huashen218/convxai-cia-dataset huggingface dataset:
Title: Interaction Illustration Taxonomy: Classification of Styles and Techniques for Visually Representing Interaction Scenarios
Authors: family: Antoine
given: Axel; family: Malacria
given: Sylvain; family: Marquardt
given: Nicolai; family: Casiez
given: Géry
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: taxonomy, interactive scenarios, figures, interaction illustrations

Abstract: Static illustrations are ubiquitous means to represent interaction scenarios. Across papers and reports, these visuals demonstrate people’s use of devices, explain systems, or show design spaces. Creating such figures is challenging, and very little is known about the overarching strategies for visually representing interaction scenarios. To mitigate this task, we contribute a unified taxonomy of design elements that compose such figures. In particular, we provide a detailed classification of Structural and Interaction strategies, such as composition, visual techniques, dynamics, representation of users, and many others – all in context of the type of scenarios. This taxonomy can inform researchers’ choices when creating new figures, by providing a concise synthesis of visual strategies, and revealing approaches they were not aware of before. Furthermore, to support the community for creating further taxonomies, we also provide three open-source software facilitating the coding process and visual exploration of the coding scheme.
Title: Color by Numbers: Interactive Structuring and Vectorization of Sketch Imagery
Authors: family: Parakkat
given: Amal Dev; family: Cani
given: Marie-Paule R.; family: Singh
given: Karan
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Abstract: We present a novel, interactive interface for the integrated cleanup, neatening, structuring and vectorization of sketch imagery. Converting scanned raster drawings into vector illustrations is a well-researched set of problems. Our approach is based on a Delaunay subdivision of the raster drawing. We algorithmically generate a colored grouping of Delaunay regions that users interactively refine by dragging and dropping colors. Sketch strokes defined as marking boundaries of different colored regions are automatically neatened using Bézier curves, and turned into closed regions suitable for fills, textures, layering and animation. We show that minimal user interaction using our technique enables better sketch vectorization than state of art automated approaches. A user study, further shows our interface to be simple, fun and easy to use, yet effectively able to process messy images with a mix of construction lines, noisy and incomplete curves, sketched with arbitrary stroke style.
Title: CASSIE: Curve and Surface Sketching in Immersive Environments
Authors: family: Yu
given: Emilie; family: Arora
given: Rahul; family: Stanko
given: Tibor; family: Bærentzen
given: J. Andreas; family: Singh
given: Karan; family: Bousseau
given: Adrien
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: ideation, concept modeling, immersive design, curve networks

Abstract: We present CASSIE, a conceptual modeling system in VR that leverages freehand mid-air sketching, and a novel 3D optimization framework to create connected curve network armatures, predictively surfaced using patches with C0 continuity. Our system provides a judicious balance of interactivity and automation, providing a homogeneous 3D drawing interface for a mix of freehand curves, curve networks, and surface patches. Our system encourages and aids users in drawing consistent networks of curves, easing the transition from freehand ideation to concept modeling. A comprehensive user study with professional designers as well as amateurs (N=12), and a diverse gallery of 3D models, show our armature and patch functionality to offer a user experience and expressivity on par with freehand ideation, while creating sophisticated concept models for downstream applications.
Title: Distractor Effects on Crossing-Based Interaction
Authors: family: Tu
given: Huawei; family: Huang
given: Jin; family: Liang
given: Hai-Ning; family: Skarbez
given: Richard; family: Tian
given: Feng; family: Duh
given: Henry Been-Lirn
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Fitts’ law, Crossing, pointing, distractor effects, models.

Abstract: Task-irrelevant distractors affect visuo-motor control for target acquisition and studying such effects has already received much attention in human-computer interaction. However, there has been little research into distractor effects on crossing-based interaction. We thus conducted an empirical study on pen-based interfaces to investigate six crossing tasks with distractor interference in comparison to two tasks without it. The six distractor-related tasks differed in movement precision constraint (directional/amplitude), target size, target distance, distractor location and target-distractor spacing. We also developed and experimentally validated six quantitative models for the six tasks. Our results show that crossing targets with distractors had longer average times and similar accuracy than that without distractors. The effects of distractors varied depending on distractor location, target-distractor spacing and movement precision constraint. When spacing is smaller than 11.27 mm, crossing tasks with distractor interference can be regarded as pointing tasks or a combination of pointing and crossing tasks, which could be better fitted with our proposed models than Fitts’ law. According to these results, we provide practical implications to crossing-based user interface design.
Title: Impact of Task on Attentional Tunneling in Handheld Augmented Reality
Authors: family: Syiem
given: Brandon Victor; family: Kelly
given: Ryan M.; family: Goncalves
given: Jorge; family: Velloso
given: Eduardo; family: Dingler
given: Tilman
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Mobile Devices, Augmented Reality, Attentional Tunneling

Abstract: Attentional tunneling describes a phenomenon in Augmented Reality (AR) where users excessively focus on virtual content while neglecting their physical surroundings. This leads to the concern that users could neglect hazardous situations when using AR applications. However, studies have often confounded the role of the virtual content with the role of the associated task in inducing attentional tunneling. In this paper, we disentangle the impact of the associated task and of the virtual content on the attentional tunneling effect by measuring reaction times to events in two user studies. We found that presenting virtual content did not significantly increase user reaction times to events, but adding a task to the content did. This work contributes towards our understanding of the attentional tunneling effect on handheld AR devices, and highlights the need to consider both task and context when evaluating AR application usage.
Title: Preserving Agency During Electrical Muscle Stimulation Training Speeds up Reaction Time Directly After Removing EMS
Authors: family: Kasahara
given: Shunichi; family: Takada
given: Kazuma; family: Nishida
given: Jun; family: Shibata
given: Kazuhisa; family: Shimojo
given: Shinsuke; family: Lopes
given: Pedro
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: electrical muscle stimulation, reaction time, agency

Abstract: Abstract: Force feedback devices, such as motor-based exoskeletons or wearables based on electrical muscle stimulation (EMS), have the unique potential to accelerate users’ own reaction time (RT). However, this speedup has only been explored while the device is attached to the user. In fact, very little is known regarding whether this faster reaction time still occurs after the user removes the device from their bodies–this is precisely what we investigated by means of a simple reaction time (RT) experiment, in which participants were asked to tap as soon as they saw an LED flashing. Participants experienced this in three EMS conditions: (1) fast-EMS, the electrical impulses were synced with the LED; (2) agency-EMS, the electrical impulse was delivered 40ms faster than the participant’s own RT, which prior work has shown to preserve one’s sense of agency over this movement; and, (3) late-EMS: the impulse was delivered after the participant’s own RT. Our results revealed that the participants’ RT was significantly reduced by approximately 8ms (up to 20ms) only after training with the agency-EMS condition. This finding suggests that the prioritizing agency during EMS training is key to motor-adaptation, i.e., it enables a faster motor response even after the user has removed the EMS device from their body.
Title: Interaction Pace and User Preferences
Authors: family: Goguey
given: Alix; family: Gutwin
given: Carl; family: Chen
given: Zhe; family: Suwanaposee
given: Pang; family: Cockburn
given: Andy
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: user preferences., timeouts, Interaction pace convergence

Abstract: The overall pace of interaction combines the user’s pace and the system’s pace, and a pace mismatch could impair user preferences (e.g., animations or timeouts that are too fast or slow for the user). Motivated by studies of speech rate convergence, we conducted an experiment to examine whether user preferences for system pace are correlated with user pace. Subjects first completed a series of trials to determine their user pace. They then completed a series of hierarchical drag-and-drop trials in which folders automatically expanded when the cursor hovered for longer than a controlled timeout. Results showed that preferences for timeout values correlated with user pace – slow-paced users preferred long timeouts, and fast-paced users preferred short timeouts. Results indicate potential benefits in moving away from fixed or customisable settings for system pace. Instead, systems could improve preferences by automatically adapting their pace to converge towards that of the user.
Title: BackSwipe: Back-of-device Word-Gesture Interaction on Smartphones
Authors: family: Cui
given: Wenzhe; family: Zhu
given: Suwen; family: Li
given: Zhi; family: Xu
given: Zheer; family: Yang
given: Xing-Dong; family: Ramakrishnan
given: IV; family: Bi
given: Xiaojun
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Text entry, command input, smartphones., touchscreen, word-gesture shortcuts

Abstract: Back-of-device interaction is a promising approach to interacting on smartphones. In this paper, we create a back-of-device command and text input technique called BackSwipe, which allows a user to hold a smartphone with one hand, and use the index finger of the same hand to draw a word-gesture anywhere at the back of the smartphone to enter commands and text. To support BackSwipe, we propose a back-of-device word-gesture decoding algorithm which infers the keyboard location from back-of-device gestures, and adjusts the keyboard size to suit the gesture scales; the inferred keyboard is then fed back into the system for decoding. Our user study shows BackSwipe is feasible and a promising input method, especially for command input in the one-hand holding posture: users can enter commands at an average accuracy of 92% with a speed of 5.32 seconds/command. The text entry performance varies across users. The average speed is 9.58 WPM with some users at 18.83 WPM; the average word error rate is 11.04% with some users at 2.85%. Overall, BackSwipe complements the extant smartphone interaction by leveraging the back of the device as a gestural input surface.
Title: PhraseFlow: Designs and Empirical Studies of Phrase-Level Input
Authors: family: Zhang
given: Mingrui Ray; family: Zhai
given: Shumin
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: phrase-level input, autocorrection, Text entry, keyboard

Abstract: Decoding on phrase-level may afford more correction accuracy than on word-level according to previous research. However, how phrase-level input affects the user typing behavior, and how to design the interaction to make it practical remain under explored. We present PhraseFlow, a phrase-level input keyboard that is able to correct previous text based on the subsequently input sequences. Computational studies show that phrase-level input reduces the error rate of autocorrection by over 16%. We found that phrase-level input introduced extra cognitive load to the user that hindered their performance. Through an iterative design-implement-research process, we optimized the design of PhraseFlow that alleviated the cognitive load. An in-lab study shows that users could adopt PhraseFlow quickly, resulting in 19% fewer error without losing speed. In real-life settings, we conducted a six-day deployment study with 42 participants, showing that 78.6% of the users would like to have the phrase-level input feature in future keyboards.
Title: PrivacyMic: Utilizing Inaudible Frequencies for Privacy Preserving Daily Activity Recognition
Authors: family: Iravantchi
given: Yasha; family: Ahuja
given: Karan; family: Goel
given: Mayank; family: Harrison
given: Chris; family: Sample
given: Alanson
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Acoustics, IoT, Ultrasound, Internet of Things, Smart Environments, Microphones, Infrasound, Sound Sensing, Ubiquitous Sensing

Abstract: Sound presents an invaluable signal source that enables computing systems to perform daily activity recognition. However, microphones are optimized for human speech and hearing ranges: capturing private content, such as speech, while omitting useful, inaudible information that can aid in acoustic recognition tasks. We simulated acoustic recognition tasks using sounds from 127 everyday household/workplace objects, finding that inaudible frequencies can act as a substitute for privacy-sensitive frequencies. To take advantage of these inaudible frequencies, we designed a Raspberry Pi-based device that captures inaudible acoustic frequencies with settings that can remove speech or all audible frequencies entirely. We conducted a perception study, where participants “eavesdropped’’ on PrivacyMic’s filtered audio and found that none of our participants could transcribe speech. Finally, PrivacyMic’s real-world activity recognition performance is comparable to our simulated results, with over 95% classification accuracy across all environments, suggesting immediate viability in performing privacy-preserving daily activity recognition.
Title: Think-Aloud Computing: Supporting Rich and Low-Effort Knowledge Capture
Authors: family: Krosnick
given: Rebecca; family: Anderson
given: Fraser; family: Matejka
given: Justin; family: Oney
given: Steve; family: S. Lasecki
given: Walter; family: Grossman
given: Tovi; family: Fitzmaurice
given: George
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: documentation, knowledge capture, Think-aloud, process

Abstract: When users complete tasks on the computer, the knowledge they leverage and their intent is often lost because it is tedious or challenging to capture. This makes it harder to understand why a colleague designed a component a certain way or to remember requirements for software you wrote a year ago. We introduce think-aloud computing, a novel application of the think-aloud protocol where computer users are encouraged to speak while working to capture rich knowledge with relatively low effort. Through a formative study we find people shared information about design intent, work processes, problems encountered, to-do items, and other useful information. We developed a prototype that supports think-aloud computing by prompting users to speak and contextualizing speech with labels and application context. Our evaluation shows more subtle design decisions and process explanations were captured in think-aloud than via traditional documentation. Participants reported that think-aloud required similar effort as traditional documentation.
Title: Comparing Perspectives Around Human and Technology Support for Contact Tracing
Authors: family: Lu
given: Xi; family: L. Reynolds
given: Tera; family: Jo
given: Eunkyung; family: Hong
given: Hwajung; family: Page
given: Xinru; family: Chen
given: Yunan; family: A. Epstein
given: Daniel
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Self-tracking, Public health, Personal informatics, Contact tracing, Crisis informatics, COVID-19

Abstract: Various contact tracing approaches have been applied to help contain the spread of COVID-19, with technology-based tracing and human tracing among the most widely adopted. However, governments and communities worldwide vary in their adoption of digital contact tracing, with many instead choosing the human approach. We investigate how people perceive the respective benefits and risks of human and digital contact tracing through a mixed-methods survey with 291 respondents from the United States. Participants perceived digital contact tracing as more beneficial for protecting privacy, providing convenience, and ensuring data accuracy, and felt that human contact tracing could help provide security, emotional reassurance, advice, and accessibility. We explore the role of self-tracking technologies in public health crisis situations, highlighting how designs must adapt to promote societal benefit rather than just self-understanding. We discuss how future digital contact tracing can better balance the benefits of human tracers and technology amidst the complex contact tracing process and context.
Title: Examining Mobility Among People Living with HIV in Rural Areas
Authors: family: F. Maestre
given: Juan; family: Dillahunt
given: Tawanna; family: Andrew Theisz
given: Alec; family: Furness
given: Megan; family: Kameswaran
given: Vaishnav; family: Veinot
given: Tiffany; family: C. Shih
given: Patrick
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: HIV Care and Management, Rural Communities, Mobility, Transportation Barriers

Abstract: The rise of ridesharing platforms has transformed traditional transportation, making it more accessible for getting to work and accessing grocery stores and healthcare providers, which are essential to physical and mental well-being. However, such technologies are not available everywhere. Additionally, there is a scarcity of HCI work that investigates how vulnerable populations such as rural-dwelling people with HIV face and overcome transportation barriers. To extend past research, we conducted 31 surveys and 18 interviews with people living with HIV (22 surveys, 14 interviews) and their case coordinators (9 surveys, 4 interviews) in rural areas. Contrary to past research, we found that the use of alternative vehicles, extensive support networks, and nonprofit health organizations facilitated transportation. However, distance, the lack of trust and infrastructure, stigma, and other cultural underpinnings made popular forms of urban transportation unappealing. We contextualize our findings with prior research and contribute implications for future research and design.
Title: Living with Uncertainty and Stigma: Self-Experimentation and Support-Seeking around Polycystic Ovary Syndrome
Authors: family: Chopra
given: Shaan; family: Zehrung
given: Rachael; family: Shanmugam
given: Tamil Arasu; family: Choe
given: Eun Kyoung
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Women’s Health, Enigmatic, Stigma, Support, Self-Experimentation, Chronic Condition, Self-Management, Self-Tracking, PCOS

Abstract: Polycystic Ovary Syndrome (PCOS) is a condition that causes hormonal imbalance and infertility in women and people with female reproductive organs. PCOS causes different symptoms for different people, with no singular or universal cure. Being a stigmatized and enigmatic condition, it is challenging to discover, diagnose, and manage PCOS. This work aims to inform the design of inclusive health technologies through an understanding of people’s lived experiences and challenges with PCOS. We conducted semi-structured interviews with 10 women diagnosed with PCOS and analyzed a PCOS-specific subreddit forum. We report people’s support-seeking, sense-making, and self-experimentation practices, and find uncertainty and stigma to be key in shaping their unique experiences of the condition. We further identify potential avenues for designing technology to support their diverse needs, such as personalized and contextual tracking, accelerated self-discovery, and co-management, contributing to a growing body of HCI literature on stigmatized topics in women’s health and well-being.
Title: StoryMap: Using Social Modeling and Self-Modeling to Support Physical Activity Among Families of Low-SES Backgrounds
Authors: family: Saksono
given: Herman; family: Castaneda-Sceppa
given: Carmen; family: Hoffman
given: Jessica A.; family: Seif El-Nasr
given: Magy; family: Parker
given: Andrea
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Social Cognitive Theory, Social Modeling, Self-Modeling, Self-Efficacy, Health, Physical Activity, Low-SES, Family

Abstract: Physical activity (PA) is crucial for reducing the risk of obesity, an epidemic that disproportionately burdens families of low-socioeconomic status (SES). While fitness tracking tools can increase PA awareness, more work is needed to examine (1) how such tools can help people benefit from their social environment, and (2) how reflections can help enhance PA attitudes. We investigated how fitness tracking tools for families can support social modeling and self-modeling (through reflection), two critical processes in Social Cognitive Theory. We developed StoryMap, a novel fitness tracking app for families aimed at supporting both modes of modeling. Then, we conducted a five-week qualitative study evaluating StoryMap with 16 low-SES families. Our findings contribute an understanding of how social and self-modeling can be implemented in fitness tracking tools and how both modes of modeling can enhance key PA attitudes: self-efficacy and outcome expectations. Finally, we propose design recommendations for social personal informatics tools.
Title: Health Data in Fertility Care: An Ecological Perspective
Authors: family: Costa Figueiredo
given: Mayara; family: Chen
given: Yunan
Venue: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Keywords: Fertility self-tracking, Ecological models, Personal Informatics

Abstract: Fertility tracking and technology are characterized by logging varied health-related data potentially associated with female fertility cycles. Such data are often seen as private and restricted to the individual level. We conducted an interview study with 21 people (16 in the U.S.) facing challenges to conceive and 5 U.S. healthcare providers specialized in infertility to analyze (in)fertility experiences with data. Our findings suggest that although fertility data are considered personal and private, they are embedded in larger ecological systems of use, influencing and being influenced by different stakeholders, institutional contexts, and sociocultural factors. Leveraging the Ecological Systems Theory, we analyze the relationships and factors shaping individuals’ fertility trajectories, discussing how the different layers influence the work individuals have to engage and the burden imposed on them through various social, institutional, and cultural boundaries. We propose an ecological perspective on fertility data practices and discuss opportunities to counter-influence broader environmental systems through data tracking.