huashen218/convxai-cia-dataset huggingface dataset:
Title: “It would probably turn into a social faux-pas”: Users’ and Bystanders’ Preferences of Privacy Awareness Mechanisms in Smart Homes
Authors: family: Thakkar
given: Parth Kirankumar; family: He
given: Shijing; family: Xu
given: Shiyu; family: Huang
given: Danny Yuxing; family: Yao
given: Yaxing
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Smart Homes, Multi-Stakeholder, Bystanders, Notifications, Privacy, Privacy Awareness

Abstract: The opaque data practices in smart home devices have raised significant privacy concerns for smart home users and bystanders. One way to learn about the data practices is through privacy-related notifications. However, how to deliver these notifications to users and bystanders and increase their awareness of data practices is not clear. We surveyed 136 users and 123 bystanders to understand their preferences of receiving privacy-related notifications in smart homes. We further collected their responses to four mechanisms that improve privacy awareness (e.g., Data Dashboard) as well as their selections of mechanisms in four different scenarios (e.g., friend visiting). Our results showed the pros and cons of each privacy awareness mechanism, e.g., Data Dashboard can help reduce bystanders’ dependence on users. We also found some unique benefits of each mechanism (e.g., Ambient Light could provide unobtrusive privacy awareness). We summarized four key design dimensions for future privacy awareness mechanisms design.
Title: Privacy Design Strategies for Home Energy Management Systems (HEMS)
Authors: family: Ramokapane
given: Kopo Marvin; family: Bird
given: Caroline; family: Rashid
given: Awais; family: Chitchyan
given: Ruzanna
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Smart Home, Smart Home Energy Management Systems, Energy Data, User Perceptions, Security and Privacy protections

Abstract: Home energy management systems (HEMS) offer control and the ability to manage energy, generating and collecting energy consumption data at the most detailed level. However, data at this level poses various privacy concerns, including, for instance, profiling consumer behaviors and large-scale surveillance. The question of how utility providers can get value from such data without infringing consumers’ privacy has remained under-investigated. We address this gap by exploring the pro-sharing attitudes and privacy perceptions of 30 HEMS users and non-users through an interview study. While participants are concerned about data misuse and stigmatization, our analysis also reveals that incentives, altruism, trust, security and privacy, transparency and accountability encourage data sharing. From this analysis, we derive privacy design strategies for HEMS that can both improve privacy and engender adoption.
Title: Permission vs. App Limiters: Profiling Smartphone Users to Understand Differing Strategies for Mobile Privacy Management
Authors: family: Alsoubai
given: Ashwaq; family: Ghaiumy Anaraky
given: Reza; family: Li
given: Yao; family: Page
given: Xinru; family: Knijnenburg
given: Bart; family: Wisniewski
given: Pamela J.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: privacy preferences, users profiling, user behaviors, smartphone users’ privacy

Abstract: We conducted a user study with 380 Android users, profiling them according to two key privacy behaviors: the number of apps installed and the Dangerous permissions granted to those apps. We identified four unique privacy profiles: 1) Privacy Balancers (49.74% of participants), 2) Permission Limiters (28.68%), 3) App Limiters (14.74%), and 4) the Privacy Unconcerned (6.84%). App and Permission Limiters were significantly more concerned about perceived surveillance than Privacy Balancers and the Privacy Unconcerned. App Limiters had the lowest number of apps installed on their devices with the lowest intention of using apps and sharing information with them, compared to Permission Limiters who had the highest number of apps installed and reported higher intention to share information with apps. The four profiles reflect the differing privacy management strategies, perceptions, and intentions of Android users that go beyond the binary decision to share or withhold information via mobile apps.
Title: Users’ Expectations About and Use of Smartphone Privacy and Security Settings
Authors: family: Frik
given: Alisa; family: Kim
given: Juliann; family: Sanchez
given: Joshua Rafael; family: Ma
given: Joanne
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: SES, self-efficacy, information architecture, cognitive interviews, security settings, digital divide, Privacy settings

Abstract: With the growing smartphone penetration rate, smartphone settings remain one of the main models for information privacy and security controls. Yet, their usability is largely understudied, especially with respect to the usability impact on underrepresented socio-economic and low-tech groups. In an online survey with 178 users, we find that many people are not aware of smartphone privacy and security settings, their defaults, and have not configured them in the past, but are willing to do it in the future. Some participants perceive low self-efficacy and expect difficulties and usability issues with configuring those settings. Finally, we find that certain socio-demographic groups are more vulnerable to risks and feel less prepared to use smartphone settings to protect their online privacy and security.
Title: Neo: Generalizing Confusion Matrix Visualization to Hierarchical and Multi-Output Labels
Authors: family: Görtler
given: Jochen; family: Hohman
given: Fred; family: Moritz
given: Dominik; family: Wongsuphasawat
given: Kanit; family: Ren
given: Donghao; family: Nair
given: Rahul; family: Kirchner
given: Marc; family: Patel
given: Kayur
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: machine learning, Confusion matrices, interactive systems, visual analytics, model evaluation

Abstract: The confusion matrix, a ubiquitous visualization for helping people evaluate machine learning models, is a tabular layout that compares predicted class labels against actual class labels over all data instances. We conduct formative research with machine learning practitioners at Apple and find that conventional confusion matrices do not support more complex data-structures found in modern-day applications, such as hierarchical and multi-output labels. To express such variations of confusion matrices, we design an algebra that models confusion matrices as probability distributions. Based on this algebra, we develop Neo, a visual analytics system that enables practitioners to flexibly author and interact with hierarchical and multi-output confusion matrices, visualize derived metrics, renormalize confusions, and share matrix specifications. Finally, we demonstrate Neo’s utility with three model evaluation scenarios that help people better understand model performance and reveal hidden confusions.
Title: Structure-aware Visualization Retrieval
Authors: family: Li
given: Haotian; family: Wang
given: Yong; family: Wu
given: Aoyu; family: Wei
given: Huan; family: Qu
given: Huamin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Visualization Retrieval, Visualization Similarity, Representation Learning, Visualization Embedding, Data Visualization

Abstract: With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can benefit various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. This paper presents a structure-aware method to advance the performance of visualization retrieval by collectively considering both the visual and structural information. We extensively evaluated our approach through quantitative comparisons, a user study and case studies. The results demonstrate the effectiveness of our approach and its advantages over existing methods.
Title: ComputableViz: Mathematical Operators as a Formalism for Visualisation Processing and Analysis
Authors: family: Wu
given: Aoyu; family: Tong
given: Wai; family: Li
given: Haotian; family: Moritz
given: Dominik; family: Wang
given: Yong; family: Qu
given: Huamin
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Data Model, Visualization, Visualization Library

Abstract: Data visualizations are created and shared on the web at an unprecedented speed, raising new needs and questions for processing and analyzing visualizations after they have been generated and digitized. However, existing formalisms focus on operating on a single visualization instead of multiple visualizations, making it challenging to perform analysis tasks such as sorting and clustering visualizations. Through a systematic analysis of previous work, we abstract visualization-related tasks into mathematical operators such as union and propose a design space of visualization operations. We realize the design by developing ComputableViz, a library that supports operations on multiple visualization specifications. To demonstrate its usefulness and extensibility, we present multiple usage scenarios concerning processing and analyzing visualization, such as generating visualization embeddings and automatically making visualizations accessible. We conclude by discussing research opportunities and challenges for managing and exploiting the massive visualizations on the web.
Title: Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health
Authors: family: Bao
given: Calvin S.; family: Li
given: Siyao; family: Flores
given: Sarah G; family: Correll
given: Michael; family: Battle
given: Leilani
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: algorithmic trust, recommendation source, Visualization recommendation systems, automation

Abstract: The promise of visualization recommendation systems is that analysts will be automatically provided with relevant and high-quality visualizations that will reduce the work of manual exploration or chart creation. However, little research to date has focused on what analysts value in the design of visualization recommendations. We interviewed 18 analysts in the public health sector and explored how they made sense of a popular in-domain dataset1 in service of generating visualizations to recommend to others. We also explored how they interacted with a corpus of both automatically- and manually-generated visualization recommendations, with the goal of uncovering how the design values of these analysts are reflected in current visualization recommendation systems. We find that analysts champion simple charts with clear takeaways that are nonetheless connected with existing semantic information or domain hypotheses. We conclude by recommending that visualization recommendation designers explore ways of integrating context and expectation into their systems.
Title: VisGuide: User-Oriented Recommendations for Data Event Extraction
Authors: family: Cao
given: Yu-Rong; family: Li
given: Xiao-Han; family: Pan
given: Jia-Yu; family: Lin
given: Wen-Chieh
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Visualization Recommendation, Visualization Sequencing, Visualization Trees

Abstract: Data exploration systems have become popular tools with which data analysts and others can explore raw data and organize their observations. However, users of such systems who are unfamiliar with their datasets face several challenges when trying to extract data events of interest to them. Those challenges include progressively discovering informative charts, organizing them into a logical order to depict a meaningful fact, and arranging one or more facts to illustrate a data event. To alleviate them, we propose VisGuide—a data exploration system that generates personalized recommendations to aid users’ discovery of data events in breadth and depth by incrementally learning their data exploration preferences and recommending meaningful charts tailored to them. As well as user preferences, VisGuide’s recommendations simultaneously consider sequence organization and chart presentation. We conducted two user studies to evaluate 1) the usability of VisGuide and 2) user satisfaction with its recommendation system. The results of those studies indicate that VisGuide can effectively help users create coherent and user-oriented visualization trees that represent meaningful data events.
Title: Visualizing Urban Accessibility: Investigating Multi-Stakeholder Perspectives through a Map-based Design Probe Study
Authors: family: Saha
given: Manaswi; family: Patil
given: Siddhant; family: Cho
given: Emily; family: Cheng
given: Evie Yu-Yen; family: Horng
given: Chris; family: Chauhan
given: Devanshi; family: Kangas
given: Rachel; family: McGovern
given: Richard; family: Li
given: Anthony; family: Heer
given: Jeffrey; family: Froehlich
given: Jon E.
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: urban tech, decision-making, geovisual analysis, visualization, sensemaking, physical accessibility

Abstract: Urban accessibility assessments are challenging: they involve varied stakeholders across decision-making contexts while serving a diverse population of people with disabilities. To better support urban accessibility assessment using data visualizations, we conducted a three-part interview study with 25 participants across five stakeholder groups using map visualization probes. We present a multi-stakeholder analysis of visualization needs and sensemaking processes to explore how interactive visualizations can support stakeholder decision making. In particular, we elaborate how stakeholders’ varying levels of familiarity with accessibility, geospatial analysis, and specific geographic locations influences their sensemaking needs. We then contribute 10 design considerations for geovisual analytic tools for urban accessibility communication, planning, policymaking, and advocacy.
Title: The Role of Staff in Facilitating Immersive Virtual Reality for Enrichment in Aged Care: An Ethic of Care Perspective
Authors: family: Waycott
given: Jenny; family: Kelly
given: Ryan M.; family: Baker
given: Steven; family: Barbosa Neves
given: Barbara; family: Thach
given: Kong Saoane; family: Lederman
given: Reeva
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Aging, Virtual Reality, Aged Care, Ethics of Care

Abstract: Immersive virtual reality (VR) is being used as an enriching experience for people living in residential aged care, or nursing homes, where care staff play a critical role supporting clients to use VR. In HCI research concerned with technology use in aged care, however, the role of formal caregivers has received limited attention. We conducted interviews with 11 caregivers working in care homes that have implemented VR as part of the social program offered to residents. Our findings highlight tensions between the opportunities created by the immersive VR experience and the risks and challenges full immersion presents for people in aged care. In this paper, we draw on an ethics of care framework to make visible the care practices involved in facilitating VR in aged care homes, highlighting the care required to ensure that older adults experience benefits when using immersive VR, while risks and challenges are carefully managed.
Title: 3D Printed Street Crossings: Supporting Orientation and Mobility Training with People who are Blind or have Low Vision
Authors: family: Holloway
given: Leona; family: Butler
given: Matthew; family: Marriott
given: Kim
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: Intersections, Maps, Blind, Orientation & Mobility, 3D Printing

Abstract: The ability to cross the street at intersections is an essential skill, often taught to people who are blind or have low vision (BLV) with the aid of tactile maps and kits or toys. However, each of the existing mapping tools has shortcomings. We investigated whether co-designed 3D printed components can offer benefits. Guided by consultation with 11 Orientation and Mobility (O&M) professionals, we co-designed a series of 3D printed kits that they then used in their practice with BLV children who showed high levels of engagement and learning. The 3D materials were found to demonstrate the key concepts for street crossings in a portable, engaging and professional manner. They will be released for free download, enabling O&M professionals to access or modify the materials as required. We hope that use of our co-designed 3D printed tools will contribute to the safety, independence and inclusion of BLV people.
Title: MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speech
Authors: family: Kim
given: Young-Ho; family: Chou
given: Diana; family: Lee
given: Bongshin; family: Danilovich
given: Margaret; family: Lazar
given: Amanda; family: Conroy
given: David E.; family: Kacorri
given: Hernisa; family: Choe
given: Eun Kyoung
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: older adults, experience sampling method, activity labeling, speech interaction, smartwatch

Abstract: Current activity tracking technologies are largely trained on younger adults’ data, which can lead to solutions that are not well-suited for older adults. To build activity trackers for older adults, it is crucial to collect training data with them. To this end, we examine the feasibility and challenges with older adults in collecting activity labels by leveraging speech. Specifically, we built MyMove, a speech-based smartwatch app to facilitate the in-situ labeling with a low capture burden. We conducted a 7-day deployment study, where 13 older adults collected their activity labels and smartwatch sensor data, while wearing a thigh-worn activity monitor. Participants were highly engaged, capturing 1,224 verbal reports in total. We extracted 1,885 activities with corresponding effort level and timespan, and examined the usefulness of these reports as activity labels. We discuss the implications of our approach and the collected dataset in supporting older adults through personalized activity tracking technologies.
Title: “I See You!”: A Design Framework for Interface Cues about Agent Visual Perception from a Thematic Analysis of Videogames
Authors: family: Rueben
given: Matthew; family: Horrocks
given: Matthew R; family: Martinez
given: Jennifer Eleanor; family: Cormier
given: Michelle V; family: LaLone
given: Nicolas; family: Fraune
given: Marlena; family: Toups Dugas
given: Z
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: qualitative methodologies, videogames, human-robot interaction, thematic analysis, sight cues

Abstract: As artificial agents proliferate, there will be more and more situations in which they must communicate their capabilities to humans, including what they can “see.” Artificial agents have existed for decades in the form of computer-controlled agents in videogames. We analyze videogames in order to not only inspire the design of better agents, but to stop agent designers from replicating research that has already been theorized, designed, and tested in-depth. We present a qualitative thematic analysis of sight cues in videogames and develop a framework to support human-agent interaction design. The framework identifies the different locations and stimulus types – both visualizations and sonifications – available to designers and the types of information they can convey as sight cues. Insights from several other cue properties are also presented. We close with suggestions for implementing such cues with existing technologies to improve the safety, privacy, and efficiency of human-agent interactions.
Title: Personalization Trade-offs in Designing a Dialogue-based Information System for Support-Seeking of Sexual Violence Survivors
Authors: family: Kim
given: Hyeok; family: Hwang
given: Youjin; family: Lee
given: Jieun; family: Kwon
given: Youngjin; family: Park
given: Yujin; family: Lee
given: Joonhwan
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: human-centered design, Sexual violence survivors

Abstract: The lack of reliable, personalized information often complicates sexual violence survivors’ support-seeking. Recently, there is an emerging approach to conversational information systems for support-seeking of sexual violence survivors, featuring personalization with wide availability and anonymity. However, a single best solution might not exist as sexual violence survivors have different needs and purposes in seeking support channels. To better envision conversational support-seeking systems for sexual violence survivors, we explore personalization trade-offs in designing such information systems. We implement a high-fidelity prototype dialogue-based information system through four design workshop sessions with three professional caregivers and interviewed with four self-identified survivors using our prototype. We then identify two forms of personalization trade-offs for conversational support-seeking systems: (1) specificity and sensitivity in understanding users and (2) relevancy and inclusiveness in providing information. To handle these trade-offs, we propose a reversed approach that starts from designing information and inclusive tailoring that considers unspecified needs, respectively.
Title: Designing Human-Agent Collaborations: Commitment, responsiveness, and support
Authors: family: Cila
given: Nazli
Venue: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
Keywords: human-agent collaboration, autonomous agent, Shared Cooperative Activity, design

Abstract: With the advancements in AI, agents (i.e., smart products, robots, software agents) are increasingly capable of working closely together with humans in a variety of ways while benefiting from each other. These human-agent collaborations have gained growing attention in the HCI community; however, the field lacks clear guidelines on how to design the agents’ behaviors in collaborations. In this paper, the qualities that are relevant for designers to create robust and pleasant human-agent collaborations were investigated. Bratman's Shared Cooperative Activity framework was used to identify the core characteristics of collaborations and survey the most important issues in the design of human-agent collaborations, namely code-of-conduct, task delegation, autonomy and control, intelligibility, common ground, offering help and requesting help. The aim of this work is to add structure to this growing and important facet of HCI research and operationalize the concept of human-agent collaboration with concrete design considerations.