield idea generation system. The
                 results show that the sequential combination system
                 produced more creative ideas in the last generation and
                 outperformed the greenfield idea generation system. The
                 design space of crowdsourced idea generation developed
                 here may be used to instantiate systems that can be
                 applied to a wide range of design problems. The work
                 has both pragmatic and theoretical implications: New
                 forms of coordination are now possible, and, using the
                 crowd, it is possible to test existing and emerging
                 theories of coordination and participatory design.
                 Moreover, it may be possible for human designers,
                 organized as a crowd, to codesign with each other and
                 with automated algorithms.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Poesio:2013:PDU,
  author =       "Massimo Poesio and Jon Chamberlain and Udo Kruschwitz
                 and Livio Robaldo and Luca Ducceschi",
  title =        "Phrase detectives: Utilizing collective intelligence
                 for {Internet}-scale language resource creation",
  journal =      j-TIIS,
  volume =       "3",
  number =       "1",
  pages =        "3:1--3:??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:17 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We are witnessing a paradigm shift in Human Language
                 Technology (HLT) that may well have an impact on the
                 field comparable to the statistical revolution:
                 acquiring large-scale resources by exploiting
                 collective intelligence. An illustration of this new
                 approach is Phrase Detectives, an interactive online
                 game with a purpose for creating anaphorically
                 annotated resources that makes use of a highly
                 distributed population of contributors with different
                 levels of expertise. The purpose of this article is to
                 first of all give an overview of all aspects of Phrase
                 Detectives, from the design of the game and the HLT
                 methods we used to the results we have obtained so far.
                 It furthermore summarizes the lessons that we have
                 learned in developing this game which should help other
                 researchers to design and implement similar games.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Console:2013:ISN,
  author =       "Luca Console and Fabrizio Antonelli and Giulia Biamino
                 and Francesca Carmagnola and Federica Cena and Elisa
                 Chiabrando and Vincenzo Cuciti and Matteo Demichelis
                 and Franco Fassio and Fabrizio Franceschi and Roberto
                 Furnari and Cristina Gena and Marina Geymonat and
                 Piercarlo Grimaldi and Pierluige Grillo and Silvia
                 Likavec and Ilaria Lombardi and Dario Mana and
                 Alessandro Marcengo and Michele Mioli and Mario
                 Mirabelli and Monica Perrero and Claudia Picardi and
                 Federica Protti and Amon Rapp and Rossana Simeoni and
                 Daniele Theseider Dupr{\'e} and Ilaria Torre and Andrea
                 Toso and Fabio Torta and Fabiana Vernero",
  title =        "Interacting with social networks of intelligent things
                 and people in the world of gastronomy",
  journal =      j-TIIS,
  volume =       "3",
  number =       "1",
  pages =        "4:1--4:??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:17 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article introduces a framework for creating rich
                 augmented environments based on a social web of
                 intelligent things and people. We target outdoor
                 environments, aiming to transform a region into a smart
                 environment that can share its cultural heritage with
                 people, promoting itself and its special qualities.
                 Using the applications developed in the framework,
                 people can interact with things, listen to the stories
                 that these things tell them, and make their own
                 contributions. The things are intelligent in the sense
                 that they aggregate information provided by users and
                 behave in a socially active way. They can autonomously
                 establish social relationships on the basis of their
                 properties and their interaction with users. Hence when
                 a user gets in touch with a thing, she is also
                 introduced to its social network consisting of other
                 things and of users; she can navigate this network to
                 discover and explore the world around the thing itself.
                 Thus the system supports serendipitous navigation in a
                 network of things and people that evolves according to
                 the behavior of users. An innovative interaction model
                 was defined that allows users to interact with objects
                 in a natural, playful way using smartphones without the
                 need for a specially created infrastructure. The
                 framework was instantiated into a suite of applications
                 called WantEat, in which objects from the domain of
                 tourism and gastronomy (such as cheese wheels or
                 bottles of wine) are taken as testimonials of the
                 cultural roots of a region. WantEat includes an
                 application that allows the definition and registration
                 of things, a mobile application that allows users to
                 interact with things, and an application that supports
                 stakeholders in getting feedback about the things that
                 they have registered in the system. WantEat was
                 developed and tested in a real-world context which
                 involved a region and gastronomy-related items from it
                 (such as products, shops, restaurants, and recipes),
                 through an early evaluation with stakeholders and a
                 final evaluation with hundreds of users.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Song:2013:PII,
  author =       "Wei Song and Andrew Finch and Kumiko Tanaka-Ishii and
                 Keiji Yasuda and Eiichiro Sumita",
  title =        "{picoTrans}: an intelligent icon-driven interface for
                 cross-lingual communication",
  journal =      j-TIIS,
  volume =       "3",
  number =       "1",
  pages =        "5:1--5:??",
  month =        apr,
  year =         "2013",
  CODEN =        "????",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Apr 30 18:37:17 MDT 2013",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "picoTrans is a prototype system that introduces a
                 novel icon-based paradigm for cross-lingual
                 communication on mobile devices. Our approach marries a
                 machine translation system with the popular picture
                 book. Users interact with picoTrans by pointing at
                 pictures as if it were a picture book; the system
                 generates natural language from these icons and the
                 user is able to interact with the icon sequence to
                 refine the meaning of the words that are generated.
                 When users are satisfied that the sentence generated
                 represents what they wish to express, they tap a
                 translate button and picoTrans displays the
                 translation. Structuring the process of communication
                 in this way has many advantages. First, tapping icons
                 is a very natural method of user input on mobile
                 devices; typing is cumbersome and speech input
                 errorful. Second, the sequence of icons which is
                 annotated both with pictures and bilingually with words
                 is meaningful to both users, and it opens up a second
                 channel of communication between them that conveys the
                 gist of what is being expressed. We performed a number
                 of evaluations of picoTrans to determine: its coverage
                 of a corpus of in-domain sentences; the input
                 efficiency in terms of the number of key presses
                 required relative to text entry; and users' overall
                 impressions of using the system compared to using a
                 picture book. Our results show that we are able to
                 cover 74\% of the expressions in our test corpus using
                 a 2000-icon set; we believe that this icon set size is
                 realistic for a mobile device. We also found that
                 picoTrans requires fewer key presses than typing the
                 input and that the system is able to predict the
                 correct, intended natural language sentence from the
                 icon sequence most of the time, making user interaction
                 with the icon sequence often unnecessary. In the user
                 evaluation, we found that in general users prefer using
                 picoTrans and are able to communicate more rapidly and
                 expressively. Furthermore, users had more confidence
                 that they were able to communicate effectively using
                 picoTrans.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Schreiber:2013:ISI,
  author =       "Daniel Schreiber and Kris Luyten and Max
                 M{\"u}hlh{\"a}user and Oliver Brdiczka and Melanie
                 Hartman",
  title =        "Introduction to the special issue on interaction with
                 smart objects",
  journal =      j-TIIS,
  volume =       "3",
  number =       "2",
  pages =        "6:1--6:??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499474.2499475",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:45 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Smart objects can be smart because of the information
                 and communication technology that is added to
                 human-made artifacts. It is not, however, the
                 technology itself that makes them smart but rather the
                 way in which the technology is integrated, and their
                 smartness surfaces through how people are able to
                 interact with these objects. Hence, the key challenge
                 for making smart objects successful is to design usable
                 and useful interactions with them. We list five
                 features that can contribute to the smartness of an
                 object, and we discuss how smart objects can help
                 resolve the simplicity-featurism paradox. We conclude
                 by introducing the three articles in this special
                 issue, which dive into various aspects of smart object
                 interaction: augmenting objects with projection,
                 service-oriented interaction with smart objects via a
                 mobile portal, and an analysis of input-output
                 relations in interaction with tangible smart objects.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Molyneaux:2013:CAM,
  author =       "David Molyneaux and Hans Gellersen and Joe Finney",
  title =        "Cooperative augmentation of mobile smart objects with
                 projected displays",
  journal =      j-TIIS,
  volume =       "3",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499474.2499476",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:45 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Sensors, processors, and radios can be integrated
                 invisibly into objects to make them smart and sensitive
                 to user interaction, but feedback is often limited to
                 beeps, blinks, or buzzes. We propose to redress this
                 input-output imbalance by augmentation of smart objects
                 with projected displays, that-unlike physical
                 displays-allow seamless integration with the natural
                 appearance of an object. In this article, we
                 investigate how, in a ubiquitous computing world, smart
                 objects can acquire and control a projection. We
                 consider that projectors and cameras are ubiquitous in
                 the environment, and we develop a novel conception and
                 system that enables smart objects to spontaneously
                 associate with projector-camera systems for cooperative
                 augmentation. Projector-camera systems are conceived as
                 generic, supporting standard computer vision methods
                 for different appearance cues, and smart objects
                 provide a model of their appearance for method
                 selection at runtime, as well as sensor observations to
                 constrain the visual detection process. Cooperative
                 detection results in accurate location and pose of the
                 object, which is then tracked for visual augmentation
                 in response to display requests by the smart object. In
                 this article, we define the conceptual framework
                 underlying our approach; report on computer vision
                 experiments that give original insight into natural
                 appearance-based detection of everyday objects; show
                 how object sensing can be used to increase speed and
                 robustness of visual detection; describe and evaluate a
                 fully implemented system; and describe two smart object
                 applications to illustrate the system's cooperative
                 augmentation process and the embodied interactions it
                 enables with smart objects.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Thebault:2013:ESP,
  author =       "Pierrick Thebault and Dominique Decotter and Mathieu
                 Boussard and Monique Lu",
  title =        "Embodying services into physical places: Toward the
                 design of a mobile environment browser",
  journal =      j-TIIS,
  volume =       "3",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499474.2499477",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:45 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The tremendous developments in mobile computing and
                 handheld devices have allowed for an increasing usage
                 of the resources of the World Wide Web. People today
                 consume information and services on the go, through
                 smart phones applications capable of exploiting their
                 location in order to adapt the content according to the
                 context of use. As location-based services gain
                 traction and reveal their limitations, we argue there
                 is a need for intelligent systems to be created to
                 better support people's activities in their experience
                 of the city, especially regarding their decision-making
                 processes. In this article, we explore the opportunity
                 to move closer to the realization of the ubiquitous
                 computing vision by turning physical places into smart
                 environments capable of cooperatively and autonomously
                 collecting, processing, and transporting information
                 about their characteristics (e.g., practical
                 information, presence of people, and ambience).
                 Following a multidisciplinary approach which leverages
                 psychology, design, and computer science, we propose to
                 investigate the potential of building communication and
                 interaction spaces, called information spheres, on top
                 of physical places such as businesses, homes, and
                 institutions. We argue that, if the latter are exposed
                 on the Web, they can act as a platform delivering
                 information and services and mediating interactions
                 with smart objects without requiring too much effort
                 for the deployment of the architecture. After
                 presenting the inherent challenges of our vision, we go
                 through the protocol of two preliminary experiments
                 that aim to evaluate users' perception of different
                 types of information (i.e., reviews, check-in
                 information, video streams, and real-time
                 representations) and their influence on the
                 decision-making process. Results of this study lead us
                 to elaborate the design considerations that must be
                 taken into account to ensure the intelligibility and
                 user acceptance of information spheres. We finally
                 describe a research prototype application called
                 Environment Browser (Env-B) and present the underlying
                 smart space middleware, before evaluating the user
                 experience with our system through quantitative and
                 qualitative methods.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{vandeGarde-Perik:2013:AIO,
  author =       "Evelien van de Garde-Perik and Serge Offermans and
                 Koen van Boerdonk and Kars-Michiel Lenssen and Elise
                 van den Hoven",
  title =        "An analysis of input-output relations in interaction
                 with smart tangible objects",
  journal =      j-TIIS,
  volume =       "3",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499474.2499478",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:45 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article focuses on the conceptual relation
                 between the user's input and a system's output in
                 interaction with smart tangible objects. Understanding
                 this input-output relation (IO relation) is a
                 prerequisite for the design of meaningful interaction.
                 A meaningful IO relation allows the user to know what
                 to do with a system to achieve a certain goal and to
                 evaluate the outcome. The work discussed in this
                 article followed a design research process in which
                 four concepts were developed and prototyped. An
                 evaluation was performed using these prototypes to
                 investigate the effect of highly different IO relations
                 on the user's understanding of the interaction. The
                 evaluation revealed two types of IO relations differing
                 in functionality and the number of mappings between the
                 user and system actions. These two types of relations
                 are described by two IO models that provide an overview
                 of these mappings. Furthermore, they illustrate the
                 role of the user and the influence of the system in the
                 process of understanding the interaction. The analysis
                 of the two types of IO models illustrates the value of
                 understanding IO relations for the design of smart
                 tangible objects.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Andre:2013:ISS,
  author =       "Elisabeth Andr{\'e} and Joyce Chai",
  title =        "Introduction to the special section on eye gaze and
                 conversation",
  journal =      j-TIIS,
  volume =       "3",
  number =       "2",
  pages =        "10:1--10:??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499474.2499479",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:45 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This editorial introduction first explains the origin
                 of this special section. It then outlines how each of
                 the two articles included sheds light on possibilities
                 for conversational dialog systems to use eye gaze as a
                 signal that reflects aspects of participation in the
                 dialog: degree of engagement and turn taking behavior,
                 respectively.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ishii:2013:GAC,
  author =       "Ryo Ishii and Yukiko I. Nakano and Toyoaki Nishida",
  title =        "Gaze awareness in conversational agents: Estimating a
                 user's conversational engagement from eye gaze",
  journal =      j-TIIS,
  volume =       "3",
  number =       "2",
  pages =        "11:1--11:??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499474.2499480",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:45 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In face-to-face conversations, speakers are
                 continuously checking whether the listener is engaged
                 in the conversation, and they change their
                 conversational strategy if the listener is not fully
                 engaged. With the goal of building a conversational
                 agent that can adaptively control conversations, in
                 this study we analyze listener gaze behaviors and
                 develop a method for estimating whether a listener is
                 engaged in the conversation on the basis of these
                 behaviors. First, we conduct a Wizard-of-Oz study to
                 collect information on a user's gaze behaviors. We then
                 investigate how conversational disengagement, as
                 annotated by human judges, correlates with gaze
                 transition, mutual gaze (eye contact) occurrence, gaze
                 duration, and eye movement distance. On the basis of
                 the results of these analyses, we identify useful
                 information for estimating a user's disengagement and
                 establish an engagement estimation method using a
                 decision tree technique. The results of these analyses
                 show that a model using the features of gaze
                 transition, mutual gaze occurrence, gaze duration, and
                 eye movement distance provides the best performance and
                 can estimate the user's conversational engagement
                 accurately. The estimation model is then implemented as
                 a real-time disengagement judgment mechanism and
                 incorporated into a multimodal dialog manager in an
                 animated conversational agent. This agent is designed
                 to estimate the user's conversational engagement and
                 generate probing questions when the user is distracted
                 from the conversation. Finally, we evaluate the
                 engagement-sensitive agent and find that asking probing
                 questions at the proper times has the expected effects
                 on the user's verbal/nonverbal behaviors during
                 communication with the agent. We also find that our
                 agent system improves the user's impression of the
                 agent in terms of its engagement awareness, behavior
                 appropriateness, conversation smoothness, favorability,
                 and intelligence.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Jokinen:2013:GTT,
  author =       "Kristiina Jokinen and Hirohisa Furukawa and Masafumi
                 Nishida and Seiichi Yamamoto",
  title =        "Gaze and turn-taking behavior in casual conversational
                 interactions",
  journal =      j-TIIS,
  volume =       "3",
  number =       "2",
  pages =        "12:1--12:??",
  month =        jul,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499474.2499481",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:45 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Eye gaze is an important means for controlling
                 interaction and coordinating the participants' turns
                 smoothly. We have studied how eye gaze correlates with
                 spoken interaction and especially focused on the
                 combined effect of the speech signal and gazing to
                 predict turn taking possibilities. It is well known
                 that mutual gaze is important in the coordination of
                 turn taking in two-party dialogs, and in this article,
                 we investigate whether this fact also holds for
                 three-party conversations. In group interactions, it
                 may be that different features are used for managing
                 turn taking than in two-party dialogs. We collected
                 casual conversational data and used an eye tracker to
                 systematically observe a participant's gaze in the
                 interactions. By studying the combined effect of speech
                 and gaze on turn taking, we aimed to answer our main
                 questions: How well can eye gaze help in predicting
                 turn taking? What is the role of eye gaze when the
                 speaker holds the turn? Is the role of eye gaze as
                 important in three-party dialogs as in two-party
                 dialogue? We used Support Vector Machines (SVMs) to
                 classify turn taking events with respect to speech and
                 gaze features, so as to estimate how well the features
                 signal a change of the speaker or a continuation of the
                 same speaker. The results confirm the earlier
                 hypothesis that eye gaze significantly helps in
                 predicting the partner's turn taking activity, and we
                 also get supporting evidence for our hypothesis that
                 the speaker is a prominent coordinator of the
                 interaction space. Such a turn taking model could be
                 used in interactive applications to improve the
                 system's conversational performance.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Jameson:2013:MJR,
  author =       "Anthony Jameson",
  title =        "In Memoriam: {John Riedl}",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "13:1--13:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2533670.2533671",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This recollection of John Riedl, founding
                 coeditor-in-chief of the ACM Transactions on
                 Interactive Intelligent Systems, presents a picture by
                 editors of the journal of what it was like to
                 collaborate and interact with him.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Amershi:2013:LAW,
  author =       "Saleema Amershi and Jalal Mahmud and Jeffrey Nichols
                 and Tessa Lau and German Attanasio Ruiz",
  title =        "{LiveAction}: Automating {Web} Task Model Generation",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "14:1--14:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2533670.2533672",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Task automation systems promise to increase human
                 productivity by assisting us with our mundane and
                 difficult tasks. These systems often rely on people to
                 (1) identify the tasks they want automated and (2)
                 specify the procedural steps necessary to accomplish
                 those tasks (i.e., to create task models). However, our
                 interviews with users of a Web task automation system
                 reveal that people find it difficult to identify tasks
                 to automate and most do not even believe they perform
                 repetitive tasks worthy of automation. Furthermore,
                 even when automatable tasks are identified, the
                 well-recognized difficulties of specifying task steps
                 often prevent people from taking advantage of these
                 automation systems. In this research, we analyze real
                 Web usage data and find that people do in fact repeat
                 behaviors on the Web and that automating these
                 behaviors, regardless of their complexity, would reduce
                 the overall number of actions people need to perform
                 when completing their tasks, potentially saving time.
                 Motivated by these findings, we developed LiveAction, a
                 fully-automated approach to generating task models from
                 Web usage data. LiveAction models can be used to
                 populate the task model repositories required by many
                 automation systems, helping us take advantage of
                 automation in our everyday lives.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Wetzler:2013:CPM,
  author =       "Philipp Wetzler and Steven Bethard and Heather Leary
                 and Kirsten Butcher and Soheil Danesh Bahreini and Jin
                 Zhao and James H. Martin and Tamara Sumner",
  title =        "Characterizing and Predicting the Multifaceted Nature
                 of Quality in Educational {Web} Resources",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "15:1--15:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2533670.2533673",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Efficient learning from Web resources can depend on
                 accurately assessing the quality of each resource. We
                 present a methodology for developing computational
                 models of quality that can assist users in assessing
                 Web resources. The methodology consists of four steps:
                 (1) a meta-analysis of previous studies to decompose
                 quality into high-level dimensions and low-level
                 indicators, (2) an expert study to identify the key
                 low-level indicators of quality in the target domain,
                 (3) human annotation to provide a collection of example
                 resources where the presence or absence of quality
                 indicators has been tagged, and (4) training of a
                 machine learning model to predict quality indicators
                 based on content and link features of Web resources. We
                 find that quality is a multifaceted construct, with
                 different aspects that may be important to different
                 users at different times. We show that machine learning
                 models can predict this multifaceted nature of quality,
                 both in the context of aiding curators as they evaluate
                 resources submitted to digital libraries, and in the
                 context of aiding teachers as they develop online
                 educational resources. Finally, we demonstrate how
                 computational models of quality can be provided as a
                 service, and embedded into applications such as Web
                 search.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Amir:2013:PRV,
  author =       "Ofra Amir and Ya'akov (Kobi) Gal",
  title =        "Plan Recognition and Visualization in Exploratory
                 Learning Environments",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "16:1--16:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2533670.2533674",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Modern pedagogical software is open-ended and
                 flexible, allowing students to solve problems through
                 exploration and trial-and-error. Such exploratory
                 settings provide for a rich educational environment for
                 students, but they challenge teachers to keep track of
                 students' progress and to assess their performance.
                 This article presents techniques for recognizing
                 students' activities in such pedagogical software and
                 visualizing these activities to teachers. It describes
                 a new plan recognition algorithm that uses a recursive
                 grammar that takes into account repetition and
                 interleaving of activities. This algorithm was
                 evaluated empirically using an exploratory environment
                 for teaching chemistry used by thousands of students in
                 several countries. It was always able to correctly
                 infer students' plans when the appropriate grammar was
                 available. We designed two methods for visualizing
                 students' activities for teachers: one that visualizes
                 students' inferred plans, and one that visualizes
                 students' interactions over a timeline. Both of these
                 visualization methods were preferred to and found more
                 helpful than a baseline method which showed a movie of
                 students' interactions. These results demonstrate the
                 benefit of combining novel AI techniques and
                 visualization methods for the purpose of designing
                 collaborative systems that support students in their
                 problem solving and teachers in their understanding of
                 students' performance.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chen:2013:HDM,
  author =       "Li Chen and Marco de Gemmis and Alexander Felfernig
                 and Pasquale Lops and Francesco Ricci and Giovanni
                 Semeraro",
  title =        "Human Decision Making and Recommender Systems",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "17:1--17:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2533670.2533675",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Recommender systems have already proved to be valuable
                 for coping with the information overload problem in
                 several application domains. They provide people with
                 suggestions for items which are likely to be of
                 interest for them; hence, a primary function of
                 recommender systems is to help people make good choices
                 and decisions. However, most previous research has
                 focused on recommendation techniques and algorithms,
                 and less attention has been devoted to the decision
                 making processes adopted by the users and possibly
                 supported by the system. There is still a gap between
                 the importance that the community gives to the
                 assessment of recommendation algorithms and the current
                 range of ongoing research activities concerning human
                 decision making. Different decision-psychological
                 phenomena can influence the decision making of users of
                 recommender systems, and research along these lines is
                 becoming increasingly important and popular. This
                 special issue highlights how the coupling of
                 recommendation algorithms with the understanding of
                 human choice and decision making theory has the
                 potential to benefit research and practice on
                 recommender systems and to enable users to achieve a
                 good balance between decision accuracy and decision
                 effort.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Dodson:2013:ELA,
  author =       "Thomas Dodson and Nicholas Mattei and Joshua T. Guerin
                 and Judy Goldsmith",
  title =        "An {English}-Language Argumentation Interface for
                 Explanation Generation with {Markov} Decision Processes
                 in the Domain of Academic Advising",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "18:1--18:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2513564",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "A Markov Decision Process (MDP) policy presents, for
                 each state, an action, which preferably maximizes the
                 expected utility accrual over time. In this article, we
                 present a novel explanation system for MDP policies.
                 The system interactively generates conversational
                 English-language explanations of the actions suggested
                 by an optimal policy, and does so in real time. We rely
                 on natural language explanations in order to build
                 trust between the user and the explanation system,
                 leveraging existing research in psychology in order to
                 generate salient explanations. Our explanation system
                 is designed for portability between domains and uses a
                 combination of domain-specific and domain-independent
                 techniques. The system automatically extracts implicit
                 knowledge from an MDP model and accompanying policy.
                 This MDP-based explanation system can be ported between
                 applications without additional effort by knowledge
                 engineers or model builders. Our system separates
                 domain-specific data from the explanation logic,
                 allowing for a robust system capable of incremental
                 upgrades. Domain-specific explanations are generated
                 through case-based explanation techniques specific to
                 the domain and a knowledge base of concept mappings
                 used to generate English-language explanations.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Freyne:2013:RBP,
  author =       "Jill Freyne and Shlomo Berkovsky and Gregory Smith",
  title =        "Rating Bias and Preference Acquisition",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "19:1--19:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499673",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Personalized systems and recommender systems exploit
                 implicitly and explicitly provided user information to
                 address the needs and requirements of those using their
                 services. User preference information, often in the
                 form of interaction logs and ratings data, is used to
                 identify similar users, whose opinions are leveraged to
                 inform recommendations or to filter information. In
                 this work we explore a different dimension of
                 information trends in user bias and reasoning learned
                 from ratings provided by users to a recommender system.
                 Our work examines the characteristics of a dataset of
                 100,000 user ratings on a corpus of recipes, which
                 illustrates stable user bias towards certain features
                 of the recipes (cuisine type, key ingredient, and
                 complexity). We exploit this knowledge to design and
                 evaluate a personalized rating acquisition tool based
                 on active learning, which leverages user biases in
                 order to obtain ratings bearing high-value information
                 and to reduce prediction errors with new users.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Knijnenburg:2013:MDA,
  author =       "Bart P. Knijnenburg and Alfred Kobsa",
  title =        "Making Decisions about Privacy: Information Disclosure
                 in Context-Aware Recommender Systems",
  journal =      j-TIIS,
  volume =       "3",
  number =       "3",
  pages =        "20:1--20:??",
  month =        oct,
  year =         "2013",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499670",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:47 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Recommender systems increasingly use contextual and
                 demographical data as a basis for recommendations.
                 Users, however, often feel uncomfortable providing such
                 information. In a privacy-minded design of
                 recommenders, users are free to decide for themselves
                 what data they want to disclose about themselves. But
                 this decision is often complex and burdensome, because
                 the consequences of disclosing personal information are
                 uncertain or even unknown. Although a number of
                 researchers have tried to analyze and facilitate such
                 information disclosure decisions, their research
                 results are fragmented, and they often do not hold up
                 well across studies. This article describes a unified
                 approach to privacy decision research that describes
                 the cognitive processes involved in users' ``privacy
                 calculus'' in terms of system-related perceptions and
                 experiences that act as mediating factors to
                 information disclosure. The approach is applied in an
                 online experiment with 493 participants using a mock-up
                 of a context-aware recommender system. Analyzing the
                 results with a structural linear model, we demonstrate
                 that personal privacy concerns and disclosure
                 justification messages affect the perception of and
                 experience with a system, which in turn drive
                 information disclosure decisions. Overall, disclosure
                 justification messages do not increase disclosure.
                 Although they are perceived to be valuable, they
                 decrease users' trust and satisfaction. Another result
                 is that manipulating the order of the requests
                 increases the disclosure of items requested early but
                 decreases the disclosure of items requested later.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Apostolopoulos:2014:IOL,
  author =       "Ilias Apostolopoulos and Navid Fallah and Eelke Folmer
                 and Kostas E. Bekris",
  title =        "Integrated online localization and navigation for
                 people with visual impairments using smart phones",
  journal =      j-TIIS,
  volume =       "3",
  number =       "4",
  pages =        "21:1--21:??",
  month =        jan,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499669",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:49 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Indoor localization and navigation systems for
                 individuals with Visual Impairments (VIs) typically
                 rely upon extensive augmentation of the physical space,
                 significant computational resources, or heavy and
                 expensive sensors; thus, few systems have been
                 implemented on a large scale. This work describes a
                 system able to guide people with VIs through indoor
                 environments using inexpensive sensors, such as
                 accelerometers and compasses, which are available in
                 portable devices like smart phones. The method takes
                 advantage of feedback from the human user, who confirms
                 the presence of landmarks, something that users with
                 VIs already do when navigating in a building. The
                 system calculates the user's location in real time and
                 uses it to provide audio instructions on how to reach
                 the desired destination. Initial early experiments
                 suggested that the accuracy of the localization depends
                 on the type of directions and the availability of an
                 appropriate transition model for the user. A critical
                 parameter for the transition model is the user's step
                 length. Consequently, this work also investigates
                 different schemes for automatically computing the
                 user's step length and reducing the dependence of the
                 approach on the definition of an accurate transition
                 model. In this way, the direction provision method is
                 able to use the localization estimate and adapt to
                 failed executions of paths by the users. Experiments
                 are presented that evaluate the accuracy of the overall
                 integrated system, which is executed online on a smart
                 phone. Both people with VIs and blindfolded sighted
                 people participated in the experiments, which included
                 paths along multiple floors that required the use of
                 stairs and elevators.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Zamborlin:2014:FGI,
  author =       "Bruno Zamborlin and Frederic Bevilacqua and Marco
                 Gillies and Mark D'inverno",
  title =        "Fluid gesture interaction design: Applications of
                 continuous recognition for the design of modern
                 gestural interfaces",
  journal =      j-TIIS,
  volume =       "3",
  number =       "4",
  pages =        "22:1--22:??",
  month =        jan,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2543921",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:49 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article presents Gesture Interaction DEsigner
                 (GIDE), an innovative application for gesture
                 recognition. Instead of recognizing gestures only after
                 they have been entirely completed, as happens in
                 classic gesture recognition systems, GIDE exploits the
                 full potential of gestural interaction by tracking
                 gestures continuously and synchronously, allowing users
                 to both control the target application moment to moment
                 and also receive immediate and synchronous feedback
                 about system recognition states. By this means, they
                 quickly learn how to interact with the system in order
                 to develop better performances. Furthermore, rather
                 than learning the predefined gestures of others, GIDE
                 allows users to design their own gestures, making
                 interaction more natural and also allowing the
                 applications to be tailored by users' specific needs.
                 We describe our system that demonstrates these new
                 qualities-that combine to provide fluid gesture
                 interaction design-through evaluations with a range of
                 performers and artists.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Young:2014:DET,
  author =       "James E. Young and Takeo Igarashi and Ehud Sharlin and
                 Daisuke Sakamoto and Jeffrey Allen",
  title =        "Design and evaluation techniques for authoring
                 interactive and stylistic behaviors",
  journal =      j-TIIS,
  volume =       "3",
  number =       "4",
  pages =        "23:1--23:??",
  month =        jan,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499671",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:49 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We present a series of projects for end-user authoring
                 of interactive robotic behaviors, with a particular
                 focus on the style of those behaviors: we call this
                 approach Style-by-Demonstration (SBD). We provide an
                 overview introduction of three different SBD platforms:
                 SBD for animated character interactive locomotion
                 paths, SBD for interactive robot locomotion paths, and
                 SBD for interactive robot dance. The primary
                 contribution of this article is a detailed
                 cross-project SBD analysis of the interaction designs
                 and evaluation approaches employed, with the goal of
                 providing general guidelines stemming from our
                 experiences, for both developing and evaluating SBD
                 systems. In addition, we provide the first full account
                 of our Puppet Master SBD algorithm, with an explanation
                 of how it evolved through the projects.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kumar:2014:TES,
  author =       "Rohit Kumar and Carolyn P. Ros{\'e}",
  title =        "Triggering effective social support for online
                 groups",
  journal =      j-TIIS,
  volume =       "3",
  number =       "4",
  pages =        "24:1--24:??",
  month =        jan,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2499672",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:49 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Conversational agent technology is an emerging
                 paradigm for creating a social environment in online
                 groups that is conducive to effective teamwork. Prior
                 work has demonstrated advantages in terms of learning
                 gains and satisfaction scores when groups learning
                 together online have been supported by conversational
                 agents that employ Balesian social strategies. This
                 prior work raises two important questions that are
                 addressed in this article. The first question is one of
                 generality. Specifically, are the positive effects of
                 the designed support specific to learning contexts? Or
                 are they in evidence in other collaborative task
                 domains as well? We present a study conducted within a
                 collaborative decision-making task where we see that
                 the positive effects of the Balesian social strategies
                 extend to this new context. The second question is
                 whether it is possible to increase the effectiveness of
                 the Balesian social strategies by increasing the
                 context sensitivity with which the social strategies
                 are triggered. To this end, we present technical work
                 that increases the sensitivity of the triggering. Next,
                 we present a user study that demonstrates an
                 improvement in performance of the support agent with
                 the new, more sensitive triggering policy over the
                 baseline approach from prior work. The technical
                 contribution of this article is that we extend prior
                 work where such support agents were modeled using a
                 composition of conversational behaviors integrated
                 within an event-driven framework. Within the present
                 approach, conversation is orchestrated through
                 context-sensitive triggering of the composed behaviors.
                 The core effort involved in applying this approach
                 involves building a set of triggering policies that
                 achieve this orchestration in a time-sensitive and
                 coherent manner. In line with recent developments in
                 data-driven approaches for building dialog systems, we
                 present a novel technique for learning
                 behavior-specific triggering policies, deploying it as
                 part of our efforts to improve a socially capable
                 conversational tutor agent that supports collaborative
                 learning.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kritikos:2014:TMD,
  author =       "K. Kritikos and D. Plexousakis and F. Patern{\`o}",
  title =        "Task model-driven realization of interactive
                 application functionality through services",
  journal =      j-TIIS,
  volume =       "3",
  number =       "4",
  pages =        "25:1--25:??",
  month =        jan,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2559979",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:49 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The Service-Oriented Computing (SOC) paradigm is
                 currently being adopted by many developers, as it
                 promises the construction of applications through reuse
                 of existing Web Services (WSs). However, current SOC
                 tools produce applications that interact with users in
                 a limited way. This limitation is overcome by
                 model-based Human-Computer Interaction (HCI) approaches
                 that support the development of applications whose
                 functionality is realized with WSs and whose User
                 Interface (UI) is adapted to the user's context.
                 Typically, such approaches do not consider various
                 functional issues, such as the applications' semantics
                 and their syntactic robustness in terms of the WSs
                 selected to implement their functionality and the
                 automation of the service discovery and selection
                 processes. To this end, we propose a model-driven
                 design method for interactive service-based
                 applications that is able to consider the functional
                 issues and their implications for the UI. This method
                 is realized by a semiautomatic environment that can be
                 integrated into current model-based HCI tools to
                 complete the development of interactive service
                 front-ends. The proposed method takes as input an HCI
                 task model, which includes the user's view of the
                 interactive system, and produces a concrete service
                 model that describes how existing services can be
                 combined to realize the application's functionality. To
                 achieve its goal, our method first transforms system
                 tasks into semantic service queries by mapping the task
                 objects onto domain ontology concepts; then it sends
                 each resulting query to a semantic service engine so as
                 to discover the corresponding services. In the end,
                 only one service from those associated with a system
                 task is selected, through the execution of a novel
                 service concretization algorithm that ensures message
                 compatibility between the selected services.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "25",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Rafailidis:2014:CBT,
  author =       "Dimitrios Rafailidis and Apostolos Axenopoulos and
                 Jonas Etzold and Stavroula Manolopoulou and Petros
                 Daras",
  title =        "Content-based tag propagation and tensor factorization
                 for personalized item recommendation based on social
                 tagging",
  journal =      j-TIIS,
  volume =       "3",
  number =       "4",
  pages =        "26:1--26:??",
  month =        jan,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2487164",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 13 06:46:49 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In this article, a novel method for personalized item
                 recommendation based on social tagging is presented.
                 The proposed approach comprises a content-based tag
                 propagation method to address the sparsity and ``cold
                 start'' problems, which often occur in social tagging
                 systems and decrease the quality of recommendations.
                 The proposed method exploits (a) the content of items
                 and (b) users' tag assignments through a relevance
                 feedback mechanism in order to automatically identify
                 the optimal number of content-based and conceptually
                 similar items. The relevance degrees between users,
                 tags, and conceptually similar items are calculated in
                 order to ensure accurate tag propagation and
                 consequently to address the issue of ``learning tag
                 relevance.'' Moreover, the ternary relation among
                 users, tags, and items is preserved by performing tag
                 propagation in the form of triplets based on users'
                 personal preferences and ``cold start'' degree. The
                 latent associations among users, tags, and items are
                 revealed based on a tensor factorization model in order
                 to build personalized item recommendations. In our
                 experiments with real-world social data, we show the
                 superiority of the proposed approach over other
                 state-of-the-art methods, since several problems in
                 social tagging systems are successfully tackled.
                 Finally, we present the recommendation methodology in
                 the multimodal engine of I-SEARCH, where users'
                 interaction capabilities are demonstrated.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "26",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Callaway:2014:EMD,
  author =       "Charles Callaway and Oliviero Stock and Elyon
                 Dekoven",
  title =        "Experiments with Mobile Drama in an Instrumented
                 Museum for Inducing Conversation in Small Groups",
  journal =      j-TIIS,
  volume =       "4",
  number =       "1",
  pages =        "2:1--2:??",
  month =        apr,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2584250",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Apr 12 11:14:27 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Small groups can have a better museum visit when that
                 visit is both a social and an educational occasion. The
                 unmediated discussion that often ensues during a shared
                 cultural experience, especially when it is with a small
                 group whose members already know each other, has been
                 shown by ethnographers to be important for a more
                 enriching experience. We present DRAMATRIC, a mobile
                 presentation system that delivers hour-long dramas to
                 small groups of museum visitors. DRAMATRIC continuously
                 receives sensor data from the museum environment during
                 a museum visit and analyzes group behavior from that
                 data. On the basis of that analysis, DRAMATRIC delivers
                 a series of dynamically coordinated dramatic scenes
                 about exhibits that the group walks near, each designed
                 to stimulate group discussion. Each drama presentation
                 contains small, complementary differences in the
                 narrative content heard by the different members of the
                 group, leveraging the tension/release cycle of
                 narrative to naturally lead visitors to fill in missing
                 pieces in their own drama by interacting with their
                 fellow group members. Using four specific techniques to
                 produce these coordinated narrative variations, we
                 describe two experiments: one in a neutral, nonmobile
                 environment, and the other a controlled experiment with
                 a full-scale drama in an actual museum. The first
                 experiment tests the hypothesis that narrative
                 differences will lead to increased conversation
                 compared to hearing identical narratives, whereas the
                 second experiment tests whether switching from
                 presenting a drama using one technique to using another
                 technique for the subsequent drama will result in
                 increased conversation. The first experiment shows that
                 hearing coordinated narrative variations can in fact
                 lead to significantly increased conversation. The
                 second experiment also serves as a framework for future
                 studies that evaluate strategies for similar adaptive
                 systems.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Martens:2014:ISI,
  author =       "Jean-Bernard Martens",
  title =        "Interactive Statistics with {Illmo}",
  journal =      j-TIIS,
  volume =       "4",
  number =       "1",
  pages =        "4:1--4:??",
  month =        apr,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2509108",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Apr 12 11:14:27 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Progress in empirical research relies on adequate
                 statistical analysis and reporting. This article
                 proposes an alternative approach to statistical
                 modeling that is based on an old but mostly forgotten
                 idea, namely Thurstone modeling. Traditional
                 statistical methods assume that either the measured
                 data, in the case of parametric statistics, or the
                 rank-order transformed data, in the case of
                 nonparametric statistics, are samples from a specific
                 (usually Gaussian) distribution with unknown
                 parameters. Consequently, such methods should not be
                 applied when this assumption is not valid. Thurstone
                 modeling similarly assumes the existence of an
                 underlying process that obeys an a priori assumed
                 distribution with unknown parameters, but combines this
                 underlying process with a flexible response mechanism
                 that can be either continuous or discrete and either
                 linear or nonlinear. One important advantage of
                 Thurstone modeling is that traditional statistical
                 methods can still be applied on the underlying process,
                 irrespective of the nature of the measured data itself.
                 Another advantage is that Thurstone models can be
                 graphically represented, which helps to communicate
                 them to a broad audience. A new interactive statistical
                 package, Interactive Log Likelihood MOdeling ( Illmo ),
                 was specifically designed for estimating and rendering
                 Thurstone models and is intended to bring Thurstone
                 modeling within the reach of persons who are not
                 experts in statistics. Illmo is unique in the sense
                 that it provides not only extensive graphical
                 renderings of the data analysis results but also an
                 interface for navigating between different model
                 options. In this way, users can interactively explore
                 different models and decide on an adequate balance
                 between model complexity and agreement with the
                 experimental data. Hypothesis testing on model
                 parameters is also made intuitive and is supported by
                 both textual and graphical feedback. The flexibility
                 and ease of use of Illmo means that it is also
                 potentially useful as a didactic tool for teaching
                 statistics.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Riveiro:2014:ENM,
  author =       "Maria Riveiro",
  title =        "Evaluation of Normal Model Visualization for Anomaly
                 Detection in Maritime Traffic",
  journal =      j-TIIS,
  volume =       "4",
  number =       "1",
  pages =        "5:1--5:??",
  month =        apr,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2591511",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Apr 12 11:14:27 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Monitoring dynamic objects in surveillance
                 applications is normally a demanding activity for
                 operators, not only because of the complexity and high
                 dimensionality of the data but also because of other
                 factors like time constraints and uncertainty. Timely
                 detection of anomalous objects or situations that need
                 further investigation may reduce operators' cognitive
                 load. Surveillance applications may include anomaly
                 detection capabilities, but their use is not
                 widespread, as they usually generate a high number of
                 false alarms, they do not provide appropriate cognitive
                 support for operators, and their outcomes can be
                 difficult to comprehend and trust. Visual analytics can
                 bridge the gap between computational and human
                 approaches to detecting anomalous behavior in traffic
                 data, making this process more transparent. As a step
                 toward this goal of transparency, this article presents
                 an evaluation that assesses whether visualizations of
                 normal behavioral models of vessel traffic support two
                 of the main analytical tasks specified during our field
                 work in maritime control centers. The evaluation
                 combines quantitative and qualitative usability
                 assessments. The quantitative evaluation, which was
                 carried out with a proof-of-concept prototype, reveals
                 that participants who used the visualization of normal
                 behavioral models outperformed the group that did not
                 do so. The qualitative assessment shows that domain
                 experts have a positive attitude toward the provision
                 of automatic support and the visualization of normal
                 behavioral models, as these aids may reduce reaction
                 time and increase trust in and comprehensibility of the
                 system.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chen:2014:EPM,
  author =       "Yingjie Victor Chen and Zhenyu Cheryl Qian and Robert
                 Woodbury and John Dill and Chris D. Shaw",
  title =        "Employing a Parametric Model for Analytic Provenance",
  journal =      j-TIIS,
  volume =       "4",
  number =       "1",
  pages =        "6:1--6:??",
  month =        apr,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2591510",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Apr 12 11:14:27 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We introduce a propagation-based parametric symbolic
                 model approach to supporting analytic provenance. This
                 approach combines a script language to capture and
                 encode the analytic process and a parametrically
                 controlled symbolic model to represent and reuse the
                 logic of the analysis process. Our approach first
                 appeared in a visual analytics system called CZSaw.
                 Using a script to capture the analyst's interactions at
                 a meaningful system action level allows the creation of
                 a parametrically controlled symbolic model in the form
                 of a Directed Acyclic Graph (DAG). Using the DAG allows
                 propagating changes. Graph nodes correspond to
                 variables in CZSaw scripts, which are results (data and
                 data visualizations) generated from user interactions.
                 The user interacts with variables representing entities
                 or relations to create the next step's results. Graph
                 edges represent dependency relationships among nodes.
                 Any change to a variable triggers the propagation
                 mechanism to update downstream dependent variables and
                 in turn updates data views to reflect the change. The
                 analyst can reuse parts of the analysis process by
                 assigning new values to a node in the graph. We
                 evaluated this symbolic model approach by solving three
                 IEEE VAST Challenge contest problems (from IEEE VAST
                 2008, 2009, and 2010). In each of these challenges, the
                 analyst first created a symbolic model to explore,
                 understand, analyze, and solve a particular subproblem
                 and then reused the model via its dependency graph
                 propagation mechanism to solve similar subproblems.
                 With the script and model, CZSaw supports the analytic
                 provenance by capturing, encoding, and reusing the
                 analysis process. The analyst can recall the
                 chronological states of the analysis process with the
                 CZSaw script and may interpret the underlying rationale
                 of the analysis with the symbolic model.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chan:2014:RCT,
  author =       "Yu-Hsuan Chan and Carlos D. Correa and Kwan-Liu Ma",
  title =        "{Regression Cube}: a Technique for Multidimensional
                 Visual Exploration and Interactive Pattern Finding",
  journal =      j-TIIS,
  volume =       "4",
  number =       "1",
  pages =        "7:1--7:??",
  month =        apr,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2590349",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Sep 13 13:17:36 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Scatterplots are commonly used to visualize
                 multidimensional data; however, 2D projections of data
                 offer limited understanding of the high-dimensional
                 interactions between data points. We introduce an
                 interactive 3D extension of scatterplots called the
                 Regression Cube (RC), which augments a 3D scatterplot
                 with three facets on which the correlations between the
                 two variables are revealed by sensitivity lines and
                 sensitivity streamlines. The sensitivity visualization
                 of local regression on the 2D projections provides
                 insights about the shape of the data through its
                 orientation and continuity cues. We also introduce a
                 series of visual operations such as clustering,
                 brushing, and selection supported in RC. By iteratively
                 refining the selection of data points of interest, RC
                 is able to reveal salient local correlation patterns
                 that may otherwise remain hidden with a global
                 analysis. We have demonstrated our system with two
                 examples and a user-oriented evaluation, and we show
                 how RCs enable interactive visual exploration of
                 multidimensional datasets via a variety of
                 classification and information retrieval tasks. A video
                 demo of RC is available.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Jawaheer:2014:MUP,
  author =       "Gawesh Jawaheer and Peter Weller and Patty Kostkova",
  title =        "Modeling User Preferences in Recommender Systems: a
                 Classification Framework for Explicit and Implicit User
                 Feedback",
  journal =      j-TIIS,
  volume =       "4",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2512208",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Sep 13 13:15:34 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Recommender systems are firmly established as a
                 standard technology for assisting users with their
                 choices; however, little attention has been paid to the
                 application of the user model in recommender systems,
                 particularly the variability and noise that are an
                 intrinsic part of human behavior and activity. To
                 enable recommender systems to suggest items that are
                 useful to a particular user, it can be essential to
                 understand the user and his or her interactions with
                 the system. These interactions typically manifest
                 themselves as explicit and implicit user feedback that
                 provides the key indicators for modeling users'
                 preferences for items and essential information for
                 personalizing recommendations. In this article, we
                 propose a classification framework for the use of
                 explicit and implicit user feedback in recommender
                 systems based on a set of distinct properties that
                 include Cognitive Effort, User Model, Scale of
                 Measurement, and Domain Relevance. We develop a set of
                 comparison criteria for explicit and implicit user
                 feedback to emphasize the key properties. Using our
                 framework, we provide a classification of recommender
                 systems that have addressed questions about user
                 feedback, and we review state-of-the-art techniques to
                 improve such user feedback and thereby improve the
                 performance of the recommender system. Finally, we
                 formulate challenges for future research on improvement
                 of user feedback.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Fang:2014:CLM,
  author =       "Yi Fang and Ziad Al Bawab and Jean-Fran{\c{c}}ois
                 Crespo",
  title =        "Collaborative Language Models for Localized Query
                 Prediction",
  journal =      j-TIIS,
  volume =       "4",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2622617",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Sep 13 13:15:34 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Localized query prediction (LQP) is the task of
                 estimating web query trends for a specific location.
                 This problem subsumes many interesting personalized web
                 applications such as personalization for buzz query
                 detection, for query expansion, and for query
                 recommendation. These personalized applications can
                 greatly enhance user interaction with web search
                 engines by providing more customized information
                 discovered from user input (i.e., queries), but the LQP
                 task has rarely been investigated in the literature.
                 Although exist abundant work on estimating global web
                 search trends does exist, it often encounters the big
                 challenge of data sparsity when personalization comes
                 into play. In this article, we tackle the LQP task by
                 proposing a series of collaborative language models
                 (CLMs). CLMs alleviate the data sparsity issue by
                 collaboratively collecting queries and trend
                 information from the other locations. The traditional
                 statistical language models assume a fixed background
                 language model, which loses the taste of
                 personalization. In contrast, CLMs are personalized
                 language models with flexible background language
                 models customized to various locations. The most
                 sophisticated CLM enables the collaboration to adapt to
                 specific query topics, which further advances the
                 personalization level. An extensive set of experiments
                 have been conducted on a large-scale web query log to
                 demonstrate the effectiveness of the proposed models.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Castellano:2014:CSA,
  author =       "Ginevra Castellano and Iolanda Leite and Andr{\'e}
                 Pereira and Carlos Martinho and Ana Paiva and Peter W.
                 Mcowan",
  title =        "Context-Sensitive Affect Recognition for a Robotic
                 Game Companion",
  journal =      j-TIIS,
  volume =       "4",
  number =       "2",
  pages =        "10:1--10:??",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2622615",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Sep 13 13:15:34 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Social perception abilities are among the most
                 important skills necessary for robots to engage humans
                 in natural forms of interaction. Affect-sensitive
                 robots are more likely to be able to establish and
                 maintain believable interactions over extended periods
                 of time. Nevertheless, the integration of affect
                 recognition frameworks in real-time human-robot
                 interaction scenarios is still underexplored. In this
                 article, we propose and evaluate a context-sensitive
                 affect recognition framework for a robotic game
                 companion for children. The robot can automatically
                 detect affective states experienced by children in an
                 interactive chess game scenario. The affect recognition
                 framework is based on the automatic extraction of task
                 features and social interaction-based features.
                 Vision-based indicators of the children's nonverbal
                 behaviour are merged with contextual features related
                 to the game and the interaction and given as input to
                 support vector machines to create a context-sensitive
                 multimodal system for affect recognition. The affect
                 recognition framework is fully integrated in an
                 architecture for adaptive human-robot interaction.
                 Experimental evaluation showed that children's affect
                 can be successfully predicted using a combination of
                 behavioural and contextual data related to the game and
                 the interaction with the robot. It was found that
                 contextual data alone can be used to successfully
                 predict a subset of affective dimensions, such as
                 interest toward the robot. Experiments also showed that
                 engagement with the robot can be predicted using
                 information about the user's valence, interest and
                 anticipatory behaviour. These results provide evidence
                 that social engagement can be modelled as a state
                 consisting of affect and attention components in the
                 context of the interaction.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Steichen:2014:IVT,
  author =       "Ben Steichen and Cristina Conati and Giuseppe
                 Carenini",
  title =        "Inferring Visualization Task Properties, User
                 Performance, and User Cognitive Abilities from Eye Gaze
                 Data",
  journal =      j-TIIS,
  volume =       "4",
  number =       "2",
  pages =        "11:1--11:??",
  month =        jul,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2633043",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Sep 13 13:15:34 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Information visualization systems have traditionally
                 followed a one-size-fits-all model, typically ignoring
                 an individual user's needs, abilities, and preferences.
                 However, recent research has indicated that
                 visualization performance could be improved by adapting
                 aspects of the visualization to the individual user. To
                 this end, this article presents research aimed at
                 supporting the design of novel user-adaptive
                 visualization systems. In particular, we discuss
                 results on using information on user eye gaze patterns
                 while interacting with a given visualization to predict
                 properties of the user's visualization task; the user's
                 performance (in terms of predicted task completion
                 time); and the user's individual cognitive abilities,
                 such as perceptual speed, visual working memory, and
                 verbal working memory. We provide a detailed analysis
                 of different eye gaze feature sets, as well as
                 over-time accuracies. We show that these predictions
                 are significantly better than a baseline classifier
                 even during the early stages of visualization usage.
                 These findings are then discussed with a view to
                 designing visualization systems that can adapt to the
                 individual user in real time.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Cuayahuitl:2014:ISI,
  author =       "Heriberto Cuay{\'a}huitl and Lutz Frommberger and Nina
                 Dethlefs and Antoine Raux and Mathew Marge and Hendrik
                 Zender",
  title =        "Introduction to the Special Issue on Machine Learning
                 for Multiple Modalities in Interactive Systems and
                 Robots",
  journal =      j-TIIS,
  volume =       "4",
  number =       "3",
  pages =        "12e:1--12e:??",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2670539",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 14 17:38:05 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This special issue highlights research articles that
                 apply machine learning to robots and other systems that
                 interact with users through more than one modality,
                 such as speech, gestures, and vision. For example, a
                 robot may coordinate its speech with its actions,
                 taking into account (audio-)visual feedback during
                 their execution. Machine learning provides interactive
                 systems with opportunities to improve performance not
                 only of individual components but also of the system as
                 a whole. However, machine learning methods that
                 encompass multiple modalities of an interactive system
                 are still relatively hard to find. The articles in this
                 special issue represent examples that contribute to
                 filling this gap.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12e",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ngo:2014:EIM,
  author =       "Hung Ngo and Matthew Luciw and Jawas Nagi and
                 Alexander Forster and J{\"u}rgen Schmidhuber and Ngo
                 Anh Vien",
  title =        "Efficient Interactive Multiclass Learning from Binary
                 Feedback",
  journal =      j-TIIS,
  volume =       "4",
  number =       "3",
  pages =        "12:1--12:??",
  month =        aug,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2629631",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Sep 13 13:15:36 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We introduce a novel algorithm called upper confidence
                 --- weighted learning (UCWL) for online multiclass
                 learning from binary feedback (e.g., feedback that
                 indicates whether the prediction was right or wrong).
                 UCWL combines the upper confidence bound (UCB)
                 framework with the soft confidence-weighted (SCW)
                 online learning scheme. In UCB, each instance is
                 classified using both score and uncertainty. For a
                 given instance in the sequence, the algorithm might
                 guess its class label primarily to reduce the class
                 uncertainty. This is a form of informed exploration,
                 which enables the performance to improve with lower
                 sample complexity compared to the case without
                 exploration. Combining UCB with SCW leads to the
                 ability to deal well with noisy and nonseparable data,
                 and state-of-the-art performance is achieved without
                 increasing the computational cost. A potential
                 application setting is human-robot interaction (HRI),
                 where the robot is learning to classify some set of
                 inputs while the human teaches it by providing only
                 binary feedback-or sometimes even the wrong answer
                 entirely. Experimental results in the HRI setting and
                 with two benchmark datasets from other settings show
                 that UCWL outperforms other state-of-the-art algorithms
                 in the online binary feedback setting-and surprisingly
                 even sometimes outperforms state-of-the-art algorithms
                 that get full feedback (e.g., the true class label),
                 whereas UCWL gets only binary feedback on the same data
                 sequence.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Benotti:2014:INL,
  author =       "Luciana Benotti and Tessa Lau and Mart{\'\i}n
                 Villalba",
  title =        "Interpreting Natural Language Instructions Using
                 Language, Vision, and Behavior",
  journal =      j-TIIS,
  volume =       "4",
  number =       "3",
  pages =        "13:1--13:??",
  month =        aug,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2629632",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Sep 13 13:15:36 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We define the problem of automatic instruction
                 interpretation as follows. Given a natural language
                 instruction, can we automatically predict what an
                 instruction follower, such as a robot, should do in the
                 environment to follow that instruction? Previous
                 approaches to automatic instruction interpretation have
                 required either extensive domain-dependent rule writing
                 or extensive manually annotated corpora. This article
                 presents a novel approach that leverages a large amount
                 of unannotated, easy-to-collect data from humans
                 interacting in a game-like environment. Our approach
                 uses an automatic annotation phase based on artificial
                 intelligence planning, for which two different
                 annotation strategies are compared: one based on
                 behavioral information and the other based on
                 visibility information. The resulting annotations are
                 used as training data for different automatic
                 classifiers. This algorithm is based on the intuition
                 that the problem of interpreting a situated instruction
                 can be cast as a classification problem of choosing
                 among the actions that are possible in the situation.
                 Classification is done by combining language, vision,
                 and behavior information. Our empirical analysis shows
                 that machine learning classifiers achieve 77\% accuracy
                 on this task on available English corpora and 74\% on
                 similar German corpora. Finally, the inclusion of human
                 feedback in the interpretation process is shown to
                 boost performance to 92\% for the English corpus and
                 90\% for the German corpus.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Keizer:2014:MLS,
  author =       "Simon Keizer and Mary Ellen Foster and Zhuoran Wang
                 and Oliver Lemon",
  title =        "Machine Learning for Social Multiparty Human--Robot
                 Interaction",
  journal =      j-TIIS,
  volume =       "4",
  number =       "3",
  pages =        "14:1--14:??",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2600021",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 14 17:38:05 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We describe a variety of machine-learning techniques
                 that are being applied to social multiuser human--robot
                 interaction using a robot bartender in our scenario. We
                 first present a data-driven approach to social state
                 recognition based on supervised learning. We then
                 describe an approach to social skills execution-that
                 is, action selection for generating socially
                 appropriate robot behavior-which is based on
                 reinforcement learning, using a data-driven simulation
                 of multiple users to train execution policies for
                 social skills. Next, we describe how these components
                 for social state recognition and skills execution have
                 been integrated into an end-to-end robot bartender
                 system, and we discuss the results of a user
                 evaluation. Finally, we present an alternative
                 unsupervised learning framework that combines social
                 state recognition and social skills execution based on
                 hierarchical Dirichlet processes and an infinite POMDP
                 interaction manager. The models make use of data from
                 both human--human interactions collected in a number of
                 German bars and human--robot interactions recorded in
                 the evaluation of an initial version of the system.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Cuayahuitl:2014:NHR,
  author =       "Heriberto Cuay{\'a}huitl and Ivana
                 Kruijff-Korbayov{\'a} and Nina Dethlefs",
  title =        "Nonstrict Hierarchical Reinforcement Learning for
                 Interactive Systems and Robots",
  journal =      j-TIIS,
  volume =       "4",
  number =       "3",
  pages =        "15:1--15:??",
  month =        oct,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2659003",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 14 17:38:05 MDT 2014",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Conversational systems and robots that use
                 reinforcement learning for policy optimization in large
                 domains often face the problem of limited scalability.
                 This problem has been addressed either by using
                 function approximation techniques that estimate the
                 approximate true value function of a policy or by using
                 a hierarchical decomposition of a learning task into
                 subtasks. We present a novel approach for dialogue
                 policy optimization that combines the benefits of both
                 hierarchical control and function approximation and
                 that allows flexible transitions between dialogue
                 subtasks to give human users more control over the
                 dialogue. To this end, each reinforcement learning
                 agent in the hierarchy is extended with a subtask
                 transition function and a dynamic state space to allow
                 flexible switching between subdialogues. In addition,
                 the subtask policies are represented with linear
                 function approximation in order to generalize the
                 decision making to situations unseen in training. Our
                 proposed approach is evaluated in an interactive
                 conversational robot that learns to play quiz games.
                 Experimental results, using simulation and real users,
                 provide evidence that our proposed approach can lead to
                 more flexible (natural) interactions than strict
                 hierarchical control and that it is preferred by human
                 users.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Bulling:2015:ISI,
  author =       "Andreas Bulling and Ulf Blanke and Desney Tan and Jun
                 Rekimoto and Gregory Abowd",
  title =        "Introduction to the Special Issue on Activity
                 Recognition for Interaction",
  journal =      j-TIIS,
  volume =       "4",
  number =       "4",
  pages =        "16:1--16:??",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2694858",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 29 10:52:31 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This editorial introduction describes the aims and
                 scope of the ACM Transactions on Interactive
                 Intelligent Systems special issue on Activity
                 Recognition for Interaction. It explains why activity
                 recognition is becoming crucial as part of the cycle of
                 interaction between users and computing systems, and it
                 shows how the five articles selected for this special
                 issue reflect this theme.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16e",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ye:2015:UUS,
  author =       "Juan Ye and Graeme Stevenson and Simon Dobson",
  title =        "{USMART}: an Unsupervised Semantic Mining Activity
                 Recognition Technique",
  journal =      j-TIIS,
  volume =       "4",
  number =       "4",
  pages =        "16:1--16:??",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2662870",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 29 10:52:31 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Recognising high-level human activities from low-level
                 sensor data is a crucial driver for pervasive systems
                 that wish to provide seamless and distraction-free
                 support for users engaged in normal activities.
                 Research in this area has grown alongside advances in
                 sensing and communications, and experiments have
                 yielded sensor traces coupled with ground truth
                 annotations about the underlying environmental
                 conditions and user actions. Traditional machine
                 learning has had some success in recognising human
                 activities; but the need for large volumes of annotated
                 data and the danger of overfitting to specific
                 conditions represent challenges in connection with the
                 building of models applicable to a wide range of users,
                 activities, and environments. We present USMART, a
                 novel unsupervised technique that combines data- and
                 knowledge-driven techniques. USMART uses a general
                 ontology model to represent domain knowledge that can
                 be reused across different environments and users, and
                 we augment a range of learning techniques with
                 ontological semantics to facilitate the unsupervised
                 discovery of patterns in how each user performs daily
                 activities. We evaluate our approach against four
                 real-world third-party datasets featuring different
                 user populations and sensor configurations, and we find
                 that USMART achieves up to 97.5\% accuracy in
                 recognising daily activities.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Dim:2015:ADS,
  author =       "Eyal Dim and Tsvi Kuflik",
  title =        "Automatic Detection of Social Behavior of Museum
                 Visitor Pairs",
  journal =      j-TIIS,
  volume =       "4",
  number =       "4",
  pages =        "17:1--17:??",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2662869",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 29 10:52:31 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In many cases, visitors come to a museum in small
                 groups. In such cases, the visitors' social context has
                 an impact on their museum visit experience. Knowing the
                 social context may allow a system to provide socially
                 aware services to the visitors. Evidence of the social
                 context can be gained from observing/monitoring the
                 visitors' social behavior. However, automatic
                 identification of a social context requires, on the one
                 hand, identifying typical social behavior patterns and,
                 on the other, using relevant sensors that measure
                 various signals and reason about them to detect the
                 visitors' social behavior. We present such typical
                 social behavior patterns of visitor pairs, identified
                 by observations, and then the instrumentation,
                 detection process, reasoning, and analysis of measured
                 signals that enable us to detect the visitors' social
                 behavior. Simple sensors' data, such as proximity to
                 other visitors, proximity to museum points of interest,
                 and visitor orientation are used to detect social
                 synchronization, attention to the social companion, and
                 interest in museum exhibits. The presented approach may
                 allow future research to offer adaptive services to
                 museum visitors based on their social context to
                 support their group visit experience better.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Caramiaux:2015:AGR,
  author =       "Baptiste Caramiaux and Nicola Montecchio and Atau
                 Tanaka and Fr{\'e}d{\'e}ric Bevilacqua",
  title =        "Adaptive Gesture Recognition with Variation Estimation
                 for Interactive Systems",
  journal =      j-TIIS,
  volume =       "4",
  number =       "4",
  pages =        "18:1--18:??",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2643204",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 29 10:52:31 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article presents a gesture recognition/adaptation
                 system for human--computer interaction applications
                 that goes beyond activity classification and that, as a
                 complement to gesture labeling, characterizes the
                 movement execution. We describe a template-based
                 recognition method that simultaneously aligns the input
                 gesture to the templates using a Sequential Monte Carlo
                 inference technique. Contrary to standard
                 template-based methods based on dynamic programming,
                 such as Dynamic Time Warping, the algorithm has an
                 adaptation process that tracks gesture variation in
                 real time. The method continuously updates, during
                 execution of the gesture, the estimated parameters and
                 recognition results, which offers key advantages for
                 continuous human--machine interaction. The technique is
                 evaluated in several different ways: Recognition and
                 early recognition are evaluated on 2D onscreen pen
                 gestures; adaptation is assessed on synthetic data; and
                 both early recognition and adaptation are evaluated in
                 a user study involving 3D free-space gestures. The
                 method is robust to noise, and successfully adapts to
                 parameter variation. Moreover, it performs recognition
                 as well as or better than nonadapting offline
                 template-based methods.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Cooney:2015:AIS,
  author =       "Martin Cooney and Shuichi Nishio and Hiroshi
                 Ishiguro",
  title =        "Affectionate Interaction with a Small Humanoid Robot
                 Capable of Recognizing Social Touch Behavior",
  journal =      j-TIIS,
  volume =       "4",
  number =       "4",
  pages =        "19:1--19:??",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2685395",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 29 10:52:31 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Activity recognition, involving a capability to
                 recognize people's behavior and its underlying
                 significance, will play a crucial role in facilitating
                 the integration of interactive robotic artifacts into
                 everyday human environments. In particular, social
                 intelligence in recognizing affectionate behavior will
                 offer value by allowing companion robots to bond
                 meaningfully with interacting persons. The current
                 article addresses the issue of designing an
                 affectionate haptic interaction between a person and a
                 companion robot by exploring how a small humanoid robot
                 can behave to elicit affection while recognizing
                 touches. We report on an experiment conducted to gain
                 insight into how people perceive three fundamental
                 interactive strategies in which a robot is either
                 always highly affectionate, appropriately affectionate,
                 or superficially unaffectionate (emphasizing
                 positivity, contingency, and challenge, respectively).
                 Results provide insight into the structure of
                 affectionate interaction between humans and humanoid
                 robots-underlining the importance of an interaction
                 design expressing sincere liking, stability and
                 variation-and suggest the usefulness of novel
                 modalities such as warmth and cold.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{DeCarolis:2015:ILD,
  author =       "Berardina {De Carolis} and Stefano Ferilli and
                 Domenico Redavid",
  title =        "Incremental Learning of Daily Routines as Workflows in
                 a {Smart} Home Environment",
  journal =      j-TIIS,
  volume =       "4",
  number =       "4",
  pages =        "20:1--20:??",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2675063",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 29 10:52:31 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Smart home environments should proactively support
                 users in their activities, anticipating their needs
                 according to their preferences. Understanding what the
                 user is doing in the environment is important for
                 adapting the environment's behavior, as well as for
                 identifying situations that could be problematic for
                 the user. Enabling the environment to exploit models of
                 the user's most common behaviors is an important step
                 toward this objective. In particular, models of the
                 daily routines of a user can be exploited not only for
                 predicting his/her needs, but also for comparing the
                 actual situation at a given moment with the expected
                 one, in order to detect anomalies in his/her behavior.
                 While manually setting up process models in business
                 and factory environments may be cost-effective,
                 building models of the processes involved in people's
                 everyday life is infeasible. This fact fully justifies
                 the interest of the Ambient Intelligence community in
                 automatically learning such models from examples of
                 actual behavior. Incremental adaptation of the models
                 and the ability to express/learn complex conditions on
                 the involved tasks are also desirable. This article
                 describes how process mining can be used for learning
                 users' daily routines from a dataset of annotated
                 sensor data. The solution that we propose relies on a
                 First-Order Logic learning approach. Indeed,
                 First-Order Logic provides a single, comprehensive and
                 powerful framework for supporting all the previously
                 mentioned features. Our experiments, performed both on
                 a proprietary toy dataset and on publicly available
                 real-world ones, indicate that this approach is
                 efficient and effective for learning and modeling daily
                 routines in Smart Home Environments.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Gianni:2015:SRF,
  author =       "Mario Gianni and Geert-Jan M. Kruijff and Fiora
                 Pirri",
  title =        "A Stimulus-Response Framework for Robot Control",
  journal =      j-TIIS,
  volume =       "4",
  number =       "4",
  pages =        "21:1--21:??",
  month =        jan,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2677198",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 29 10:52:31 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We propose in this article a new approach to robot
                 cognitive control based on a stimulus-response
                 framework that models both a robot's stimuli and the
                 robot's decision to switch tasks in response to or
                 inhibit the stimuli. In an autonomous system, we expect
                 a robot to be able to deal with the whole system of
                 stimuli and to use them to regulate its behavior in
                 real-world applications. The proposed framework
                 contributes to the state of the art of robot planning
                 and high-level control in that it provides a novel
                 perspective on the interaction between robot and
                 environment. Our approach is inspired by Gibson's
                 constructive view of the concept of a stimulus and by
                 the cognitive control paradigm of task switching. We
                 model the robot's response to a stimulus in three
                 stages. We start by defining the stimuli as perceptual
                 functions yielded by the active robot processes and
                 learned via an informed logistic regression. Then we
                 model the stimulus-response relationship by estimating
                 a score matrix that leads to the selection of a single
                 response task for each stimulus, basing the estimation
                 on low-rank matrix factorization. The decision about
                 switching takes into account both an interference cost
                 and a reconfiguration cost. The interference cost
                 weighs the effort of discontinuing the current robot
                 mental state to switch to a new state, whereas the
                 reconfiguration cost weighs the effort of activating
                 the response task. A choice is finally made based on
                 the payoff of switching. Because processes play such a
                 crucial role both in the stimulus model and in the
                 stimulus-response model, and because processes are
                 activated by actions, we address also the process
                 model, which is built on a theory of action. The
                 framework is validated by several experiments that
                 exploit a full implementation on an advanced robotic
                 platform and is compared with two known approaches to
                 replanning. Results demonstrate the practical value of
                 the system in terms of robot autonomy, flexibility, and
                 usability.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Taranta:2015:EBC,
  author =       "Eugene M. {Taranta II} and Thaddeus K. Simons and
                 Rahul Sukthankar and Joseph J. {Laviola, Jr.}",
  title =        "Exploring the Benefits of Context in {$3$D} Gesture
                 Recognition for Game-Based Virtual Environments",
  journal =      j-TIIS,
  volume =       "5",
  number =       "1",
  pages =        "1:1--1:??",
  month =        mar,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2656345",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 26 05:43:35 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We present a systematic exploration of how to utilize
                 video game context (e.g., player and environmental
                 state) to modify and augment existing 3D gesture
                 recognizers to improve accuracy for large gesture sets.
                 Specifically, our work develops and evaluates three
                 strategies for incorporating context into 3D gesture
                 recognizers. These strategies include modifying the
                 well-known Rubine linear classifier to handle
                 unsegmented input streams and per-frame retraining
                 using contextual information (CA-Linear); a GPU
                 implementation of dynamic time warping (DTW) that
                 reduces the overhead of traditional DTW by utilizing
                 context to evaluate only relevant time sequences inside
                 of a multithreaded kernel (CA-DTW); and a multiclass
                 SVM with per-class probability estimation that is
                 combined with a contextually based prior probability
                 distribution (CA-SVM). We evaluate each strategy using
                 a Kinect-based third-person perspective VE game
                 prototype that combines parkour-style navigation with
                 hand-to-hand combat. Using a simple gesture collection
                 application to collect a set of 57 gestures and the
                 game prototype that implements 37 of these gestures, we
                 conduct three experiments. In the first experiment, we
                 evaluate the effectiveness of several established
                 classifiers on our gesture set and demonstrate
                 state-of-the-art results using our proposed method. In
                 our second experiment, we generate 500 random scenarios
                 having between 5 and 19 of the 57 gestures in context.
                 We show that the contextually aware classifiers
                 CA-Linear, CA-DTW, and CA-SVM significantly outperform
                 their non--contextually aware counterparts by 37.74\%,
                 36.04\%, and 20.81\%, respectively. On the basis of the
                 results of the second experiment, we derive upper-bound
                 expectations for in-game performance for the three CA
                 classifiers: 96.61\%, 86.79\%, and 96.86\%,
                 respectively. Finally, our third experiment is an
                 in-game evaluation of the three CA classifiers with and
                 without context. Our results show that through the use
                 of context, we are able to achieve an average in-game
                 recognition accuracy of 89.67\% with CA-Linear compared
                 to 65.10\% without context, 79.04\% for CA-DTW compared
                 to 58.1\% without context, and 90.85\% with CA-SVM
                 compared to 75.2\% without context.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Gil:2015:HTI,
  author =       "Yolanda Gil",
  title =        "Human Tutorial Instruction in the Raw",
  journal =      j-TIIS,
  volume =       "5",
  number =       "1",
  pages =        "2:1--2:??",
  month =        mar,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2531920",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 26 05:43:35 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Humans learn procedures from one another through a
                 variety of methods, such as observing someone do the
                 task, practicing by themselves, reading manuals or
                 textbooks, or getting instruction from a teacher. Some
                 of these methods generate examples that require the
                 learner to generalize appropriately. When procedures
                 are complex, however, it becomes unmanageable to induce
                 the procedures from examples alone. An alternative and
                 very common method for teaching procedures is tutorial
                 instruction, where a teacher describes in general terms
                 what actions to perform and possibly includes
                 explanations of the rationale for the actions. This
                 article provides an overview of the challenges in using
                 human tutorial instruction for teaching procedures to
                 computers. First, procedures can be very complex and
                 can involve many different types of interrelated
                 information, including (1) situating the instruction in
                 the context of relevant objects and their properties,
                 (2) describing the steps involved, (3) specifying the
                 organization of the procedure in terms of relationships
                 among steps and substeps, and (4) conveying control
                 structures. Second, human tutorial instruction is
                 naturally plagued with omissions, oversights,
                 unintentional inconsistencies, errors, and simply poor
                 design. The article presents a survey of work from the
                 literature that highlights the nature of these
                 challenges and illustrates them with numerous examples
                 of instruction in many domains. Major research
                 challenges in this area are highlighted, including the
                 difficulty of the learning task when procedures are
                 complex, the need to overcome omissions and errors in
                 the instruction, the design of a natural user interface
                 to specify procedures, the management of the
                 interaction of a human with a learning system, and the
                 combination of tutorial instruction with other teaching
                 modalities.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Pejsa:2015:GAM,
  author =       "Tomislav Pejsa and Sean Andrist and Michael Gleicher
                 and Bilge Mutlu",
  title =        "Gaze and Attention Management for Embodied
                 Conversational Agents",
  journal =      j-TIIS,
  volume =       "5",
  number =       "1",
  pages =        "3:1--3:??",
  month =        mar,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2724731",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 26 05:43:35 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "To facilitate natural interactions between humans and
                 embodied conversational agents (ECAs), we need to endow
                 the latter with the same nonverbal cues that humans use
                 to communicate. Gaze cues in particular are integral in
                 mechanisms for communication and management of
                 attention in social interactions, which can trigger
                 important social and cognitive processes, such as
                 establishment of affiliation between people or learning
                 new information. The fundamental building blocks of
                 gaze behaviors are gaze shifts: coordinated movements
                 of the eyes, head, and body toward objects and
                 information in the environment. In this article, we
                 present a novel computational model for gaze shift
                 synthesis for ECAs that supports parametric control
                 over coordinated eye, head, and upper body movements.
                 We employed the model in three studies with human
                 participants. In the first study, we validated the
                 model by showing that participants are able to
                 interpret the agent's gaze direction accurately. In the
                 second and third studies, we showed that by adjusting
                 the participation of the head and upper body in gaze
                 shifts, we can control the strength of the attention
                 signals conveyed, thereby strengthening or weakening
                 their social and cognitive effects. The second study
                 shows that manipulation of eye--head coordination in
                 gaze enables an agent to convey more information or
                 establish stronger affiliation with participants in a
                 teaching task, while the third study demonstrates how
                 manipulation of upper body coordination enables the
                 agent to communicate increased interest in objects in
                 the environment.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Deng:2015:ESA,
  author =       "James J. Deng and Clement H. C. Leung and Alfredo
                 Milani and Li Chen",
  title =        "Emotional States Associated with Music:
                 Classification, Prediction of Changes, and
                 Consideration in Recommendation",
  journal =      j-TIIS,
  volume =       "5",
  number =       "1",
  pages =        "4:1--4:??",
  month =        mar,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2723575",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 26 05:43:35 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We present several interrelated technical and
                 empirical contributions to the problem of emotion-based
                 music recommendation and show how they can be applied
                 in a possible usage scenario. The contributions are (1)
                 a new three-dimensional resonance-arousal-valence model
                 for the representation of emotion expressed in music,
                 together with methods for automatically classifying a
                 piece of music in terms of this model, using robust
                 regression methods applied to musical/acoustic
                 features; (2) methods for predicting a listener's
                 emotional state on the assumption that the emotional
                 state has been determined entirely by a sequence of
                 pieces of music recently listened to, using conditional
                 random fields and taking into account the decay of
                 emotion intensity over time; and (3) a method for
                 selecting a ranked list of pieces of music that match a
                 particular emotional state, using a minimization
                 iteration method. A series of experiments yield
                 information about the validity of our
                 operationalizations of these contributions. Throughout
                 the article, we refer to an illustrative usage scenario
                 in which all of these contributions can be exploited,
                 where it is assumed that (1) a listener's emotional
                 state is being determined entirely by the music that he
                 or she has been listening to and (2) the listener wants
                 to hear additional music that matches his or her
                 current emotional state. The contributions are intended
                 to be useful in a variety of other scenarios as well.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Mazilu:2015:WAG,
  author =       "Sinziana Mazilu and Ulf Blanke and Moran Dorfman and
                 Eran Gazit and Anat Mirelman and Jeffrey M. Hausdorff
                 and Gerhard Tr{\"o}ster",
  title =        "A Wearable Assistant for Gait Training for
                 {Parkinson}'s Disease with Freezing of Gait in
                 Out-of-the-Lab Environments",
  journal =      j-TIIS,
  volume =       "5",
  number =       "1",
  pages =        "5:1--5:??",
  month =        mar,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2701431",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Mar 26 05:43:35 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "People with Parkinson's disease (PD) suffer from
                 declining mobility capabilities, which cause a
                 prevalent risk of falling. Commonly, short periods of
                 motor blocks occur during walking, known as freezing of
                 gait (FoG). To slow the progressive decline of motor
                 abilities, people with PD usually undertake stationary
                 motor-training exercises in the clinics or supervised
                 by physiotherapists. We present a wearable system for
                 the support of people with PD and FoG. The system is
                 designed for independent use. It enables motor training
                 and gait assistance at home and other unsupervised
                 environments. The system consists of three components.
                 First, FoG episodes are detected in real time using
                 wearable inertial sensors and a smartphone as the
                 processing unit. Second, a feedback mechanism triggers
                 a rhythmic auditory signal to the user to alleviate
                 freeze episodes in an assistive mode. Third, the
                 smartphone-based application features support for
                 training exercises. Moreover, the system allows
                 unobtrusive and long-term monitoring of the user's
                 clinical condition by transmitting sensing data and
                 statistics to a telemedicine service. We investigate
                 the at-home acceptance of the wearable system in a
                 study with nine PD subjects. Participants deployed and
                 used the system on their own, without any clinical
                 support, at their homes during three protocol sessions
                 in 1 week. Users' feedback suggests an overall positive
                 attitude toward adopting and using the system in their
                 daily life, indicating that the system supports them in
                 improving their gait. Further, in a data-driven
                 analysis with sensing data from five participants, we
                 study whether there is an observable effect on the gait
                 during use of the system. In three out of five
                 subjects, we observed a decrease in FoG duration
                 distributions over the protocol days during
                 gait-training exercises. Moreover, sensing data-driven
                 analysis shows a decrease in FoG duration and FoG
                 number in four out of five participants when they use
                 the system as a gait-assistive tool during normal daily
                 life activities at home.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Salah:2015:BIS,
  author =       "Albert Ali Salah and Hayley Hung and Oya Aran and
                 Hatice Gunes and Matthew Turk",
  title =        "Brief Introduction to the Special Issue on Behavior
                 Understanding for Arts and Entertainment",
  journal =      j-TIIS,
  volume =       "5",
  number =       "2",
  pages =        "6:1--6:??",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2786762",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Aug 7 09:18:56 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This editorial introduction describes the aims and
                 scope of the special issue of the ACM Transactions on
                 Interactive Intelligent Systems on Behavior
                 Understanding for Arts and Entertainment, which is
                 being published in issues 2 and 3 of volume 5 of the
                 journal. Here we offer a brief introduction to the use
                 of behavior analysis for interactive systems that
                 involve creativity in either the creator or the
                 consumer of a work of art. We then characterize each of
                 the five articles included in this first part of the
                 special issue, which span a wide range of
                 applications.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Grenader:2015:VIA,
  author =       "Emily Grenader and Danilo Gasques Rodrigues and
                 Fernando Nos and Nadir Weibel",
  title =        "The {VideoMob} Interactive Art Installation Connecting
                 Strangers through Inclusive Digital Crowds",
  journal =      j-TIIS,
  volume =       "5",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2768208",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Aug 7 09:18:56 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "VideoMob is an interactive video platform and an
                 artwork that enables strangers visiting different
                 installation locations to interact across time and
                 space through a computer interface that detects their
                 presence, video-records their actions while
                 automatically removing the video background through
                 computer vision, and co-situates visitors as part of
                 the same digital environment. Through the combination
                 of individual user videos to form a digital crowd,
                 strangers are connected through the graphic display.
                 Our work is inspired by the way distant people can
                 interact with each other through technology and
                 influenced by artists working in the realm of
                 interactive art. We deployed VideoMob in a variety of
                 settings, locations, and contexts to observe hundreds
                 of visitors' reactions. By analyzing behavioral data
                 collected through depth cameras from our 1,068
                 recordings across eight venues, we studied how
                 participants behave when given the opportunity to
                 record their own video portrait into the artwork. We
                 report the specific activity performed in front of the
                 camera and the influences that existing crowds impose
                 on new participants. Our analysis informs the
                 integration of a series of possible novel interaction
                 paradigms based on real-time analysis of the visitors'
                 behavior through specific computer vision and machine
                 learning techniques that have the potential to increase
                 the engagement of the artwork's visitors and to impact
                 user experience.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Sartori:2015:AAP,
  author =       "Andreza Sartori and Victoria Yanulevskaya and Almila
                 Akdag Salah and Jasper Uijlings and Elia Bruni and Nicu
                 Sebe",
  title =        "Affective Analysis of Professional and Amateur
                 Abstract Paintings Using Statistical Analysis and Art
                 Theory",
  journal =      j-TIIS,
  volume =       "5",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2768209",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Aug 7 09:18:56 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "When artists express their feelings through the
                 artworks they create, it is believed that the resulting
                 works transform into objects with ``emotions'' capable
                 of conveying the artists' mood to the audience. There
                 is little to no dispute about this belief: Regardless
                 of the artwork, genre, time, and origin of creation,
                 people from different backgrounds are able to read the
                 emotional messages. This holds true even for the most
                 abstract paintings. Could this idea be applied to
                 machines as well? Can machines learn what makes a work
                 of art ``emotional''? In this work, we employ a
                 state-of-the-art recognition system to learn which
                 statistical patterns are associated with positive and
                 negative emotions on two different datasets that
                 comprise professional and amateur abstract artworks.
                 Moreover, we analyze and compare two different
                 annotation methods in order to establish the ground
                 truth of positive and negative emotions in abstract
                 art. Additionally, we use computer vision techniques to
                 quantify which parts of a painting evoke positive and
                 negative emotions. We also demonstrate how the
                 quantification of evidence for positive and negative
                 emotions can be used to predict which parts of a
                 painting people prefer to focus on. This method opens
                 new opportunities of research on why a specific
                 painting is perceived as emotional at global and local
                 scales.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Sanchez-Cortes:2015:MVM,
  author =       "Dairazalia Sanchez-Cortes and Shiro Kumano and
                 Kazuhiro Otsuka and Daniel Gatica-Perez",
  title =        "In the Mood for Vlog: Multimodal Inference in
                 Conversational Social Video",
  journal =      j-TIIS,
  volume =       "5",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2641577",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Aug 7 09:18:56 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The prevalent ``share what's on your mind'' paradigm
                 of social media can be examined from the perspective of
                 mood: short-term affective states revealed by the
                 shared data. This view takes on new relevance given the
                 emergence of conversational social video as a popular
                 genre among viewers looking for entertainment and among
                 video contributors as a channel for debate, expertise
                 sharing, and artistic expression. From the perspective
                 of human behavior understanding, in conversational
                 social video both verbal and nonverbal information is
                 conveyed by speakers and decoded by viewers. We present
                 a systematic study of classification and ranking of
                 mood impressions in social video, using vlogs from
                 YouTube. Our approach considers eleven natural mood
                 categories labeled through crowdsourcing by external
                 observers on a diverse set of conversational vlogs. We
                 extract a comprehensive number of nonverbal and verbal
                 behavioral cues from the audio and video channels to
                 characterize the mood of vloggers. Then we implement
                 and validate vlog classification and vlog ranking tasks
                 using supervised learning methods. Following a
                 reliability and correlation analysis of the mood
                 impression data, our study demonstrates that, while the
                 problem is challenging, several mood categories can be
                 inferred with promising performance. Furthermore,
                 multimodal features perform consistently better than
                 single-channel features. Finally, we show that
                 addressing mood as a ranking problem is a promising
                 practical direction for several of the mood categories
                 studied.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Vezzani:2015:GPS,
  author =       "Roberto Vezzani and Martino Lombardi and Augusto
                 Pieracci and Paolo Santinelli and Rita Cucchiara",
  title =        "A General-Purpose Sensing Floor Architecture for
                 Human-Environment Interaction",
  journal =      j-TIIS,
  volume =       "5",
  number =       "2",
  pages =        "10:1--10:??",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2751566",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Aug 7 09:18:56 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Smart environments are now designed as natural
                 interfaces to capture and understand human behavior
                 without a need for explicit human-computer interaction.
                 In this article, we present a general-purpose
                 architecture that acquires and understands human
                 behaviors through a sensing floor. The pressure field
                 generated by moving people is captured and analyzed.
                 Specific actions and events are then detected by a
                 low-level processing engine and sent to high-level
                 interfaces providing different functions. The proposed
                 architecture and sensors are modular, general-purpose,
                 cheap, and suitable for both small- and large-area
                 coverage. Some sample entertainment and virtual reality
                 applications that we developed to test the platform are
                 presented.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Baur:2015:CAA,
  author =       "Tobias Baur and Gregor Mehlmann and Ionut Damian and
                 Florian Lingenfelser and Johannes Wagner and Birgit
                 Lugrin and Elisabeth Andr{\'e} and Patrick Gebhard",
  title =        "Context-Aware Automated Analysis and Annotation of
                 Social Human--Agent Interactions",
  journal =      j-TIIS,
  volume =       "5",
  number =       "2",
  pages =        "11:1--11:??",
  month =        jul,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2764921",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Aug 7 09:18:56 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The outcome of interpersonal interactions depends not
                 only on the contents that we communicate verbally, but
                 also on nonverbal social signals. Because a lack of
                 social skills is a common problem for a significant
                 number of people, serious games and other training
                 environments have recently become the focus of
                 research. In this work, we present NovA ( No n v erbal
                 behavior A nalyzer), a system that analyzes and
                 facilitates the interpretation of social signals
                 automatically in a bidirectional interaction with a
                 conversational agent. It records data of interactions,
                 detects relevant social cues, and creates descriptive
                 statistics for the recorded data with respect to the
                 agent's behavior and the context of the situation. This
                 enhances the possibilities for researchers to
                 automatically label corpora of human--agent
                 interactions and to give users feedback on strengths
                 and weaknesses of their social behavior.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Salah:2015:BUA,
  author =       "Albert Ali Salah and Hayley Hung and Oya Aran and
                 Hatice Gunes and Matthew Turk",
  title =        "Behavior Understanding for Arts and Entertainment",
  journal =      j-TIIS,
  volume =       "5",
  number =       "3",
  pages =        "12:1--12:??",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2817208",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Oct 17 18:18:51 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This editorial introduction complements the shorter
                 introduction to the first part of the two-part special
                 issue on Behavior Understanding for Arts and
                 Entertainment. It offers a more expansive discussion of
                 the use of behavior analysis for interactive systems
                 that involve creativity, either for the producer or the
                 consumer of such a system. We first summarise the two
                 articles that appear in this second part of the special
                 issue. We then discuss general questions and challenges
                 in this domain that were suggested by the entire set of
                 seven articles of the special issue and by the comments
                 of the reviewers of these articles.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Alaoui:2015:IVM,
  author =       "Sarah Fdili Alaoui and Frederic Bevilacqua and
                 Christian Jacquemin",
  title =        "Interactive Visuals as Metaphors for Dance Movement
                 Qualities",
  journal =      j-TIIS,
  volume =       "5",
  number =       "3",
  pages =        "13:1--13:??",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2738219",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Oct 17 18:18:51 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The notion of ``movement qualities'' is central in
                 contemporary dance; it describes the manner in which a
                 movement is executed. Movement qualities convey
                 information revealing movement expressiveness; their
                 use has strong potential for movement-based interaction
                 with applications in arts, entertainment, education, or
                 rehabilitation. The purpose of our research is to
                 design and evaluate interactive reflexive visuals for
                 movement qualities. The theoretical basis for this
                 research is drawn from a collaboration with the members
                 of the international dance company Emio Greco|PC to
                 study their formalization of movement qualities. We
                 designed a pedagogical interactive installation called
                 Double Skin/Double Mind (DS/DM) for the analysis and
                 visualization of movement qualities through physical
                 model-based interactive renderings. In this article, we
                 first evaluate dancers' perception of the visuals as
                 metaphors for movement qualities. This evaluation shows
                 that, depending on the physical model parameterization,
                 the visuals are capable of generating dynamic behaviors
                 that the dancers associate with DS/DM movement
                 qualities. Moreover, we evaluate dance students' and
                 professionals' experience of the interactive visuals in
                 the context of a dance pedagogical workshop and a
                 professional dance training. The results of these
                 evaluations show that the dancers consider the
                 interactive visuals to be a reflexive system that
                 encourages them to perform, improves their experience,
                 and contributes to a better understanding of movement
                 qualities. Our findings support research on interactive
                 systems for real-time analysis and visualization of
                 movement qualities, which open new perspectives in
                 movement-based interaction design.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Yang:2015:QSM,
  author =       "Yi-Hsuan Yang and Yuan-Ching Teng",
  title =        "Quantitative Study of Music Listening Behavior in a
                 {Smartphone} Context",
  journal =      j-TIIS,
  volume =       "5",
  number =       "3",
  pages =        "14:1--14:??",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2738220",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Oct 17 18:18:51 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Context-based services have attracted increasing
                 attention because of the prevalence of sensor-rich
                 mobile devices such as smartphones. The idea is to
                 recommend information that a user would be interested
                 in according to the user's surrounding context.
                 Although remarkable progress has been made to
                 contextualize music playback, relatively little
                 research has been made using a large collection of
                 real-life listening records collected in situ. In light
                 of this fact, we present in this article a quantitative
                 study of the personal, situational, and musical factors
                 of musical preference in a smartphone context, using a
                 new dataset comprising the listening records and
                 self-report context annotation of 48 participants
                 collected over 3wk via an Android app. Although the
                 number of participants is limited and the population is
                 biased towards students, the dataset is unique in that
                 it is collected in a daily context, with sensor data
                 and music listening profiles recorded at the same time.
                 We investigate 3 core research questions evaluating the
                 strength of a rich set of low-level and high-level
                 audio features for music usage auto-tagging (i.e.,
                 music preference in different user activities), the
                 strength of time-domain and frequency-domain sensor
                 features for user activity classification, and how user
                 factors such as personality traits are correlated with
                 the predictability of music usage and user activity,
                 using a closed set of 8 activity classes. We provide an
                 in-depth discussion of the main findings of this study
                 and their implications for the development of
                 context-based music services for smartphones.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Bott:2015:WRW,
  author =       "Jared N. Bott and Joseph J. {Laviola Jr.}",
  title =        "The {WOZ Recognizer}: a {Wizard of Oz} Sketch
                 Recognition System",
  journal =      j-TIIS,
  volume =       "5",
  number =       "3",
  pages =        "15:1--15:??",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2743029",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Oct 17 18:18:51 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Sketch recognition has the potential to be an
                 important input method for computers in the coming
                 years, particularly for STEM (science, technology,
                 engineering, and math) education. However, designing
                 and building an accurate and sophisticated sketch
                 recognition system is a time-consuming and daunting
                 task. Since sketch recognition mistakes are still
                 common, it is important to understand how users
                 perceive and tolerate recognition errors and other user
                 interface elements with these imperfect systems. In
                 order to solve this problem, we developed a Wizard of
                 Oz sketch recognition tool, the WOZ Recognizer, that
                 supports controlled recognition accuracy, multiple
                 recognition modes, and multiple sketching domains for
                 performing controlled experiments. We present the
                 design of the WOZ Recognizer and our process for
                 representing recognition domains using graphs and
                 symbol alphabets. In addition, we discuss how sketches
                 are altered, how to control the WOZ Recognizer, and how
                 users interact with it. Finally, we present an expert
                 user case study that examines the WOZ Recognizer's
                 usability.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Soto:2015:EVA,
  author =       "Axel J. Soto and Ryan Kiros and Vlado Keselj and
                 Evangelos Milios",
  title =        "Exploratory Visual Analysis and Interactive Pattern
                 Extraction from Semi-Structured Data",
  journal =      j-TIIS,
  volume =       "5",
  number =       "3",
  pages =        "16:1--16:??",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2812115",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Oct 17 18:18:51 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Semi-structured documents are a common type of data
                 containing free text in natural language (unstructured
                 data) as well as additional information about the
                 document, or meta-data, typically following a schema or
                 controlled vocabulary (structured data). Simultaneous
                 analysis of unstructured and structured data enables
                 the discovery of hidden relationships that cannot be
                 identified from either of these sources when analyzed
                 independently of each other. In this work, we present a
                 visual text analytics tool for semi-structured
                 documents (ViTA-SSD), that aims to support the user in
                 the exploration and finding of insightful patterns in a
                 visual and interactive manner in a semi-structured
                 collection of documents. It achieves this goal by
                 presenting to the user a set of coordinated
                 visualizations that allows the linking of the metadata
                 with interactively generated clusters of documents in
                 such a way that relevant patterns can be easily
                 spotted. The system contains two novel approaches in
                 its back end: a feature-learning method to learn a
                 compact representation of the corpus and a
                 fast-clustering approach that has been redesigned to
                 allow user supervision. These novel contributions make
                 it possible for the user to interact with a large and
                 dynamic document collection and to perform several text
                 analytical tasks more efficiently. Finally, we present
                 two use cases that illustrate the suitability of the
                 system for in-depth interactive exploration of
                 semi-structured document collections, two user studies,
                 and results of several evaluations of our text-mining
                 components.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Meignan:2015:RTI,
  author =       "David Meignan and Sigrid Knust and Jean-Marc Frayret
                 and Gilles Pesant and Nicolas Gaud",
  title =        "A Review and Taxonomy of Interactive Optimization
                 Methods in Operations Research",
  journal =      j-TIIS,
  volume =       "5",
  number =       "3",
  pages =        "17:1--17:??",
  month =        oct,
  year =         "2015",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2808234",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Oct 17 18:18:51 MDT 2015",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article presents a review and a classification of
                 interactive optimization methods. These interactive
                 methods are used for solving optimization problems. The
                 interaction with an end user or decision maker aims at
                 improving the efficiency of the optimization procedure,
                 enriching the optimization model, or informing the user
                 regarding the solutions proposed by the optimization
                 system. First, we present the challenges of using
                 optimization methods as a tool for supporting decision
                 making, and we justify the integration of the user in
                 the optimization process. This integration is generally
                 achieved via a dynamic interaction between the user and
                 the system. Next, the different classes of interactive
                 optimization approaches are presented. This detailed
                 review includes trial and error, interactive
                 reoptimization, interactive multiobjective
                 optimization, interactive evolutionary algorithms,
                 human-guided search, and other approaches that are less
                 well covered in the research literature. On the basis
                 of this review, we propose a classification that aims
                 to better describe and compare interaction mechanisms.
                 This classification offers two complementary views on
                 interactive optimization methods. The first perspective
                 focuses on the user's contribution to the optimization
                 process, and the second concerns the components of
                 interactive optimization systems. Finally, on the basis
                 of this review and classification, we identify some
                 open issues and potential perspectives for interactive
                 optimization methods.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Wang:2016:ART,
  author =       "Weiyi Wang and Valentin Enescu and Hichem Sahli",
  title =        "Adaptive Real-Time Emotion Recognition from Body
                 Movements",
  journal =      j-TIIS,
  volume =       "5",
  number =       "4",
  pages =        "18:1--18:??",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2738221",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 7 16:06:24 MST 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We propose a real-time system that continuously
                 recognizes emotions from body movements. The combined
                 low-level 3D postural features and high-level kinematic
                 and geometrical features are fed to a Random Forests
                 classifier through summarization (statistical values)
                 or aggregation (bag of features). In order to improve
                 the generalization capability and the robustness of the
                 system, a novel semisupervised adaptive algorithm is
                 built on top of the conventional Random Forests
                 classifier. The MoCap UCLIC affective gesture database
                 (labeled with four emotions) was used to train the
                 Random Forests classifier, which led to an overall
                 recognition rate of 78\% using a 10-fold
                 cross-validation. Subsequently, the trained classifier
                 was used in a stream-based semisupervised Adaptive
                 Random Forests method for continuous unlabeled Kinect
                 data classification. The very low update cost of our
                 adaptive classifier makes it highly suitable for data
                 stream applications. Tests performed on the publicly
                 available emotion datasets (body gestures and facial
                 expressions) indicate that our new classifier
                 outperforms existing algorithms for data streams in
                 terms of accuracy and computational costs.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Harper:2016:MDH,
  author =       "F. Maxwell Harper and Joseph A. Konstan",
  title =        "The {MovieLens} Datasets: History and Context",
  journal =      j-TIIS,
  volume =       "5",
  number =       "4",
  pages =        "19:1--19:??",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2827872",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 7 16:06:24 MST 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The MovieLens datasets are widely used in education,
                 research, and industry. They are downloaded hundreds of
                 thousands of times each year, reflecting their use in
                 popular press programming books, traditional and online
                 courses, and software. These datasets are a product of
                 member activity in the MovieLens movie recommendation
                 system, an active research platform that has hosted
                 many experiments since its launch in 1997. This article
                 documents the history of MovieLens and the MovieLens
                 datasets. We include a discussion of lessons learned
                 from running a long-standing, live research platform
                 from the perspective of a research organization. We
                 document best practices and limitations of using the
                 MovieLens datasets in new research.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Yordanova:2016:PSD,
  author =       "Kristina Yordanova and Thomas Kirste",
  title =        "A Process for Systematic Development of Symbolic
                 Models for Activity Recognition",
  journal =      j-TIIS,
  volume =       "5",
  number =       "4",
  pages =        "20:1--20:??",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2806893",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 7 16:06:24 MST 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Several emerging approaches to activity recognition
                 (AR) combine symbolic representation of user actions
                 with probabilistic elements for reasoning under
                 uncertainty. These approaches provide promising results
                 in terms of recognition performance, coping with the
                 uncertainty of observations, and model size explosion
                 when complex problems are modelled. But experience has
                 shown that it is not always intuitive to model even
                 seemingly simple problems. To date, there are no
                 guidelines for developing such models. To address this
                 problem, in this work we present a development process
                 for building symbolic models that is based on
                 experience acquired so far as well as on existing
                 engineering and data analysis workflows. The proposed
                 process is a first attempt at providing structured
                 guidelines and practices for designing, modelling, and
                 evaluating human behaviour in the form of symbolic
                 models for AR. As an illustration of the process, a
                 simple example from the office domain was developed.
                 The process was evaluated in a comparative study of an
                 intuitive process and the proposed process. The results
                 showed a significant improvement over the intuitive
                 process. Furthermore, the study participants reported
                 greater ease of use and perceived effectiveness when
                 following the proposed process. To evaluate the
                 applicability of the process to more complex AR
                 problems, it was applied to a problem from the kitchen
                 domain. The results showed that following the proposed
                 process yielded an average accuracy of 78\%. The
                 developed model outperformed state-of-the-art methods
                 applied to the same dataset in previous work, and it
                 performed comparably to a symbolic model developed by a
                 model expert without following the proposed development
                 process.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Yamazaki:2016:ITN,
  author =       "Keiichi Yamazaki and Akiko Yamazaki and Keiko Ikeda
                 and Chen Liu and Mihoko Fukushima and Yoshinori
                 Kobayashi and Yoshinori Kuno",
  title =        "{``I'll Be There Next''}: a Multiplex Care Robot
                 System that Conveys Service Order Using Gaze Gestures",
  journal =      j-TIIS,
  volume =       "5",
  number =       "4",
  pages =        "21:1--21:??",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2844542",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 7 16:06:24 MST 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In this article, we discuss our findings from an
                 ethnographic study at an elderly care center where we
                 observed the utilization of two different functions of
                 human gaze to convey service order (i.e., ``who is
                 served first and who is served next''). In one case,
                 when an elderly person requested assistance, the gaze
                 of the care worker communicated that he/she would serve
                 that client next in turn. In the other case, the gaze
                 conveyed a request to the service seeker to wait until
                 the care worker finished attending the current client.
                 Each gaze function depended on the care worker's
                 current engagement and other behaviors. We sought to
                 integrate these findings into the development of a
                 robot that might function more effectively in multiple
                 human-robot party settings. We focused on the multiple
                 functions of gaze and bodily actions, implementing
                 those functions into our robot. We conducted three
                 experiments to gauge a combination of gestures and
                 gazes performed by our robot. This article demonstrates
                 that the employment of gaze is an important
                 consideration when developing robots that can interact
                 effectively in multiple human-robot party settings.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Nakano:2016:GRG,
  author =       "Yukiko I. Nakano and Takashi Yoshino and Misato
                 Yatsushiro and Yutaka Takase",
  title =        "Generating Robot Gaze on the Basis of Participation
                 Roles and Dominance Estimation in Multiparty
                 Interaction",
  journal =      j-TIIS,
  volume =       "5",
  number =       "4",
  pages =        "22:1--22:??",
  month =        jan,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2743028",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Thu Jan 7 16:06:24 MST 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Gaze is an important nonverbal feedback signal in
                 multiparty face-to-face conversations. It is well known
                 that gaze behaviors differ depending on participation
                 role: speaker, addressee, or side participant. In this
                 study, we focus on dominance as another factor that
                 affects gaze. First, we conducted an empirical study
                 and analyzed its results that showed how gaze behaviors
                 are affected by both dominance and participation roles.
                 Then, using speech and gaze information that was
                 statistically significant for distinguishing the more
                 dominant and less dominant person in an empirical
                 study, we established a regression-based model for
                 estimating conversational dominance. On the basis of
                 the model, we implemented a dominance estimation
                 mechanism that processes online speech and head
                 direction data. Then we applied our findings to
                 human-robot interaction. To design robot gaze
                 behaviors, we analyzed gaze transitions with respect to
                 participation roles and dominance and implemented
                 gaze-transition models as robot gaze behavior
                 generation rules. Finally, we evaluated a humanoid
                 robot that has dominance estimation functionality and
                 determines its gaze based on the gaze models, and we
                 found that dominant participants had a better
                 impression of less dominant robot gaze behaviors. This
                 suggests that a robot using our gaze models was
                 preferred to a robot that was simply looking at the
                 speaker. We have demonstrated the importance of
                 considering dominance in human-robot multiparty
                 interaction.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Nakano:2016:ISI,
  author =       "Yukiko I. Nakano and Roman Bednarik and Hung-Hsuan
                 Huang and Kristiina Jokinen",
  title =        "Introduction to the Special Issue on New Directions in
                 Eye Gaze for Interactive Intelligent Systems",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "1:1--1:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2893485",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Eye gaze has been used broadly in interactive
                 intelligent systems. The research area has grown in
                 recent years to cover emerging topics that go beyond
                 the traditional focus on interaction between a single
                 user and an interactive system. This special issue
                 presents five articles that explore new directions of
                 gaze-based interactive intelligent systems, ranging
                 from communication robots in dyadic and multiparty
                 conversations to a driving simulator that uses eye gaze
                 evidence to critique learners' behavior.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Xu:2016:SYS,
  author =       "Tian (Linger) Xu and Hui Zhang and Chen Yu",
  title =        "See You See Me: The Role of Eye Contact in Multimodal
                 Human-Robot Interaction",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "2:1--2:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2882970",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We focus on a fundamental looking behavior in
                 human-robot interactions-gazing at each other's face.
                 Eye contact and mutual gaze between two social partners
                 are critical in smooth human-human interactions.
                 Therefore, investigating at what moments and in what
                 ways a robot should look at a human user's face as a
                 response to the human's gaze behavior is an important
                 topic. Toward this goal, we developed a gaze-contingent
                 human-robot interaction system, which relied on
                 momentary gaze behaviors from a human user to control
                 an interacting robot in real time. Using this system,
                 we conducted an experiment in which human participants
                 interacted with the robot in a joint-attention task. In
                 the experiment, we systematically manipulated the
                 robot's gaze toward the human partner's face in real
                 time and then analyzed the human's gaze behavior as a
                 response to the robot's gaze behavior. We found that
                 more face looks from the robot led to more look-backs
                 (to the robot's face) from human participants, and
                 consequently, created more mutual gaze and eye contact
                 between the two. Moreover, participants demonstrated
                 more coordinated and synchronized multimodal behaviors
                 between speech and gaze when more eye contact was
                 successfully established and maintained.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Wade:2016:GCA,
  author =       "Joshua Wade and Lian Zhang and Dayi Bian and Jing Fan
                 and Amy Swanson and Amy Weitlauf and Medha Sarkar and
                 Zachary Warren and Nilanjan Sarkar",
  title =        "A Gaze-Contingent Adaptive Virtual Reality Driving
                 Environment for Intervention in Individuals with Autism
                 Spectrum Disorders",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "3:1--3:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2892636",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In addition to social and behavioral deficits,
                 individuals with Autism Spectrum Disorder (ASD) often
                 struggle to develop the adaptive skills necessary to
                 achieve independence. Driving intervention in
                 individuals with ASD is a growing area of study, but it
                 is still widely under-researched. We present the
                 development and preliminary assessment of a
                 gaze-contingent adaptive virtual reality driving
                 simulator that uses real-time gaze information to adapt
                 the driving environment with the aim of providing a
                 more individualized method of driving intervention. We
                 conducted a small pilot study of 20 adolescents with
                 ASD using our system: 10 with the adaptive
                 gaze-contingent version of the system and 10 in a
                 purely performance-based version. Preliminary results
                 suggest that the novel intervention system may be
                 beneficial in teaching driving skills to individuals
                 with ASD.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ishii:2016:PWW,
  author =       "Ryo Ishii and Kazuhiro Otsuka and Shiro Kumano and
                 Junji Yamato",
  title =        "Prediction of Who Will Be the Next Speaker and When
                 Using Gaze Behavior in Multiparty Meetings",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "4:1--4:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2757284",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In multiparty meetings, participants need to predict
                 the end of the speaker's utterance and who will start
                 speaking next, as well as consider a strategy for good
                 timing to speak next. Gaze behavior plays an important
                 role in smooth turn-changing. This article proposes a
                 prediction model that features three processing steps
                 to predict (I) whether turn-changing or turn-keeping
                 will occur, (II) who will be the next speaker in
                 turn-changing, and (III) the timing of the start of the
                 next speaker's utterance. For the feature values of the
                 model, we focused on gaze transition patterns and the
                 timing structure of eye contact between a speaker and a
                 listener near the end of the speaker's utterance. Gaze
                 transition patterns provide information about the order
                 in which gaze behavior changes. The timing structure of
                 eye contact is defined as who looks at whom and who
                 looks away first, the speaker or listener, when eye
                 contact between the speaker and a listener occurs. We
                 collected corpus data of multiparty meetings, using the
                 data to demonstrate relationships between gaze
                 transition patterns and timing structure and situations
                 (I), (II), and (III). The results of our analyses
                 indicate that the gaze transition pattern of the
                 speaker and listener and the timing structure of eye
                 contact have a strong association with turn-changing,
                 the next speaker in turn-changing, and the start time
                 of the next utterance. On the basis of the results, we
                 constructed prediction models using the gaze transition
                 patterns and timing structure. The gaze transition
                 patterns were found to be useful in predicting
                 turn-changing, the next speaker in turn-changing, and
                 the start time of the next utterance. Contrary to
                 expectations, we did not find that the timing structure
                 is useful for predicting the next speaker and the start
                 time. This study opens up new possibilities for
                 predicting the next speaker and the timing of the next
                 utterance using gaze transition patterns in multiparty
                 meetings.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Dardard:2016:ACL,
  author =       "Floriane Dardard and Giorgio Gnecco and Donald
                 Glowinski",
  title =        "Automatic Classification of Leading Interactions in a
                 String Quartet",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "5:1--5:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2818739",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The aim of the present work is to analyze
                 automatically the leading interactions between the
                 musicians of a string quartet, using machine-learning
                 techniques applied to nonverbal features of the
                 musicians' behavior, which are detected through the
                 help of a motion-capture system. We represent these
                 interactions by a graph of ``influence'' of the
                 musicians, which displays the relations ``is
                 following'' and ``is not following'' with weighted
                 directed arcs. The goal of the machine-learning problem
                 investigated is to assign weights to these arcs in an
                 optimal way. Since only a subset of the available
                 training examples are labeled, a semisupervised support
                 vector machine is used, which is based on a linear
                 kernel to limit its model complexity. Specific
                 potential applications within the field of
                 human-computer interaction are also discussed, such as
                 e-learning, networked music performance, and social
                 active listening.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Piana:2016:ABG,
  author =       "Stefano Piana and Alessandra Staglian{\`o} and
                 Francesca Odone and Antonio Camurri",
  title =        "Adaptive Body Gesture Representation for Automatic
                 Emotion Recognition",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "6:1--6:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2818740",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We present a computational model and a system for the
                 automated recognition of emotions starting from
                 full-body movement. Three-dimensional motion data of
                 full-body movements are obtained either from
                 professional optical motion-capture systems (Qualisys)
                 or from low-cost RGB-D sensors (Kinect and Kinect2). A
                 number of features are then automatically extracted at
                 different levels, from kinematics of a single joint to
                 more global expressive features inspired by psychology
                 and humanistic theories (e.g., contraction index,
                 fluidity, and impulsiveness). An abstraction layer
                 based on dictionary learning further processes these
                 movement features to increase the model generality and
                 to deal with intraclass variability, noise, and
                 incomplete information characterizing emotion
                 expression in human movement. The resulting feature
                 vector is the input for a classifier performing
                 real-time automatic emotion recognition based on linear
                 support vector machines. The recognition performance of
                 the proposed model is presented and discussed,
                 including the tradeoff between precision of the
                 tracking measures (we compare the Kinect RGB-D sensor
                 and the Qualisys motion-capture system) versus
                 dimension of the training dataset. The resulting model
                 and system have been successfully applied in the
                 development of serious games for helping autistic
                 children learn to recognize and express emotions by
                 means of their full-body movement.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hoque:2016:ITM,
  author =       "Enamul Hoque and Giuseppe Carenini",
  title =        "Interactive Topic Modeling for Exploring Asynchronous
                 Online Conversations: Design and Evaluation of
                 {ConVisIT}",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "7:1--7:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2854158",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Since the mid-2000s, there has been exponential growth
                 of asynchronous online conversations, thanks to the
                 rise of social media. Analyzing and gaining insights
                 from such conversations can be quite challenging for a
                 user, especially when the discussion becomes very long.
                 A promising solution to this problem is topic modeling,
                 since it may help the user to understand quickly what
                 was discussed in a long conversation and to explore the
                 comments of interest. However, the results of topic
                 modeling can be noisy, and they may not match the
                 user's current information needs. To address this
                 problem, we propose a novel topic modeling system for
                 asynchronous conversations that revises the model on
                 the fly on the basis of users' feedback. We then
                 integrate this system with interactive visualization
                 techniques to support the user in exploring long
                 conversations, as well as in revising the topic model
                 when the current results are not adequate to fulfill
                 the user's information needs. Finally, we report on an
                 evaluation with real users that compared the resulting
                 system with both a traditional interface and an
                 interactive visual interface that does not support
                 human-in-the-loop topic modeling. Both the quantitative
                 results and the subjective feedback from the
                 participants illustrate the potential benefits of our
                 interactive topic modeling approach for exploring
                 conversations, relative to its counterparts.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Jannach:2016:SDM,
  author =       "Dietmar Jannach and Michael Jugovac and Lukas Lerche",
  title =        "Supporting the Design of Machine Learning Workflows
                 with a Recommendation System",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "8:1--8:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2852082",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Machine learning and data analytics tasks in practice
                 require several consecutive processing steps.
                 RapidMiner is a widely used software tool for the
                 development and execution of such analytics workflows.
                 Unlike many other algorithm toolkits, it comprises a
                 visual editor that allows the user to design processes
                 on a conceptual level. This conceptual and visual
                 approach helps the user to abstract from the technical
                 details during the development phase and to retain a
                 focus on the core modeling task. The large set of
                 preimplemented data analysis and machine learning
                 operations available in the tool, as well as their
                 logical dependencies, can, however, be overwhelming in
                 particular for novice users. In this work, we present
                 an add-on to the RapidMiner framework that supports the
                 user during the modeling phase by recommending
                 additional operations to insert into the currently
                 developed machine learning workflow. First, we propose
                 different recommendation techniques and evaluate them
                 in an offline setting using a pool of several thousand
                 existing workflows. Second, we present the results of a
                 laboratory study, which show that our tool helps users
                 to significantly increase the efficiency of the
                 modeling process. Finally, we report on analyses using
                 data that were collected during the real-world
                 deployment of the plug-in component and compare the
                 results of the live deployment of the tool with the
                 results obtained through an offline analysis and a
                 replay simulation.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Malik:2016:HVH,
  author =       "Sana Malik and Ben Shneiderman and Fan Du and
                 Catherine Plaisant and Margret Bjarnadottir",
  title =        "High-Volume Hypothesis Testing: Systematic Exploration
                 of Event Sequence Comparisons",
  journal =      j-TIIS,
  volume =       "6",
  number =       "1",
  pages =        "9:1--9:??",
  month =        may,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2890478",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat May 21 08:06:01 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Cohort comparison studies have traditionally been
                 hypothesis driven and conducted in carefully controlled
                 environments (such as clinical trials). Given two
                 groups of event sequence data, researchers test a
                 single hypothesis (e.g., does the group taking
                 Medication A exhibit more deaths than the group taking
                 Medication B?). Recently, however, researchers have
                 been moving toward more exploratory methods of
                 retrospective analysis with existing data. In this
                 article, we begin by showing that the task of cohort
                 comparison is specific enough to support automatic
                 computation against a bounded set of potential
                 questions and objectives, a method that we refer to as
                 High-Volume Hypothesis Testing (HVHT). From this
                 starting point, we demonstrate that the diversity of
                 these objectives, both across and within different
                 domains, as well as the inherent complexities of
                 real-world datasets, still requires human involvement
                 to determine meaningful insights. We explore how
                 visualization and interaction better support the task
                 of exploratory data analysis and the understanding of
                 HVHT results (how significant they are, why they are
                 meaningful, and whether the entire dataset has been
                 exhaustively explored). Through interviews and case
                 studies with domain experts, we iteratively design and
                 implement visualization and interaction techniques in a
                 visual analytics tool, CoCo. As a result of our
                 evaluation, we propose six design guidelines for
                 enabling users to explore large result sets of HVHT
                 systematically and flexibly in order to glean
                 meaningful insights more quickly. Finally, we
                 illustrate the utility of this method with three case
                 studies in the medical domain.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Pan:2016:TLS,
  author =       "Weike Pan and Qiang Yang and Yuchao Duan and Zhong
                 Ming",
  title =        "Transfer Learning for Semisupervised Collaborative
                 Recommendation",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "10:1--10:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2835497",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Users' online behaviors such as ratings and
                 examination of items are recognized as one of the most
                 valuable sources of information for learning users'
                 preferences in order to make personalized
                 recommendations. But most previous works focus on
                 modeling only one type of users' behaviors such as
                 numerical ratings or browsing records, which are
                 referred to as explicit feedback and implicit feedback,
                 respectively. In this article, we study a
                 Semisupervised Collaborative Recommendation (SSCR)
                 problem with labeled feedback (for explicit feedback)
                 and unlabeled feedback (for implicit feedback), in
                 analogy to the well-known Semisupervised Learning (SSL)
                 setting with labeled instances and unlabeled instances.
                 SSCR is associated with two fundamental challenges,
                 that is, heterogeneity of two types of users' feedback
                 and uncertainty of the unlabeled feedback. As a
                 response, we design a novel Self-Transfer Learning
                 (sTL) algorithm to iteratively identify and integrate
                 likely positive unlabeled feedback, which is inspired
                 by the general forward/backward process in machine
                 learning. The merit of sTL is its ability to learn
                 users' preferences from heterogeneous behaviors in a
                 joint and selective manner. We conduct extensive
                 empirical studies of sTL and several very competitive
                 baselines on three large datasets. The experimental
                 results show that our sTL is significantly better than
                 the state-of-the-art methods.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Verbert:2016:AVU,
  author =       "Katrien Verbert and Denis Parra and Peter
                 Brusilovsky",
  title =        "Agents Vs. Users: Visual Recommendation of Research
                 Talks with Multiple Dimension of Relevance",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "11:1--11:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2946794",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Several approaches have been researched to help people
                 deal with abundance of information. An important
                 feature pioneered by social tagging systems and later
                 used in other kinds of social systems is the ability to
                 explore different community relevance prospects by
                 examining items bookmarked by a specific user or items
                 associated by various users with a specific tag. A
                 ranked list of recommended items offered by a specific
                 recommender engine can be considered as another
                 relevance prospect. The problem that we address is that
                 existing personalized social systems do not allow their
                 users to explore and combine multiple relevance
                 prospects. Only one prospect can be explored at any
                 given time-a list of recommended items, a list of items
                 bookmarked by a specific user, or a list of items
                 marked with a specific tag. In this article, we explore
                 the notion of combining multiple relevance prospects as
                 a way to increase effectiveness and trust. We used a
                 visual approach to recommend articles at a conference
                 by explicitly presenting multiple dimensions of
                 relevance. Suggestions offered by different
                 recommendation techniques were embodied as recommender
                 agents to put them on the same ground as users and
                 tags. The results of two user studies performed at
                 academic conferences allowed us to obtain interesting
                 insights to enhance user interfaces of personalized
                 social systems. More specifically, effectiveness and
                 probability of item selection increase when users are
                 able to explore and interrelate prospects of items
                 relevance-that is, items bookmarked by users,
                 recommendations and tags. Nevertheless, a
                 less-technical audience may require guidance to
                 understand the rationale of such intersections.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Cafaro:2016:EIA,
  author =       "Angelo Cafaro and Brian Ravenet and Magalie Ochs and
                 Hannes H{\"o}gni Vilhj{\'a}lmsson and Catherine
                 Pelachaud",
  title =        "The Effects of Interpersonal Attitude of a Group of
                 Agents on User's Presence and Proxemics Behavior",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "12:1--12:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2914796",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In the everyday world people form small conversing
                 groups where social interaction takes place, and much
                 of the social behavior takes place through managing
                 interpersonal space (i.e., proxemics) and group
                 formation, signaling their attention to others (i.e.,
                 through gaze behavior), and expressing certain
                 attitudes, for example, friendliness, by smiling,
                 getting close through increased engagement and
                 intimacy, and welcoming newcomers. Many real-time
                 interactive systems feature virtual anthropomorphic
                 characters in order to simulate conversing groups and
                 add plausibility and believability to the simulated
                 environments. However, only a few have dealt with
                 autonomous behavior generation, and in those cases, the
                 agents' exhibited behavior should be evaluated by users
                 in terms of appropriateness, believability, and
                 conveyed meaning (e.g., attitudes). In this article we
                 present an integrated intelligent interactive system
                 for generating believable nonverbal behavior exhibited
                 by virtual agents in small simulated group
                 conversations. The produced behavior supports group
                 formation management and the expression of
                 interpersonal attitudes (friendly vs. unfriendly) both
                 among the agents in the group (i.e., in-group attitude)
                 and towards an approaching user in an avatar-based
                 interaction (out-group attitude). A user study
                 investigating the effects of these attitudes on users'
                 social presence evaluation and proxemics behavior (with
                 their avatar) in a three-dimensional virtual city
                 environment is presented. We divided the study into two
                 trials according to the task assigned to users, that
                 is, joining a conversing group and reaching a target
                 destination behind the group. Results showed that the
                 out-group attitude had a major impact on social
                 presence evaluations in both trials, whereby friendly
                 groups were perceived as more socially rich. The user's
                 proxemics behavior depended on both out-group and
                 in-group attitudes expressed by the agents.
                 Implications of these results for the design and
                 implementation of similar intelligent interactive
                 systems for the autonomous generation of agents'
                 multimodal behavior are briefly discussed.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Taranta:2016:DPB,
  author =       "Eugene M. {Taranta II} and Andr{\'e}s N. Vargas and
                 Spencer P. Compton and Joseph J. {Laviola, Jr.}",
  title =        "A Dynamic Pen-Based Interface for Writing and Editing
                 Complex Mathematical Expressions With Math Boxes",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "13:1--13:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2946795",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Math boxes is a recently introduced pen-based user
                 interface for simplifying the task of hand writing
                 difficult mathematical expressions. Visible bounding
                 boxes around subexpressions are automatically generated
                 as the system detects relevant spatial relationships
                 between symbols including superscripts, subscripts, and
                 fractions. Subexpressions contained in a math box can
                 then be extended by adding new terms directly into its
                 given bounds. When new characters are accepted, box
                 boundaries are dynamically resized and neighboring
                 terms are translated to make room for the larger box.
                 Feedback on structural recognition is given via the
                 boxes themselves. In this work, we extend the math
                 boxes interface to include support for subexpression
                 modifications via a new set of pen-based interactions.
                 Specifically, techniques to expand and rearrange terms
                 in a given expression are introduced. To evaluate the
                 usefulness of our proposed methods, we first conducted
                 a user study in which participants wrote a variety of
                 equations ranging in complexity from a simple
                 polynomial to the more difficult expected value of the
                 logistic distribution. The math boxes interface is
                 compared against the commonly used offset typeset
                 (small) method, where recognized expressions are
                 typeset in a system font near the user's unmodified
                 ink. In this initial study, we find that the fluidness
                 of the offset method is preferred for simple
                 expressions but that, as difficulty increases, our math
                 boxes method is overwhelmingly preferred. We then
                 conducted a second user study that focused only on
                 modifying various mathematical expressions. In general,
                 participants worked faster with the math boxes
                 interface, and most new techniques were well received.
                 On the basis of the two user studies, we discuss the
                 implications of the math boxes interface and identify
                 areas where improvements are possible.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Yang:2016:SUS,
  author =       "Yi Yang and Shimei Pan and Jie Lu and Mercan Topkara
                 and Yangqiu Song",
  title =        "The Stability and Usability of Statistical Topic
                 Models",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "14:1--14:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2954002",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Statistical topic models have become a useful and
                 ubiquitous tool for analyzing large text corpora. One
                 common application of statistical topic models is to
                 support topic-centric navigation and exploration of
                 document collections. Existing work on topic modeling
                 focuses on the inference of model parameters so the
                 resulting model fits the input data. Since the exact
                 inference is intractable, statistical inference
                 methods, such as Gibbs Sampling, are commonly used to
                 solve the problem. However, most of the existing work
                 ignores an important aspect that is closely related to
                 the end user experience: topic model stability. When
                 the model is either re-trained with the same input data
                 or updated with new documents, the topic previously
                 assigned to a document may change under the new model,
                 which may result in a disruption of end users' mental
                 maps about the relations between documents and topics,
                 thus undermining the usability of the applications. In
                 this article, we propose a novel user-directed
                 non-disruptive topic model update method that balances
                 the tradeoff between finding the model that fits the
                 data and maintaining the stability of the model from
                 end users' perspective. It employs a novel constrained
                 LDA algorithm to incorporate pairwise document
                 constraints, which are converted from user feedback
                 about topics, to achieve topic model stability.
                 Evaluation results demonstrate the advantages of our
                 approach over previous methods.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kveton:2016:MIC,
  author =       "Branislav Kveton and Shlomo Berkovsky",
  title =        "Minimal Interaction Content Discovery in Recommender
                 Systems",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "15:1--15:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2845090",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Many prior works in recommender systems focus on
                 improving the accuracy of item rating predictions. In
                 comparison, the areas of recommendation interfaces and
                 user-recommender interaction remain underexplored. In
                 this work, we look into the interaction of users with
                 the recommendation list, aiming to devise a method that
                 simplifies content discovery and minimizes the cost of
                 reaching an item of interest. We quantify this cost by
                 the number of user interactions (clicks and scrolls)
                 with the recommendation list. To this end, we propose
                 generalized linear search (GLS), an adaptive
                 combination of the established linear and generalized
                 search (GS) approaches. GLS leverages the advantages of
                 these two approaches, and we prove formally that it
                 performs at least as well as GS. We also conduct a
                 thorough experimental evaluation of GLS and compare it
                 to several baselines and heuristic approaches in both
                 an offline and live evaluation. The results of the
                 evaluation show that GLS consistently outperforms the
                 baseline approaches and is also preferred by users. In
                 summary, GLS offers an efficient and easy-to-use means
                 for content discovery in recommender systems.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Zhang:2016:BTE,
  author =       "Cheng Zhang and Anhong Guo and Dingtian Zhang and Yang
                 Li and Caleb Southern and Rosa I. Arriaga and Gregory
                 D. Abowd",
  title =        "Beyond the Touchscreen: an Exploration of Extending
                 Interactions on Commodity {Smartphones}",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "16:1--16:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2954003",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Most smartphones today have a rich set of sensors that
                 could be used to infer input (e.g., accelerometer,
                 gyroscope, microphone); however, the primary mode of
                 interaction is still limited to the front-facing
                 touchscreen and several physical buttons on the case.
                 To investigate the potential opportunities for
                 interactions supported by built-in sensors, we present
                 the implementation and evaluation of BeyondTouch, a
                 family of interactions to extend and enrich the input
                 experience of a smartphone. Using only existing sensing
                 capabilities on a commodity smartphone, we offer the
                 user a wide variety of additional inputs on the case
                 and the surface adjacent to the smartphone. Although
                 most of these interactions are implemented with machine
                 learning methods, compact and robust rule-based
                 detection methods can also be applied for recognizing
                 some interactions by analyzing physical characteristics
                 of tapping events on the phone. This article is an
                 extended version of Zhang et al. [2015], which solely
                 covered gestures implemented by machine learning
                 methods. We extended our previous work by adding
                 gestures implemented with rule-based methods, which
                 works well with different users across devices without
                 collecting any training data. We outline the
                 implementation of both machine learning and rule-based
                 methods for these interaction techniques and
                 demonstrate empirical evidence of their effectiveness
                 and usability. We also discuss the practicality of
                 BeyondTouch for a variety of application scenarios and
                 compare the two different implementation methods.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Bosch:2016:UVA,
  author =       "Nigel Bosch and Sidney K. D'mello and Jaclyn Ocumpaugh
                 and Ryan S. Baker and Valerie Shute",
  title =        "Using Video to Automatically Detect Learner Affect in
                 Computer-Enabled Classrooms",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "17:1--17:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2946837",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Affect detection is a key component in intelligent
                 educational interfaces that respond to students'
                 affective states. We use computer vision and
                 machine-learning techniques to detect students' affect
                 from facial expressions (primary channel) and gross
                 body movements (secondary channel) during interactions
                 with an educational physics game. We collected data in
                 the real-world environment of a school computer lab
                 with up to 30 students simultaneously playing the game
                 while moving around, gesturing, and talking to each
                 other. The results were cross-validated at the student
                 level to ensure generalization to new students.
                 Classification accuracies, quantified as area under the
                 receiver operating characteristic curve (AUC), were
                 above chance (AUC of 0.5) for all the affective states
                 observed, namely, boredom (AUC = .610), confusion (AUC
                 = .649), delight (AUC = .867), engagement (AUC = .679),
                 frustration (AUC = .631), and for off-task behavior
                 (AUC = .816). Furthermore, the detectors showed
                 temporal generalizability in that there was less than a
                 2\% decrease in accuracy when tested on data collected
                 from different times of the day and from different
                 days. There was also some evidence of generalizability
                 across ethnicity (as perceived by human coders) and
                 gender, although with a higher degree of variability
                 attributable to differences in affect base rates across
                 subpopulations. In summary, our results demonstrate the
                 feasibility of generalizable video-based detectors of
                 naturalistic affect in a real-world setting, suggesting
                 that the time is ripe for affect-sensitive
                 interventions in educational games and other
                 intelligent interfaces.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Tanaka:2016:TSC,
  author =       "Hiroki Tanaka and Sakti Sakriani and Graham Neubig and
                 Tomoki Toda and Hideki Negoro and Hidemi Iwasaka and
                 Satoshi Nakamura",
  title =        "Teaching Social Communication Skills Through
                 Human-Agent Interaction",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "18:1--18:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2937757",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "There are a large number of computer-based systems
                 that aim to train and improve social skills. However,
                 most of these do not resemble the training regimens
                 used by human instructors. In this article, we propose
                 a computer-based training system that follows the
                 procedure of social skills training (SST), a
                 well-established method to decrease human anxiety and
                 discomfort in social interaction, and acquire social
                 skills. We attempt to automate the process of SST by
                 developing a dialogue system named the automated social
                 skills trainer, which teaches social communication
                 skills through human-agent interaction. The system
                 includes a virtual avatar that recognizes user speech
                 and language information and gives feedback to users.
                 Its design is based on conventional SST performed by
                 human participants, including defining target skills,
                 modeling, role-play, feedback, reinforcement, and
                 homework. We performed a series of three experiments
                 investigating (1) the advantages of using
                 computer-based training systems compared to human-human
                 interaction (HHI) by subjectively evaluating
                 nervousness, ease of talking, and ability to talk well;
                 (2) the relationship between speech language features
                 and human social skills; and (3) the effect of
                 computer-based training using our proposed system.
                 Results of our first experiment show that interaction
                 with an avatar decreases nervousness and increases the
                 user's subjective impression of his or her ability to
                 talk well compared to interaction with an unfamiliar
                 person. The experimental evaluation measuring the
                 relationship between social skill and speech and
                 language features shows that these features have a
                 relationship with social skills. Finally, experiments
                 measuring the effect of performing SST with the
                 proposed application show that participants
                 significantly improve their skill, as assessed by
                 separate evaluators, by using the system for 50
                 minutes. A user survey also shows that the users
                 thought our system is useful and easy to use, and that
                 interaction with the avatar felt similar to HHI.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Mahmoud:2016:AAN,
  author =       "Marwa Mahmoud and Tadas Baltrusaitis and Peter
                 Robinson",
  title =        "Automatic Analysis of Naturalistic Hand-Over-Face
                 Gestures",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "19:1--19:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2946796",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "One of the main factors that limit the accuracy of
                 facial analysis systems is hand occlusion. As the face
                 becomes occluded, facial features are lost, corrupted,
                 or erroneously detected. Hand-over-face occlusions are
                 considered not only very common but also very
                 challenging to handle. However, there is empirical
                 evidence that some of these hand-over-face gestures
                 serve as cues for recognition of cognitive mental
                 states. In this article, we present an analysis of
                 automatic detection and classification of
                 hand-over-face gestures. We detect hand-over-face
                 occlusions and classify hand-over-face gesture
                 descriptors in videos of natural expressions using
                 multi-modal fusion of different state-of-the-art
                 spatial and spatio-temporal features. We show
                 experimentally that we can successfully detect face
                 occlusions with an accuracy of 83\%. We also
                 demonstrate that we can classify gesture descriptors (
                 hand shape, hand action, and facial region occluded )
                 significantly better than a na{\"\i}ve baseline. Our
                 detailed quantitative analysis sheds some light on the
                 challenges of automatic classification of
                 hand-over-face gestures in natural expressions.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ishii:2016:URP,
  author =       "Ryo Ishii and Kazuhiro Otsuka and Shiro Kumano and
                 Junji Yamato",
  title =        "Using Respiration to Predict Who Will Speak Next and
                 When in Multiparty Meetings",
  journal =      j-TIIS,
  volume =       "6",
  number =       "2",
  pages =        "20:1--20:??",
  month =        aug,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2946838",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:13 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Techniques that use nonverbal behaviors to predict
                 turn-changing situations-such as, in multiparty
                 meetings, who the next speaker will be and when the
                 next utterance will occur-have been receiving a lot of
                 attention in recent research. To build a model for
                 predicting these behaviors we conducted a research
                 study to determine whether respiration could be
                 effectively used as a basis for the prediction. Results
                 of analyses of utterance and respiration data collected
                 from participants in multiparty meetings reveal that
                 the speaker takes a breath more quickly and deeply
                 after the end of an utterance in turn-keeping than in
                 turn-changing. They also indicate that the listener who
                 will be the next speaker takes a bigger breath more
                 quickly and deeply in turn-changing than the other
                 listeners. On the basis of these results, we
                 constructed and evaluated models for predicting the
                 next speaker and the time of the next utterance in
                 multiparty meetings. The results of the evaluation
                 suggest that the characteristics of the speaker's
                 inhalation right after an utterance unit-the points in
                 time at which the inhalation starts and ends after the
                 end of the utterance unit and the amplitude, slope, and
                 duration of the inhalation phase-are effective for
                 predicting the next speaker in multiparty meetings.
                 They further suggest that the characteristics of
                 listeners' inhalation-the points in time at which the
                 inhalation starts and ends after the end of the
                 utterance unit and the minimum and maximum inspiration,
                 amplitude, and slope of the inhalation phase-are
                 effective for predicting the next speaker. The start
                 time and end time of the next speaker's inhalation are
                 also useful for predicting the time of the next
                 utterance in turn-changing.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Prendinger:2016:IBT,
  author =       "Helmut Prendinger and Nahum Alvarez and Antonio
                 Sanchez-Ruiz and Marc Cavazza and jo{\~a}o Catarino and
                 Jo{\~a}o Oliveira and Rui Prada and Shuji Fujimoto and
                 Mika Shigematsu",
  title =        "Intelligent Biohazard Training Based on Real-Time Task
                 Recognition",
  journal =      j-TIIS,
  volume =       "6",
  number =       "3",
  pages =        "21:1--21:??",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2883617",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:14 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Virtual environments offer an ideal setting to develop
                 intelligent training applications. Yet, their ability
                 to support complex procedures depends on the
                 appropriate integration of knowledge-based techniques
                 and natural interaction. In this article, we describe
                 the implementation of an intelligent rehearsal system
                 for biohazard laboratory procedures, based on the
                 real-time instantiation of task models from the
                 trainee's actions. A virtual biohazard laboratory has
                 been recreated using the Unity3D engine, in which users
                 interact with laboratory objects using keyboard/mouse
                 input or hand gestures through a Kinect device.
                 Realistic behavior for objects is supported by the
                 implementation of a relevant subset of common sense and
                 physics knowledge. User interaction with objects leads
                 to the recognition of specific actions, which are used
                 to progressively instantiate a task-based
                 representation of biohazard procedures. The dynamics of
                 this instantiation process supports trainee evaluation
                 as well as real-time assistance. This system is
                 designed primarily as a rehearsal system providing
                 real-time advice and supporting user performance
                 evaluation. We provide detailed examples illustrating
                 error detection and recovery, and results from on-site
                 testing with students from the Faculty of Medical
                 Sciences at Kyushu University. In the study, we
                 investigate the usability aspect by comparing
                 interaction with mouse and Kinect devices and the
                 effect of real-time task recognition on recovery time
                 after user mistakes.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Sappelli:2016:AIA,
  author =       "Maya Sappelli and Suzan Verberne and Wessel Kraaij",
  title =        "Adapting the Interactive Activation Model for Context
                 Recognition and Identification",
  journal =      j-TIIS,
  volume =       "6",
  number =       "3",
  pages =        "22:1--22:??",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2873067",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:14 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In this article, we propose and implement a new model
                 for context recognition and identification. Our work is
                 motivated by the importance of ``working in context''
                 for knowledge workers to stay focused and productive. A
                 computer application that can identify the current
                 context in which the knowledge worker is working can
                 (among other things) provide the worker with contextual
                 support, for example, by suggesting relevant
                 information sources, or give an overview of how he or
                 she spent his or her time during the day. We present a
                 descriptive model for the context of a knowledge
                 worker. This model describes the contextual elements in
                 the work environment of the knowledge worker and how
                 these elements relate to each other. This model is
                 operationalized in an algorithm, the contextual
                 interactive activation model (CIA), which is based on
                 the interactive activation model by Rumelhart and
                 McClelland. It consists of a layered connected network
                 through which activation flows. We have tested CIA in a
                 context identification setting. In this case, the data
                 that we use as input is low-level computer interaction
                 logging data. We found that topical information and
                 entities were the most relevant types of information
                 for context identification. Overall the proposed CIA
                 model is more effective than traditional supervised
                 methods in identifying the active context from sparse
                 input data, with less labelled training data.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Aslan:2016:DEM,
  author =       "Ilhan Aslan and Andreas Uhl and Alexander
                 Meschtscherjakov and Manfred Tscheligi",
  title =        "Design and Exploration of Mid-Air Authentication
                 Gestures",
  journal =      j-TIIS,
  volume =       "6",
  number =       "3",
  pages =        "23:1--23:??",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2832919",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:14 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Authentication based on touchless mid-air gestures
                 would benefit a multitude of ubiquitous computing
                 applications, especially those that are used in clean
                 environments (e.g., medical environments or clean
                 rooms). In order to explore the potential of mid-air
                 gestures for novel authentication approaches, we
                 performed a series of studies and design experiments.
                 First, we collected data from more then 200 users
                 during a 3-day science event organized within a
                 shopping mall. These data were used to investigate
                 capabilities of the Leap Motion sensor, observe
                 interaction in the wild, and to formulate an initial
                 design problem. The design problem, as well as the
                 design of mid-air gestures for authentication purposes,
                 were iterated in subsequent design activities. In a
                 final study with 13 participants, we evaluated two
                 mid-air gestures for authentication purposes in
                 different situations, including different body
                 positions. Our results highlight a need for different
                 mid-air gestures for differing situations and carefully
                 chosen constraints for mid-air gestures. We conclude by
                 proposing an exemplary system, which aims to provide
                 tool-support for designers and engineers, allowing them
                 to explore authentication gestures in the original
                 context of use and thus support them with the design of
                 contextual mid-air authentication gestures.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{El-Glaly:2016:RWY,
  author =       "Yasmine N. El-Glaly and Francis Quek",
  title =        "Read What You Touch with Intelligent Audio System for
                 Non-Visual Interaction",
  journal =      j-TIIS,
  volume =       "6",
  number =       "3",
  pages =        "24:1--24:??",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2822908",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:14 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Slate-type devices allow Individuals with Blindness or
                 Severe Visual Impairment (IBSVI) to read in place with
                 the touch of their fingertip by audio-rendering the
                 words they touch. Such technologies are helpful for
                 spatial cognition while reading. However, users have to
                 move their fingers slowly, or they may lose their place
                 on screen. Also, IBSVI may wander between lines without
                 realizing they did. We addressed these two interaction
                 problems by introducing a dynamic speech-touch
                 interaction model and an intelligent reading support
                 system. With this model, the speed of the speech will
                 dynamically change with the user's finger speed. The
                 proposed model is composed of (1) an Audio Dynamics
                 Model and (2) an Off-line Speech Synthesis Technique.
                 The intelligent reading support system predicts the
                 direction of reading, corrects the reading word if the
                 user drifts, and notifies the user using a sonic gutter
                 to help him/her from straying off the reading line. We
                 tested the new audio dynamics model, the sonic gutter,
                 and the reading support model in two user studies. The
                 participants' feedback helped us fine-tune the
                 parameters of the two models. A decomposition study was
                 conducted to evaluate the main components of the
                 system. The results showed that both intelligent
                 reading support with tactile feedback are required to
                 achieve the best performance in terms of efficiency and
                 effectiveness. Finally, we ran an evaluation study
                 where the reading support system is compared to other
                 VoiceOver technologies. The results showed
                 preponderance to the reading support system with its
                 audio dynamics and intelligent reading support
                 components.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Park:2016:MAP,
  author =       "Sunghyun Park and Han Suk Shim and Moitreya Chatterjee
                 and Kenji Sagae and Louis-Philippe Morency",
  title =        "Multimodal Analysis and Prediction of Persuasiveness
                 in Online Social Multimedia",
  journal =      j-TIIS,
  volume =       "6",
  number =       "3",
  pages =        "25:1--25:??",
  month =        oct,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2897739",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Tue Oct 18 11:51:14 MDT 2016",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Our lives are heavily influenced by persuasive
                 communication, and it is essential in almost any type
                 of social interaction from business negotiation to
                 conversation with our friends and family. With the
                 rapid growth of social multimedia websites, it is
                 becoming ever more important and useful to understand
                 persuasiveness in the context of social multimedia
                 content online. In this article, we introduce a newly
                 created multimedia corpus of 1,000 movie review videos
                 with subjective annotations of persuasiveness and
                 related high-level characteristics or attributes (e.g.,
                 confidence). This dataset will be made freely available
                 to the research community. We designed our experiments
                 around the following five main research hypotheses.
                 First, we study if computational descriptors derived
                 from verbal and nonverbal behavior can be predictive of
                 persuasiveness. We further explore combining
                 descriptors from multiple communication modalities
                 (acoustic, verbal, para-verbal, and visual) for
                 predicting persuasiveness and compare with using a
                 single modality alone. Second, we investigate how
                 certain high-level attributes, such as credibility or
                 expertise, are related to persuasiveness and how the
                 information can be used in modeling and predicting
                 persuasiveness. Third, we investigate differences when
                 speakers are expressing a positive or negative opinion
                 and if the opinion polarity has any influence in the
                 persuasiveness prediction. Fourth, we further study if
                 gender has any influence in the prediction performance.
                 Last, we test if it is possible to make comparable
                 predictions of persuasiveness by only looking at thin
                 slices (i.e., shorter time windows) of a speaker's
                 behavior.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "25",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Tintarev:2016:ISI,
  author =       "Nava Tintarev and John O'donovan and Alexander
                 Felfernig",
  title =        "Introduction to the Special Issue on Human Interaction
                 with Artificial Advice Givers",
  journal =      j-TIIS,
  volume =       "6",
  number =       "4",
  pages =        "26:1--26:??",
  month =        dec,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3014432",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Many interactive systems in today's world can be
                 viewed as providing advice to their users. Commercial
                 examples include recommender systems, satellite
                 navigation systems, intelligent personal assistants on
                 smartphones, and automated checkout systems in
                 supermarkets. We will call these systems that support
                 people in making choices and decisions artificial
                 advice givers (AAGs): They propose and evaluate options
                 while involving their human users in the
                 decision-making process. This special issue addresses
                 the challenge of improving the interaction between
                 artificial and human agents. It answers the question of
                 how an agent of each type (human and artificial) can
                 influence and understand the reasoning, working models,
                 and conclusions of the other agent by means of novel
                 forms of interaction. To address this challenge, the
                 articles in the special issue are organized around
                 three themes: (a) human factors to consider when
                 designing interactions with AAGs (e.g., over- and
                 under-reliance, overestimation of the system's
                 capabilities), (b) methods for supporting interaction
                 with AAGs (e.g., natural language, visualization, and
                 argumentation), and (c) considerations for evaluating
                 AAGs (both criteria and methodology for applying
                 them).",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "26",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Sutherland:2016:EAE,
  author =       "Steven C. Sutherland and Casper Harteveld and Michael
                 E. Young",
  title =        "Effects of the Advisor and Environment on Requesting
                 and Complying With Automated Advice",
  journal =      j-TIIS,
  volume =       "6",
  number =       "4",
  pages =        "27:1--27:??",
  month =        dec,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2905370",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Given the rapid technological advances in our society
                 and the increase in artificial and automated advisors
                 with whom we interact on a daily basis, it is becoming
                 increasingly necessary to understand how users interact
                 with and why they choose to request and follow advice
                 from these types of advisors. More specifically, it is
                 necessary to understand errors in advice utilization.
                 In the present study, we propose a methodological
                 framework for studying interactions between users and
                 automated or other artificial advisors. Specifically,
                 we propose the use of virtual environments and the tarp
                 technique for stimulus sampling, ensuring sufficient
                 sampling of important extreme values and the stimulus
                 space between those extremes. We use this proposed
                 framework to identify the impact of several factors on
                 when and how advice is used. Additionally, because
                 these interactions take place in different
                 environments, we explore the impact of where the
                 interaction takes place on the decision to interact. We
                 varied the cost of advice, the reliability of the
                 advisor, and the predictability of the environment to
                 better understand the impact of these factors on the
                 overutilization of suboptimal advisors and
                 underutilization of optimal advisors. We found that
                 less predictable environments, more reliable advisors,
                 and lower costs for advice led to overutilization,
                 whereas more predictable environments and less reliable
                 advisors led to underutilization. Moreover, once advice
                 was received, users took longer to make a final
                 decision, suggesting less confidence and trust in the
                 advisor when the reliability of the advisor was lower,
                 the environment was less predictable, and the advice
                 was not consistent with the environmental cues. These
                 results contribute to a more complete understanding of
                 advice utilization and trust in advisors.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "27",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Knijnenburg:2016:ICI,
  author =       "Bart P. Knijnenburg and Martijn C. Willemsen",
  title =        "Inferring Capabilities of Intelligent Agents from
                 Their External Traits",
  journal =      j-TIIS,
  volume =       "6",
  number =       "4",
  pages =        "28:1--28:??",
  month =        dec,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2963106",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "We investigate the usability of humanlike agent-based
                 interfaces for interactive advice-giving systems. In an
                 experiment with a travel advisory system, we manipulate
                 the ``humanlikeness'' of the agent interface. We
                 demonstrate that users of the more humanlike agents try
                 to exploit capabilities that were not signaled by the
                 system. This severely reduces the usability of systems
                 that look human but lack humanlikehumanlike
                 capabilities (overestimation effect). We explain this
                 effect by showing that users of humanlike agents form
                 anthropomorphic beliefs (a user's ``mental model'')
                 about the system: They act humanlike towards the system
                 and try to exploit typical humanlike capabilities they
                 believe the system possesses. Furthermore, we
                 demonstrate that the mental model users form of an
                 agent-based system is inherently integrated (as opposed
                 to the compositional mental model they form of
                 conventional interfaces): Cues provided by the system
                 do not instill user responses in a one-to-one matter
                 but are instead integrated into a single mental
                 model.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "28",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Clark:2016:MAA,
  author =       "Leigh Clark and Abdulmalik Ofemile and Svenja Adolphs
                 and Tom Rodden",
  title =        "A Multimodal Approach to Assessing User Experiences
                 with Agent Helpers",
  journal =      j-TIIS,
  volume =       "6",
  number =       "4",
  pages =        "29:1--29:??",
  month =        dec,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2983926",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The study of agent helpers using linguistic strategies
                 such as vague language and politeness has often come
                 across obstacles. One of these is the quality of the
                 agent's voice and its lack of appropriate fit for using
                 these strategies. The first approach of this article
                 compares human vs. synthesised voices in agents using
                 vague language. This approach analyses the 60,000-word
                 text corpus of participant interviews to investigate
                 the differences of user attitudes towards the agents,
                 their voices and their use of vague language. It
                 discovers that while the acceptance of vague language
                 is still met with resistance in agent instructors,
                 using a human voice yields more positive results than
                 the synthesised alternatives. The second approach in
                 this article discusses the development of a novel
                 multimodal corpus of video and text data to create
                 multiple analyses of human-agent interaction in
                 agent-instructed assembly tasks. The second approach
                 analyses user spontaneous facial actions and gestures
                 during their interaction in the tasks. It found that
                 agents are able to elicit these facial actions and
                 gestures and posits that further analysis of this
                 nonverbal feedback may help to create a more adaptive
                 agent. Finally, the approaches used in this article
                 suggest these can contribute to furthering the
                 understanding of what it means to interact with
                 software agents.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "29",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Rosenfeld:2016:PAD,
  author =       "Ariel Rosenfeld and Sarit Kraus",
  title =        "Providing Arguments in Discussions on the Basis of the
                 Prediction of Human Argumentative Behavior",
  journal =      j-TIIS,
  volume =       "6",
  number =       "4",
  pages =        "30:1--30:??",
  month =        dec,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2983925",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Argumentative discussion is a highly demanding task.
                 In order to help people in such discussions, this
                 article provides an innovative methodology for
                 developing agents that can support people in
                 argumentative discussions by proposing possible
                 arguments. By gathering and analyzing human
                 argumentative behavior from more than 1000 human study
                 participants, we show that the prediction of human
                 argumentative behavior using Machine Learning (ML) is
                 possible and useful in designing argument provision
                 agents. This paper first demonstrates that ML
                 techniques can achieve up to 76\% accuracy when
                 predicting people's top three argument choices given a
                 partial discussion. We further show that
                 well-established Argumentation Theory is not a good
                 predictor of people's choice of arguments. Then, we
                 present 9 argument provision agents, which we
                 empirically evaluate using hundreds of human study
                 participants. We show that the Predictive and
                 Relevance-Based Heuristic agent (PRH), which uses ML
                 prediction with a heuristic that estimates the
                 relevance of possible arguments to the current state of
                 the discussion, results in significantly higher levels
                 of satisfaction among study participants compared with
                 the other evaluated agents. These other agents propose
                 arguments based on Argumentation Theory; propose
                 predicted arguments without the heuristics or with only
                 the heuristics; or use Transfer Learning methods. Our
                 findings also show that people use the PRH agents
                 proposed arguments significantly more often than those
                 proposed by the other agents.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "30",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Mutlu:2016:VRP,
  author =       "Belgin Mutlu and Eduardo Veas and Christoph Trattner",
  title =        "{VizRec}: Recommending Personalized Visualizations",
  journal =      j-TIIS,
  volume =       "6",
  number =       "4",
  pages =        "31:1--31:??",
  month =        dec,
  year =         "2016",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2983923",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Visualizations have a distinctive advantage when
                 dealing with the information overload problem: Because
                 they are grounded in basic visual cognition, many
                 people understand them. However, creating proper
                 visualizations requires specific expertise of the
                 domain and underlying data. Our quest in this article
                 is to study methods to suggest appropriate
                 visualizations autonomously. To be appropriate, a
                 visualization has to follow known guidelines to find
                 and distinguish patterns visually and encode data
                 therein. A visualization tells a story of the
                 underlying data; yet, to be appropriate, it has to
                 clearly represent those aspects of the data the viewer
                 is interested in. Which aspects of a visualization are
                 important to the viewer? Can we capture and use those
                 aspects to recommend visualizations? This article
                 investigates strategies to recommend visualizations
                 considering different aspects of user preferences. A
                 multi-dimensional scale is used to estimate aspects of
                 quality for visualizations for collaborative filtering.
                 Alternatively, tag vectors describing visualizations
                 are used to recommend potentially interesting
                 visualizations based on content. Finally, a hybrid
                 approach combines information on what a visualization
                 is about (tags) and how good it is (ratings). We
                 present the design principles behind VizRec, our visual
                 recommender. We describe its architecture, the data
                 acquisition approach with a crowd sourced study, and
                 the analysis of strategies for visualization
                 recommendation.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "31",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Paudel:2017:UAD,
  author =       "Bibek Paudel and Fabian Christoffel and Chris Newell
                 and Abraham Bernstein",
  title =        "Updatable, Accurate, Diverse, and Scalable
                 Recommendations for Interactive Applications",
  journal =      j-TIIS,
  volume =       "7",
  number =       "1",
  pages =        "1:1--1:??",
  month =        mar,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2955101",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Recommender systems form the backbone of many
                 interactive systems. They incorporate user feedback to
                 personalize the user experience typically via
                 personalized recommendation lists. As users interact
                 with a system, an increasing amount of data about a
                 user's preferences becomes available, which can be
                 leveraged for improving the systems' performance.
                 Incorporating these new data into the underlying
                 recommendation model is, however, not always
                 straightforward. Many models used by recommender
                 systems are computationally expensive and, therefore,
                 have to perform offline computations to compile the
                 recommendation lists. For interactive applications, it
                 is desirable to be able to update the computed values
                 as soon as new user interaction data is available:
                 updating recommendations in interactive time using new
                 feedback data leads to better accuracy and increases
                 the attraction of the system to the users.
                 Additionally, there is a growing consensus that
                 accuracy alone is not enough and user satisfaction is
                 also dependent on diverse recommendations. In this
                 work, we tackle this problem of updating personalized
                 recommendation lists for interactive applications in
                 order to provide both accurate and diverse
                 recommendations. To that end, we explore algorithms
                 that exploit random walks as a sampling technique to
                 obtain diverse recommendations without compromising on
                 efficiency and accuracy. Specifically, we present a
                 novel graph vertex ranking recommendation algorithm
                 called RP$^3_\beta $ that reranks items based on
                 three-hop random walk transition probabilities. We show
                 empirically that RP$^3_\beta $ provides accurate
                 recommendations with high long-tail item frequency at
                 the top of the recommendation list. We also present
                 approximate versions of RP$^3_\beta $ and the two most
                 accurate previously published vertex ranking algorithms
                 based on random walk transition probabilities and show
                 that these approximations converge with an increasing
                 number of samples. To obtain interactively updatable
                 recommendations, we additionally show how our algorithm
                 can be extended for online updates at interactive
                 speeds. The underlying random walk sampling technique
                 makes it possible to perform the updates without having
                 to recompute the values for the entire dataset. In an
                 empirical evaluation with three real-world datasets, we
                 show that RP$^3_\beta $ provides highly accurate and
                 diverse recommendations that can easily be updated with
                 newly gathered information at interactive speeds ($ \ll
                 $ 100 ms ).",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kaminskas:2017:DSN,
  author =       "Marius Kaminskas and Derek Bridge",
  title =        "Diversity, Serendipity, Novelty, and Coverage: a
                 Survey and Empirical Analysis of Beyond-Accuracy
                 Objectives in Recommender Systems",
  journal =      j-TIIS,
  volume =       "7",
  number =       "1",
  pages =        "2:1--2:??",
  month =        mar,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2926720",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "What makes a good recommendation or good list of
                 recommendations? Research into recommender systems has
                 traditionally focused on accuracy, in particular how
                 closely the recommender's predicted ratings are to the
                 users' true ratings. However, it has been recognized
                 that other recommendation qualities-such as whether the
                 list of recommendations is diverse and whether it
                 contains novel items-may have a significant impact on
                 the overall quality of a recommender system.
                 Consequently, in recent years, the focus of recommender
                 systems research has shifted to include a wider range
                 of ``beyond accuracy'' objectives. In this article, we
                 present a survey of the most discussed beyond-accuracy
                 objectives in recommender systems research: diversity,
                 serendipity, novelty, and coverage. We review the
                 definitions of these objectives and corresponding
                 metrics found in the literature. We also review works
                 that propose optimization strategies for these
                 beyond-accuracy objectives. Since the majority of works
                 focus on one specific objective, we find that it is not
                 clear how the different objectives relate to each
                 other. Hence, we conduct a set of offline experiments
                 aimed at comparing the performance of different
                 optimization approaches with a view to seeing how they
                 affect objectives other than the ones they are
                 optimizing. We use a set of state-of-the-art
                 recommendation algorithms optimized for recall along
                 with a number of reranking strategies for optimizing
                 the diversity, novelty, and serendipity of the
                 generated recommendations. For each reranking strategy,
                 we measure the effects on the other beyond-accuracy
                 objectives and demonstrate important insights into the
                 correlations between the discussed objectives. For
                 instance, we find that rating-based diversity is
                 positively correlated with novelty, and we demonstrate
                 the positive influence of novelty on recommendation
                 coverage.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Zhang:2017:EEI,
  author =       "Ting Zhang and Yu-Ting Li and Juan P. Wachs",
  title =        "The Effect of Embodied Interaction in Visual-Spatial
                 Navigation",
  journal =      j-TIIS,
  volume =       "7",
  number =       "1",
  pages =        "3:1--3:??",
  month =        mar,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2953887",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article aims to assess the effect of embodied
                 interaction on attention during the process of solving
                 spatio-visual navigation problems. It presents a method
                 that links operator's physical interaction, feedback,
                 and attention. Attention is inferred through networks
                 called Bayesian Attentional Networks (BANs). BANs are
                 structures that describe cause-effect relationship
                 between attention and physical action. Then, a utility
                 function is used to determine the best combination of
                 interaction modalities and feedback. Experiments
                 involving five physical interaction modalities
                 (vision-based gesture interaction, glove-based gesture
                 interaction, speech, feet, and body stance) and two
                 feedback modalities (visual and sound) are described.
                 The main findings are: (i) physical expressions have an
                 effect in the quality of the solutions to spatial
                 navigation problems; (ii) the combination of feet
                 gestures with visual feedback provides the best task
                 performance.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ochs:2017:UPB,
  author =       "Magalie Ochs and Catherine Pelachaud and Gary
                 Mckeown",
  title =        "A User Perception--Based Approach to Create Smiling
                 Embodied Conversational Agents",
  journal =      j-TIIS,
  volume =       "7",
  number =       "1",
  pages =        "4:1--4:??",
  month =        mar,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2925993",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In order to improve the social capabilities of
                 embodied conversational agents, we propose a
                 computational model to enable agents to automatically
                 select and display appropriate smiling behavior during
                 human--machine interaction. A smile may convey
                 different communicative intentions depending on subtle
                 characteristics of the facial expression and contextual
                 cues. To construct such a model, as a first step, we
                 explore the morphological and dynamic characteristics
                 of different types of smiles (polite, amused, and
                 embarrassed smiles) that an embodied conversational
                 agent may display. The resulting lexicon of smiles is
                 based on a corpus of virtual agents' smiles directly
                 created by users and analyzed through a
                 machine-learning technique. Moreover, during an
                 interaction, a smiling expression impacts on the
                 observer's perception of the interpersonal stance of
                 the speaker. As a second step, we propose a
                 probabilistic model to automatically compute the user's
                 potential perception of the embodied conversational
                 agent's social stance depending on its smiling behavior
                 and on its physical appearance. This model, based on a
                 corpus of users' perceptions of smiling and nonsmiling
                 virtual agents, enables a virtual agent to determine
                 the appropriate smiling behavior to adopt given the
                 interpersonal stance it wants to express. An experiment
                 using real human--virtual agent interaction provided
                 some validation of the proposed model.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ramirez-Amaro:2017:AVG,
  author =       "Karinne Ramirez-Amaro and Humera Noor Minhas and
                 Michael Zehetleitner and Michael Beetz and Gordon
                 Cheng",
  title =        "Added Value of Gaze-Exploiting Semantic Representation
                 to Allow Robots Inferring Human Behaviors",
  journal =      j-TIIS,
  volume =       "7",
  number =       "1",
  pages =        "5:1--5:??",
  month =        mar,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2939381",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Mar 25 07:51:07 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Neuroscience studies have shown that incorporating
                 gaze view with third view perspective has a great
                 influence to correctly infer human behaviors. Given the
                 importance of both first and third person observations
                 for the recognition of human behaviors, we propose a
                 method that incorporates these observations in a
                 technical system to enhance the recognition of human
                 behaviors, thus improving beyond third person
                 observations in a more robust human activity
                 recognition system. First, we present the extension of
                 our proposed semantic reasoning method by including
                 gaze data and external observations as inputs to
                 segment and infer human behaviors in complex real-world
                 scenarios. Then, from the obtained results we
                 demonstrate that the combination of gaze and external
                 input sources greatly enhance the recognition of human
                 behaviors. Our findings have been applied to a humanoid
                 robot to online segment and recognize the observed
                 human activities with better accuracy when using both
                 input sources; for example, the activity recognition
                 increases from 77\% to 82\% in our proposed
                 pancake-making dataset. To provide completeness of our
                 system, we have evaluated our approach with another
                 dataset with a similar setup as the one proposed in
                 this work, that is, the CMU-MMAC dataset. In this case,
                 we improved the recognition of the activities for the
                 egg scrambling scenario from 54\% to 86\% by combining
                 the external views with the gaze information, thus
                 showing the benefit of incorporating gaze information
                 to infer human behaviors across different datasets.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Cena:2017:ISI,
  author =       "F. Cena and C. Gena and G. J. Houben and M.
                 Strohmaier",
  title =        "Introduction to the {Special Issue on Big Personal
                 Data in Interactive Intelligent Systems}",
  journal =      j-TIIS,
  volume =       "7",
  number =       "2",
  pages =        "6:1--6:??",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3101102",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Sep 8 08:41:25 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This brief introduction begins with an overview of the
                 types of research that are relevant to the special
                 issue on Big Personal Data in Interactive Intelligent
                 Systems. The overarching question is: How can big
                 personal data be collected, analyzed, and exploited so
                 as to provide new or improved forms of interaction with
                 intelligent systems, and what new issues have to be
                 taken into account? The three articles accepted for the
                 special issue are then characterized in terms of the
                 concepts of this overview.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Cassavia:2017:DUB,
  author =       "Nunziato Cassavia and Elio Masciari and Chiara Pulice
                 and Domenico Sacc{\`a}",
  title =        "Discovering User Behavioral Features to Enhance
                 Information Search on Big Data",
  journal =      j-TIIS,
  volume =       "7",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2856059",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Sep 8 08:41:25 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Due to the emerging Big Data paradigm, driven by the
                 increasing availability of intelligent services easily
                 accessible by a large number of users (e.g., social
                 networks), traditional data management techniques are
                 inadequate in many real-life scenarios. In particular,
                 the availability of huge amounts of data pertaining to
                 user social interactions, user preferences, and
                 opinions calls for advanced analysis strategies to
                 understand potentially interesting social dynamics.
                 Furthermore, heterogeneity and high speed of
                 user-generated data require suitable data storage and
                 management tools to be designed from scratch. This
                 article presents a framework tailored for analyzing
                 user interactions with intelligent systems while
                 seeking some domain-specific information (e.g.,
                 choosing a good restaurant in a visited area). The
                 framework enhances a user's quest for information by
                 exploiting previous knowledge about their social
                 environment, the extent of influence the users are
                 potentially subject to, and the influence they may
                 exert on other users. User influence spread across the
                 network is dynamically computed as well to improve user
                 search strategy by providing specific suggestions,
                 represented as tailored faceted features. Such features
                 are the result of data exchange activity (called data
                 posting) that enriches information sources with
                 additional background information and knowledge derived
                 from experiences and behavioral properties of domain
                 experts and users. The approach is tested in an
                 important application scenario such as tourist
                 recommendation, but it can be profitably exploited in
                 several other contexts, for example, viral marketing
                 and food education.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kim:2017:MML,
  author =       "Seungjun Kim and Dan Tasse and Anind K. Dey",
  title =        "Making Machine-Learning Applications for Time-Series
                 Sensor Data Graphical and Interactive",
  journal =      j-TIIS,
  volume =       "7",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2983924",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Sep 8 08:41:25 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The recent profusion of sensors has given consumers
                 and researchers the ability to collect significant
                 amounts of data. However, understanding sensor data can
                 be a challenge, because it is voluminous,
                 multi-sourced, and unintelligible. Nonetheless,
                 intelligent systems, such as activity recognition,
                 require pattern analysis of sensor data streams to
                 produce compelling results; machine learning (ML)
                 applications enable this type of analysis. However, the
                 number of ML experts able to proficiently classify
                 sensor data is limited, and there remains a lack of
                 interactive, usable tools to help intermediate users
                 perform this type of analysis. To learn which features
                 these tools must support, we conducted interviews with
                 intermediate users of ML and conducted two probe-based
                 studies with a prototype ML and visual analytics
                 system, Gimlets. Our system implements ML applications
                 for sensor-based time-series data as a novel
                 domain-specific prototype that integrates interactive
                 visual analytic features into the ML pipeline. We
                 identify future directions for usable ML systems based
                 on sensor data that will enable intermediate users to
                 build systems that have been prohibitively difficult.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Zanzotto:2017:YLT,
  author =       "Fabio Massimo Zanzotto and Lorenzo Ferrone",
  title =        "Have You Lost the Thread? {Discovering} Ongoing
                 Conversations in Scattered Dialog Blocks",
  journal =      j-TIIS,
  volume =       "7",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jul,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2885501",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Fri Sep 8 08:41:25 MDT 2017",
  bibsource =    "http://portal.acm.org/;
                 https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Finding threads in textual dialogs is emerging as a
                 need to better organize stored knowledge. We capture
                 this need by introducing the novel task of discovering
                 ongoing conversations in scattered dialog blocks. Our
                 aim in this article is twofold. First, we propose a
                 publicly available testbed for the task by solving the
                 insurmountable problem of privacy of Big Personal Data.
                 In fact, we showed that personal dialogs can be
                 surrogated with theatrical plays. Second, we propose a
                 suite of computationally light learning models that can
                 use syntactic and semantic features. With this suite,
                 we showed that models for this challenging task should
                 include features capturing shifts in language use and,
                 possibly, modeling underlying scripts.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Jugovac:2017:IRO,
  author =       "Michael Jugovac and Dietmar Jannach",
  title =        "Interacting with Recommenders-Overview and Research
                 Directions",
  journal =      j-TIIS,
  volume =       "7",
  number =       "3",
  pages =        "10:1--10:??",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3001837",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Automated recommendations have become a ubiquitous
                 part of today's online user experience. These systems
                 point us to additional items to purchase in online
                 shops, they make suggestions to us on movies to watch,
                 or recommend us people to connect with on social
                 websites. In many of today's applications, however, the
                 only way for users to interact with the system is to
                 inspect the recommended items. Often, no mechanisms are
                 implemented for users to give the system feedback on
                 the recommendations or to explicitly specify
                 preferences, which can limit the potential overall
                 value of the system for its users. Academic research in
                 recommender systems is largely focused on algorithmic
                 approaches for item selection and ranking. Nonetheless,
                 over the years a variety of proposals were made on how
                 to design more interactive recommenders. This work
                 provides a comprehensive overview on the existing
                 literature on user interaction aspects in recommender
                 systems. We cover existing approaches for preference
                 elicitation and result presentation, as well as
                 proposals that consider recommendation as an
                 interactive process. Throughout the work, we
                 furthermore discuss examples of real-world systems and
                 outline possible directions for future works.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Paiva:2017:EVA,
  author =       "Ana Paiva and Iolanda Leite and Hana Boukricha and
                 Ipke Wachsmuth",
  title =        "Empathy in Virtual Agents and Robots: a Survey",
  journal =      j-TIIS,
  volume =       "7",
  number =       "3",
  pages =        "11:1--11:??",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2912150",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article surveys the area of computational
                 empathy, analysing different ways by which artificial
                 agents can simulate and trigger empathy in their
                 interactions with humans. Empathic agents can be seen
                 as agents that have the capacity to place themselves
                 into the position of a user's or another agent's
                 emotional situation and respond appropriately. We also
                 survey artificial agents that, by their design and
                 behaviour, can lead users to respond emotionally as if
                 they were experiencing the agent's situation. In the
                 course of this survey, we present the research
                 conducted to date on empathic agents in light of the
                 principles and mechanisms of empathy found in humans.
                 We end by discussing some of the main challenges that
                 this exciting area will be facing in the future.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Nourbakhsh:2017:DUC,
  author =       "Nargess Nourbakhsh and Fang Chen and Yang Wang and
                 Rafael A. Calvo",
  title =        "Detecting Users' Cognitive Load by Galvanic Skin
                 Response with Affective Interference",
  journal =      j-TIIS,
  volume =       "7",
  number =       "3",
  pages =        "12:1--12:??",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2960413",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Experiencing high cognitive load during complex and
                 demanding tasks results in performance reduction,
                 stress, and errors. However, these could be prevented
                 by a system capable of constantly monitoring users'
                 cognitive load fluctuations and adjusting its
                 interactions accordingly. Physiological data and
                 behaviors have been found to be suitable measures of
                 cognitive load and are now available in many consumer
                 devices. An advantage of these measures over subjective
                 and performance-based methods is that they are captured
                 in real time and implicitly while the user interacts
                 with the system, which makes them suitable for
                 real-world applications. On the other hand, emotion
                 interference can change physiological responses and
                 make accurate cognitive load measurement more
                 challenging. In this work, we have studied six galvanic
                 skin response (GSR) features in detection of four
                 cognitive load levels with the interference of
                 emotions. The data was derived from two arithmetic
                 experiments and emotions were induced by displaying
                 pleasant and unpleasant pictures in the background. Two
                 types of classifiers were applied to detect cognitive
                 load levels. Results from both studies indicate that
                 the features explored can detect four and two cognitive
                 load levels with high accuracy even under emotional
                 changes. More specifically, rise duration and
                 accumulative GSR are the common best features in all
                 situations, having the highest accuracy especially in
                 the presence of emotions.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Duncan:2017:ESC,
  author =       "Brittany A. Duncan and Robin R. Murphy",
  title =        "Effects of Speed, Cyclicity, and Dimensionality on
                 Distancing, Time, and Preference in Human--Aerial
                 Vehicle Interactions",
  journal =      j-TIIS,
  volume =       "7",
  number =       "3",
  pages =        "13:1--13:??",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2983927",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article will present a simulation-based approach
                 to testing multiple variables in the behavior of a
                 small Unmanned Aerial Vehicle (sUAV), inspired by
                 insect and animal motions, to understand how these
                 variables impact time of interaction, preference for
                 interaction, and distancing in Human-Robot Interaction
                 (HRI). Previous work has focused on communicating
                 directionality of flight, intentionality of the robot,
                 and perception of motion in sUAVs, while interactions
                 involving direct distancing from these vehicles have
                 been limited to a single study (likely due to safety
                 concerns). This study takes place in a Cave Automatic
                 Virtual Environment (CAVE) to maintain a sense of scale
                 and immersion with the users, while also allowing for
                 safe interaction. Additionally, the two-alternative
                 forced-choice method is employed as a unique
                 methodology to the study of collocated HRI in order to
                 both study the impact of these variables on preference
                 and allow participants to choose whether or not to
                 interact with a specific robot. This article will be of
                 interest to end-users of sUAV technologies to encourage
                 appropriate distancing based on their application,
                 practitioners in HRI to understand the use of this new
                 methodology, and human-aerial vehicle researchers to
                 understand the perception of these vehicles by 64 naive
                 users. Results suggest that low speed (by 0.27m, $ p <
                 0.02$) and high cyclicity (by 0.28m, $ p < 0.01$)
                 expressions can be used to increase distancing; that
                 low speed (by 4.4s, $ p < 0.01$) and three-dimensional
                 (by 2.6s, $ p < 0.01$) expressions can be used to
                 decrease time of interaction; and low speed (by 10.4\%,
                 $ p < 0.01$) expressions are less preferred for
                 passability in human-aerial vehicle interactions.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kucher:2017:ALV,
  author =       "Kostiantyn Kucher and Carita Paradis and Magnus
                 Sahlgren and Andreas Kerren",
  title =        "Active Learning and Visual Analytics for Stance
                 Classification with {ALVA}",
  journal =      j-TIIS,
  volume =       "7",
  number =       "3",
  pages =        "14:1--14:??",
  month =        oct,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3132169",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The automatic detection and classification of stance
                 (e.g., certainty or agreement) in text data using
                 natural language processing and machine-learning
                 methods creates an opportunity to gain insight into the
                 speakers' attitudes toward their own and other people's
                 utterances. However, identifying stance in text
                 presents many challenges related to training data
                 collection and classifier training. To facilitate the
                 entire process of training a stance classifier, we
                 propose a visual analytics approach, called ALVA, for
                 text data annotation and visualization. ALVA's
                 interplay with the stance classifier follows an active
                 learning strategy to select suitable candidate
                 utterances for manual annotaion. Our approach supports
                 annotation process management and provides the
                 annotators with a clean user interface for labeling
                 utterances with multiple stance categories. ALVA also
                 contains a visualization method to help analysts of the
                 annotation and training process gain a better
                 understanding of the categories used by the annotators.
                 The visualization uses a novel visual representation,
                 called CatCombos, which groups individual annotation
                 items by the combination of stance categories.
                 Additionally, our system makes a visualization of a
                 vector space model available that is itself based on
                 utterances. ALVA is already being used by our domain
                 experts in linguistics and computational linguistics to
                 improve the understanding of stance phenomena and to
                 build a stance classifier for applications such as
                 social media monitoring.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Masai:2017:EFE,
  author =       "Katsutoshi Masai and Kai Kunze and Yuta Sugiura and
                 Masa Ogata and Masahiko Inami and Maki Sugimoto",
  title =        "Evaluation of Facial Expression Recognition by a Smart
                 Eyewear for Facial Direction Changes, Repeatability,
                 and Positional Drift",
  journal =      j-TIIS,
  volume =       "7",
  number =       "4",
  pages =        "15:1--15:??",
  month =        dec,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3012941",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article presents a novel smart eyewear that
                 recognizes the wearer's facial expressions in daily
                 scenarios. Our device uses embedded photo-reflective
                 sensors and machine learning to recognize the wearer's
                 facial expressions. Our approach focuses on skin
                 deformations around the eyes that occur when the wearer
                 changes his or her facial expressions. With small
                 photo-reflective sensors, we measure the distances
                 between the skin surface on the face and the 17 sensors
                 embedded in the eyewear frame. A Support Vector Machine
                 (SVM) algorithm is then applied to the information
                 collected by the sensors. The sensors can cover various
                 facial muscle movements. In addition, they are small
                 and light enough to be integrated into daily-use
                 glasses. Our evaluation of the device shows the
                 robustness to the noises from the wearer's facial
                 direction changes and the slight changes in the
                 glasses' position, as well as the reliability of the
                 device's recognition capacity. The main contributions
                 of our work are as follows: (1) We evaluated the
                 recognition accuracy in daily scenes, showing 92.8\%
                 accuracy regardless of facial direction and
                 removal/remount. Our device can recognize facial
                 expressions with 78.1\% accuracy for repeatability and
                 87.7\% accuracy in case of its positional drift. (2) We
                 designed and implemented the device by taking usability
                 and social acceptability into account. The device looks
                 like a conventional eyewear so that users can wear it
                 anytime, anywhere. (3) Initial field trials in a daily
                 life setting were undertaken to test the usability of
                 the device. Our work is one of the first attempts to
                 recognize and evaluate a variety of facial expressions
                 with an unobtrusive wearable device.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Yan:2017:EAR,
  author =       "Shuo Yan and Gangyi Ding and Hongsong Li and Ningxiao
                 Sun and Zheng Guan and Yufeng Wu and Longfei Zhang and
                 Tianyu Huang",
  title =        "Exploring Audience Response in Performing Arts with a
                 Brain-Adaptive Digital Performance System",
  journal =      j-TIIS,
  volume =       "7",
  number =       "4",
  pages =        "16:1--16:??",
  month =        dec,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3009974",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Audience response is an important indicator of the
                 quality of performing arts. Psychophysiological
                 measurements enable researchers to perceive and
                 understand audience response by collecting their
                 bio-signals during a live performance. However, how the
                 audience respond and how the performance is affected by
                 these responses are the key elements but are hard to
                 implement. To address this issue, we designed a
                 brain-computer interactive system called Brain-Adaptive
                 Digital Performance ( BADP ) for the measurement and
                 analysis of audience engagement level through an
                 interactive three-dimensional virtual theater. The BADP
                 system monitors audience engagement in real time using
                 electroencephalography (EEG) measurement and tries to
                 improve it by applying content-related performing cues
                 when the engagement level decreased. In this article,
                 we generate EEG-based engagement level and build
                 thresholds to determine the decrease and re-engage
                 moments. In the experiment, we simulated two types of
                 theatre performance to provide participants a
                 high-fidelity virtual environment using the BADP
                 system. We also create content-related performing cues
                 for each performance under three different conditions.
                 The results of these evaluations show that our
                 algorithm could accurately detect the engagement status
                 and the performing cues have a positive impact on
                 regaining audience engagement across different
                 performance types. Our findings open new perspectives
                 in audience-based theatre performance design.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Gotz:2017:ACM,
  author =       "David Gotz and Shun Sun and Nan Cao and Rita Kundu and
                 Anne-Marie Meyer",
  title =        "Adaptive Contextualization Methods for Combating
                 Selection Bias during High-Dimensional Visualization",
  journal =      j-TIIS,
  volume =       "7",
  number =       "4",
  pages =        "17:1--17:??",
  month =        dec,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3009973",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Large and high-dimensional real-world datasets are
                 being gathered across a wide range of application
                 disciplines to enable data-driven decision making.
                 Interactive data visualization can play a critical role
                 in allowing domain experts to select and analyze data
                 from these large collections. However, there is a
                 critical mismatch between the very large number of
                 dimensions in complex real-world datasets and the much
                 smaller number of dimensions that can be concurrently
                 visualized using modern techniques. This gap in
                 dimensionality can result in high levels of selection
                 bias that go unnoticed by users. The bias can in turn
                 threaten the very validity of any subsequent insights.
                 This article describes Adaptive Contextualization (AC),
                 a novel approach to interactive visual data selection
                 that is specifically designed to combat the invisible
                 introduction of selection bias. The AC approach (1)
                 monitors and models a user's visual data selection
                 activity, (2) computes metrics over that model to
                 quantify the amount of selection bias after each step,
                 (3) visualizes the metric results, and (4) provides
                 interactive tools that help users assess and avoid
                 bias-related problems. This article expands on an
                 earlier article presented at ACM IUI 2016 [16] by
                 providing a more detailed review of the AC methodology
                 and additional evaluation results.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{DiSciascio:2017:SES,
  author =       "Cecilia {Di Sciascio} and Vedran Sabol and Eduardo
                 Veas",
  title =        "Supporting Exploratory Search with a Visual
                 User-Driven Approach",
  journal =      j-TIIS,
  volume =       "7",
  number =       "4",
  pages =        "18:1--18:??",
  month =        dec,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3009976",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Whenever users engage in gathering and organizing new
                 information, searching and browsing activities emerge
                 at the core of the exploration process. As the process
                 unfolds and new knowledge is acquired, interest drifts
                 occur inevitably and need to be accounted for. Despite
                 the advances in retrieval and recommender algorithms,
                 real-world interfaces have remained largely unchanged:
                 results are delivered in a relevance-ranked list.
                 However, it quickly becomes cumbersome to reorganize
                 resources along new interests, as any new search brings
                 new results. We introduce an interactive user-driven
                 tool that aims at supporting users in understanding,
                 refining, and reorganizing documents on the fly as
                 information needs evolve. Decisions regarding visual
                 and interactive design aspects are tightly grounded on
                 a conceptual model for exploratory search. In other
                 words, the different views in the user interface
                 address stages of awareness, exploration, and
                 explanation unfolding along the discovery process,
                 supported by a set of text-mining methods. A formal
                 evaluation showed that gathering items relevant to a
                 particular topic of interest with our tool incurs in a
                 lower cognitive load compared to a traditional ranked
                 list. A second study reports on usage patterns and
                 usability of the various interaction techniques within
                 a free, unsupervised setting.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Davis:2017:QCC,
  author =       "N. Davis and C. Hsiao and K. Y. Singh and B. Lin and
                 B. Magerko",
  title =        "Quantifying Collaboration with a Co-Creative Drawing
                 Agent",
  journal =      j-TIIS,
  volume =       "7",
  number =       "4",
  pages =        "19:1--19:??",
  month =        dec,
  year =         "2017",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3009981",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Jan 22 17:18:51 MST 2018",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article describes a new technique for quantifying
                 creative collaboration and applies it to the user study
                 evaluation of a co-creative drawing agent. We present a
                 cognitive framework called creative sense-making that
                 provides a new method to visualize and quantify the
                 interaction dynamics of creative collaboration, for
                 example, the rhythm of interaction, style of turn
                 taking, and the manner in which participants are
                 mutually making sense of a situation. The creative
                 sense-making framework includes a qualitative coding
                 technique, interaction coding software, an analysis
                 method, and the cognitive theory behind these
                 applications. This framework and analysis method are
                 applied to empirical studies of the Drawing Apprentice
                 collaborative sketching system to compare human
                 collaboration with a co-creative AI agent vs. a Wizard
                 of Oz setup. The analysis demonstrates how the proposed
                 technique can be used to analyze interaction data using
                 continuous functions (e.g., integrations and moving
                 averages) to measure and evaluate how collaborations
                 unfold through time.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Lin:2018:GES,
  author =       "Yu-Ru Lin and Nan Cao",
  title =        "Guest Editorial: Special Issue on Interactive Visual
                 Analysis of Human and Crowd Behaviors",
  journal =      j-TIIS,
  volume =       "8",
  number =       "1",
  pages =        "1:1--1:??",
  month =        mar,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3178569",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The analysis of human behaviors has impacted many
                 social and commercial domains. How could interactive
                 visual analytic systems be used to further provide
                 behavioral insights? This editorial introduction
                 features emerging research trend related to this
                 question. The four articles accepted for this special
                 issue represent recent progress: they identify research
                 challenges arising from analysis of human and crowd
                 behaviors, and present novel methods in visual analysis
                 to address those challenges and help make behavioral
                 data more useful.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Polack:2018:CIM,
  author =       "Peter J. {Polack Jr.} and Shang-Tse Chen and Minsuk
                 Kahng and Kaya {De Barbaro} and Rahul Basole and
                 Moushumi Sharmin and Duen Horng Chau",
  title =        "Chronodes: Interactive Multifocus Exploration of Event
                 Sequences",
  journal =      j-TIIS,
  volume =       "8",
  number =       "1",
  pages =        "2:1--2:??",
  month =        mar,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3152888",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The advent of mobile health (mHealth) technologies
                 challenges the capabilities of current visualizations,
                 interactive tools, and algorithms. We present
                 Chronodes, an interactive system that unifies data
                 mining and human-centric visualization techniques to
                 support explorative analysis of longitudinal mHealth
                 data. Chronodes extracts and visualizes frequent event
                 sequences that reveal chronological patterns across
                 multiple participant timelines of mHealth data. It then
                 combines novel interaction and visualization techniques
                 to enable multifocus event sequence analysis, which
                 allows health researchers to interactively define,
                 explore, and compare groups of participant behaviors
                 using event sequence combinations. Through summarizing
                 insights gained from a pilot study with 20 behavioral
                 and biomedical health experts, we discuss Chronodes's
                 efficacy and potential impact in the mHealth domain.
                 Ultimately, we outline important open challenges in
                 mHealth, and offer recommendations and design
                 guidelines for future research.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Fu:2018:VVA,
  author =       "Siwei Fu and Yong Wang and Yi Yang and Qingqing Bi and
                 Fangzhou Guo and Huamin Qu",
  title =        "{VisForum}: a Visual Analysis System for Exploring
                 User Groups in Online Forums",
  journal =      j-TIIS,
  volume =       "8",
  number =       "1",
  pages =        "3:1--3:??",
  month =        mar,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3162075",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "User grouping in asynchronous online forums is a
                 common phenomenon nowadays. People with similar
                 backgrounds or shared interests like to get together in
                 group discussions. As tens of thousands of archived
                 conversational posts accumulate, challenges emerge for
                 forum administrators and analysts to effectively
                 explore user groups in large-volume threads and gain
                 meaningful insights into the hierarchical discussions.
                 Identifying and comparing groups in discussion threads
                 are nontrivial, since the number of users and posts
                 increases with time and noises may hamper the detection
                 of user groups. Researchers in data mining fields have
                 proposed a large body of algorithms to explore user
                 grouping. However, the mining result is not intuitive
                 to understand and difficult for users to explore the
                 details. To address these issues, we present VisForum,
                 a visual analytic system allowing people to
                 interactively explore user groups in a forum. We work
                 closely with two educators who have released courses in
                 Massive Open Online Courses (MOOC) platforms to compile
                 a list of design goals to guide our design. Then, we
                 design and implement a multi-coordinated interface as
                 well as several novel glyphs, i.e., group glyph, user
                 glyph, and set glyph, with different granularities.
                 Accordingly, we propose the group Detecting 8 Sorting
                 Algorithm to reduce noises in a collection of posts,
                 and employ the concept of ``forum-index'' for users to
                 identify high-impact forum members. Two case studies
                 using real-world datasets demonstrate the usefulness of
                 the system and the effectiveness of novel glyph
                 designs. Furthermore, we conduct an in-lab user study
                 to present the usability of VisForum.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Steptoe:2018:VAF,
  author =       "Michael Steptoe and Robert Kr{\"u}ger and Rolando
                 Garcia and Xing Liang and Ross Maciejewski",
  title =        "A Visual Analytics Framework for Exploring Theme Park
                 Dynamics",
  journal =      j-TIIS,
  volume =       "8",
  number =       "1",
  pages =        "4:1--4:??",
  month =        mar,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3162076",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "In 2015, the top 10 largest amusement park
                 corporations saw a combined annual attendance of over
                 400 million visitors. Daily average attendance in some
                 of the most popular theme parks in the world can
                 average 44,000 visitors per day. These visitors ride
                 attractions, shop for souvenirs, and dine at local
                 establishments; however, a critical component of their
                 visit is the overall park experience. This experience
                 depends on the wait time for rides, the crowd flow in
                 the park, and various other factors linked to the crowd
                 dynamics and human behavior. As such, better insight
                 into visitor behavior can help theme parks devise
                 competitive strategies for improved customer
                 experience. Research into the use of attractions,
                 facilities, and exhibits can be studied, and as
                 behavior profiles emerge, park operators can also
                 identify anomalous behaviors of visitors which can
                 improve safety and operations. In this article, we
                 present a visual analytics framework for analyzing
                 crowd dynamics in theme parks. Our proposed framework
                 is designed to support behavioral analysis by
                 summarizing patterns and detecting anomalies. We
                 provide methodologies to link visitor movement data,
                 communication data, and park infrastructure data. This
                 combination of data sources enables a semantic analysis
                 of who, what, when, and where, enabling analysts to
                 explore visitor-visitor interactions and
                 visitor-infrastructure interactions. Analysts can
                 identify behaviors at the macro level through semantic
                 trajectory clustering views for group behavior
                 dynamics, as well as at the micro level using
                 trajectory traces and a novel visitor network analysis
                 view. We demonstrate the efficacy of our framework
                 through two case studies of simulated theme park
                 visitors.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Wang:2018:VRI,
  author =       "Yong Wang and Conglei Shi and Liangyue Li and Hanghang
                 Tong and Huamin Qu",
  title =        "Visualizing Research Impact through Citation Data",
  journal =      j-TIIS,
  volume =       "8",
  number =       "1",
  pages =        "5:1--5:??",
  month =        mar,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3132744",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Research impact plays a critical role in evaluating
                 the research quality and influence of a scholar, a
                 journal, or a conference. Many researchers have
                 attempted to quantify research impact by introducing
                 different types of metrics based on citation data, such
                 as h -index, citation count, and impact factor. These
                 metrics are widely used in the academic community.
                 However, quantitative metrics are highly aggregated in
                 most cases and sometimes biased, which probably results
                 in the loss of impact details that are important for
                 comprehensively understanding research impact. For
                 example, which research area does a researcher have
                 great research impact on? How does the research impact
                 change over time? How do the collaborators take effect
                 on the research impact of an individual? Simple
                 quantitative metrics can hardly help answer such kind
                 of questions, since more detailed exploration of the
                 citation data is needed. Previous work on visualizing
                 citation data usually only shows limited aspects of
                 research impact and may suffer from other problems
                 including visual clutter and scalability issues. To
                 fill this gap, we propose an interactive visualization
                 tool, ImpactVis, for better exploration of research
                 impact through citation data. Case studies and in-depth
                 expert interviews are conducted to demonstrate the
                 effectiveness of ImpactVis.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Nourashrafeddin:2018:VAI,
  author =       "Seyednaser Nourashrafeddin and Ehsan Sherkat and
                 Rosane Minghim and Evangelos E. Milios",
  title =        "A Visual Approach for Interactive Keyterm-Based
                 Clustering",
  journal =      j-TIIS,
  volume =       "8",
  number =       "1",
  pages =        "6:1--6:??",
  month =        mar,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3181669",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "The keyterm-based approach is arguably intuitive for
                 users to direct text-clustering processes and adapt
                 results to various applications in text analysis. Its
                 way of markedly influencing the results, for instance,
                 by expressing important terms in relevance order,
                 requires little knowledge of the algorithm and has
                 predictable effect, speeding up the task. This article
                 first presents a text-clustering algorithm that can
                 easily be extended into an interactive algorithm. We
                 evaluate its performance against state-of-the-art
                 clustering algorithms in unsupervised mode. Next, we
                 propose three interactive versions of the algorithm
                 based on keyterm labeling, document labeling, and
                 hybrid labeling. We then demonstrate that keyterm
                 labeling is more effective than document labeling in
                 text clustering. Finally, we propose a visual approach
                 to support the keyterm-based version of the algorithm.
                 Visualizations are provided for the whole collection as
                 well as for detailed views of document and cluster
                 relationships. We show the effectiveness and
                 flexibility of our framework, Vis-Kt, by presenting
                 typical clustering cases on real text document
                 collections. A user study is also reported that reveals
                 overwhelmingly positive acceptance toward keyterm-based
                 clustering.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Fiebrink:2018:ISI,
  author =       "Rebecca Fiebrink and Marco Gillies",
  title =        "Introduction to the Special Issue on Human-Centered
                 Machine Learning",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3205942",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Machine learning is one of the most important and
                 successful techniques in contemporary computer science.
                 Although it can be applied to myriad problems of human
                 interest, research in machine learning is often framed
                 in an impersonal way, as merely algorithms being
                 applied to model data. However, this viewpoint hides
                 considerable human work of tuning the algorithms,
                 gathering the data, deciding what should be modeled in
                 the first place, and using the outcomes of machine
                 learning in the real world. Examining machine learning
                 from a human-centered perspective includes explicitly
                 recognizing human work, as well as reframing machine
                 learning workflows based on situated human working
                 practices, and exploring the co-adaptation of humans
                 and intelligent systems. A human-centered understanding
                 of machine learning in human contexts can lead not only
                 to more usable machine learning tools, but to new ways
                 of understanding what machine learning is good for and
                 how to make it more useful. This special issue brings
                 together nine articles that present different ways to
                 frame machine learning in a human context. They
                 represent very different application areas (from
                 medicine to audio) and methodologies (including machine
                 learning methods, human-computer interaction methods,
                 and hybrids), but they all explore the human contexts
                 in which machine learning is used. This introduction
                 summarizes the articles in this issue and draws out
                 some common themes.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Dudley:2018:RUI,
  author =       "John J. Dudley and Per Ola Kristensson",
  title =        "A Review of User Interface Design for Interactive
                 Machine Learning",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3185517",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Interactive Machine Learning (IML) seeks to complement
                 human perception and intelligence by tightly
                 integrating these strengths with the computational
                 power and speed of computers. The interactive process
                 is designed to involve input from the user but does not
                 require the background knowledge or experience that
                 might be necessary to work with more traditional
                 machine learning techniques. Under the IML process,
                 non-experts can apply their domain knowledge and
                 insight over otherwise unwieldy datasets to find
                 patterns of interest or develop complex data-driven
                 applications. This process is co-adaptive in nature and
                 relies on careful management of the interaction between
                 human and machine. User interface design is fundamental
                 to the success of this approach, yet there is a lack of
                 consolidated principles on how such an interface should
                 be implemented. This article presents a detailed review
                 and characterisation of Interactive Machine Learning
                 from an interactive systems perspective. We propose and
                 describe a structural and behavioural model of a
                 generalised IML system and identify solution principles
                 for building effective interfaces for IML. Where
                 possible, these emergent solution principles are
                 contextualised by reference to the broader
                 human-computer interaction literature. Finally, we
                 identify strands of user interface research key to
                 unlocking more efficient and productive non-expert
                 interactive machine learning applications.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chen:2018:UML,
  author =       "Nan-Chen Chen and Margaret Drouhard and Rafal
                 Kocielnik and Jina Suh and Cecilia R. Aragon",
  title =        "Using Machine Learning to Support Qualitative Coding
                 in Social Science: Shifting the Focus to Ambiguity",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3185515",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Machine learning (ML) has become increasingly
                 influential to human society, yet the primary
                 advancements and applications of ML are driven by
                 research in only a few computational disciplines. Even
                 applications that affect or analyze human behaviors and
                 social structures are often developed with limited
                 input from experts outside of computational fields.
                 Social scientists-experts trained to examine and
                 explain the complexity of human behavior and
                 interactions in the world-have considerable expertise
                 to contribute to the development of ML applications for
                 human-generated data, and their analytic practices
                 could benefit from more human-centered ML methods.
                 Although a few researchers have highlighted some gaps
                 between ML and social sciences [51, 57, 70], most
                 discussions only focus on quantitative methods. Yet
                 many social science disciplines rely heavily on
                 qualitative methods to distill patterns that are
                 challenging to discover through quantitative data. One
                 common analysis method for qualitative data is
                 qualitative coding. In this article, we highlight three
                 challenges of applying ML to qualitative coding.
                 Additionally, we utilize our experience of designing a
                 visual analytics tool for collaborative qualitative
                 coding to demonstrate the potential in using ML to
                 support qualitative coding by shifting the focus to
                 identifying ambiguity. We illustrate dimensions of
                 ambiguity and discuss the relationship between
                 disagreement and ambiguity. Finally, we propose three
                 research directions to ground ML applications for
                 social science as part of the progression toward
                 human-centered machine learning.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Smith:2018:PUC,
  author =       "Jim Smith and Phil Legg and Milos Matovic and
                 Kristofer Kinsey",
  title =        "Predicting User Confidence During Visual Decision
                 Making",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "10:1--10:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3185524",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "People are not infallible consistent ``oracles'':
                 their confidence in decision-making may vary
                 significantly between tasks and over time. We have
                 previously reported the benefits of using an interface
                 and algorithms that explicitly captured and exploited
                 users' confidence: error rates were reduced by up to
                 50\% for an industrial multi-class learning problem;
                 and the number of interactions required in a
                 design-optimisation context was reduced by 33\%. Having
                 access to users' confidence judgements could
                 significantly benefit intelligent interactive systems
                 in industry, in areas such as intelligent tutoring
                 systems and in health care. There are many reasons for
                 wanting to capture information about confidence
                 implicitly. Some are ergonomic, but others are more
                 ``social''-such as wishing to understand (and possibly
                 take account of) users' cognitive state without
                 interrupting them. We investigate the hypothesis that
                 users' confidence can be accurately predicted from
                 measurements of their behaviour. Eye-tracking systems
                 were used to capture users' gaze patterns as they
                 undertook a series of visual decision tasks, after each
                 of which they reported their confidence on a 5-point
                 Likert scale. Subsequently, predictive models were
                 built using ``conventional'' machine learning
                 approaches for numerical summary features derived from
                 users' behaviour. We also investigate the extent to
                 which the deep learning paradigm can reduce the need to
                 design features specific to each application by
                 creating ``gaze maps''-visual representations of the
                 trajectories and durations of users' gaze fixations-and
                 then training deep convolutional networks on these
                 images. Treating the prediction of user confidence as a
                 two-class problem (confident/not confident), we
                 attained classification accuracy of 88\% for the
                 scenario of new users on known tasks, and 87\% for
                 known users on new tasks. Considering the confidence as
                 an ordinal variable, we produced regression models with
                 a mean absolute error of \approx 0.7 in both cases.
                 Capturing just a simple subset of non-task-specific
                 numerical features gave slightly worse, but still quite
                 high accuracy (e.g., MAE \approx 1.0). Results obtained
                 with gaze maps and convolutional networks are
                 competitive, despite not having access to longer-term
                 information about users and tasks, which was vital for
                 the ``summary'' feature sets. This suggests that the
                 gaze-map-based approach forms a viable, transferable
                 alternative to handcrafting features for each different
                 application. These results provide significant evidence
                 to confirm our hypothesis, and offer a way of
                 substantially improving many interactive artificial
                 intelligence applications via the addition of cheap
                 non-intrusive hardware and computationally cheap
                 prediction algorithms.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Dumitrache:2018:CGT,
  author =       "Anca Dumitrache and Lora Aroyo and Chris Welty",
  title =        "Crowdsourcing Ground Truth for Medical Relation
                 Extraction",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "11:1--11:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3152889",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Cognitive computing systems require human labeled data
                 for evaluation and often for training. The standard
                 practice used in gathering this data minimizes
                 disagreement between annotators, and we have found this
                 results in data that fails to account for the ambiguity
                 inherent in language. We have proposed the CrowdTruth
                 method for collecting ground truth through
                 crowdsourcing, which reconsiders the role of people in
                 machine learning based on the observation that
                 disagreement between annotators provides a useful
                 signal for phenomena such as ambiguity in the text. We
                 report on using this method to build an annotated data
                 set for medical relation extraction for the cause and
                 treat relations, and how this data performed in a
                 supervised training experiment. We demonstrate that by
                 modeling ambiguity, labeled data gathered from crowd
                 workers can (1) reach the level of quality of domain
                 experts for this task while reducing the cost, and (2)
                 provide better training data at scale than distant
                 supervision. We further propose and validate new
                 weighted measures for precision, recall, and F-measure,
                 which account for ambiguity in both human and machine
                 performance on this task.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Morrison:2018:VUS,
  author =       "Cecily Morrison and Kit Huckvale and Bob Corish and
                 Richard Banks and Martin Grayson and Jonas Dorn and
                 Abigail Sellen and S{\^a}n Lindley",
  title =        "Visualizing Ubiquitously Sensed Measures of Motor
                 Ability in Multiple Sclerosis: Reflections on
                 Communicating Machine Learning in Practice",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "12:1--12:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3181670",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Sophisticated ubiquitous sensing systems are being
                 used to measure motor ability in clinical settings.
                 Intended to augment clinical decision-making, the
                 interpretability of the machine-learning measurements
                 underneath becomes critical to their use. We explore
                 how visualization can support the interpretability of
                 machine-learning measures through the case of Assess
                 MS, a system to support the clinical assessment of
                 Multiple Sclerosis. A substantial design challenge is
                 to make visible the algorithm's decision-making process
                 in a way that allows clinicians to integrate the
                 algorithm's result into their own decision process. To
                 this end, we present a series of design iterations that
                 probe the challenges in supporting interpretability in
                 a real-world system. The key contribution of this
                 article is to illustrate that simply making visible the
                 algorithmic decision-making process is not helpful in
                 supporting clinicians in their own decision-making
                 process. It disregards that people and algorithms make
                 decisions in different ways. Instead, we propose that
                 visualisation can provide context to algorithmic
                 decision-making, rendering observable a range of
                 internal workings of the algorithm from data quality
                 issues to the web of relationships generated in the
                 machine-learning process.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Kim:2018:HLS,
  author =       "Bongjun Kim and Bryan Pardo",
  title =        "A Human-in-the-Loop System for Sound Event Detection
                 and Annotation",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "13:1--13:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3214366",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Labeling of audio events is essential for many tasks.
                 However, finding sound events and labeling them within
                 a long audio file is tedious and time-consuming. In
                 cases where there is very little labeled data (e.g., a
                 single labeled example), it is often not feasible to
                 train an automatic labeler because many techniques
                 (e.g., deep learning) require a large number of
                 human-labeled training examples. Also, fully automated
                 labeling may not show sufficient agreement with human
                 labeling for many uses. To solve this issue, we present
                 a human-in-the-loop sound labeling system that helps a
                 user quickly label target sound events in a long audio.
                 It lets a user reduce the time required to label a long
                 audio file (e.g., 20 hours) containing target sounds
                 that are sparsely distributed throughout the recording
                 (10\% or less of the audio contains the target) when
                 there are too few labeled examples (e.g., one) to train
                 a state-of-the-art machine audio labeling system. To
                 evaluate the effectiveness of our tool, we performed a
                 human-subject study. The results show that it helped
                 participants label target sound events twice as fast as
                 labeling them manually. In addition to measuring the
                 overall performance of the proposed system, we also
                 measure interaction overhead and machine accuracy,
                 which are two key factors that determine the overall
                 performance. The analysis shows that an ideal interface
                 that does not have interaction overhead at all could
                 speed labeling by as much as a factor of four.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Zhang:2018:ERC,
  author =       "Amy X. Zhang and Jilin Chen and Wei Chai and Jinjun Xu
                 and Lichan Hong and Ed Chi",
  title =        "Evaluation and Refinement of Clustered Search Results
                 with the Crowd",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "14:1--14:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3158226",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "When searching on the web or in an app, results are
                 often returned as lists of hundreds to thousands of
                 items, making it difficult for users to understand or
                 navigate the space of results. Research has
                 demonstrated that using clustering to partition search
                 results into coherent, topical clusters can aid in both
                 exploration and discovery. Yet clusters generated by an
                 algorithm for this purpose are often of poor quality
                 and do not satisfy users. To achieve acceptable
                 clustered search results, experts must manually
                 evaluate and refine the clustered results for each
                 search query, a process that does not scale to large
                 numbers of search queries. In this article, we
                 investigate using crowd-based human evaluation to
                 inspect, evaluate, and improve clusters to create
                 high-quality clustered search results at scale. We
                 introduce a workflow that begins by using a collection
                 of well-known clustering algorithms to produce a set of
                 clustered search results for a given query. Then, we
                 use crowd workers to holistically assess the quality of
                 each clustered search result to find the best one.
                 Finally, the workflow has the crowd spot and fix
                 problems in the best result to produce a final output.
                 We evaluate this workflow on 120 top search queries
                 from the Google Play Store, some of whom have clustered
                 search results as a result of evaluations and
                 refinements by experts. Our evaluations demonstrate
                 that the workflow is effective at reproducing the
                 evaluation of expert judges and also improves clusters
                 in a way that agrees with experts and crowds alike.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Self:2018:OLP,
  author =       "Jessica Zeitz Self and Michelle Dowling and John
                 Wenskovitch and Ian Crandell and Ming Wang and Leanna
                 House and Scotland Leman and Chris North",
  title =        "Observation-Level and Parametric Interaction for
                 High-Dimensional Data Analysis",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "15:1--15:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3158230",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Exploring high-dimensional data is challenging.
                 Dimension reduction algorithms, such as weighted
                 multidimensional scaling, support data exploration by
                 projecting datasets to two dimensions for
                 visualization. These projections can be explored
                 through parametric interaction, tweaking underlying
                 parameterizations, and observation-level interaction,
                 directly interacting with the points within the
                 projection. In this article, we present the results of
                 a controlled usability study determining the
                 differences, advantages, and drawbacks among parametric
                 interaction, observation-level interaction, and their
                 combination. The study assesses both interaction
                 technique effects on domain-specific high-dimensional
                 data analyses performed by non-experts of statistical
                 algorithms. This study is performed using Andromeda, a
                 tool that enables both parametric and observation-level
                 interaction to provide in-depth data exploration. The
                 results indicate that the two forms of interaction
                 serve different, but complementary, purposes in gaining
                 insight through steerable dimension reduction
                 algorithms.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Francoise:2018:MSM,
  author =       "Jules Fran{\c{c}}oise and Fr{\'e}d{\'e}ric
                 Bevilacqua",
  title =        "Motion-Sound Mapping through Interaction: an Approach
                 to User-Centered Design of Auditory Feedback Using
                 Machine Learning",
  journal =      j-TIIS,
  volume =       "8",
  number =       "2",
  pages =        "16:1--16:??",
  month =        jul,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3211826",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:40 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Technologies for sensing movement are expanding toward
                 everyday use in virtual reality, gaming, and artistic
                 practices. In this context, there is a need for
                 methodologies to help designers and users create
                 meaningful movement experiences. This article discusses
                 a user-centered approach for the design of interactive
                 auditory feedback using interactive machine learning.
                 We discuss Mapping through Interaction, a method for
                 crafting sonic interactions from corporeal
                 demonstrations of embodied associations between motion
                 and sound. It uses an interactive machine learning
                 approach to build the mapping from user demonstrations,
                 emphasizing an iterative design process that integrates
                 acted and interactive experiences of the relationships
                 between movement and sound. We examine Gaussian Mixture
                 Regression and Hidden Markov Regression for continuous
                 movement recognition and real-time sound parameter
                 generation. We illustrate and evaluate this approach
                 through an application in which novice users can create
                 interactive sound feedback based on coproduced gestures
                 and vocalizations. Results indicate that Gaussian
                 Mixture Regression and Hidden Markov Regression can
                 efficiently learn complex motion-sound mappings from
                 few examples.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Sidner:2018:CNT,
  author =       "Candace L. Sidner and Timothy Bickmore and Bahador
                 Nooraie and Charles Rich and Lazlo Ring and Mahni
                 Shayganfar and Laura Vardoulakis",
  title =        "Creating New Technologies for Companionable Agents to
                 Support Isolated Older Adults",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "17:1--17:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3213050",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "This article reports on the development of
                 capabilities for (on-screen) virtual agents and robots
                 to support isolated older adults in their homes. A
                 real-time architecture was developed to use a virtual
                 agent or a robot interchangeably to interact via dialog
                 and gesture with a human user. Users could interact
                 with either agent on 12 different activities, some of
                 which included on-screen games, and forms to complete.
                 The article reports on a pre-study that guided the
                 choice of interaction activities. A month-long study
                 with 44 adults between the ages of 55 and 91 assessed
                 differences in the use of the robot and virtual
                 agent.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Oviatt:2018:DHS,
  author =       "S. Oviatt and K. Hang and J. Zhou and K. Yu and F.
                 Chen",
  title =        "Dynamic Handwriting Signal Features Predict Domain
                 Expertise",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "18:1--18:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3213309",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "As commercial pen-centric systems proliferate, they
                 create a parallel need for analytic techniques based on
                 dynamic writing. Within educational applications,
                 recent empirical research has shown that signal-level
                 features of students' writing, such as stroke distance,
                 pressure and duration, are adapted to conserve total
                 energy expenditure as they consolidate expertise in a
                 domain. The present research examined how accurately
                 three different machine-learning algorithms could
                 automatically classify users' domain expertise based on
                 signal features of their writing, without any content
                 analysis. Compared with an unguided machine-learning
                 classification accuracy of 71\%, hybrid methods using
                 empirical-statistical guidance correctly classified
                 79-92\% of students by their domain expertise level. In
                 addition to improved accuracy, the hybrid approach
                 contributed a causal understanding of prediction
                 success and generalization to new data. These novel
                 findings open up opportunities to design new automated
                 learning analytic systems and student-adaptive
                 educational technologies for the rapidly expanding
                 sector of commercial pen systems.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hammond:2018:JAA,
  author =       "Tracy Hammond and Shalini Priya Ashok Kumar and
                 Matthew Runyon and Josh Cherian and Blake Williford and
                 Swarna Keshavabhotla and Stephanie Valentine and Wayne
                 Li and Julie Linsey",
  title =        "It's Not Just about Accuracy: Metrics That Matter When
                 Modeling Expert Sketching Ability",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "19:1--19:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3181673",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Design sketching is an important skill for designers,
                 engineers, and creative professionals, as it allows
                 them to express their ideas and concepts in a visual
                 medium. Being a critical and versatile skill for many
                 different disciplines, courses on design sketching are
                 often taught in universities. Courses today
                 predominately rely on pen and paper; however, this
                 traditional pedagogy is limited by the availability of
                 human instructors, who can provide personalized
                 feedback. Using a stylus-based intelligent tutoring
                 system called SketchTivity, we aim to eventually mimic
                 the feedback given by an instructor and assess
                 student-drawn sketches to give students insight into
                 areas for improvement. To provide effective feedback to
                 users, it is important to identify what aspects of
                 their sketches they should work on to improve their
                 sketching ability. After consulting with several domain
                 experts in sketching, we came up with several classes
                 of features that could potentially differentiate expert
                 and novice sketches. Because improvement on one metric,
                 such as speed, may result in a decrease in another
                 metric, such as accuracy, the creation of a single
                 score may not mean much to the user. We attempted to
                 create a single internal score that represents overall
                 drawing skill so that the system can track improvement
                 over time and found that this score correlates highly
                 with expert rankings. We gathered over 2,000 sketches
                 from 20 novices and four experts for analysis. We
                 identified key metrics for quality assessment that were
                 shown to significantly correlate with the quality of
                 expert sketches and provide insight into providing
                 intelligent user feedback in the future.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Koskela:2018:PIR,
  author =       "Markus Koskela and Petri Luukkonen and Tuukka Ruotsalo
                 and Mats Sj{\"O}berg and Patrik Flor{\'e}en",
  title =        "Proactive Information Retrieval by Capturing Search
                 Intent from Primary Task Context",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "20:1--20:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3150975",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "A significant fraction of information searches are
                 motivated by the user's primary task. An ideal search
                 engine would be able to use information captured from
                 the primary task to proactively retrieve useful
                 information. Previous work has shown that many
                 information retrieval activities depend on the primary
                 task in which the retrieved information is to be used,
                 but fairly little research has been focusing on methods
                 that automatically learn the informational intents from
                 the primary task context. We study how the implicit
                 primary task context can be used to model the user's
                 search intent and to proactively retrieve relevant and
                 useful information. Data comprising of logs from a user
                 study, in which users are writing an essay, demonstrate
                 that users' search intents can be captured from the
                 task and relevant and useful information can be
                 proactively retrieved. Data from simulations with
                 several datasets of different complexity show that the
                 proposed approach of using primary task context
                 generalizes to a variety of data. Our findings have
                 implications for the design of proactive search systems
                 that can infer users' search intent implicitly by
                 monitoring users' primary task activities.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Narzt:2018:ECA,
  author =       "Wolfgang Narzt and Otto Weichselbaum and Gustav
                 Pomberger and Markus Hofmarcher and Michael Strauss and
                 Peter Holzkorn and Roland Haring and Monika Sturm",
  title =        "Estimating Collective Attention toward a Public
                 Display",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "21:1--21:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3230715",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Enticing groups of passers-by to focused interaction
                 with a public display requires the display system to
                 take appropriate action that depends on how much
                 attention the group is already paying to the display.
                 In the design of such a system, we might want to
                 present the content so that it indicates that a part of
                 the group that is looking head-on at the display has
                 already been registered and is addressed individually,
                 whereas it simultaneously emits a strong audio signal
                 that makes the inattentive rest of the group turn
                 toward it. The challenge here is to define and delimit
                 adequate mixed attention states for groups of people,
                 allowing for classifying collective attention based on
                 inhomogeneous variants of individual attention, i.e.,
                 where some group members might be highly attentive,
                 others even interacting with the public display, and
                 some unperceptive. In this article, we present a model
                 for estimating collective human attention toward a
                 public display and investigate technical methods for
                 practical implementation that employs measurement of
                 physical expressive features of people appearing within
                 the display's field of view (i.e., the basis for
                 deriving a person's attention). We delineate strengths
                 and weaknesses and prove the potentials of our model by
                 experimentally exerting influence on the attention of
                 groups of passers-by in a public gaming scenario.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hossain:2018:ASM,
  author =       "H. M. Sajjad Hossain and Sreenivasan R. Ramamurthy and
                 Md Abdullah {Al Hafiz Khan} and Nirmalya Roy",
  title =        "An Active Sleep Monitoring Framework Using Wearables",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "22:1--22:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3185516",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Sleep is the most important aspect of healthy and
                 active living. The right amount of sleep at the right
                 time helps an individual to protect his or her
                 physical, mental, and cognitive health and maintain his
                 or her quality of life. The most durative of the
                 Activities of Daily Living (ADL), sleep has a major
                 synergic influence on a person's fuctional, behavioral,
                 and cognitive health. A deep understanding of sleep
                 behavior and its relationship with its physiological
                 signals, and contexts (such as eye or body movements),
                 is necessary to design and develop a robust intelligent
                 sleep monitoring system. In this article, we propose an
                 intelligent algorithm to detect the microscopic states
                 of sleep that fundamentally constitute the components
                 of good and bad sleeping behaviors and thus help shape
                 the formative assessment of sleep quality. Our initial
                 analysis includes the investigation of several
                 classification techniques to identify and correlate the
                 relationship of microscopic sleep states with overall
                 sleep behavior. Subsequently, we also propose an online
                 algorithm based on change point detection to process
                 and classify the microscopic sleep states. We also
                 develop a lightweight version of the proposed algorithm
                 for real-time sleep monitoring, recognition, and
                 assessment at scale. For a larger deployment of our
                 proposed model across a community of individuals, we
                 propose an active-learning-based methodology to reduce
                 the effort of ground-truth data collection and
                 labeling. Finally, we evaluate the performance of our
                 proposed algorithms on real data traces and demonstrate
                 the efficacy of our models for detecting and assessing
                 the fine-grained sleep states beyond an individual.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Park:2018:MFU,
  author =       "Souneil Park and Joan Serr{\`a} and Enrique Frias
                 Martinez and Nuria Oliver",
  title =        "{MobInsight}: a Framework Using Semantic Neighborhood
                 Features for Localized Interpretations of Urban
                 Mobility",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "23:1--23:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3158433",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Collective urban mobility embodies the residents'
                 local insights on the city. Mobility practices of the
                 residents are produced from their spatial choices,
                 which involve various considerations such as the
                 atmosphere of destinations, distance, past experiences,
                 and preferences. The advances in mobile computing and
                 the rise of geo-social platforms have provided the
                 means for capturing the mobility practices; however,
                 interpreting the residents' insights is challenging due
                 to the scale and complexity of an urban environment and
                 its unique context. In this article, we present
                 MobInsight, a framework for making localized
                 interpretations of urban mobility that reflect various
                 aspects of the urbanism. MobInsight extracts a rich set
                 of neighborhood features through holistic semantic
                 aggregation, and models the mobility between all-pairs
                 of neighborhoods. We evaluate MobInsight with the
                 mobility data of Barcelona and demonstrate diverse
                 localized and semantically rich interpretations.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Carreno-Medrano:2018:PVG,
  author =       "Pamela Carreno-Medrano and Sylvie Gibet and
                 Pierre-Fran{\c{C}}ois Marteau",
  title =        "Perceptual Validation for the Generation of Expressive
                 Movements from End-Effector Trajectories",
  journal =      j-TIIS,
  volume =       "8",
  number =       "3",
  pages =        "24:1--24:??",
  month =        aug,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3150976",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Endowing animated virtual characters with emotionally
                 expressive behaviors is paramount to improving the
                 quality of the interactions between humans and virtual
                 characters. Full-body motion, in particular, with its
                 subtle kinematic variations, represents an effective
                 way of conveying emotionally expressive content.
                 However, before synthesizing expressive full-body
                 movements, it is necessary to identify and understand
                 what qualities of human motion are salient to the
                 perception of emotions and how these qualities can be
                 exploited to generate novel and equally expressive
                 full-body movements. Based on previous studies, we
                 argue that it is possible to perceive and generate
                 expressive full-body movements from a limited set of
                 joint trajectories, including end-effector trajectories
                 and additional constraints such as pelvis and elbow
                 trajectories. Hence, these selected trajectories define
                 a significant and reduced motion space, which is
                 adequate for the characterization of the expressive
                 qualities of human motion and that is both suitable for
                 the analysis and generation of emotionally expressive
                 full-body movements. The purpose and main contribution
                 of this work is the methodological framework we defined
                 and used to assess the validity and applicability of
                 the selected trajectories for the perception and
                 generation of expressive full-body movements. This
                 framework consists of the creation of a motion capture
                 database of expressive theatrical movements, the
                 development of a motion synthesis system based on
                 trajectories re-played or re-sampled and inverse
                 kinematics, and two perceptual studies.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Knott:2018:ATI,
  author =       "Benjamin A. Knott and Jonathan Gratch and Angelo
                 Cangelosi and James Caverlee",
  title =        "{{\booktitle{ACM Transactions on Interactive
                 Intelligent Systems (TiiS)}}} Special Issue on Trust
                 and Influence in Intelligent Human-Machine
                 Interaction",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "25:1--25:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3281451",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3281451",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "25",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Wagner:2018:MHR,
  author =       "Alan R. Wagner and Paul Robinette and Ayanna Howard",
  title =        "Modeling the Human-Robot Trust Phenomenon: a
                 Conceptual Framework based on Risk",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "26:1--26:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3152890",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3152890",
  abstract =     "This article presents a conceptual framework for
                 human-robot trust which uses computational
                 representations inspired by game theory to represent a
                 definition of trust, derived from social psychology.
                 This conceptual framework generates several testable
                 hypotheses related to human-robot trust. This article
                 examines these hypotheses and a series of experiments
                 we have conducted which both provide support for and
                 also conflict with our framework for trust. We also
                 discuss the methodological challenges associated with
                 investigating trust. The article concludes with a
                 description of the important areas for future research
                 on the topic of human-robot trust.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "26",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Akash:2018:CMS,
  author =       "Kumar Akash and Wan-Lin Hu and Neera Jain and Tahira
                 Reid",
  title =        "A Classification Model for Sensing Human Trust in
                 Machines Using {EEG} and {GSR}",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "27:1--27:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3132743",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3132743",
  abstract =     "Today, intelligent machines interact and collaborate
                 with humans in a way that demands a greater level of
                 trust between human and machine. A first step toward
                 building intelligent machines that are capable of
                 building and maintaining trust with humans is the
                 design of a sensor that will enable machines to
                 estimate human trust level in real time. In this
                 article, two approaches for developing classifier-based
                 empirical trust-sensor models are presented that
                 specifically use electroencephalography and galvanic
                 skin response measurements. Human subject data
                 collected from 45 participants is used for feature
                 extraction, feature selection, classifier training, and
                 model validation. The first approach considers a
                 general set of psychophysiological features across all
                 participants as the input variables and trains a
                 classifier-based model for each participant, resulting
                 in a trust-sensor model based on the general feature
                 set (i.e., a ``general trust-sensor model''). The
                 second approach considers a customized feature set for
                 each individual and trains a classifier-based model
                 using that feature set, resulting in improved mean
                 accuracy but at the expense of an increase in training
                 time. This work represents the first use of real-time
                 psychophysiological measurements for the development of
                 a human trust sensor. Implications of the work, in the
                 context of trust management algorithm design for
                 intelligent machines, are also discussed.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "27",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Holbrook:2018:CVI,
  author =       "Colin Holbrook",
  title =        "Cues of Violent Intergroup Conflict Diminish
                 Perceptions of Robotic Personhood",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "28:1--28:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3181674",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3181674",
  abstract =     "Convergent lines of evidence indicate that
                 anthropomorphic robots are represented using
                 neurocognitive mechanisms typically employed in social
                 reasoning about other people. Relatedly, a growing
                 literature documents that contexts of threat can
                 exacerbate coalitional biases in social perceptions.
                 Integrating these research programs, the present
                 studies test whether cues of violent intergroup
                 conflict modulate perceptions of the intelligence,
                 emotional experience, or overall personhood of robots.
                 In Studies 1 and 2, participants evaluated a large,
                 bipedal all-terrain robot; in Study 3, participants
                 evaluated a small, social robot with humanlike facial
                 and vocal characteristics. Across all studies, cues of
                 violent conflict caused significant decreases in
                 perceived robotic personhood, and these shifts were
                 mediated by parallel reductions in emotional connection
                 with the robot (with no significant effects of threat
                 on attributions of intelligence/skill). In addition, in
                 Study 2, participants in the conflict condition
                 estimated the large bipedal robot to be less effective
                 in military combat, and this difference was mediated by
                 the reduction in perceived robotic personhood. These
                 results are discussed as they motivate future
                 investigation into the links among threat, coalitional
                 bias and human-robot interaction.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "28",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chien:2018:ECT,
  author =       "Shih-Yi Chien and Michael Lewis and Katia Sycara and
                 Jyi-Shane Liu and Asiye Kumru",
  title =        "The Effect of Culture on Trust in Automation:
                 Reliability and Workload",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "29:1--29:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3230736",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3230736",
  abstract =     "Trust in automation has become a topic of intensive
                 study since the late 1990s and is of increasing
                 importance with the advent of intelligent interacting
                 systems. While the earliest trust experiments involved
                 human interventions to correct failures/errors in
                 automated control systems, a majority of subsequent
                 studies have investigated information acquisition and
                 analysis decision aiding tasks such as target detection
                 for which automation reliability is more easily
                 manipulated. Despite the high level of international
                 dependence on automation in industry, almost all
                 current studies have employed Western samples primarily
                 from the U.S. The present study addresses these gaps by
                 running a large sample experiment in three (U.S.,
                 Taiwan, and Turkey) diverse cultures using a ``trust
                 sensitive task'' consisting of both automated control
                 and target detection subtasks. This article presents
                 results for the target detection subtask for which
                 reliability and task load were manipulated. The current
                 experiments allow us to determine whether reported
                 effects are universal or specific to Western culture,
                 vary in baseline or magnitude, or differ across
                 cultures. Results generally confirm consistent effects
                 of manipulations across the three cultures as well as
                 cultural differences in initial trust and variation in
                 effects of manipulations consistent with 10 cultural
                 hypotheses based on Hofstede's Cultural Dimensions and
                 Leung and Cohen's theory of Cultural Syndromes. These
                 results provide critical implications and insights for
                 correct trust calibration and to enhance human trust in
                 intelligent automation systems across cultures.
                 Additionally, our results would be useful in designing
                 intelligent systems for users of different cultures.
                 Our article presents the following contributions:
                 First, to the best of our knowledge, this is the first
                 set of studies that deal with cultural factors across
                 all the cultural syndromes identified in the literature
                 by comparing trust in the Honor, Face, Dignity
                 cultures. Second, this is the first set of studies that
                 uses a validated cross-cultural trust measure for
                 measuring trust in automation. Third, our experiments
                 are the first to study the dynamics of trust across
                 cultures.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "29",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Baker:2018:TUT,
  author =       "Anthony L. Baker and Elizabeth K. Phillips and Daniel
                 Ullman and Joseph R. Keebler",
  title =        "Toward an Understanding of Trust Repair in Human-Robot
                 Interaction: Current Research and Future Directions",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "30:1--30:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3181671",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3181671",
  abstract =     "Gone are the days of robots solely operating in
                 isolation, without direct interaction with people.
                 Rather, robots are increasingly being deployed in
                 environments and roles that require complex social
                 interaction with humans. The implementation of
                 human-robot teams continues to increase as technology
                 develops in tandem with the state of human-robot
                 interaction (HRI) research. Trust, a major component of
                 human interaction, is an important facet of HRI.
                 However, the ideas of trust repair and trust violations
                 are understudied in the HRI literature. Trust repair is
                 the activity of rebuilding trust after one party breaks
                 the trust of another. These trust breaks are referred
                 to as trust violations. Just as with humans, trust
                 violations with robots are inevitable; as a result, a
                 clear understanding of the process of HRI trust repair
                 must be developed in order to ensure that a human-robot
                 team can continue to perform well after a trust
                 violation. Previous research on human-automation trust
                 and human-human trust can serve as starting places for
                 exploring trust repair in HRI. Although existing models
                 of human-automation and human-human trust are helpful,
                 they do not account for some of the complexities of
                 building and maintaining trust in unique relationships
                 between humans and robots. The purpose of this article
                 is to provide a foundation for exploring human-robot
                 trust repair by drawing upon prior work in the
                 human-robot, human-automation, and human-human trust
                 literature, concluding with recommendations for
                 advancing this body of work.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "30",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Wang:2018:TBM,
  author =       "Yue Wang and Laura R. Humphrey and Zhanrui Liao and
                 Huanfei Zheng",
  title =        "Trust-Based Multi-Robot Symbolic Motion Planning with
                 a Human-in-the-Loop",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "31:1--31:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3213013",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3213013",
  abstract =     "Symbolic motion planning for robots is the process of
                 specifying and planning robot tasks in a discrete
                 space, then carrying them out in a continuous space in
                 a manner that preserves the discrete-level task
                 specifications. Despite progress in symbolic motion
                 planning, many challenges remain, including addressing
                 scalability for multi-robot systems and improving
                 solutions by incorporating human intelligence. In this
                 article, distributed symbolic motion planning for
                 multi-robot systems is developed to address
                 scalability. More specifically, compositional reasoning
                 approaches are developed to decompose the global
                 planning problem, and atomic propositions for
                 observation, communication, and control are proposed to
                 address inter-robot collision avoidance. To improve
                 solution quality and adaptability, a hypothetical
                 dynamic, quantitative, and probabilistic human-to-robot
                 trust model is developed to aid this decomposition.
                 Furthermore, a trust-based real-time switching
                 framework is proposed to switch between autonomous and
                 manual motion planning for tradeoffs between task
                 safety and efficiency. Deadlock- and livelock-free
                 algorithms are designed to guarantee reachability of
                 goals with a human-in-the-loop. A set of nontrivial
                 multi-robot simulations with direct human inputs and
                 trust evaluation is provided, demonstrating the
                 successful implementation of the trust-based
                 multi-robot symbolic motion planning methods.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "31",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Afergan:2018:DR,
  author =       "Daniel Afergan",
  title =        "Distinguished Reviewers",
  journal =      j-TIIS,
  volume =       "8",
  number =       "4",
  pages =        "32:1--32:??",
  month =        nov,
  year =         "2018",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3283374",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3283374",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "32",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Niewiadomski:2019:AMQ,
  author =       "Radoslaw Niewiadomski and Ksenia Kolykhalova and
                 Stefano Piana and Paolo Alborno and Gualtiero Volpe and
                 Antonio Camurri",
  title =        "Analysis of Movement Quality in Full-Body Physical
                 Activities",
  journal =      j-TIIS,
  volume =       "9",
  number =       "1",
  pages =        "1:1--1:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3132369",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3132369",
  abstract =     "Full-body human movement is characterized by
                 fine-grain expressive qualities that humans are easily
                 capable of exhibiting and recognizing in others'
                 movement. In sports (e.g., martial arts) and performing
                 arts (e.g., dance), the same sequence of movements can
                 be performed in a wide range of ways characterized by
                 different qualities, often in terms of subtle (spatial
                 and temporal) perturbations of the movement. Even a
                 non-expert observer can distinguish between a top-level
                 and average performance by a dancer or martial artist.
                 The difference is not in the performed movements--the
                 same in both cases--but in the ``quality'' of their
                 performance. In this article, we present a
                 computational framework aimed at an automated
                 approximate measure of movement quality in full-body
                 physical activities. Starting from motion capture data,
                 the framework computes low-level (e.g., a limb
                 velocity) and high-level (e.g., synchronization between
                 different limbs) movement features. Then, this vector
                 of features is integrated to compute a value aimed at
                 providing a quantitative assessment of movement quality
                 approximating the evaluation that an external expert
                 observer would give of the same sequence of movements.
                 Next, a system representing a concrete implementation
                 of the framework is proposed. Karate is adopted as a
                 testbed. We selected two different katas (i.e.,
                 detailed choreographies of movements in karate)
                 characterized by different overall attitudes and
                 expressions (aggressiveness, meditation), and we asked
                 seven athletes, having various levels of experience and
                 age, to perform them. Motion capture data were
                 collected from the performances and were analyzed with
                 the system. The results of the automated analysis were
                 compared with the scores given by 14 karate experts who
                 rated the same performances. Results show that the
                 movement-quality scores computed by the system and the
                 ratings given by the human observers are highly
                 correlated (Pearson's correlations r = 0.84, p = 0.001
                 and r = 0.75, p = 0.005).",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Ramachandran:2019:TER,
  author =       "Aditi Ramachandran and Chien-Ming Huang and Brian
                 Scassellati",
  title =        "Toward Effective Robot--Child Tutoring: Internal
                 Motivation, Behavioral Intervention, and Learning
                 Outcomes",
  journal =      j-TIIS,
  volume =       "9",
  number =       "1",
  pages =        "2:1--2:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3213768",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3213768",
  abstract =     "Personalized learning environments have the potential
                 to improve learning outcomes for children in a variety
                 of educational domains, as they can tailor instruction
                 based on the unique learning needs of individuals.
                 Robot tutoring systems can further engage users by
                 leveraging their potential for embodied social
                 interaction and take into account crucial aspects of a
                 learner, such as a student's motivation in learning. In
                 this article, we demonstrate that motivation in young
                 learners corresponds to observable behaviors when
                 interacting with a robot tutoring system, which, in
                 turn, impact learning outcomes. We first detail a user
                 study involving children interacting one on one with a
                 robot tutoring system over multiple sessions. Based on
                 empirical data, we show that academic motivation
                 stemming from one's own values or goals as assessed by
                 the Academic Self-Regulation Questionnaire (SRQ-A)
                 correlates to observed suboptimal help-seeking behavior
                 during the initial tutoring session. We then show how
                 an interactive robot that responds intelligently to
                 these observed behaviors in subsequent tutoring
                 sessions can positively impact both student behavior
                 and learning outcomes over time. These results provide
                 empirical evidence for the link between internal
                 motivation, observable behavior, and learning outcomes
                 in the context of robot--child tutoring. We also
                 identified an additional suboptimal behavioral feature
                 within our tutoring environment and demonstrated its
                 relationship to internal factors of motivation,
                 suggesting further opportunities to design robot
                 intervention to enhance learning. We provide insights
                 on the design of robot tutoring systems aimed to
                 deliver effective behavioral intervention during
                 learning interactions for children and present a
                 discussion on the broader challenges currently faced by
                 robot--child tutoring systems.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Marge:2019:MDR,
  author =       "Matthew Marge and Alexander I. Rudnicky",
  title =        "Miscommunication Detection and Recovery in Situated
                 Human--Robot Dialogue",
  journal =      j-TIIS,
  volume =       "9",
  number =       "1",
  pages =        "3:1--3:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3237189",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3237189",
  abstract =     "Even without speech recognition errors, robots may
                 face difficulties interpreting natural-language
                 instructions. We present a method for robustly handling
                 miscommunication between people and robots in
                 task-oriented spoken dialogue. This capability is
                 implemented in TeamTalk, a conversational interface to
                 robots that supports detection and recovery from the
                 situated grounding problems of referential ambiguity
                 and impossible actions. We introduce a representation
                 that detects these problems and a nearest-neighbor
                 learning algorithm that selects recovery strategies for
                 a virtual robot. When the robot encounters a grounding
                 problem, it looks back on its interaction history to
                 consider how it resolved similar situations. The
                 learning method is trained initially on crowdsourced
                 data but is then supplemented by interactions from a
                 longitudinal user study in which six participants
                 performed navigation tasks with the robot. We compare
                 results collected using a general model to
                 user-specific models and find that user-specific models
                 perform best on measures of dialogue efficiency, while
                 the general model yields the highest agreement with
                 human judges. Our overall contribution is a novel
                 approach to detecting and recovering from
                 miscommunication in dialogue by including situated
                 context, namely, information from a robot's path
                 planner and surroundings.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Guo:2019:VEA,
  author =       "Fangzhou Guo and Tianlong Gu and Wei Chen and Feiran
                 Wu and Qi Wang and Lei Shi and Huamin Qu",
  title =        "Visual Exploration of Air Quality Data with a
                 Time-correlation-partitioning Tree Based on Information
                 Theory",
  journal =      j-TIIS,
  volume =       "9",
  number =       "1",
  pages =        "4:1--4:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3182187",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3182187",
  abstract =     "Discovering the correlations among variables of air
                 quality data is challenging, because the correlation
                 time series are long-lasting, multi-faceted, and
                 information-sparse. In this article, we propose a novel
                 visual representation, called
                 Time-correlation-partitioning (TCP) tree, that
                 compactly characterizes correlations of multiple air
                 quality variables and their evolutions. A TCP tree is
                 generated by partitioning the information-theoretic
                 correlation time series into pieces with respect to the
                 variable hierarchy and temporal variations, and
                 reorganizing these pieces into a hierarchically nested
                 structure. The visual exploration of a TCP tree
                 provides a sparse data traversal of the correlation
                 variations and a situation-aware analysis of
                 correlations among variables. This can help
                 meteorologists understand the correlations among air
                 quality variables better. We demonstrate the efficiency
                 of our approach in a real-world air quality
                 investigation scenario.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Krokos:2019:EDL,
  author =       "Eric Krokos and Hsueh-Chen Cheng and Jessica Chang and
                 Bohdan Nebesh and Celeste Lyn Paul and Kirsten Whitley
                 and Amitabh Varshney",
  title =        "Enhancing Deep Learning with Visual Interactions",
  journal =      j-TIIS,
  volume =       "9",
  number =       "1",
  pages =        "5:1--5:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3150977",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Deep learning has emerged as a powerful tool for
                 feature-driven labeling of datasets. However, for it to
                 be effective, it requires a large and finely labeled
                 training dataset. Precisely labeling a large training
                 dataset is expensive, time-consuming, and error prone.
                 In this article, we present a visually driven
                 deep-learning approach that starts with a coarsely
                 labeled training dataset and iteratively refines the
                 labeling through intuitive interactions that leverage
                 the latent structures of the dataset. Our approach can
                 be used to (a) alleviate the burden of intensive manual
                 labeling that captures the fine nuances in a
                 high-dimensional dataset by simple visual interactions,
                 (b) replace a complicated (and therefore difficult to
                 design) labeling algorithm by a simpler (but coarse)
                 labeling algorithm supplemented by user interaction to
                 refine the labeling, or (c) use low-dimensional
                 features (such as the RGB colors) for coarse labeling
                 and turn to higher-dimensional latent structures that
                 are progressively revealed by deep learning, for fine
                 labeling. We validate our approach through use cases on
                 three high-dimensional datasets and a user study.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Koh:2019:DHG,
  author =       "Jung In Koh and Josh Cherian and Paul Taele and Tracy
                 Hammond",
  title =        "Developing a Hand Gesture Recognition System for
                 Mapping Symbolic Hand Gestures to Analogous Emojis in
                 Computer-Mediated Communication",
  journal =      j-TIIS,
  volume =       "9",
  number =       "1",
  pages =        "6:1--6:??",
  month =        feb,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3297277",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Mon Mar 4 08:29:41 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  abstract =     "Recent trends in computer-mediated communication (CMC)
                 have not only led to expanded instant messaging through
                 the use of images and videos but have also expanded
                 traditional text messaging with richer content in the
                 form of visual communication markers (VCMs) such as
                 emoticons, emojis, and stickers. VCMs could prevent a
                 potential loss of subtle emotional conversation in CMC,
                 which is delivered by nonverbal cues that convey
                 affective and emotional information. However, as the
                 number of VCMs grows in the selection set, the problem
                 of VCM entry needs to be addressed. Furthermore,
                 conventional means of accessing VCMs continue to rely
                 on input entry methods that are not directly and
                 intimately tied to expressive nonverbal cues. In this
                 work, we aim to address this issue by facilitating the
                 use of an alternative form of VCM entry: hand gestures.
                 To that end, we propose a user-defined hand gesture set
                 that is highly representative of a number of VCMs and a
                 two-stage hand gesture recognition system
                 (trajectory-based, shape-based) that can identify these
                 user-defined hand gestures with an accuracy of 82\%. By
                 developing such a system, we aim to allow people using
                 low-bandwidth forms of CMCs to still enjoy their
                 convenient and discreet properties while also allowing
                 them to experience more of the intimacy and
                 expressiveness of higher-bandwidth online
                 communication.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chen:2019:SIH,
  author =       "Fang Chen and Carlos Duarte and Wai-Tat Fu",
  title =        "Special Issue on Highlights of {ACM Intelligent User
                 Interface (IUI) 2017}",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "7:1--7:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3301292",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3301292",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Pham:2019:AMA,
  author =       "Phuong Pham and Jingtao Wang",
  title =        "{AttentiveVideo}: a Multimodal Approach to Quantify
                 Emotional Responses to Mobile Advertisements",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "8:1--8:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3232233",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3232233",
  abstract =     "Understanding a target audience's emotional responses
                 to a video advertisement is crucial to evaluate the
                 advertisement's effectiveness. However, traditional
                 methods for collecting such information are slow,
                 expensive, and coarse grained. We propose
                 AttentiveVideo, a scalable intelligent mobile interface
                 with corresponding inference algorithms to monitor and
                 quantify the effects of mobile video advertising in
                 real time. Without requiring additional sensors,
                 AttentiveVideo employs a combination of implicit
                 photoplethysmography (PPG) sensing and facial
                 expression analysis (FEA) to detect the attention,
                 engagement, and sentiment of viewers as they watch
                 video advertisements on unmodified smartphones. In a
                 24-participant study, AttentiveVideo achieved good
                 accuracy on a wide range of emotional measures (the
                 best average accuracy = 82.6\% across nine measures).
                 While feature fusion alone did not improve prediction
                 accuracy with a single model, it significantly improved
                 the accuracy when working together with model fusion.
                 We also found that the PPG sensing channel and the FEA
                 technique have different strength in data availability,
                 latency detection, accuracy, and usage environment.
                 These findings show the potential for both low-cost
                 collection and deep understanding of emotional
                 responses to mobile video advertisements.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Mihoub:2019:WSS,
  author =       "Alaeddine Mihoub and Gr{\'e}goire Lefebvre",
  title =        "Wearables and Social Signal Processing for Smarter
                 Public Presentations",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "9:1--9:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3234507",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3234507",
  abstract =     "Social Signal Processing techniques have given the
                 opportunity to analyze in-depth human behavior in
                 social face-to-face interactions. With recent
                 advancements, it is henceforth possible to use these
                 techniques to augment social interactions, especially
                 human behavior in oral presentations. The goal of this
                 study is to train a computational model able to provide
                 a relevant feedback to a public speaker concerning
                 his/her coverbal communication. Hence, the role of this
                 model is to augment the social intelligence of the
                 orator and then the relevance of his/her presentation.
                 To this end, we present an original interaction setting
                 in which the speaker is equipped with only wearable
                 devices. Several coverbal modalities have been
                 extracted and automatically annotated namely speech
                 volume, intonation, speech rate, eye gaze, hand
                 gestures, and body movements. In this article, which is
                 an extension of our previous article published in
                 IUI'17, we compare our Dynamic Bayesian Network design
                 to classical J48/Multi-Layer Perceptron/Support Vector
                 Machine classifiers, propose a subjective evaluation of
                 presenter skills with a discussion in regards to our
                 automatic evaluation, and we add a complementary study
                 about using DBScan versus k -means algorithm in the
                 design process of our Dynamic Bayesian Network.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Zhou:2019:TVA,
  author =       "Michelle X. Zhou and Gloria Mark and Jingyi Li and
                 Huahai Yang",
  title =        "Trusting Virtual Agents: The Effect of Personality",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "10:1--10:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3232077",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3232077",
  abstract =     "We present artificial intelligent (AI) agents that act
                 as interviewers to engage with a user in a text-based
                 conversation and automatically infer the user's
                 personality traits. We investigate how the personality
                 of an AI interviewer and the inferred personality of a
                 user influences the user's trust in the AI interviewer
                 from two perspectives: the user's willingness to
                 confide in and listen to an AI interviewer. We have
                 developed two AI interviewers with distinct
                 personalities and deployed them in a series of
                 real-world events. We present findings from four such
                 deployments involving 1,280 users, including 606 actual
                 job applicants. Notably, users are more willing to
                 confide in and listen to an AI interviewer with a
                 serious, assertive personality in a high-stakes job
                 interview. Moreover, users' personality traits,
                 inferred from their chat text, along with interview
                 context, influence their perception of and their
                 willingness to confide in and listen to an AI
                 interviewer. Finally, we discuss the design
                 implications of our work on building
                 hyper-personalized, intelligent agents.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Santos:2019:PPT,
  author =       "Carlos Pereira Santos and Kevin Hutchinson and
                 Vassilis-Javed Khan and Panos Markopoulos",
  title =        "Profiling Personality Traits with Games",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "11:1--11:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3230738",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3230738",
  abstract =     "Trying to understand a player's characteristics with
                 regards to a computer game is a major line of research
                 known as player modeling. The purpose of player
                 modeling is typically the adaptation of the game
                 itself. We present two studies that extend player
                 modeling into player profiling by trying to identify
                 abstract personality traits, such as the need for
                 cognition and self-esteem, through a player's in-game
                 behavior. We present evidence that game mechanics that
                 can be broadly adopted by several game genres, such as
                 hints and a player's self-evaluation at the end of a
                 level, correlate with the aforementioned personality
                 traits. We conclude by presenting future directions for
                 research regarding this topic, discuss the direct
                 applications for the games industry, and explore how
                 games can be developed as profiling tools with
                 applications to other contexts.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Sen:2019:TUS,
  author =       "Shilad Sen and Anja Beth Swoap and Qisheng Li and Ilse
                 Dippenaar and Monica Ngo and Sarah Pujol and Rebecca
                 Gold and Brooke Boatman and Brent Hecht and Bret
                 Jackson",
  title =        "Toward Universal Spatialization Through
                 {Wikipedia}-Based Semantic Enhancement",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "12:1--12:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3213769",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3213769",
  abstract =     "This article introduces Cartograph, a visualization
                 system that harnesses the vast world knowledge encoded
                 within Wikipedia to create thematic maps of almost any
                 data. Cartograph extends previous systems that
                 visualize non-spatial data using geographic approaches.
                 Although these systems required data with an existing
                 semantic structure, Cartograph unlocks spatial
                 visualization for a much larger variety of datasets by
                 enhancing input datasets with semantic information
                 extracted from Wikipedia. Cartograph's map embeddings
                 use neural networks trained on Wikipedia article
                 content and user navigation behavior. Using these
                 embeddings, the system can reveal connections between
                 points that are unrelated in the original datasets but
                 are related in meaning and therefore embedded close
                 together on the map. We describe the design of the
                 system and key challenges we encountered. We present
                 findings from two user studies exploring design choices
                 and use of the system.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{DiSciascio:2019:IQA,
  author =       "Cecilia {Di Sciascio} and David Strohmaier and Marcelo
                 Errecalde and Eduardo Veas",
  title =        "Interactive Quality Analytics of User-generated
                 Content: an Integrated Toolkit for the Case of
                 {Wikipedia}",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "13:1--13:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3150973",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3150973",
  abstract =     "Digital libraries and services enable users to access
                 large amounts of data on demand. Yet, quality
                 assessment of information encountered on the Internet
                 remains an elusive open issue. For example, Wikipedia,
                 one of the most visited platforms on the Web, hosts
                 thousands of user-generated articles and undergoes 12
                 million edits/contributions per month. User-generated
                 content is undoubtedly one of the keys to its success
                 but also a hindrance to good quality. Although
                 Wikipedia has established guidelines for the ``perfect
                 article,'' authors find it difficult to assert whether
                 their contributions comply with them and reviewers
                 cannot cope with the ever-growing amount of articles
                 pending review. Great efforts have been invested in
                 algorithmic methods for automatic classification of
                 Wikipedia articles (as featured or non-featured) and
                 for quality flaw detection. Instead, our contribution
                 is an interactive tool that combines automatic
                 classification methods and human interaction in a
                 toolkit, whereby experts can experiment with new
                 quality metrics and share them with authors that need
                 to identify weaknesses to improve a particular article.
                 A design study shows that experts are able to
                 effectively create complex quality metrics in a visual
                 analytics environment. In turn, a user study evidences
                 that regular users can identify flaws, as well as
                 high-quality content based on the inspection of
                 automatic quality scores.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Paudyal:2019:CTS,
  author =       "Prajwal Paudyal and Junghyo Lee and Ayan Banerjee and
                 Sandeep K. S. Gupta",
  title =        "A Comparison of Techniques for Sign Language Alphabet
                 Recognition Using Armband Wearables",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "14:1--14:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3150974",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3150974",
  abstract =     "Recent research has shown that reliable recognition of
                 sign language words and phrases using user-friendly and
                 noninvasive armbands is feasible and desirable. This
                 work provides an analysis and implementation of
                 including fingerspelling recognition (FR) in such
                 systems, which is a much harder problem due to lack of
                 distinctive hand movements. A novel algorithm called
                 DyFAV (Dynamic Feature Selection and Voting) is
                 proposed for this purpose that exploits the fact that
                 fingerspelling has a finite corpus (26 alphabets for
                 the American Sign Language (ASL)). Detailed analysis of
                 the algorithm used as well as comparisons with other
                 traditional machine-learning algorithms is provided.
                 The system uses an independent multiple-agent voting
                 approach to identify letters with high accuracy. The
                 independent voting of the agents ensures that the
                 algorithm is highly parallelizable and thus recognition
                 times can be kept low to suit real-time mobile
                 applications. A thorough explanation and analysis is
                 presented on results obtained on the ASL alphabet
                 corpus for nine people with limited training. An
                 average recognition accuracy of 95.36\% is reported and
                 compared with recognition results from other
                 machine-learning techniques. This result is extended by
                 including six additional validation users with data
                 collected under similar settings as the previous
                 dataset. Furthermore, a feature selection schema using
                 a subset of the sensors is proposed and the results are
                 evaluated. The mobile, noninvasive, and real-time
                 nature of the technology is demonstrated by evaluating
                 performance on various types of Android phones and
                 remote server configurations. A brief discussion of the
                 user interface is provided along with guidelines for
                 best practices.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Katsuragawa:2019:BLT,
  author =       "Keiko Katsuragawa and Ankit Kamal and Qi Feng Liu and
                 Matei Negulescu and Edward Lank",
  title =        "Bi-Level Thresholding: Analyzing the Effect of
                 Repeated Errors in Gesture Input",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "15:1--15:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3181672",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3181672",
  abstract =     "In gesture recognition, one challenge that researchers
                 and developers face is the need for recognition
                 strategies that mediate between false positives and
                 false negatives. In this article, we examine bi-level
                 thresholding, a recognition strategy that uses two
                 thresholds: a tighter threshold limits false positives
                 and recognition errors, and a looser threshold prevents
                 repeated errors (false negatives) by analyzing
                 movements in sequence. We first describe early
                 observations that led to the development of the
                 bi-level thresholding algorithm. Next, using a
                 Wizard-of-Oz recognizer, we hold recognition rates
                 constant and adjust for fixed versus bi-level
                 thresholding; we show that systems using bi-level
                 thresholding result in significantly lower workload
                 scores on the NASA-TLX and significantly lower
                 accelerometer variance when performing gesture input.
                 Finally, we examine the effect that bi-level
                 thresholding has on a real-world dataset of wrist and
                 finger gestures, showing an ability to significantly
                 improve measures of precision and recall. Overall,
                 these results argue for the viability of bi-level
                 thresholding as an effective technique for balancing
                 between false positives, recognition errors, and false
                 negatives.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Intharah:2019:HDI,
  author =       "Thanapong Intharah and Daniyar Turmukhambetov and
                 Gabriel J. Brostow",
  title =        "{HILC}: Domain-Independent {PbD} System Via Computer
                 Vision and Follow-Up Questions",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "16:1--16:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3234508",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3234508",
  abstract =     "Creating automation scripts for tasks involving
                 Graphical User Interface (GUI) interactions is hard. It
                 is challenging because not all software applications
                 allow access to a program's internal state, nor do they
                 all have accessibility APIs. Although much of the
                 internal state is exposed to the user through the GUI,
                 it is hard to programmatically operate the GUI's
                 widgets. To that end, we developed a system prototype
                 that learns by demonstration, called HILC (Help, It
                 Looks Confusing). Users, both programmers and
                 non-programmers, train HILC to synthesize a task script
                 by demonstrating the task. A demonstration produces the
                 needed screenshots and their corresponding
                 mouse-keyboard signals. After the demonstration, the
                 user answers follow-up questions. We propose a
                 user-in-the-loop framework that learns to generate
                 scripts of actions performed on visible elements of
                 graphical applications. Although pure programming by
                 demonstration is still unrealistic due to a computer's
                 limited understanding of user intentions, we use
                 quantitative and qualitative experiments to show that
                 non-programming users are willing and effective at
                 answering follow-up queries posed by our system, to
                 help with confusing parts of the demonstrations. Our
                 models of events and appearances are surprisingly
                 simple but are combined effectively to cope with
                 varying amounts of supervision. The best available
                 baseline, Sikuli Slides, struggled to assist users in
                 the majority of the tests in our user study
                 experiments. The prototype with our proposed approach
                 successfully helped users accomplish simple linear
                 tasks, complicated tasks (monitoring, looping, and
                 mixed), and tasks that span across multiple
                 applications. Even when both systems could ultimately
                 perform a task, ours was trained and refined by the
                 user in less time.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Thomason:2019:CAV,
  author =       "John Thomason and Photchara Ratsamee and Jason Orlosky
                 and Kiyoshi Kiyokawa and Tomohiro Mashita and Yuki
                 Uranishi and Haruo Takemura",
  title =        "A Comparison of Adaptive View Techniques for
                 Exploratory {$3$D} Drone Teleoperation",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "17:1--17:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3232232",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3232232",
  abstract =     "Drone navigation in complex environments poses many
                 problems to teleoperators. Especially in three
                 dimensional (3D) structures such as buildings or
                 tunnels, viewpoints are often limited to the drone's
                 current camera view, nearby objects can be collision
                 hazards, and frequent occlusion can hinder accurate
                 manipulation. To address these issues, we have
                 developed a novel interface for teleoperation that
                 provides a user with environment-adaptive viewpoints
                 that are automatically configured to improve safety and
                 provide smooth operation. This real-time adaptive
                 viewpoint system takes robot position, orientation, and
                 3D point-cloud information into account to modify the
                 user's viewpoint to maximize visibility. Our prototype
                 uses simultaneous localization and mapping (SLAM) based
                 reconstruction with an omnidirectional camera, and we
                 use the resulting models as well as simulations in a
                 series of preliminary experiments testing navigation of
                 various structures. Results suggest that automatic
                 viewpoint generation can outperform first- and
                 third-person view interfaces for virtual teleoperators
                 in terms of ease of control and accuracy of robot
                 operation.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Oraby:2019:MCC,
  author =       "Shereen Oraby and Mansurul Bhuiyan and Pritam Gundecha
                 and Jalal Mahmud and Rama Akkiraju",
  title =        "Modeling and Computational Characterization of
                 {Twitter} Customer Service Conversations",
  journal =      j-TIIS,
  volume =       "9",
  number =       "2--3",
  pages =        "18:1--18:??",
  month =        apr,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3213014",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:19 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3213014",
  abstract =     "Given the increasing popularity of customer service
                 dialogue on Twitter, analysis of conversation data is
                 essential to understanding trends in customer and agent
                 behavior for the purpose of automating customer service
                 interactions. In this work, we develop a novel taxonomy
                 of fine-grained ``dialogue acts'' frequently observed
                 in customer service, showcasing acts that are more
                 suited to the domain than the more generic existing
                 taxonomies. Using a sequential SVM-HMM model, we model
                 conversation flow, predicting the dialogue act of a
                 given turn in real time, and showcase this using our
                 ``PredDial'' portal. We characterize differences
                 between customer and agent behavior in Twitter customer
                 service conversations and investigate the effect of
                 testing our system on different customer service
                 industries. Finally, we use a data-driven approach to
                 predict important conversation outcomes: customer
                 satisfaction, customer frustration, and overall problem
                 resolution. We show that the type and location of
                 certain dialogue acts in a conversation have a
                 significant effect on the probability of desirable and
                 undesirable outcomes and present actionable rules based
                 on our findings. We explore the correlations between
                 different dialogue acts and the outcome of the
                 conversations in detail using an actionable-rule
                 discovery task by leveraging a state-of-the-art
                 sequential rule mining algorithm while modeling a set
                 of conversations as a set of sequences. The patterns
                 and rules we derive can be used as guidelines for
                 outcome-driven automated customer service platforms.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Sharma:2019:LSI,
  author =       "Mohit Sharma and F. Maxwell Harper and George
                 Karypis",
  title =        "Learning from Sets of Items in Recommender Systems",
  journal =      j-TIIS,
  volume =       "9",
  number =       "4",
  pages =        "19:1--19:??",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3326128",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:20 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3326128",
  abstract =     "Most of the existing recommender systems use the
                 ratings provided by users on individual items. An
                 additional source of preference information is to use
                 the ratings that users provide on sets of items. The
                 advantages of using preferences on sets are twofold.
                 First, a rating provided on a set conveys some
                 preference information about each of the set's items,
                 which allows us to acquire a user's preferences for
                 more items than the number of ratings that the user
                 provided. Second, due to privacy concerns, users may
                 not be willing to reveal their preferences on
                 individual items explicitly but may be willing to
                 provide a single rating to a set of items, since it
                 provides some level of information hiding. This article
                 investigates two questions related to using set-level
                 ratings in recommender systems. First, how users'
                 item-level ratings relate to their set-level ratings.
                 Second, how collaborative filtering-based models for
                 item-level rating prediction can take advantage of such
                 set-level ratings. We have collected set-level ratings
                 from active users of Movielens on sets of movies that
                 they have rated in the past. Our analysis of these
                 ratings shows that though the majority of the users
                 provide the average of the ratings on a set's
                 constituent items as the rating on the set, there
                 exists a significant number of users that tend to
                 consistently either under- or over-rate the sets. We
                 have developed collaborative filtering-based methods to
                 explicitly model these user behaviors that can be used
                 to recommend items to users. Experiments on real data
                 and on synthetic data that resembles the under- or
                 over-rating behavior in the real data demonstrate that
                 these models can recover the overall characteristics of
                 the underlying data and predict the user's ratings on
                 individual items.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Chen:2019:UES,
  author =       "Li Chen and Dongning Yan and Feng Wang",
  title =        "User Evaluations on Sentiment-based Recommendation
                 Explanations",
  journal =      j-TIIS,
  volume =       "9",
  number =       "4",
  pages =        "20:1--20:??",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3282878",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:20 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3282878",
  abstract =     "The explanation interface has been recognized as
                 important in recommender systems because it can allow
                 users to better judge the relevance of recommendations
                 to their preferences and, hence, make more informed
                 decisions. In different product domains, the specific
                 purpose of explanation can be different. For
                 high-investment products (e.g., digital cameras,
                 laptops), how to educate the typical type of new buyers
                 about product knowledge and, consequently, improve
                 their preference certainty and decision quality is
                 essentially crucial. With this objective, we have
                 developed a novel tradeoff-oriented explanation
                 interface that particularly takes into account
                 sentiment features as extracted from product reviews to
                 generate recommendations and explanations in a category
                 structure. In this manuscript, we first reported the
                 results of an earlier user study (in both before-after
                 and counter-balancing setups) that compared our
                 prototype system with the traditional one that purely
                 considers static specifications for explanations. This
                 experiment revealed that adding sentiment-based
                 explanations can significantly increase users' product
                 knowledge, preference certainty, perceived information
                 usefulness, perceived recommendation transparency and
                 quality, and purchase intention. In order to further
                 identify the reason behind users' perception
                 improvements on the sentiment-based explanation
                 interface, we performed a follow-up lab controlled
                 eye-tracking experiment that investigated how users
                 viewed information and compared products on the
                 interface. This study shows that incorporating
                 sentiment features into the tradeoff-oriented
                 explanations can significantly affect users' eye-gaze
                 pattern. They were stimulated to not only notice bottom
                 categories of products, but also, more frequently, to
                 compare products across categories. The results also
                 disclose users' inherent information needs for
                 sentiment-based explanations, as they allow users to
                 better understand the recommended products and gain
                 more knowledge about static specifications.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Du:2019:EVA,
  author =       "Fan Du and Catherine Plaisant and Neil Spring and
                 Kenyon Crowley and Ben Shneiderman",
  title =        "{EventAction}: a Visual Analytics Approach to
                 Explainable Recommendation for Event Sequences",
  journal =      j-TIIS,
  volume =       "9",
  number =       "4",
  pages =        "21:1--21:??",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3301402",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:20 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3301402",
  abstract =     "People use recommender systems to improve their
                 decisions; for example, item recommender systems help
                 them find films to watch or books to buy. Despite the
                 ubiquity of item recommender systems, they can be
                 improved by giving users greater transparency and
                 control. This article develops and assesses interactive
                 strategies for transparency and control, as applied to
                 event sequence recommender systems, which provide
                 guidance in critical life choices such as medical
                 treatments, careers decisions, and educational course
                 selections. This article's main contribution is the use
                 of both record attributes and temporal event
                 information as features to identify similar records and
                 provide appropriate recommendations. While traditional
                 item recommendations are based on choices by people
                 with similar attributes, such as those who looked at
                 this product or watched this movie, our event sequence
                 recommendation approach allows users to select records
                 that share similar attribute values and start with a
                 similar event sequence. Then users see how different
                 choices of actions and the orders and times between
                 them might lead to users' desired outcomes. This paper
                 applies a visual analytics approach to present and
                 explain recommendations of event sequences. It presents
                 a workflow for event sequence recommendation that is
                 implemented in EventAction and reports on three case
                 studies in two domains to illustrate the use of
                 generating event sequence recommendations based on
                 personal histories. It also offers design guidelines
                 for the construction of user interfaces for event
                 sequence recommendation and discusses ethical issues in
                 dealing with personal histories. A demo video of
                 EventAction is available at
                 https://hcil.umd.edu/eventaction.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Lee:2019:UAM,
  author =       "Junghyo Lee and Prajwal Paudyal and Ayan Banerjee and
                 Sandeep K. S. Gupta",
  title =        "A User-adaptive Modeling for Eating Action
                 Identification from Wristband Time Series",
  journal =      j-TIIS,
  volume =       "9",
  number =       "4",
  pages =        "22:1--22:??",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3300149",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:20 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3300149",
  abstract =     "Eating activity monitoring using wearable sensors can
                 potentially enable interventions based on eating speed
                 to mitigate the risks of critical healthcare problems
                 such as obesity or diabetes. Eating actions are
                 poly-componential gestures composed of sequential
                 arrangements of three distinct components interspersed
                 with gestures that may be unrelated to eating. This
                 makes it extremely challenging to accurately identify
                 eating actions. The primary reasons for the lack of
                 acceptance of state-of-the-art eating action monitoring
                 techniques include the following: (i) the need to
                 install wearable sensors that are cumbersome to wear or
                 limit the mobility of the user, (ii) the need for
                 manual input from the user, and (iii) poor accuracy in
                 the absence of manual inputs. In this work, we propose
                 a novel methodology, IDEA, that performs accurate
                 eating action identification within eating episodes
                 with an average F1 score of 0.92. This is an
                 improvement of 0.11 for precision and 0.15 for recall
                 for the worst-case users as compared to the state of
                 the art. IDEA uses only a single wristband and provides
                 feedback on eating speed every 2 min without obtaining
                 any manual input from the user.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Hezarjaribi:2019:HLL,
  author =       "Niloofar Hezarjaribi and Sepideh Mazrouee and Saied
                 Hemati and Naomi S. Chaytor and Martine Perrigue and
                 Hassan Ghasemzadeh",
  title =        "Human-in-the-loop Learning for Personalized Diet
                 Monitoring from Unstructured Mobile Data",
  journal =      j-TIIS,
  volume =       "9",
  number =       "4",
  pages =        "23:1--23:??",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3319370",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:20 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3319370",
  abstract =     "Lifestyle interventions with the focus on diet are
                 crucial in self-management and prevention of many
                 chronic conditions, such as obesity, cardiovascular
                 disease, diabetes, and cancer. Such interventions
                 require a diet monitoring approach to estimate overall
                 dietary composition and energy intake. Although
                 wearable sensors have been used to estimate eating
                 context (e.g., food type and eating time), accurate
                 monitoring of dietary intake has remained a challenging
                 problem. In particular, because monitoring dietary
                 intake is a self-administered task that involves the
                 end-user to record or report their nutrition intake,
                 current diet monitoring technologies are prone to
                 measurement errors related to challenges of human
                 memory, estimation, and bias. New approaches based on
                 mobile devices have been proposed to facilitate the
                 process of dietary intake recording. These technologies
                 require individuals to use mobile devices such as
                 smartphones to record nutrition intake by either
                 entering text or taking images of the food. Such
                 approaches, however, suffer from errors due to low
                 adherence to technology adoption and time sensitivity
                 to the dietary intake context. In this article, we
                 introduce EZNutriPal, an interactive diet monitoring
                 system that operates on unstructured mobile data such
                 as speech and free-text to facilitate dietary
                 recording, real-time prompting, and personalized
                 nutrition monitoring. EZNutriPal features a natural
                 language processing unit that learns incrementally to
                 add user-specific nutrition data and rules to the
                 system. To prevent missing data that are required for
                 dietary monitoring (e.g., calorie intake estimation),
                 EZNutriPal devises an interactive operating mode that
                 prompts the end-user to complete missing data in
                 real-time. Additionally, we propose a combinatorial
                 optimization approach to identify the most appropriate
                 pairs of food names and food quantities in complex
                 input sentences. We evaluate the performance of
                 EZNutriPal using real data collected from 23 human
                 subjects who participated in two user studies conducted
                 in 13 days each. The results demonstrate that
                 EZNutriPal achieves an accuracy of 89.7\% in calorie
                 intake estimation. We also assess the impacts of the
                 incremental training and interactive prompting
                 technologies on the accuracy of nutrient intake
                 estimation and show that incremental training and
                 interactive prompting improve the performance of diet
                 monitoring by 49.6\% and 29.1\%, respectively, compared
                 to a system without such computing units.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Juvina:2019:TUT,
  author =       "Ion Juvina and Michael G. Collins and Othalia Larue
                 and William G. Kennedy and Ewart {De Visser} and Celso
                 {De Melo}",
  title =        "Toward a Unified Theory of Learned Trust in
                 Interpersonal and Human-Machine Interactions",
  journal =      j-TIIS,
  volume =       "9",
  number =       "4",
  pages =        "24:1--24:??",
  month =        dec,
  year =         "2019",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3230735",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Wed Dec 11 06:36:20 MST 2019",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/ft_gateway.cfm?id=3230735",
  abstract =     "A proposal for a unified theory of learned trust
                 implemented in a cognitive architecture is presented.
                 The theory is instantiated as a computational cognitive
                 model of learned trust that integrates several
                 seemingly unrelated categories of findings from the
                 literature on interpersonal and human-machine
                 interactions and makes unintuitive predictions for
                 future studies. The model relies on a combination of
                 learning mechanisms to explain a variety of phenomena
                 such as trust asymmetry, the higher impact of early
                 trust breaches, the black-hat/white-hat effect, the
                 correlation between trust and cognitive ability, and
                 the higher resilience of interpersonal as compared to
                 human-machine trust. In addition, the model predicts
                 that trust decays in the absence of evidence of
                 trustworthiness or untrustworthiness. The implications
                 of the model for the advancement of the theory on trust
                 are discussed. Specifically, this work suggests two
                 more trust antecedents on the trustor's side: perceived
                 trust necessity and cognitive ability to detect cues of
                 trustworthiness.",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Billinghurst:2020:SIH,
  author =       "Mark Billinghurst and Margaret Burnett and Aaron
                 Quigley",
  title =        "Special Issue on Highlights of {ACM Intelligent User
                 Interface (IUI) 2018}",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "1:1--1:3",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3357206",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:20 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3357206",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Vanderdonckt:2020:EDS,
  author =       "Jean Vanderdonckt and Sara Bouzit and Ga{\"e}lle
                 Calvary and Denis Ch{\^e}ne",
  title =        "Exploring a Design Space of Graphical Adaptive Menus:
                 Normal vs. Small Screens",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "2:1--2:40",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3237190",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:20 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3237190",
  abstract =     "Graphical Adaptive Menus are Graphical User Interface
                 menus whose predicted items of immediate use can be
                 automatically rendered in a prediction window.
                 Rendering this prediction window is a key question for
                 adaptivity to enable the end-user to \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Song:2020:FLT,
  author =       "Jean Y. Song and Raymond Fok and Juho Kim and Walter
                 S. Lasecki",
  title =        "{FourEyes}: Leveraging Tool Diversity as a Means to
                 Improve Aggregate Accuracy in Crowdsourcing",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "3:1--3:30",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3237188",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:20 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3237188",
  abstract =     "Crowdsourcing is a common means of collecting image
                 segmentation training data for use in a variety of
                 computer vision applications. However, designing
                 accurate crowd-powered image segmentation systems is
                 challenging, because defining object boundaries
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Broden:2020:BBE,
  author =       "Bj{\"o}rn Brod{\'e}n and Mikael Hammar and Bengt J.
                 Nilsson and Dimitris Paraschakis",
  title =        "A Bandit-Based Ensemble Framework for
                 Exploration\slash Exploitation of Diverse
                 Recommendation Components: an Experimental Study within
                 E-Commerce",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "4:1--4:32",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3237187",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Jan 11 08:20:51 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3237187",
  abstract =     "This work presents an extension of Thompson Sampling
                 bandit policy for orchestrating the collection of base
                 recommendation algorithms for e-commerce. We focus on
                 the problem of item-to-item recommendations, for which
                 multiple behavioral and attribute-\ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Tsai:2020:ESR,
  author =       "Chun-Hua Tsai and Peter Brusilovsky",
  title =        "Exploring Social Recommendations with Visual
                 Diversity-Promoting Interfaces",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "5:1--5:34",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3231465",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:20 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3231465",
  abstract =     "The beyond-relevance objectives of recommender systems
                 have been drawing more and more attention. For example,
                 a diversity-enhanced interface has been shown to
                 associate positively with overall levels of user
                 satisfaction. However, little is known about \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Sherkat:2020:VAA,
  author =       "Ehsan Sherkat and Evangelos E. Milios and Rosane
                 Minghim",
  title =        "A Visual Analytics Approach for Interactive Document
                 Clustering",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "6:1--6:33",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3241380",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:20 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3241380",
  abstract =     "Document clustering is a necessary step in various
                 analytical and automated activities. When guided by the
                 user, algorithms are tailored to imprint a perspective
                 on the clustering process that reflects the user's
                 understanding of the dataset. More than \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Suh:2020:AFS,
  author =       "Jina Suh and Soroush Ghorashi and Gonzalo Ramos and
                 Nan-Chen Chen and Steven Drucker and Johan Verwey and
                 Patrice Simard",
  title =        "{AnchorViz}: Facilitating Semantic Data Exploration
                 and Concept Discovery for Interactive Machine
                 Learning",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "7:1--7:38",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3241379",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:20 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3241379",
  abstract =     "When building a classifier in interactive machine
                 learning (iML), human knowledge about the target class
                 can be a powerful reference to make the classifier
                 robust to unseen items. The main challenge lies in
                 finding unlabeled items that can either help \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Sciascio:2020:RUC,
  author =       "Cecilia {Di Sciascio} and Peter Brusilovsky and
                 Christoph Trattner and Eduardo Veas",
  title =        "A Roadmap to User-Controllable Social Exploratory
                 Search",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "8:1--8:38",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3241382",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Jan 11 08:20:51 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3241382",
  abstract =     "Information-seeking tasks with learning or
                 investigative purposes are usually referred to as
                 exploratory search. Exploratory search unfolds as a
                 dynamic process where the user, amidst navigation,
                 trial and error, and on-the-fly selections, gathers and
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Todi:2020:IGL,
  author =       "Kashyap Todi and Jussi Jokinen and Kris Luyten and
                 Antti Oulasvirta",
  title =        "Individualising Graphical Layouts with Predictive
                 Visual Search Models",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "9:1--9:24",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3241381",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:20 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3241381",
  abstract =     "In domains where users are exposed to large variations
                 in visuo-spatial features among designs, they often
                 spend excess time searching for common elements
                 (features) on an interface. This article contributes
                 individualised predictive models of visual \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{He:2020:DDA,
  author =       "Yangyang He and Paritosh Bahirat and Bart P.
                 Knijnenburg and Abhilash Menon",
  title =        "A Data-Driven Approach to Designing for Privacy in
                 Household {IoT}",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "10:1--10:47",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3241378",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Jan 11 08:20:51 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3241378",
  abstract =     "In this article, we extend and improve upon a
                 previously developed data-driven approach to design
                 privacy-setting interfaces for users of household IoT
                 devices. The essence of this approach is to gather
                 users' feedback on household IoT scenarios
                 before\ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Avrahami:2020:UAR,
  author =       "Daniel Avrahami and Mitesh Patel and Yusuke Yamaura
                 and Sven Kratz and Matthew Cooper",
  title =        "Unobtrusive Activity Recognition and Position
                 Estimation for Work Surfaces Using {RF}-Radar Sensing",
  journal =      j-TIIS,
  volume =       "10",
  number =       "1",
  pages =        "11:1--11:28",
  month =        jan,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3241383",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  bibdate =      "Sat Jan 11 08:20:51 MST 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3241383",
  abstract =     "Activity recognition is a core component of many
                 intelligent and context-aware systems. We present a
                 solution for discreetly and unobtrusively recognizing
                 common work activities above a work surface without
                 using cameras. We demonstrate our approach, \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J1341",
}

@Article{Conati:2020:CCI,
  author =       "Cristina Conati and S{\'e}bastien Lall{\'e} and Md
                 Abed Rahman and Dereck Toker",
  title =        "Comparing and Combining Interaction Data and
                 Eye-tracking Data for the Real-time Prediction of User
                 Cognitive Abilities in Visualization Tasks",
  journal =      j-TIIS,
  volume =       "10",
  number =       "2",
  pages =        "12:1--12:41",
  month =        jun,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3301400",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sat Jun 27 14:42:35 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3301400",
  abstract =     "Previous work has shown that some user cognitive
                 abilities relevant for processing information
                 visualizations can be predicted from eye-tracking data.
                 Performing this type of user modeling is important for
                 devising visualizations that can detect a user'.
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Ahn:2020:PIP,
  author =       "Yongsu Ahn and Yu-Ru Lin",
  title =        "{PolicyFlow}: Interpreting Policy Diffusion in
                 Context",
  journal =      j-TIIS,
  volume =       "10",
  number =       "2",
  pages =        "13:1--13:23",
  month =        jun,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3385729",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sat Jun 27 14:42:35 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3385729",
  abstract =     "Stability in social, technical, and financial systems,
                 as well as the capacity of organizations to work across
                 borders, requires consistency in public policy across
                 jurisdictions. The diffusion of laws and regulations
                 across political boundaries can \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mohan:2020:DAH,
  author =       "Shiwali Mohan and Anusha Venkatakrishnan and Andrea L.
                 Hartzler",
  title =        "Designing an {AI} Health Coach and Studying Its
                 Utility in Promoting Regular Aerobic Exercise",
  journal =      j-TIIS,
  volume =       "10",
  number =       "2",
  pages =        "14:1--14:30",
  month =        jun,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3366501",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sat Jun 27 14:42:35 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3366501",
  abstract =     "Our research aims to develop interactive, social
                 agents that can coach people to learn new tasks,
                 skills, and habits. In this article, we focus on
                 coaching sedentary, overweight individuals (i.e.,
                 ``trainees'') to exercise regularly. We employ adaptive
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Anderson:2020:MMM,
  author =       "Andrew Anderson and Jonathan Dodge and Amrita
                 Sadarangani and Zoe Juozapaitis and Evan Newman and Jed
                 Irvine and Souti Chattopadhyay and Matthew Olson and
                 Alan Fern and Margaret Burnett",
  title =        "Mental Models of Mere Mortals with Explanations of
                 Reinforcement Learning",
  journal =      j-TIIS,
  volume =       "10",
  number =       "2",
  pages =        "15:1--15:37",
  month =        jun,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3366485",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sat Jun 27 14:42:35 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3366485",
  abstract =     "How should reinforcement learning (RL) agents explain
                 themselves to humans not trained in AI? To gain
                 insights into this question, we conducted a
                 124-participant, four-treatment experiment to compare
                 participants' mental models of an RL agent in the
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Fan:2020:ADU,
  author =       "Mingming Fan and Yue Li and Khai N. Truong",
  title =        "Automatic Detection of Usability Problem Encounters in
                 Think-aloud Sessions",
  journal =      j-TIIS,
  volume =       "10",
  number =       "2",
  pages =        "16:1--16:24",
  month =        jun,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3385732",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sat Jun 27 14:42:35 MDT 2020",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/abs/10.1145/3385732",
  abstract =     "Think-aloud protocols are a highly valued usability
                 testing method for identifying usability problems.
                 Despite the value of conducting think-aloud usability
                 test sessions, analyzing think-aloud sessions is often
                 time-consuming and labor-intensive. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Pan:2020:SID,
  author =       "Shimei Pan and Oliver Brdiczka and Andrea Kleinsmith
                 and Yangqiu Song",
  title =        "Special Issue on Data-Driven Personality Modeling for
                 Intelligent Human-Computer Interaction",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "17:1--17:3",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3402522",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3402522",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Taib:2020:PSD,
  author =       "Ronnie Taib and Shlomo Berkovsky and Irena Koprinska
                 and Eileen Wang and Yucheng Zeng and Jingjie Li",
  title =        "Personality Sensing: Detection of Personality Traits
                 Using Physiological Responses to Image and Video
                 Stimuli",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "18:1--18:32",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3357459",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3357459",
  abstract =     "Personality detection is an important task in
                 psychology, as different personality traits are linked
                 to different behaviours and real-life outcomes.
                 Traditionally it involves filling out lengthy
                 questionnaires, which is time-consuming, and may also
                 be \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Dotti:2020:BCA,
  author =       "Dario Dotti and Mirela Popa and Stylianos Asteriadis",
  title =        "Being the Center of Attention: a Person-Context {CNN}
                 Framework for Personality Recognition",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "19:1--19:20",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3338245",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3338245",
  abstract =     "This article proposes a novel study on personality
                 recognition using video data from different scenarios.
                 Our goal is to jointly model nonverbal behavioral cues
                 with contextual information for a robust,
                 multi-scenario, personality recognition system.
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Bagheri:2020:ACE,
  author =       "Elahe Bagheri and Pablo G. Esteban and Hoang-Long Cao
                 and Albert {De Beir} and Dirk Lefeber and Bram
                 Vanderborght",
  title =        "An Autonomous Cognitive Empathy Model Responsive to
                 Users' Facial Emotion Expressions",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "20:1--20:23",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3341198",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3341198",
  abstract =     "Successful social robot services depend on how robots
                 can interact with users. The effective service can be
                 obtained through smooth, engaged, and humanoid
                 interactions in which robots react properly to a user's
                 affective state. This article proposes a \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Wang:2020:MDS,
  author =       "Ruijie Wang and Liming Chen and Ivar Solheim",
  title =        "Modeling Dyslexic Students' Motivation for Enhanced
                 Learning in E-learning Systems",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "21:1--21:34",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3341197",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3341197",
  abstract =     "E-Learning systems can support real-time monitoring of
                 learners' learning desires and effects, thus offering
                 opportunities for enhanced personalized learning.
                 Recognition of the determinants of dyslexic users'
                 motivation to use e-learning systems is \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Khan:2020:PUM,
  author =       "Euna Mehnaz Khan and Md. Saddam Hossain Mukta and
                 Mohammed Eunus Ali and Jalal Mahmud",
  title =        "Predicting Users' Movie Preference and Rating Behavior
                 from Personality and Values",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "22:1--22:25",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3338244",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3338244",
  abstract =     "In this article, we propose novel techniques to
                 predict a user's movie genre preference and rating
                 behavior from her psycholinguistic attributes obtained
                 from the social media interactions. The motivation of
                 this work comes from various psychological \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Higuchi:2020:LCD,
  author =       "Keita Higuchi and Hiroki Tsuchida and Eshed Ohn-Bar
                 and Yoichi Sato and Kris Kitani",
  title =        "Learning Context-dependent Personal Preferences for
                 Adaptive Recommendation",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "23:1--23:26",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3359755",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3359755",
  abstract =     "We propose two online-learning algorithms for modeling
                 the personal preferences of users of interactive
                 systems. The proposed algorithms leverage user feedback
                 to estimate user behavior and provide personalized
                 adaptive recommendation for supporting \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Hailpern:2020:HIP,
  author =       "Joshua Hailpern and Mark Huber and Ronald Calvo",
  title =        "How Impactful Is Presentation in Email? {The} Effect
                 of Avatars and Signatures",
  journal =      j-TIIS,
  volume =       "10",
  number =       "3",
  pages =        "24:1--24:26",
  month =        nov,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3345641",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:21 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3345641",
  abstract =     "A primary well-controlled study of 900 participants
                 found that personal presentation choices in
                 professional emails (non-content changes like Profile
                 Avatar 8 Signature) impact the recipient's perception
                 of the sender's personality and the quality of
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zhou:2020:ITS,
  author =       "Michele X. Zhou",
  title =        "Introduction to the {TiiS} Special Column",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "25:1--25:1",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3427592",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3427592",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "25",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Shneiderman:2020:BGB,
  author =       "Ben Shneiderman",
  title =        "Bridging the Gap Between Ethics and Practice:
                 Guidelines for Reliable, Safe, and Trustworthy
                 Human-centered {AI} Systems",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "26:1--26:31",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3419764",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3419764",
  abstract =     "This article attempts to bridge the gap between widely
                 discussed ethical principles of Human-centered AI
                 (HCAI) and practical steps for effective governance.
                 Since HCAI systems are developed and implemented in
                 multiple organizational structures, I \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "26",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Brdiczka:2020:ISI,
  author =       "Oliver Brdiczka and Duen Horng Chau and Minsuk Kahng
                 and Ga{\"e}lle Calvary",
  title =        "Introduction to the Special Issue on Highlights of
                 {ACM Intelligent User Interface (IUI) 2019}",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "27:1--27:2",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3429946",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3429946",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "27",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Eiband:2020:MAE,
  author =       "Malin Eiband and Sarah Theres V{\"o}lkel and Daniel
                 Buschek and Sophia Cook and Heinrich Hussmann",
  title =        "A Method and Analysis to Elicit User-Reported Problems
                 in Intelligent Everyday Applications",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "28:1--28:27",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3370927",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3370927",
  abstract =     "The complex nature of intelligent systems motivates
                 work on supporting users during interaction, for
                 example, through explanations. However, as of yet,
                 there is little empirical evidence in regard to
                 specific problems users face when applying such
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "28",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Springer:2020:PDW,
  author =       "Aaron Springer and Steve Whittaker",
  title =        "Progressive Disclosure: When, Why, and How Do Users
                 Want Algorithmic Transparency Information?",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "29:1--29:32",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3374218",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3374218",
  abstract =     "It is essential that users understand how algorithmic
                 decisions are made, as we increasingly delegate
                 important decisions to intelligent systems. Prior work
                 has often taken a techno-centric approach, focusing on
                 new computational techniques to support \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "29",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Dominguez:2020:AHA,
  author =       "Vicente Dominguez and Ivania Donoso-Guzm{\'a}n and
                 Pablo Messina and Denis Parra",
  title =        "Algorithmic and {HCI} Aspects for Explaining
                 Recommendations of Artistic Images",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "30:1--30:31",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3369396",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3369396",
  abstract =     "Explaining suggestions made by recommendation systems
                 is key to make users trust and accept these systems.
                 This is specially critical in areas such as art image
                 recommendation. Traditionally, artworks are sold in
                 galleries where people can see them \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "30",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Kouki:2020:GUP,
  author =       "Pigi Kouki and James Schaffer and Jay Pujara and John
                 O'Donovan and Lise Getoor",
  title =        "Generating and Understanding Personalized Explanations
                 in Hybrid Recommender Systems",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "31:1--31:40",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3365843",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3365843",
  abstract =     "Recommender systems are ubiquitous and shape the way
                 users access information and make decisions. As these
                 systems become more complex, there is a growing need
                 for transparency and interpretability. In this article,
                 we study the problem of generating \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "31",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Hsu:2020:SPE,
  author =       "Yen-Chia Hsu and Jennifer Cross and Paul Dille and
                 Michael Tasota and Beatrice Dias and Randy Sargent and
                 Ting-Hao (Kenneth) Huang and Illah Nourbakhsh",
  title =        "{Smell Pittsburgh}: Engaging Community Citizen Science
                 for Air Quality",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "32:1--32:49",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3369397",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3369397",
  abstract =     "Urban air pollution has been linked to various human
                 health concerns, including cardiopulmonary diseases.
                 Communities who suffer from poor air quality often rely
                 on experts to identify pollution sources due to the
                 lack of accessible tools. Taking this \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "32",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mohanty:2020:PSI,
  author =       "Vikram Mohanty and David Thames and Sneha Mehta and
                 Kurt Luther",
  title =        "{Photo Sleuth}: Identifying Historical Portraits with
                 Face Recognition and Crowdsourced Human Expertise",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "33:1--33:36",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3365842",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3365842",
  abstract =     "Identifying people in historical photographs is
                 important for preserving material culture, correcting
                 the historical record, and creating economic value, but
                 it is also a complex and challenging task. In this
                 article, we focus on identifying portraits \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "33",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Kulahcioglu:2020:AAW,
  author =       "Tugba Kulahcioglu and Gerard {De Melo}",
  title =        "Affect-Aware Word Clouds",
  journal =      j-TIIS,
  volume =       "10",
  number =       "4",
  pages =        "34:1--34:25",
  month =        dec,
  year =         "2020",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3370928",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Sun Mar 28 07:49:22 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3370928",
  abstract =     "Word clouds are widely used for non-analytic purposes,
                 such as introducing a topic to students, or creating a
                 gift with personally meaningful text. Surveys show that
                 users prefer tools that yield word clouds with a
                 stronger emotional impact. Fonts and \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "34",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mohan:2021:ERC,
  author =       "Shiwali Mohan",
  title =        "Exploring the Role of Common Model of Cognition in
                 Designing Adaptive Coaching Interactions for Health
                 Behavior Change",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "1:1--1:30",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3375790",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3375790",
  abstract =     "Our research aims to develop intelligent collaborative
                 agents that are human-aware: They can model, learn, and
                 reason about their human partner's physiological,
                 cognitive, and affective states. In this article, we
                 study how adaptive coaching interactions \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Penney:2021:SGE,
  author =       "Sean Penney and Jonathan Dodge and Andrew Anderson and
                 Claudia Hilderbrand and Logan Simpson and Margaret
                 Burnett",
  title =        "The Shoutcasters, the Game Enthusiasts, and the {AI}:
                 Foraging for Explanations of Real-time Strategy
                 Players",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "2:1--2:46",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3396047",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3396047",
  abstract =     "Assessing and understanding intelligent agents is a
                 difficult task for users who lack an AI background.
                 ``Explainable AI'' (XAI) aims to address this problem,
                 but what should be in an explanation? One route toward
                 answering this question is to turn to \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Bessghaier:2021:DSA,
  author =       "Narjes Bessghaier and Makram Soui and Christophe
                 Kolski and Mabrouka Chouchane",
  title =        "On the Detection of Structural Aesthetic Defects of
                 {Android} Mobile User Interfaces with a Metrics-based
                 Tool",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "3:1--3:27",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3410468",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3410468",
  abstract =     "Smartphone users are striving for easy-to-learn and
                 use mobile apps user interfaces. Accomplishing these
                 qualities demands an iterative evaluation of the Mobile
                 User Interface (MUI). Several studies stress the value
                 of providing a MUI with a pleasing look \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Oviatt:2021:KWY,
  author =       "Sharon Oviatt and Jionghao Lin and Abishek Sriramulu",
  title =        "{I} Know What You Know: What Hand Movements Reveal
                 about Domain Expertise",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "4:1--4:26",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3423049",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3423049",
  abstract =     "This research investigates whether students' level of
                 domain expertise can be detected during authentic
                 learning activities by analyzing their physical
                 activity patterns. More expert students reduced their
                 manual activity by a substantial 50\%, which was
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Douer:2021:TMS,
  author =       "Nir Douer and Joachim Meyer",
  title =        "Theoretical, Measured, and Subjective Responsibility
                 in Aided Decision Making",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "5:1--5:37",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3425732",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3425732",
  abstract =     "When humans interact with intelligent systems, their
                 causal responsibility for outcomes becomes equivocal.
                 We analyze the descriptive abilities of a newly
                 developed responsibility quantification model (ResQu)
                 to predict actual human responsibility and \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Bhattacharya:2021:RTI,
  author =       "Samit Bhattacharya and Viral Bharat Shah and Krishna
                 Kumar and Ujjwal Biswas",
  title =        "A Real-time Interactive Visualizer for Large
                 Classroom",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "6:1--6:26",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3418529",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3418529",
  abstract =     "In improving the teaching and learning experience in a
                 classroom environment, it is crucial for a teacher to
                 have a fair idea about the students who need help
                 during a lecture. However, teachers of large classes
                 usually face difficulties in identifying \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Chen:2021:PPR,
  author =       "Xiaoyu Chen and Nathan Lau and Ran Jin",
  title =        "{PRIME}: a Personalized Recommender System for
                 Information Visualization Methods via Extended Matrix
                 Completion",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "7:1--7:30",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3366484",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3366484",
  abstract =     "Adapting user interface designs for specific tasks
                 performed by different users is a challenging yet
                 important problem. Automatically adapting visualization
                 designs to users and contexts (e.g., tasks, display
                 devices, environments, etc.) can theoretically
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Ma:2021:HTR,
  author =       "Wanqi Ma and Xiaoxiao Liao and Wei Dai and Weike Pan
                 and Zhong Ming",
  title =        "Holistic Transfer to Rank for Top-{$N$}
                 Recommendation",
  journal =      j-TIIS,
  volume =       "11",
  number =       "1",
  pages =        "8:1--8:1",
  month =        apr,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3434360",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Apr 27 08:00:40 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3434360",
  abstract =     "Recommender systems have been a valuable component in
                 various online services such as e-commerce and
                 entertainment. To provide an accurate top-N
                 recommendation list of items for each target user, we
                 have to answer a very basic question of how to model
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Tran:2021:HRS,
  author =       "Thi Ngoc Trang Tran and Alexander Felfernig and Nava
                 Tintarev",
  title =        "Humanized Recommender Systems: State-of-the-art and
                 Research Issues",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "9:1--9:41",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3446906",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3446906",
  abstract =     "Psychological factors such as personality, emotions,
                 social connections, and decision biases can
                 significantly affect the outcome of a decision process.
                 These factors are also prevalent in the existing
                 literature related to the inclusion of psychological
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Shatilov:2021:EEB,
  author =       "Kirill A. Shatilov and Dimitris Chatzopoulos and
                 Lik-Hang Lee and Pan Hui",
  title =        "Emerging {ExG}-based {NUI} Inputs in Extended
                 Realities: a Bottom-up Survey",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "10:1--10:49",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3457950",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3457950",
  abstract =     "Incremental and quantitative improvements of two-way
                 interactions with e x tended realities (XR) are
                 contributing toward a qualitative leap into a state of
                 XR ecosystems being efficient, user-friendly, and
                 widely adopted. However, there are multiple \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Gil:2021:AIM,
  author =       "Yolanda Gil and Daniel Garijo and Deborah Khider and
                 Craig A. Knoblock and Varun Ratnakar and Maximiliano
                 Osorio and Hern{\'a}n Vargas and Minh Pham and Jay
                 Pujara and Basel Shbita and Binh Vu and Yao-Yi Chiang
                 and Dan Feldman and Yijun Lin and Hayley Song and Vipin
                 Kumar and Ankush Khandelwal and Michael Steinbach and
                 Kshitij Tayal and Shaoming Xu and Suzanne A. Pierce and
                 Lissa Pearson and Daniel Hardesty-Lewis and Ewa Deelman
                 and Rafael Ferreira {Da Silva} and Rajiv Mayani and
                 Armen R. Kemanian and Yuning Shi and Lorne Leonard and
                 Scott Peckham and Maria Stoica and Kelly Cobourn and
                 Zeya Zhang and Christopher Duffy and Lele Shu",
  title =        "Artificial Intelligence for Modeling Complex Systems:
                 Taming the Complexity of Expert Models to Improve
                 Decision Making",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "11:1--11:49",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3453172",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3453172",
  abstract =     "Major societal and environmental challenges involve
                 complex systems that have diverse multi-scale
                 interacting processes. Consider, for example, how
                 droughts and water reserves affect crop production and
                 how agriculture and industrial needs affect water
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Rosenberg:2021:ECA,
  author =       "Maor Rosenberg and Hae Won Park and Rinat
                 Rosenberg-Kima and Safinah Ali and Anastasia K.
                 Ostrowski and Cynthia Breazeal and Goren Gordon",
  title =        "Expressive Cognitive Architecture for a Curious Social
                 Robot",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "12:1--12:25",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3451531",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3451531",
  abstract =     "Artificial curiosity, based on developmental
                 psychology concepts wherein an agent attempts to
                 maximize its learning progress, has gained much
                 attention in recent years. Similarly, social robots are
                 slowly integrating into our daily lives, in schools,
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Roffarello:2021:UDM,
  author =       "Alberto Monge Roffarello and Luigi {De Russis}",
  title =        "Understanding, Discovering, and Mitigating Habitual
                 Smartphone Use in Young Adults",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "13:1--13:34",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3447991",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3447991",
  abstract =     "People, especially young adults, often use their
                 smartphones out of habit: They compulsively browse
                 social networks, check emails, and play video-games
                 with little or no awareness at all. While previous
                 studies analyzed this phenomena qualitatively, e.g.,
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Spiller:2021:PVS,
  author =       "Moritz Spiller and Ying-Hsang Liu and Md Zakir Hossain
                 and Tom Gedeon and Julia Geissler and Andreas
                 N{\"u}rnberger",
  title =        "Predicting Visual Search Task Success from Eye Gaze
                 Data as a Basis for User-Adaptive Information
                 Visualization Systems",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "14:1--14:25",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3446638",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3446638",
  abstract =     "Information visualizations are an efficient means to
                 support the users in understanding large amounts of
                 complex, interconnected data; user comprehension,
                 however, depends on individual factors such as their
                 cognitive abilities. The research literature \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Banisetty:2021:SAN,
  author =       "Santosh Balajee Banisetty and Scott Forer and Logan
                 Yliniemi and Monica Nicolescu and David Feil-Seifer",
  title =        "Socially Aware Navigation: a Non-linear
                 Multi-objective Optimization Approach",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "15:1--15:26",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3453445",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3453445",
  abstract =     "Mobile robots are increasingly populating homes,
                 hospitals, shopping malls, factory floors, and other
                 human environments. Human society has social norms that
                 people mutually accept; obeying these norms is an
                 essential signal that someone is participating
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mousas:2021:PSV,
  author =       "Christos Mousas and Claudia Krogmeier and Zhiquan
                 Wang",
  title =        "Photo Sequences of Varying Emotion: Optimization with
                 a Valence-Arousal Annotated Dataset",
  journal =      j-TIIS,
  volume =       "11",
  number =       "2",
  pages =        "16:1--16:19",
  month =        jul,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3458844",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Jul 22 08:06:11 MDT 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3458844",
  abstract =     "Synthesizing photo products such as photo strips and
                 slideshows using a database of images is a
                 time-consuming and tedious process that requires
                 significant manual work. To overcome this limitation,
                 we developed a method that automatically synthesizes
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Turkay:2021:SII,
  author =       "Cagatay Turkay and Tatiana {Von Landesberger} and
                 Daniel Archambault and Shixia Liu and Remco Chang",
  title =        "Special Issue on Interactive Visual Analytics for
                 Making Explainable and Accountable Decisions",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "17:1--17:4",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3471903",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3471903",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zhang:2021:MMI,
  author =       "Yu Zhang and Bob Coecke and Min Chen",
  title =        "{MI3}: Machine-initiated Intelligent Interaction for
                 Interactive Classification and Data Reconstruction",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "18:1--18:34",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3412848",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3412848",
  abstract =     "In many applications, while machine learning (ML) can
                 be used to derive algorithmic models to aid decision
                 processes, it is often difficult to learn a precise
                 model when the number of similar data points is
                 limited. One example of such applications is \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Sevastjanova:2021:QGA,
  author =       "Rita Sevastjanova and Wolfgang Jentner and Fabian
                 Sperrle and Rebecca Kehlbeck and J{\"u}rgen Bernard and
                 Mennatallah El-assady",
  title =        "{QuestionComb}: a Gamification Approach for the Visual
                 Explanation of Linguistic Phenomena through Interactive
                 Labeling",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "19:1--19:38",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3429448",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3429448",
  abstract =     "Linguistic insight in the form of high-level
                 relationships and rules in text builds the basis of our
                 understanding of language. However, the data-driven
                 generation of such structures often lacks labeled
                 resources that can be used as training data for
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Bernard:2021:TPM,
  author =       "J{\"u}rgen Bernard and Marco Hutter and Michael
                 Sedlmair and Matthias Zeppelzauer and Tamara Munzner",
  title =        "A Taxonomy of Property Measures to Unify Active
                 Learning and Human-centered Approaches to Data
                 Labeling",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "20:1--20:42",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3439333",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3439333",
  abstract =     "Strategies for selecting the next data instance to
                 label, in service of generating labeled data for
                 machine learning, have been considered separately in
                 the machine learning literature on active learning and
                 in the visual analytics literature on human-.
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Segura:2021:BBO,
  author =       "Vin{\'i}cius Segura and Simone D. J. Barbosa",
  title =        "{BONNIE}: Building Online Narratives from Noteworthy
                 Interaction Events",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "21:1--21:31",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3423048",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3423048",
  abstract =     "Nowadays, we have access to data of unprecedented
                 volume, high dimensionality, and complexity. To extract
                 novel insights from such complex and dynamic data, we
                 need effective and efficient strategies. One such
                 strategy is to combine data analysis and \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Hinterreiter:2021:PPE,
  author =       "Andreas Hinterreiter and Christian Steinparz and
                 Moritz Sch{\"O}fl and Holger Stitz and Marc Streit",
  title =        "Projection Path Explorer: Exploring Visual Patterns in
                 Projected Decision-making Paths",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "22:1--22:29",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3387165",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3387165",
  abstract =     "In problem-solving, a path towards a solutions can be
                 viewed as a sequence of decisions. The decisions, made
                 by humans or computers, describe a trajectory through a
                 high-dimensional representation space of the problem.
                 By means of dimensionality reduction,. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Kim:2021:LGR,
  author =       "Chris Kim and Xiao Lin and Christopher Collins and
                 Graham W. Taylor and Mohamed R. Amer",
  title =        "Learn, Generate, Rank, Explain: a Case Study of Visual
                 Explanation by Generative Machine Learning",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "23:1--23:34",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3465407",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3465407",
  abstract =     "While the computer vision problem of searching for
                 activities in videos is usually addressed by using
                 discriminative models, their decisions tend to be
                 opaque and difficult for people to understand. We
                 propose a case study of a novel machine learning
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mohseni:2021:MSF,
  author =       "Sina Mohseni and Niloofar Zarei and Eric D. Ragan",
  title =        "A Multidisciplinary Survey and Framework for Design
                 and Evaluation of Explainable {AI} Systems",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "24:1--24:45",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3387166",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3387166",
  abstract =     "The need for interpretable and accountable intelligent
                 systems grows along with the prevalence of artificial
                 intelligence (AI) applications used in everyday life.
                 Explainable AI (XAI) systems are intended to
                 self-explain the reasoning behind system \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Hepenstal:2021:DCA,
  author =       "Sam Hepenstal and Leishi Zhang and Neesha Kodagoda and
                 B. L. William Wong",
  title =        "Developing Conversational Agents for Use in Criminal
                 Investigations",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "25:1--25:35",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3444369",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3444369",
  abstract =     "The adoption of artificial intelligence (AI) systems
                 in environments that involve high risk and high
                 consequence decision-making is severely hampered by
                 critical design issues. These issues include system
                 transparency and brittleness, where transparency
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "25",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Meng:2021:VVA,
  author =       "Linhao Meng and Yating Wei and Rusheng Pan and Shuyue
                 Zhou and Jianwei Zhang and Wei Chen",
  title =        "{VADAF}: Visualization for Abnormal Client Detection
                 and Analysis in Federated Learning",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "26:1--26:23",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3426866",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3426866",
  abstract =     "Federated Learning (FL) provides a powerful solution
                 to distributed machine learning on a large corpus of
                 decentralized data. It ensures privacy and security by
                 performing computation on devices (which we refer to as
                 clients) based on local data to \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "26",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Li:2021:ASE,
  author =       "Mingzhao Li and Zhifeng Bao and Farhana Choudhury and
                 Hanan Samet and Matt Duckham and Timos Sellis",
  title =        "{AOI}-shapes: an Efficient Footprint Algorithm to
                 Support Visualization of User-defined Urban Areas of
                 Interest",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "27:1--27:32",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3431817",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3431817",
  abstract =     "Understanding urban areas of interest (AOIs) is
                 essential in many real-life scenarios, and such AOIs
                 can be computed based on the geographic points that
                 satisfy user queries. In this article, we study the
                 problem of efficient and effective visualization
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "27",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Barral:2021:EAG,
  author =       "Oswald Barral and S{\'e}bastien Lall{\'e} and Alireza
                 Iranpour and Cristina Conati",
  title =        "Effect of Adaptive Guidance and Visualization Literacy
                 on Gaze Attentive Behaviors and Sequential Patterns on
                 Magazine-Style Narrative Visualizations",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "28:1--28:46",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3447992",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3447992",
  abstract =     "We study the effectiveness of adaptive interventions
                 at helping users process textual documents with
                 embedded visualizations, a form of multimodal documents
                 known as Magazine-Style Narrative Visualizations
                 (MSNVs). The interventions are meant to \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "28",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Dodge:2021:AAR,
  author =       "Jonathan Dodge and Roli Khanna and Jed Irvine and
                 Kin-ho Lam and Theresa Mai and Zhengxian Lin and
                 Nicholas Kiddle and Evan Newman and Andrew Anderson and
                 Sai Raja and Caleb Matthews and Christopher Perdriau
                 and Margaret Burnett and Alan Fern",
  title =        "After-Action Review for {AI (AAR\slash AI)}",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "29:1--29:35",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3453173",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3453173",
  abstract =     "Explainable AI is growing in importance as AI pervades
                 modern society, but few have studied how explainable AI
                 can directly support people trying to assess an AI
                 agent. Without a rigorous process, people may approach
                 assessment in ad hoc ways-leading to \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "29",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Starke:2021:PEE,
  author =       "Alain Starke and Martijn Willemsen and Chris
                 Snijders",
  title =        "Promoting Energy-Efficient Behavior by Depicting
                 Social Norms in a Recommender Interface",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "30:1--30:32",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3460005",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3460005",
  abstract =     "How can recommender interfaces help users to adopt new
                 behaviors? In the behavioral change literature, social
                 norms and other nudges are studied to understand how
                 people can be convinced to take action (e.g., towel
                 re-use is boosted when stating that \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "30",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zhou:2021:ISC,
  author =       "Michelle X. Zhou",
  title =        "Introduction to the Special Column for Human-Centered
                 Artificial Intelligence",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "31:1--31:1",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3490553",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3490553",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "31",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Yang:2021:TRA,
  author =       "Qiang Yang",
  title =        "Toward Responsible {AI}: an Overview of Federated
                 Learning for User-centered Privacy-preserving
                 Computing",
  journal =      j-TIIS,
  volume =       "11",
  number =       "3--4",
  pages =        "32:1--32:22",
  month =        dec,
  year =         "2021",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3485875",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Dec 10 11:35:09 MST 2021",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3485875",
  abstract =     "With the rapid advances of Artificial Intelligence
                 (AI) technologies and applications, an increasing
                 concern is on the development and application of
                 responsible AI technologies. Building AI technologies
                 or machine-learning models often requires massive
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "32",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Khanna:2022:FAF,
  author =       "Roli Khanna and Jonathan Dodge and Andrew Anderson and
                 Rupika Dikkala and Jed Irvine and Zeyad Shureih and
                 Kin-Ho Lam and Caleb R. Matthews and Zhengxian Lin and
                 Minsuk Kahng and Alan Fern and Margaret Burnett",
  title =        "Finding {AI's} Faults with {AAR\slash AI}: an
                 Empirical Study",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "1:1--1:33",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3487065",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3487065",
  abstract =     "Would you allow an AI agent to make decisions on your
                 behalf? If the answer is ``not always,'' the next
                 question becomes ``in what circumstances''? Answering
                 this question requires human users to be able to assess
                 an AI agent-and not just with overall pass/. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{VanBerkel:2022:IRF,
  author =       "Niels {Van Berkel} and Jeremy Opie and Omer F. Ahmad
                 and Laurence Lovat and Danail Stoyanov and Ann
                 Blandford",
  title =        "Initial Responses to False Positives in {AI}-Supported
                 Continuous Interactions: a Colonoscopy Case Study",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "2:1--2:18",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3480247",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3480247",
  abstract =     "The use of artificial intelligence (AI) in clinical
                 support systems is increasing. In this article, we
                 focus on AI support for continuous interaction
                 scenarios. A thorough understanding of end-user
                 behaviour during these continuous human-AI
                 interactions, \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zini:2022:ACT,
  author =       "Floriano Zini and Fabio {Le Piane} and Mauro Gaspari",
  title =        "Adaptive Cognitive Training with Reinforcement
                 Learning",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "3:1--3:29",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3476777",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3476777",
  abstract =     "Computer-assisted cognitive training can help patients
                 affected by several illnesses alleviate their cognitive
                 deficits or healthy people improve their mental
                 performance. In most computer-based systems, training
                 sessions consist of graded exercises, \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Tang:2022:SOA,
  author =       "Tan Tang and Junxiu Tang and Jiewen Lai and Lu Ying
                 and Yingcai Wu and Lingyun Yu and Peiran Ren",
  title =        "{SmartShots}: an Optimization Approach for Generating
                 Videos with Data Visualizations Embedded",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "4:1--4:21",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3484506",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3484506",
  abstract =     "Videos are well-received methods for storytellers to
                 communicate various narratives. To further engage
                 viewers, we introduce a novel visual medium where data
                 visualizations are embedded into videos to present data
                 insights. However, creating such data-. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Ahn:2022:TCI,
  author =       "Yongsu Ahn and Muheng Yan and Yu-Ru Lin and Wen-Ting
                 Chung and Rebecca Hwa",
  title =        "Tribe or Not? {Critical} Inspection of Group
                 Differences Using {TribalGram}",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "5:1--5:34",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3484509",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3484509",
  abstract =     "With the rise of AI and data mining techniques, group
                 profiling and group-level analysis have been
                 increasingly used in many domains, including policy
                 making and direct marketing. In some cases, the
                 statistics extracted from data may provide insights to
                 a \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Bruckner:2022:LGC,
  author =       "Lukas Br{\"u}ckner and Luis A. Leiva and Antti
                 Oulasvirta",
  title =        "Learning {GUI} Completions with User-defined
                 Constraints",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "6:1--6:40",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3490034",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3490034",
  abstract =     "A key objective in the design of graphical user
                 interfaces (GUIs) is to ensure consistency across
                 screens of the same product. However, designing a
                 compliant layout is time-consuming and can distract
                 designers from creative thinking. This paper studies
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zhou:2022:EIT,
  author =       "Michelle X. Zhou",
  title =        "Editorial Introduction to {TiiS} Special Category
                 Article: Practitioners' Toolbox",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "7:1--7:1",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3519381",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3519381",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mascarenhas:2022:FTT,
  author =       "Samuel Mascarenhas and Manuel Guimar{\~a}es and Rui
                 Prada and Pedro A. Santos and Jo{\~a}o Dias and Ana
                 Paiva",
  title =        "{FAtiMA} Toolkit: Toward an Accessible Tool for the
                 Development of Socio-emotional Agents",
  journal =      j-TIIS,
  volume =       "12",
  number =       "1",
  pages =        "8:1--8:30",
  month =        mar,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3510822",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Fri Mar 25 07:11:26 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3510822",
  abstract =     "More than a decade has passed since the development of
                 FearNot!, an application designed to help children deal
                 with bullying through role-playing with virtual
                 characters. It was also the application that led to the
                 creation of FAtiMA, an affective agent \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Kocaballi:2022:SIC,
  author =       "A. Baki Kocaballi and Liliana Laranjo and Leigh Clark
                 and Rafa{\l} Kocielnik and Robert J. Moore and Q. Vera
                 Liao and Timothy Bickmore",
  title =        "Special Issue on Conversational Agents for Healthcare
                 and Wellbeing",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "9:1--9:3",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3532860",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3532860",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Maharjan:2022:ESE,
  author =       "Raju Maharjan and Kevin Doherty and Darius Adam Rohani
                 and Per B{\ae}kgaard and Jakob E. Bardram",
  title =        "Experiences of a Speech-enabled Conversational Agent
                 for the Self-report of Well-being among People Living
                 with Affective Disorders: an In-the-Wild Study",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "10:1--10:29",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3484508",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3484508",
  abstract =     "The growing commercial success of smart speaker
                 devices following recent advancements in speech
                 recognition technology has surfaced new opportunities
                 for collecting self-reported health and well-being
                 data. Speech-enabled conversational agents (CAs) in
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Koulouri:2022:CSY,
  author =       "Theodora Koulouri and Robert D. Macredie and David
                 Olakitan",
  title =        "Chatbots to Support Young Adults' Mental Health: an
                 Exploratory Study of Acceptability",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "11:1--11:39",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3485874",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3485874",
  abstract =     "Despite the prevalence of mental health conditions,
                 stigma, lack of awareness, and limited resources impede
                 access to care, creating a need to improve mental
                 health support. The recent surge in scientific and
                 commercial interest in conversational agents \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Eagle:2022:DKW,
  author =       "Tessa Eagle and Conrad Blau and Sophie Bales and
                 Noopur Desai and Victor Li and Steve Whittaker",
  title =        "{``I don't know what you mean by `I am anxious'''}: a
                 New Method for Evaluating Conversational Agent
                 Responses to Standardized Mental Health Inputs for
                 Anxiety and Depression",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "12:1--12:23",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3488057",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3488057",
  abstract =     "Conversational agents (CAs) are increasingly
                 ubiquitous and are now commonly used to access medical
                 information. However, we lack systematic data about the
                 quality of advice such agents provide. This paper
                 evaluates CA advice for mental health (MH) \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Brewer:2022:ESO,
  author =       "Robin Brewer and Casey Pierce and Pooja Upadhyay and
                 Leeseul Park",
  title =        "An Empirical Study of Older Adult's Voice Assistant
                 Use for Health Information Seeking",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "13:1--13:32",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3484507",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3484507",
  abstract =     "Although voice assistants are increasingly being
                 adopted by older adults, we lack empirical research on
                 how they interact with these devices for health
                 information seeking. Also, prior work shows how voice
                 assistant responses can provide misleading or
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Razavi:2022:DBO,
  author =       "S. Zahra Razavi and Lenhart K. Schubert and Kimberly
                 van Orden and Mohammad Rafayet Ali and Benjamin Kane
                 and Ehsan Hoque",
  title =        "Discourse Behavior of Older Adults Interacting with a
                 Dialogue Agent Competent in Multiple Topics",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "14:1--14:21",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3484510",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3484510",
  abstract =     "We present a conversational agent designed to provide
                 realistic conversational practice to older adults at
                 risk of isolation or social anxiety, and show the
                 results of a content analysis on a corpus of data
                 collected from experiments with elderly patients
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Jang:2022:RAH,
  author =       "Yi Hyun Jang and Soo Han Im and Younah Kang and Joon
                 Sang Baek",
  title =        "Relational Agents for the Homeless with Tuberculosis
                 Experience: Providing Social Support Through
                 Human-agent Relationships",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "15:1--15:22",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3488056",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3488056",
  abstract =     "In human-computer interaction (HCI) research,
                 relational agents (RAs) are increasingly used to
                 improve social support for vulnerable groups including
                 people exposed to stigmas, alienation, and isolation.
                 However, technical support for tuberculosis (TB)
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zorrilla:2022:MNC,
  author =       "Asier L{\'o}pez Zorrilla and M. In{\'e}s Torres",
  title =        "A Multilingual Neural Coaching Model with Enhanced
                 Long-term Dialogue Structure",
  journal =      j-TIIS,
  volume =       "12",
  number =       "2",
  pages =        "16:1--16:47",
  month =        jun,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3487066",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 25 09:40:04 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3487066",
  abstract =     "In this work we develop a fully data driven
                 conversational agent capable of carrying out
                 motivational coaching sessions in Spanish, French,
                 Norwegian, and English. Unlike the majority of
                 coaching, and in general well-being related
                 conversational agents \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Caldwell:2022:ANR,
  author =       "Sabrina Caldwell and Penny Sweetser and Nicholas
                 O'Donnell and Matthew J. Knight and Matthew Aitchison
                 and Tom Gedeon and Daniel Johnson and Margot Brereton
                 and Marcus Gallagher and David Conroy",
  title =        "An Agile New Research Framework for Hybrid Human-{AI}
                 Teaming: Trust, Transparency, and Transferability",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "17:1--17:36",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3514257",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3514257",
  abstract =     "We propose a new research framework by which the
                 nascent discipline of human-AI teaming can be explored
                 within experimental environments in preparation for
                 transferal to real-world contexts. We examine the
                 existing literature and unanswered research \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Nakao:2022:TIE,
  author =       "Yuri Nakao and Simone Stumpf and Subeida Ahmed and
                 Aisha Naseer and Lorenzo Strappelli",
  title =        "Toward Involving End-users in Interactive
                 Human-in-the-loop {AI} Fairness",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "18:1--18:30",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3514258",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3514258",
  abstract =     "Ensuring fairness in artificial intelligence (AI) is
                 important to counteract bias and discrimination in
                 far-reaching applications. Recent work has started to
                 investigate how humans judge fairness and how to
                 support machine learning experts in making their
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Kruse:2022:EMA,
  author =       "Jan Kruse and Andy M. Connor and Stefan Marks",
  title =        "Evaluation of a Multi-agent {``Human-in-the-loop''}
                 Game Design System",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "19:1--19:26",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3531009",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3531009",
  abstract =     "Designing games is a complicated and time-consuming
                 process, where developing new levels for existing games
                 can take weeks. Procedural content generation offers
                 the potential to shorten this timeframe, however,
                 automated design tools are not adopted \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Alhejaili:2022:ELF,
  author =       "Abdullah Alhejaili and Shaheen Fatima",
  title =        "Expressive Latent Feature Modelling for Explainable
                 Matrix Factorisation-based Recommender Systems",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "20:1--20:30",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3530299",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3530299",
  abstract =     "The traditional matrix factorisation (MF)-based
                 recommender system methods, despite their success in
                 making the recommendation, lack explainable
                 recommendations as the produced latent features are
                 meaningless and cannot explain the recommendation. This
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Hsieh:2022:ADA,
  author =       "Sheng-Jen Hsieh and Andy R. Wang and Anna Madison and
                 Chad Tossell and Ewart de Visser",
  title =        "Adaptive Driving Assistant Model {(ADAM)} for Advising
                 Drivers of Autonomous Vehicles",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "21:1--21:28",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3545994",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3545994",
  abstract =     "Fully autonomous driving is on the horizon; vehicles
                 with advanced driver assistance systems (ADAS) such as
                 Tesla's Autopilot are already available to consumers.
                 However, all currently available ADAS applications
                 require a human driver to be alert and \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Handler:2022:CIQ,
  author =       "Abram Handler and Narges Mahyar and Brendan O'Connor",
  title =        "{ClioQuery}: Interactive Query-oriented Text Analytics
                 for Comprehensive Investigation of Historical News
                 Archives",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "22:1--22:49",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3524025",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3524025",
  abstract =     "Historians and archivists often find and analyze the
                 occurrences of query words in newspaper archives to
                 help answer fundamental questions about society. But
                 much work in text analytics focuses on helping people
                 investigate other textual units, such as \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Liu:2022:SSE,
  author =       "Fang Liu and Xiaoming Deng and Jiancheng Song and
                 Yu-Kun Lai and Yong-Jin Liu and Hao Wang and Cuixia Ma
                 and Shengfeng Qin and Hongan Wang",
  title =        "{SketchMaker}: Sketch Extraction and Reuse for
                 Interactive Scene Sketch Composition",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "23:1--23:26",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3543956",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3543956",
  abstract =     "Sketching is an intuitive and simple way to depict
                 sciences with various object form and appearance
                 characteristics. In the past few years, widely
                 available touchscreen devices have increasingly made
                 sketch-based human-AI co-creation applications popular.
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Kronenberg:2022:IOW,
  author =       "Rotem Kronenberg and Tsvi Kuflik and Ilan Shimshoni",
  title =        "Improving Office Workers' Workspace Using a
                 Self-adjusting Computer Screen",
  journal =      j-TIIS,
  volume =       "12",
  number =       "3",
  pages =        "24:1--24:32",
  month =        sep,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3545993",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Sep 20 09:43:15 MDT 2022",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3545993",
  abstract =     "With the rapid evolution of technology, computers and
                 their users' workspaces have become an essential part
                 of our life in general. Today, many people use
                 computers both for work and for personal needs,
                 spending long hours sitting at a desk in front of a
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Hammond:2022:SIH,
  author =       "Tracy Hammond and Bart Knijnenburg and John O'Donovan
                 and Paul Taele",
  title =        "Special Issue on Highlights of {IUI} 2021:
                 Introduction",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "25:1--25:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3561516",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3561516",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "25",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Sovrano:2022:GUC,
  author =       "Francesco Sovrano and Fabio Vitali",
  title =        "Generating User-Centred Explanations via Illocutionary
                 Question Answering: From Philosophy to Interfaces",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "26:1--26:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3519265",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3519265",
  abstract =     "We propose a new method for generating explanations
                 with Artificial Intelligence (AI) and a tool to test
                 its expressive power within a user interface. In order
                 to bridge the gap between philosophy and human-computer
                 interfaces, we show a new approach for \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "26",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Wang:2022:EEA,
  author =       "Xinru Wang and Ming Yin",
  title =        "Effects of Explanations in {AI}-Assisted Decision
                 Making: Principles and Comparisons",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "27:1--27:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3519266",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3519266",
  abstract =     "Recent years have witnessed the growing literature in
                 empirical evaluation of explainable AI (XAI) methods.
                 This study contributes to this ongoing conversation by
                 presenting a comparison on the effects of a set of
                 established XAI methods in AI-assisted \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "27",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Nourani:2022:IUB,
  author =       "Mahsan Nourani and Chiradeep Roy and Jeremy E. Block
                 and Donald R. Honeycutt and Tahrima Rahman and Eric D.
                 Ragan and Vibhav Gogate",
  title =        "On the Importance of User Backgrounds and Impressions:
                 Lessons Learned from Interactive {AI} Applications",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "28:1--28:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3531066",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3531066",
  abstract =     "While EXplainable Artificial Intelligence (XAI)
                 approaches aim to improve human-AI collaborative
                 decision-making by improving model transparency and
                 mental model formations, experiential factors
                 associated with human users can cause challenges in
                 ways \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "28",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Buschek:2022:HSU,
  author =       "Daniel Buschek and Malin Eiband and Heinrich
                 Hussmann",
  title =        "How to Support Users in Understanding Intelligent
                 Systems? An Analysis and Conceptual Framework of User
                 Questions Considering User Mindsets, Involvement, and
                 Knowledge Outcomes",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "29:1--29:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3519264",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3519264",
  abstract =     "The opaque nature of many intelligent systems violates
                 established usability principles and thus presents a
                 challenge for human-computer interaction. Research in
                 the field therefore highlights the need for
                 transparency, scrutability, intelligibility, \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "29",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Ramos:2022:FAO,
  author =       "Gonzalo Ramos and Napol Rachatasumrit and Jina Suh and
                 Rachel Ng and Christopher Meek",
  title =        "{ForSense}: Accelerating Online Research Through
                 Sensemaking Integration and Machine Research Support",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "30:1--30:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3532853",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3532853",
  abstract =     "Online research is a frequent and important activity
                 people perform on the Internet, yet current support for
                 this task is basic, fragmented and not well integrated
                 into web browser experiences. Guided by sensemaking
                 theory, we present ForSense, a browser \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "30",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Karimi:2022:TTS,
  author =       "Pegah Karimi and Emanuele Plebani and Aqueasha
                 Martin-Hammond and Davide Bolchini",
  title =        "Textflow: Toward Supporting Screen-free Manipulation
                 of Situation-Relevant Smart Messages",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "31:1--31:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3519263",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3519263",
  abstract =     "Texting relies on screen-centric prompts designed for
                 sighted users, still posing significant barriers to
                 people who are blind and visually impaired (BVI). Can
                 we re-imagine texting untethered from a visual display?
                 In an interview study, 20 BVI adults \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "31",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Svikhnushina:2022:PMK,
  author =       "Ekaterina Svikhnushina and Pearl Pu",
  title =        "{PEACE}: a Model of Key Social and Emotional Qualities
                 of Conversational Chatbots",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "32:1--32:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3531064",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3531064",
  abstract =     "Open-domain chatbots engage with users in natural
                 conversations to socialize and establish bonds.
                 However, designing and developing an effective
                 open-domain chatbot is challenging. It is unclear what
                 qualities of a chatbot most correspond to users'
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "32",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Das:2022:DRD,
  author =       "Kapotaksha Das and Michalis Papakostas and Kais Riani
                 and Andrew Gasiorowski and Mohamed Abouelenien and
                 Mihai Burzo and Rada Mihalcea",
  title =        "Detection and Recognition of Driver Distraction Using
                 Multimodal Signals",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "33:1--33:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3519267",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3519267",
  abstract =     "Distracted driving is a leading cause of accidents
                 worldwide. The tasks of distraction detection and
                 recognition have been traditionally addressed as
                 computer vision problems. However, distracted behaviors
                 are not always expressed in a visually observable
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "33",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Ang:2022:LSR,
  author =       "Gary Ang and Ee-Peng Lim",
  title =        "Learning Semantically Rich Network-based Multi-modal
                 Mobile User Interface Embeddings",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "34:1--34:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3533856",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3533856",
  abstract =     "Semantically rich information from multiple
                 modalities-text, code, images, categorical and
                 numerical data-co-exist in the user interface (UI)
                 design of mobile applications. Moreover, each UI design
                 is composed of inter-linked UI entities that support
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "34",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Yagi:2022:GFR,
  author =       "Takuma Yagi and Takumi Nishiyasu and Kunimasa Kawasaki
                 and Moe Matsuki and Yoichi Sato",
  title =        "{GO-Finder}: a Registration-free Wearable System for
                 Assisting Users in Finding Lost Hand-held Objects",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "35:1--35:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3519268",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3519268",
  abstract =     "People spend an enormous amount of time and effort
                 looking for lost objects. To help remind people of the
                 location of lost objects, various computational systems
                 that provide information on their locations have been
                 developed. However, prior systems for \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "35",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Feng:2022:AIA,
  author =       "Sidong Feng and Minmin Jiang and Tingting Zhou and
                 Yankun Zhen and Chunyang Chen",
  title =        "{Auto-Icon+}: an Automated End-to-End Code Generation
                 Tool for Icon Designs in {UI} Development",
  journal =      j-TIIS,
  volume =       "12",
  number =       "4",
  pages =        "36:1--36:??",
  month =        dec,
  year =         "2022",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3531065",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:07 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3531065",
  abstract =     "Approximately 50\% of development resources are
                 devoted to user interface (UI) development tasks [ 9 ].
                 Occupying a large proportion of development resources,
                 developing icons can be a time-consuming task, because
                 developers need to consider not only \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "36",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Li:2023:ESE,
  author =       "Xingjun Li and Yizhi Zhang and Justin Leung and
                 Chengnian Sun and Jian Zhao",
  title =        "{EDAssistant}: Supporting Exploratory Data Analysis in
                 Computational Notebooks with In Situ Code Search and
                 Recommendation",
  journal =      j-TIIS,
  volume =       "13",
  number =       "1",
  pages =        "1:1--1:??",
  month =        mar,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3545995",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:08 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3545995",
  abstract =     "Using computational notebooks (e.g., Jupyter
                 Notebook), data scientists rationalize their
                 exploratory data analysis (EDA) based on their prior
                 experience and external knowledge, such as online
                 examples. For novices or data scientists who lack
                 specific \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Liu:2023:SGL,
  author =       "Huimin Liu and Minsoo Choi and Dominic Kao and
                 Christos Mousas",
  title =        "Synthesizing Game Levels for Collaborative Gameplay in
                 a Shared Virtual Environment",
  journal =      j-TIIS,
  volume =       "13",
  number =       "1",
  pages =        "2:1--2:??",
  month =        mar,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3558773",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:08 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3558773",
  abstract =     "We developed a method to synthesize game levels that
                 accounts for the degree of collaboration required by
                 two players to finish a given game level. We first
                 asked a game level designer to create playable game
                 level chunks. Then, two artificial \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Yan:2023:IPT,
  author =       "Dongning Yan and Li Chen",
  title =        "The Influence of Personality Traits on User
                 Interaction with Recommendation Interfaces",
  journal =      j-TIIS,
  volume =       "13",
  number =       "1",
  pages =        "3:1--3:??",
  month =        mar,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3558772",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:08 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3558772",
  abstract =     "Users' personality traits can take an active role in
                 affecting their behavior when they interact with a
                 computer interface. However, in the area of recommender
                 systems (RS), though personality-based RS has been
                 extensively studied, most works focus on \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Lin:2023:PIM,
  author =       "Yi-Ling Lin and Shao-Wei Lee",
  title =        "A Personalized Interaction Mechanism Framework for
                 Micro-moment Recommender Systems",
  journal =      j-TIIS,
  volume =       "13",
  number =       "1",
  pages =        "4:1--4:??",
  month =        mar,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3569586",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:08 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3569586",
  abstract =     "The emergence of the micro-moment concept highlights
                 the influence of context; recommender system design
                 should reflect this trend. In response to different
                 contexts, a micro-moment recommender system (MMRS)
                 requires an effective interaction mechanism \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Afzal:2023:VVA,
  author =       "Shehzad Afzal and Sohaib Ghani and Mohamad Mazen
                 Hittawe and Sheikh Faisal Rashid and Omar M. Knio and
                 Markus Hadwiger and Ibrahim Hoteit",
  title =        "Visualization and Visual Analytics Approaches for
                 Image and Video Datasets: a Survey",
  journal =      j-TIIS,
  volume =       "13",
  number =       "1",
  pages =        "5:1--5:??",
  month =        mar,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3576935",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Mar 21 06:18:08 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3576935",
  abstract =     "Image and video data analysis has become an
                 increasingly important research area with applications
                 in different domains such as security surveillance,
                 healthcare, augmented and virtual reality, video and
                 image editing, activity analysis and recognition,
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Hernandez-Bocanegra:2023:ERT,
  author =       "Diana C. Hernandez-Bocanegra and J{\"u}rgen Ziegler",
  title =        "Explaining Recommendations through Conversations:
                 Dialog Model and the Effects of Interface Type and
                 Degree of Interactivity",
  journal =      j-TIIS,
  volume =       "13",
  number =       "2",
  pages =        "6:1--6:??",
  month =        jun,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3579541",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 3 06:48:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3579541",
  abstract =     "Explaining system-generated recommendations based on
                 user reviews can foster users' understanding and
                 assessment of the recommended items and the recommender
                 system (RS) as a whole. While up to now explanations
                 have mostly been static, shown in a single \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Das:2023:EAR,
  author =       "Devleena Das and Yasutaka Nishimura and Rajan P. Vivek
                 and Naoto Takeda and Sean T. Fish and Thomas Pl{\"o}tz
                 and Sonia Chernova",
  title =        "Explainable Activity Recognition for Smart Home
                 Systems",
  journal =      j-TIIS,
  volume =       "13",
  number =       "2",
  pages =        "7:1--7:??",
  month =        jun,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3561533",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 3 06:48:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3561533",
  abstract =     "Smart home environments are designed to provide
                 services that help improve the quality of life for the
                 occupant via a variety of sensors and actuators
                 installed throughout the space. Many automated actions
                 taken by a smart home are governed by the output
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Rudrauf:2023:CPC,
  author =       "D. Rudrauf and G. Sergeant-Perhtuis and Y. Tisserand
                 and T. Monnor and V. {De Gevigney} and O. Belli",
  title =        "Combining the Projective Consciousness Model and
                 Virtual Humans for Immersive Psychological Research: a
                 Proof-of-concept Simulating a {ToM} Assessment",
  journal =      j-TIIS,
  volume =       "13",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jun,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3583886",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 3 06:48:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3583886",
  abstract =     "Relating explicit psychological mechanisms and
                 observable behaviours is a central aim of psychological
                 and behavioural science. One of the challenges is to
                 understand and model the role of consciousness and, in
                 particular, its subjective perspective as an \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Guo:2023:GGF,
  author =       "Mengtian Guo and Zhilan Zhou and David Gotz and Yue
                 Wang",
  title =        "{GRAFS}: Graphical Faceted Search System to Support
                 Conceptual Understanding in Exploratory Search",
  journal =      j-TIIS,
  volume =       "13",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jun,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3588319",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 3 06:48:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3588319",
  abstract =     "When people search for information about a new topic
                 within large document collections, they implicitly
                 construct a mental model of the unfamiliar information
                 space to represent what they currently know and guide
                 their exploration into the unknown. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Jentner:2023:VAC,
  author =       "Wolfgang Jentner and Giuliana Lindholz and Hanna
                 Hauptmann and Mennatallah El-Assady and Kwan-Liu Ma and
                 Daniel Keim",
  title =        "Visual Analytics of Co-Occurrences to Discover
                 Subspaces in Structured Data",
  journal =      j-TIIS,
  volume =       "13",
  number =       "2",
  pages =        "10:1--10:??",
  month =        jun,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3579031",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Mon Jul 3 06:48:34 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3579031",
  abstract =     "We present an approach that shows all relevant
                 subspaces of categorical data condensed in a single
                 picture. We model the categorical values of the
                 attributes as co-occurrences with data partitions
                 generated from structured data using pattern mining. We
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Yalcin:2023:IIP,
  author =       "{\"O}zge Nilay Yal{\c{c}}{\i}n and S{\'e}bastien
                 Lall{\'e} and Cristina Conati",
  title =        "The Impact of Intelligent Pedagogical Agents'
                 Interventions on Student Behavior and Performance in
                 Open-Ended Game Design Environments",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "11:1--11:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3578523",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3578523",
  abstract =     "Research has shown that free-form Game-Design (GD)
                 environments can be very effective in fostering
                 Computational Thinking (CT) skills at a young
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Ang:2023:LUU,
  author =       "Gary Ang and Ee-Peng Lim",
  title =        "Learning and Understanding User Interface Semantics
                 from Heterogeneous Networks with Multimodal and
                 Positional Attributes",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "12:1--12:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3578522",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3578522",
  abstract =     "User interfaces (UI) of desktop, web, and mobile
                 applications involve a hierarchy of objects (e.g.,
                 applications, screens, view class, and other types of
                 design \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Ferdous:2023:EEW,
  author =       "Javedul Ferdous and Hae-Na Lee and Sampath Jayarathna
                 and Vikas Ashok",
  title =        "Enabling Efficient {Web} Data-Record Interaction for
                 People with Visual Impairments via Proxy Interfaces",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "13:1--13:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3579364",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3579364",
  abstract =     "Web data records are usually accompanied by auxiliary
                 webpage segments, such as filters, sort options, search
                 form, and multi-page links, to enhance interaction
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Aguirre:2023:CTC,
  author =       "Carlos Aguirre and Shiye Cao and Amama Mahmood and
                 Chien-Ming Huang",
  title =        "Crowdsourcing Thumbnail Captions: Data Collection and
                 Validation",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "14:1--14:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3589346",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3589346",
  abstract =     "Speech interfaces, such as personal assistants and
                 screen readers, read image captions to users.
                 Typically, however, only one caption is available per
                 image, \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Shibata:2023:CCS,
  author =       "Ryoichi Shibata and Shoya Matsumori and Yosuke Fukuchi
                 and Tomoyuki Maekawa and Mitsuhiko Kimoto and Michita
                 Imai",
  title =        "Conversational Context-sensitive Ad Generation with a
                 Few Core-Queries",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "15:1--15:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3588578",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3588578",
  abstract =     "When people are talking together in front of digital
                 signage, advertisements that are aware of the context
                 of the dialogue will work the most effectively.
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Sluyters:2023:RAR,
  author =       "Arthur Slu{\"y}ters and S{\'e}bastien Lambot and Jean
                 Vanderdonckt and Radu-Daniel Vatavu",
  title =        "{RadarSense}: Accurate Recognition of Mid-air Hand
                 Gestures with Radar Sensing and Few Training Examples",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "16:1--16:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3589645",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3589645",
  abstract =     "Microwave radars bring many benefits to mid-air
                 gesture sensing due to their large field of view and
                 independence from environmental conditions, such as
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Wang:2023:WBH,
  author =       "Clarice Wang and Kathryn Wang and Andrew Y. Bian and
                 Rashidul Islam and Kamrun Naher Keya and James Foulds
                 and Shimei Pan",
  title =        "When Biased Humans Meet Debiased {AI}: a Case Study in
                 College Major Recommendation",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "17:1--17:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3611313",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3611313",
  abstract =     "Currently, there is a surge of interest in fair
                 Artificial Intelligence (AI) and Machine Learning (ML)
                 research which aims to mitigate discriminatory bias in
                 AI \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Smith:2023:GDB,
  author =       "Ronnie Smith and Mauro Dragone",
  title =        "Generalisable Dialogue-based Approach for Active
                 Learning of Activities of Daily Living",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "18:1--18:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3616017",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3616017",
  abstract =     "While Human Activity Recognition systems may benefit
                 from Active Learning by allowing users to self-annotate
                 their Activities of Daily Living (ADLs), \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zhou:2023:TBP,
  author =       "Michelle Zhou and Shlomo Berkovsky",
  title =        "2022 {TiiS} Best Paper Announcement",
  journal =      j-TIIS,
  volume =       "13",
  number =       "3",
  pages =        "19:1--19:??",
  month =        sep,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3615590",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 13 06:40:19 MDT 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3615590",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Li:2023:VAN,
  author =       "Yiran Li and Junpeng Wang and Takanori Fujiwara and
                 Kwan-Liu Ma",
  title =        "Visual Analytics of Neuron Vulnerability to
                 Adversarial Attacks on Convolutional Neural Networks",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "20:1--20:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3587470",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3587470",
  abstract =     "Adversarial attacks on a convolutional neural network
                 (CNN)-injecting human-imperceptible perturbations into
                 an input image-could fool a high-performance CNN into
                 making incorrect predictions. The success of
                 adversarial attacks raises serious concerns \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Panigutti:2023:CDH,
  author =       "Cecilia Panigutti and Andrea Beretta and Daniele Fadda
                 and Fosca Giannotti and Dino Pedreschi and Alan Perotti
                 and Salvatore Rinzivillo",
  title =        "Co-design of Human-centered, Explainable {AI} for
                 Clinical Decision Support",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "21:1--21:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3587271",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3587271",
  abstract =     "eXplainable AI (XAI) involves two intertwined but
                 separate challenges: the development of techniques to
                 extract explanations from black-box AI models and the
                 way such explanations are presented to users, i.e., the
                 explanation user interface. Despite its \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Cau:2023:EAL,
  author =       "Federico Maria Cau and Hanna Hauptmann and Lucio
                 Davide Spano and Nava Tintarev",
  title =        "Effects of {AI} and Logic-Style Explanations on Users'
                 Decisions Under Different Levels of Uncertainty",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "22:1--22:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3588320",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3588320",
  abstract =     "Existing eXplainable Artificial Intelligence (XAI)
                 techniques support people in interpreting AI advice.
                 However, although previous work evaluates the users'
                 understanding of explanations, factors influencing the
                 decision support are largely overlooked in \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Singh:2023:DEA,
  author =       "Ronal Singh and Tim Miller and Henrietta Lyons and Liz
                 Sonenberg and Eduardo Velloso and Frank Vetere and
                 Piers Howe and Paul Dourish",
  title =        "Directive Explanations for Actionable Explainability
                 in Machine Learning Applications",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "23:1--23:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3579363",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3579363",
  abstract =     "In this article, we show that explanations of
                 decisions made by machine learning systems can be
                 improved by not only explaining why a decision was made
                 but also explaining how an individual could obtain
                 their desired outcome. We formally define the
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Lee:2023:LAE,
  author =       "Benjamin Charles Germain Lee and Doug Downey and Kyle
                 Lo and Daniel S. Weld",
  title =        "{LIMEADE}: From {AI} Explanations to Advice Taking",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "24:1--24:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3589345",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3589345",
  abstract =     "Research in human-centered AI has shown the benefits
                 of systems that can explain their predictions. Methods
                 that allow AI to take advice from humans in response to
                 explanations are similarly useful. While both
                 capabilities are well developed for ...$^$",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Schrills:2023:HDU,
  author =       "Tim Schrills and Thomas Franke",
  title =        "How Do Users Experience Traceability of {AI} Systems?
                 {Examining} Subjective Information Processing Awareness
                 in Automated Insulin Delivery {(AID)} Systems",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "25:1--25:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3588594",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3588594",
  abstract =     "When interacting with artificial intelligence (AI) in
                 the medical domain, users frequently face automated
                 information processing, which can remain opaque to
                 them. For example, users with diabetes may interact
                 daily with automated insulin delivery (AID). \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "25",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Vainio-Pekka:2023:REA,
  author =       "Heidi Vainio-Pekka and Mamia Ori-Otse Agbese and
                 Marianna Jantunen and Ville Vakkuri and Tommi Mikkonen
                 and Rebekah Rousi and Pekka Abrahamsson",
  title =        "The Role of Explainable {AI} in the Research Field of
                 {AI} Ethics",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "26:1--26:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3599974",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3599974",
  abstract =     "Ethics of Artificial Intelligence (AI) is a growing
                 research field that has emerged in response to the
                 challenges related to AI. Transparency poses a key
                 challenge for implementing AI ethics in practice. One
                 solution to transparency issues is AI systems
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "26",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Martinez:2023:DEH,
  author =       "Miguel Angel Meza Mart{\'\i}nez and Mario Nadj and
                 Moritz Langner and Peyman Toreini and Alexander
                 Maedche",
  title =        "Does this Explanation Help? {Designing} Local
                 Model-agnostic Explanation Representations and an
                 Experimental Evaluation Using Eye-tracking Technology",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "27:1--27:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3607145",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3607145",
  abstract =     "In Explainable Artificial Intelligence (XAI) research,
                 various local model-agnostic methods have been proposed
                 to explain individual predictions to users in order to
                 increase the transparency of the underlying Artificial
                 Intelligence (AI) systems. However,. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "27",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zoller:2023:XVA,
  author =       "Marc-Andr{\'e} Z{\"o}ller and Waldemar Titov and
                 Thomas Schlegel and Marco F. Huber",
  title =        "{XAutoML}: a Visual Analytics Tool for Understanding
                 and Validating Automated Machine Learning",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "28:1--28:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3625240",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3625240",
  abstract =     "In the last 10 years, various automated machine
                 learning (AutoML) systems have been proposed to build
                 end-to-end machine learning (ML) pipelines with minimal
                 human interaction. Even though such automatically
                 synthesized ML pipelines are able to achieve \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "28",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Roy:2023:EAR,
  author =       "Chiradeep Roy and Mahsan Nourani and Shivvrat Arya and
                 Mahesh Shanbhag and Tahrima Rahman and Eric D. Ragan
                 and Nicholas Ruozzi and Vibhav Gogate",
  title =        "Explainable Activity Recognition in Videos using Deep
                 Learning and Tractable Probabilistic Models",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "29:1--29:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3626961",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3626961",
  abstract =     "We consider the following video activity recognition
                 (VAR) task: given a video, infer the set of activities
                 being performed in the video and assign each frame to
                 an activity. Although VAR can be solved accurately
                 using existing deep learning techniques, \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "29",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Larasati:2023:MEE,
  author =       "Retno Larasati and Anna {De Liddo} and Enrico Motta",
  title =        "Meaningful Explanation Effect on {User}'s Trust in an
                 {AI} Medical System: Designing Explanations for
                 Non-Expert Users",
  journal =      j-TIIS,
  volume =       "13",
  number =       "4",
  pages =        "30:1--30:??",
  month =        dec,
  year =         "2023",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3631614",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Thu Dec 21 10:44:24 MST 2023",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3631614",
  abstract =     "Whereas most research in AI system explanation for
                 healthcare applications looks at developing algorithmic
                 explanations targeted at AI experts or medical
                 professionals, the question we raise is: How do we
                 build meaningful explanations for laypeople? And
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "30",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zhang:2024:SBO,
  author =       "Yu Zhang and Martijn Tennekes and Tim {De Jong} and
                 Lyana Curier and Bob Coecke and Min Chen",
  title =        "Simulation-based Optimization of User Interfaces for
                 Quality-assuring Machine Learning Model Predictions",
  journal =      j-TIIS,
  volume =       "14",
  number =       "1",
  pages =        "1:1--1:??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3594552",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:09 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3594552",
  abstract =     "Quality-sensitive applications of machine learning
                 (ML) require quality assurance (QA) by humans before
                 the predictions of an ML model can be deployed. QA for
                 ML (QA4ML) interfaces require users to view a large
                 amount of data and perform many interactions \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "1",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Wenskovitch:2024:TAA,
  author =       "John Wenskovitch and Michelle Dowling and Chris
                 North",
  title =        "Toward Addressing Ambiguous Interactions and Inferring
                 User Intent with Dimension Reduction and Clustering
                 Combinations in Visual Analytics",
  journal =      j-TIIS,
  volume =       "14",
  number =       "1",
  pages =        "2:1--2:??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3588565",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:09 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3588565",
  abstract =     "Direct manipulation interactions on projections are
                 often incorporated in visual analytics applications.
                 These interactions enable analysts to provide
                 incremental feedback to the system in a semi-supervised
                 manner, demonstrating relationships that the \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "2",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Rathore:2024:VVI,
  author =       "Archit Rathore and Sunipa Dev and Jeff M. Phillips and
                 Vivek Srikumar and Yan Zheng and Chin-Chia Michael Yeh
                 and Junpeng Wang and Wei Zhang and Bei Wang",
  title =        "{VERB}: Visualizing and Interpreting Bias Mitigation
                 Techniques Geometrically for Word Representations",
  journal =      j-TIIS,
  volume =       "14",
  number =       "1",
  pages =        "3:1--3:??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3604433",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:09 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3604433",
  abstract =     "Word vector embeddings have been shown to contain and
                 amplify biases in the data they are extracted from.
                 Consequently, many techniques have been proposed to
                 identify, mitigate, and attenuate these biases in word
                 representations. In this article, we \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "3",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mehrotra:2024:IBE,
  author =       "Siddharth Mehrotra and Carolina Centeio Jorge and
                 Catholijn M. Jonker and Myrthe L. Tielman",
  title =        "Integrity-based Explanations for Fostering Appropriate
                 Trust in {AI} Agents",
  journal =      j-TIIS,
  volume =       "14",
  number =       "1",
  pages =        "4:1--4:??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3610578",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:09 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3610578",
  abstract =     "Appropriate trust is an important component of the
                 interaction between people and AI systems, in that
                 ``inappropriate'' trust can cause disuse, misuse, or
                 abuse of AI. To foster appropriate trust in AI, we need
                 to understand how AI systems can elicit \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "4",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Jorge:2024:HSA,
  author =       "Carolina Centeio Jorge and Catholijn M. Jonker and
                 Myrthe L. Tielman",
  title =        "How Should an {AI} Trust its Human Teammates?
                 {Exploring} Possible Cues of Artificial Trust",
  journal =      j-TIIS,
  volume =       "14",
  number =       "1",
  pages =        "5:1--5:??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3635475",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:09 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3635475",
  abstract =     "In teams composed of humans, we use trust in others to
                 make decisions, such as what to do next, who to help
                 and who to ask for help. When a team member is
                 artificial, they should also be able to assess whether
                 a human teammate is trustworthy for a certain
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "5",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Zhang:2024:KLB,
  author =       "Rui Zhang and Christopher Flathmann and Geoff Musick
                 and Beau Schelble and Nathan J. McNeese and Bart
                 Knijnenburg and Wen Duan",
  title =        "{I} Know This Looks Bad, But {I} Can Explain:
                 Understanding When {AI} Should Explain Actions In
                 {Human--AI} Teams",
  journal =      j-TIIS,
  volume =       "14",
  number =       "1",
  pages =        "6:1--6:??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3635474",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:09 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3635474",
  abstract =     "Explanation of artificial intelligence (AI)
                 decision-making has become an important research area
                 in human-computer interaction (HCI) and
                 computer-supported teamwork research. While plenty of
                 research has investigated AI explanations with an
                 intent to \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "6",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Emamgholizadeh:2024:PGC,
  author =       "Hanif Emamgholizadeh and Amra Deli{\'c} and Francesco
                 Ricci",
  title =        "Predicting Group Choices from Group Profiles",
  journal =      j-TIIS,
  volume =       "14",
  number =       "1",
  pages =        "7:1--7:??",
  month =        mar,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3639710",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:09 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3639710",
  abstract =     "Group recommender systems (GRSs) identify items to
                 recommend to a group of people by aggregating group
                 members' individual preferences into a group profile
                 and selecting the items that have the largest score in
                 the group profile. The GRS predicts that \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "7",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Guo:2024:TAN,
  author =       "Yi Guo and Danqing Shi and Mingjuan Guo and Yanqiu Wu
                 and Nan Cao and Qing Chen",
  title =        "{Talk$2$Data}: a Natural Language Interface for
                 Exploratory Visual Analysis via Question
                 Decomposition",
  journal =      j-TIIS,
  volume =       "14",
  number =       "2",
  pages =        "8:1--8:??",
  month =        jun,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3643894",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:10 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3643894",
  abstract =     "Through a natural language interface (NLI) for
                 exploratory visual analysis, users can directly ``ask''
                 analytical questions about the given tabular data. This
                 process greatly improves user experience and lowers the
                 technical barriers of data analysis. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "8",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Yousefi:2024:EFM,
  author =       "Zeinab R. Yousefi and Tung Vuong and Marie AlGhossein
                 and Tuukka Ruotsalo and Giulio Jaccuci and Samuel
                 Kaski",
  title =        "Entity Footprinting: Modeling Contextual User States
                 via Digital Activity Monitoring",
  journal =      j-TIIS,
  volume =       "14",
  number =       "2",
  pages =        "9:1--9:??",
  month =        jun,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3643893",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:10 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3643893",
  abstract =     "Our digital life consists of activities that are
                 organized around tasks and exhibit different user
                 states in the digital contexts around these activities.
                 Previous works have shown that digital activity
                 monitoring can be used to predict entities that
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "9",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Radeta:2024:MME,
  author =       "Marko Radeta and Ruben Freitas and Claudio Rodrigues
                 and Agustin Zuniga and Ngoc Thi Nguyen and Huber Flores
                 and Petteri Nurmi",
  title =        "Man and the Machine: Effects of {AI}-assisted Human
                 Labeling on Interactive Annotation of Real-time Video
                 Streams",
  journal =      j-TIIS,
  volume =       "14",
  number =       "2",
  pages =        "10:1--10:??",
  month =        jun,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3649457",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:10 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3649457",
  abstract =     "AI-assisted interactive annotation is a powerful way
                 to facilitate data annotation-a prerequisite for
                 constructing robust AI models. While AI-assisted
                 interactive annotation has been extensively studied in
                 static settings, less is known about its usage in
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "10",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Cheng:2024:IWW,
  author =       "Ruijia Cheng and Ruotong Wang and Thomas Zimmermann
                 and Denae Ford",
  title =        "``{It} would work for me too'': How Online Communities
                 Shape Software Developers' Trust in {AI}-Powered Code
                 Generation Tools",
  journal =      j-TIIS,
  volume =       "14",
  number =       "2",
  pages =        "11:1--11:??",
  month =        jun,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3651990",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:10 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3651990",
  abstract =     "While revolutionary AI-powered code generation tools
                 have been rising rapidly, we know little about how and
                 how to help software developers form appropriate trust
                 in those AI tools. Through a two-phase formative study,
                 we investigate how online \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "11",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Liu:2024:SCM,
  author =       "Can Liu and Yu Zhang and Cong Wu and Chen Li and
                 Xiaoru Yuan",
  title =        "A Spatial Constraint Model for Manipulating Static
                 Visualizations",
  journal =      j-TIIS,
  volume =       "14",
  number =       "2",
  pages =        "12:1--12:??",
  month =        jun,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3657642",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:10 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3657642",
  abstract =     "We introduce a spatial constraint model to
                 characterize the positioning and interactions in
                 visualizations, thereby facilitating the activation of
                 static visualizations. Our model provides users with
                 the capability to manipulate visualizations through
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "12",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mo:2024:CMO,
  author =       "George Mo and John Dudley and Liwei Chan and Yi-Chi
                 Liao and Antti Oulasvirta and Per Ola Kristensson",
  title =        "Cooperative Multi-Objective {Bayesian} Design
                 Optimization",
  journal =      j-TIIS,
  volume =       "14",
  number =       "2",
  pages =        "13:1--13:??",
  month =        jun,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3657643",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:10 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3657643",
  abstract =     "Computational methods can potentially facilitate user
                 interface design by complementing designer intuition,
                 prior experience, and personal preference. Framing a
                 user interface design task as a multi-objective
                 optimization problem can help with \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "13",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Spinner:2024:TEG,
  author =       "Thilo Spinner and Rebecca Kehlbeck and Rita
                 Sevastjanova and Tobias St{\"a}hle and Daniel A. Keim
                 and Oliver Deussen and Mennatallah El-Assady",
  title =        "{[tree-emoji]-generAItor}: Tree-in-the-loop Text
                 Generation for Language Model Explainability and
                 Adaptation",
  journal =      j-TIIS,
  volume =       "14",
  number =       "2",
  pages =        "14:1--14:??",
  month =        jun,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3652028",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Tue Jun 25 07:33:10 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3652028",
  abstract =     "Large language models (LLMs) are widely deployed in
                 various downstream tasks, e.g., auto-completion, aided
                 writing, or chat-based text generation. However, the
                 considered output candidates of the underlying search
                 algorithm are under-explored and under-. \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "14",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Li:2024:ISS,
  author =       "Yante Li and Yang Liu and Andy Nguyen and Henglin Shi
                 and Eija Vuorenmaa and Sanna J{\"a}rvel{\"a} and
                 Guoying Zhao",
  title =        "Interactions for Socially Shared Regulation in
                 Collaborative Learning: an Interdisciplinary Multimodal
                 Dataset",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "15:1--15:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3658376",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3658376",
  abstract =     "Socially shared regulation plays a pivotal role in the
                 success of collaborative learning. However, evaluating
                 socially shared regulation of learning (SSRL) proves
                 challenging due to the dynamic and infrequent cognitive
                 and socio-emotional interactions, \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "15",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Humer:2024:RMD,
  author =       "Christina Humer and Andreas Hinterreiter and Benedikt
                 Leichtmann and Martina Mara and Marc Streit",
  title =        "Reassuring, Misleading, Debunking: Comparing Effects
                 of {XAI} Methods on Human Decisions",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "16:1--16:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3665647",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3665647",
  abstract =     "Trust calibration is essential in AI-assisted
                 decision-making. If human users understand the
                 rationale on which an AI model has made a prediction,
                 they can decide whether they consider this prediction
                 reasonable. Especially in high-risk tasks such as
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "16",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Mcintosh:2024:RVA,
  author =       "Timothy R. Mcintosh and Tong Liu and Teo Susnjak and
                 Paul Watters and Malka N. Halgamuge",
  title =        "A Reasoning and Value Alignment Test to Assess
                 Advanced {GPT} Reasoning",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "17:1--17:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3670691",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3670691",
  abstract =     "In response to diverse perspectives on artificial
                 general intelligence (AGI), ranging from potential
                 safety and ethical concerns to more extreme views about
                 the threats it poses to humanity, this research
                 presents a generic method to gauge the reasoning
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "17",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Tsiakas:2024:UHA,
  author =       "Konstantinos Tsiakas and Dave Murray-Rust",
  title =        "Unpacking Human-{AI} interactions: From Interaction
                 Primitives to a Design Space",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "18:1--18:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3664522",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3664522",
  abstract =     "This article aims to develop a semi-formal
                 representation for Human-AI (HAI) interactions, by
                 building a set of interaction primitives which can
                 specify the information exchanges between users and AI
                 systems during their interaction. We show how these
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "18",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Chatti:2024:VRE,
  author =       "Mohamed Amine Chatti and Mouadh Guesmi and Arham
                 Muslim",
  title =        "Visualization for Recommendation Explainability: a
                 Survey and New Perspectives",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "19:1--19:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3672276",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3672276",
  abstract =     "Providing system-generated explanations for
                 recommendations represents an important step toward
                 transparent and trustworthy recommender systems.
                 Explainable recommender systems provide a
                 human-understandable rationale for their outputs. Over
                 the past two \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "19",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Antony:2024:ICC,
  author =       "Victor Nikhil Antony and Chien-Ming Huang",
  title =        "{ID.8}: Co-Creating Visual Stories with Generative
                 {AI}",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "20:1--20:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3672277",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3672277",
  abstract =     "Storytelling is an integral part of human culture and
                 significantly impacts cognitive and socio-emotional
                 development and connection. Despite the importance of
                 interactive visual storytelling, the process of
                 creating such content requires specialized \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "20",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Anderson:2024:MUE,
  author =       "Andrew Anderson and Jimena Noa Guevara and Fatima
                 Moussaoui and Tianyi Li and Mihaela Vorvoreanu and
                 Margaret Burnett",
  title =        "Measuring User Experience Inclusivity in {Human-AI}
                 Interaction via Five User Problem-Solving Styles",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "21:1--21:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3663740",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3663740",
  abstract =     "Motivations: Recent research has emerged on generally
                 how to improve AI products' human-AI interaction (HAI)
                 user experience (UX), but relatively little is known
                 about HAI-UX inclusivity. For example, what kinds of
                 users are supported, and who are left \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "21",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Lawless:2024:WIW,
  author =       "Connor Lawless and Jakob Schoeffer and Lindy Le and
                 Kael Rowan and Shilad Sen and Cristina {St. Hill} and
                 Jina Suh and Bahareh Sarrafzadeh",
  title =        "{``I Want It That Way''}: Enabling Interactive
                 Decision Support Using Large Language Models and
                 Constraint Programming",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "22:1--22:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3685053",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3685053",
  abstract =     "A critical factor in the success of many decision
                 support systems is the accurate modeling of user
                 preferences. Psychology research has demonstrated that
                 users often develop their preferences during the
                 elicitation process, highlighting the pivotal role
                 \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "22",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Choi:2024:EES,
  author =       "Minsoo Choi and Siqi Guo and Alexandros Koilias and
                 Matias Volonte and Dominic Kao and Christos Mousas",
  title =        "Exploring the Effects of Self-Correction Behavior of
                 an Intelligent Virtual Character during a Jigsaw Puzzle
                 Co-Solving Task",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "23:1--23:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3688006",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3688006",
  abstract =     "Although researchers have explored how humans perceive
                 the intelligence of virtual characters, few studies
                 have focused on the ability of intelligent virtual
                 characters to fix their mistakes. Thus, we explored the
                 self-correction behavior of a virtual \ldots{}",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "23",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}

@Article{Berkovsky:2024:TBP,
  author =       "Shlomo Berkovsky",
  title =        "{2023 TiiS Best Paper} announcement",
  journal =      j-TIIS,
  volume =       "14",
  number =       "3",
  pages =        "24:1--24:??",
  month =        sep,
  year =         "2024",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/3690000",
  ISSN =         "2160-6455 (print), 2160-6463 (electronic)",
  ISSN-L =       "2160-6455",
  bibdate =      "Wed Sep 25 11:23:38 MDT 2024",
  bibsource =    "https://www.math.utah.edu/pub/tex/bib/tiis.bib",
  URL =          "https://dl.acm.org/doi/10.1145/3690000",
  acknowledgement = ack-nhfb,
  ajournal =     "ACM Trans. Interact. Intell. Syst.",
  articleno =    "24",
  fjournal =     "ACM Transactions on Interactive Intelligent Systems
                 (TIIS)",
  journal-URL =  "https://dl.acm.org/loi/tiis",
}