Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Printed Street Crossings: Supporting Orientation and Mobility Training with People who are Blind or have Low Vision

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.
Assessing the Spatial Distribution of Visual Attention in a Virtual Environment: Development and Validation of a Novel VR-based Attentional Visual Field (AVF) Task

Visual attention is critical for everyday task performance and safety. The Attentional Visual Field Task (AVF) is an established, computerized method for assessing the distribution of visual attention across a wide visual field. High-fidelity virtual reality (VR) presents an opportunity for more ecological methods for assessing and training visual attention; however, this novel approach has not been examined. We developed a new VR-based AVF task, AVF-VE, using a Head-Mounted Display (HMD) VR device with an integrated eye-tracker, and conducted a study to validate this newly developed visual attention task. We further examined how visual attention is distributed in a virtual visual field. The findings suggest that the VR-based visual attention task is a valid and useful tool that can be used for future attention research and training. Unique characteristics of the spatial distribution of visual attention in the virtual environment observed in the current evaluation study are discussed.
Finding Strategies Against Misinformation in Social Media: A Qualitative Study

Misinformation spread through social media has become a fundamental challenge in modern society. Recent studies have evaluated various strategies for addressing this problem, such as by modifying social media platforms or educating people about misinformation, to varying degrees of success. Our goal is to develop a new strategy for countering misinformation: intelligent tools that encourage social media users to foster metacognitive skills ”in the wild. ” As a first step, we conducted focus groups with social media users to discover how they can be best supported in combating misinformation. Qualitative analyses of the discussions revealed that people find it difficult to detect misinformation. Findings also indicated a need for but lack of resources to support cross-validation of information. Moreover, misinformation had a nuanced emotional impact on people. Suggestions for the design of intelligent tools that support social media users in information selection, information engagement, and emotional response management are presented.
Virtual Feed: Design and Evaluation of a Virtual Reality Simulation Addressing the Lived Experience of Breastfeeding

Breastfeeding can be challenging, but it is difficult for antenatal education to convey issues associated with the lived experience of breastfeeding. In our work, we explore the potential of interactive simulations to support antenatal education, and present Virtual Feed, a Virtual Reality breastfeeding simulation for parents-to-be developed following a three-step process. (1) We created an experience prototype that features basic VR scenarios and a tangible baby, (2) we engaged in design sessions with 19 parents and parents-to-be to derive design implications to further refine the simulation, and (3) we evaluated the system through case studies to examine the perspectives of parents and parents-to-be on the simulation. Our results show that the simulation successfully engaged users and sparked curiosity, while also encouraging reflection about the challenges of breastfeeding. On this basis, we discuss challenges for the design of simulations with the purpose of supplementing antenatal education.
A Large-Scale Measurement of Cybercrime Against Individuals

We know surprisingly little about the prevalence and severity of cybercrime in the U.S. Yet, in order to prioritize the development and distribution of advice and technology to protect end users, we require empirical evidence regarding cybercrime. Measuring crime, including cybercrime, is a challenging problem that relies on a combination of direct crime reports to the government – which have known issues of under-reporting – and assessment via carefully-designed self-report surveys. We report on the first large-scale, nationally representative academic survey (n=11,953) of consumer cybercrime experiences in the U.S. Our analysis answers four research questions: (1) What is the prevalence and (2) the monetary impact of these cybercrimes we measure in the U.S.? , (3) Do inequities exist in victimization? , and (4) Can we improve cybercrime measurement by leveraging social-reporting techniques used to measure physical crime? Our analysis also offers insight toward improving future measurement of cybercrime and protecting users.
Understanding iOS Privacy Nutrition Labels: An Exploratory Large-Scale Analysis of App Store Data

Since December 2020, the Apple App Store has required all developers to create a privacy label when submitting new apps or app updates. However, there has not been a comprehensive study on how developers responded to this requirement. We present the first measurement study of Apple privacy nutrition labels to understand how apps on the U.S. App Store create and update privacy labels. We collected weekly snapshots of the privacy label and other metadata for all the 1.4 million apps on the U.S. App Store from April 2 to November 5, 2021. Our analysis showed that 51.6% of apps still do not have a privacy label as of November 5, 2021. Although 35.3% of old apps have created a privacy label, only 2.7% of old apps created a privacy label without app updates (i.e., voluntary adoption). Our findings suggest that inactive apps have little incentive to create privacy labels.
Re-imagining the Power of Priming and Framing Effects in the Context of Political Crowdfunding Campaigns

In recent years, political crowdfunding campaigns have emerged through which politicians raise money to fund their election campaigns. Divisive issues discussed in these campaigns may not only motivate donations but also could have a broader priming effect on people’s social opinions. In the U.S., more than one-third of the population with moderate opinions show a tendency to swing their opinion based on recent and more accessible events. In this paper, we ask: can such campaigns further prime people’s responses to partisan topics, even when we discuss those topics in a non-political context? To answer this question, we analyzed the influence of exposure to a political candidate’s crowdfunding campaign on responses to a subsequently seen, unrelated scientific topic that is not inherently political but is seen as partisan in the U.S. (climate change). We found that exposure to an attitude-inconsistent political candidate’s crowdfunding campaign (a campaign that is counter to someone’s existing political beliefs) can have a significant priming effect on subsequently seen politically charged topics. This effect may occur due to the activation of in-group identity by the candidate’s partisan campaign. Guided by these findings, we investigated elements that can mitigate this self-categorization effect. We found that carefully designed content following framing techniques such as schema framing and threat/safety framing can mitigate people’s sense of self-categorization toward non-political topics.
“It’s Kind of Like Code-Switching”: Black Older Adults’ Experiences with a Voice Assistant for Health Information Seeking

Black older adults from lower socioeconomic environments are often neglected in health technology interventions. Voice assistants have a potential to make healthcare more accessible to older adults, yet, little is known about their experiences with this type of health information seeking, especially Black older adults. Through a three-phase exploratory study, we explored health information seeking with 30 Black older adults in lower-income environments to understand how they ask health-related questions, and their perceptions of the Google Home being used for that purpose. Through our analysis, we identified the health information needs and common search topics, and discussed the communication breakdowns and types of repair performed. We contribute an understanding of cultural code-switching that has to be done by these older adults when interacting with voice assistants, and the importance of such phenomenon when designing for historically excluded groups.
A Multiresolution Analysis of Temporal Logic

Is it possible to determine whether a signal violates a formula in Signal Temporal Logic (STL), if the monitor only has access to a low-resolution version of the signal? We answer this question affirmatively by demonstrating that temporal logic has a multiresolution structure, which parallels the multiresolution structure of signals. A formula in discrete-time Signal Temporal Logic (STL) is equivalently defined via the set of signals that satisfy it, known as its language. If a wavelet decomposition x = y + d is performed on each signal x in the language, we end up with two signal sets Y and D, where Y contains the low-resolution approximation signals y, and D contains the detail signals d needed to reconstruct the x’s. This paper provides a complete computational characterization of both Y and D using a novel constraint set encoding of STL, s.t. x satisfies a formula if and only if its decomposition signals satisfy their respective encoding constraints. Then a conservative logical approximation of Y is also provided: namely, we show that Y is over approximated by the language of a formula − 1. By iterating the decomposition, we obtain a sequence of lower-resolution formulas − 1, − 2, − 3,. which thus constitute a multiresolution analysis of. This work lays the foundation for multiresolution monitoring in distributed systems. One potential application of these results is a multiresolution monitor that can detect specification violation early by simply observing a low-resolution version of the signal to be monitored. 1
Care Workers Making Use of Robots: Results of a Three-Month Study on Human-Robot Interaction within a Care Home

Research on social robots in care has often focused on either the care recipients or the technology itself, neglecting the care workers who, in and through their collaborative and coordinative practices, will need to work with the robots. To better understand these interactions with a social robot (Pepper), we undertook a 3 month long-term study within a care home to gain empirical insights into the way the robot was used. We observed how care workers learned to use the device, applied it to their daily work life, and encountered obstacles. Our findings show that the care workers used the robot regularly (1:07 hours/day) mostly in one-to-one interactions with residents. While the robot had a limited effect on reducing the workload of care workers, it had other positive effects, demonstrating the potential to enhance the quality of care.
”It Matches My Worldview”: Examining Perceptions and Attitudes Around Fake Videos

We present a qualitative study with 36 diverse social media users in India to critically examine how low-resource communities engage with fake videos, including cheapfakes and AI-generated deepfakes. We find that most users are unaware of digitally manipulated fake videos and perceive videos to be fake only when they present inaccurate information. Few users who know about doctored videos expect them to be of poor quality and know nothing about sophisticated deepfakes. Moreover, most users lack the skills and willingness to spot fake videos and some were oblivious to the risks and harms of fake videos. Even when users know a video to be fake, they prefer to take no action and sometimes willingly share fake videos that favor their worldview. Drawing on our findings, we discuss design recommendations for social media platforms to curb the spread of fake videos.
COVID Student Study: A Year in the Life of College Students during the COVID-19 Pandemic Through the Lens of Mobile Phone Sensing

The COVID-19 pandemic continues to affect the daily life of college students, impacting their social life, education, stress levels and overall mental well-being. We study and assess behavioral changes of N=180 undergraduate college students one year prior to the pandemic as a baseline and then during the first year of the pandemic using mobile phone sensing and behavioral inference. We observe that certain groups of students experience the pandemic very differently. Furthermore, we explore the association of self-reported COVID-19 concern with students’ behavior and mental health. We find that heightened COVID-19 concern is correlated with increased depression, anxiety and stress. We evaluate the performance of different deep learning models to classify student COVID-19 concerns with an AUROC and F1 score of 0.70 and 0.71, respectively. Our study spans a two-year period and provides a number of important insights into the life of college students during this period.
EntityBot: Actionable Entity Recommendations for Everyday Digital Task

Our everyday digital tasks require access to information from a wide range of applications and systems. Although traditional search systems can help find information, they usually operate within one application (e.g., email client or web browser) and require the user’s cognitive effort and attention to formulate proper search queries. In this paper, we demonstrate EntityBot, a system that proactively provides useful and supporting entities across application boundaries without requiring explicit query formulation. Our methodology is to exploit the context from screen frames captured every 2 seconds to recommend relevant entities for the current task. Recommendations are not restricted to only documents but include various kinds of entities, such as applications, documents, contact persons, and keywords representing the tasks. Recommendations are actionable, that is, a user can perform actions on recommended entities, such as opening documents and applications. The EntityBot also includes support for interactivity, allowing the user to affect the recommendations by providing explicit feedback on the entities. The usefulness of entity recommendations and their impact on user behavior has been evaluated in a user study based on real-world tasks. Quantitative and qualitative results suggest that the system had an actual impact on the tasks and led to high user satisfaction.
The Deskilling of Domain Expertise in AI Development

Field workers, like farmers and radiologists, play a crucial role in dataset collection for AI models in low-resource settings. However, we know little about how field workers’ expertise is leveraged in dataset and model development. Based on 68 interviews with AI developers building for low-resource contexts, we find that developers reduced field workers to data collectors. Attributing poor data quality to worker practices, developers conceived of workers as corrupt, lazy, non-compliant, and as datasets themselves, pursuing surveillance and gamification to discipline workers to collect better quality data. Even though models sought to emulate the expertise of field workers, AI developers treated workers as non-essential and deskilled their expertise in service of building machine intelligence. We make the case for why field workers should be recognised as domain experts and re-imagine domain expertise as an essential partnership for AI development.
Including the Experiences of Physically Disabled Players in Mainstream Guidelines for Movement-Based Games

Movement-based video games can provide engaging play experiences, and also have the potential to encourage physical activity. However, existing design guidelines for such games overwhelmingly focus on non-disabled players. Here, we explore wheelchair users’ perspectives on movement-based games as an enjoyable play activity. We created eight game concepts as discussion points for semi-structured interviews (N=6) with wheelchair users, and used Interpretative Phenomenological Analysis to understand their perspectives on physical activity and play. Themes focus on independent access, challenges in social settings, and the need for comprehensive adaptation. We also conducted an online survey (N=21) using the same game concepts, and thematic analysis highlighted the importance of adequate challenge, and considerations around multiplayer experiences. Based on these findings, we re-contextualize and expand guidelines for movement-based games previously established by Mueller and Isbister to include disabled players, and suggest design strategies that take into account their perspectives on play.
EmoBalloon - Conveying Emotional Arousal in Text Chats with Speech Balloons

Text chat applications are an integral part of daily social and professional communication. However, messages sent over text chat applications do not convey vocal or nonverbal information from the sender, and detecting the emotional tone in text-only messages is challenging. In this paper, we explore the effects of speech balloon shapes on the sender-receiver agreement regarding the emotionality of a text message. We first investigated the relationship between the shape of a speech balloon and the emotionality of speech text in Japanese manga. Based on these results, we created a system that automatically generates speech balloons matching linear emotional arousal intensity by Auxiliary Classifier Generative Adversarial Networks (ACGAN). Our evaluation results from a controlled experiment suggested that the use of emotional speech balloons outperforms the use of emoticons in decreasing the differences between message senders’ and receivers’ perceptions about the level of emotional arousal in text messages.
Measuring the User Experience of Vibrotactile Feedback on the Finger, Wrist, and Forearm for Touch Input on Large Displays

We examine vibrotactile feedback delivered on the finger, wrist, and forearm with the goal of enriching the experience of touch input interactions with public displays. We focus on understanding the user experience of such interactions, which we characterize with a wide spectrum of UX measures, including subjective perceptions of the enjoyment, efficiency, input confidence, integration between touch input and on-body vibrations, distraction, confusion, and complexity of vibrotactile feedback for touch input with public displays. Our empirical findings, from a controlled experiment with fourteen participants, show positive and favorable perceptions of vibrotactile feedback as well as a significant preference for feedback on the finger compared to the wrist and forearm.
A Large-Scale Longitudinal Analysis of Missing Label Accessibility Failures in Android Apps

We present the first large-scale longitudinal analysis of missing label accessibility failures in Android apps. We developed a crawler and collected monthly snapshots of 312 apps over 16 months. We use this unique dataset in empirical examinations of accessibility not possible in prior datasets. Key large-scale findings include missing label failures in 55.6% of unique image-based elements, longitudinal improvement in ImageButton elements but not in more prevalent ImageView elements, that 8.8% of unique screens are unreachable without navigating at least one missing label failure, that app failure rate does not improve with number of downloads, and that effective labeling is neither limited to nor guaranteed by large software organizations. We then examine longitudinal data in individual apps, presenting illustrative examples of accessibility impacts of systematic improvements, incomplete improvements, interface redesigns, and accessibility regressions. We discuss these findings and potential opportunities for tools and practices to improve label-based accessibility.
Bridging the Gap Between UX Practitioners’ Work Practices and AI-Enabled Design Support Tools

User interface (UI) and user experience (UX) design have become an indispensable part of today’s tech industry. Recently, much progress has been made in machine-learning-enabled design support tools for UX designers. However, few of these tools have been adopted by practitioners. To learn the underlying reasons and understand user needs for bridging this gap, we conducted a retrospective analysis with 8 UX professionals to understand their practice and identify opportunities for future research. We found that the current AI-enabled systems to support UX work mainly work on graphical interface elements, while design activities that involve more ‘design thinking” such as user interviews and user testings are more helpful for designers. Many current systems were also designed for overly-simplistic and generic use scenarios. We identified 4 areas in the UX workflow that can benefit from additional AI-enabled assistance: design inspiration search, design alternative exploration, design system customization, and design guideline violation check.
Monitoring Pets, Deterring Intruders, and Casually Spying on Neighbors: Everyday Uses of Smart Home Cameras

The increased adoption of smart home cameras (SHCs) foregrounds issues of surveillance, power, and privacy in homes and neighborhoods. However, questions remain about how people are currently using these devices to monitor and surveil, what the benefits and limitations are for users, and what privacy and security tensions arise between primary users and other stakeholders. We present an empirical study with 14 SHC users to understand how these devices are used and integrated within everyday life. Based on semi-structured qualitative interviews, we investigate users’ motivations, practices, privacy concerns, and social negotiations. Our findings highlight the SHC as a perceptually powerful and spatially sensitive device that enables a variety of surveillant uses outside of basic home security—from formally surveilling domestic workers, to casually spying on neighbors, to capturing memories. We categorize surveillant SHC uses, clarify distinctions between primary and non-primary users, and highlight under-considered design directions for addressing power imbalances among primary and non-primary users.
Improving Agent-Based Route Predictions for As-The-Crow-Flies Navigation

Mobile navigation has become a ubiquitous technology through mobile devices and smartphones. One of these devices’ most-used navigation methods is the turn-by-turn method, first used in car-based navigation systems. However, pedestrians and cyclists increasingly use other methods such as the as-the-crow-flies (ATCF) navigation method instead of the classical turn-by-turn navigation. Instead of giving fixed instruction, ATCF navigation only indicates the straight-line direction to the destination and users have to make their own decision when and where to turn. In this paper, we improve the route predictions for such alternative navigation methods as ATFC by incorporating a model on users’ angle estimation error. We show that the route predictions are closer to actual human navigation behavior than methods proposed in related work.