huashen218/convxai-cia-dataset huggingface dataset:
Title: "I just leaned on it!" Exploring Opportunistic Social Discovery of a Technologically Augmented Cushion
Authors: family: Monastero
given: Beatrice; family: McGookin
given: David; family: Takala
given: Tapio
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: While personal devices are often used to connect online with others far away, public media rarely offers opportunities to connect with collocated individuals. We explore novel interaction strategies to enhance opportunistic collocated sociality through technologically augmented daily objects. ThinkCushion is an augmented cushion allowing users to record and playback audio messages either explicitly or implicitly by leaning on it. We deployed ThinkCushion in an open coworking space and gathered quantitative and qualitative data over one month to unveil how individuals discovered it and interacted. We individuate three modes of discovery (serendipitous, spectated and facilitated) and their relations with situated socio-spatial aspects. We discuss the interplay of active and passive interaction modalities for locally accessing and creating content, and how verbal content can be used in either performative or informative ways. We suggest future research on how to design public technologies supporting collocated sociality already from the phase of technological discovery.
Title: HiveFive: Immersion Preserving Attention Guidance in Virtual Reality
Authors: family: Lange
given: Daniel; family: Stratmann
given: Tim Claudius; family: Gruenefeld
given: Uwe; family: Boll
given: Susanne
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Recent advances in Virtual Reality (VR) technology, such as larger fields of view, have made VR increasingly immersive. However, a larger field of view often results in a user focusing on certain directions and missing relevant content presented elsewhere on the screen. With HiveFive, we propose a technique that uses swarm motion to guide user attention in VR. The goal is to seamlessly integrate directional cues into the scene without losing immersiveness. We evaluate HiveFive in two studies. First, we compare biological motion (from a prerecorded swarm) with non-biological motion (from an algorithm), finding further evidence that humans can distinguish between these motion types and that, contrary to our hypothesis, non-biological swarm motion results in significantly faster response times. Second, we compare HiveFive to four other techniques and show that it not only results in fast response times but also has the smallest negative effect on immersion.
Title: Textlets: Supporting Constraints and Consistency in Text Documents
Authors: family: Han
given: Han L.; family: Renom
given: Miguel A.; family: Mackay
given: Wendy E.; family: Beaudouin-Lafon
given: Michel
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Writing technical documents frequently requires following constraints and consistently using domain-specific terms. We interviewed 12 legal professionals and found that they all use a standard word processor, but must rely on their memory to manage dependencies and maintain consistent vocabulary within their documents. We introduce Textlets, interactive objects that reify text selections into persistent items. We show how Textlets help manage consistency and constraints within the document, including selective search and replace, word count, and alternative wording. Eight participants tested a search-and-replace Textlet as a technology probe. All successfully interacted directly with the Textlet to perform advanced tasks; and most (6/8) spontaneously generated a novel replace-all-then-correct strategy. Participants suggested additional ideas, such as supporting collaborative editing over time by embedding a Textlet into the document to flag forbidden words. We argue that Textlets serve as a generative concept for creating powerful new tools for document editing.
Title: Facilitating Democracy: Concerns from Participatory Design with Asymmetric Stakeholder Relations in Health Care
Authors: family: Dahl
given: Yngve; family: Svanæs
given: Dag
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: This paper addresses how facilitation can implicate what, whose and how perspectives and values become embedded in the results from participatory design activities. Inspired by Donald Schön's reflection-on-action theory, an analysis of our facilitator performances in three design activities involving health care stakeholder groups with asymmetric relations has been performed. The analysis highlights the often subtle and unforeseen ways by which facilitator actions influence who "has a say". The results emphasize how continuous introspective analyses and reflections may improve the facilitator's attentiveness to actions that may inadvertently impede the disfavored party. In the long-term, neglect may threaten the integrity of participatory design as a democratic and empowering design approach. The shift towards a practice-perspective on facilitation goes beyond the efforts of the individual practitioner. The cultivation of the reflective facilitator, a concern of relevance for the Human?Computer Interaction and Participatory Design community as a whole, is considered.
Title: Trigeminal-based Temperature Illusions
Authors: family: Brooks
given: Jas; family: Nagels
given: Steven; family: Lopes
given: Pedro
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: We explore a temperature illusion that uses low-powered electronics and enables the miniaturization of simple warm and cool sensations. Our illusion relies on the properties of certain scents, such as the coolness of mint or hotness of peppers. These odors trigger not only the olfactory bulb, but also the nose's trigeminal nerve, which has receptors that respond to both temperature and chemicals. To exploit this, we engineered a wearable device based on micropumps and an atomizer that emits up to three custom-made "thermal" scents directly to the user's nose. Breathing in these scents causes the user to feel warmer or cooler. We demonstrate how our device renders warmth and cooling sensations in virtual experiences. In our first study, we evaluated six candidate "thermal" scents. We found two hot-cold pairs, with one pair being less identifiable by odor. In our second study, pParticipants rated VR experiences with our device trigeminal stimulants as significantly warmer or cooler than the baseline conditions. Lastly, we believe this offers an alternative to existing thermal feedback devices, which unfortunately rely on power-hungry heat-lamps or Peltier-elements.
Title: CheXplain: Enabling Physicians to Explore and Understand Data-Driven, AI-Enabled Medical Imaging Analysis
Authors: family: Xie
given: Yao; family: Chen
given: Melody; family: Kao
given: David; family: Gao
given: Ge; family: Chen
given: Xiang 'Anthony'
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The recent development of data-driven AI promises to automate medical diagnosis; however, most AI functions as 'black boxes' to physicians with limited computational knowledge. Using medical imaging as a point of departure, we conducted three iterations of design activities to formulate CheXplain — a system that enables physicians to explore and understand AI-enabled chest X-ray analysis: (i) a paired survey between referring physicians and radiologists reveals whether, when, and what kinds of explanations are needed; (ii) a low-fidelity prototype co-designed with three physicians formulates eight key features; and (iii) a high-fidelity prototype evaluated by another six physicians provides detailed summative insights on how each feature enables the exploration and understanding of AI. We summarize by discussing recommendations for future work to design and implement explainable medical AI systems that encompass four recurring themes: motivation, constraint, explanation, and justification.
Title: RCEA: Real-time, Continuous Emotion Annotation for Collecting Precise Mobile Video Ground Truth Labels
Authors: family: Zhang
given: Tianyi; family: El Ali
given: Abdallah; family: Wang
given: Chen; family: Hanjalic
given: Alan; family: Cesar
given: Pablo
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Collecting accurate and precise emotion ground truth labels for mobile video watching is essential for ensuring meaningful predictions. However, video-based emotion annotation techniques either rely on post-stimulus discrete self-reports, or allow real-time, continuous emotion annotations (RCEA) only for desktop settings. Following a user-centric approach, we designed an RCEA technique for mobile video watching, and validated its usability and reliability in a controlled, indoor (N=12) and later outdoor (N=20) study. Drawing on physiological measures, interaction logs, and subjective workload reports, we show that (1) RCEA is perceived to be usable for annotating emotions while mobile video watching, without increasing users' mental workload (2) the resulting time-variant annotations are comparable with intended emotion attributes of the video stimuli (classification error for valence: 8.3%; arousal: 25%). We contribute a validated annotation technique and associated annotation fusion method, that is suitable for collecting fine-grained emotion annotations while users watch mobile videos.
Title: Trackly: A Customisable and Pictorial Self-Tracking App to Support Agency in Multiple Sclerosis Self-Care
Authors: family: Ayobi
given: Amid; family: Marshall
given: Paul; family: Cox
given: Anna L.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Self-tracking is an important part of self-care. However, predefined self-tracking approaches can impede people's agency in managing their health. We investigated a customisable and pictorial self-tracking approach in multiple sclerosis self-management by implementing and conducting a field study of Trackly: a prototype app that supports people in defining and colouring pictorial trackers, such as body shapes. We found that participants utilised the elements of Trackly designed to support agentive behaviour: they defined personally meaningful tracking parameters in their own words, and particularly valued being able to flexibly colour in and make sense of their pictorial trackers. Having been able to support their individual self-care intentions with Trackly, participants reported a spectrum of interrelated experiences of agency, including a sense of ownership, identity, self-awareness, mindfulness, and control. Our findings demonstrate the importance of supporting people's individual needs and creative capacities to foster mindful and personally meaningful engagement with health and wellbeing data.
Title: FrownOnError: Interrupting Responses from Smart Speakers by Facial Expressions
Authors: family: Yan
given: Yukang; family: Yu
given: Chun; family: Zheng
given: Wengrui; family: Tang
given: Ruining; family: Xu
given: Xuhai; family: Shi
given: Yuanchun
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: In the conversations with smart speakers, misunderstandings of users' requests lead to erroneous responses. We propose FrownOnError, a novel interaction technique that enables users to interrupt the responses by intentional but natural facial expressions. This method leverages the human nature that the facial expression changes when we receive unexpected responses. We conducted a first user study (N=12) to understand users' intuitive reactions to the correct and incorrect responses. Our results reveal the significant difference in the frequency of occurrence and intensity of users' facial expressions between two conditions, and frowning and raising eyebrows are intuitive to perform and easy to control. Our second user study (N=16) evaluated the user experience and interruption efficiency of FrownOnError and the third user study (N=12) explored suitable conversation recovery strategies after the interruptions. Our results show that FrownOnError can be accurately detected (precision: 97.4%, recall: 97.6%), provides the most timely interruption compared to the baseline methods of wake-up word and button press, and is rated as most intuitive and easiest to be performed by users.
Title: Understanding User Perception of Automated News Generation System
Authors: family: Oh
given: Changhoon; family: Choi
given: Jinhan; family: Lee
given: Sungwoo; family: Park
given: SoHyun; family: Kim
given: Daeryong; family: Song
given: Jungwoo; family: Kim
given: Dongwhan; family: Lee
given: Joonhwan; family: Suh
given: Bongwon
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Automated journalism refers to the generation of news articles using computer programs. Although it is widely used in practice, its user experience and interface design remain largely unexplored. To understand the user perception of an automated news system, we designed NewsRobot, a research prototype that automatically generated news on major events of the PyeongChang 2018 Winter Olympic Games in real-time. It produces six types of news by combining two kinds of content (general/individualized) and three styles (text, text+image, text+image+sound). A total of 30 users participated in using NewsRobot, completing surveys and interviews on their experience. Our findings are as follows: (1) Users preferred individualized news yet considered it less credible, (2) more presentation elements were appreciated but only if their quality was assured, and (3) NewsRobot was considered factual and accurate yet shallow in depth. Based on our findings, we discuss implications for designing automated journalism user interfaces.
Title: Soma Design and Sensory Misalignment
Authors: family: Tennent
given: Paul; family: Marshall
given: Joe; family: Tsaknaki
given: Vasiliki; family: Windlin
given: Charles; family: Höök
given: Kristina; family: Alfaras
given: Miquel
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: We report on a workshop bringing together researchers working in soma design and sensory misalignment. Creating experiences that make use of sensory misalignment has become increasingly common, often associated with virtual reality research. However, little attention has been paid to how to design such experiences. We argue that the practice of soma design is a relevant candidate method for designing misalignment experiences, since soma design brings with it concepts such as estrangement and disrupting the habitual as a path to design. We further argue that sensory misalignment may in turn extend soma design methods, adding methods for explicitly disrupting sensory perception using technology interventions. Finally, we draw on the findings of that workshop to discuss the ideas of: pluralism in experience; orchestration of overall experience; as well as the broader intersection of soma design and sensory misalignment approaches.
Title: Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences
Authors: family: Wang
given: Ruotong; family: Harper
given: F. Maxwell; family: Zhu
given: Haiyi
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
Keywords: perceived fairness, algorithmoutcome, algorithm development, algorithmic decision-making

Abstract: Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of "outcome favorability" bias.
Title: Platform for Studying Self-Repairing Auto-Corrections in Mobile Text Entry based on Brain Activity, Gaze, and Context
Authors: family: Putze
given: Felix; family: Ihrig
given: Tilman; family: Schultz
given: Tanja; family: Stuerzlinger
given: Wolfgang
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Auto-correction is a standard feature of mobile text entry. While the performance of state-of-the-art auto-correct methods is usually relatively high, any errors that occur are cumbersome to repair, interrupt the flow of text entry, and challenge the user's agency over the process. In this paper, we describe a system that aims to automatically identify and repair auto-correction errors. This system comprises a multi-modal classifier for detecting auto-correction errors from brain activity, eye gaze, and context information, as well as a strategy to repair such errors by replacing the erroneous correction or suggesting alternatives. We integrated both parts in a generic Android component and thus present a research platform for studying self-repairing end-to-end systems. To demonstrate its feasibility, we performed a user study to evaluate the classification performance and usability of our approach.
Title: Non-Verbal Auditory Input for Controlling Binary, Discrete, and Continuous Input in Automotive User Interfaces
Authors: family: Funk
given: Markus; family: Tobisch
given: Vanessa; family: Emfield
given: Adam
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Using auditory input while driving is becoming increasingly popular for making distraction-free inputs while driving. However, we argue that auditory input is more than just using speech. Thus, in this work, we explore using Non-Verbal Auditory Input (NVAI) for interacting with smart assistants while driving. Through an online study with 100 participants, we initially investigated users' input preferences for binary, discrete, and continuous data types. After identifying the top three modalities for NVAI, we subsequently conducted an in-person study with 16 participants. In our study, the participants tested these input modalities for three different input data types regarding their accuracy, driver-distraction, and social acceptability, while operating a driving simulator. The results reveal that, although clapping hands for making input was initially preferred in our online survey, it is snapping fingers for binary input and discrete input and humming for making continuous input that is the preferred NVAI modality while driving.
Title: Optimizing for Happiness and Productivity: Modeling Opportune Moments for Transitions and Breaks at Work
Authors: family: Kaur
given: Harmanpreet; family: Williams
given: Alex C.; family: McDuff
given: Daniel; family: Czerwinski
given: Mary; family: Teevan
given: Jaime; family: Iqbal
given: Shamsi T.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Information workers perform jobs that demand constant multitasking, leading to context switches, productivity loss, stress, and unhappiness. Systems that can mediate task transitions and breaks have the potential to keep people both productive and happy. We explore a crucial initial step for this goal: finding opportune moments to recommend transitions and breaks without disrupting people during focused states. Using affect, workstation activity, and task data from a three-week field study (N=25), we build models to predict whether a person should continue their task, transition to a new task, or take a break. The R-squared values of our models are as high as 0.7, with only 15% error cases. We ask users to evaluate the timing of recommendations provided by a recommender that relies on these models. Our study shows that users find our transition and break recommendations to be well-timed, rating them as 86% and 77% accurate, respectively. We conclude with a discussion of the implications for intelligent systems that seek to guide task transitions and manage interruptions at work.
Title: Right Information, Right Time, Right Place: Physical Alignment and Misalignment in Healthcare Practice
Authors: family: Pine
given: Kathleen H.; family: Chen
given: Yunan
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Implementation of new health information systems such as Electronic Health Records (EHR) is expected to reap many benefits. However, the transition from one information system to another is often associated with inefficiency, ineffectiveness, and patient safety hazards. These negative consequences are difficult to predict and avoid before system transitions take place. The changed physical form of information remains an unexamined facet of healthcare system transitions. Using ethnographic methods in two clinical sites, we discovered a recurrent set of problems that emerged due to physical disconnections between information and practice predicated on implementation of new information systems. "Physical misalignments" are instances where workers cannot bring information sources to hand in the precise time and place in which they are needed. We identify three types of physical misalignments, then discuss how physical misalignments can be proactively identified and corrected before, during, and after implementation of new health information systems.
Title: Improving Reliability of Virtual Collision Responses: A Cue Integration Technique
Authors: family: Do
given: Seungwon; family: Lee
given: Byungjoo
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: In virtual reality (VR), a user's virtual avatar can interact with a virtual object by colliding with it. If collision responses do not occur in the direction that the user expects, the user experiences degradation of accuracy and precision in applications such as VR sports games. In determining the response of a virtual collision, existing physics engines have not considered the direction in which the user perceived and estimated the collision. Based on the cue integration theory, this study presents a statistical model explaining how users estimate the direction of a virtual collision from their body's orientation and velocity vectors. The accuracy and precision of virtual collisions can be improved by 8.77% and 30.29%, respectively, by setting the virtual collision response in the direction that users perceive.