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Predicting User Confidence During Visual Decision Making

Crowdsourcing Ground Truth for Medical Relation Extraction

Evaluation and Refinement of Clustered Search Results with the Crowd

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

NEWS

TiiS 2017 Best Paper Award winners are Marius Kaminskas and Derek Bridge for Diversity, Serendipity, Novelty, and Coverage: A Survey and Empirical Analysis of Beyond-Accuracy Objectives in Recommender Systems from TiiS 7(1)! 

TiiS 2016 Best Paper Award is granted to Weike Pan, Qiang Yang, Yuchao Duan, and Zhong Ming, for their article "Transfer Learning for Semi-Supervised Collaborative Recommendation", appeared in TiiS 6(2). Congratulations to all the authors! 

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Forthcoming Articles
Bi-Level Thresholding: Analyzing the Effect of Repeated Errors in Gesture Input

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 paper, we examine bi-level thresholding, a recognition strategy that uses two threshold: 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 lead 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 significant 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 data set 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.

Creating new technologies for companionable agents to support isolated older adults

This paper 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 twelve different activities, some of which included on-screen games, and forms to complete. The paper 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.

Modeling and Computational Characterization of Twitter Customer Service Conversations

Given the increasing popularity of customer service dialogue on Twitter, analysis of conversation data is essential to understand 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 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.

Interactive Quality Analytics of User-Generated Content. An Integrated Toolkit for the Case of Wikipedia

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: contributions can be of poor quality because anyone, even anonymous users, can participate. Though Wikipedia has defined guidelines as to what makes 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. However, little has been done to support quality assessment of user-generated content through interactive tools that combine automatic methods and human intelligence. We developed WikiLyzer, a Web toolkit comprising three interactive applications designed to assist (i) knowledge discovery experts in creating and testing metrics for quality measurement, (ii) Wikipedia users searching for good articles, and (iii) Wikipedia authors that need to identify weaknesses to improve a particular article. A design study sheds a light on how experts could create complex quality metrics with our tool, while a user study reports on its usefulness to identify high-quality content.

MobInsight: A Framework Using Semantic Neighborhood Features for Localized Interpretations of Urban Mobility

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. ŒThe 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 paper, we present MobInsight, a framework for making localized interpretations of urban mobility that reflƒect 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.

AttentiveVideo : A Multimodal Approach to Quantify Emotional Responses to Mobile Advertisements

Understanding a target audiences emotional responses to video advertisements is crucial to stakeholders. However, traditional methods for collecting such information are slow, expensive, and coarse-grained. We propose AttentiveVideo, an intelligent mobile interface with corresponding inference algorithms to monitor and quantify the effects of mobile video advertising in real time. AttentiveVideo employs a combination of implicit photoplethysmography (PPG) sensing and facial expression analysis (FEA) to predict viewers attention, engagement, and sentiment when watching video advertisements on unmodified smartphones. In a 24-participant study, AttentiveVideo achieved good accuracy on a wide range of emotional measures (the best accuracy = 73.4%, kappa = 0.46 across 9 measures). We also found that the PPG sensing channel and the FEA technique are complementary in both prediction accuracy and signal availability. These findings show the potential for both low-cost collection and deep understanding of emotional responses to mobile video advertisements.

An Active Sleep Monitoring Framework Using Wearables

Sleep is the most important aspect of healthy and active living. Right amount of sleep at the right time helps an individual to protect his physical, mental, cognitive health and maintain his quality of life. The most durative of the Activities of Daily Living (ADL), sleep, has a major synergic influence on a persons 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 paper, we propose an intelligent algorithm to detect the microscopic states of the sleep, which fundamentally constitute the components of a good and bad sleeping behavior 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 the 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.

Enhancing Deep Learning with Visual Interactions

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 paper 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 (hyperspectral) latent structures, that are progressively revealed by deep learning, for fine labeling. We validate our approach through use cases on three high-dimensional datasets.

Exploring Social Recommendations with Visual Diversity-Promoting Interfaces

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 how users adopt diversity-enhanced interfaces to accomplish various real-world tasks. In this paper, we present two attempts at creating a visual diversity-enhanced interface that presents recommendations beyond a simple ranked list. Our goal was to design a recommender system interface to help users explore the different relevance prospects of recommended items in parallel and to stress their diversity. Two within-subject user studies in the context of social recommendation at academic conferences were conducted to compare our visual interfaces. Results from our user study show that the visual interfaces significantly reduced the exploration efforts required for given tasks and helped users to perceive the recommendation diversity. We show that the users examined a diverse set of recommended items while experiencing an improvement in overall user satisfaction. Also, the users' subjective evaluations show significant improvement in many user-centric metrics. Experiences are discussed that shed light on avenues for future interface designs.

Profiling Personality Traits with Games

Trying to understand a players characteristics with regards to a computer game is a major line of research known as player modeling. The purpose of such 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 through a players in-game behavior more abstract personality traits such as the need for cognition and self-esteem. We present evidence that game mechanics that can be broadly adopted by several game genres, such as hints and a players 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.

Its Not Just About Accuracy: Metrics that Matter when Modeling Expert Sketching Ability

Design sketching is an important tool for designers and creative professionals 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 sometimes 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 PerSketchTivity, we aim to mimic the feedback given by an instructor and assess the student drawn sketches to give them insight into the areas they need to improve on. In order to provide effective feedback to users, it is important to identify what features of their sketches they should work on to improve their sketching ability. After consulting with several domain experts in sketching, we came up with an initial list of 22 different metrics that could potentially differentiate expert and novice sketches. We gathered over 2000 sketches from 20 novices and four experts for analysis. Seven metrics were shown to significantly correlate with the quality of expert sketches and provided insight into providing intelligent user feedback as well as an overall model of expert sketching ability.

Toward an Understanding of Trust Repair in Human-Robot Interaction: Current Research and Future Directions

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 much 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. As HRI becomes widespread, so will trust violations; 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 trust is violated. 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. As such, the purpose of this paper is to provide a foundation for exploring human-robot trust repair by drawing upon prior work in the human-robot and human-human trust literature, concluding with recommendations for advancing this body of work.

Towards Universal Spatialization Through Wikipedia-Based Semantic Enhancement

This paper 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. While 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 data sets, 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 an exploratory user study and introduce a novel human-centered evaluation technique that can be used on a variety of scatterplot visualizations.

Dynamic Handwriting Signal Features Predict Domain Expertise

As pen-centric systems increase in the marketplace, they create a parallel need for learning analytic techniques based on dynamic writing. Recent empirical research has shown that signal-level features of dynamic handwriting, such as stroke distance, pressure, and duration, are adapted to conserve total energy expenditure as individuals consolidate expertise in a domain. The aim of this research was to examine how accurately three different machine learning algorithms could automatically classify students by their level of domain expertise, without conducting any written content analysis. Compared with an unsupervised classification accuracy of 71%, a hybrid approach that combined empirical-statistical guidance of machine learning consistently led to correctly classifying 79-92% of students by their expertise level. The hybrid approach also enabled deriving a causal understanding of the basis for prediction success, improved transparency, and a foundation for generalizing results. These findings open up opportunities to design new student-adaptive educational technologies based on individualized data for existing pen-centric systems.

Perceptual Validation for the Generation of Expressive Movements from End-Effector Trajectories

Endowing animated virtual characters with emotionally expressive behaviors is paramount to improve the quality of the interactions between humans and virtual characters. Full-body motion, in particular 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 when generating 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 end-effector trajectories alone. Hence, end-effector trajectories define a reduced motion space that is adequate for the characterization of the expressive qualities of human motion and that is both fitting 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 end-effector 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.

Trusting Virtual Agents: The Effect of Personality

We present an intelligent virtual interviewer that engages with a user in a text-based conversation and automatically infers the users personality traits. We investigate how the personality of a virtual interviewer as well as the personality of a user inferred from a virtual interview influences the users trust in the virtual interviewer from two perspectives: the users willingness to confide in, and listen to, a virtual interviewer. We have developed two virtual interviewers with distinct personalities and deployed them in a series of real-world events. We present findings from four real-world deployments with completed interviews of 1280 users, including 606 actual job applicants. Notably, users are more willing to confide in and listen to a virtual 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 a virtual interviewer, and their willingness to confide in and listen to a virtual interviewer. Finally, we discuss the implications of our work on building hyper-personalized, intelligent agents based on user traits.

Proactive Information Retrieval by Capturing Search Intent from Primary Task Context

A significant fraction of information searches are motivated by the user's primary task, such as documents that the user is reading or writing that trigger the information need and search activity. An ideal search engine would be able to use information inferred from the primary task in order to 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 inferred from the task and relevant and useful information can be proactively retrieved. Data from simulations with several data sets 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.

A Comparison of Adaptive View Techniques for Exploratory 3D Drone Teleoperation

Drone navigation in complex environments poses many problems to teleoperators. Especially in 3D structures like 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 smooth user operation. This real-time adaptive viewpoint system takes robot position, orientation, and 3D pointcloud information into account to modify user-viewpoint to maximize visibility. Our prototype uses simultaneous localization and mapping (SLAM) based reconstruction with an omnidirectional camera and we use 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.

Visual Exploration of Air Quality Data with A Time-Correlation Partitioning Tree Based on Information Theory

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 paper, 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.

Trust-based Multi-Robot Symbolic Motion Planning with a Human-in-the-Loop

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 paper, 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 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 non-trivial multi-robot simulations with direct human input and trust evaluation are provided demonstrating the successful implementation of the trust-based multi-robot symbolic motion planning methods.

Estimating Collective Attention toward a Public Display

Enticing passers-by to a focused interaction with a public display requires taking appropriate action depending on how much attention visitors are already paying to the display. A suchlike system might want to emit a strong signal that makes the inattentive visitor look or turn towards it or choose to give the actual content in a way that indicates that a head-on looking visitor has been registered and is addressed individually (as opposed to a dumb system just playing a message in a loop). The challenge in this connection is to reliably determine the attention of passers-by  both considering single persons and groups of visitors simultaneously appearing within the displays field of view. In this article, we present a model for estimating individual and collective human attention towards a focal stimulus and investigate different technical methods for measuring physical expressive features (i.e. the basis for deriving a persons attention). In the course of an experimental setup we compare a Support Vector Machine (SVM) as a measuring technique, a neural network using a Multilayer Perceptron (MLP) and a Finite State Machine (FSM) and compare the results to a manual reference classification. We carve out strengths and weaknesses and identify the most feasible measuring method with regard to precision of recognition and practical application.

The Effect of Culture on Trust in Automation: Reliability and Workload

Trust in automation has become a topic of intensive study over the past two decades. 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 and transport almost all current studies have employed Western samples primarily from the US. The present study addresses these gaps by running a large sample experiment in three (US, Taiwan and Turkey) diverse cultures using a trust sensitive task consisting of both automated control and target detection subtasks. This paper 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 Hofstedes Cultural Dimensions and Leung and Cohens theory of Cultural Syndromes. These results provide critical implications and insights for enhancing human trust in intelligent automation systems across cultures. Our paper 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

Cues of Violent Intergroup Conflict Diminish Perceptions of Robotic Personhood

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 this shift was mediated by parallel reductions in emotional sympathy with the robot (with no significant effects of threat on attributions of intelligence). 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 between threat, coalitional bias and human-robot interaction.

Visualizing Ubiquitously Sensed Measures of Motor Ability in Multiple Sclerosis: Reflections on communicating machine learning in practice

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 algorithms decision-making process in a way that allows clinicians to integrate the algorithms result into their own decision process. To this end, we present an iterative design research study that draws out challenges of supporting interpretability in a real-world system. The key contribution of this paper 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.

Bibliometrics

Publication Years 2011-2018
Publication Count 170
Citation Count 721
Available for Download 170
Downloads (6 weeks) 1689
Downloads (12 Months) 13552
Downloads (cumulative) 79762
Average downloads per article 469
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 Fellows (2017)
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)
James E. Smith ACM-IEEE CS Eckert-Mauchly Award (1999)
Matthew A Turk ACM Senior Member (2007)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)
Qiang Yang ACM Fellows (2017)
ACM Distinguished Member (2011)

First Name Last Name Paper Counts
Frédéric Bevilacqua 4
Albert Salah 3
Elisabeth André 3
Shiro Kumano 3
Joseph LaViola, 3
John Riedl 3
Ryo Ishii 3
Kazuhiro Otsuka 3
Anthony Jameson 3
Hatice Gunes 2
Kristiina Jokinen 2
Bilge Mutlu 2
Magalie Ochs 2
Ana Paiva 2
Yukiko Nakano 2
Yang Wang 2
Louis Morency 2
Catherine Havasi 2
Oya Aran 2
Federica Cena 2
Catherine Pélachaud 2
Giuseppe Carenini 2
Henry Lieberman 2
Lola Cañamero 2
Heriberto Cuayáhuitl 2
Nina Dethlefs 2
Cristina Gena 2
Ginevra Castellano 2
Alexander Felfernig 2
Shlomo Berkovsky 2
Iolanda Leite 2
Matthew Turk 2
Ulf Blanke 2
Nan Cao 2
Chen Yu 2
Michael Jugovac 2
Bart Knijnenburg 2
Kim Bard 2
Yolanda Gil 2
Dietmar Jannach 2
Gregory Abowd 2
Sidney D'mello 2
Eduardo Veas 2
Hiroshi Ishiguro 2
Evangelos Milios 2
Hayley Hung 2
Eugene Taranta 2
Junji Yamato 2
Joshua Guerin 1
Pasquale Lops 1
Stefan Scherer 1
Björn Schuller 1
Aryel Beck 1
Antoine Hiolle 1
Marina Davila-Ross 1
Jean Vesin 1
David Demirdjian 1
Randall Davis 1
Seyednaser Nourashrafeddin 1
Liangyue Li 1
Moushumi Sharmin 1
Rolando Garcia 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
Stefano Piana 1
Joshua Wade 1
Amy Weitlauf 1
Hunghsuan Huang 1
Tian(Linger) Xu 1
Margrét Bjarnadóttir 1
Martino Lombardi 1
Johannes Wagner 1
Mika Shigematsu 1
Moitreya Chatterjee 1
Fabian Bohnert 1
Robert Woodbury 1
Maria Riveiro 1
Daniel Keim 1
Vlado Kešelj 1
Yoshinori Kuno 1
Hichem Sahli 1
Yukiko Nakano 1
David Molyneaux 1
Dominique Decotter 1
Michael Dorr 1
Jonas Beskow 1
Cecilia Sciascio 1
Nicholas Davis 1
Brian Magerko 1
Kai Kunze 1
Lixiu Yu 1
Luca Console 1
Mario Mirabelli 1
Federica Protti 1
Giulia Biamino 1
Franco Fassio 1
Wei Song 1
Peter Weller 1
Peter McOwan 1
Branislav Kveton 1
Jaclyn Ocumpaugh 1
Caleb Southern 1
Fiora Pirri 1
Priscilla Moraes 1
Pascal Poupart 1
Andrew Monk 1
Ronnie Taib 1
Bo Yin 1
Ed CHI 1
Per Kristensson 1
Brittany Duncan 1
Saleema Amershi 1
Ya'akov Gal 1
Jill Freyne 1
Jin Zhao 1
Marco De Gemmis 1
Florian Eyben 1
Laurel Riek 1
Christopher Peters 1
Brett Stevens 1
Conglei Shi 1
Minsuk Kahng 1
Duenhorng Chau 1
Yuru Lin 1
Yong Wang 1
Qingqing Bi 1
Lutz Frommberger 1
Matthew Marge 1
Tessa Lau 1
Anat Mirelman 1
Alessandra Staglianò 1
Enamul Hoque 1
Augusto Pieracci 1
Hiroki Tanaka 1
Andrew Finch 1
Ben Steichen 1
Gawesh Jawaheer 1
Katrien Verbert 1
Juan Ye 1
Graeme Stevenson 1
Jun Rekimoto 1
Domenico Redavid 1
Geert Kruijff 1
Panos Markopoulos 1
Steven Sutherland 1
Tom Rodden 1
Sandra Carberry 1
David Oliver 1
Fang Chen 1
Lichan Hong 1
Philip Legg 1
Nargess Nourbakhsh 1
Magnus Sahlgren 1
Hana Boukricha 1
Ipke Wachsmuth 1
Philipp Wetzler 1
Li Chen 1
Günther Palm 1
Martin WöLlmer 1
Yale Song 1
Fangzhou Guo 1
Ngoanh Vien 1
Ivana Kruijff-Korbayová 1
Sinziana Mazilu 1
Gerhard Tröster, 1
Donald Glowinski 1
Zachary Warren 1
Roman Bednarik 1
Hui Zhang 1
Victoria Yanulevskaya 1
Daniel Gatica-Perez 1
Nahum Álvarez 1
Manfred Tscheligi 1
Sunghyun Park 1
Andrew Gordon 1
Andreas Forsblom 1
Kwanliu Ma 1
David Ebert 1
Gilles Pesant 1
Abraham Bernstein 1
Domenico Saccà 1
Ehud Sharlin 1
Daisuke Sakamoto 1
Michael Beetz 1
Shun Sun 1
Rita Kundu 1
Hongsong Li 1
Zheng Guan 1
Jeffrey Nickerson 1
Fabrizio Franceschi 1
Silvia Likavec 1
Patty Kostkova 1
Yangqiu Song 1
Tomoki Toda 1
Marwa Mahmoud 1
Brian Ravenet 1
Qiang Yang 1
Yang Li 1
Nicola Montecchio 1
Casper Harteveld 1
Svenja Adolphs 1
Stephanie Elzer 1
Judy Kay 1
Amy Zhang 1
Jessica Self 1
Jules Françoise 1
Robin Murphy 1
Jalal Mahmud 1
German Ruiz 1
Gregory Smith 1
Francesco Ricci 1
Touradj Ebrahimi 1
Yong Wang 1
Robert Krüger 1
Jawad Nagi 1
Jing Fan 1
Ben Shneiderman 1
Tobias Baur 1
Patrick Gebhard 1
Emily Grenader 1
Dairazalia Sanchez-Cortes 1
Fernando Nos 1
Elia Bruni 1
Nicu Sebe 1
Jane Hsu 1
Rosalind Picard 1
John Dill 1
Chris Shaw 1
Sarah Fdili Alaoui 1
Yoshinori Kobayashi 1
Misato Yatsushiro 1
Weiyi Wang 1
Joe Finney 1
Monique Lu 1
Serge Offermans 1
Paul Schermerhorn 1
Matthias Scheutz 1
Dan Tasse 1
Ravi Sarvadevabhatla 1
Brandon Paulson 1
Sylvie Gibet 1
Wengkeen Wong 1
Dimitrios Rafailidis 1
Dimitris Plexousakis 1
Fabian Christoffel 1
Longfei Zhang 1
Masahiko Inami 1
Livio Robaldo 1
Pierluigi Grillo 1
Michele Mioli 1
Rossana Simeoni 1
Kumiko Tanaka-Ishii 1
Keiji Yasuda 1
Yi Yang 1
Sakti Sakriani 1
Hideki Negoro 1
Yuchao Duan 1
Cheng Zhang 1
Simon Dobson 1
Desney Tan 1
Berardina Carolis 1
Paolo Cremonesi 1
Abdulmalik Ofemile 1
Peng Wu 1
Charles Greenbacker 1
Daniel Chester 1
Eelke Folmer 1
Ryan Kiros 1
Andreas Dengel 1
Seungjun Kim 1
Jeffrey Allen 1
Marco Gillies 1
Bibek Paudel 1
Karinne Ramirez-Amaro 1
Humera Minhas 1
Ting Zhang 1
Yuting Li 1
Marius Kaminskas 1
Shuo Yan 1
Yufeng Wu 1
Kunwar Singh 1
Luca Ducceschi 1
Marina Geymonat 1
Piercarlo Grimaldi 1
Vincenzo Cuciti 1
Fausto Giunchiglia 1
Jean Crespo 1
André Pereira 1
Mercan Topkara 1
Hidemi Iwasaka 1
Peter Robinson 1
Peter Brusilovsky 1
Weike Pan 1
Andrés Vargas 1
Dingtian Zhang 1
Andreas Bulling 1
Stefano Ferilli 1
Martin Cooney 1
Franca Garzotto 1
Roberto Turrin 1
Sangyoung Chung 1
Boris De Ruyter 1
Leigh Clark 1
Kathleen McCoy 1
Edward Schwartz 1
Alex Mihailidis 1
Bob Kummerfeld 1
Michelle Dowling 1
Milos Matovic 1
Carita Paradis 1
Kirsten Butcher 1
Tamara Sumner 1
Anca Dumitrache 1
Jesse Hoey 1
M Khawaja 1
Arthur Graesser 1
Ming Wang 1
Chris North 1
James Smith 1
Kristofer Kinsey 1
Fang Chen 1
Kostiantyn Kucher 1
Steven Bethard 1
Soheil Bahreini 1
James Martin 1
Michael Glodek 1
Jean Martin 1
Jongseok Lee 1
Hanghang Tong 1
Peter Polack 1
Yi Yang 1
Simon Keizer 1
Sean Andrist 1
Michael Gleicher 1
Francesca Odone 1
Amy Swanson 1
Nilanjan Sarkar 1
Catherine Plaisant 1
Danilo Rodrigues 1
Andreza Sartori 1
Jasper Uijlings 1
Helmut Prendinger 1
Antonio Sánchez-Ruiz 1
Alexander Meschtscherjakov 1
Yasmine El-Glaly 1
Karthik Dinakar 1
Varun Ratnakar 1
Paul Groth 1
Patrik Floréen 1
Charles Callaway 1
Carlos Correa 1
Yingjievictor Chen 1
Remco Chang 1
Mihoko Fukushima 1
Joseph Konstan 1
Toyoaki Nishida 1
Seiichi Yamamoto 1
Rosane Minghim 1
Kostas Karpouzis 1
Ashkan Yazdani 1
Ehsan Sherkat 1
Xing Liang 1
Huamin Qu 1
Hung Ngo 1
Matthew Luciw 1
Antoine Raux 1
Zhuoran Wang 1
James Deng 1
Tomislav Pejša 1
Floriane Dardard 1
Giorgio Gnecco 1
Roberto Vezzani 1
Paolo Santinelli 1
Gregor Mehlmann 1
Florian Lingenfelser 1
Nadir Weibel 1
Wessel Kraaij 1
Marc Cavazza 1
Joao Catarino 1
Hansuk Shim 1
Yenling Kuo 1
Jesse Vig 1
Birago Jones 1
Denny Vrandečić 1
Elyon DeKoven 1
Zhenyucheryl Qian 1
Yihsuan Yang 1
Yuanching Teng 1
Jared Bott 1
Jean Frayret 1
Nicolas Gaud 1
Axel Soto 1
Franklin Harper 1
Masafumi Nishida 1
Melanie Hartman 1
Erhardt Barth 1
Pablo Varona 1
Lorenzo Ferrone 1
Elio Masciari 1
Victor Ng-Thow-Hing 1
Kyle Duarte 1
Margaret Burnett 1
Navid Fallah 1
Kostas Bekris 1
Chiara Pulice 1
Hans Gellersen 1
Mathieu Boussard 1
Jodi Forlizzi 1
Thibaut Naour 1
Simone Stumpf 1
Stephen Perona 1
Andrew Ko 1
James Young 1
Rohit Kumar 1
Petros Daras 1
Derek Bridge 1
Gangyi Ding 1
Tianyu Huang 1
Brenda Lin 1
Yuta Sugiura 1
Masa Ogata 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
Ziad Bawab 1
Jie Lu 1
Satoshi Nakamura 1
Angelo Cafaro 1
Hannes Vilhjálmsson 1
Nigel Bosch 1
Valerie Shute 1
Rosa Arriaga 1
Eyal Dim 1
Tsvi Kuflik 1
Souneil Park 1
Maurits Kaptein 1
Emile Aarts 1
Belgin Mutlu 1
Ariel Rosenfeld 1
John O'Donovan 1
Patrick Olivier 1
Eric Choi 1
Julien Epps 1
John Wenskovitch 1
Scotland Leman 1
Andreas Kerren 1
Katsutoshi Masai 1
Apostolos Axenopoulos 1
Stavroula Manolopoulou 1
Juan Wachs 1
Udo Kruschwitz 1
Roberto Furnari 1
Ilaria Lombardi 1
Dario Mana 1
Denis Parra 1
Anhong Guo 1
Atau Tanaka 1
Mario Gianni 1
Martijn Willemsen 1
Jilin Chen 1
Ian Crandell 1
John Dudley 1
Rafael Calvo 1
Thomas Dodson 1
Jeffrey Nichols 1
Ofra Amir 1
Friedhelm Schwenker 1
Huamin Qu 1
Siwei Fu 1
Luciana Benotti 1
Martin Villalba 1
Alfredo Milani 1
Rahul Sukthankar 1
Antonio Camurri 1
Lian Zhang 1
Dayi Bian 1
Medha Sarkar 1
Sana Malik 1
Fan Du 1
Ionut Damian 1
Suzan Verberne 1
Rui Prada 1
Shuji Fujimoto 1
Andreas Uhl 1
Francis Quek 1
Shilad Sen 1
Reid Swanson 1
Yuhsuan Chan 1
Chen Liu 1
Takashi Yoshino 1
Yutaka Takase 1
Kristina Yordanova 1
Valentin Enescu 1
Maya Sappelli 1
Joao Oliveira 1
Ilhan Aslan 1
Kenji Sagae 1
Jean Martens 1
Petteri Nurmi 1
Antti Salovaara 1
Sigrid Knust 1
Akiko Yamazaki 1
Keiko Ikeda 1
Thomas Kirste 1
Hirohisa Furukawa 1
Evelien Van De Garde-Perik 1
Elise Hoven 1
Laura Pomarjanschi 1
David Rozado 1
Francisco Rodrıguez 1
Takayuki Kanda 1
Ludger Van Elst 1
Sandra Okita 1
Steven Hoi 1
Todd Kulesza 1
Ian Oberst 1
Takeo Igarashi 1
Kyriakos Kritikos 1
Mark D'Inverno 1
Chris Newell 1
Gordon Cheng 1
Gary McKeown 1
Alfred Kobsa 1
Nicholas Mattei 1
Judy Goldsmith 1
Tessa Lau 1
Heather Leary 1
Giovanni Semeraro 1
Nick Campbell 1
Shangtse Chen 1
Kaya De Barbaro 1
Rahul Basole 1
Michael Steptoe 1
Ross Maciejewski 1
Mary Ellen Foster 1
Thaddeus Simons 1
Lukas Lerche 1
Rita Cucchiara 1
Birgit Lugrin 1
Timothy Chklovski 1
Oliviero Stock 1
Christian Jacquemin 1
David Meignan 1
Keiichi Yamazaki 1
Joyce Chai 1
Kris Luyten 1
Pierrick Thébault 1
Koen Van Boerdonk 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
Rong Jin 1
Jonas Etzold 1
Fabio Paternò 1
Michael Zehetleitner 1
Anne Meyer 1
Ningxiao Sun 1
Chihpin Hsiao 1
Fabrizio Antonelli 1
Claudia Picardi 1
Daniele Dupré 1
Elisa Chiabrando 1
Matteo Demichelis 1
Kars Lenssen 1
Daniel Schreiber 1
Max Mühlhäuser 1
Oliver Brdiczka 1
Jessica Hodgins 1
Joyce Chai 1
Anind Dey 1
Nunziato Cassavia 1
Tracy Hammond 1
Nicolas Courty 1
Ilias Apostolopoulos 1
Carolyn Rosé 1
Bruno Zamborlin 1
David Gotz 1
Vedran Sabol 1
Maki Sugimoto 1
Andrea Toso 1
Fabiana Vernero 1
Francesca Carmagnola 1
David Robertson 1
Yi Fang 1
Cristina Conati 1
Carlos Martinho 1
Shimei Pan 1
Graham Neubig 1
Tadas Baltrušaitis 1
Zhong Ming 1
Ryan Baker 1
Spencer Compton 1
Shuichi Nishio 1
Baptiste Caramiaux 1
Pierre Andrews 1
Seungwoo Kang 1
Junehwa Song 1
Christoph Trattner 1
Sarit Kraus 1
Michael Young 1
Nava Tintarev 1
Seniz Demir 1
Craig Boutilier 1
Natalie Ruiz 1
Lora Aroyo 1
Christopher Welty 1
Wei Chai 1
Jinjun Xu 1
Leanna House 1

Affiliation Paper Counts
Ritsumeikan University 1
Beihang University 1
Karlsruhe Institute of Technology 1
Lawrence Livermore National Laboratory 1
Federal University of Sao Carlos 1
University of Dublin, Trinity College 1
Laobratoire d'Informatique pour la Mecanique et les Sciences de l'Ingenieur 1
Harvard Medical School 1
National Institute of Infectious Diseases 1
Universite Paris-Sud XI 1
Microsoft Research 1
IBM, Argentina 1
Rutgers, The State University of New Jersey 1
University of Calgary 1
Middle Tennessee State University 1
University of Miyazaki 1
University of Konstanz 1
Stevens Institute of Technology 1
Hasselt University 1
Palo Alto Research Center Incorporated 1
Osaka University 1
University College London 1
University of Illinois at Urbana-Champaign 1
Pontificia Universidad Catolica de Chile 1
Florida State University 1
University of Kent 1
University of New South Wales 1
Institutions Markets Technologies, Lucca 1
University of Louisville 1
Swedish Institute of Computer Science 1
Vrije Universiteit Amsterdam 1
Fondazione Bruno Kessler 1
University of Amsterdam 1
University of Memphis 1
Coventry University 1
National Technical University of Athens 1
Complutense University of Madrid 1
Institut de Recherche et Coordination Acoustique Musique 1
Norwegian University of Science and Technology 1
Clemson University 1
Canon Inc. 1
University of Maryland, Baltimore County 1
University of Southern California, Information Sciences Institute 1
Kyushu University, Faculty of Medical Sciences 1
University of Tennessee at Martin 1
University of Kentucky 1
Ben-Gurion University of the Negev 1
Fulda University of Applied Sciences 1
University of Geneva 1
University of Eastern Finland 1
Laboratoire Traitement et Communication de l'Information 1
Queen's University Belfast 1
Tokyo University of Technology 1
Macalester College 1
University of California, Santa Cruz 1
Monash University 1
University of Michigan 1
IT University of Copenhagen 1
Microsoft Corporation 1
Yale University 1
University of Sao Paulo 1
Western Washington University 1
University of Padua 1
University of Utah 1
Northeastern University 1
Bournemouth University 1
BBN Technologies 1
Institut Mines Telecom 1
British Broadcasting Corporation 1
National Institute of Advanced Industrial Science and Technology 1
Kansai University 1
University of Eastern Piedmont Amedeo Avogadro 1
Kyushu University 1
Yonsei University 1
University of Koblenz-Landau 1
IBM Thomas J. Watson Research Center 1
Catholic University of Leuven, Leuven 1
Universite de Technologie Belfort-Montbeliard 1
Harvard School of Engineering and Applied Sciences 1
Istituto di Scienza e Tecnologie dell'Informazione A. Faedo 1
Lund University 1
University of Skovde 1
Santa Clara University 1
Reykjavik University 1
University of Perugia 1
University of Manitoba 1
Japan Science and Technology Agency 1
Newcastle University, United Kingdom 1
Nokia Corporation 1
West Virginia University 1
Max Planck Institute for Informatics 1
University of Pennsylvania 1
National University of Singapore 1
University of Pittsburgh 2
Google Inc. 2
Osnabruck University 2
University of Haifa 2
University College Cork 2
Know-Center, Graz 2
University of Edinburgh 2
Technical University of Darmstadt 2
Laboratoire des sciences de l'information et des sytemes, Marseille 2
University of Roma La Sapienza 2
University of York 2
National University of Cuyo 2
Southern Illinois University at Carbondale 2
University of Rostock 2
University of California, San Diego 2
IBM Research 2
National Taiwan University 2
University of Gastronomic Sciences 2
Bar-Ilan University 2
Ludwig Maximilian University of Munich 2
University of Bielefeld 2
University of Stuttgart 2
Nanyang Technological University 2
University of Washington, Seattle 2
Research Organization of Information and Systems National Institute of Informatics 2
Bogazici University 2
Polytechnic School of Montreal 2
Philips Research 2
Linnaeus University, Vaxjo 2
University of Waterloo 2
Kyoto University 2
University of Roma Tor Vergata 2
Texas A and M University 2
Nara University of Education 2
Lubeck University 2
National University of Cordoba 2
Academia Sinica Taiwan 2
Free University of Bozen-Bolzano 2
University of Minnesota Twin Cities 2
University of California, Davis 2
University of Birmingham 2
Tongji University 2
Michigan State University 2
Politecnico di Milano 2
University of California, Irvine 2
Graz University of Technology 2
Institute of Computer Science Crete 2
University of Helsinki 2
Tufts University 2
Millersville University 2
University of Cambridge 3
University of Nevada, Reno 3
Radboud University Nijmegen 3
Delft University of Technology 3
University of St Andrews 3
Nokia Bell Labs 3
Columbia University 3
Queen Mary, University of London 3
Lancaster University 3
Shenzhen University 3
Bremen University 3
University of Toronto 3
University of Essex 3
Doshisha University 3
Institut Dalle Molle D'intelligence Artificielle Perceptive 3
Commonwealth Scientific and Industrial Research Organization 3
University of California, Santa Barbara 3
University of Zurich 3
Vrije Universiteit Brussel 3
Japan National Institute of Information and Communications Technology 3
Massachusetts Institute of Technology 3
Universidad Autonoma de Madrid 3
City University London 3
Texas A and M University System 3
CNRS Centre National de la Recherche Scientifique 3
Universite Paris Saclay 3
The University of North Carolina at Chapel Hill 4
Indiana University 4
Hong Kong Baptist University 4
University of Portsmouth 4
University of Notre Dame 4
Simon Fraser University 4
Advanced Telecommunications Research Institute International (ATR) 4
The University of Sydney 4
University of Hertfordshire 4
University of Minnesota System 4
Universite de Bretagne-Sud 4
University of Ulm 4
University of Nottingham 4
Keio University 4
University of Trento 4
Korea Advanced Institute of Science & Technology 4
Helsinki Institute for Information Technology 4
Swiss Federal Institute of Technology, Zurich 4
Tel Aviv Sourasky Medical Center 4
University of Salzburg 4
University of Genoa 4
Saitama University 4
Technical University of Munich 4
Swiss Federal Institute of Technology, Lausanne 4
University of Tokyo 4
Goldsmiths, University of London 4
Royal Institute of Technology 4
Oregon State University 5
TU Dortmund University 5
University of Wisconsin Madison 5
University of Maryland 5
University of Modena and Reggio Emilia 5
Nara Institute of Science and Technology 5
Dalle Molle Institute for Artificial Intelligence 5
The University of British Columbia 5
Dalhousie University 6
Arizona State University 6
University of Colorado at Boulder 6
University of Delaware 6
Purdue University 6
University of Bari 6
Seikei University 6
Hong Kong University of Science and Technology 7
Beijing Institute of Technology 7
MIT Media Laboratory 7
Instituto Superior Tecnico 7
Heriot-Watt University, Edinburgh 8
Vanderbilt University 8
University of Southern California 8
University of Central Florida 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
Carnegie Mellon University 11
CSIRO Data61 11
Georgia Institute of Technology 17
University of Turin 20

ACM Transactions on Interactive Intelligent Systems (TiiS)
Archive


2018
Volume 8 Issue 2, June 2018  Issue-in-Progress
Volume 8 Issue 1, March 2018 Special Issue on Interactive Visual Analysis of Human Crowd Behaviors and Regular Paper

2017
Volume 7 Issue 4, December 2017 Special Issue on IUI 2016 Highlights
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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