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It's A Match! Gesture Generation Using Expressive Parameter Matching arXiv.cs.HC Pub Date : 2021-03-04 Ylva Ferstl; Michael Neff; Rachel McDonnell
Automatic gesture generation from speech generally relies on implicit modelling of the nondeterministic speech-gesture relationship and can result in averaged motion lacking defined form. Here, we propose a database-driven approach of selecting gestures based on specific motion characteristics that have been shown to be associated with the speech audio. We extend previous work that identified expressive
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Gaze-contingent decoding of human navigation intention on an autonomous wheelchair platform arXiv.cs.HC Pub Date : 2021-03-04 Mahendran Subramanian; Suhyung Park; Pavel Orlov; Ali Shafti; A. Aldo Faisal
We have pioneered the Where-You-Look-Is Where-You-Go approach to controlling mobility platforms by decoding how the user looks at the environment to understand where they want to navigate their mobility device. However, many natural eye-movements are not relevant for action intention decoding, only some are, which places a challenge on decoding, the so-called Midas Touch Problem. Here, we present a
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FootApp: an AI-Powered System for Football Match Annotation arXiv.cs.HC Pub Date : 2021-03-04 Silvio Barra; Salvatore M. Carta; Alessandro Giuliani; Alessia Pisu; Alessandro Sebastian Podda; DanieleRiboni
In the last years, scientific and industrial research has experienced a growing interest in acquiring large annotated data sets to train artificial intelligence algorithms for tackling problems in different domains. In this context, we have observed that even the market for football data has substantially grown. The analysis of football matches relies on the annotation of both individual players' and
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Visual Motion Imagery Classification with Deep Neural Network based on Functional Connectivity arXiv.cs.HC Pub Date : 2021-03-04 Byoung-Hee Kwon; Ji-Hoon Jeong; Seong-Whan Lee
Brain-computer interfaces (BCIs) use brain signals such as electroencephalography to reflect user intention and enable two-way communication between computers and users. BCI technology has recently received much attention in healthcare applications, such as neurorehabilitation and diagnosis. BCI applications can also control external devices using only brain activity, which can help people with physical
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Measuring Presence in Augmented Reality Environments: Design and a First Test of a Questionnaire arXiv.cs.HC Pub Date : 2021-03-04 Holger Regenbrecht; Thomas Schubert
Augmented Reality (AR) enriches a user's real environment by adding spatially aligned virtual objects (3D models, 2D textures, textual annotations, etc) by means of special display technologies. These are either worn on the body or placed in the working environment. From a technical point of view, AR faces three major challenges: (1) to generate a high quality rendering, (2) to precisely register (in
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Remote Observation of Field Work on the Farm arXiv.cs.HC Pub Date : 2021-03-04 Wendy Ju; Ilan Mandel; Kevin Weatherwax; Leila Takayama; Nikolas Martelaro; Denis Willett
Travel restrictions and social distancing measures make it difficult to observe, monitor or manage physical fieldwork. We describe research in progress that applies technologies for real-time remote observation and conversation in on-road vehicles to observe field work on a farm. We collaborated on a pilot deployment of this project at Kreher Eggs in upstate New York. We instrumented a tractor with
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Advances in Multi-turn Dialogue Comprehension: A Survey arXiv.cs.HC Pub Date : 2021-03-04 Zhuosheng Zhang; Hai Zhao
Training machines to understand natural language and interact with humans is an elusive and essential task in the field of artificial intelligence. In recent years, a diversity of dialogue systems has been designed with the rapid development of deep learning researches, especially the recent pre-trained language models. Among these studies, the fundamental yet challenging part is dialogue comprehension
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FAtiMA Toolkit -- Toward an effective and accessible tool for the development of intelligent virtual agents and social robots arXiv.cs.HC Pub Date : 2021-03-04 Samuel Mascarenhas; Manuel Guimarães; Pedro A. Santos; João Dias; Rui Prada; Ana Paiva
More than a decade has passed since the development of FearNot!, an application designed to help children deal with bullying through role-playing with virtual characters. It was also the application that led to the creation of FAtiMA, an affective agent architecture for creating autonomous characters that can evoke empathic responses. In this paper, we describe FAtiMA Toolkit, a collection of open-source
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Preference-based Learning of Reward Function Features arXiv.cs.HC Pub Date : 2021-03-03 Sydney M. Katz; Amir Maleki; Erdem Bıyık; Mykel J. Kochenderfer
Preference-based learning of reward functions, where the reward function is learned using comparison data, has been well studied for complex robotic tasks such as autonomous driving. Existing algorithms have focused on learning reward functions that are linear in a set of trajectory features. The features are typically hand-coded, and preference-based learning is used to determine a particular user's
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A Bounded Measure for Estimating the Benefit of Visualization: Case Studies and Empirical Evaluation arXiv.cs.HC Pub Date : 2021-03-03 Min Chen; Alfie Abdul-Rahman; Deborah Silver; Mateu Sbert
Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed
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Decision-makers Processing of AI Algorithmic Advice: Automation Bias versus Selective Adherence arXiv.cs.HC Pub Date : 2021-03-03 Saar Alon-Barkat; Madalina Busuioc
Artificial intelligence algorithms are increasingly adopted as decisional aides by public organisations, with the promise of overcoming biases of human decision-makers. At the same time, the use of algorithms may introduce new biases in the human-algorithm interaction. A key concern emerging from psychology studies regards human overreliance on algorithmic advice even in the face of warning signals
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EmoWrite: A Sentiment Analysis-Based Thought to Text Conversion arXiv.cs.HC Pub Date : 2021-03-03 A. ShahidCOMSATS University Islamabad, Lahore Campus; I. RazaCOMSATS University Islamabad, Lahore Campus; S. A. HussainCOMSATS University Islamabad, Lahore Campus
Brain Computer Interface (BCI) helps in processing and extraction of useful information from the acquired brain signals having applications in diverse fields such as military, medicine, neuroscience, and rehabilitation. BCI has been used to support paralytic patients having speech impediments with severe disabilities. To help paralytic patients communicate with ease, BCI based systems convert silent
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Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment arXiv.cs.HC Pub Date : 2021-03-03 Young-Eun Lee; Seong-Whan Lee
Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by movement artifacts and electromyography signals when users are moving, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer
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Methodology to Assess Quality, Presence, Empathy, Attitude, and Attention in Social VR: International Experiences Use Case arXiv.cs.HC Pub Date : 2021-03-03 Marta Orduna; Pablo Pérez; Jesús Gutiérrez; Narciso García
This paper analyzes the joint assessment of quality, spatial and social presence, empathy, attitude, and attention in three conditions: (A)visualizing and rating the quality of contents in a Head-Mounted Display (HMD), (B)visualizing the contents in an HMD,and (C)visualizing the contents in an HMD where participants can see their hands and take notes. The experiment simulates an immersive communication
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Personal Productivity and Well-being -- Chapter 2 of the 2021 New Future of Work Report arXiv.cs.HC Pub Date : 2021-03-03 Jenna Butler; Mary Czerwinski; Shamsi Iqbal; Sonia Jaffe; Kate Nowak; Emily Peloquin; Longqi Yang
We now turn to understanding the impact that COVID-19 had on the personal productivity and well-being of information workers as their work practices were impacted by remote work. This chapter overviews people's productivity, satisfaction, and work patterns, and shows that the challenges and benefits of remote work are closely linked. Looking forward, the infrastructure surrounding work will need to
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A Bounded Measure for Estimating the Benefit of Visualization: Theoretical Discourse and Conceptual Evaluation arXiv.cs.HC Pub Date : 2021-03-03 Min Chen; Mateu Sbert
Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson-Shannon
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Shape-driven Coordinate Ordering for Star Glyph Sets via Reinforcement Learning arXiv.cs.HC Pub Date : 2021-03-03 Ruizhen Hu; Bin Chen; Juzhan Xu; Oliver van Kaick; Oliver Deussen; Hui Huang
We present a neural optimization model trained with reinforcement learning to solve the coordinate ordering problem for sets of star glyphs. Given a set of star glyphs associated to multiple class labels, we propose to use shape context descriptors to measure the perceptual distance between pairs of glyphs, and use the derived silhouette coefficient to measure the perception of class separability within
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Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks arXiv.cs.HC Pub Date : 2021-03-03 Zhen Fu; Bo Wang; Fei Chen; Xihong Wu; Jing Chen
Hearing-impaired listeners usually have troubles attending target talker in multi-talker scenes, even with hearing aids (HAs). The problem can be solved with eye-gaze steering HAs, which requires listeners eye-gazing on the target. In a situation where head rotates, eye-gaze is subject to both behaviors of saccade and head rotation. However, existing methods of eye-gaze estimation did not work reliably
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The Personalization Paradox: the Conflict between Accurate User Models and Personalized Adaptive Systems arXiv.cs.HC Pub Date : 2021-03-02 Santiago Ontañón; Jichen Zhu
Personalized adaptation technology has been adopted in a wide range of digital applications such as health, training and education, e-commerce and entertainment. Personalization systems typically build a user model, aiming to characterize the user at hand, and then use this model to personalize the interaction. Personalization and user modeling, however, are often intrinsically at odds with each other
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Does Interaction Improve Bayesian Reasoning with Visualization? arXiv.cs.HC Pub Date : 2021-03-02 Ab Mosca; Alvitta Ottley; Remco Chang
Interaction enables users to navigate large amounts of data effectively, supports cognitive processing, and increases data representation methods. However, there have been few attempts to empirically demonstrate whether adding interaction to a static visualization improves its function beyond popular beliefs. In this paper, we address this gap. We use a classic Bayesian reasoning task as a testbed
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Towards a Better Understanding of Social Acceptability arXiv.cs.HC Pub Date : 2021-03-02 Alarith Uhde; Marc Hassenzahl
Social contexts play an important role in understanding acceptance and use of technology. However, current approaches used in HCI to describe contextual influence do not capture it appropriately. On the one hand, the often used Technology Acceptance Model and related frameworks are too rigid to account for the nuanced variations of social situations. On the other hand, Goffman's dramaturgical model
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Natural interaction with traffic control cameras through multimodal interfaces arXiv.cs.HC Pub Date : 2021-03-02 Marco Grazioso; Alessandro Sebastian Podda; Silvio Barra; Francesco Cutugno
Human-Computer Interfaces have always played a fundamental role in usability and commands' interpretability of the modern software systems. With the explosion of the Artificial Intelligence concept, such interfaces have begun to fill the gap between the user and the system itself, further evolving in Adaptive User Interfaces (AUI). Meta Interfaces are a further step towards the user, and they aim at
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I'm all Ears! Listening to Software Developers on Putting GDPR Principles into Software Development Practice arXiv.cs.HC Pub Date : 2021-03-02 Abdulrahman Alhazmi; Nalin Asanka Gamagedara Arachchilage
Previous research has been carried out to identify the impediments that prevent developers from incorporating privacy protocols into software applications. No research has been carried out to find out why developers are not able to develop systems that preserve-privacy while specifically considering the General Data Protection Regulation principles (GDPR principles). Consequently, this paper aims to
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Supporting a Crowd-powered Accessible Online Art Gallery for People with Visual Impairments: A Feasibility Study arXiv.cs.HC Pub Date : 2021-03-01 Nahyun Kwon; Yunjung Lee; Uran Oh
While people with visual impairments are interested in artwork as much as their sighted peers, their experience is limited to few selective artworks that are exhibited at certain museums. To enable people with visual impairments to access and appreciate as many artworks as possible at ease, we propose an online art gallery that allows users to explore different parts of a painting displayed on their
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Between Post-Flaneur and Smartphone Zombie Smartphone Users Altering Visual Attention and Walking Behavior in Public Space arXiv.cs.HC Pub Date : 2021-02-26 Gorsev Argin; Burak Pak; Handan Turkoglu
The extensive use of smartphones in our everyday lives has created new modes of appropriation and behavior in public spaces. Recognition of these are essential for urban design and planning practices which help us to improve the relationship between humans, technologies, and urban environment. This study aims to research smartphone users in public space by observing their altering visual attention
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Um Estudo sobre Atividades Participativas para Soluções IoT para o Home care de Pessoas Idosas arXiv.cs.HC Pub Date : 2021-03-01 Renata de Podestá Gaspar; Rodrigo Bonacin; Vinícius Gonçalves
Population aging in Brazil and in the world occurs at the same time of advances and evolutions in technology. Thus, opportunities for new solutions arise for the elderly, such as innovations in Home Care. With the Internet of Things, it is possible to improve the elderly autonomy, safety and quality of life. However, the design of IoT solutions for elderly Home Care poses new challenges. In this context
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Visualizing Rule Sets: Exploration and Validation of a Design Space arXiv.cs.HC Pub Date : 2021-03-01 Jun Yuan; Oded Nov; Enrico Bertini
Rule sets are often used in Machine Learning (ML) as a way to communicate the model logic in settings where transparency and intelligibility are necessary. Rule sets are typically presented as a text-based list of logical statements (rules). Surprisingly, to date there has been limited work on exploring visual alternatives for presenting rules. In this paper, we explore the idea of designing alternative
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Anticipation Next -- System-sensitive technology development and integration in work contexts arXiv.cs.HC Pub Date : 2021-03-01 Sarah Janboecke; Susanne Zajitschek
When discussing future concerns within socio-technical systems in work contexts, we often find descriptions of missed technology development and integration. The experience of technology that fails whilst being integrated is often rooted in dysfunctional epistemological approaches within the research and development process. Thus, ultimately leading to sustainable technology-distrust in work contexts
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GestureMap: Supporting Visual Analytics and Quantitative Analysis of Motion Elicitation Data by Learning 2D Embeddings arXiv.cs.HC Pub Date : 2021-03-01 Hai Dang; Daniel Buschek
This paper presents GestureMap, a visual analytics tool for gesture elicitation which directly visualises the space of gestures. Concretely, a Variational Autoencoder embeds gestures recorded as 3D skeletons on an interactive 2D map. GestureMap further integrates three computational capabilities to connect exploration to quantitative measures: Leveraging DTW Barycenter Averaging (DBA), we compute average
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Deep Colormap Extraction from Visualizations arXiv.cs.HC Pub Date : 2021-03-01 Lin-Ping Yuan; Wei Zeng; Siwei Fu; Zhiliang Zeng; Haotian Li; Chi-Wing Fu; Huamin Qu
This work presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of 64K visualizations that cover
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Music Genre Bars arXiv.cs.HC Pub Date : 2021-02-27 Swaroop Panda; S. T. Roy
Music Genres, as a popular meta-data of music, are very useful to organize, explore or search music datasets. Soft music genres are weighted multiple-genre annotations to songs. In this initial work, we propose horizontally stacked bar charts to represent a music dataset annotated by these soft music genres. For this purpose, we take an example of a toy dataset consisting of songs labelled with help
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Visualizing Music Genres using a Topic Model arXiv.cs.HC Pub Date : 2021-02-27 Swaroop Panda; V. Namboodiri; S. T. Roy
Music Genres serve as an important meta-data in the field of music information retrieval and have been widely used for music classification and analysis tasks. Visualizing these music genres can thus be helpful for music exploration, archival and recommendation. Probabilistic topic models have been very successful in modelling text documents. In this work, we visualize music genres using a probabilistic
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Software-Supported Audits of Decision-Making Systems: Testing Google and Facebook's Political Advertising Policies arXiv.cs.HC Pub Date : 2021-02-26 J. Nathan Matias; Austin Hounsel; Nick Feamster
How can society understand and hold accountable complex human and algorithmic decision-making systems whose systematic errors are opaque to the outside? These systems routinely make decisions on individual rights and well-being, and on protecting society and the democratic process. Practical and statistical constraints on external audits can lead researchers to miss important sources of error in these
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Casual and Hardcore Player Traits and Gratifications of Pokémon GO, Harry Potter: Wizards Unite, Ingress arXiv.cs.HC Pub Date : 2021-02-26 John Dunham; Konstantinos Papangelis; Nicolas LaLone; Yihong Wang
Location-based games (LBG) impose virtual spaces on top of physical locations. Studies have explored LBG from various perspectives. However, a comprehensive study of who these players are, their traits, their gratifications, and the links between them is conspicuously absent from the literature. In this paper, we aim to address this lacuna through a series of surveys with 2390 active LBG players utilizing
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Learning Human-like Hand Reaching for Human-Robot Handshaking arXiv.cs.HC Pub Date : 2021-02-28 Vignesh Prasad; Ruth Stock-Homburg; Jan Peters
One of the first and foremost non-verbal interactions that humans perform is a handshake. It has an impact on first impressions as touch can convey complex emotions. This makes handshaking an important skill for the repertoire of a social robot. In this paper, we present a novel framework for learning human-robot handshaking behaviours for humanoid robots solely using third-person human-human interaction
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Towards Conversational Humor Analysis and Design arXiv.cs.HC Pub Date : 2021-02-28 Tanishq Chaudhary; Mayank Goel; Radhika Mamidi
Well-defined jokes can be divided neatly into a setup and a punchline. While most works on humor today talk about a joke as a whole, the idea of generating punchlines to a setup has applications in conversational humor, where funny remarks usually occur with a non-funny context. Thus, this paper is based around two core concepts: Classification and the Generation of a punchline from a particular setup
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Cybersecurity Awareness arXiv.cs.HC Pub Date : 2021-02-28 Jason R. C. Nurse
Cybersecurity awareness can be viewed as the level of appreciation, understanding or knowledge of cybersecurity or information security aspects. Such aspects include cognizance of cyber risks and threats, but also appropriate protection measures.
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Model-Agnostic Explainability for Visual Search arXiv.cs.HC Pub Date : 2021-02-28 Mark Hamilton; Scott Lundberg; Lei Zhang; Stephanie Fu; William T. Freeman
What makes two images similar? We propose new approaches to generate model-agnostic explanations for image similarity, search, and retrieval. In particular, we extend Class Activation Maps (CAMs), Additive Shapley Explanations (SHAP), and Locally Interpretable Model-Agnostic Explanations (LIME) to the domain of image retrieval and search. These approaches enable black and grey-box model introspection
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Object affordance as a guide for grasp-type recognition arXiv.cs.HC Pub Date : 2021-02-27 Naoki Wake; Daichi Saito; Kazuhiro Sasabuchi; Hideki Koike; Katsushi Ikeuchi
Recognizing human grasping strategies is an important factor in robot teaching as these strategies contain the implicit knowledge necessary to perform a series of manipulations smoothly. This study analyzed the effects of object affordance-a prior distribution of grasp types for each object-on convolutional neural network (CNN)-based grasp-type recognition. To this end, we created datasets of first-person
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A Brief Survey of Current Software Engineering Practices in Continuous Integration and Automated Accessibility Testing arXiv.cs.HC Pub Date : 2021-02-27 Parth Sane
It's long been accepted that continuous integration (CI) in software engineering increases the code quality of enterprise projects when adhered to by it's practitioners. But is any of that effort to increase code quality and velocity directed towards improving software accessibility accommodations? What are the potential benefits quoted in literature? Does it fit with the modern agile way that teams
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SocNavBench: A Grounded Simulation Testing Framework for Evaluating Social Navigation arXiv.cs.HC Pub Date : 2021-02-26 Abhijat Biswas; Allan Wang; Gustavo Silvera; Aaron Steinfeld; Henny Admoni
The human-robot interaction (HRI) community has developed many methods for robots to navigate safely and socially alongside humans. However, experimental procedures to evaluate these works are usually constructed on a per-method basis. Such disparate evaluations make it difficult to compare the performance of such methods across the literature. To bridge this gap, we introduce SocNavBench, a simulation
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Eliciting and Analysing Users' Envisioned Dialogues with Perfect Voice Assistants arXiv.cs.HC Pub Date : 2021-02-26 Sarah Theres Völkel; Daniel Buschek; Malin Eiband; Benjamin R. Cowan; Heinrich Hussmann
We present a dialogue elicitation study to assess how users envision conversations with a perfect voice assistant (VA). In an online survey, N=205 participants were prompted with everyday scenarios, and wrote the lines of both user and VA in dialogues that they imagined as perfect. We analysed the dialogues with text analytics and qualitative analysis, including number of words and turns, social aspects
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The Virtual Emotion Loop: Towards Emotion-Driven Services via Virtual Reality arXiv.cs.HC Pub Date : 2021-02-26 Davide Andreoletti; Luca Luceri; Tiziano Leidi; Achille Peternier; Silvia Giordano
The importance of emotions in service and product design is well known. Despite this, however, it is still not very well understood how users' emotions can be incorporated in a product or service lifecycle. In this paper, we argue that this gap is due to a lack of a methodological framework for an effective investigation of the emotional response of persons when using products and services. Indeed
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Music-Circles: Can Music Be Represented With Numbers? arXiv.cs.HC Pub Date : 2021-02-26 Seokgi Kim; Jihye Park; Kihong Seong; Namwoo Cho; Junho Min; Hwajung Hong
The world today is experiencing an abundance of music like no other time, and attempts to group music into clusters have become increasingly prevalent. Common standards for grouping music were songs, artists, and genres, with artists or songs exploring similar genres of music seen as related. These clustering attempts serve critical purposes for various stakeholders involved in the music industry.
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Perspectives and solutions towards intelligent ambient assisted living systems arXiv.cs.HC Pub Date : 2021-02-25 Hong Sun; Vincenzo De Florio
The population of the elderly people has kept increasing rapidly over the world in the past decades. Solutions that are able to effectively support the elderly people to live independently at their home are thus urgently needed. Ambient assisted living (AAL) aims to provide products and services with ambient intelligence to build a safe environment around people in need. With the high prevalence of
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Images, Emotions, and Credibility: Effect of Emotional Facial Images on Perceptions of News Content Bias and Source Credibility in Social Media arXiv.cs.HC Pub Date : 2021-02-25 Alireza Karduni; Ryan Wesslen; Douglas Markant; Wenwen Dou
Images are an indispensable part of the news content we consume. Highly emotional images from sources of misinformation can greatly influence our judgements. We present two studies on the effects of emotional facial images on users' perception of bias in news content and the credibility of sources. In study 1, we investigate the impact of happy and angry facial images on users' decisions. In study
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Methods for the Design and Evaluation of HCI+NLP Systems arXiv.cs.HC Pub Date : 2021-02-26 Hendrik Heuer; Daniel Buschek
HCI and NLP traditionally focus on different evaluation methods. While HCI involves a small number of people directly and deeply, NLP traditionally relies on standardized benchmark evaluations that involve a larger number of people indirectly. We present five methodological proposals at the intersection of HCI and NLP and situate them in the context of ML-based NLP models. Our goal is to foster interdisciplinary
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Novelty and Primacy: A Long-Term Estimator for Online Experiments arXiv.cs.HC Pub Date : 2021-02-18 Soheil Sadeghi; Somit Gupta; Stefan Gramatovici; Jiannan Lu; Hao Ai; Ruhan Zhang
Online experiments are the gold standard for evaluating impact on user experience and accelerating innovation in software. However, since experiments are typically limited in duration, observed treatment effects are not always permanently stable, sometimes revealing increasing or decreasing patterns over time. There are multiple causes for a treatment effect to change over time. In this paper, we focus
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3D4ALL: Toward an Inclusive Pipeline to Classify 3D Contents arXiv.cs.HC Pub Date : 2021-02-24 Nahyun Kwon; Chen Liang; Jeeeun Kim
Algorithmic content moderation manages an explosive number of user-created content shared online everyday. Despite a massive number of 3D designs that are free to be downloaded, shared, and 3D printed by the users, detecting sensitivity with transparency and fairness has been controversial. Although sensitive 3D content might have a greater impact than other media due to its possible reproducibility
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Themisto: Towards Automated Documentation Generation in Computational Notebooks arXiv.cs.HC Pub Date : 2021-02-24 April Yi Wang; Dakuo Wang; Jaimie Drozdal; Michael Muller; Soya Park; Justin D. Weisz; Xuye Liu; Lingfei Wu; Casey Dugan
Computational notebooks allow data scientists to express their ideas through a combination of code and documentation. However, data scientists often pay attention only to the code, and neglect creating or updating their documentation during quick iterations, which leads to challenges in sharing their notebooks with others and future selves. Inspired by human documentation practices from analyzing 80
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TeethTap: Recognizing Discrete Teeth Gestures Using Motion and Acoustic Sensing on an Earpiece arXiv.cs.HC Pub Date : 2021-02-24 Wei Sun; Franklin Mingzhe Li; Benjamin Steeper; Songlin Xu; Feng Tian; Cheng Zhang
Teeth gestures become an alternative input modality for different situations and accessibility purposes. In this paper, we present TeethTap, a novel eyes-free and hands-free input technique, which can recognize up to 13 discrete teeth tapping gestures. TeethTap adopts a wearable 3D printed earpiece with an IMU sensor and a contact microphone behind both ears, which works in tandem to detect jaw movement
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Online Mobile App Usage as an Indicator of Sleep Behavior and Job Performance arXiv.cs.HC Pub Date : 2021-02-24 Chunjong Park; Morelle Arian; Xin Liu; Leon Sasson; Jeffrey Kahn; Shwetak Patel; Alex Mariakakis; Tim Althoff
Sleep is critical to human function, mediating factors like memory, mood, energy, and alertness; therefore, it is commonly conjectured that a good night's sleep is important for job performance. However, both real-world sleep behavior and job performance are hard to measure at scale. In this work, we show that people's everyday interactions with online mobile apps can reveal insights into their job
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AutoPreview: A Framework for Autopilot Behavior Understanding arXiv.cs.HC Pub Date : 2021-02-25 Yuan Shen; Niviru Wijayaratne; Peter Du; Shanduojiao Jiang; Katherine Driggs Campbell
The behavior of self driving cars may differ from people expectations, (e.g. an autopilot may unexpectedly relinquish control). This expectation mismatch can cause potential and existing users to distrust self driving technology and can increase the likelihood of accidents. We propose a simple but effective framework, AutoPreview, to enable consumers to preview a target autopilot potential actions
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Facilitating Asynchronous Participatory Design of Open Source Software: Bringing End Users into the Loop arXiv.cs.HC Pub Date : 2021-02-24 Jazlyn Hellman; Jinghui Cheng; Jin L. C. Guo
As open source software (OSS) becomes increasingly mature and popular, there are significant challenges with properly accounting for usability concerns for the diverse end users. Participatory design, where multiple stakeholders collaborate on iterating the design, can be an efficient way to address the usability concerns for OSS projects. However, barriers such as a code-centric mindset and insufficient
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Gaze-Informed Multi-Objective Imitation Learning from Human Demonstrations arXiv.cs.HC Pub Date : 2021-02-25 Ritwik Bera; Vinicius G. Goecks; Gregory M. Gremillion; Vernon J. Lawhern; John Valasek; Nicholas R. Waytowich
In the field of human-robot interaction, teaching learning agents from human demonstrations via supervised learning has been widely studied and successfully applied to multiple domains such as self-driving cars and robot manipulation. However, the majority of the work on learning from human demonstrations utilizes only behavioral information from the demonstrator, i.e. what actions were taken, and
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Towards Unbiased and Accurate Deferral to Multiple Experts arXiv.cs.HC Pub Date : 2021-02-25 Vijay Keswani; Matthew Lease; Krishnaram Kenthapadi
Machine learning models are often implemented in cohort with humans in the pipeline, with the model having an option to defer to a domain expert in cases where it has low confidence in its inference. Our goal is to design mechanisms for ensuring accuracy and fairness in such prediction systems that combine machine learning model inferences and domain expert predictions. Prior work on "deferral systems"
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A New Neuromorphic Computing Approach for Epileptic Seizure Prediction arXiv.cs.HC Pub Date : 2021-02-25 Fengshi Tian; Jie Yang; Shiqi Zhao; Mohamad Sawan
Several high specificity and sensitivity seizure prediction methods with convolutional neural networks (CNNs) are reported. However, CNNs are computationally expensive and power hungry. These inconveniences make CNN-based methods hard to be implemented on wearable devices. Motivated by the energy-efficient spiking neural networks (SNNs), a neuromorphic computing approach for seizure prediction is proposed
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vrCAPTCHA: Exploring CAPTCHA Designs in Virtual Reality arXiv.cs.HC Pub Date : 2021-02-24 Xiang Li; Yuzheng Chen; Rakesh Patibanda; Florian 'Floyd' Mueller
With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) designs for VR devices. In this paper, we first present four traditional two-dimensional (2D) CAPTCHAs (i.e., text-based, image-rotated, image-puzzled, and image-selected CAPTCHAs)
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A Framework for Integrating Gesture Generation Models into Interactive Conversational Agents arXiv.cs.HC Pub Date : 2021-02-24 Rajmund Nagy; Taras Kucherenko; Birger Moell; André Pereira; Hedvig Kjellström; Ulysses Bernardet
Embodied conversational agents (ECAs) benefit from non-verbal behavior for natural and efficient interaction with users. Gesticulation - hand and arm movements accompanying speech - is an essential part of non-verbal behavior. Gesture generation models have been developed for several decades: starting with rule-based and ending with mainly data-driven methods. To date, recent end-to-end gesture generation
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Comparing Completion Time, Accuracy, and Satisfaction in Virtual Reality vs. Desktop Implementation of the Common Coordinate Framework Registration User Interface (CCF RUI) arXiv.cs.HC Pub Date : 2021-02-24 Andreas Bueckle; Kilian Buehling; Patrick C. Shih; Katy Börner
Working with organs and tissue blocks is an essential task in medical environments. In order to prepare specimens for further analysis, wet-bench workers must dissect tissue and collect spatial metadata. The Registration User Interface (RUI) was developed to allow stakeholders in the Human Biomolecular Atlas Program (HuBMAP) to register tissue blocks by size, position, and orientation. The RUI has
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