huashen218/convxai-cia-dataset huggingface dataset:
Title: Towards Complete Icon Labeling in Mobile Applications
Authors: family: Chen
given: Jieshan; family: Swearngin
given: Amanda; family: Wu
given: Jason; family: Barik
given: Titus; family: Nichols
given: Jeffrey; family: Zhang
given: Xiaoyi
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Abstract: Accurately recognizing icon types in mobile applications is integral to many tasks, including accessibility improvement, UI design search, and conversational agents. Existing research focuses on recognizing the most frequent icon types, but these technologies fail when encountering an unrecognized low-frequency icon. In this paper, we work towards complete coverage of icons in the wild. After annotating a large-scale icon dataset (327,879 icons) from iPhone apps, we found a highly uneven distribution: 98 common icon types covered 92.8% of icons, while 7.2% of icons were covered by more than 331 long-tail icon types. In order to label icons with widely varying occurrences in apps, our system uses an image classification model to recognize common icon types with an average of 3,000 examples each (96.3% accuracy) and applies a few-shot learning model to classify long-tail icon types with an average of 67 examples each (78.6% accuracy). Our system also detects contextual information that helps characterize icon semantics, including nearby text (95.3% accuracy) and modifier symbols added to the icon (87.4% accuracy). In a validation study with workers (n = 23), we verified the usefulness of our generated icon labels. The icon types supported by our work cover 99.5% of collected icons, improving on the previously highest 78% coverage in icon classification work.
Title: CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities
Authors: family: Lee
given: Mina; family: Liang
given: Percy; family: Yang
given: Qian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Human-AI collaborative writing, language models, dataset, writing assistants., natural language generation, GPT-3, crowdsourcing

Abstract: Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design. However, their highly context-dependent capabilities are difficult to grasp and are often subjectively interpreted. In this paper, we argue that by curating and analyzing large interaction datasets, the HCI community can foster more incisive examinations of LMs’ generative capabilities. Exemplifying this approach, we present CoAuthor, a dataset designed for revealing GPT-3’s capabilities in assisting creative and argumentative writing. CoAuthor captures rich interactions between 63 writers and four instances of GPT-3 across 1445 writing sessions. We demonstrate that CoAuthor can address questions about GPT-3’s language, ideation, and collaboration capabilities, and reveal its contribution as a writing “collaborator” under various definitions of good collaboration. Finally, we discuss how this work may facilitate a more principled discussion around LMs’ promises and pitfalls in relation to interaction design. The dataset and an interface for replaying the writing sessions are publicly available at https://coauthor.stanford.edu.
Title: Next Steps in Epidermal Computing: Opportunities and Challenges for Soft On-Skin Devices
Authors: family: Nittala
given: Aditya Shekhar; family: Steimle
given: Jürgen
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: epidermal devices, soft wearables, survey, wearable devices

Abstract: Skin is a promising interaction medium and has been widely explored for mobile, and expressive interaction. Recent research in HCI has seen the development of Epidermal Computing Devices: ultra-thin and non-invasive devices which reside on the user’s skin, offering intimate integration with the curved surfaces of the body, while having physical and mechanical properties that are akin to skin, expanding the horizon of on-body interaction. However, with rapid technological advancements in multiple disciplines, we see a need to synthesize the main open research questions and opportunities for the HCI community to advance future research in this area. By systematically analyzing Epidermal Devices contributed in the HCI community, physical sciences research and from our experiences in designing and building Epidermal Devices, we identify opportunities and challenges for advancing research across five themes. This multi-disciplinary synthesis enables multiple research communities to facilitate progression towards more coordinated endeavors for advancing Epidermal Computing.
Title: Meander Coil++: A Body-scale Wireless Power Transmission Using Safe-to-body and Energy-efficient Transmitter Coil
Authors: family: Takahashi
given: Ryo; family: Yukita
given: Wakako; family: Yokota
given: Tomoyuki; family: Someya
given: Takao; family: Kawahara
given: Yoshihiro
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: liquid metal, knit, magnetic resonant coupling, wireless power transfer, coil

Abstract: Wearable devices for life-logging and healthcare have been studied, but the need for frequent charging imposes inconvenience for long-term use. Integrating textile-based wireless chargers (i.e., coil) into clothing enables sustainable wearable computing by charging the on-body devices in use. However, the electromagnetic field generated by conventional coil chargers strongly interferes with human body, and the high resistance of conductive threads leads to inefficient power delivery. This paper presents Meander Coil++, enabling safe, energy-efficient, and body-scale wireless power delivery. Meander Coil++ uses a wiring pattern that suppresses electromagnetic exposure to the human body without compromising power delivery performance and a liquid-metal-based low-loss conductive cord. With these advancements, Meander Coil++ transmits a few watts of power to on-body devices at 25% DC-to-DC efficiency while complying with international safety guidelines regarding electromagnetic exposure. We envision Meander Coil++ can maintain multiple devices on body for weeks beyond the confines of their small battery capacity.
Title: KnitSkin: Machine-Knitted Scaled Skin for Locomotion
Authors: family: Kim
given: Jin Hee (Heather); family: Patil
given: Shreyas Dilip; family: Matson
given: Sarina; family: Conroy
given: Melissa; family: Kao
given: Cindy Hsin-Liu
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Machine Knitting, Mobile Robots, Smart Textiles, Wearable Robots

Abstract: We present KnitSkin, a bio-inspired sleeve that can traverse diverse cylindrical terrains, ranging from a user’s forearm at a wearable scale, to pipes and tree branches at an environmental scale. Fabricated with a machine knitted substrate, the sleeve configures a stepped array of knitted scales that exhibit anisotropic friction. Coupled with the extension of actuators enclosed in the sleeve, the scales enable effective directional locomotion on cylindrical surfaces with varying slopes, textures, and curvatures. KnitSkin’s substrates are characterized by scales whose geometries and materials can be fine-tuned and channels that can accommodate diverse actuators. We introduce the design elements of KnitSkin in which we characterize a series of substrate parameters and their resulting anisotropic behaviors. In evaluating the locomotion, we examine the variables associated with the surface and actuator characteristics. KnitSkin obtains diverse applications across different scales, including wearable interfaces, industrial pipe-monitoring, to agricultural robots.
Title: ARGONAUT: An Inclusive Design Process for Wearable Health Monitoring Systems
Authors: family: Schauss
given: Gabriella; family: Arquilla
given: Katya; family: Anderson
given: Allison
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Electrocardiogram (ECG), iterative design, smart textiles, technological integration, motion artifact

Abstract: Wearable biosignal monitoring systems are becoming increasingly ubiquitous as tools for autonomous health monitoring and other real-world applications. Despite the continual advancements in this field, anthropometric considerations for women’s form are often overlooked in the design process, making systems ill fit and less effective. In this paper, we present a full garment assembly, ARGONAUT, with integrated textile electrocardiogram (ECG) electrodes in a 3-lead configuration that is designed specifically for women’s form. Through the exploration of materials, anthropometry, and garment assembly, we designed and tested ARGONAUT against the laboratory standard to determine performance through R-peak detection and noise interference. We investigated common issues faced when designing a wearable ECG garment, such as fit, motion artifact mitigation, and social wearability, to develop a dynamic design process that can be used to expand the advancing technology sensor integrated garments to all individuals in order to allow for equal access to potential health benefits.
Title: HydroMod : Constructive Modules for Prototyping Hydraulic Physical Interfaces
Authors: family: Morita
given: Takafumi; family: Kuwajima
given: Yu; family: Minaminosono
given: Ayato; family: Maeda
given: Shingo; family: Kakehi
given: Yasuaki
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Programmable Materials, Wearables, Electrohydrodynamics, Fluid Interface, Toolkit, Prototyping

Abstract: In recent years, actuators that handle fluids such as gases and liquids have been attracting attention for their applications in soft robots and shape-changing interfaces. In the field of HCI, there have been various inflatable prototyping tools that utilize air control, however, very few tools for liquid control have been developed. In this study, we propose HydroMod, new constructive modules that can easily generate liquid flow and programmatically control liquid flow, with the aim of lowering the barrier to entry for prototyping with liquids. HydroMod consists of palm-sized small modules, which can generate liquid flow with the electrohydrodynamics (EHD) phenomenon by simply connecting the modules. Moreover, users can configure and control the flow path by simply recombining the modules. In this paper, we propose the design of the modules, evaluate the performance of HydroMod as a fluid system, and also show the possible application scenarios of fluid prototyping using this system.
Title: “So Close, yet So Far”: Exploring Sexual-minority Women’s Relationship-building via Online Dating in China
Authors: family: Cui
given: Yichao; family: Yamashita
given: Naomi; family: Liu
given: Mingjie; family: Lee
given: Yi-Chieh
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Relationship Building, Online Dating, Sexual-minority Women

Abstract: Sexual-minority women (SMWs) in China are often subject to strong stigmatization and tend to have limited opportunities to connect with other SMWs in offline contexts. Although dating apps help them connect and seek social support, little is known about SMWs’ practices of self-disclosure and connection-building through those apps. To address this gap, we interviewed 43 SMW dating-app users in China. We found that these SMWs developed distinctive self-disclosure strategies, such as posting non-facial photos and implicitly disclosing their whereabouts by blending location information into photos that only those in the know could understand, to avoid interference from aggressive acquaintances and other risks of unintentional disclosure of their SMW identities. Moreover, they used dating apps not only to recognize other SMWs offline and build relationships with them, but to exchange emotional support in the process of SMW identity development. Our findings have design implications for supporting SMWs and improving their online dating experiences.
Title: Nice Guys, Virgins, and Incels: Gender in Remixing and Sharing Memes at Hackathons
Authors: family: Brooke
given: Sian JM
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Sexism, Programming, Gender, Memes, Ethnography, Culture, Hackathons

Abstract: This paper investigates how the conceptions of gender in memes are central to socializing at hackathons. Drawing on a multi-sited ethnography of seven hackathons, I provide insight into how references to memes and informal technology culture shape interaction in local manifestations of this culture. The contribution of this paper is twofold. First, I show how vocabularies and artifacts of technology culture move between on and offline spaces. These findings have implications for HCI research that investigates questions of materiality in computer-mediated communication. Second, I show how even the mundane memes of technology culture can reveal the toxic masculinity and ideology of Incels. By tying these internet memes to a physical context, I unpack how humor can reveal and perpetuate the enduring masculine dominance of technology. I end with recommendations for increasing inclusivity at hackathons, based on how HCI is uniquely positioned to understand how Internet symbols and interactions manifest offline.
Title: Who am I, and who are you, and who are we? A Scientometric Analysis of Gender and Geography in HCI
Authors: family: McKay
given: Dana; family: Zhang
given: Huiwen; family: Buchanan
given: George
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: geography, gender, authorship in HCI, diversity, Equity

Abstract: Conference authorship, attendance and presentation is a key measure of quality in the HCI academic, yet we know conferences are not equally accessible for reasons that have nothing to do with quality. In this paper we examine two axes of diversity: gender, and geographic location (of both authors and conferences) and examine how they affect participation at major HCI conferences. Diversity in a group is associated with better outcomes from its work, and HCI has made numerous contributions to increasing representation in other communities. Reflecting on our own situation can produce recommendations for the planning of future HCI conferences, and identify challenges of representation that the HCI community should endeavor to address.
Title: “Brush it Off”: How Women Workers Manage and Cope with Bias and Harassment in Gender-agnostic Gig Platforms
Authors: family: Ma
given: Ning F.; family: Rivera
given: Veronica A.; family: Yao
given: Zheng; family: Yoon
given: Dongwook
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Labor, Gender, Uber, Harassment, TaskRabbit, Bias, Women, DoorDash, Gig work

Abstract: Women make up approximately half of the workforce in ride-hailing, food delivery, and home service platforms in North America. While studies have reported that gig workers face bias, harassment, and a gender pay gap, we have limited understanding of women’s perspectives of these issues and their coping mechanisms. We interviewed 20 women gig workers to hear their unique experiences with these challenges. We found that gig platforms are gender-agnostic, meaning they do not acknowledge women’s experiences and the value they bring. By not enforcing anti-harassment policies in design, gig platforms also leave women workers vulnerable to bias and harassment. Due to the lack of support for immediate actions and in fear of losing access to work, women workers “brush off” harassment. In addition, the platforms’ dispatching and recommendation mechanisms do not acknowledge women’s contributions in perceived safety for customers and social support for peer workers.
Title: Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messages
Authors: family: Sun
given: Jiao; family: Wu
given: Tongshuang; family: Jiang
given: Yue; family: Awalegaonkar
given: Ronil; family: Lin
given: Xi Victoria; family: Yang
given: Diyi
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: greeting card messages, visualization system, gender role awareness

Abstract: People write personalized greeting cards on various occasions. While prior work has studied gender roles in greeting card messages, systematic analysis at scale and tools for raising the awareness of gender stereotyping remain under-investigated. To this end, we collect a large greeting card message corpus covering three different occasions (birthday, Valentine’s Day and wedding) from three sources (exemplars from greeting message websites, real-life greetings from social media and language model generated ones). We uncover a wide range of gender stereotypes in this corpus via topic modeling, odds ratio and Word Embedding Association Test (WEAT). We further conduct a survey to understand people’s perception of gender roles in messages from this corpus and if gender stereotyping is a concern. The results show that people want to be aware of gender roles in the messages, but remain unconcerned unless the perceived gender roles conflict with the recipient’s true personality. In response, we developed GreetA, an interactive visualization and writing assistant tool to visualize fine-grained topics in greeting card messages drafted by the users and the associated gender perception scores, but without suggesting text changes as an intervention.
Title: ”Your Eyes Tell You Have Used This Password Before”: Identifying Password Reuse from Gaze and Keystroke Dynamics
Authors: family: Abdrabou
given: Yasmeen; family: Schütte
given: Johannes; family: Shams
given: Ahmed; family: Pfeuffer
given: Ken; family: Buschek
given: Daniel; family: Khamis
given: Mohamed; family: Alt
given: Florian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Gaze Behavior, Machine Learning, Passwords, Keystroke Dynamics

Abstract: A significant drawback of text passwords for end-user authentication is password reuse. We propose a novel approach to detect password reuse by leveraging gaze as well as typing behavior and study its accuracy. We collected gaze and typing behavior from 49 users while creating accounts for 1) a webmail client and 2) a news website. While most participants came up with a new password, 32% reported having reused an old password when setting up their accounts. We then compared different ML models to detect password reuse from the collected data. Our models achieve an accuracy of up to 87.7% in detecting password reuse from gaze, 75.8% accuracy from typing, and 88.75% when considering both types of behavior. We demonstrate that using gaze, password reuse can already be detected during the registration process, before users entered their password. Our work paves the road for developing novel interventions to prevent password reuse.
Title: Can’t Touch This: Rethinking Public Technology in a COVID-19 Era
Authors: family: Pearson
given: Jennifer; family: Bailey
given: Gavin; family: Robinson
given: Simon; family: Jones
given: Matt; family: Owen
given: Tom; family: Zhang
given: Chi; family: Reitmaier
given: Thomas; family: Steer
given: Cameron; family: Carter
given: Anna; family: Sahoo
given: Deepak Ranjan; family: Raju
given: Dani Kalarikalayil
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: prototyping/implementation., Public displays, field studies

Abstract: What do pedestrian crossings, ATMs, elevators and ticket machines have in common? These are just a few of the ubiquitous yet essential elements of public-space infrastructure that rely on physical buttons or touchscreens; common interactions that, until recently, were considered perfectly safe to perform. This work investigates how we might integrate touchless technologies into public-space infrastructure in order to minimise physical interaction with shared devices in light of the ongoing COVID-19 pandemic. Drawing on an ethnographic exploration into how public utilities are being used, adapted or avoided, we developed and evaluated a suite of technology probes that can be either retrofitted into, or replace, these services. In-situ community deployments of our probes demonstrate strong uptake and provide insight into how hands-free technologies can be adapted and utilised for the public domain; and, in turn, used to inform the future of walk-up-and use public technologies.
Title: Mold-It: Understanding how Physical Shapes affect Interaction with Handheld Freeform Devices
Authors: family: Serrano
given: Marcos; family: Finch
given: Jolee; family: Irani
given: Pourang; family: Lucero
given: Andrés; family: Roudaut
given: Anne
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Freeform interfaces, device form factor, handheld devices

Abstract: Advanced technologies are increasingly enabling the creation of interactive devices with non-rectangular form-factors but it is currently unclear what alternative form-factors are desirable for end-users. We contribute an understanding of the interplay between the rationale for the form factors of such devices and their interactive content through think-aloud design sessions in which participants could mold devices as they wished using clay. We analysed their qualitative reflections on how the shapes affected interaction. Using thematic analysis, we identified shape features desirable on handheld freeform devices and discuss the particularity of three themes central to the choice of form factors: freeform dexterity, shape features discoverability and shape adaptability (to the task and context). In a second study following the same experimental set-up, we focused on the trade off between dexterity and discoverability and the relation to the concept of affordance. Our work reveals the shape features that impact the most the choice of grasps on freeform devices from which we derive design guidelines for the design of such devices.
Title: Affective State Prediction from Smartphone Touch and Sensor Data in the Wild
Authors: family: Wampfler
given: Rafael; family: Klingler
given: Severin; family: Solenthaler
given: Barbara; family: Schinazi
given: Victor R.; family: Gross
given: Markus; family: Holz
given: Christian
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Deep Learning, Classification, Smartphone, Affective Computing

Abstract: Knowledge of users’ affective states can improve their interaction with smartphones by providing more personalized experiences (e.g., search results and news articles). We present an affective state classification model based on data gathered on smartphones in real-world environments. From touch events during keystrokes and the signals from the inertial sensors, we extracted two-dimensional heat maps as input into a convolutional neural network to predict the affective states of smartphone users. For evaluation, we conducted a data collection in the wild with 82 participants over 10 weeks. Our model accurately predicts three levels (low, medium, high) of valence (AUC up to 0.83), arousal (AUC up to 0.85), and dominance (AUC up to 0.84). We also show that using the inertial sensor data alone, our model achieves a similar performance (AUC up to 0.83), making our approach less privacy-invasive. By personalizing our model to the user, we show that performance increases by an additional 0.07 AUC.