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Introduction to the Special Issue on Highlights of ACM Intelligent User Interface (IUI) 2019 ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-12-02 Oliver Brdiczka; Duen Horng Chau; Minsuk Kahng; Gaëlle Calvary
No abstract available.
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Introduction to the TiiS Special Column ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-23 Michele X. Zhou
No abstract available.
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How Impactful Is Presentation in Email? The Effect of Avatars and Signatures ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-13 Joshua Hailpern; Mark Huber; Ronald Calvo
A primary well-controlled study of 900 participants found that personal presentation choices in professional emails (non-content changes like Profile Avatar 8 Signature) impact the recipient’s perception of the sender’s personality and the quality of the email itself. By understanding the role these choices play, employees can gain better control over how they influence the recipient of their messages
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Personality Sensing: Detection of Personality Traits Using Physiological Responses to Image and Video Stimuli ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-10-15 Ronnie Taib; Shlomo Berkovsky; Irena Koprinska; Eileen Wang; Yucheng Zeng; Jingjie Li
Personality detection is an important task in psychology, as different personality traits are linked to different behaviours and real-life outcomes. Traditionally it involves filling out lengthy questionnaires, which is time-consuming, and may also be unreliable if respondents do not fully understand the questions or are not willing to honestly answer them. In this article, we propose a framework for
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Being the Center of Attention: A Person-Context CNN Framework for Personality Recognition ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-09 Dario Dotti; Mirela Popa; Stylianos Asteriadis
This article proposes a novel study on personality recognition using video data from different scenarios. Our goal is to jointly model nonverbal behavioral cues with contextual information for a robust, multi-scenario, personality recognition system. Therefore, we build a novel multi-stream Convolutional Neural Network (CNN) framework, which considers multiple sources of information. From a given scenario
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An Autonomous Cognitive Empathy Model Responsive to Users’ Facial Emotion Expressions ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-09 Elahe Bagheri; Pablo G. Esteban; Hoang-Long Cao; Albert De Beir; Dirk Lefeber; Bram Vanderborght
Successful social robot services depend on how robots can interact with users. The effective service can be obtained through smooth, engaged, and humanoid interactions in which robots react properly to a user’s affective state. This article proposes a novel Automatic Cognitive Empathy Model, ACEM, for humanoid robots to achieve longer and more engaged human-robot interactions (HRI) by considering humans’
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Modeling Dyslexic Students’ Motivation for Enhanced Learning in E-learning Systems ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-09 Ruijie Wang; Liming Chen; Ivar Solheim
E-Learning systems can support real-time monitoring of learners’ learning desires and effects, thus offering opportunities for enhanced personalized learning. Recognition of the determinants of dyslexic users’ motivation to use e-learning systems is important to help developers improve the design of e-learning systems and educators direct their efforts to relevant factors to enhance dyslexic students’
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Predicting Users’ Movie Preference and Rating Behavior from Personality and Values ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-10-15 Euna Mehnaz Khan; Md. Saddam Hossain Mukta; Mohammed Eunus Ali; Jalal Mahmud
In this article, we propose novel techniques to predict a user’s movie genre preference and rating behavior from her psycholinguistic attributes obtained from the social media interactions. The motivation of this work comes from various psychological studies that demonstrate that psychological attributes such as personality and values can influence one’s decision or choice in real life. In this work
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Learning Context-dependent Personal Preferences for Adaptive Recommendation ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-09 Keita Higuchi; Hiroki Tsuchida; Eshed Ohn-Bar; Yoichi Sato; Kris Kitani
We propose two online-learning algorithms for modeling the personal preferences of users of interactive systems. The proposed algorithms leverage user feedback to estimate user behavior and provide personalized adaptive recommendation for supporting context-dependent decision-making. We formulate preference modeling as online prediction algorithms over a set of learned policies, i.e., policies generated
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A Method and Analysis to Elicit User-Reported Problems in Intelligent Everyday Applications ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-08 Malin Eiband; Sarah Theres Völkel; Daniel Buschek; Sophia Cook; Heinrich Hussmann
The complex nature of intelligent systems motivates work on supporting users during interaction, for example, through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such systems in everyday situations. This article contributes a novel method and analysis to investigate such problems as reported by users: We analysed 45,448
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Algorithmic and HCI Aspects for Explaining Recommendations of Artistic Images ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-08 Vicente Dominguez; Ivania Donoso-Guzmán; Pablo Messina; Denis Parra
Explaining suggestions made by recommendation systems is key to make users trust and accept these systems. This is specially critical in areas such as art image recommendation. Traditionally, artworks are sold in galleries where people can see them physically, and artists have the chance to persuade the people into buying them. On the other side, online art stores only offer the user the action of
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Generating and Understanding Personalized Explanations in Hybrid Recommender Systems ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-08 Pigi Kouki; James Schaffer; Jay Pujara; John O’Donovan; Lise Getoor
Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating and visualizing personalized explanations for recommender systems that incorporate signals from many different data sources. We use a flexible, extendable
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Smell Pittsburgh: Engaging Community Citizen Science for Air Quality ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-08 Yen-Chia Hsu; Jennifer Cross; Paul Dille; Michael Tasota; Beatrice Dias; Randy Sargent; Ting-Hao (Kenneth) Huang; Illah Nourbakhsh
Urban air pollution has been linked to various human health concerns, including cardiopulmonary diseases. Communities who suffer from poor air quality often rely on experts to identify pollution sources due to the lack of accessible tools. Taking this into account, we developed Smell Pittsburgh, a system that enables community members to report odors and track where these odors are frequently concentrated
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Affect-Aware Word Clouds ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-11-08 Tugba Kulahcioglu; Gerard De Melo
Word clouds are widely used for non-analytic purposes, such as introducing a topic to students, or creating a gift with personally meaningful text. Surveys show that users prefer tools that yield word clouds with a stronger emotional impact. Fonts and color palettes are powerful typographical signals that may determine this impact. Typically, these signals are assigned randomly, or expected to be chosen
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Bridging the Gap Between Ethics and Practice: Guidelines for Reliable, Safe, and Trustworthy Human-centered AI Systems ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-10-16 Ben Shneiderman
This article attempts to bridge the gap between widely discussed ethical principles of Human-centered AI (HCAI) and practical steps for effective governance. Since HCAI systems are developed and implemented in multiple organizational structures, I propose 15 recommendations at three levels of governance: team, organization, and industry. The recommendations are intended to increase the reliability
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Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-10-16 Aaron Springer; Steve Whittaker
It is essential that users understand how algorithmic decisions are made, as we increasingly delegate important decisions to intelligent systems. Prior work has often taken a techno-centric approach, focusing on new computational techniques to support transparency. In contrast, this article employs empirical methods to better understand user reactions to transparent systems to motivate user-centric
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Photo Sleuth: Identifying Historical Portraits with Face Recognition and Crowdsourced Human Expertise ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-10-16 Vikram Mohanty; David Thames; Sneha Mehta; Kurt Luther
Identifying people in historical photographs is important for preserving material culture, correcting the historical record, and creating economic value, but it is also a complex and challenging task. In this article, we focus on identifying portraits of soldiers who participated in the American Civil War (1861--65), the first widely photographed conflict. Many thousands of these portraits survive
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PolicyFlow: Interpreting Policy Diffusion in Context ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-06-11 Yongsu Ahn; Yu-Ru Lin
Stability in social, technical, and financial systems, as well as the capacity of organizations to work across borders, requires consistency in public policy across jurisdictions. The diffusion of laws and regulations across political boundaries can reduce the tension that arises between innovation and consistency. Policy diffusion has been a topic of focus across the social sciences for several decades
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Comparing and Combining Interaction Data and Eye-tracking Data for the Real-time Prediction of User Cognitive Abilities in Visualization Tasks ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-05-30 Cristina Conati; Sébastien Lallé; Md Abed Rahman; Dereck Toker
Previous work has shown that some user cognitive abilities relevant for processing information visualizations can be predicted from eye-tracking data. Performing this type of user modeling is important for devising visualizations that can detect a user's abilities and adapt accordingly during the interaction. In this article, we extend previous user modeling work by investigating for the first time
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Designing an AI Health Coach and Studying Its Utility in Promoting Regular Aerobic Exercise ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-05-30 Shiwali Mohan; Anusha Venkatakrishnan; Andrea L. Hartzler
Our research aims to develop interactive, social agents that can coach people to learn new tasks, skills, and habits. In this article, we focus on coaching sedentary, overweight individuals (i.e., “trainees”) to exercise regularly. We employ adaptive goal setting in which the intelligent health coach generates, tracks, and revises personalized exercise goals for a trainee. The goals become incrementally
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Mental Models of Mere Mortals with Explanations of Reinforcement Learning ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-05-30 Andrew Anderson; Jonathan Dodge; Amrita Sadarangani; Zoe Juozapaitis; Evan Newman; Jed Irvine; Souti Chattopadhyay; Matthew Olson; Alan Fern; Margaret Burnett
How should reinforcement learning (RL) agents explain themselves to humans not trained in AI? To gain insights into this question, we conducted a 124-participant, four-treatment experiment to compare participants’ mental models of an RL agent in the context of a simple Real-Time Strategy (RTS) game. The four treatments isolated two types of explanations vs. neither vs. both together. The two types
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Automatic Detection of Usability Problem Encounters in Think-aloud Sessions ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-05-30 Mingming Fan; Yue Li; Khai N. Truong
Think-aloud protocols are a highly valued usability testing method for identifying usability problems. Despite the value of conducting think-aloud usability test sessions, analyzing think-aloud sessions is often time-consuming and labor-intensive. Consequently, previous research has urged the community to develop techniques to support fast-paced analysis. In this work, we took the first step to design
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The Shoutcasters, the Game Enthusiasts, and the AI: Foraging for Explanations of Real-Time Strategy Players ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2020-04-18 Sean Penney; Jonathan Dodge; Andrew Anderson; Claudia Hilderbrand; Logan Simpson; Margaret Burnett
Assessing and understanding intelligent agents is a difficult task for users who lack an AI background. ?Explainable AI? (XAI) aims to address this problem, but what should be in an explanation? One route toward answering this question is to turn to theories of how humans try to obtain information they seek. Information Foraging Theory (IFT) is one such theory. In this paper, we present a series of
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Chronodes: Interactive Multifocus Exploration of Event Sequences. ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2018-03-09 Peter J Polack,Shang-Tse Chen,Minsuk Kahng,Kaya DE Barbaro,Rahul Basole,Moushumi Sharmin,Duen Horng Chau
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
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See You See Me: the Role of Eye Contact in Multimodal Human-Robot Interaction. ACM Trans. Interact. Intell. Syst. (IF 1.63) Pub Date : 2016-05-01 Tian Linger Xu,Hui Zhang,Chen Yu
We focus on a fundamental looking behavior in human-robot interactions - gazing at each other's face. Eye contact and mutual gaze between two social partners are critical in smooth human-human interactions. Therefore, investigating at what moments and in what ways a robot should look at a human user's face as a response to the human's gaze behavior is an important topic. Toward this goal, we developed