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ACM Transactions on

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Adaptive Contextualization Methods for Combating Selection Bias during High-Dimensional Visualization

Large and high-dimensional real-world datasets are being gathered across a wide range of application... (more)

Interacting with Recommenders—Overview and Research Directions

Automated recommendations have become a ubiquitous part of today’s online user experience. These systems point us to additional items to... (more)

Empathy in Virtual Agents and Robots: A Survey

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... (more)

Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference

Experiencing high cognitive load during complex and demanding tasks results in performance reduction, stress, and errors. However, these could be... (more)

Effects of Speed, Cyclicity, and Dimensionality on Distancing, Time, and Preference in Human-Aerial Vehicle Interactions

This article will present a simulation-based approach to testing multiple variables in the behavior... (more)

Active Learning and Visual Analytics for Stance Classification with ALVA

The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and... (more)

NEWS

Most Recent News

  • Call for papers: TiiS special issue on Trust and Influence in Intelligent Human-Machine Interaction. Submission deadline: Nov. 30th, 2016.
  • Call for papers: TiiS special issue on Human-Centered Machine Learning. Submission deadline: December 2nd, 2016.
  • The editors-in-chief of TiiS are pleased to join the ACM Publications Board in announcing the appointment as new editor-in-chief of Dr. Michelle X. Zhou, a leading researcher in the field of interactive intelligent systems and a prominent representative of the Intelligent User Interfaces community of ACM. Dr. Zhou will be supported by Dr. Anbang Xu as information director. The new 3-year editor-in-chief term begins on February 1st; the transition will become visible step by step between now and about mid-February.
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Forthcoming Articles
A Classification Model for Sensing Human Trust in Machines Using EEG and GSR

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 towards 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 paper, two approaches for developing classifier-based empirical trust sensor models are presented that specifically use electroencephalography (EEG) and galvanic skin response (GSR) 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.

Exploring Audience Response in Performing Arts with a Brain┬┐Adaptive Digital Performance System

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 live performance. However, how the audience respond, and how the performance is affected by these responses are the key elements but 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 theatre. 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 BADP system. We also create content-related performing cues for each performance in three different modes. The results of these evaluations show that our algorithm could accurately detect the engagement status and the performing cues have positive impacts on regain audience engagement across different performance types. Our findings open new perspectives in audience-based theatre performance design.

Crowdsourcing Ground Truth for Medical Relation Extraction

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, that 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, that account for ambiguity in both human and machine performance on this task.

Evaluation and Refinement of Clustered Search Results with the Crowd

When searching on the web, 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. As a result, 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 work, 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 in order to find the best one. Finally, the workflow has the crowd spot and fix problems in the best result in order 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.

Chronodes: Interactive Multi-focus Exploration of Event Sequences

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 multi-focus 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 Chronodess efficacy and potential impact in themHealth domain. Ultimately we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research. For a video demonstration of Chronodes, please refer to the provided video figure.

Quantifying Collaboration with a Co-Creative Drawing Agent

This paper reports on the design and evaluation of a co-creative drawing partner called the Drawing Apprentice, which was designed to improvise and collaborate on abstract sketches with users in real time. The system qualifies as a new genre of creative technologies termed casual creators that are meant to creatively engage users and provide enjoyable creative experiences rather than necessarily helping users make a higher quality creative product. We introduce the conceptual framework of participatory sense-making and describe how it can help model and understand open-ended collaboration. We report the results of user studies evaluating different prototypes of the system during an iterative design process. Based on insights from the user studies, we present design recommendations for co-creative agents.

Evaluation of Facial Expression Recognition by A Smart Eyewear for Facial Direction Change, Repeatability and Positional Drift

This paper presents a novel smart eyewear that recognizes a wearer's facial expression in daily scenes. We evaluated our device and showed the robustness to the noise from a wearer's facial direction change, repeatability and the positional drift of the glasses. Our device uses embedded photo reflective sensors and machine learning to recognize a wearer's facial expressions. We leverage the skin deformation when a wearer changes their facial expressions. With small photo reflective sensors, we measure the proximity between the skin surface on a face and the eyewear frame where 17 sensors are integrated. A Support Vector Machine (SVM) algorithm was applied for the sensor information. The sensors can cover various facial muscle movements and can be integrated into everyday glasses.There are various possible scenarios of our devices such as a care system for older adults and mental management. The main contributions of our work are as follows. (1) We evaluated the recognition accuracy in daily scenes. We showed 92.8% accuracy regardless of facial direction, taking on/off by learning those data. Our device can recognize facial expressions with 78.1% accuracy for repeatability, with 87.7% accuracy in case of its positional drift. (2) It is designed and implemented considering social acceptability. The device looks like normal eyewear, so users can wear it anytime, anywhere. (3) Initial field trials in daily life were undertaken. Our work is one of the first attempts to recognize and evaluate a variety of facial expressions in the form of an unobtrusive wearable.

Analysis of Movement Quality in Full-Body Physical Activities

Full-body human movement is characterized by fine-grain expressive qualities that humans are easily capable to exhibit and recognize in others movement. In sports (e.g., martial arts) as well as in 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 an 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 paper, we present a computational framework aiming 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 aiming at providing a quantitative assessment of movement quality, approximating the evaluation 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 attitude and expression (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 fourteen 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 (Pearsons correlations r = 0.84, p = 0.001 and r = 0.75, p = 0.005).

Visualizing Research Impact Through Citation Data

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 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.

VisForum: A Visual Analysis System for Exploring User Groups in Online Forums

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 insight into hierarchical discussions. Simply identifying and comparing groups in a single thread are nontrivial tasks as the number of users and posts increases with time and noises hamper detections of user groups. Researchers in data mining field have proposed a large body of algorithms to explore user grouping, however the result is not revealing to laymen. To address these problems, we present VisForum, a visual analytic system which allows people to interactively explore user groups in a forum. We work closely with two educators who have released courses in MOOC platforms and compile a list of design goals to guide our design. Following a set of design rationales and tasks, 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 & Sorting Algorithm to reduce noises in a collection of posts, and employ the concept of `Forum-index' for end users to identify high-impact forum members. Two case studies using different 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.

A Visual Analytics Framework for Exploring Theme Park Dynamics

In 2015, the top ten 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 paper, 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 analysis, enabling analysts to explore visitor-visitor interactions and visitor-infrastructure interactions. Analysts can explore 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.

Observation-Level and Parametric Interaction for High-Dimensional Data Analysis

Exploring high-dimensional data is challenging. Dimension reduction algorithms, such as weighted multi- dimensional scaling, support data explorations 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 paper, 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 techniques affects 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.

Modeling the Human-Robot Trust Phenomenon: A Conceptual Framework based on Risk

This paper presents a conceptual framework for human-robot trust which uses 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 paper 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 paper concludes with a description of the important areas for future research on the topic of human-robot trust.

Bibliometrics

Publication Years 2011-2017
Publication Count 154
Citation Count 604
Available for Download 154
Downloads (6 weeks) 1544
Downloads (12 Months) 12174
Downloads (cumulative) 58322
Average downloads per article 379
Average citations per article 4
First Name Last Name Award
Gregory Abowd ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics (2009)
ACM Fellows (2008)
ACM Senior Member (2008)
Craig Boutilier ACM Fellows (2012)
Oliver Brdiczka ACM Senior Member (2015)
Peter Brusilovsky ACM Senior Member (2008)
Margaret Burnett ACM Distinguished Member (2015)
Yolanda Gil ACM Fellows (2016)
Michael L Gleicher ACM Distinguished Member (2011)
Tracy Anne Hammond ACM Senior Member (2015)
Andreas Kerren ACM Senior Member (2013)
Joseph A Konstan ACM Software System Award (2010)
ACM Fellows (2008)
ACM Distinguished Member (2006)
Wessel Kraaij ACM Distinguished Member (2017)
ACM Senior Member (2007)
Sarit Kraus ACM Fellows (2014)
Tsvi Kuflik ACM Distinguished Member (2013)
ACM Senior Member (2012)
Robin R Murphy ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science and Informatics (2014)
Jeffrey Nichols ACM Senior Member (2013)
Fabio Paterno ACM Distinguished Member (2009)
Stefano Piana ACM Gordon Bell Prize
Special Category (2009) ACM Gordon Bell Prize
Special Category (2009)
John T Riedl ACM Software System Award (2010)
ACM Fellows (2009)
ACM Distinguished Member (2007)
Tom Rodden ACM Fellows (2014)
Domenico Sacca ACM Senior Member (2007)
Ben Shneiderman ACM Fellows (1997)
Matthew A Turk ACM Senior Member (2007)
Qiang Yang ACM Distinguished Member (2011)

First Name Last Name Paper Counts
Joseph LaViola, 3
Albert Salah 3
Frédéric Bevilacqua 3
Anthony Jameson 3
Kazuhiro Otsuka 3
Shiro Kumano 3
John Riedl 3
Elisabeth André 3
Ryo Ishii 3
Yolanda Gil 2
Magalie Ochs 2
Junji Yamato 2
Kristiina Jokinen 2
Yukiko Nakano 2
Heriberto Cuayáhuitl 2
Nina Dethlefs 2
Louis Morency 2
Catherine Pélachaud 2
Catherine Havasi 2
Federica Cena 2
Lola Cañamero 2
Giuseppe Carenini 2
Alexander Felfernig 2
Henry Lieberman 2
Cristina Gena 2
Ginevra Castellano 2
Eugene Taranta 2
Shlomo Berkovsky 2
Iolanda Leite 2
Matthew Turk 2
Chen Yu 2
Ulf Blanke 2
Michael Jugovac 2
Bart Knijnenburg 2
Kim Bard 2
Bilge Mutlu 2
Ana Paiva 2
Yang Wang 2
Hatice Gunes 2
Gregory Abowd 2
Dietmar Jannach 2
Sidney D'mello 2
Hiroshi Ishiguro 2
Hayley Hung 2
Oya Aran 2
Matthew Marge 1
Tessa Lau 1
Anat Mirelman 1
Maya Sappelli 1
Joao Oliveira 1
Ilhan Aslan 1
Kenji Sagae 1
Jaclyn Ocumpaugh 1
Caleb Southern 1
Branislav Kveton 1
Jean Martens 1
Petteri Nurmi 1
Antti Salovaara 1
Augusto Pieracci 1
Dairazalia Sanchez-Cortes 1
Priscilla Moraes 1
Fernando Nos 1
Elia Bruni 1
Nicu Sebe 1
Pascal Poupart 1
Andrew Monk 1
Ronnie Taib 1
Bo Yin 1
Lixiu Yu 1
Luca Console 1
Mario Mirabelli 1
Federica Protti 1
Giulia Biamino 1
Franco Fassio 1
Wei Song 1
Laurel Riek 1
Christopher Peters 1
Florian Eyben 1
Brett Stevens 1
Hirohisa Furukawa 1
Evelien Van De Garde-Perik 1
Laura Pomarjanschi 1
David Rozado 1
Francisco Rodrıguez 1
Takayuki Kanda 1
Ludger Van Elst 1
Peter Weller 1
Elise Hoven 1
Peter McOwan 1
Alessandra Staglianò 1
Enamul Hoque 1
Chris Newell 1
Gordon Cheng 1
Gary McKeown 1
Sarah Fdili Alaoui 1
Misato Yatsushiro 1
Weiyi Wang 1
Abdulmalik Ofemile 1
Brandon Paulson 1
Sylvie Gibet 1
Wengkeen Wong 1
Yoshinori Kobayashi 1
Ravi Sarvadevabhatla 1
Fang Chen 1
Kostiantyn Kucher 1
Simon Dobson 1
Berardina Carolis 1
Dimitris Plexousakis 1
Steven Bethard 1
Soheil Bahreini 1
James Martin 1
Simon Keizer 1
Yuchao Duan 1
Cheng Zhang 1
Sakti Sakriani 1
Hideki Negoro 1
Jane Hsu 1
Rosalind Picard 1
Paolo Cremonesi 1
Sean Andrist 1
Michael Gleicher 1
Yi Yang 1
John Dill 1
Chris Shaw 1
Peng Wu 1
Charles Greenbacker 1
Daniel Chester 1
Desney Tan 1
Dimitrios Rafailidis 1
Danilo Rodrigues 1
Jasper Uijlings 1
Andreza Sartori 1
Jesse Hoey 1
M Khawaja 1
Arthur Graesser 1
Livio Robaldo 1
Pierluigi Grillo 1
Michele Mioli 1
Rossana Simeoni 1
Kumiko Tanaka-Ishii 1
Keiji Yasuda 1
Michael Glodek 1
Jean Martin 1
Jongseok Lee 1
Joe Finney 1
Monique Lu 1
Serge Offermans 1
Paul Schermerhorn 1
Matthias Scheutz 1
Dan Tasse 1
Francesca Odone 1
Amy Swanson 1
Nilanjan Sarkar 1
Catherine Plaisant 1
Fabian Christoffel 1
Joseph Konstan 1
Belgin Mutlu 1
Ariel Rosenfeld 1
Thibaut Naour 1
Stephen Perona 1
John O'Donovan 1
Simone Stumpf 1
Andrew Ko 1
Mihoko Fukushima 1
Andreas Kerren 1
Eyal Dim 1
Tsvi Kuflik 1
James Young 1
Rohit Kumar 1
Souneil Park 1
Emile Aarts 1
Nicholas Mattei 1
Judy Goldsmith 1
Tessa Lau 1
Heather Leary 1
Giovanni Semeraro 1
Mary Ellen Foster 1
Thaddeus Simons 1
Helmut Prendinger 1
Antonio Sánchez-Ruiz 1
Alexander Meschtscherjakov 1
Yasmine El-Glaly 1
Angelo Cafaro 1
Hannes Vilhjálmsson 1
Nigel Bosch 1
Valerie Shute 1
Rosa Arriaga 1
Satoshi Nakamura 1
Karthik Dinakar 1
Varun Ratnakar 1
Paul Groth 1
Patrik Floréen 1
Charles Callaway 1
Remco Chang 1
Rita Cucchiara 1
Birgit Lugrin 1
Maurits Kaptein 1
Alfred Kobsa 1
Jie Lu 1
Yingjievictor Chen 1
Petros Daras 1
Carlos Correa 1
Patrick Olivier 1
Eric Choi 1
Julien Epps 1
Massimo Poesio 1
Jon Chamberlain 1
Alessandro Marcengo 1
Monica Perrero 1
Amon Rapp 1
Ilaria Torre 1
Fabio Torta 1
Eiichiro Sumita 1
Nick Campbell 1
Toyoaki Nishida 1
Seiichi Yamamoto 1
Hans Gellersen 1
Mathieu Boussard 1
Jodi Forlizzi 1
Chiara Pulice 1
Ziad Bawab 1
Lukas Lerche 1
Derek Bridge 1
Christian Jacquemin 1
David Meignan 1
Evangelos Milios 1
Eduardo Veas 1
Steven Sutherland 1
Tom Rodden 1
Rong Jin 1
Keiichi Yamazaki 1
Nargess Nourbakhsh 1
Magnus Sahlgren 1
Hana Boukricha 1
Ipke Wachsmuth 1
Juan Ye 1
Graeme Stevenson 1
Domenico Redavid 1
Geert Kruijff 1
Fabio Paternò 1
Philipp Wetzler 1
Li Chen 1
Ngoanh Vien 1
Ivana Kruijff-Korbayová 1
Sinziana Mazilu 1
Gerhard Tröster, 1
Katrien Verbert 1
Hiroki Tanaka 1
Timothy Chklovski 1
Oliviero Stock 1
Daniel Gatica-Perez 1
Sandra Carberry 1
Panos Markopoulos 1
David Oliver 1
Jun Rekimoto 1
Jonas Etzold 1
Victoria Yanulevskaya 1
Fang Chen 1
Fabrizio Antonelli 1
Claudia Picardi 1
Daniele Dupré 1
Elisa Chiabrando 1
Matteo Demichelis 1
Andrew Finch 1
Günther Palm 1
Martin WöLlmer 1
Yale Song 1
Joyce Chai 1
Kris Luyten 1
Pierrick Thébault 1
Javier San Agustin 1
Samer Al Moubayed 1
Jens Edlund 1
Georg Buscher 1
Ralf Biedert 1
Fabio Zanzotto 1
Geert Houben 1
Markus Strohmaier 1
Ben Steichen 1
Gawesh Jawaheer 1
Koen Van Boerdonk 1
Donald Glowinski 1
Zachary Warren 1
Roman Bednarik 1
Michael Zehetleitner 1
Hui Zhang 1
Gilles Pesant 1
Ryan Kiros 1
Leigh Clark 1
Carita Paradis 1
Stefano Ferilli 1
Martin Cooney 1
Eelke Folmer 1
Marco Gillies 1
Sangyoung Chung 1
Kirsten Butcher 1
Tamara Sumner 1
Hung Ngo 1
Matthew Luciw 1
Antoine Raux 1
Zhuoran Wang 1
James Deng 1
Nahum Álvarez 1
Manfred Tscheligi 1
Sunghyun Park 1
Peter Brusilovsky 1
Weike Pan 1
Andrés Vargas 1
Dingtian Zhang 1
Hidemi Iwasaka 1
Andrew Gordon 1
Andreas Forsblom 1
David Ebert 1
Roberto Vezzani 1
Paolo Santinelli 1
Gregor Mehlmann 1
Florian Lingenfelser 1
Jeffrey Allen 1
Franca Garzotto 1
Roberto Turrin 1
Boris De Ruyter 1
Tomislav Pejša 1
Mercan Topkara 1
Peter Robinson 1
Kathleen McCoy 1
Edward Schwartz 1
Andreas Bulling 1
Kwanliu Ma 1
Nadir Weibel 1
Alex Mihailidis 1
Bob Kummerfeld 1
Luca Ducceschi 1
Marina Geymonat 1
Piercarlo Grimaldi 1
Vincenzo Cuciti 1
Fausto Giunchiglia 1
Kostas Karpouzis 1
Ashkan Yazdani 1
Andreas Dengel 1
Seungjun Kim 1
Jean Crespo 1
André Pereira 1
Floriane Dardard 1
Giorgio Gnecco 1
Bibek Paudel 1
Karinne Ramirez-Amaro 1
Humera Minhas 1
Marius Kaminskas 1
Yuting Li 1
Ting Zhang 1
Yihsuan Yang 1
Yuanching Teng 1
Jared Bott 1
Jean Frayret 1
Nicolas Gaud 1
Axel Soto 1
Franklin Harper 1
Martijn Willemsen 1
Kyle Duarte 1
Margaret Burnett 1
Victor Ng-Thow-Hing 1
Rafael Calvo 1
Atau Tanaka 1
Mario Gianni 1
Navid Fallah 1
Kostas Bekris 1
Takeo Igarashi 1
Thomas Dodson 1
Jeffrey Nichols 1
Ofra Amir 1
Luciana Benotti 1
Martin Villalba 1
Alfredo Milani 1
Rahul Sukthankar 1
Wessel Kraaij 1
Marc Cavazza 1
Joao Catarino 1
Hansuk Shim 1
Denis Parra 1
Anhong Guo 1
Yenling Kuo 1
Birago Jones 1
Denny Vrandečić 1
Elyon DeKoven 1
Ionut Damian 1
Jesse Vig 1
Zhenyucheryl Qian 1
Apostolos Axenopoulos 1
Stavroula Manolopoulou 1
Udo Kruschwitz 1
Roberto Furnari 1
Ilaria Lombardi 1
Dario Mana 1
Friedhelm Schwenker 1
Masafumi Nishida 1
Melanie Hartman 1
Erhardt Barth 1
Pablo Varona 1
Lorenzo Ferrone 1
Elio Masciari 1
Antonio Camurri 1
Lian Zhang 1
Dayi Bian 1
Medha Sarkar 1
Sana Malik 1
Fan Du 1
Juan Wachs 1
Takashi Yoshino 1
Yutaka Takase 1
Valentin Enescu 1
Christoph Trattner 1
Sarit Kraus 1
Michael Young 1
Tracy Hammond 1
Nicolas Courty 1
Kristina Yordanova 1
Nava Tintarev 1
Chen Liu 1
Shuichi Nishio 1
Baptiste Caramiaux 1
Ilias Apostolopoulos 1
Carolyn Rosé 1
Bruno Zamborlin 1
Seungwoo Kang 1
Junehwa Song 1
Joshua Guerin 1
Pasquale Lops 1
Alexander Förster 1
Jürgen Schmidhuber 1
Hendrik Zender 1
Oliver Lemon 1
Clement Leung 1
Li Chen 1
Moran Dorfman 1
Eran Gazit 1
Jeffrey Hausdorff 1
Suzan Verberne 1
Rui Prada 1
Shuji Fujimoto 1
Andreas Uhl 1
Francis Quek 1
Zhong Ming 1
Ryan Baker 1
Spencer Compton 1
Graham Neubig 1
Tadas Baltrušaitis 1
Reid Swanson 1
Martino Lombardi 1
Johannes Wagner 1
Seniz Demir 1
Pierre Andrews 1
Shimei Pan 1
Shilad Sen 1
Yuhsuan Chan 1
Craig Boutilier 1
Natalie Ruiz 1
Andrea Toso 1
Fabiana Vernero 1
Francesca Carmagnola 1
David Robertson 1
Stefan Scherer 1
Aryel Beck 1
Antoine Hiolle 1
Marina Davila-Ross 1
Jean Vesin 1
David Demirdjian 1
Randall Davis 1
Daniel Schreiber 1
Max Mühlhäuser 1
Oliver Brdiczka 1
Jessica Hodgins 1
Anind Dey 1
Nunziato Cassavia 1
Yi Fang 1
Cristina Conati 1
Kars Lenssen 1
Joyce Chai 1
Carlos Martinho 1
Stefano Piana 1
Joshua Wade 1
Amy Weitlauf 1
Hunghsuan Huang 1
Margrét Bjarnadóttir 1
Tian(Linger) Xu 1
Vlado Kešelj 1
Hichem Sahli 1
Casper Harteveld 1
Svenja Adolphs 1
Yoshinori Kuno 1
Robin Murphy 1
Nicola Montecchio 1
Ehud Sharlin 1
Jalal Mahmud 1
German Ruiz 1
Gregory Smith 1
Francesco Ricci 1
Jawad Nagi 1
Mika Shigematsu 1
Moitreya Chatterjee 1
Brian Ravenet 1
Qiang Yang 1
Yang Li 1
Tomoki Toda 1
Marwa Mahmoud 1
Fabian Bohnert 1
Maria Riveiro 1
Daniel Keim 1
Tobias Baur 1
Patrick Gebhard 1
Daisuke Sakamoto 1
Yangqiu Song 1
Robert Woodbury 1
Stephanie Elzer 1
Emily Grenader 1
Judy Kay 1
Jeffrey Nickerson 1
Fabrizio Franceschi 1
Silvia Likavec 1
Björn Schuller 1
Yukiko Nakano 1
Touradj Ebrahimi 1
David Molyneaux 1
Dominique Decotter 1
Michael Dorr 1
Jonas Beskow 1
Domenico Saccà 1
Patty Kostkova 1
Jing Fan 1
Ben Shneiderman 1
Abraham Bernstein 1
Michael Beetz 1
Sigrid Knust 1
Steven Hoi 1
Todd Kulesza 1
Thomas Kirste 1
Ian Oberst 1
Akiko Yamazaki 1
Keiko Ikeda 1
Sandra Okita 1
Brittany Duncan 1
Fiora Pirri 1
Kyriakos Kritikos 1
Mark D'Inverno 1
Saleema Amershi 1
Ya'akov Gal 1
Jill Freyne 1
Jin Zhao 1
Marco De Gemmis 1
Lutz Frommberger 1

Affiliation Paper Counts
Kansai University 1
Tokyo University of Technology 1
University of New South Wales 1
Palo Alto Research Center Incorporated 1
University of Memphis 1
Northeastern University 1
Macalester College 1
TELECOM ParisTech 1
Lund University 1
Bournemouth University 1
Pontificia Universidad Catolica de Chile 1
Queen's University Belfast 1
Rutgers, The State University of New Jersey 1
National Institute of Infectious Diseases 1
Swedish Institute of Computer Science 1
Ritsumeikan University 1
Laobratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur 1
Fulda University of Applied Sciences 1
Reykjavik University 1
Laboratoire Traitement et Communication de l'Information 1
Harvard School of Engineering and Applied Sciences 1
Fondazione Bruno Kessler 1
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 1
British Broadcasting Corporation 1
Institutions Markets Technologies, Lucca 1
Karlsruhe Institute of Technology 1
University of Eastern Finland 1
IBM, Argentina 1
Max Planck Institute for Informatics 1
University of Padua 1
University of Illinois at Urbana-Champaign 1
University of Michigan 1
University of Miyazaki 1
Florida State University 1
University of Calgary 1
University of Amsterdam 1
Harvard Medical School 1
National Technical University of Athens 1
University of Perugia 1
University of Eastern Piedmont Amedeo Avogadro 1
University of Geneva 1
Yale University 1
Nanyang Technological University 1
Newcastle University, United Kingdom 1
University of Pennsylvania 1
University of Koblenz-Landau 1
Hong Kong University of Science and Technology 1
Vrije Universiteit Amsterdam 1
University of Manitoba 1
University of Skovde 1
Hasselt University 1
Santa Clara University 1
University of Kent 1
University of Dublin, Trinity College 1
BBN Technologies 1
Norwegian University of Science and Technology 1
Institut de Recherche et Coordination Acoustique Musique 1
Federal University of Sao Carlos 1
University of Southern California, Information Sciences Institute 1
University of Pittsburgh 1
Japan Science and Technology Agency 1
University of Stuttgart 1
University of Kentucky 1
University of Tennessee at Martin 1
Yonsei University 1
National University of Singapore 1
Coventry University 1
IBM Thomas J. Watson Research Center 1
Middle Tennessee State University 1
IT University of Copenhagen 1
Monash University 1
University of Louisville 1
West Virginia University 1
Microsoft Research 1
Canon Inc. 1
University College London 1
National University of Cuyo 1
Universite de Technologie Belfort-Montbeliard 1
Osaka University 1
Universite Paris-Sud XI 1
University of California, Santa Cruz 1
University of Maryland, Baltimore County 1
Catholic University of Leuven, Leuven 1
University of Utah 1
University of Konstanz 1
Lawrence Livermore National Laboratory 1
Microsoft Corporation 1
Clemson University 1
Nokia Corporation 1
Ben-Gurion University of the Negev 1
Complutense University of Madrid 1
Stevens Institute of Technology 1
National University of Cordoba 2
Millersville University 2
Bar-Ilan University 2
University of Helsinki 2
University of Haifa 2
University of Rostock 2
National Taiwan University 2
Bogazici University 2
Google Inc. 2
University of California, Irvine 2
Osnabruck University 2
Free University of Bozen-Bolzano 2
Kyoto University 2
Kyushu University 2
IBM Research 2
Technical University of Darmstadt 2
Lubeck University 2
Nara University of Education 2
University of Waterloo 2
University of California, Davis 2
University of Washington, Seattle 2
University of Bielefeld 2
University of York 2
Philips Research 2
Academia Sinica Taiwan 2
Polytechnic School of Montreal 2
Graz University of Technology 2
University of Birmingham 2
Texas A and M University 2
Tufts University 2
Politecnico di Milano 2
University College Cork 2
University of Edinburgh 2
University of Roma Tor Vergata 2
Ludwig Maximilian University of Munich 2
Research Organization of Information and Systems National Institute of Informatics 2
University of Minnesota Twin Cities 2
Southern Illinois University at Carbondale 2
University of California, San Diego 2
Michigan State University 2
University of Roma La Sapienza 2
Institute of Computer Science Crete 2
University of Gastronomic Sciences 2
Laboratoire des sciences de l'information et des sytemes, Marseille 2
Linnaeus University, Vaxjo 2
Shenzhen University 3
University of Essex 3
University of Nevada, Reno 3
Delft University of Technology 3
CNRS Centre National de la Recherche Scientifique 3
Bremen University 3
Doshisha University 3
University of Zurich 3
Dalhousie University 3
Radboud University Nijmegen 3
University of Toronto 3
Texas A and M University System 3
Nokia Bell Labs 3
Queen Mary, University of London 3
University of St Andrews 3
Massachusetts Institute of Technology 3
Lancaster University 3
University of Tokyo 3
City University London 3
Universidad Autonoma de Madrid 3
Vrije Universiteit Brussel 3
University of California, Santa Barbara 3
Commonwealth Scientific and Industrial Research Organization 3
Institut Dalle Molle D'intelligence Artificielle Perceptive 3
Columbia University 3
University of Cambridge 3
Japan National Institute of Information and Communications Technology 3
Universite Paris Saclay 3
Helsinki Institute for Information Technology 4
Advanced Telecommunications Research Institute International (ATR) 4
Royal Institute of Technology 4
University of Salzburg 4
Universite de Bretagne-Sud 4
University of Minnesota System 4
Tel Aviv Sourasky Medical Center 4
University of Ulm 4
Goldsmiths, University of London 4
Hong Kong Baptist University 4
University of Nottingham 4
University of Trento 4
Simon Fraser University 4
Technical University of Munich 4
Indiana University 4
University of Notre Dame 4
University of Genoa 4
University of Portsmouth 4
University of Sydney 4
Swiss Federal Institute of Technology, Zurich 4
Swiss Federal Institute of Technology, Lausanne 4
Saitama University 4
Korea Advanced Institute of Science & Technology 4
University of Hertfordshire 4
University of Maryland 5
TU Dortmund University 5
University of Wisconsin Madison 5
University of Modena and Reggio Emilia 5
The University of British Columbia 5
Dalle Molle Institute for Artificial Intelligence 5
Oregon State University 5
Nara Institute of Science and Technology 5
Georgia Institute of Technology 6
University of Bari 6
University of Delaware 6
Seikei University 6
Purdue University 6
University of Colorado at Boulder 6
Instituto Superior Tecnico 7
MIT Media Laboratory 7
Vanderbilt University 8
Heriot-Watt University, Edinburgh 8
University of Central Florida 8
University of Southern California 8
University of Augsburg 9
German Research Center for Artificial Intelligence (DFKI) 9
Eindhoven University of Technology 9
Telecom Italia 10
Nippon Telegraph and Telephone Corporation 10
CSIRO Data61 11
Carnegie Mellon University 11
University of Turin 20

ACM Transactions on Interactive Intelligent Systems (TiiS)
Archive


2017
Volume 7 Issue 4, November 2017  Issue-in-Progress
Volume 7 Issue 3, October 2017
Volume 7 Issue 2, July 2017
Volume 7 Issue 1, March 2017

2016
Volume 6 Issue 4, December 2016 Special Issue on Human Interaction with Artificial Advice Givers
Volume 6 Issue 3, October 2016 Regular Articles and Special Issue on Highlights of ICMI 2014 (Part 2 of 2)
Volume 6 Issue 2, August 2016 Regular Articles, Special Issue on Highlights of IUI 2015 (Part 2 of 2) and Special Issue on Highlights of ICMI 2014 (Part 1 of 2)
Volume 6 Issue 1, May 2016 Special Issue on New Directions in Eye Gaze for Interactive Intelligent Systems (Part 2 of 2), Regular Articles and Special Issue on Highlights of IUI 2015 (Part 1 of 2)
Volume 5 Issue 4, January 2016 Regular Articles and Special issue on New Directions in Eye Gaze for Interactive Intelligent Systems (Part 1 of 2)

2015
Volume 5 Issue 3, October 2015 Special Issue on Behavior Understanding for Arts and Entertainment (Part 2 of 2) and Regular Articles
Volume 5 Issue 2, July 2015 Special Issue on Behavior Understanding for Arts and Entertainment (Part 1 of 2)
Volume 5 Issue 1, March 2015
Volume 4 Issue 4, January 2015 Special Issue on Activity Recognition for Interaction and Regular Article

2014
Volume 4 Issue 3, October 2014 Special Issue on Multiple Modalities in Interactive Systems and Robots
Volume 4 Issue 2, July 2014
Volume 4 Issue 1, April 2014 Special Issue on Interactive Computational Visual Analytics
Volume 3 Issue 4, January 2014

2013
Volume 3 Issue 3, October 2013
Volume 3 Issue 2, July 2013 Special issue on interaction with smart objects, Special section on eye gaze and conversation
Volume 3 Issue 1, April 2013 Special section on internet-scale human problem solving and regular papers

2012
Volume 2 Issue 4, December 2012 Special issue on highlights of the decade in interactive intelligent systems
Volume 2 Issue 3, September 2012 Special Issue on Common Sense for Interactive Systems
Volume 2 Issue 2, June 2012
Volume 2 Issue 1, March 2012 Special Issue on Affective Interaction in Natural Environments
Volume 1 Issue 2, January 2012

2011
Volume 1 Issue 1, October 2011
 
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