Reviewerly/exHarmony huggingface dataset:
GlanceWriter: Writing Text by Glancing Over Letters with Gaze

Writing text with eye gaze only is an appealing hands-free text entry method. However, existing gaze-based text entry methods introduce eye fatigue and are slow in typing speed because they often require users to dwell on letters of a word, or mark the starting and ending positions of a gaze path with extra operations for entering a word. In this paper, we propose GlanceWriter, a text entry method that allows users to enter text by glancing over keys one by one without any need to dwell on any keys or specify the starting and ending positions of a gaze path when typing a word. To achieve so, GlanceWriter probabilistically determines the letters to be typed based on the dynamics of gaze movements and gaze locations. Our user studies demonstrate that GlanceWriter significantly improves the text entry performance over EyeSwipe, a dwell-free input method using "reverse crossing" to identify the starting and ending keys. GlanceWriter also outperforms the dwell-free gaze input method of Tobii's Communicator 5, a commercial eye gaze-based communication system. Overall, GlanceWriter achieves dwell-free and crossing-free text entry by probabilistically decoding gaze paths, offering a promising gaze-based text entry method.
How can HCI support end-of-life care? Critical perspectives on sociotechnical imaginaries for palliative care

Designing technologies in the palliative care environment is difficult due to the sensitivity of the context and vulnerability of the patients and their families in a critical stage of life. We conducted two focus groups with 9 healthcare workers to capture sociotechnical imaginaries to support physical, psychological, social and spiritual aspects of the patient care in the palliative environment. The findings reveal recurring themes of care: expressing respect in safe spaces, transitioning into the unknown and experiencing home again, taking opportunities for uplifting care, communication for empowerment and risk mitigation, and connection to support networks. These are discussed through a lens of the materiality of care provided to patients, where tangible materials of care are entwined with patients’ values and culture. This in turn offers insight into opportunities for palliative care technology.
Three Design Directions for a Diversity Computing Design Space

We present the insights from participatory design work that explores notions of Diversity Computing (DivComp) and how HCI can meaningfully engage with designing technology around diversity without resorting to tokenistic approaches. A future goal overarching the initial findings in this paper is to design technologically mediated, physical spaces (DivComp Spaces) within a school context where children meet, experiment and learn the complex dynamics of othering. We report on a series of nine workshops with 48 children. Based on a thematic analysis, we present four themes — Technology as Utility and Authority, Individual and Collective Place-Making, Staged and Emerging Conflicts, Belonging to the Group and Self-Expression — which we use to inform three design directions for developing DivComp Spaces specifically in the context of school. Finally, we critically reflect on our design practice and the difficulties of designing not only for, but also with diversity meaningfully embedded into design processes.
Efficient Video Anomaly Detection using Residual Variational Autoencoder

Video anomaly detection is a critical task in various fields such as surveillance, security, and transportation, and has been gaining significant attention in recent years.Manually monitoring such anomalies can be time-consuming and monotonous in result. Detecting anomalies in videos is difficult because of erratic nature of the event. Motivated by these issues we purpose a promising approach for video anomaly detection by using Residual Variational Autoencoder(RVAE) model which is able to detect anomalies in an unsupervised manner.RVAE can capture more complex patterns in the data and improve the reconstruction error of the model, In this model, the encoder takes the input and provides a low-dimensional latent representation of it, and the decoder learns to reconstruct the original input with minimum loss. ConvLSTM layer is used to make better Spatio-temporal learning and residual connection to reduce the vanishing gradient problem. This model is implemented on three benchmark datasets ucds pedl, ped2, and Avenue datasets given the result shows good potential and it could be a step forward to improve the performance
VRtoER: When Virtual Reality leads to Accidents

Virtual Reality (VR) has gained a relevant market share as a product for consumers in the gaming industry. Compared to other hardware, VR headsets isolate users from reality visually to create immersive experiences. However, when not used correctly, VR can lead to accidents, for instance, when colliding with objects in the real environment. A community on Reddit has formed around the topic of VR-related accidents and injuries called r/VRtoER, in which users upload media of accidents and injuries through the use of VR. 82 posts of the community were analyzed to understand 1) the cause for the accident, 2) why it happened, and 3) how it could be prevented. Preliminary findings are presented based on the analysis. The results show that in 26.83% of analyzed cases, the environment was not suitable for VR use. The analysis has also shown six cases in which VR use led to child endangerment.
Dancing with the Avatars: Minimal Avatar Customisation Enhances Learning in a Psychomotor Task

Virtual environments can support psychomotor learning by allowing learners to observe instructor avatars. Instructor avatars that look like the learner hold promise in enhancing learning; however, it is unclear whether this works for psychomotor tasks and how similar avatars need to be. We investigated 'minimal' customisation of instructor avatars, approximating a learner's appearance by matching only key visual features: gender, skin-tone, and hair colour. These avatars can be created easily and avoid problems of highly similar avatars. Using modern dancing as a skill to learn, we compared the effects of visually similar and dissimilar avatars, considering both learning on a screen (n=59) and in VR (n=38). Our results indicate that minimal avatar customisation leads to significantly more vivid visual imagery of the dance moves than dissimilar avatars. We analyse variables affecting interindividual differences, discuss the results in relation to theory, and derive design implications for psychomotor training in virtual environments.
A Benchmark for Performance Evaluation of a Multi-Model Database vs. Polyglot Persistence

As the need for handling data from various sources becomes crucial for making optimal decisions, managing multi-model data has become a key area of research. Currently, it is challenging to strike a balance between two methods: polyglot persistence and multi-model databases. Moreover, existing studies suggest that current benchmarks are not completely suitable for comparing these two methods, whether in terms of test datasets, workloads, or metrics. To address this issue, the authors introduce MDBench, an end-to-end benchmark tool. Based on the multi-model dataset and proposed workloads, the experiments reveal that ArangoDB is superior at insertion operations of graph data, while the polyglot persistence instance is better at handling the deletion operations of document data. When it comes to multi-thread and associated queries to multiple tables, the polyglot persistence outperforms ArangoDB in both execution time and resource usage. However, ArangoDB has the edge over MongoDB and Neo4j regarding reliability and availability.
A new fuzzy support vector machine with pinball loss

Abstract The fuzzy support vector machine (FSVM) assigns each sample a fuzzy membership value based on its relevance, making it less sensitive to noise or outliers in the data. Although FSVM has had some success in avoiding the negative effects of noise, it uses hinge loss, which maximizes the shortest distance between two classes and is ineffective in dealing with feature noise near the decision boundary. Furthermore, whereas FSVM concentrates on misclassification errors, it neglects to consider the critical within-class scatter minimization. We present a Fuzzy support vector machine with pinball loss (FPin-SVM), which is a fuzzy extension of a reformulation of a recently proposed support vector machine with pinball loss (Pin-SVM) with several significant improvements, to improve the performance of FSVM. First, because we used the squared L2- norm of errors variables instead of the L1 norm, our FPin-SVM is a strongly convex minimization problem; second, to speed up the training procedure, solutions of the proposed FPin-SVM, as an unconstrained minimization problem, are obtained using the functional iterative and Newton methods. Third, it is proposed to solve the minimization problem directly in primal. Unlike FSVM and Pin-SVM, our FPin-SVM does not require a toolbox for optimization. We dig deeper into the features of FPin-SVM, such as noise insensitivity and within-class scatter minimization. We conducted experiments on synthetic and real-world datasets with various sounds to validate the usefulness of the suggested approach. Compared to the SVM, FSVM, and Pin-SVM, the presented approaches demonstrate equivalent or superior generalization performance in less training time.
Invisibility or Visibility in Intimate Care at the Workplace? Examining the Use of Breast Pumps

Advances in intimate care technologies and on-body wearables are disrupting how and where we think about and care for our bodies. The boundaries between private and public are increasingly porous. This offers new sites for studying intimate care as technology-use-in-practice. We present a qualitative study on the use of breast pumps in the workplace, based on semi-structured interviews with 19 individuals. Through this, we contribute an illustration of the complexities in carrying out intimate care work at the workplace and what it means to be pumping at the workplace. Our analysis unpacks (in)visibility as a crucial tension in the use of breast pumps in the workplace. We discuss how (in)visibility of personal medical devices plays a mediating role in how individuals exercise bodily rights, and the norms of who fits into professional settings.
A Case Study on Student Experiences of Social VR in a Remote STEM Classroom

Extended reality (XR) technologies continue gaining traction in multiple higher education contexts. As XR becomes more commercially accessible to students and universities, its convenience for educational purposes presents a renewed potential for exploration. Due to Covid-19 restrictions, there is also a growing interest in cross-platform, socially orientated software for remote educational practices. However, the precise role of XR technologies and how they contribute to student experiences of remote learning, particularly the unique affordances of social virtual reality (VR) for evoking an embodied sense of presence, is relatively unknown. Based on real-world experiences, we present a case study on a social VR intervention in a remote higher education classroom to inspire Human-Computer Interaction (HCI) researchers to investigate further the issues that arise from our practice-based research. Our motivations were to report, analyze, and summarize everyday virtual learning environment (VLE) challenges, identify design considerations for VLE technologies, and comment on social VR's utility in delivering Science, Technology, Engineering, and Mathematics (STEM) subjects in a remote setting. We apply a practical approach to investigate and identify potential HCI problems, capture the unique experiences of STEM students during the lockdown, and explore the effects of tutorial activities that give students agency in constructing VLEs. The findings of this student-focused case study draw attention to the design of social VR activities that support conventional, web browser-based VLEs.
Point Cloud Alignment through Mid-Air Gestures on a Stereoscopic Display

Manual point cloud registration is often a crucial step during the mapping of 3D point clouds and usually performed on a conventional desktop setup with mouse interaction. Since 3D point clouds are inherently spatial, these 2D applications suffer from impaired depth perception and inconvenient interaction. Nonetheless, there are few efforts to improve the usability of these applications. To address this, we propose an alternative setup, consisting of a stereoscopic display and an external hand tracker, allowing for enhanced depth perception and natural interaction without the need for body-worn devices or handheld controllers. We developed interaction techniques for point cloud alignment in 3D space, including visual feedback during alignment, and implemented a proof-of-concept prototype in the context of a surgical use case. We describe the use case, design and implementation of our concepts and outline future work. Herewith we provide a user-centered alternative to desktop applications for manual point cloud registration.
A Case Study on Scaffolding Exploratory Data Analysis for AI Pair Programmers

Recent advances in automatic code generation have made tools like GitHub Copilot attractive for programmers, as they allow for the creation of code blocks by simply providing descriptive prompts to the AI. While researchers have studied the performance of these AI-based tools in general-purpose programming, their effectiveness in data analysis is understudied. Unlike general-purpose programming which focuses more on algorithm-driven tasks like building novel software, data analysis requires a data-driven approach to actually gain insights. It remains unclear how these tools could be utilized to help data scientists analyze real-world problems. In this paper, we conducted a qualitative user study with 5 participants to understand the use of GitHub Copilot in solving problems by scaffolding prompts at different levels of specificity among data scientists. We discovered that effective prompts require carefully selected terminology, properly arranged word order, and sufficiently established interaction between humans and GitHub Copilot. We also spot some potential flaws in GitHub Copilot that hinder data scientists from efficiently scaffolding prompts. Our work points out some improvement directions for both data scientists and GitHub Copilot in the future.
GestureExplorer: Immersive Visualisation and Exploration of Gesture Data

This paper presents the design and evaluation of GestureExplorer, an Immersive Analytics tool that supports the interactive exploration, classification and sensemaking with large sets of 3D temporal gesture data. GestureExplorer features 3D skeletal and trajectory visualisations of gestures combined with abstract visualisations of clustered sets of gestures. By leveraging the large immersive space afforded by a Virtual Reality interface our tool allows free navigation and control of viewing perspective for users to gain a better understanding of gestures.
Engaging Passers-by with Rhythm: Applying Feedforward Learning to a Xylophonic Media Architecture Facade

Media architecture exploits interactive technology to encourage passers-by to engage with an architectural environment. Whereas most media architecture installations focus on visual stimulation, we developed a permanent media facade that rhythmically knocks xylophone blocks embedded beneath 11 window sills, according to the human actions constantly traced via an overhead camera. In an attempt to overcome its apparent limitations in engaging passers-by more enduringly and purposefully, our study investigates the impact of feedforward learning, a constructive interaction method that instructs passers-by about the results of their actions. Based on a comparative (n=25) and a one-month in-the-wild (n=1877) study, we propose how feedforward learning could empower passers-by to understand the interaction of more abstract types of media architecture, and how particular quantitative indicators capturing this learning could predict how enduringly and purposefully a passer-might engage. We believe these contributions could inspire more creative integrations of non-visual modalities in future public interactive interventions.
Vehicle Damage Analysis Using Computer Vision: Survey

One of the biggest problems in the transportation industry is vehicle damage. The manual inspection of these damaged automobiles takes a long time. Processing vehicle insurance using pictures of damaged cars is a crucial industry with lots of possibilities for automation. A segmentation method for detecting vehicle damage that is based on machine learning. When submitting insurance claims, using photos taken at the scene of an accident can expedite the process and save time and money while also improving driver convenience. This work examines how much automotive damage is worth. This research focuses on computer vision-based studies from the previous five years. This Convolutional Neural Networks serve as the foundation for the methods utilised. Based on the study, a majority of the work focuses on Mask R-CNN, but there is still potential to increase the performance. This research widens the window for auto insurance and damage detection.
A Special Interest Group on Developing Theories of Language Use in Interaction with Conversational User Interfaces

extended-abstract Share on A Special Interest Group on Developing Theories of Language Use in Interaction with Conversational User Interfaces Authors: Paola Raquel Peña School of Information and Communication Studies, University College Dublin, Ireland School of Information and Communication Studies, University College Dublin, Ireland 0000-0002-6371-8074View Profile , Philip R Doyle School of Information and Communication Studies, University College Dublin, Ireland School of Information and Communication Studies, University College Dublin, Ireland 0000-0002-2686-8962View Profile , Emily Yj Ip School of Computer Science and Statistics, Trinity College Dublin, Ireland School of Computer Science and Statistics, Trinity College Dublin, Ireland 0000-0001-6307-9666View Profile , Giovanni Di Liberto School of Computer Science and Statistics, Trinity College Dublin, Ireland School of Computer Science and Statistics, Trinity College Dublin, Ireland 0000-0002-7361-0980View Profile , Darragh Higgins School of Computer Science and Statistics, Trinity College, Dublin, Ireland School of Computer Science and Statistics, Trinity College, Dublin, Ireland 0000-0003-1008-0051View Profile , Rachel Mcdonnell School of Computer Science and Statistics, Trinity College Dublin, Ireland School of Computer Science and Statistics, Trinity College Dublin, Ireland 0000-0002-1957-2506View Profile , Holly Branigan Department of Psychology, University of Edinburgh, United Kingdom Department of Psychology, University of Edinburgh, United Kingdom 0000-0002-7845-8850View Profile , Joakim Gustafson Division of Speech, Music and Hearing, KTH Royal Institute of Technology, Sweden Division of Speech, Music and Hearing, KTH Royal Institute of Technology, Sweden 0000-0002-0397-6442View Profile , Donald Mcmillan Department of Computer and Systems Sciences, Stockholm University, Sweden Department of Computer and Systems Sciences, Stockholm University, Sweden 0000-0002-1392-5737View Profile , Robert J Moore IBM Research, United States IBM Research, United States 0000-0002-5636-9822View Profile , Benjamin R. Cowan School of Information and Communication Studies, University College Dublin, Ireland School of Information and Communication Studies, University College Dublin, Ireland 0000-0002-8595-8132View Profile Authors Info & Claims CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing SystemsApril 2023Article No.: 509Pages 1–4https://doi.org/10.1145/3544549.3583179Published:19 April 2023Publication History 1citation101DownloadsMetricsTotal Citations1Total Downloads101Last 12 Months101Last 6 weeks101 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Beyond Text-to-Image: Multimodal Prompts to Explore Generative AI

Text-to-image AI systems have proven to have extraordinary generative capacities that have facilitated widespread adoption. However, these systems are primarily text-based, which is a fundamental inversion of what many artists are traditionally used to: having full control over the composition of their work. Prior work has shown that there is great utility in using text prompts and that AI augmented workflows can increase momentum on creative tasks for end users. However, multimodal interactions beyond text need to be further defined, so end users can have rich points of interaction that allow them to truly co-pilot AI-generated content creation. To this end, the goal of my research is to equip creators with workflows that 1) translate abstract design goals into prompts of visual language, 2) structure exploration of design outcomes, and 3) integrate creator contributions into generations.
Dispensing with Humans in Human-Computer Interaction Research

Machine Learning models have become more advanced than could have been supposed even a few years ago, often surpassing human performance on many tasks. Large language models (LLM) can produce text indistinguishable from human-produced text. This begs the question, how necessary are humans - even for tasks where humans appear indispensable?
Empowering Students as Leaders of Co-Design for Block-Based Programming

Educational software is rarely co-designed with teachers and students. We have created a novel co-design process that can help both teachers and students learn about human-computer interaction (HCI) and research methods while contributing to the design of educational systems that will benefit future students and teachers. We investigated the results of our process of mentoring two undergraduate students to lead participatory design (PD) sessions with three K-12 teachers. We found that this process exposed teachers and undergraduates to HCI research and design techniques, empowered undergraduates to lead design sessions, and encouraged knowledge transfer between the groups. Our lessons learned include ways to prepare students inexperienced with HCI to lead PD, with training in the participants' domain, practice sessions, and predefined PD methods to adapt for their context. Our recommendations for future design collaborations can also provide insights to empower undergraduate students or less experienced teams and participants to benefit from participatory design efforts.
Social Dynamics of AI Support in Creative Writing

Recently, large language models have made huge advances in generating coherent, creative text. While much research focuses on how users can interact with language models, less work considers the social-technical gap that this technology poses. What are the social nuances that underlie receiving support from a generative AI? In this work we ask when and why a creative writer might turn to a computer versus a peer or mentor for support. We interview 20 creative writers about their writing practice and their attitudes towards both human and computer support. We discover three elements that govern a writer's interaction with support actors: 1) what writers desire help with, 2) how writers perceive potential support actors, and 3) the values writers hold. We align our results with existing frameworks of writing cognition and creativity support, uncovering the social dynamics which modulate user responses to generative technologies.