huashen218/convxai-cia-dataset huggingface dataset:
Title: Learning to Denoise Raw Mobile UI Layouts for Improving Datasets at Scale
Authors: family: Li
given: Gang; family: Baechler
given: Gilles; family: Tragut
given: Manuel; family: Li
given: Yang
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: mobile UI layouts, neural networks, Graph Neural Networks, Convolutional Neural Networks, Transformers, Datasets

Abstract: The layout of a mobile screen is a critical data source for UI design research and semantic understanding of the screen. However, UI layouts in existing datasets are often noisy, have mismatches with their visual representation, or consists of generic or app-specific types that are difficult to analyze and model. In this paper, we propose the CLAY pipeline that uses a deep learning approach for denoising UI layouts, allowing us to automatically improve existing mobile UI layout datasets at scale. Our pipeline takes both the screenshot and the raw UI layout, and annotates the raw layout by removing incorrect nodes and assigning a semantically meaningful type to each node. To experiment with our data-cleaning pipeline, we create the CLAY dataset of 59,555 human-annotated screen layouts, based on screenshots and raw layouts from Rico, a public mobile UI corpus. Our deep models achieve high accuracy with F1 scores of 82.7% for detecting layout objects that do not have a valid visual representation and 85.9% for recognizing object types, which significantly outperforms a heuristic baseline. Our work lays a foundation for creating large-scale high quality UI layout datasets for data-driven mobile UI research and reduces the need of manual labeling efforts that are prohibitively expensive.
Title: Crystalline: Lowering the Cost for Developers to Collect and Organize Information for Decision Making
Authors: family: Liu
given: Michael Xieyang; family: Kittur
given: Aniket; family: Myers
given: Brad A.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Sensemaking, Developer tools, Implicit signals, Behavior patterns, Decision making

Abstract: Developers perform online sensemaking on a daily basis, such as researching and choosing libraries and APIs. Prior research has introduced tools that help developers capture information from various sources and organize it into structures useful for subsequent decision-making. However, it remains a laborious process for developers to manually identify and clip content, maintaining its provenance and synthesizing it with other content. In this work, we introduce a new system called Crystalline that automatically collects and organizes information into tabular structures as the user searches and browses the web. It leverages natural language processing to automatically group similar criteria together to reduce clutter, and uses passive behavioral signals such as mouse movement and dwell time to infer what information to collect and how to visualize and prioritize it. Our user study suggests that developers are able to create comparison tables about 20% faster with a 60% reduction in operational cost without sacrificing the quality of the tables.
Title: Understanding How Programmers Can Use Annotations on Documentation
Authors: family: Horvath
given: Amber; family: Liu
given: Michael Xieyang; family: Hendriksen
given: River; family: Shannon
given: Connor; family: Paterson
given: Emma; family: Jawad
given: Kazi; family: Macvean
given: Andrew; family: Myers
given: Brad A
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: documentation, Annotations, note taking, software engineering, application programming interfaces (APIs)

Abstract: Modern software development requires developers to find and effectively utilize new APIs and their documentation, but documentation has many well-known issues. Despite this, developers eventually overcome these issues but have no way of sharing what they learned. We investigate sharing this documentation-specific information through annotations, which have advantages over developer forums as the information is contextualized, not disruptive, and is short, thus easy to author. Developers can also author annotations to support their own comprehension. In order to support the documentation usage behaviors we found, we built the Adamite annotation tool, which provides features such as multiple anchors, annotation types, and pinning. In our user study, we found that developers are able to create annotations that are useful to themselves and are able to utilize annotations created by other developers when learning a new API, with readers of the annotations completing 67% more of the task, on average, than the baseline.
Title: How to Save Lives with Microblogs? Lessons From the Usage of Weibo for Requests for Medical Assistance During COVID-19
Authors: family: Yang
given: Wenjie; family: Wu
given: Zhiyang; family: Mok
given: Nga Yiu; family: Ma
given: Xiaojuan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Crisis informatics, social media, disaster response, help-seeking, COVID-19

Abstract: During recent crises like COVID-19, microblogging platforms have become popular channels for affected people seeking assistance such as medical supplies and rescue operations from emergency responders and the public. Despite this common practice, the affordances of microblogging services for help-seeking during crises that needs immediate attention are not well understood. To fill this gap, we analyzed 8K posts from COVID-19 patients or caregivers requesting urgent medical assistance on Weibo, the largest microblogging site in China. Our mixed-methods analyses suggest that existing microblogging functions need to be improved in multiple aspects to sufficiently facilitate help-seeking in emergencies, including capabilities of search and tracking requests, ease of use, and privacy protection. We also find that people tend to stick to certain well-established functions for publishing requests, even after better alternatives emerge. These findings have implications for designing microblogging tools to better support help requesting and responding during crises.
Title: How Much Do Platform Workers Value Reviews? An Experimental Method
Authors: family: Holtz
given: David M; family: Scult
given: Liane; family: Suri
given: Siddharth
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: online labor market, reputation, algorithmic management, upwork, behavioral experiment, crowdsourcing

Abstract: Previous qualitative work has documented that platform workers place an immense importance on their reputation due to the use of algorithmic management by online labor platforms. We provide a general experimental method, which can be used across platforms and time, for numerically quantifying the intensity with which platform workers experience reputation system-based algorithmic management. Our method works via an experiment where workers choose between a monetary bonus or a positive review. We demonstrate this method by measuring the value that freelancers assigned to positive feedback on Upwork in June 2020. The median freelancer in our sample valued a single positive review at ∼ $49 USD. We also find that less experienced freelancers valued a positive review more highly than those with more experience. Qualitative data collected during the experiment indicates that many freelancers considered issues related to reputation system-based algorithmic management while choosing between the monetary reward and the positive review.
Title: Studying How Digital Luthiers Choose Their Tools
Authors: family: Renney
given: Nathan; family: Gaster
given: Benedict; family: Mitchell
given: Tom; family: Renney
given: Harri
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Programming Languages, Design Tools, Design Practice, Digital Musical Instruments, Thematic Analysis, Digital Luthier, Qualitative

Abstract: Digital lutherie is a sub-domain of digital craft focused on creating digital musical instruments: high-performance devices for musical expression. It represents a nuanced and challenging area of human-computer interaction that is well established and mature, offering the opportunity to observe designers’ work on highly demanding human-computer interfaces. This paper explores how and why digital luthiers choose their tools and how these tools relate to the challenges they face. Findings from 27 standardised open-ended interviews with prominent digital luthiers from commercial, research, independent and artistic backgrounds are analysed through reflexive thematic analysis. Our discussion explores their perspectives, finding that a process of pragmatic rationalisation and environmental influences play a significant role in tool selection. We also present how challenges faced by digital luthiers relate to social creativity and meta-design. These findings build upon the existing literature that examines the designer-tool relationship.
Title: A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing Research
Authors: family: Cooper
given: Ned; family: Horne
given: Tiffanie; family: Hayes
given: Gillian R; family: Heldreth
given: Courtney; family: Lahav
given: Michal; family: Holbrook
given: Jess; family: Wilcox
given: Lauren
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Meta-Analysis/Literature Survey, Research Methods

Abstract: HCI researchers have been gradually shifting attention from individual users to communities when engaging in research, design, and system development. However, our field has yet to establish a cohesive, systematic understanding of the challenges, benefits, and commitments of community-collaborative approaches to research. We conducted a systematic review and thematic analysis of 47 computing research papers discussing participatory research with communities for the development of technological artifacts and systems, published over the last two decades. From this review, we identified seven themes associated with the evolution of a project: from establishing community partnerships to sustaining results. Our findings suggest that several tensions characterize these projects, many of which relate to the power and position of researchers, and the computing research environment, relative to community partners. We discuss the implications of our findings and offer methodological proposals to guide HCI, and computing research more broadly, towards practices that center communities.
Title: A Personalized Visual Aid for Selections of Appearance Building Products with Long-term Effects
Authors: family: Shi
given: Chuhan; family: Jiang
given: Zhihan; family: Ma
given: Xiaojuan; family: Luo
given: Qiong
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: virtual try-on, Appearance building products, decision-making, personalized visual aid

Abstract: It is challenging for customers to select appearance building products (e.g., skincare products, weight loss programs) that suit them personally as such products usually demonstrate efficacy only after long-term usage. Although e-retailers generally provide product descriptions or other customers’ reviews, users often find it hard to relate to their own situations. In this work, we proposed a pipeline to display envisioned users’ appearance after long-term use of appearance building products to deliver their efficacy on each individual visually. We selected skincare as a case and developed SkincareMirror which predicts skincare effects on users’ facial images by analyzing product function labels, efficacy ratings, and skin models’ images. The results of a between-subjects study (N=48) show that (1) SkincareMirror outperforms the baseline shopping site in terms of perceived usability, usefulness, user satisfaction and helps users select products faster; (2) SkincareMirror is especially effective to males and users with limited product domain knowledge.
Title: Public Views on Digital COVID-19 Certificates: a Mixed Methods User Study
Authors: family: Nurgalieva
given: Leysan; family: Ryan
given: Seamus; family: Balaskas
given: Andreas; family: Lindqvist
given: Janne; family: Doherty
given: Gavin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: privacy, COVID-19, COVID certificate, security, fairness, digital health

Abstract: The COVID-19 pandemic has led governments worldwide to introduce various measures restricting human activity and mobility. Along with the administration of COVID-19 vaccinations and rapid testing, socio-technological solutions such as digital COVID-19 certificates have been considered as a strategy to lessen these restrictions and allow the resumption of routine activities. Using a mixed-methods approach – a survey (n=1008) and 27 semi-structured interviews – this study explores the attitudes of residents in the Republic of Ireland towards the idea of introducing digital COVID-19 certificates. We examine the topics of acceptability, fairness, security and privacy of COVID-related personal data, and practical considerations for implementation. Our study reveals the conditional and contextual nature of the acceptability of digital certificates, identifying specific factors that affect it, associated data practices, and related public concerns and expectations of such technologies.
Title: Supporting the Contact Tracing Process with WiFi Location Data: Opportunities and Challenges
Authors: family: Hall
given: Kaely; family: Yoo
given: Dong Whi; family: Zhang
given: Wenrui; family: Bin Morshed
given: Mehrab; family: Das Swain
given: Vedant; family: Abowd
given: Gregory D.; family: De Choudhury
given: Munmun; family: Endert
given: Alex; family: Stasko
given: John; family: Kim
given: Jennifer G
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: contact tracing, visualization, WiFi data

Abstract: Contact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member’s recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member’s memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making.
Title: Does Mode of Digital Contact Tracing Affect User Willingness to Share Information? A Quantitative Study
Authors: family: Zakaria
given: Camellia; family: Foong
given: Pin Sym; family: Lim
given: Chang Siang; family: V. S. Pakianathan
given: Pavithren; family: Koh
given: Gerald Huat Choon; family: Perrault
given: Simon Tangi
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: contact tracing, willingness, pandemic, trust, public health

Abstract: Digital contact tracing can limit the spread of infectious diseases. Nevertheless, barriers remain to attain sufficient adoption. In this study, we investigate how willingness to participate in contact tracing is affected by two critical factors: the modes of data collection and the type of data collected. We conducted a scenario-based survey study among 220 respondents in the United States (U.S.) to understand their perceptions about contact tracing associated with automated and manual contact tracing methods. The findings indicate a promising use of smartphones and a combination of public health officials and medical health records as information sources. Through a quantitative analysis, we describe how different modalities and individual demographic factors may affect user compliance when participants are asked to provide four key information pieces for contact tracing.
Title: Shifting Trust: Examining How Trust and Distrust Emerge, Transform, and Collapse in COVID-19 Information Seeking
Authors: family: Zhang
given: Yixuan; family: Suhaimi
given: Nurul; family: Yongsatianchot
given: Nutchanon; family: Gaggiano
given: Joseph D; family: Kim
given: Miso; family: Patel
given: Shivani A; family: Sun
given: Yifan; family: Marsella
given: Stacy; family: Griffin
given: Jacqueline; family: Parker
given: Andrea G
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: trust, information infrastructure, crisis informatics, misinformation, longitudinal research, COVID-19, social media, distrust, information behaviors, mixed methods

Abstract: During crises like COVID-19, individuals are inundated with conflicting and time-sensitive information that drives a need for rapid assessment of the trustworthiness and reliability of information sources and platforms. This parallels evolutions in information infrastructures, ranging from social media to government data platforms. Distinct from current literature, which presumes a static relationship between the presence or absence of trust and people’s behaviors, our mixed-methods research focuses on situated trust, or trust that is shaped by people’s information-seeking and assessment practices through emerging information platforms (e.g., social media, crowdsourced systems, COVID data platforms). Our findings characterize the shifts in trustee (what/who people trust) from information on social media to the social media platform(s), how distrust manifests skepticism in issues of data discrepancy, the insufficient presentation of uncertainty, and how this trust and distrust shift over time. We highlight the deep challenges in existing information infrastructures that influence trust and distrust formation.
Title: Annotating Line Charts for Addressing Deception
Authors: family: Fan
given: Arlen; family: Ma
given: Yuxin; family: Mancenido
given: Michelle; family: Maciejewski
given: Ross
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: deceptive visualization, visual literacy, educational tools

Abstract: Deceptive visualizations are visualizations that, whether intentionally or not, lead the reader to an understanding of the data which varies from the actual data. Examples of deceptive visualizations can be found in every digital platform, and, despite their widespread use in the wild, there have been limited efforts to alert laypersons to common deceptive visualization practices. In this paper, we present a tool for annotating line charts in the wild that reads line chart images and outputs text and visual annotations to assess the line charts for distortions and help guide the reader towards an honest understanding of the chart data. We demonstrate the usefulness of our tool through a series of case studies on real-world charts. Finally, we perform a crowdsourced experiment to evaluate the ability of the proposed tool to educate readers about potentially deceptive visualization practices.
Title: Making Data Tangible: A Cross-disciplinary Design Space for Data Physicalization
Authors: family: Bae
given: S. Sandra; family: Zheng
given: Clement; family: West
given: Mary Etta; family: Do
given: Ellen Yi-Luen; family: Huron
given: Samuel; family: Szafir
given: Danielle Albers
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: design, data visualization, design space, tangible user interface, data physicalization

Abstract: Designing a data physicalization requires a myriad of different considerations. Despite the cross-disciplinary nature of these considerations, research currently lacks a synthesis across the different communities data physicalization sits upon, including their approaches, theories, and even terminologies. To bridge these communities synergistically, we present a design space that describes and analyzes physicalizations according to three facets: context (end-user considerations), structure (the physical structure of the artifact), and interactions (interactions with both the artifact and data). We construct this design space through a systematic review of 47 physicalizations and analyze the interrelationships of key factors when designing a physicalization. This design space cross-pollinates knowledge from relevant HCI communities, providing a cohesive overview of what designers should consider when creating a data physicalization while suggesting new design possibilities. We analyze the design decisions present in current physicalizations, discuss emerging trends, and identify underlying open challenges.
Title: Put a Label On It! Approaches for Constructing and Contextualizing Bar Chart Physicalizations
Authors: family: Sauvé
given: Kim; family: Ramirez Gomez
given: Argenis; family: Houben
given: Steven
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Constructive Visualization, Data Labels, Data Physicalization, Physical Visualization

Abstract: Physicalizations represent data through their tangible and material properties. In contrast to screen-based visualizations, there is currently very limited understanding of how to label or annotate physicalizations to support people in interpreting the data encoded by the physicalization. Because of its spatiality, contextualization through labeling or annotation is crucial to communicate data across different orientations. In this paper, we study labeling approaches as part of the overall construction process of bar chart physicalizations. We designed a toolkit of physical tokens and paper data labels and asked 16 participants to construct and contextualize their own data physicalizations. We found that (i) the construction and contextualization of physicalizations is a highly intertwined process, (ii) data labels are integrated with physical constructs in the final design, and (iii) these are both influenced by orientation changes. We contribute with an understanding of the role of data labeling in the creation and contextualization of physicalizations.
Title: Visualization Accessibility in the Wild: Challenges Faced by Visualization Designers
Authors: family: Joyner
given: Shakila Cherise S; family: Riegelhuth
given: Amalia; family: Garrity
given: Kathleen; family: Kim
given: Yea-Seul; family: Kim
given: Nam Wook
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: a11y, data visualization, visual impairment, inclusive design

Abstract: Data visualizations are now widely used across many disciplines. However, many of them are not easily accessible for visually impaired people. In this work, we use three-staged mixed methods to understand the current practice of accessible visualization design for visually impaired people. We analyzed 95 visualizations from various venues to inspect how they are made inaccessible. To understand the rationale and context behind the design choices, we also conducted surveys with 144 practitioners in the U.S. and follow-up interviews with ten selected survey participants. Our findings include the difficulties of handling modern complex and interactive visualizations and the lack of accessibility support from visualization tools in addition to personal and organizational factors making it challenging to perform accessible design practices.