Reviewerly/exHarmony huggingface dataset:
Can Voice Assistants Be Microaggressors? Cross-Race Psychological Responses to Failures of Automatic Speech Recognition

Language technologies have a racial bias, committing greater errors for Black users than for white users. However, little work has evaluated what effect these disparate error rates have on users themselves. The present study aims to understand if speech recognition errors in human-computer interactions may mirror the same effects as misunderstandings in interpersonal cross-race communication. In a controlled experiment (N=108), we randomly assigned Black and white participants to interact with a voice assistant pre-programmed to exhibit a high versus low error rate. Results revealed that Black participants in the high error rate condition, compared to Black participants in the low error rate condition, exhibited significantly higher levels of self-consciousness, lower levels of self-esteem and positive affect, and less favorable ratings of the technology. White participants did not exhibit this disparate pattern. We discuss design implications and the diverse research directions to which this initial study aims to contribute.
Living with Light Touch

We are a mother and son who have been using a pair of simple, self-build communication devices to maintain a feeling of connection while separated by over 5,000 miles. The devices, called Light Touch, only allow us to send one another slowly-fading, coloured lights, yet we have been surprised by how much our ongoing interaction with them means to us. This paper contributes an autoethnographical account of our experiences over the last two years, including our initial experiences with the devices, and focusing on various aspects of our day-to-day use. Based on our observations, we discuss the features that have proven important in mediating our feelings of connection. We point out, however, that their success is contingent on our context of use and the nature of our bond, and suggest that simple systems like Light Touch may support emotional communication, but only if they are well-matched to settings and relationships.
HotFoot: Foot-Based User Identification Using Thermal Imaging

We propose a novel method for seamlessly identifying users by combining thermal and visible feet features. While it is known that users' feet have unique characteristics, these have so far been underutilized for biometric identification, as observing those features often requires the removal of shoes and socks. As thermal cameras are becoming ubiquitous, we foresee a new form of identification, using feet features and heat traces to reconstruct the footprint even while wearing shoes or socks. We collected a dataset of users' feet (N = 21), wearing three types of footwear (personal shoes, standard shoes, and socks) on three floor types (carpet, laminate, and linoleum). By combining visual and thermal features, an AUC between 91.1% and 98.9%, depending on floor type and shoe type can be achieved, with personal shoes on linoleum floor performing best. Our findings demonstrate the potential of thermal imaging for continuous and unobtrusive user identification.
The Hidden Rules of Hanabi: How Humans Outperform AI Agents

Games that feature multiple players, limited communication, and partial information are particularly challenging for AI agents. In the cooperative card game Hanabi, which possesses all of these attributes, AI agents fail to achieve scores comparable to even first-time human players. Through an observational study of three mixed-skill Hanabi play groups, we identify the techniques used by humans that help to explain their superior performance compared to AI. These concern physical artefact manipulation, coordination play, role establishment, and continual rule negotiation. Our findings extend previous accounts of human performance in Hanabi, which are purely in terms of theory-of-mind reasoning, by revealing more precisely how this form of collective decision-making is enacted in skilled human play. Our interpretation points to a gap in the current capabilities of AI agents to perform cooperative tasks.
Infrastructuring Care: How Trans and Non-Binary People Meet Health and Well-Being Needs through Technology

We present a cross-cultural diary study with 64 transgender (trans) and non-binary adults in Mexico, the U.S., and India, to understand experiences keeping track of and managing aspects of personal health and well-being. Based on a reflexive thematic analysis of diary data, we highlight sociotechnical interactions that shape how trans and non-binary people track and manage aspects of their health and well-being. Specifically, we surface the ways in which trans and non-binary people infrastructure forms of care, by assembling together elements of informal social ecologies, formalized knowledge sources, and self-reflective media. We examine the forms of precarity that interact with care infrastructure and shape management of health and well-being, including management of gender identity transitions. We discuss the ways in which our findings extend knowledge at the intersection of technology and marginalized health needs, and conclude by arguing for the importance of a research agenda to move toward TGNB-inclusive design.
OmniSense: Exploring Novel Input Sensing and Interaction Techniques on Mobile Device with an Omni-Directional Camera

An omni-directional (360°) camera captures the entire viewing sphere surrounding its optical center. Such cameras are growing in use to create highly immersive content and viewing experiences. When such a camera is held by a user, the view includes the user's hand grip, finger, body pose, face, and the surrounding environment, providing a complete understanding of the visual world and context around it. This capability opens up numerous possibilities for rich mobile input sensing. In OmniSense, we explore the broad input design space for mobile devices with a built-in omni-directional camera and broadly categorize them into three sensing pillars: i) near device ii) around device and iii) surrounding device. In addition we explore potential use cases and applications that leverage these sensing capabilities to solve user needs. Following this, we develop a working system to put these concepts into action, by leveraging these sensing capabilities to enable potential use cases and applications. We studied the system in a technical evaluation and a preliminary user study to gain initial feedback and insights. Collectively these techniques illustrate how a single, omni-purpose sensor on a mobile device affords many compelling ways to enable expressive input, while also affording a broad range of novel applications that improve user experience during mobile interaction.
Digital Storytelling for the Support of Maternal and Child-wellbeing Through Community-based Co-design

In rural, under-resourced, and marginalised regions, digital health information has become a tool with which to reach new and expecting parents for the care of their and their children's wellbeing. However, the types of health information shared is often inaccessible since "top-down" approaches are utilised in creating and sharing content, with limited input by recipients of the information. Furthermore, access to health information has been prioritised over the learning thereof, indicating a need to refocus efforts on how health information is curated. I aim to explore how community-based co-design can be leveraged to design and develop digital storytelling content for the support of maternal and child-wellbeing (MCWB). With the consideration of intersectional feminist methods, participatory visual techniques, and Lambert's taxonomy of participatory media practices, I seek to understand existing storytelling and communication practices, as well as dissemination mechanisms in two rural South African communities for the support of MCWB. This will be achieved through four phases, namely understanding, making, connecting, and embracing, using in-depth interviews, photovoice, generative workshops, and focus groups. I am interested in how various community stakeholders participate in the MCWB and health information sharing ecosystem, thus I will be including mothers, fathers, other caregivers, healthcare workers and community leaders. With the creation of digital story artefacts, community-based methods and techniques, and an understanding of health information content creation and sharing via community-based co-design, the intended contributions are: describing how existing communal practices can influence novel approaches to promoting MCWB; developing an understanding of how different degrees of participation in the creation of digital stories impact how people create, share, and learn from digital stories; advances in community-based co-design for rural and under-resourced populations; and explaining how digital stories could impact MCWB within a community.
Viewpoint-Controllable Telepresence: A Robotic-Arm-Based Mixed-Reality Telecollaboration System

In mixed-reality (MR) telecollaboration, the local environment is remotely presented to a remote user wearing a virtual reality (VR) head-mounted display (HMD) via a video capture device. However, remote users frequently face challenges in naturally and actively manipulating their viewpoints. In this paper, we propose a telepresence system with viewpoint control, which involves a robotic arm equipped with a stereo camera in the local environment. This system enables remote users to actively and flexibly observe the local environment by moving their heads to manipulate the robotic arm. Additionally, to solve the problem of the limited field of view of the stereo camera and limited movement range of the robotic arm, we propose a 3D reconstruction method combined with a stereo video field-of-view enhancement technique to guide remote users to move within the movement range of the robotic arm and provide them with a larger range of local environment perception. Finally, a mixed-reality telecollaboration prototype was built, and two user studies were conducted to evaluate the overall system. User study A evaluated the interaction efficiency, system usability, workload, copresence, and user satisfaction of our system from the remote user's perspective, and the results showed that our system can effectively improve the interaction efficiency while achieving a better user experience than two traditional view-sharing techniques based on 360 video and based on the local user's first-person view. User study B evaluated our MR telecollaboration system prototype from both the remote-user side and the local-user side as a whole, providing directions and suggestions for the subsequent design and improvement of our mixed-reality telecollaboration system.
Physicalization from Theory to Practice: Exploring Physicalization Design across Domains

Currently, physicalization research is dominated by technology-centric explorations with limited insights into the broader domain implications. The goal of this workshop is to bring together researchers and practitioners who share an interest in using data physicalizations to solve real-world problems. Hence, we aim to further explore the utility of physicalization for different domains that (already) apply data physicalization in their practices (e.g., sustainability, office vitality, education, and personal informatics). The objective of the workshop is to combine the expertise of researchers working in physicalization and/or exemplar domains to (i) develop an understanding of common challenges, (ii) map out overarching factors across domains, (ii) operationalize design strategies for common domains, and (iv) reflect on the implementation of data physicalizations for different domains. Upon completion of our workshop, we plan to create a BIT Special Issue addressing the challenges and potential directions of the domain application of data physicalizations.
Human-Centered Explainable AI (HCXAI): Coming of Age

Explainability is an essential pillar of Responsible AI that calls for equitable and ethical Human-AI interaction. Explanations are essential to hold AI systems and their producers accountable, and can serve as a means to ensure humans' right to understand and contest AI decisions. Human-centered XAI (HCXAI) argues that there is more to making AI explainable than algorithmic transparency. Explainability of AI is more than just "opening" the black box — who opens it matters just as much, if not more, as the ways of opening it. In this third CHI workshop on Human-centered XAI (HCXAI), we build on the maturation through the first two installments to craft the coming-of-age story of HCXAI, which embodies a deeper discourse around operationalizing human-centered perspectives in XAI. We aim towards actionable interventions that recognize both affordances and potential pitfalls of XAI. The goal of the third installment is to go beyond the black box and examine how human-centered perspectives in XAI can be operationalized at the conceptual, methodological, and technical levels. Encouraging holistic (historical, sociological, and technical) approaches, we emphasize "operationalizing." Within our research agenda for XAI, we seek actionable analysis frameworks, concrete design guidelines, transferable evaluation methods, and principles for accountability.
Parallel matters: Efficient polyp segmentation with parallel structured feature augmentation modules

Abstract The large variations of polyp sizes and shapes and the close resemblances of polyps to their surroundings call for features with long‐range information in rich scales and strong discrimination. This article proposes two parallel structured modules for building those features. One is the Transformer Inception module (TI) which applies Transformers with different reception fields in parallel to input features and thus enriches them with more long‐range information in more scales. The other is the Local‐Detail Augmentation module (LDA) which applies the spatial and channel attentions in parallel to each block and thus locally augments the features from two complementary dimensions for more object details. Integrating TI and LDA, a new Transformer encoder based framework, Parallel‐Enhanced Network (PENet), is proposed, where LDA is specifically adopted twice in a coarse‐to‐fine way for accurate prediction. PENet is efficient in segmenting polyps with different sizes and shapes without the interference from the background tissues. Experimental comparisons with state‐of‐the‐arts methods show its merits.
Effective 2D Stroke-based Gesture Augmentation for RNNs

Recurrent neural networks (RNN) require large training datasets from which they learn new class models. This limitation prohibits their use in custom gesture applications where only one or two end user samples are given per gesture class. One common way to enhance sparse datasets is to use data augmentation to synthesize new samples. Although there are numerous known techniques, they are often treated as standalone approaches when in reality they are often complementary. We show that by intelligently chaining augmentation techniques together that simulate different gesture production variability types, such as those affecting the temporal and spatial qualities of a gesture, we can significantly increase RNN accuracy without sacrificing training time. Through experimentation on four public stroke-based 2D gesture datasets, we show that RNNs trained with our data augmentation chaining technique achieves state-of-the-art recognition accuracy in both writer-dependent and writer-independent test scenarios.
The Halting problem: Video analysis of self-driving cars in traffic

Using publicly uploaded videos of the Waymo and Tesla FSD self-driving cars, this paper documents how self-driving vehicles still struggle with some basics of road interaction. To drive safely self-driving cars need to interact in traffic with other road users. Yet traffic is a complex, long established social domain. We focus on one core element of road interaction: when road users yield for each other. Yielding – such as by slowing down for others in traffic – involves communication between different road users to decide who will 'go' and who will 'yield'. Videos of the Waymo and Tesla FSD self-driving cars show how these systems fail to both yield for others, as well as failing to go when yielded to. In discussion, we explore how these 'problems' illustrate both the complexity of designing for road interaction, but also how the space of physical machine/human social interactions more broadly can be designed for.
From Asymptomatics to Zombies: Visualization-Based Education of Disease Modeling for Children

Throughout the COVID-19 pandemic, visualizations became commonplace in public communications to help people make sense of the world and the reasons behind government-imposed restrictions. Though the adult population were the main target of these messages, children were affected by restrictions through not being able to see friends and virtual schooling. However, through these daily models and visualizations, the pandemic response provided a way for children to understand what data scientists really do and provided new routes for engagement with STEM subjects. In this paper, we describe the development of an interactive and accessible visualization tool to be used in workshops for children to explain computational modeling of diseases, in particular COVID-19. We detail our design decisions based on approaches evidenced to be effective and engaging such as unplugged activities and interactivity. We share reflections and learnings from delivering these workshops to 140 children and assess their effectiveness.
Who Do We Mean When We Talk About Visualization Novices?

As more people rely on visualization to inform their personal and collective decisions, researchers have focused on a broader range of audiences, including "novices." But successfully applying, interrogating, or advancing visualization research for novices demands a clear understanding of what "novice" means in theory and practice. Misinterpreting who a "novice" is could lead to misapplying guidelines and overgeneralizing results. In this paper, we investigated how visualization researchers define novices and how they evaluate visualizations intended for novices. We analyzed 79 visualization papers that used "novice," "non-expert," "laypeople," or "general public" in their titles or abstracts. We found ambiguity within papers and disagreement between papers regarding what defines a novice. Furthermore, we found a mismatch between the broad language describing novices and the narrow population representing them in evaluations (i.e., young people, students, and US residents). We suggest directions for inclusively supporting novices in both theory and practice.
Does Repeatedly Typing the Same Phrase Provide a Good Estimate of Expert Text Entry Performance?

To identify if novel/unfamiliar keyboard layouts like OPTI can outperform QWERTY, lengthy training through longitudinal studies is typically required. To reduce this logistical bottleneck, a popular approach in the literature requires participants to type the same phrase repeatedly. However, it is still unknown whether this approach provides a good estimate of expert performance. To validate this method, we set up a study where participants were tasked with typing the same phrase 96 times for both OPTI and QWERTY. Results showed that this approach has the potential to estimate expert performance for novel/unfamiliar keyboards faster than the traditional approach with different phrases. Yet, we also found that accurate estimates still require training over several days and, therefore, do not eliminate the need for a longitudinal study. Our findings thus show the need for research on faster, easier, and more reliable empirical approaches to evaluate text entry systems.
Phone Sleight of Hand: Finger-Based Dexterous Gestures for Physical Interaction with Mobile Phones

We identify and evaluate single-handed "dexterous gestures" to physically manipulate a phone using the fine motor skills of fingers. Four manipulations are defined: shift, spin (yaw axis), rotate (roll axis) and flip (pitch axis), with a formative survey showing all except flip have been performed for various reasons. A controlled experiment examines the speed, behaviour, and preference of manipulations in the form of dexterous gestures, by considering two directions and two movement magnitudes. Results show rotate is rated as easiest and most comfortable, while flip is rated lowest. Using a heuristic recognizer for spin, rotate, and flip, a one-week usability experiment finds increased practice and familiarity improve the speed and comfort of dexterous gestures. Design guidelines are developed to consider comfort, ability, and confidence when mapping dexterous gestures to interactions, and demonstrations show how such gestures can be used in smartphone applications.
Selecting Real-World Objects via User-Perspective Phone Occlusion

Perceiving the region of interest (ROI) and target object by smartphones from the user's first-person perspective can enable diverse spatial interactions. In this paper, we propose a novel ROI input method and a target selecting method for smartphones by utilizing the user-perspective phone occlusion. This concept of turning the phone into real-world physical cursor benefits from the proprioception, gets rid of the constraint of camera preview, and allows users to rapidly and accurately select the target object. Meanwhile, our method can provide a resizable and rotatable rectangular ROI to disambiguate dense targets. We implemented the prototype system by positioning the user's iris with the front camera and estimating the rectangular area blocked by the phone with the rear camera simultaneously, followed by a target prediction algorithm with the distance-weighted Jaccard index. We analyzed the behavioral models of using our method and evaluated our prototype system's pointing accuracy and usability. Results showed that our method is well-accepted by the users for its convenience, accuracy, and efficiency.
PyArabic: A Python package for Arabic text

Because text is the most common type of information representation, text processing and manipulation require recurring routines and functions.Every day, massive amounts of text are processed.Indeed, with the advent of artificial intelligence and new machine learning and deep learning enhancements, natural language processing has become a critical domain.PyArabic is a collection of modules that provide basic functionality for manipulating Arabic texts, phrases, words, numbers, and letters.It primarily provides preprocessing tools such as normalization, tokenization, diacritics removal, number conversion, transliteration, and so on.For years, researchers and developers who worked on machine learning algorithms for natural language processing have used the library for Arabic text preprocessing and cleaning.The library becomes more important for machine learning.