huashen218/convxai-cia-dataset huggingface dataset:
Title: Celebrating Everyday Success: Improving Engagement and Motivation using a System for Recording Daily Highlights
Authors: family: Avrahami
given: Daniel; family: Williams
given: Kristin; family: Lee
given: Matthew L.; family: Tokunaga
given: Nami; family: Tjahjadi
given: Yulius; family: Marlow
given: Jennifer
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The demands of daily work offer few opportunities for workers to take stock of their own progress, big or small, which can lead to lower motivation, engagement, and higher risk of burnout. We present Highlight Matome, a personal online tool that encourages workers to quickly record and rank a single work highlight each day, helping them gain awareness of their own successes. We describe results from a field experiment investigating our tool's effectiveness for improving workers' engagement, perceptions, and affect. Thirty-three knowledge workers in Japan and the U.S. used Highlight Matome for six weeks. Our results show that using our tool for less than one minute each day significantly increased measures of work engagement, dedication, and positivity. A qualitative analysis of the highlights offers a window into participants' emotions and perceptions. We discuss implications for theories of inner work life and worker well-being.
Title: Embedding a VR Game Studio in a Sedentary Workplace: Use, Experience and Exercise Benefits
Authors: family: Yoo
given: Soojeong; family: Gough
given: Phillip; family: Kay
given: Judy
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Many people, especially those in sedentary occupations, fail to achieve the recommended levels of physical activity. Virtual reality (VR) games have the potential to overcome this because they are fun and also can be physically demanding. This paper explores whether a VR game studio can help workers in sedentary jobs to get valuable levels of exercise. We studied how 11 participants used our VR game studio in a sedentary workplace over 8-weeks and their perceptions of the experience. We analysed the physical exertion in the VR game studio, comparing this to their step counts from a smartwatch. All participants achieved valuable levels of physical activity and mood benefits. Importantly, for 6 participants, only with the VR game studio did they meet recommended activity levels. Our key contributions are insights about the use of a workplace VR game studio and its health benefits.
Title: Embodiment Effects in Interactions with Failing Robots
Authors: family: Kontogiorgos
given: Dimosthenis; family: van Waveren
given: Sanne; family: Wallberg
given: Olle; family: Pereira
given: Andre; family: Leite
given: Iolanda; family: Gustafson
given: Joakim
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The increasing use of robots in real-world applications will inevitably cause users to encounter more failures in interactions. While there is a longstanding effort in bringing human-likeness to robots, how robot embodiment affects users' perception of failures remains largely unexplored. In this paper, we extend prior work on robot failures by assessing the impact that embodiment and failure severity have on people's behaviours and their perception of robots. Our findings show that when using a smart-speaker embodiment, failures negatively affect users' intention to frequently interact with the device, however not when using a human-like robot embodiment. Additionally, users significantly rate the human-like robot higher in terms of perceived intelligence and social presence. Our results further suggest that in higher severity situations, human-likeness is distracting and detrimental to the interaction. Drawing on quantitative findings, we discuss benefits and drawbacks of embodiment in robot failures that occur in guided tasks.
Title: PuffPacket: A Platform for Unobtrusively Tracking the Fine-grained Consumption Patterns of E-cigarette Users
Authors: family: Adams
given: Alexander T.; family: Mandel
given: Ilan; family: Shats
given: Anna; family: Robin
given: Alina; family: Choudhury
given: Tanzeem
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The proliferation of e-cigarettes and portable vaporizers presents new opportunities for accurately and unobtrusively tracking e-cigarette use. PuffPacket is a hardware and soft-ware research platform that leverages the technology built into vaporizers, e-cigarettes and other electronic drug delivery devices to ubiquitously track their usage. The system piggybacks on the signals these devices use to directly measure and track the nicotine consumed by users. PuffPacket augments e-cigarettes with Bluetooth to calculate the frequency, intensity, and duration of each inhalation. This information is augmented with smartphone-based location and activity information to help identify potential contextual triggers. Puff-Packet is generalizable to a wide variety of electronic nicotine,THC, and other drug delivery devices currently on the mar-ket. The hardware and software for PuffPacket is open-source so it can be expanded upon and leveraged for mobile health tracking research.
Title: WeDA: Designing and Evaluating A Scale-driven Wearable Diagnostic Assessment System for Children with ADHD
Authors: family: Jiang
given: Xinlong; family: Chen
given: Yiqiang; family: Huang
given: Wuliang; family: Zhang
given: Teng; family: Gao
given: Chenlong; family: Xing
given: Yunbing; family: Zheng
given: Yi
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders affecting children. Because the etiology of ADHD is complex and its symptoms are not specific, there is a lack of feasible quantitative diagnostic methods. Pursuing objective and non-invasive detection methods and standards is of great practical significance to prevent the development of the disease. In this study, we aim to address one specific concern about the objectivity and quantification of ADHD diagnosis. Over a year, we iteratively designed and tested WeDA, a scale-driven wearable diagnostic assessment system. This system contains an Android computer machine with a large touchscreen, a suite of 3D printed interactive devices, and six wearable motion sensors. We implement ten diagnostic tasks drawing on the symptoms of ADHD based on DSM-5. The experimental results of classifying children with ADHD and typically developing children and subjective evaluations from doctors, parents, and children validate the effectiveness and acceptability of WeDA.
Title: Crowdsourced Detection of Emotionally Manipulative Language
Authors: family: Huffaker
given: Jordan S.; family: Kummerfeld
given: Jonathan K.; family: Lasecki
given: Walter S.; family: Ackerman
given: Mark S.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Detecting rhetoric that manipulates readers' emotions requires distinguishing intrinsically emotional content (IEC; e.g., a parent losing a child) from emotionally manipulative language (EML; e.g., using fear-inducing language to spread anti-vaccine propaganda). However, this remains an open classification challenge for both automatic and crowdsourcing approaches. Machine Learning approaches only work in narrow domains where labeled training data is available, and non-expert annotators tend to conflate IEC with EML. We introduce an approach, anchor comparison, that leverages workers' ability to identify and remove instances of EML in text to create a paraphrased "anchor text", which is then used as a comparison point to classify EML in the original content. We evaluate our approach with a dataset of news-style text snippets and show that precision and recall can be tuned for system builders' needs. Our contribution is a crowdsourcing approach that enables non-expert disentanglement of social references from content.
Title: Conversational Agency in Augmentative and Alternative Communication
Authors: family: Valencia
given: Stephanie; family: Pavel
given: Amy; family: Santa Maria
given: Jared; family: Yu
given: Seunga (Gloria); family: Bigham
given: Jeffrey P.; family: Admoni
given: Henny
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Augmented communicators (ACs) use augmentative and alternative communication (AAC) technologies to speak. Prior work in AAC research has looked to improve efficiency and expressivity of AAC via device improvements and user training. However, ACs also face constraints in communication beyond their device and individual abilities such as when they can speak, what they can say, and who they can address. In this work, we recast and broaden this prior work using conversational agency as a new frame to study AC communication. We investigate AC conversational agency with a study examining different conversational tasks between four triads of expert ACs, their close conversation partners (paid aide or parent), and a third party (experimenter). We define metrics to analyze AAC conversational agency quantitatively and qualitatively. We conclude with implications for future research to enable ACs to easily exercise conversational agency.
Title: Prior Setting in Practice: Strategies and Rationales Used in Choosing Prior Distributions for Bayesian Analysis
Authors: family: Sarma
given: Abhraneel; family: Kay
given: Matthew
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Bayesian statistical analysis is steadily growing in popularity and use. Choosing priors is an integral part of Bayesian inference. While there exist extensive normative recommendations for prior setting, little is known about how priors are chosen in practice. We conducted a survey (N = 50) and interviews (N = 9) where we used interactive visualizations to elicit prior distributions from researchers experienced withBayesian statistics and asked them for rationales for those priors. We found that participants' experience and philosophy influence how much and what information they are willing to incorporate into their priors, manifesting as different levels of informativeness and skepticism. We also identified three broad strategies participants use to set their priors: centrality matching, interval matching, and visual mass allocation. We discovered that participants' understanding of the notion of 'weakly informative priors"-a commonly-recommended normative approach to prior setting-manifests very differently across participants. Our results have implications both for how to develop prior setting recommendations and how to design tools to elicit priors in Bayesian analysis.
Title: Pac-Euglena: A Living Cellular Pac-Man Meets Virtual Ghosts
Authors: family: Lam
given: Amy T.; family: Griffin
given: Jonathan; family: Loeun
given: Matthew Austin; family: Cira
given: Nate J.; family: Lee
given: Seung Ah; family: Riedel-Kruse
given: Ingmar H.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: The advancement of biotechnology enabled the development of "biotic video games", where human players manipulate real biological samples for fun and educational human-biology interactions. However, new design principles are needed to both leverage and mitigate biological properties (e.g., variability and stochasticity), and create unique play experiences that transcend traditional video games. This paper describes the implementation of Pac-Euglena, a biotic Pac-Man analog, where players guide live microscopic Euglena cells with light stimuli through a physical microfluidic maze. Through use of multi-modal stimuli, a mixed biology-digital-human reality is achieved, enabling cell interactions with virtual ghosts and collectibles. Through an iterative design process, we illustrate challenges and strategies for designing games with living organisms. A user study (n=18, conducted at a university event) showed that Pac-Euglena was fun, stimulated curiosity, and taught users about Euglena. We conclude with five general guidelines for the design and development of biotic games and HBI interfaces.
Title: Proxemics Beyond Proximity: Designing for Flexible Social Interaction Through Cross-Device Interaction
Authors: family: Grønbæk
given: Jens Emil; family: Knudsen
given: Mille Skovhus; family: O'Hara
given: Kenton; family: Krogh
given: Peter Gall; family: Vermeulen
given: Jo; family: Petersen
given: Marianne Graves
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Cross-device interactions enable ad hoc sharing of content and control in co-located collaboration. Cross-device research often draws from proxemics theory for designing interactions based on detection of spatial relations such as distance and orientation between people and devices. However, detection of human-human or human-device proximity also constrains flexibility in co-located social interaction. We suggest a proxemics-based approach to designing flexible cross-device interactions. From observations in a field study, we articulate how co-located sharing practices are shaped by the interplay between everyday mobile devices and the physical environment. Based on these insights, we present three cross-device prototypes as proofs-of-concept, demonstrating three design sensitivities for considering proxemics beyond proximity; incorporating features in the environment, enabling flexibility in interpersonal distance and orientation, and providing multiple alternative action possibilities. Drawing from characteristics of our prototypes, we discuss concrete proposals for designing cross-device interactions to enable flexible social interaction.
Title: Decipher: An Interactive Visualization Tool for Interpreting Unstructured Design Feedback from Multiple Providers
Authors: family: Yen
given: Yu-Chun Grace; family: Kim
given: Joy O.; family: Bailey
given: Brian P.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Feedback from diverse audiences can vary in focus, differ in structure, and contradict each other, making it hard to interpret and act on. While prior work has explored generating quality feedback, our work helps a designer interpret that feedback. Through a formative study with professional designers (N=10), we discovered that the interpretation process includes categorizing feedback, identifying valuable feedback, and prioritizing which feedback to incorporate in a revision. We also found that designers leverage feedback topic and sentiment, and the status of the provider to aid interpretation. Based on the findings, we created a new tool (Decipher) that enables designers to visualize and navigate a collection of feedback using its topic and sentiment structure. In a preliminary evaluation (N=20), we found that Decipher helped users feel less overwhelmed during feedback interpretation tasks and better attend to critical issues and conflicting opinions compared to using a typical document-editing tool.
Title: Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach
Authors: family: Nobre
given: Carolina; family: Wootton
given: Dylan; family: Harrison
given: Lane; family: Lex
given: Alexander
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking.
Title: Enabling Data-Driven API Design with Community Usage Data: A Need-Finding Study
Authors: family: Zhang
given: Tianyi; family: Hartmann
given: Björn; family: Kim
given: Miryung; family: Glassman
given: Elena L.
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: APIs are becoming the fundamental building block of modern software and their usability is crucial to programming efficiency and software quality. Yet API designers find it hard to gather and interpret user feedback on their APIs. To close the gap, we interviewed 23 API designers from 6 companies and 11 open-source projects to understand their practices and needs. The primary way of gathering user feedback is through bug reports and peer reviews, as formal usability testing is prohibitively expensive to conduct in practice. Participants expressed a strong desire to gather real-world use cases and understand users' mental models, but there was a lack of tool support for such needs. In particular, participants were curious about where users got stuck, their workarounds, common mistakes, and unanticipated corner cases. We highlight several opportunities to address those unmet needs, including developing new mechanisms that systematically elicit users' mental models, building mining frameworks that identify recurring patterns beyond shallow statistics about API usage, and exploring alternative design choices made in similar libraries.
Title: Synthesized Social Signals: Computationally-Derived Social Signals from Account Histories
Authors: family: Im
given: Jane; family: Tandon
given: Sonali; family: Chandrasekharan
given: Eshwar; family: Denby
given: Taylor; family: Gilbert
given: Eric
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Social signals are crucial when we decide if we want to interact with someone online. However, social signals are typically limited to the few that platform designers provide, and most can be easily manipulated. In this paper, we propose a new idea called synthesized social signals (S3s): social signals computationally derived from an account's history, and then rendered into the profile. Unlike conventional social signals such as profile bios, S3s use computational summarization to reduce receiver costs and raise the cost of faking signals. To demonstrate and explore the concept, we built Sig, an extensible Chrome extension that computes and visualizes S3s. After a formative study, we conducted a field deployment of Sig on Twitter, targeting two well-known problems on social media: toxic accounts and misinformation. Results show that Sig reduced receiver costs, added important signals beyond conventionally available ones, and that a few users felt safer using Twitter as a result. We conclude by reflecting on the opportunities and challenges S3s provide for augmenting interaction on social platforms.
Title: Evaluating the Information Security Awareness of Smartphone Users
Authors: family: Bitton
given: Ron; family: Boymgold
given: Kobi; family: Puzis
given: Rami; family: Shabtai
given: Asaf
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Information security awareness (ISA) is a practice focused on the set of skills which help a user successfully mitigate social engineering (SE) attacks. Evaluating the ISA of users is crucial, since early identification of users who are more vulnerable to SE attacks improves system security. Previous studies for evaluating the ISA of smartphone users rely on subjective data sources (questionnaires) and do not address the differences between classes of SE attacks. This paper presents a framework for evaluating the ISA of smartphone users for specific attack classes. In addition to questionnaires, we utilize objective data sources: a mobile agent, a network traffic monitor, and cybersecurity challenges. We evaluated the framework by conducting a long-term user study involving 162 users. The results show that: the self-reported behavior of users differs significantly from their actual behavior and the ISA level derived from the actual behavior of users is highly correlated with their ability to mitigate SE attacks.
Title: Choice-Point: Fostering Awareness and Choice with Perpetrators in Domestic Violence Interventions
Authors: family: Bellini
given: Rosanna; family: Forrest
given: Simon; family: Westmarland
given: Nicole; family: Jackson
given: Dan; family: Smeddinck
given: Jan David
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Learning about alternatives to violence is an essential part of change work with domestic violence perpetrators. This is complex work, seeking to tackle a sensitive issue by involving the development of deep, embodied learning for perpetrators who may lack perspective on their behaviour. Interactive storytelling has been providing users with the opportunity to explore speculative scenarios in a controlled environment. We discuss the design of Choice-Point: a web-based application that allows perpetrators adopt the role of different fictional characters in an abusive scenario for conveying the essential skill of perspective-taking. We evaluated Choice-Point through trials with three groups of perpetrators, a support group of victim-survivors and an expert critique from support workers. We discuss challenges in using such technologies - such as our system - for engagement; the value of perpetrator agency in supporting non-violent behaviours, and the potential to positively shape perpetrators' journeys to non-violence within social care settings.
Title: Sensock: 3D Foot Reconstruction with Flexible Sensors
Authors: family: Zhang
given: Hechuan; family: Chen
given: Zhiyong; family: Guo
given: Shihui; family: Lin
given: Juncong; family: Shi
given: Yating; family: Liu
given: Xiangyang; family: Ma
given: Yong
Venue: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Abstract: Capturing 3D foot models is important for applications such as manufacturing customized shoes and creating clubfoot orthotics. In this paper, we propose a novel prototype, Sensock, to offer a fully wearable solution for the task of 3D foot reconstruction. The prototype consists of four soft stretchable sensors, made from silk fibroin yarn. We identify four characteristic foot girths based on the existing knowledge of foot anatomy, and measure their lengths with the resistance value of the stretchable sensors. A learning-based model is trained offline and maps the foot girths to the corresponding 3D foot shapes. We compare our method with existing solutions using red-green-blue (RGB) or RGBD (RGB-depth) cameras, and show the advantages of our method in terms of both efficiency and accuracy. In the user experiment, we find that the relative error of Sensock is lower than 0.55%. It performs consistently across different trials and is considered comfortable and suitable for long-term wearing.