Miracle-dz/UniFAR-FTUs huggingface dataset:
Accessible Text Tools: Where They Are Needed & What They Should Look Like

Many people have problems with reading, which limits their ability to participate in society. This paper explores tools that make text more accessible. For this, we interviewed experts who proposed scenarios and tools. Frequently mentioned scenarios are public administration, the medical domain, and everyday life. The accessible text tools proposed by experts support readers by improving how text is compressed, expanded, reviewed, and experienced. We provide the Accessible Text Framework to help researchers understand how the different software tools can be combined and discuss how individual tools can be implemented.
Contextualizing User Perceptions about Biases for Human-Centered Explainable Artificial Intelligence

Biases in Artificial Intelligence (AI) systems or their results are one important issue that demands AI explainability. Despite the prevalence of AI applications, the general public are not necessarily equipped with the ability to understand how the black-box algorithms work and how to deal with biases. To inform designs for explainable AI (XAI), we conducted in-depth interviews with major stakeholders, both end-users (n = 24) and engineers (n = 15), to investigate how they made sense of AI applications and the associated biases according to situations of high and low stakes. We discussed users’ perceptions and attributions about AI biases and their desired levels and types of explainability. We found that personal relevance and boundaries as well as the level of stake are two major dimensions for developing user trust especially during biased situations and informing XAI designs.
It is Okay to be Distracted: How Real-time Transcriptions Facilitate Online Meeting with Distraction

Online meetings are indispensable in collaborative remote work environments, but they are vulnerable to distractions due to their distributed and location-agnostic nature. While distraction often leads to a decrease in online meeting quality due to loss of engagement and context, natural multitasking has positive tradeoff effects, such as increased productivity within a given time unit. In this study, we investigate the impact of real-time transcriptions (i.e., full-transcripts, summaries, and keywords) as a solution to help facilitate online meetings during distracting moments while still preserving multitasking behaviors. Through two rounds of controlled user studies, we qualitatively and quantitatively show that people can better catch up with the meeting flow and feel less interfered with when using real-time transcriptions. The benefits of real-time transcriptions were more pronounced after distracting activities. Furthermore, we reveal additional impacts of real-time transcriptions (e.g., supporting recalling contents) and suggest design implications for future online meeting platforms where these could be adaptively provided to users with different purposes.
StyleMe: Towards Intelligent Fashion Generation with Designer Style

Hand-drawn sketches and sketch colourization are the most laborious but necessary steps for fashion designers to design exquisite clothes, especially when the fashion design requires distinctive and personal characteristics from designer style. This paper presents an artificial intelligent aided fashion design system, namely StyleMe, to support the automatic generation of clothing sketches with designer style. Given the clothing pictures specified by the designer, StyleMe can use deep learning based generative model to generate clothing sketches that are consistent with the designer style. The system also supports intelligent colourization on clothing sketch by style transfer, according to specified styles from the real fashion images. Through a series of performance evaluations and user studies, we found that our system can generate effective clothing sketches as good as fashion designers’ human work, and significantly improve the efficiency of fashion design with its sketch colourization method.
Dozer: Towards understanding the design of closed-loop wearables for sleep

Sleep plays a paramount role in maintaining healthy bodily functioning. Yet, poor sleep is an increasingly prevalent global health concern. Most current sleep technology tracks sleep, but how to design for promoting sleep is relatively underexplored. We highlight the potential of employing closed-loop systems for promoting sleep onset and explore this through the design and study of “Dozer”, a closed-loop beanie that accelerates sleep onset through auditory and electrical brain stimulation after detecting drowsiness in EEG. In an in-the-wild study, participant interviews revealed three UX themes (closed-loop neurocentric agency, awareness of hardware, and awareness of feedback), which ultimately suggested that participants fell asleep in spite of Dozer, rather than through its assistance. We interpret these results and provide actionable design tactics to inform the design of closed-loop sleep systems moving forward. We hope this work gives rise to a deeper understanding of designing closed techno-physiological loops.
Programming without a Programming Language: Challenges and Opportunities for Designing Developer Tools for Prompt Programming

Existing tools for writing prompts for language models (known as “prompt programming”) provide little support to prompt programmers. Consequently, as prompts become more complex with the addition of multiple input/output examples (“few-shot” prompts), they can be hard to read, understand, and edit. In this work, we observe that prompts are often used to solve complex problems, but lack the strict grammar of a traditional programming language. We describe methods for extracting the semantically meaningful structure of natural language prompts (e.g., regions of the prompt representing a preamble or input/output examples) in the absence of a rigid formal grammar, and demonstrate a range of editor features that can leverage this information to assist prompt programmers. Finally, we relate initial feedback from design probe explorations with a set of domain experts and provide insights to help guide the development of future prompt editors.
Energy equipartition and unidirectional emission in a spaser nanolaser

A spaser is a nanoplasmonic counterpart of a laser, with photons replaced by surface plasmon polaritons and a resonant cavity replaced by a metallic nanostructure supporting localized plasmonic modes. By combining analytical results and first‐principle numerical simulations, we provide a comprehensive study of the ultrafast dynamics of a spaser. Due to its highly‐nonlinear nature, the spaser is characterized by a large number of interacting degrees of freedom, which sustain a rich manifold of different phases we discover, describe and analyze here. In the regime of strong interaction, the system manifests an irreversible ergodic evolution towards the configuration where energy is equally shared among all the available degrees of freedom. Under this condition, the spaser generates ultrafast vortex‐like lasing modes that are spinning on the femtosecond scale and whose direction of rotation is dictated by quantum noise. In this regime, the spaser acquires the character of a nanoparticle with an effective spin. This opens up a range of interesting possibilities for achieving unidirectional emission from a symmetric nanostructure, stimulating a broad range of applications for nanoplasmonic lasers as unidirectional couplers and random information sources.
Tesseract: Querying Spatial Design Recordings by Manipulating Worlds in Miniature

New immersive 3D design tools enable the creation of spatial design recordings, capturing collaborative design activities. By reviewing captured spatial design sessions, which include user activities, workflows, and tool use, users can reflect on their own design processes, learn new workflows, and understand others’ design rationale. However, finding interesting moments in design activities can be challenging: they contain multimodal data (such as user motion and logged events) occurring over time which can be difficult to specify when searching, and are typically distributed over many sessions or recordings. We present Tesseract, a Worlds-in-Miniature-based system to expressively query VR spatial design recordings. Tesseract consists of the Search Cube interface acting as a centralized stage-to-search container, and four querying tools for specifying multimodal data to enable users to find interesting moments in past design activities. We studied ten participants who used Tesseract and found support for our miniature-based stage-to-search approach.
“I Would Like to Design”: Black Girls Analyzing and Ideating Fair and Accountable AI

Artificial intelligence (AI) literacy is especially important for those who may not be well-represented in technology design. We worked with ten Black girls in fifth and sixth grade from a predominantly Black school to understand their perceptions around fair and accountable AI and how they can have an empowered role in the creation of AI. Thematic analysis of discussions and activity artifacts from a summer camp and after-school session revealed a number of findings around how Black girls: perceive AI, primarily consider fairness as niceness and equality (but may need support considering other notions, such as equity), consider accountability, and envision a just future. We also discuss how the learners can be positioned as decision-making designers in creating AI technology, as well as how AI literacy learning experiences can be empowering.
CrowdIDEA: Blending Crowd Intelligence and Data Analytics to Empower Causal Reasoning

Causal reasoning is crucial for people to understand data, make decisions, or take action. However, individuals often have blind spots and overlook alternative hypotheses, and using only data is insufficient for causal reasoning. We designed and implemented CrowdIDEA, a novel tool consisting of a three-panel integration incorporating the crowd’s beliefs (Crowd Panel with two designs), data analytics (Data Panel), and user’s causal diagram (Diagram Panel) to stimulate causal reasoning. Through an experiment with 54 participants, we showed the significant effects of the Crowd Panel designs on the outcomes of causal reasoning, such as an increased number of causal beliefs generated. Participants also devised new strategies for bootstrapping, strengthening, deepening, and explaining their causal beliefs, as well as taking advantage of the unique characteristics of both qualitative and quantitative data sources to reduce potential biases in reasoning. Our work makes theoretical and design implications for exploratory causal reasoning.
ImpactBot: Chatbot Leveraging Language Models to Automate Feedback and Promote Critical Thinking Around Impact Statements

Impact statements articulate the impacts of a research project with concise and unambiguous statements about problems addressed, actions to resolve, and explanations of any impacts. Researchers and technologists often rely on impact statements as means to provoke introspective critical thinking around the impacts of technology being developed. However, due to factors such as technocentrism, positivity bias, marketization, or hyperinflation of impact statements, the claims presented in these statements do not cover all important aspects when creating technology – for instance, negative and delayed impacts. This work contributes to the development of a chatbot called ImpactBot to promote critical thinking while researchers create impact statements for research projects or scientific papers. The proposed chatbot leverages two fine-tuned state-of-the-art RoBERTa models for sequence classification and was assessed in this case study with 5 researchers from a large information technology company and 7 university engineering research scientists or students. This approach may be reused as part of content management or a paper submission system, for instance, to dialogue with researchers and promote critical thinking about negative impacts and how to mitigate them (if any) while creating impact statements for their projects or scientific papers.
Characterizing the Technology Needs of Vulnerable Populations for Participation in Research and Design by Adopting Maslow’s Hierarchy of Needs

While various frameworks and heuristics exist within the HCI community to guide research and design for vulnerable populations, most are centered on the researcher’s involvement. In this work, we developed a conceptual framework for supporting the participation of vulnerable populations in the research and design of technologies. Building upon Maslow’s hierarchy of needs, we synthesized 84 research articles that focus on vulnerable populations and technology to develop our framework. This framework conceptualizes both the barriers, such as lack of technology access and digital literacy, and assets, like social relationships, that impact effective participation in research and design. Using our framework can guide researchers in identifying and fulfilling the technology-related needs of vulnerable populations, leading to more empowering research participation for these groups. The framework’s guiding questions offer researchers the opportunity to reflect on their approach prior to and during their collaboration with vulnerable populations in technology research and design.
DataHalo: A Customizable Notification Visualization System for Personalized and Longitudinal Interactions

People struggle with the overflow of smartphone notifications but often face two challenges: (1) prioritizing the informative notifications as they wish and (2) retaining the delivered information as long as they want to utilize it. In this paper, we present DataHalo, a customizable notification visualization system that represents notifications as prolonged ambient visualizations on the home screen. DataHalo supports keyword-based filtering and categorization, and draws graphical marks based on time-varying importance model to enable longitudinal interaction with the notifications. We evaluated DataHalo through a usability study (N = 17), from which we improved the interface. We then conducted a three-week deployment study (N = 12) to assess how people use DataHalo in their domestic contexts. Our study revealed that people generated various visualization settings for different kinds of apps. Drawing on both quantitative and qualitative findings, we discussed implications for supporting effective notification management through customizable ambient visualizations.
PathFinder: Designing a Map-less Navigation System for Blind People in Unfamiliar Buildings

Indoor navigation systems with prebuilt maps have shown great potential in navigating blind people even in unfamiliar buildings. However, blind people cannot always benefit from them in every building, as prebuilt maps are expensive to build. This paper explores a map-less navigation system for blind people to reach destinations in unfamiliar buildings, which is implemented on a robot. We first conducted a participatory design with five blind people, which revealed that intersections and signs are the most relevant information in unfamiliar buildings. Then, we prototyped PathFinder, a navigation system that allows blind people to determine their way by detecting and conveying information about intersections and signs. Through a participatory study, we improved the interface of PathFinder, such as the feedback for conveying the detection results. Finally, a study with seven blind participants validated that PathFinder could assist users in navigating unfamiliar buildings with increased confidence compared to their regular aid.
Online Proctoring: Privacy Invasion or Study Alleviation?: Discovering Acceptability Using Contextual Integrity

Detecting fraud during online exams using proctoring software comes with substantial privacy challenges. Previous work argues students experience heightened anxiety and have privacy concerns. However, little is known about which specific aspects of online proctoring cause these concerns. This study contributes such insights by using the Contextual Integrity (CI) framework to discover how students (N = 456) rate the acceptability of 1064 proctoring information flows with varying information types, recipients, and transmission principles. We find that the acceptability varies considerably depending on the context. Besides exposing obvious privacy violations, we find that, under certain conditions, students consider it acceptable to share data with teachers - despite their lack of involvement in proctoring. Also, the acceptability of sharing highly sensitive information - which should under no circumstances be shared - sometimes increases. We discuss the implications of these and other findings and provide concrete recommendations for educational institutions using online proctoring.
Fostering Youth’s Critical Thinking Competency About AI through Exhibition

Today’s youth lives in a world deeply intertwined with AI, which has become an integral part of everyday life. For this reason, it is important for youth to critically think about and examine AI to become responsible users in the future. Although recent attempts have educated youth on AI with focus on delivering critical perspectives within a structured curriculum, opportunities to develop critical thinking competencies that can be reflected in their lives must be provided. With this background, we designed an informal learning experience through an AI-related exhibition to cultivate critical thinking competency. To explore changes before and after the exhibition, 23 participants were invited to experience the exhibition. We found that the exhibition can support the youth in relating AI to their lives through critical thinking processes. Our findings suggest implications for designing learning experiences to foster critical thinking competency for better coexistence with AI.
Shifting from Surveillance-as-Safety to Safety-through-Noticing: A Photovoice Study with Eastside Detroit Residents

Safety has been used to justify the expansion of today’s large-scale surveillance infrastructures in American cities. Our work offers empirical and theoretical groundings on why and how the safety-surveillance conflation that reproduces harm toward communities of color must be denaturalized. In a photovoice study conducted in collaboration with a Detroit community organization and a university team, we invited 11 Black mid-aged and senior Detroiters to use photography to capture their lived experiences of navigating personal and community safety. Their photographic narratives unveil acts of “everyday noticing” in negotiating and maintaining their intricate and interdependent relations with human, non-human animals, plants, spaces, and material things, through which a multiplicity of meaning and senses of safety are produced and achieved. Everyday noticing, as simultaneously a survival skill and a more-than-human care act, is situated in residents’ lived materialities, while also serving as a site for critiquing the reductive and exclusionary vision embedded in large-scale surveillance infrastructures. By proposing an epistemological shift from surveillance-as-safety to safety-through-noticing, we invite future HCI work to attend to the fluid and relational forms of safety that emerge from local entanglement and sensibilities.
Measuring the Stigmatizing Effects of a Highly Publicized Event on Online Mental Health Discourse

Media coverage has historically played an influential and often stigmatizing role in the public’s understanding of mental illness through harmful language and inaccurate portrayals of those with mental health issues. However, it is unknown how and to what extent media events may affect stigma in online discourse regarding mental health. In this study, we examine a highly publicized event – the celebrity defamation trial between Johnny Depp and Amber Heard – to uncover how stigmatizing and destigmatizing language on Twitter changed during and after the course of the trial. Using causal impact and language analysis methods, we provided a first look at how external events can lead to significantly greater levels of stigmatization and lower levels of destigmatization on Twitter towards not only particular disorders targeted in the coverage of external events but also general mental health discourse.
Impressions and Strategies of Academic Advisors When Using a Grade Prediction Tool During Term Planning

Academic advising brings numerous benefits to the mission of Higher Education Institutions. One central and challenging duty of advisors is course recommendation for term planning. This task requires both knowledge of the study programs as well as a thorough analysis of the students’ unique circumstances. Limited time and a large student population make this task overwhelming. As a result, an important body of research has sought to expedite term planning via data-oriented decision-support tools. The impact of such tools on students has been extensively studied. However, the advisors’ perspective remains largely unexplored. We contribute to redressing this gap by studying how a grade prediction tool shapes academic advisors’ approach to course recommendation. We found that while the advisors’ usual strategies tend to prevail, their recommendations largely depend on the advisee’s historical performance. That said, advisors also acknowledge the limitations of grades as a measure of academic success.
The Effects of Body Location and Biosignal Feedback Modality on Performance and Workload Using Electromyography in Virtual Reality

Using biosignals through electromyography (EMG) and rendering them as feedback for hands-free interaction finally migrates to engaging virtual reality (VR) experiences for health and fitness-related applications. Previous work proposes various body locations as input sources and different output modalities for creating effective biofeedback loops. However, it is currently unknown which muscles and sensory modalities can provide optimal real-time interaction regarding the performance and perceived workload of the users. In two VR studies (N=18 and N=40) based on a Fitts’ law target selection task, we explored sensor placement at different body locations and investigate auditory, tactile, and visual feedback modalities. Objective and subjective results indicate that input performance can be improved by presenting muscle tension as simultaneous tactile and visual feedback. We contribute with recommendations for registration of isometric muscle contraction at different body locations and conclude that reproducing physiological feedback through multimodal channels can assist users interacting with EMG devices.
Matching Mind and Method: Augmented Decision-Making with Digital Companions based on Regulatory Mode Theory

Digital companions shall augment complex human processes like extensive decision-making. However, their acceptance may depend upon their ability to adapt to individuals’ psychological states and preferred decision strategies. Regulatory Mode Theory divides human self-regulation into assessment (i.e., making comparisons) and locomotion (i.e., movement from state to state). These regulatory modes are more or less compatible with different decision strategies. In an experimental study (N=81, 2x2-between-subjects design) we explored whether digital companions can gain higher acceptance by considering these compatibilities. Participants were confronted with a decision task. The assisting digital companion first induced a regulatory mode (assessment vs. locomotion) and subsequently presented information according to one of two decision strategies (full evaluation vs. progressive elimination). We show that a fit between regulatory mode and decision strategy (assessment/full evaluation or locomotion/progressive elimination) leads to a favorable evaluation of decisions and the digital companion. No differences regarding decision accuracy and speed were observed.