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page: <a href=/list/cs.HC/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> <dl id='articles'> <h3>New submissions (showing 51 of 51 entries)</h3> <dt> <a name='item1'>[1]</a> <a href ="/abs/2503.15488" title="Abstract" id="2503.15488"> arXiv:2503.15488 </a> [<a href="/pdf/2503.15488" title="Download PDF" id="pdf-2503.15488" aria-labelledby="pdf-2503.15488">pdf</a>, <a href="/format/2503.15488" title="Other formats" id="oth-2503.15488" aria-labelledby="oth-2503.15488">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Human-AI Collaboration for Wearable Technology Component Standardization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lydner,+A+M">Andrew M. Lydner</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Due to the multidisciplinary nature of wearable technology, the industry faces potential limitations in innovation. The wearable technology industry is still in its infancy and increased applicable use faces stagnation in the despite the plethora of technologies that have been largely wrist worn. This could be a result of the lack of multidisciplinary expert knowledge disseminating through the industry. Unlike other technologies which have standardizations and processes for how they are developed, wearable technologies exist in a realm of perpetual change as given the various materials and subcomponents that continue to be developed. It is essential that expert opinions form a collaborative foundation, and even more so that intelligent systems foster that collaboration. The caveat though, is likeliness of these artificial intelligence (AI) collaboration tools to be utilized by industry experts. Mental model development for AI tool usage could be applied to wearable technology innovation in this regard, thus the goal of this paper and focus of research. </p> </div> </dd> <dt> <a name='item2'>[2]</a> <a href ="/abs/2503.15489" title="Abstract" id="2503.15489"> arXiv:2503.15489 </a> [<a href="/pdf/2503.15489" title="Download PDF" id="pdf-2503.15489" aria-labelledby="pdf-2503.15489">pdf</a>, <a href="https://arxiv.org/html/2503.15489v1" title="View HTML" id="html-2503.15489" aria-labelledby="html-2503.15489" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15489" title="Other formats" id="oth-2503.15489" aria-labelledby="oth-2503.15489">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> PersonaAI: Leveraging Retrieval-Augmented Generation and Personalized Context for AI-Driven Digital Avatars </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kimara,+E">Elvis Kimara</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Oguntoye,+K+S">Kunle S. Oguntoye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+J">Jian Sun</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> This paper introduces PersonaAI, a cutting-edge application that leverages Retrieval-Augmented Generation (RAG) and the LLAMA model to create highly personalized digital avatars capable of accurately mimicking individual personalities. Designed as a cloud-based mobile application, PersonaAI captures user data seamlessly, storing it in a secure database for retrieval and analysis. The result is a system that provides context-aware, accurate responses to user queries, enhancing the potential of AI-driven personalization. <br>Why should you care? PersonaAI combines the scalability of RAG with the efficiency of prompt-engineered LLAMA3, offering a lightweight, sustainable alternative to traditional large language model (LLM) training methods. The system's novel approach to data collection, utilizing real-time user interactions via a mobile app, ensures enhanced context relevance while maintaining user privacy. By open-sourcing our implementation, we aim to foster adaptability and community-driven development. <br>PersonaAI demonstrates how AI can transform interactions by merging efficiency, scalability, and personalization, making it a significant step forward in the future of digital avatars and personalized AI. </p> </div> </dd> <dt> <a name='item3'>[3]</a> <a href ="/abs/2503.15490" title="Abstract" id="2503.15490"> arXiv:2503.15490 </a> [<a href="/pdf/2503.15490" title="Download PDF" id="pdf-2503.15490" aria-labelledby="pdf-2503.15490">pdf</a>, <a href="/format/2503.15490" title="Other formats" id="oth-2503.15490" aria-labelledby="oth-2503.15490">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards an AI task tensor: A taxonomy for organizing work in the age of generative AI </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Doshi,+A+R">Anil R. Doshi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Moore,+A">Alastair Moore</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> We introduce the AI task tensor, a framework for understanding the impact of generative AI on human work. The tensor is based on eight dimensions of tasks performed by a human-AI dyad, organized around the areas of a task's formulation, implementation, and resolution. The dimensions include task definition, AI integration, modality. audit requirement, output definition, decision-making authority, AI type, and human user type. After describing the eight dimensions of the AI task tensor, we provide illustrative projections that show how the tensor might be used to build analytical tractability and management intuition. We demonstrate how the AI task tensor can be used to organize emerging research on generative AI. We propose that the AI task tensor offers a starting point for understanding how work will be performed with the emergence of generative AI. </p> </div> </dd> <dt> <a name='item4'>[4]</a> <a href ="/abs/2503.15491" title="Abstract" id="2503.15491"> arXiv:2503.15491 </a> [<a href="/pdf/2503.15491" title="Download PDF" id="pdf-2503.15491" aria-labelledby="pdf-2503.15491">pdf</a>, <a href="https://arxiv.org/html/2503.15491v1" title="View HTML" id="html-2503.15491" aria-labelledby="html-2503.15491" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15491" title="Other formats" id="oth-2503.15491" aria-labelledby="oth-2503.15491">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Agreeing to Interact in Human-Robot Interaction using Large Language Models and Vision Language Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sasabuchi,+K">Kazuhiro Sasabuchi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wake,+N">Naoki Wake</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kanehira,+A">Atsushi Kanehira</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Takamatsu,+J">Jun Takamatsu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ikeuchi,+K">Katsushi Ikeuchi</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Computation and Language (cs.CL); Machine Learning (cs.LG); Robotics (cs.RO) </div> <p class='mathjax'> In human-robot interaction (HRI), the beginning of an interaction is often complex. Whether the robot should communicate with the human is dependent on several situational factors (e.g., the current human's activity, urgency of the interaction, etc.). We test whether large language models (LLM) and vision language models (VLM) can provide solutions to this problem. We compare four different system-design patterns using LLMs and VLMs, and test on a test set containing 84 human-robot situations. The test set mixes several publicly available datasets and also includes situations where the appropriate action to take is open-ended. Our results using the GPT-4o and Phi-3 Vision model indicate that LLMs and VLMs are capable of handling interaction beginnings when the desired actions are clear, however, challenge remains in the open-ended situations where the model must balance between the human and robot situation. </p> </div> </dd> <dt> <a name='item5'>[5]</a> <a href ="/abs/2503.15492" title="Abstract" id="2503.15492"> arXiv:2503.15492 </a> [<a href="/pdf/2503.15492" title="Download PDF" id="pdf-2503.15492" aria-labelledby="pdf-2503.15492">pdf</a>, <a href="/format/2503.15492" title="Other formats" id="oth-2503.15492" aria-labelledby="oth-2503.15492">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> World of ScoreCraft: Novel Multi Scorer Experiment on the Impact of a Decision Support System in Sleep Staging </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Holm,+B">Benedikt Holm</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=%C3%93skarsson,+A">Arnar 脫skarsson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=%C3%9Eorleifsson,+B+E">Bj枚rn Elvar 脼orleifsson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hafsteinsson,+H+%C3%9E">H枚r冒ur 脼贸r Hafsteinsson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sigur%C3%B0ard%C3%B3ttir,+S">Sigr铆冒ur Sigur冒ard贸ttir</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gr%C3%A9tarsd%C3%B3ttir,+H">Hei冒ur Gr茅tarsd贸ttir</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hoelke,+K">Kenan Hoelke</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jouan,+G+M+M">Gabriel Marc Marie Jouan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Penzel,+T">Thomas Penzel</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Arnardottir,+E+S">Erna Sif Arnardottir</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=%C3%93skarsd%C3%B3ttir,+M">Mar铆a 脫skarsd贸ttir</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 12 pages, 13 figures, 2 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> Manual scoring of polysomnography (PSG) is a time intensive task, prone to inter scorer variability that can impact diagnostic reliability. This study investigates the integration of decision support systems (DSS) into PSG scoring workflows, focusing on their effects on accuracy, scoring time, and potential biases toward recommendations from artificial intelligence (AI) compared to human generated recommendations. Using a novel online scoring platform, we conducted a repeated measures study with sleep technologists, <br>who scored traditional and self applied PSGs. Participants were occasionally presented with recommendations labeled as either human or AI generated. We found that traditional PSGs tended to be scored slightly more accurately than self applied PSGs, but this difference was not statistically significant. Correct recommendations significantly improved scoring accuracy for both PSG types, while incorrect recommendations reduced accuracy. No significant bias was observed toward or against AI generated recommendations compared to human generated recommendations. These findings highlight the potential of AI to enhance PSG scoring reliability. However, ensuring the accuracy of AI outputs is critical to maximizing its benefits. Future research should explore the long term impacts of DSS on scoring workflows and strategies for integrating AI in clinical practice. </p> </div> </dd> <dt> <a name='item6'>[6]</a> <a href ="/abs/2503.15493" title="Abstract" id="2503.15493"> arXiv:2503.15493 </a> [<a href="/pdf/2503.15493" title="Download PDF" id="pdf-2503.15493" aria-labelledby="pdf-2503.15493">pdf</a>, <a href="/format/2503.15493" title="Other formats" id="oth-2503.15493" aria-labelledby="oth-2503.15493">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Is Negative Representation More Engaging? The Influence of News Title Framing of Older Adults on Viewer Behavior </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+Z">Zhilong Zhao</a> (1), <a href="https://arxiv.org/search/cs?searchtype=author&query=Xia,+J">Jiaxin Xia</a> (2) ((1) School of Journalism and Communication, South China University of Technology,(2) Department of Communication, Faculty of Social Sciences, University of Macau)</div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 32 pages, 1 figure, 3 tables. Submitted to the 2024 Intelligent and Computational Communication Annual Conference, Hangzhou, China. Accepted on October 13, 2024. For supplementary materials and data, see <a href="https://www.scidb.cn/en/anonymous/aVliQUZ6" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Computers and Society (cs.CY) </div> <p class='mathjax'> Grounded in framing theory, this study examines how news titles about older adults shape user engagement on a Chinese video-sharing platform. We analyzed 2,017 video news titles from 2016 to 2021, identifying nine frames. Negative frames produced higher views and shares, suggesting that negative portrayals garner attention and encourage further distribution. In contrast, positive frames led to more collections and rewards, reflecting viewer preference and financial support for favorable depictions. These findings underscore how framing aligns with ageism concerns and highlight the need for more balanced media portrayals of older adults. </p> </div> </dd> <dt> <a name='item7'>[7]</a> <a href ="/abs/2503.15494" title="Abstract" id="2503.15494"> arXiv:2503.15494 </a> [<a href="/pdf/2503.15494" title="Download PDF" id="pdf-2503.15494" aria-labelledby="pdf-2503.15494">pdf</a>, <a href="https://arxiv.org/html/2503.15494v1" title="View HTML" id="html-2503.15494" aria-labelledby="html-2503.15494" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15494" title="Other formats" id="oth-2503.15494" aria-labelledby="oth-2503.15494">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> AI-Powered Assistive Technologies for Visual Impairment </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Naayini,+P">Prudhvi Naayini</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Myakala,+P+K">Praveen Kumar Myakala</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bura,+C">Chiranjeevi Bura</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jonnalagadda,+A+K">Anil Kumar Jonnalagadda</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kamatala,+S">Srikanth Kamatala</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) </div> <p class='mathjax'> Artificial Intelligence (AI) is revolutionizing assistive technologies. It offers innovative solutions to enhance the quality of life for individuals with visual impairments. This review examines the development, applications, and impact of AI-powered tools in key domains, such as computer vision, natural language processing (NLP), and wearable devices. Specific advancements include object recognition for identifying everyday items, scene description for understanding surroundings, and NLP-driven text-to-speech systems for accessing digital information. Assistive technologies like smart glasses, smartphone applications, and AI-enabled navigation aids are discussed, demonstrating their ability to support independent travel, facilitate social interaction, and increase access to education and employment opportunities. <br>The integration of deep learning models, multimodal interfaces, and real-time data processing has transformed the functionality and usability of these tools, fostering inclusivity and empowerment. This article also addresses critical challenges, including ethical considerations, affordability, and adaptability in diverse environments. Future directions highlight the need for interdisciplinary collaboration to refine these technologies, ensuring equitable access and sustainable innovation. By providing a comprehensive overview, this review underscores AI's transformative potential in promoting independence, enhancing accessibility, and fostering social inclusion for visually impaired individuals. </p> </div> </dd> <dt> <a name='item8'>[8]</a> <a href ="/abs/2503.15495" title="Abstract" id="2503.15495"> arXiv:2503.15495 </a> [<a href="/pdf/2503.15495" title="Download PDF" id="pdf-2503.15495" aria-labelledby="pdf-2503.15495">pdf</a>, <a href="/format/2503.15495" title="Other formats" id="oth-2503.15495" aria-labelledby="oth-2503.15495">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Entwicklung einer Webanwendung zur Generierung von skolemisierten RDF Daten f眉r die Verwaltung von Lieferketten </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Laas,+R">Roman Laas</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Master's thesis </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> F眉r eine fr眉hzeitige Erkennung von Lieferengp盲ssen m眉ssen Lieferketten in einer geeigneten digitalen Form vorliegen, damit sie verarbeitet werden k枚nnen. Der f眉r die Datenmodellierung ben枚tigte Arbeitsaufwand ist jedoch, gerade IT-fremden Personen, nicht zuzumuten. Es wurde deshalb im Rahmen dieser Arbeit eine Webanwendung entwickelt, welche die zugrunde liegende Komplexit盲t f眉r den Benutzer verschleiern soll. Konkret handelt es sich dabei um eine grafische Benutzeroberfl盲che, auf welcher Templates instanziiert und miteinander verkn眉pft werden k枚nnen. F眉r die Definition dieser Templates wurden in dieser Arbeit geeignete Konzepte erarbeitet und erweitert. Zur Erhebung der Benutzerfreundlichkeit der Webanwendung wurde abschlie脽end eine Nutzerstudie mit mehreren Testpersonen durchgef眉hrt. Diese legte eine Vielzahl von n眉tzlichen Verbesserungsvorschl盲gen offen. <br>-- <br>For early detection of supply bottlenecks, supply chains must be available in a suitable digital form so that they can be processed. However, the amount of work required for data modeling cannot be expected of people who are not familiar with IT topics. Therefore, a web application was developed in the context of this thesis, which is supposed to disguise the underlying complexity for the user. Specifically, this is a graphical user interface on which templates can be instantiated and linked to each other. Suitable concepts for the definition of these templates were developed and extended in this thesis. Finally, a user study with several test persons was conducted to determine the usability of the web application. This revealed a large number of useful suggestions for improvement. </p> </div> </dd> <dt> <a name='item9'>[9]</a> <a href ="/abs/2503.15496" title="Abstract" id="2503.15496"> arXiv:2503.15496 </a> [<a href="/pdf/2503.15496" title="Download PDF" id="pdf-2503.15496" aria-labelledby="pdf-2503.15496">pdf</a>, <a href="https://arxiv.org/html/2503.15496v1" title="View HTML" id="html-2503.15496" aria-labelledby="html-2503.15496" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15496" title="Other formats" id="oth-2503.15496" aria-labelledby="oth-2503.15496">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Fast Multi-Party Open-Ended Conversation with a Social Robot </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Abbo,+G+A">Giulio Antonio Abbo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pinto-Bernal,+M+J">Maria Jose Pinto-Bernal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Catrycke,+M">Martijn Catrycke</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Belpaeme,+T">Tony Belpaeme</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 18 pages, 6 figures, 1 appendix </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Robotics (cs.RO) </div> <p class='mathjax'> This paper presents the implementation and evaluation of a conversational agent designed for multi-party open-ended interactions. Leveraging state-of-the-art technologies such as voice direction of arrival, voice recognition, face tracking, and large language models, the system aims to facilitate natural and intuitive human-robot conversations. Deployed on the Furhat robot, the system was tested with 30 participants engaging in open-ended group conversations and then in two overlapping discussions. Quantitative metrics, such as latencies and recognition accuracy, along with qualitative measures from user questionnaires, were collected to assess performance. The results highlight the system's effectiveness in managing multi-party interactions, though improvements are needed in response relevance and latency. This study contributes valuable insights for advancing human-robot interaction, particularly in enhancing the naturalness and engagement in group conversations. </p> </div> </dd> <dt> <a name='item10'>[10]</a> <a href ="/abs/2503.15497" title="Abstract" id="2503.15497"> arXiv:2503.15497 </a> [<a href="/pdf/2503.15497" title="Download PDF" id="pdf-2503.15497" aria-labelledby="pdf-2503.15497">pdf</a>, <a href="https://arxiv.org/html/2503.15497v1" title="View HTML" id="html-2503.15497" aria-labelledby="html-2503.15497" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15497" title="Other formats" id="oth-2503.15497" aria-labelledby="oth-2503.15497">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Impact of Big Five Personality Traits on AI Agent Decision-Making in Public Spaces: A Social Simulation Study </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ren,+M">Mingjun Ren</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+W">Wentao Xu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) </div> <p class='mathjax'> This study investigates how the Big Five personality traits influence decision-making processes in AI agents within public spaces. Using AgentVerse framework and GPT-3.5-turbo, we simulated interactions among 10 AI agents, each embodying different dimensions of the Big Five personality traits, in a classroom environment responding to misinformation. The experiment assessed both public expressions ([Speak]) and private thoughts ([Think]) of agents, revealing significant correlations between personality traits and decision-making patterns. Results demonstrate that Openness to Experience had the strongest impact on information acceptance, with curious agents showing high acceptance rates and cautious agents displaying strong skepticism. Extraversion and Conscientiousness also showed notable influence on decision-making, while Neuroticism and Agreeableness exhibited more balanced responses. Additionally, we observed significant discrepancies between public expressions and private thoughts, particularly in agents with friendly and extroverted personalities, suggesting that social context influences decision-making behavior. Our findings contribute to understanding how personality traits shape AI agent behavior in social settings and have implications for developing more nuanced and context-aware AI systems. </p> </div> </dd> <dt> <a name='item11'>[11]</a> <a href ="/abs/2503.15498" title="Abstract" id="2503.15498"> arXiv:2503.15498 </a> [<a href="/pdf/2503.15498" title="Download PDF" id="pdf-2503.15498" aria-labelledby="pdf-2503.15498">pdf</a>, <a href="https://arxiv.org/html/2503.15498v1" title="View HTML" id="html-2503.15498" aria-labelledby="html-2503.15498" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15498" title="Other formats" id="oth-2503.15498" aria-labelledby="oth-2503.15498">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Revival: Collaborative Artistic Creation through Human-AI Interactions in Musical Creativity </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Lee,+K+J+M">Keon Ju M. Lee</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pasquier,+P">Philippe Pasquier</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yuri,+J">Jun Yuri</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Keon Ju M. Lee, Philippe Pasquier and Jun Yuri. 2024. In Proceedings of the Creativity and Generative AI NIPS (Neural Information Processing Systems) Workshop </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); Multimedia (cs.MM); Sound (cs.SD); Audio and Speech Processing (eess.AS) </div> <p class='mathjax'> Revival is an innovative live audiovisual performance and music improvisation by our artist collective K-Phi-A, blending human and AI musicianship to create electronic music with audio-reactive visuals. The performance features real-time co-creative improvisation between a percussionist, an electronic music artist, and AI musical agents. Trained in works by deceased composers and the collective's compositions, these agents dynamically respond to human input and emulate complex musical styles. An AI-driven visual synthesizer, guided by a human VJ, produces visuals that evolve with the musical landscape. Revival showcases the potential of AI and human collaboration in improvisational artistic creation. </p> </div> </dd> <dt> <a name='item12'>[12]</a> <a href ="/abs/2503.15499" title="Abstract" id="2503.15499"> arXiv:2503.15499 </a> [<a href="/pdf/2503.15499" title="Download PDF" id="pdf-2503.15499" aria-labelledby="pdf-2503.15499">pdf</a>, <a href="/format/2503.15499" title="Other formats" id="oth-2503.15499" aria-labelledby="oth-2503.15499">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Approach to Visual Attractiveness of Event Space Through Data-Driven Environment and Spatial Perception </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Majiid,+A">Aliffi Majiid</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mian,+R">Riaz-Ul-Haque Mian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kurohara,+K">Kouki Kurohara</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nguyen-Tran,+Y">Yen-Khang Nguyen-Tran</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> Revitalizing Japan's remote areas has become a crucial task, and Matsue City exemplifies this effort in its temporary event spaces, created through collective efforts to foster urban vibrancy and bring together residents and visitors. This research examines the relationship between data-driven in-sights using generative AI and visual attractiveness by evaluating tempo-rary events in Matsue City, particularly considering the cognitive-cultural differences in processing visual information of the participants. The first phase employs semantic keyword extraction from interviews, categorizing responses into physical elements, activities, and atmosphere. The second phase analyzes spatial perception through three categories: layout hierar-chy, product visibility, and visual attention. The correlation indicates that successful event design requires a balance between spatial efficiency and diverse needs, with a spatial organization that optimizes visitor flow and visibility strategies considering cultural and demographic diversity. These findings contribute to understanding the urban quality of temporary event spaces and offer a replicable framework for enhancing the visual appeal of events in remote areas throughout Japan. </p> </div> </dd> <dt> <a name='item13'>[13]</a> <a href ="/abs/2503.15500" title="Abstract" id="2503.15500"> arXiv:2503.15500 </a> [<a href="/pdf/2503.15500" title="Download PDF" id="pdf-2503.15500" aria-labelledby="pdf-2503.15500">pdf</a>, <a href="https://arxiv.org/html/2503.15500v1" title="View HTML" id="html-2503.15500" aria-labelledby="html-2503.15500" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15500" title="Other formats" id="oth-2503.15500" aria-labelledby="oth-2503.15500">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ImageInThat: Manipulating Images to Convey User Instructions to Robots </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Mahadevan,+K">Karthik Mahadevan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lewis,+B">Blaine Lewis</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+J">Jiannan Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mutlu,+B">Bilge Mutlu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+A">Anthony Tang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Grossman,+T">Tovi Grossman</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> In Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Robotics (cs.RO) </div> <p class='mathjax'> Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using various methods-natural language conveys immediate instructions but can be abstract or ambiguous, whereas end-user programming supports longer horizon tasks but interfaces face difficulties in capturing user intent. In this work, we propose using direct manipulation of images as an alternative paradigm to instruct robots, and introduce a specific instantiation called ImageInThat which allows users to perform direct manipulation on images in a timeline-style interface to generate robot instructions. Through a user study, we demonstrate the efficacy of ImageInThat to instruct robots in kitchen manipulation tasks, comparing it to a text-based natural language instruction method. The results show that participants were faster with ImageInThat and preferred to use it over the text-based method. Supplementary material including code can be found at: <a href="https://image-in-that.github.io/" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item14'>[14]</a> <a href ="/abs/2503.15501" title="Abstract" id="2503.15501"> arXiv:2503.15501 </a> [<a href="/pdf/2503.15501" title="Download PDF" id="pdf-2503.15501" aria-labelledby="pdf-2503.15501">pdf</a>, <a href="https://arxiv.org/html/2503.15501v1" title="View HTML" id="html-2503.15501" aria-labelledby="html-2503.15501" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15501" title="Other formats" id="oth-2503.15501" aria-labelledby="oth-2503.15501">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Development of an Inclusive Educational Platform Using Open Technologies and Machine Learning: A Case Study on Accessibility Enhancement </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Togni,+J">Jimi Togni</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 14 pages, 1 figure </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) </div> <p class='mathjax'> This study addresses the pressing challenge of educational inclusion for students with special needs by proposing and developing an inclusive educational platform. Integrating machine learning, natural language processing, and cross-platform interfaces, the platform features key functionalities such as speech recognition functionality to support voice commands and text generation via voice input; real-time object recognition using the YOLOv5 model, adapted for educational environments; Grapheme-to-Phoneme (G2P) conversion for Text-to-Speech systems using seq2seq models with attention, ensuring natural and fluent voice synthesis; and the development of a cross-platform mobile application in Flutter with on-device inference execution using TensorFlow Lite. The results demonstrated high accuracy, usability, and positive impact in educational scenarios, validating the proposal as an effective tool for educational inclusion. This project underscores the importance of open and accessible technologies in promoting inclusive and quality education. </p> </div> </dd> <dt> <a name='item15'>[15]</a> <a href ="/abs/2503.15502" title="Abstract" id="2503.15502"> arXiv:2503.15502 </a> [<a href="/pdf/2503.15502" title="Download PDF" id="pdf-2503.15502" aria-labelledby="pdf-2503.15502">pdf</a>, <a href="/format/2503.15502" title="Other formats" id="oth-2503.15502" aria-labelledby="oth-2503.15502">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> MapColorAI: Designing Contextually Relevant Choropleth Map Color Schemes Using a Large Language Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+N">Nai Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yijie Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wu,+F">Fan Wu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wei,+Z">Zhiwei Wei</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> Choropleth maps, which utilize color schemes to visualize spatial patterns and trends, are simple yet effective tools for geographic data analysis. As such, color scheme design is a critical aspect of choropleth map creation. The traditional coloring methods offered by GIS tools such as ArcGIS and QGIS are not user-friendly for non-professionals. On the one hand, these tools provide numerous color schemes, making it hard to decide which one best matches the theme. On the other hand, it is difficult to fulfill some ambiguous and personalized coloring needs of users, such as requests for 'summer-like' map colors. To address these shortcomings, we develop a novel system that leverages a large language model and map color design principles to generate contextually relevant and user-aligned choropleth map color schemes. The system follows a three-stage process: Data processing, which provides an overview of the data and classifies the data into meaningful classes; Color Concept Design, where the color theme and color mode are conceptualized based on data characteristics and user intentions; and Color Scheme Design, where specific colors are assigned to classes based on generated color theme, color mode, and user requirements. Our system incorporates an interactive interface, providing necessary visualization for choropleth map color design and allowing users to customize and refine color choices flexibly. Through user studies and evaluations, the system demonstrates acceptable usability, accuracy, and flexibility, with users highlighting the tool's efficiency and ease of use. </p> </div> </dd> <dt> <a name='item16'>[16]</a> <a href ="/abs/2503.15503" title="Abstract" id="2503.15503"> arXiv:2503.15503 </a> [<a href="/pdf/2503.15503" title="Download PDF" id="pdf-2503.15503" aria-labelledby="pdf-2503.15503">pdf</a>, <a href="/format/2503.15503" title="Other formats" id="oth-2503.15503" aria-labelledby="oth-2503.15503">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Impact of Extended Reality on Robot-Assisted Surgery Training </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bickford,+M">Michael Bickford</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Alruwaili,+F">Fayez Alruwaili</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ragab,+S">Sara Ragab</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rothenberg,+H">Hanna Rothenberg</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Abedin-Nasab,+M">Mohammad Abedin-Nasab</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> The article is under review for publication </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Robotics (cs.RO) </div> <p class='mathjax'> Robot Assisted Surgeries (RAS) have one of the steepest learning curves of any type of surgery. Because of this, methods to practice RAS outside the operating room have been developed to improve the surgeons skills. These strategies include the incorporation of extended reality simulators into surgical training programs. In this Systematic review, we seek to determine if extended reality simulators can improve the performance of novice surgeons and how their performance compares to the conventional training of surgeons on Surgical robots. Using the PRISMA 2020 guidelines, a systematic review and meta-analysis was performed searching PubMed, Embase, Web of Science, and Cochrane library for studies that compared the performance of novice surgeons that received no additional training, trained with extended reality, or trained with inanimate physical simulators (conventional additional training). We included articles that gauged performance using either GEARS or Time to complete measurements and used SPSS to perform a meta-analysis to compare the performance outcomes of the surgeons after training. Surgeons trained using extended reality completed their surgical tasks statistically significantly faster than those who did not receive training (Cohen's d=-0.95, p=0.02), and moderately slower than those conventionally trained (Cohen's d=0.65, p=0.14). However, this difference was not statistically significant. Surgeons trained on extended reality demonstrated a statistically significant improvement in GEARS scores over those who did not train (Cohen's d=0.964, p<0.001). While surgeons trained in extended reality had comparable GEARS scores to surgeons trained conventionally (Cohen's d=0.65, p=0.14). This meta-analysis demonstrates that extended reality simulators translated complex skills to surgeons in a low cost and low risk environment. </p> </div> </dd> <dt> <a name='item17'>[17]</a> <a href ="/abs/2503.15504" title="Abstract" id="2503.15504"> arXiv:2503.15504 </a> [<a href="/pdf/2503.15504" title="Download PDF" id="pdf-2503.15504" aria-labelledby="pdf-2503.15504">pdf</a>, <a href="/format/2503.15504" title="Other formats" id="oth-2503.15504" aria-labelledby="oth-2503.15504">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> GRETA: Modular Platform to Create Adaptive Socially Interactive Agents </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Grimaldi,+M">Michele Grimaldi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Woo,+J">Jieyeon Woo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Boucaud,+F">Fabien Boucaud</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Galland,+L">Lucie Galland</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Younsi,+N">Nezih Younsi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+L">Liu Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fares,+M">Mireille Fares</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Graux,+S">Sean Graux</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gauthier,+P">Philippe Gauthier</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pelachaud,+C">Catherine Pelachaud</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Robotics (cs.RO) </div> <p class='mathjax'> The interaction between humans is very complex to describe since it is composed of different elements from different modalities such as speech, gaze, and gestures influenced by social attitudes and emotions. Furthermore, the interaction can be affected by some features which refer to the interlocutor's state. Actual Socially Interactive Agents SIAs aim to adapt themselves to the state of the interaction partner. In this paper, we discuss this adaptation by describing the architecture of the GRETA platform which considers external features while interacting with humans and/or another ECA and process the dialogue incrementally. We illustrate the new architecture of GRETA which deals with the external features, the adaptation, and the incremental approach for the dialogue processing. </p> </div> </dd> <dt> <a name='item18'>[18]</a> <a href ="/abs/2503.15505" title="Abstract" id="2503.15505"> arXiv:2503.15505 </a> [<a href="/pdf/2503.15505" title="Download PDF" id="pdf-2503.15505" aria-labelledby="pdf-2503.15505">pdf</a>, <a href="https://arxiv.org/html/2503.15505v1" title="View HTML" id="html-2503.15505" aria-labelledby="html-2503.15505" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15505" title="Other formats" id="oth-2503.15505" aria-labelledby="oth-2503.15505">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Sensitivity to Redirected Walking Considering Gaze, Posture, and Luminance </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Williams,+N+L">Niall L. Williams</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Stevens,+L+C">Logan C. Stevens</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bera,+A">Aniket Bera</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Manocha,+D">Dinesh Manocha</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted for publication in TVCG, proceedings of IEEE VR 2025 conference </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Graphics (cs.GR) </div> <p class='mathjax'> We study the correlations between redirected walking (RDW) rotation gains and patterns in users' posture and gaze data during locomotion in virtual reality (VR). To do this, we conducted a psychophysical experiment to measure users' sensitivity to RDW rotation gains and collect gaze and posture data during the experiment. Using multilevel modeling, we studied how different factors of the VR system and user affected their physiological signals. In particular, we studied the effects of redirection gain, trial duration, trial number (i.e., time spent in VR), and participant gender on postural sway, gaze velocity (a proxy for gaze stability), and saccade and blink rate. Our results showed that, in general, physiological signals were significantly positively correlated with the strength of redirection gain, the duration of trials, and the trial number. Gaze velocity was negatively correlated with trial duration. Additionally, we measured users' sensitivity to rotation gains in well-lit (photopic) and dimly-lit (mesopic) virtual lighting conditions. Results showed that there were no significant differences in RDW detection thresholds between the photopic and mesopic luminance conditions. </p> </div> </dd> <dt> <a name='item19'>[19]</a> <a href ="/abs/2503.15506" title="Abstract" id="2503.15506"> arXiv:2503.15506 </a> [<a href="/pdf/2503.15506" title="Download PDF" id="pdf-2503.15506" aria-labelledby="pdf-2503.15506">pdf</a>, <a href="/format/2503.15506" title="Other formats" id="oth-2503.15506" aria-labelledby="oth-2503.15506">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Effectiveness of machining equipment user guides: A comparative study of augmented reality and traditional media </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Ghobrial,+M">Mina Ghobrial</a> (INSA Toulouse, INUC), <a href="https://arxiv.org/search/cs?searchtype=author&query=Seitier,+P">Philippe Seitier</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lagarrigue,+P">Pierre Lagarrigue</a> (ICA), <a href="https://arxiv.org/search/cs?searchtype=author&query=Galaup,+M">Michel Galaup</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gilles,+P">Patrick Gilles</a> (ICA)</div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> Material Forming, Apr 2024, toulouse, France. pp.2320-2328 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Graphics (cs.GR) </div> <p class='mathjax'> In the rapidly evolving landscape of manufacturing and material forming, innovative strategies are imperative for maintaining a competitive edge. Augmented Reality (AR) has emerged as a groundbreaking technology, offering new dimensions in how information is displayed and interacted with. It holds particular promise in the panel of instructional guides for complex machinery, potentially enhance traditional methods of knowledge transfer and operator training. Material forming, a key discipline within mechanical engineering, requires high-precision and skill, making it an ideal candidate for the integration of advanced instructional technologies like AR. This study aims to explore the efficiency of three distinct types of user manuals-video, paper, and augmented reality (AR)-on performance and acceptability in a material forming workshop environment. The focus will be on how AR can be specifically applied to improve task execution and understanding in material forming operations. Participants are mechanical engineering students specializing in material forming. They will engage in a series of standardized tasks related to machining processes. Performance will be gauged by metrics like task completion time and error rates, while task load will be assessed via the NASA Task Load Index (NASA-TLX) [1]. Acceptability of each manual type will be evaluated using the System Usability Scale (SUS) [2]. By comparing these various instructional formats, this research seeks to shed light on the most effective mediums for enhancing both operator performance and experience. </p> </div> </dd> <dt> <a name='item20'>[20]</a> <a href ="/abs/2503.15507" title="Abstract" id="2503.15507"> arXiv:2503.15507 </a> [<a href="/pdf/2503.15507" title="Download PDF" id="pdf-2503.15507" aria-labelledby="pdf-2503.15507">pdf</a>, <a href="https://arxiv.org/html/2503.15507v1" title="View HTML" id="html-2503.15507" aria-labelledby="html-2503.15507" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15507" title="Other formats" id="oth-2503.15507" aria-labelledby="oth-2503.15507">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> CvhSlicer 2.0: Immersive and Interactive Visualization of Chinese Visible Human Data in XR Environments </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Qiu,+Y">Yue Qiu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tong,+Y">Yuqi Tong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+Y">Yu Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liu,+Q">Qixuan Liu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pei,+J">Jialun Pei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qiu,+S">Shi Qiu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Heng,+P">Pheng-Ann Heng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fu,+C">Chi-Wing Fu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> IEEE VR 2025 Posters </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Graphics (cs.GR); Multimedia (cs.MM) </div> <p class='mathjax'> The study of human anatomy through advanced visualization techniques is crucial for medical research and education. In this work, we introduce CvhSlicer 2.0, an innovative XR system designed for immersive and interactive visualization of the Chinese Visible Human (CVH) dataset. Particularly, our proposed system operates entirely on a commercial XR headset, offering a range of visualization and interaction tools for dynamic 2D and 3D data exploration. By conducting comprehensive evaluations, our CvhSlicer 2.0 demonstrates strong capabilities in visualizing anatomical data, enhancing user engagement and improving educational effectiveness. A demo video is available at <a href="https://youtu.be/CfR72S_0N-4" rel="external noopener nofollow" class="link-external link-https">this https URL</a> </p> </div> </dd> <dt> <a name='item21'>[21]</a> <a href ="/abs/2503.15508" title="Abstract" id="2503.15508"> arXiv:2503.15508 </a> [<a href="/pdf/2503.15508" title="Download PDF" id="pdf-2503.15508" aria-labelledby="pdf-2503.15508">pdf</a>, <a href="https://arxiv.org/html/2503.15508v1" title="View HTML" id="html-2503.15508" aria-labelledby="html-2503.15508" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15508" title="Other formats" id="oth-2503.15508" aria-labelledby="oth-2503.15508">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Assessing Human Intelligence Augmentation Strategies Using Brain Machine Interfaces and Brain Organoids in the Era of AI Advancement </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kitamura,+K">Kenta Kitamura</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Cryptography and Security (cs.CR); Emerging Technologies (cs.ET) </div> <p class='mathjax'> The rapid advancement of Artificial Intelligence (AI) technologies, including the potential emergence of Artificial General Intelligence (AGI) and Artificial Superintelligence (ASI), has raised concerns about AI surpassing human cognitive capabilities. To address this challenge, intelligence augmentation approaches, such as Brain Machine Interfaces (BMI) and Brain Organoid (BO) integration have been proposed. In this study, we compare three intelligence augmentation strategies, namely BMI, BO, and a hybrid approach combining both. These strategies are evaluated from three key perspectives that influence user decisions in selecting an augmentation method: information processing capacity, identity risk, and consent authenticity risk. First, we model these strategies and assess them across the three perspectives. The results reveal that while BO poses identity risks and BMI has limitations in consent authenticity capacity, the hybrid approach mitigates these weaknesses by striking a balance between the two. Second, we investigate how users might choose among these intelligence augmentation strategies in the context of evolving AI capabilities over time. As the result, we find that BMI augmentation alone is insufficient to compete with advanced AI, and while BO augmentation offers scalability, BO increases identity risks as the scale grows. Moreover, the hybrid approach provides a balanced solution by adapting to AI advancements. This study provides a novel framework for human capability augmentation in the era of advancing AI and serves as a guideline for adapting to AI development. </p> </div> </dd> <dt> <a name='item22'>[22]</a> <a href ="/abs/2503.15509" title="Abstract" id="2503.15509"> arXiv:2503.15509 </a> [<a href="/pdf/2503.15509" title="Download PDF" id="pdf-2503.15509" aria-labelledby="pdf-2503.15509">pdf</a>, <a href="https://arxiv.org/html/2503.15509v1" title="View HTML" id="html-2503.15509" aria-labelledby="html-2503.15509" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15509" title="Other formats" id="oth-2503.15509" aria-labelledby="oth-2503.15509">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Representing data in words </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Caut,+A+M">Amandine M. Caut</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rouillard,+A">Amy Rouillard</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zenebe,+B">Beimnet Zenebe</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Green,+M">Matthias Green</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Morthens,+%C3%81+P">脕g煤st P谩lmason Morthens</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sumpter,+D+J+T">David J. T. Sumpter</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Computation and Language (cs.CL) </div> <p class='mathjax'> An important part of data science is the use of visualisations to display data in a way that is easy to digest. Visualisations often rely on underlying statistical or machine learning models -- ranging from basic calculations like category means to advanced methods such as principal component analysis of multidimensional datasets -- to convey insights. We introduce an analogous concept for word descriptions of data, which we call wordalisations. Wordalisations describe data in easy to digest words, without necessarily reporting numerical values from the data. We show how to create wordalisations using large language models, through prompt templates engineered according to a task-agnostic structure which can be used to automatically generate prompts from data. We show how to produce reliable and engaging texts on three application areas: scouting football players, personality tests, and international survey data. Using the model cards framework, we emphasise the importance of clearly stating the model we are imposing on the data when creating the wordalisation, detailing how numerical values are translated into words, incorporating background information into prompts for the large language model, and documenting the limitations of the wordalisations. We argue that our model cards approach is a more appropriate framework for setting best practices in wordalisation of data than performance tests on benchmark datasets. </p> </div> </dd> <dt> <a name='item23'>[23]</a> <a href ="/abs/2503.15510" title="Abstract" id="2503.15510"> arXiv:2503.15510 </a> [<a href="/pdf/2503.15510" title="Download PDF" id="pdf-2503.15510" aria-labelledby="pdf-2503.15510">pdf</a>, <a href="https://arxiv.org/html/2503.15510v1" title="View HTML" id="html-2503.15510" aria-labelledby="html-2503.15510" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15510" title="Other formats" id="oth-2503.15510" aria-labelledby="oth-2503.15510">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Joint Decision-Making in Robot Teleoperation: When are Two Heads Better Than One? </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nguyen,+D">Duc-An Nguyen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bhattacharyya,+R">Raunak Bhattacharyya</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Colombatto,+C">Clara Colombatto</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fleming,+S">Steve Fleming</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Posner,+I">Ingmar Posner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hawes,+N">Nick Hawes</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To be published in the 2025 20th ACM/IEEE International Conference on Human-Robot Interaction (HRI) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Robotics (cs.RO) </div> <p class='mathjax'> Operators working with robots in safety-critical domains have to make decisions under uncertainty, which remains a challenging problem for a single human operator. An open question is whether two human operators can make better decisions jointly, as compared to a single operator alone. While prior work has shown that two heads are better than one, such studies have been mostly limited to static and passive tasks. We investigate joint decision-making in a dynamic task involving humans teleoperating robots. We conduct a human-subject experiment with $N=100$ participants where each participant performed a navigation task with two mobiles robots in simulation. We find that joint decision-making through confidence sharing improves dyad performance beyond the better-performing individual (p<0.0001). Further, we find that the extent of this benefit is regulated both by the skill level of each individual, as well as how well-calibrated their confidence estimates are. Finally, we present findings on characterising the human-human dyad's confidence calibration based on the individuals constituting the dyad. Our findings demonstrate for the first time that two heads are better than one, even on a spatiotemporal task which includes active operator control of robots. </p> </div> </dd> <dt> <a name='item24'>[24]</a> <a href ="/abs/2503.15511" title="Abstract" id="2503.15511"> arXiv:2503.15511 </a> [<a href="/pdf/2503.15511" title="Download PDF" id="pdf-2503.15511" aria-labelledby="pdf-2503.15511">pdf</a>, <a href="/format/2503.15511" title="Other formats" id="oth-2503.15511" aria-labelledby="oth-2503.15511">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Trust Calibration Maturity Model for Characterizing and Communicating Trustworthiness of AI Systems </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Steinmetz,+S+T">Scott T Steinmetz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Naugle,+A">Asmeret Naugle</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schutte,+P">Paul Schutte</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sweitzer,+M">Matt Sweitzer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Washburne,+A">Alex Washburne</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Linville,+L">Lisa Linville</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Krofcheck,+D">Daniel Krofcheck</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kucer,+M">Michal Kucer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Myren,+S">Samuel Myren</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 19 pages, 4 figures, 2 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Computers and Society (cs.CY); Machine Learning (cs.LG) </div> <p class='mathjax'> The proliferation of powerful AI capabilities and systems necessitates a commensurate focus on user trust. We introduce the Trust Calibration Maturity Model (TCMM) to capture and communicate the maturity of AI system trustworthiness. The TCMM scores maturity along 5 dimensions that drive user trust: Performance Characterization, Bias & Robustness Quantification, Transparency, Safety & Security, and Usability. Information captured in the TCMM can be presented along with system performance information to help a user to appropriately calibrate trust, to compare requirements with current states of development, and to clarify trustworthiness needs. We present the TCMM and demonstrate its use on two AI system-target task pairs. </p> </div> </dd> <dt> <a name='item25'>[25]</a> <a href ="/abs/2503.15512" title="Abstract" id="2503.15512"> arXiv:2503.15512 </a> [<a href="/pdf/2503.15512" title="Download PDF" id="pdf-2503.15512" aria-labelledby="pdf-2503.15512">pdf</a>, <a href="https://arxiv.org/html/2503.15512v1" title="View HTML" id="html-2503.15512" aria-labelledby="html-2503.15512" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15512" title="Other formats" id="oth-2503.15512" aria-labelledby="oth-2503.15512">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Beyond Accuracy, SHAP, and Anchors -- On the difficulty of designing effective end-user explanations </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Omar,+Z+A">Zahra Abba Omar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Nahar,+N">Nadia Nahar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tjaden,+J">Jacob Tjaden</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gilles,+I+M">In猫s M. Gilles</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mekonnen,+F">Fikir Mekonnen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hsieh,+J">Jane Hsieh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=K%C3%A4stner,+C">Christian K盲stner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Menon,+A">Alka Menon</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> Modern machine learning produces models that are impossible for users or developers to fully understand -- raising concerns about trust, oversight and human dignity. Transparency and explainability methods aim to provide some help in understanding models, but it remains challenging for developers to design explanations that are understandable to target users and effective for their purpose. Emerging guidelines and regulations set goals but may not provide effective actionable guidance to developers. In a controlled experiment with 124 participants, we investigate whether and how specific forms of policy guidance help developers design explanations for an ML-powered screening tool for diabetic retinopathy. Contrary to our expectations, we found that participants across the board struggled to produce quality explanations, comply with the provided policy requirements for explainability, and provide evidence of compliance. We posit that participant noncompliance is in part due to a failure to imagine and anticipate the needs of their audience, particularly non-technical stakeholders. Drawing on cognitive process theory and the sociological imagination to contextualize participants' failure, we recommend educational interventions. </p> </div> </dd> <dt> <a name='item26'>[26]</a> <a href ="/abs/2503.15513" title="Abstract" id="2503.15513"> arXiv:2503.15513 </a> [<a href="/pdf/2503.15513" title="Download PDF" id="pdf-2503.15513" aria-labelledby="pdf-2503.15513">pdf</a>, <a href="/format/2503.15513" title="Other formats" id="oth-2503.15513" aria-labelledby="oth-2503.15513">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Designing an intelligent computer game for predicting dysgraphia </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Nevisi,+Z">Zahra Nevisi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tahmasbi,+M">Maryam Tahmasbi</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Dysgraphia is a key cognitive disorder impacting writing skills. Current tests often identify dysgraphia after writing issues emerge. This paper presents a set of computer games and uses machine learning to analyze the results, predicting if a child is at risk. The games focus on cognitive differences like visual attention between dysgraphic and typical children. The machine learning model forecasts dysgraphia by observing how kids interact with these games. We also create an algorithm to detect unsuitable testing conditions, acting as a preprocess to avoid mislabeling them as dysgraphia. We developed a machine learning model capable of predicting dysgraphia with 93.24% accuracy in a test group of 74 participants. </p> </div> </dd> <dt> <a name='item27'>[27]</a> <a href ="/abs/2503.15514" title="Abstract" id="2503.15514"> arXiv:2503.15514 </a> [<a href="/pdf/2503.15514" title="Download PDF" id="pdf-2503.15514" aria-labelledby="pdf-2503.15514">pdf</a>, <a href="https://arxiv.org/html/2503.15514v1" title="View HTML" id="html-2503.15514" aria-labelledby="html-2503.15514" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15514" title="Other formats" id="oth-2503.15514" aria-labelledby="oth-2503.15514">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Superhuman AI Disclosure: Impacts on Toxicity, Fairness, and Trust Vary by Expertise and Persona Attributes </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chua,+J">Jaymari Chua</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+C">Chen Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yao,+L">Lina Yao</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Emerging Technologies (cs.ET) </div> <p class='mathjax'> As artificial intelligence demonstrates surpassing human performance across real-world tasks, disclosing superhuman capabilities poses challenges for fairness, accountability, and trust. To investigate how transparency impacts attitudes and perceptions, we introduce a grounded and validated set of synthetic personas reflecting diverse fairness concerns and technology acceptance levels. Then we evaluate responses in two contrasting domains: (1) a competitive player in StarCraft II, where strategy and high-skill gameplay often elicit toxic interactions, and (2) a cooperative personal-assistant in providing information. Across numerous interactions spanning persona profiles, we test non-disclosure versus explicit superhuman labelling under controlled game outcomes and usage contexts. Our findings reveal sharp domain-specific effects: in StarCraft II, explicitly labelling AI as superhuman, novice personas who learned of it reported lower toxicity and higher fairness-attributing defeat to advanced skill rather than hidden cheating-whereas expert personas found the disclosure statements irksome but still less deceptive than non-disclosure. Conversely, in the LLM as personal-assistant setting, disclosure of superhuman capabilities improved perceived trustworthiness, though it risked AI overreliance among certain persona segments. We release Dataset X-containing persona cards-including profile attributes, disclosure prompts, and detailed interaction logs, accompanied by reproducible protocols and disclaimers for adapting them to diverse tasks. Our results demonstrate that transparency is not a cure-all: while it reduces suspicion and enhances trust in cooperative contexts, it may inflame resistance or disappointment in competitive domains. </p> </div> </dd> <dt> <a name='item28'>[28]</a> <a href ="/abs/2503.15515" title="Abstract" id="2503.15515"> arXiv:2503.15515 </a> [<a href="/pdf/2503.15515" title="Download PDF" id="pdf-2503.15515" aria-labelledby="pdf-2503.15515">pdf</a>, <a href="https://arxiv.org/html/2503.15515v1" title="View HTML" id="html-2503.15515" aria-labelledby="html-2503.15515" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15515" title="Other formats" id="oth-2503.15515" aria-labelledby="oth-2503.15515">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Towards Computer-Using Personal Agents </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Bonatti,+P+A">Piero A. Bonatti</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Domingue,+J">John Domingue</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gentile,+A+L">Anna Lisa Gentile</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Harth,+A">Andreas Harth</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hartig,+O">Olaf Hartig</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hogan,+A">Aidan Hogan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hose,+K">Katja Hose</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Jimenez-Ruiz,+E">Ernesto Jimenez-Ruiz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=McGuinness,+D+L">Deborah L. McGuinness</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sun,+C">Chang Sun</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Verborgh,+R">Ruben Verborgh</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wright,+J">Jesse Wright</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> This report is a result of Dagstuhl Seminar 25051 "Trust and Accountability in Knowledge Graph-Based AI for Self Determination", which took place in January 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA) </div> <p class='mathjax'> Computer-Using Agents (CUA) enable users to automate increasingly-complex tasks using graphical interfaces such as browsers. As many potential tasks require personal data, we propose Computer-Using Personal Agents (CUPAs) that have access to an external repository of the user's personal data. Compared with CUAs, CUPAs offer users better control of their personal data, the potential to automate more tasks involving personal data, better interoperability with external sources of data, and better capabilities to coordinate with other CUPAs in order to solve collaborative tasks involving the personal data of multiple users. </p> </div> </dd> <dt> <a name='item29'>[29]</a> <a href ="/abs/2503.15516" title="Abstract" id="2503.15516"> arXiv:2503.15516 </a> [<a href="/pdf/2503.15516" title="Download PDF" id="pdf-2503.15516" aria-labelledby="pdf-2503.15516">pdf</a>, <a href="https://arxiv.org/html/2503.15516v1" title="View HTML" id="html-2503.15516" aria-labelledby="html-2503.15516" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15516" title="Other formats" id="oth-2503.15516" aria-labelledby="oth-2503.15516">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> In Pursuit of Predictive Models of Human Preferences Toward AI Teammates </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Siu,+H+C">Ho Chit Siu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pe%C3%B1a,+J+D">Jaime D. Pe帽a</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhou,+Y">Yutai Zhou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Allen,+R+E">Ross E. Allen</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> We seek measurable properties of AI agents that make them better or worse teammates from the subjective perspective of human collaborators. Our experiments use the cooperative card game Hanabi -- a common benchmark for AI-teaming research. We first evaluate AI agents on a set of objective metrics based on task performance, information theory, and game theory, which are measurable without human interaction. Next, we evaluate subjective human preferences toward AI teammates in a large-scale (N=241) human-AI teaming experiment. Finally, we correlate the AI-only objective metrics with the human subjective preferences. Our results refute common assumptions from prior literature on reinforcement learning, revealing new correlations between AI behaviors and human preferences. We find that the final game score a human-AI team achieves is less predictive of human preferences than esoteric measures of AI action diversity, strategic dominance, and ability to team with other AI. In the future, these correlations may help shape reward functions for training human-collaborative AI. </p> </div> </dd> <dt> <a name='item30'>[30]</a> <a href ="/abs/2503.15517" title="Abstract" id="2503.15517"> arXiv:2503.15517 </a> [<a href="/pdf/2503.15517" title="Download PDF" id="pdf-2503.15517" aria-labelledby="pdf-2503.15517">pdf</a>, <a href="/format/2503.15517" title="Other formats" id="oth-2503.15517" aria-labelledby="oth-2503.15517">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Analysis of AI Effectiveness in Reducing Human Errors in Processing Transportation Requests </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Korostin,+O">Oleksandr Korostin</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 4 pages, 1 figure </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> German International Journal of Modern Science 88 (2024) 67-70 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> This article examines the characteristics of human errors in processing transportation requests. The role of artificial intelligence (AI) in maritime transportation is explored. The main methods and technologies used for automating and optimizing the handling of transportation requests are analyzed, along with their impact on reducing the number of errors. Examples of successful AI implementation in large companies are provided, confirming the positive influence of these technologies on overall operational efficiency and customer service levels. </p> </div> </dd> <dt> <a name='item31'>[31]</a> <a href ="/abs/2503.15518" title="Abstract" id="2503.15518"> arXiv:2503.15518 </a> [<a href="/pdf/2503.15518" title="Download PDF" id="pdf-2503.15518" aria-labelledby="pdf-2503.15518">pdf</a>, <a href="https://arxiv.org/html/2503.15518v1" title="View HTML" id="html-2503.15518" aria-labelledby="html-2503.15518" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15518" title="Other formats" id="oth-2503.15518" aria-labelledby="oth-2503.15518">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Robot Character Generation and Adaptive Human-Robot Interaction with Personality Shaping </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+C">Cheng Tang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+C">Chao Tang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gong,+S">Steven Gong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kwok,+T">Thomas Kwok</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hu,+Y">Yue Hu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> We present a novel framework for designing emotionally agile robots with dynamic personalities and memory-based learning, with the aim of performing adaptive and non-deterministic interactions with humans while conforming to shared social understanding. While existing work has largely focused on emotion recognition and static response systems, many approaches rely on sentiment analysis and action mapping frameworks that are pre-defined with limited dimensionality and fixed configurations, lacking the flexibility of dynamic personality traits and memory-enabled adaptation. Other systems are often restricted to limited modes of expression and fail to develop a causal relationship between human behavior and the robot's proactive physical actions, resulting in constrained adaptability and reduced responsiveness in complex, dynamic interactions. Our methodology integrates the Big Five Personality Traits, Appraisal Theory, and abstracted memory layers through Large Language Models (LLMs). The LLM generates a parameterized robot personality based on the Big Five, processes human language and sentiments, evaluates human behavior using Appraisal Theory, and generates emotions and selects appropriate actions adapted by historical context over time. We validated the framework by testing three robots with distinct personalities in identical background contexts and found that personality, appraisal, and memory influence the adaptability of human-robot interactions. The impact of the individual components was further validated through ablation tests. We conclude that this system enables robots to engage in meaningful and personalized interactions with users, and holds significant potential for applications in domains such as pet robots, assistive robots, educational robots, and collaborative functional robots, where cultivating tailored relationships and enriching user experiences are essential. </p> </div> </dd> <dt> <a name='item32'>[32]</a> <a href ="/abs/2503.15520" title="Abstract" id="2503.15520"> arXiv:2503.15520 </a> [<a href="/pdf/2503.15520" title="Download PDF" id="pdf-2503.15520" aria-labelledby="pdf-2503.15520">pdf</a>, <a href="https://arxiv.org/html/2503.15520v1" title="View HTML" id="html-2503.15520" aria-labelledby="html-2503.15520" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15520" title="Other formats" id="oth-2503.15520" aria-labelledby="oth-2503.15520">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Agent-S: LLM Agentic workflow to automate Standard Operating Procedures </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kulkarni,+M">Mandar Kulkarni</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> AI agents using Large Language Models (LLMs) as foundations have shown promise in solving complex real-world tasks. In this paper, we propose an LLM-based agentic workflow for automating Standard Operating Procedures (SOP). For customer care operations, an SOP defines a logical step-by-step process for human agents to resolve customer issues. We observe that any step in the SOP can be categorized as user interaction or API call, while the logical flow in the SOP defines the navigation. We use LLMs augmented with memory and environments (API tools, user interface, external knowledge source) for SOP automation. Our agentic architecture consists of three task-specific LLMs, a Global Action Repository (GAR), execution memory, and multiple environments. SOP workflow is written as a simple logical block of text. Based on the current execution memory and the SOP, the agent chooses the action to execute; it interacts with an appropriate environment (user/API) to collect observations and feedback, which are, in turn, inputted to memory to decide the next action. The agent is designed to be fault-tolerant, where it dynamically decides to repeat an action or seek input from an external knowledge source. We demonstrate the efficacy of the proposed agent on the three SOPs from the e-commerce seller domain. The experimental results validate the agent's performance under complex real-world scenarios. </p> </div> </dd> <dt> <a name='item33'>[33]</a> <a href ="/abs/2503.15521" title="Abstract" id="2503.15521"> arXiv:2503.15521 </a> [<a href="/pdf/2503.15521" title="Download PDF" id="pdf-2503.15521" aria-labelledby="pdf-2503.15521">pdf</a>, <a href="/format/2503.15521" title="Other formats" id="oth-2503.15521" aria-labelledby="oth-2503.15521">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> From Divergence to Consensus: Evaluating the Role of Large Language Models in Facilitating Agreement through Adaptive Strategies </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Triantafyllopoulos,+L">Loukas Triantafyllopoulos</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kalles,+D">Dimitris Kalles</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 32 pages, 5 figures, 4 tables </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) </div> <p class='mathjax'> Achieving consensus in group decision-making often involves overcoming significant challenges, particularly in reconciling diverse perspectives and mitigating biases that hinder agreement. Traditional methods relying on human facilitators are often constrained by scalability and efficiency, especially in large-scale, fast-paced discussions. To address these challenges, this study proposes a novel framework employing large language models (LLMs) as automated facilitators within a custom-built multi-user chat system. Leveraging cosine similarity as a core metric, this approach evaluates the ability of three state-of-the-art LLMs- ChatGPT 4.0, Mistral Large 2, and AI21 Jamba Instruct- to synthesize consensus proposals that align with participants' viewpoints. Unlike conventional techniques, the system integrates adaptive facilitation strategies, including clarifying misunderstandings, summarizing discussions, and proposing compromises, enabling the LLMs to iteratively refine consensus proposals based on user feedback. Experimental results demonstrate the superiority of ChatGPT 4.0, which achieves higher alignment with participant opinions, requiring fewer iterations to reach consensus compared to its counterparts. Moreover, analysis reveals the nuanced performance of the models across various sustainability-focused discussion topics, such as climate action, quality education, good health and well-being, and access to clean water and sanitation. These findings highlight the transformative potential of LLM-driven facilitation for improving collective decision-making processes and underscore the importance of advancing evaluation metrics and cross-cultural adaptability in future research. </p> </div> </dd> <dt> <a name='item34'>[34]</a> <a href ="/abs/2503.15522" title="Abstract" id="2503.15522"> arXiv:2503.15522 </a> [<a href="/pdf/2503.15522" title="Download PDF" id="pdf-2503.15522" aria-labelledby="pdf-2503.15522">pdf</a>, <a href="https://arxiv.org/html/2503.15522v1" title="View HTML" id="html-2503.15522" aria-labelledby="html-2503.15522" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15522" title="Other formats" id="oth-2503.15522" aria-labelledby="oth-2503.15522">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> "I don't like things where I do not have control": Participants' Experience of Trustworthy Interaction with Autonomous Vehicles </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Tanevska,+A">Ana Tanevska</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Winkle,+K">Katie Winkle</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Castellano,+G">Ginevra Castellano</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> With the rapid advancement of autonomous vehicle (AV) technology, AVs are progressively seen as interactive agents with some level of autonomy, as well as some context-dependent social features. <br>This introduces new challenges and questions, already relevant in other areas of human-robot interaction (HRI) - namely, if an AV is perceived as a social agent by the human with whom it is interacting, how are the various facets of its design and behaviour impacting its human partner? And how can we foster a successful human-agent interaction (HAI) between the AV and the human, maximizing the human's comfort, acceptance, and trust in the AV? <br>In this work, we attempt to understand the various factors that could influence na茂ve participants' acceptance and trust when interacting with an AV in the role of a driver. Through a large-scale online study, we investigate the effect of the AV's autonomy on the human driver, as well as explore which parameters of the interaction have the highest impact on the user's sense of trust in the AV. Finally, we analyze our preliminary findings from the user study within existing guidelines on Trustworthy HAI/HRI. </p> </div> </dd> <dt> <a name='item35'>[35]</a> <a href ="/abs/2503.15523" title="Abstract" id="2503.15523"> arXiv:2503.15523 </a> [<a href="/pdf/2503.15523" title="Download PDF" id="pdf-2503.15523" aria-labelledby="pdf-2503.15523">pdf</a>, <a href="/format/2503.15523" title="Other formats" id="oth-2503.15523" aria-labelledby="oth-2503.15523">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> InteractiveEdu: An Open-source Interactive Floor for Exergame as a Learning Platform </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=da+Rosa,+E+B">Everson Borges da Rosa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Albonico,+M">Michel Albonico</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Varela,+P+J">Paulo Juunior Varela</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted at CSEDU 2025. Not published (unable to attend the venue) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Children tend to be constantly exposed to technologies, such as smartphones, tablets, and gaming consoles, drawn by the interactive and visually stimulating nature of digital platforms. Thus, integrating the teaching process with technological gadgets may enhance engagement and foster interactive learning experiences, besides equipping students with the digital skills for today's increasingly technology-driven world. The main goal of this work is to provide an open-source and manageable tool that teachers can use as an everyday activity and as an exergame. For this, we present a prototype of an interactive platform that students use to answer a quiz by moving to segments available on an interactive floor. All the platform design and implementation directions are publicly available. </p> </div> </dd> <dt> <a name='item36'>[36]</a> <a href ="/abs/2503.15524" title="Abstract" id="2503.15524"> arXiv:2503.15524 </a> [<a href="/pdf/2503.15524" title="Download PDF" id="pdf-2503.15524" aria-labelledby="pdf-2503.15524">pdf</a>, <a href="/format/2503.15524" title="Other formats" id="oth-2503.15524" aria-labelledby="oth-2503.15524">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> KHAIT: K-9 Handler Artificial Intelligence Teaming for Collaborative Sensemaking </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wilchek,+M">Matthew Wilchek</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+L">Linhan Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dickinson,+S">Sally Dickinson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Feuerbacher,+E">Erica Feuerbacher</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Luther,+K">Kurt Luther</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Batarseh,+F+A">Feras A. Batarseh</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 13 pages, 7 figures, ACM 30th International Conference on Intelligent User Interfaces (IUI 25) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Emerging Technologies (cs.ET); Multiagent Systems (cs.MA) </div> <p class='mathjax'> In urban search and rescue (USAR) operations, communication between handlers and specially trained canines is crucial but often complicated by challenging environments and the specific behaviors canines are trained to exhibit when detecting a person. Since a USAR canine often works out of sight of the handler, the handler lacks awareness of the canine's location and situation, known as the 'sensemaking gap.' In this paper, we propose KHAIT, a novel approach to close the sensemaking gap and enhance USAR effectiveness by integrating object detection-based Artificial Intelligence (AI) and Augmented Reality (AR). Equipped with AI-powered cameras, edge computing, and AR headsets, KHAIT enables precise and rapid object detection from a canine's perspective, improving survivor localization. We evaluate this approach in a real-world USAR environment, demonstrating an average survival allocation time decrease of 22%, enhancing the speed and accuracy of operations. </p> </div> </dd> <dt> <a name='item37'>[37]</a> <a href ="/abs/2503.15525" title="Abstract" id="2503.15525"> arXiv:2503.15525 </a> [<a href="/pdf/2503.15525" title="Download PDF" id="pdf-2503.15525" aria-labelledby="pdf-2503.15525">pdf</a>, <a href="/format/2503.15525" title="Other formats" id="oth-2503.15525" aria-labelledby="oth-2503.15525">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Use of Artificial Intelligence Tools in Assessing Content Validity: A Comparative Study with Human Experts </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gurdil,+H">Hatice Gurdil</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Anadol,+H+O">Hatice Ozlem Anadol</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Soguksu,+Y+B">Yesim Beril Soguksu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Computers and Society (cs.CY) </div> <p class='mathjax'> In this study, it was investigated whether AI evaluators assess the content validity of B1-level English reading comprehension test items in a manner similar to human evaluators. A 25-item multiple-choice test was developed, and these test items were evaluated by four human and four AI evaluators. No statistically significant difference was found between the scores given by human and AI evaluators, with similar evaluation trends observed. The Content Validity Ratio (CVR) and the Item Content Validity Index (I-CVI) were calculated and analyzed using the Wilcoxon Signed-Rank Test, with no statistically significant difference. The findings revealed that in some cases, AI evaluators could replace human evaluators. However, differences in specific items were thought to arise from varying interpretations of the evaluation criteria. Ensuring linguistic clarity and clearly defining criteria could contribute to more consistent evaluations. In this regard, the development of hybrid evaluation systems, in which AI technologies are used alongside human experts, is recommended. </p> </div> </dd> <dt> <a name='item38'>[38]</a> <a href ="/abs/2503.15526" title="Abstract" id="2503.15526"> arXiv:2503.15526 </a> [<a href="/pdf/2503.15526" title="Download PDF" id="pdf-2503.15526" aria-labelledby="pdf-2503.15526">pdf</a>, <a href="https://arxiv.org/html/2503.15526v1" title="View HTML" id="html-2503.15526" aria-labelledby="html-2503.15526" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15526" title="Other formats" id="oth-2503.15526" aria-labelledby="oth-2503.15526">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Assessment of AI-Generated Pediatric Rehabilitation SOAP-Note Quality </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Amenyo,+S">Solomon Amenyo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Grossman,+M+R">Maura R. Grossman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Brown,+D+G">Daniel G. Brown</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wylie-Toal,+B">Brendan Wylie-Toal</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> This study explores the integration of artificial intelligence (AI) or large language models (LLMs) into pediatric rehabilitation clinical documentation, focusing on the generation of SOAP (Subjective, Objective, Assessment, Plan) notes, which are essential for patient care. Creating complex documentation is time-consuming in pediatric settings. We evaluate the effectiveness of two AI tools; Copilot, a commercial LLM, and KAUWbot, a fine-tuned LLM developed for KidsAbility Centre for Child Development (an Ontario pediatric rehabilitation facility), in simplifying and automating this process. We focus on two key questions: (i) How does the quality of AI-generated SOAP notes based on short clinician summaries compare to human-authored notes, and (ii) To what extent is human editing necessary for improving AI-generated SOAP notes? We found no evidence of prior work assessing the quality of AI-generated clinical notes in pediatric rehabilitation. <br>We used a sample of 432 SOAP notes, evenly divided among human-authored, Copilot-generated, and KAUWbot-generated notes. We employ a blind evaluation by experienced clinicians based on a custom rubric. Statistical analysis is conducted to assess the quality of the notes and the impact of human editing. The results suggest that AI tools such as KAUWbot and Copilot can generate SOAP notes with quality comparable to those authored by humans. We highlight the potential for combining AI with human expertise to enhance clinical documentation and offer insights for the future integration of AI into pediatric rehabilitation practice and other settings for the management of clinical conditions. </p> </div> </dd> <dt> <a name='item39'>[39]</a> <a href ="/abs/2503.15527" title="Abstract" id="2503.15527"> arXiv:2503.15527 </a> [<a href="/pdf/2503.15527" title="Download PDF" id="pdf-2503.15527" aria-labelledby="pdf-2503.15527">pdf</a>, <a href="/format/2503.15527" title="Other formats" id="oth-2503.15527" aria-labelledby="oth-2503.15527">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Exploring the Panorama of Anxiety Levels: A Multi-Scenario Study Based on Human-Centric Anxiety Level Detection and Personalized Guidance </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xian,+L">Longdi Xian</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+J">Junhao Xu</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) </div> <p class='mathjax'> More and more people are experiencing pressure from work, life, and education. These pressures often lead to an anxious state of mind, or even the early symptoms of suicidal ideation. With the advancement of artificial intelligence (AI) technology, large language models have become one of the most prominent technologies. They are often used for detecting psychological disorders. However, current studies primarily provide categorization results without offering interpretable explanations for these results. To address this gap, this study adopts a person-centered perspective and focuses on GPT-generated multi-scenario simulated conversations. These simulated conversations were selected as data samples for the study. Various transformer-based encoder models were utilized to develop a classification model capable of identifying different levels of anxiety. Additionally, a knowledge base focusing on anxiety was constructed using LangChain and GPT-4. When analyzing classification results, this knowledge base was able to provide explanations and reasons most relevant to the interlocutor's anxiety situation. The study demonstrates that the proposed model achieves over 94% accuracy in categorical prediction, and the advice provided is highly personalized and relevant. </p> </div> </dd> <dt> <a name='item40'>[40]</a> <a href ="/abs/2503.15528" title="Abstract" id="2503.15528"> arXiv:2503.15528 </a> [<a href="/pdf/2503.15528" title="Download PDF" id="pdf-2503.15528" aria-labelledby="pdf-2503.15528">pdf</a>, <a href="https://arxiv.org/html/2503.15528v1" title="View HTML" id="html-2503.15528" aria-labelledby="html-2503.15528" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15528" title="Other formats" id="oth-2503.15528" aria-labelledby="oth-2503.15528">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Seifi,+S">Sarah Seifi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sukianto,+T">Tobias Sukianto</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Carbonelli,+C">Cecilia Carbonelli</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Servadei,+L">Lorenzo Servadei</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wille,+R">Robert Wille</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> The EU AI Act underscores the importance of transparency, user-centricity, and robustness in AI systems, particularly for high-risk systems. In response, we present advancements in XentricAI, an explainable hand gesture recognition (HGR) system designed to meet these regulatory requirements. XentricAI adresses fundamental challenges in HGR, such as the opacity of black-box models using explainable AI methods and the handling of distributional shifts in real-world data through transfer learning techniques. We extend an existing radar-based HGR dataset by adding 28,000 new gestures, with contributions from multiple users across varied locations, including 24,000 out-of-distribution gestures. Leveraging this real-world dataset, we enhance XentricAI's capabilities by integrating a variational autoencoder module for improved gesture anomaly detection, incorporating user-specific thresholding. This integration enables the identification of 11.50% more anomalous gestures. Our extensive evaluations demonstrate a 97.5% sucess rate in characterizing these anomalies, significantly improving system explainability. Furthermore, the implementation of transfer learning techniques has shown a substantial increase in user adaptability, with an average improvement of at least 15.17%. This work contributes to the development of trustworthy AI systems by providing both technical advancements and regulatory compliance, offering a commercially viable solution that aligns with the EU AI Act requirements. </p> </div> </dd> <dt> <a name='item41'>[41]</a> <a href ="/abs/2503.15529" title="Abstract" id="2503.15529"> arXiv:2503.15529 </a> [<a href="/pdf/2503.15529" title="Download PDF" id="pdf-2503.15529" aria-labelledby="pdf-2503.15529">pdf</a>, <a href="https://arxiv.org/html/2503.15529v1" title="View HTML" id="html-2503.15529" aria-labelledby="html-2503.15529" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15529" title="Other formats" id="oth-2503.15529" aria-labelledby="oth-2503.15529">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Reflections on the Use of Dashboards in the Covid-19 Pandemic </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Arleo,+A">Alessio Arleo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Borgo,+R">Rita Borgo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kohlhammer,+J">J枚rn Kohlhammer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ruddle,+R">Roy Ruddle</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Scharlach,+H">Holger Scharlach</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yuan,+X">Xiaoru Yuan</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Computers and Society (cs.CY) </div> <p class='mathjax'> Dashboards have arguably been the most used visualizations during the COVID-19 pandemic. They were used to communicate its evolution to national governments for disaster mitigation, to the public domain to inform about its status, and to epidemiologists to comprehend and predict the evolution of the disease. Each design had to be tailored for different tasks and to varying audiences - in many cases set up in a very short time due to the urgent need. In this paper, we collect notable examples of dashboards and reflect on their use and design during the pandemic from a user-oriented perspective: we interview a group of researchers with varying visualization expertise who actively used dashboards during the pandemic as part of their daily workflow. We discuss our findings and compile a list of lessons learned to support future visualization researchers and dashboard designers. </p> </div> </dd> <dt> <a name='item42'>[42]</a> <a href ="/abs/2503.15530" title="Abstract" id="2503.15530"> arXiv:2503.15530 </a> [<a href="/pdf/2503.15530" title="Download PDF" id="pdf-2503.15530" aria-labelledby="pdf-2503.15530">pdf</a>, <a href="/format/2503.15530" title="Other formats" id="oth-2503.15530" aria-labelledby="oth-2503.15530">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> A Beautiful Mind: Principles and Strategies for AI-Augmented Human Reasoning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Koon,+S">Sean Koon</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 13 pages, 6493 words, 1 table </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> Amidst the race to create more intelligent machines, this paper asserts a critical need to invest in human reasoning so that people can manage the many new challenges and opportunities of the future. As people face accelerating changes and complexities in our society, there is a risk that we will rely on AI in ways that reduce our own agency as humans. This paper outlines a human-centered augmented reasoning paradigm by 1. Articulating fundamental principles for augmented reasoning tools, emphasizing their ergonomic, pre-conclusive, directable, exploratory, enhancing, and integrated nature; 2. Proposing a 'many tasks, many tools' approach to ensuring human control, and 3. Offering examples of interaction modes that can serve as bridges between human reasoning and AI algorithms. </p> </div> </dd> <dt> <a name='item43'>[43]</a> <a href ="/abs/2503.15637" title="Abstract" id="2503.15637"> arXiv:2503.15637 </a> [<a href="/pdf/2503.15637" title="Download PDF" id="pdf-2503.15637" aria-labelledby="pdf-2503.15637">pdf</a>, <a href="https://arxiv.org/html/2503.15637v1" title="View HTML" id="html-2503.15637" aria-labelledby="html-2503.15637" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15637" title="Other formats" id="oth-2503.15637" aria-labelledby="oth-2503.15637">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Understanding State Social Anxiety in Virtual Social Interactions using Multimodal Wearable Sensing Indicators </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Larrazabal,+M+A">Maria A. Larrazabal</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Z">Zhiyuan Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rucker,+M">Mark Rucker</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Toner,+E+R">Emma R. Toner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Boukhechba,+M">Mehdi Boukhechba</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Teachman,+B+A">Bethany A. Teachman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barnes,+L+E">Laura E. Barnes</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 28 pages, 13 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Mobile sensing is ubiquitous and offers opportunities to gain insight into state mental health functioning. Detecting state elevations in social anxiety would be especially useful given this phenomenon is highly prevalent and impairing, but often not disclosed. Although anxiety is highly dynamic, fluctuating rapidly over the course of minutes, most work to date has examined anxiety at a scale of hours, days, or longer. In the present work, we explore the feasibility of detecting fluctuations in state social anxiety among N = 46 undergraduate students with elevated symptoms of trait social anxiety. Participants engaged in two dyadic and two group social interactions via Zoom. We evaluated participants' state anxiety levels as they anticipated, immediately after experiencing, and upon reflecting on each social interaction, spanning a time frame of 2-6 minutes. We collected biobehavioral features (i.e., PPG, EDA, skin temperature, and accelerometer) via Empatica E4 devices as they participated in the varied social contexts (e.g., dyadic vs. group; anticipating vs. experiencing the interaction; experiencing varying levels of social evaluation). We additionally measured their trait mental health functioning. Mixed-effect logistic regression and leave-one-subject-out machine learning modeling indicated biobehavioral features significantly predict state fluctuations in anxiety, though balanced accuracy tended to be modest (59%). However, our capacity to identify instances of heightened versus low state anxiety significantly increased (with balanced accuracy ranging from 69% to 84% across different operationalizations of state anxiety) when we integrated contextual data alongside trait mental health functioning into our predictive models.. We discuss these and other findings in the context of the broader anxiety detection literature. </p> </div> </dd> <dt> <a name='item44'>[44]</a> <a href ="/abs/2503.15808" title="Abstract" id="2503.15808"> arXiv:2503.15808 </a> [<a href="/pdf/2503.15808" title="Download PDF" id="pdf-2503.15808" aria-labelledby="pdf-2503.15808">pdf</a>, <a href="/format/2503.15808" title="Other formats" id="oth-2503.15808" aria-labelledby="oth-2503.15808">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ChatGPT and U(X): A Rapid Review on Measuring the User Experience </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Seaborn,+K">Katie Seaborn</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY) </div> <p class='mathjax'> ChatGPT, powered by a large language model (LLM), has revolutionized everyday human-computer interaction (HCI) since its 2022 release. While now used by millions around the world, a coherent pathway for evaluating the user experience (UX) ChatGPT offers remains missing. In this rapid review (N = 58), I explored how ChatGPT UX has been approached quantitatively so far. I focused on the independent variables (IVs) manipulated, the dependent variables (DVs) measured, and the methods used for measurement. Findings reveal trends, gaps, and emerging consensus in UX assessments. This work offers a first step towards synthesizing existing approaches to measuring ChatGPT UX, urgent trajectories to advance standardization and breadth, and two preliminary frameworks aimed at guiding future research and tool development. I seek to elevate the field of ChatGPT UX by empowering researchers and practitioners in optimizing user interactions with ChatGPT and similar LLM-based systems. </p> </div> </dd> <dt> <a name='item45'>[45]</a> <a href ="/abs/2503.15926" title="Abstract" id="2503.15926"> arXiv:2503.15926 </a> [<a href="/pdf/2503.15926" title="Download PDF" id="pdf-2503.15926" aria-labelledby="pdf-2503.15926">pdf</a>, <a href="https://arxiv.org/html/2503.15926v1" title="View HTML" id="html-2503.15926" aria-labelledby="html-2503.15926" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15926" title="Other formats" id="oth-2503.15926" aria-labelledby="oth-2503.15926">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Modeling Face Emotion Perception from Naturalistic Face Viewing: Insights from Fixational Events and Gaze Strategies </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Seikavandi,+M+J">Meisam J. Seikavandi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Barrett,+M+J">Maria J. Barrett</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Burelli,+P">Paolo Burelli</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Face Emotion Recognition (FER) is essential for social interactions and understanding others' mental states. Utilizing eye tracking to investigate FER has yielded insights into cognitive processes. In this study, we utilized an instructionless paradigm to collect eye movement data from 21 participants, examining two FER processes: free viewing and grounded FER. We analyzed fixational, pupillary, and microsaccadic events from eye movements, establishing their correlation with emotion perception and performance in the grounded task. By identifying regions of interest on the face, we explored the impact of eye-gaze strategies on face processing, their connection to emotions, and performance in emotion perception. During free viewing, participants displayed specific attention patterns for various emotions. In grounded tasks, where emotions were interpreted based on words, we assessed performance and contextual understanding. Notably, gaze patterns during free viewing predicted success in grounded FER tasks, underscoring the significance of initial gaze behavior. We also employed features from pre-trained deep-learning models for face recognition to enhance the scalability and comparability of attention analysis during free viewing across different datasets and populations. This method facilitated the prediction and modeling of individual emotion perception performance from minimal observations. Our findings advance the understanding of the link between eye movements and emotion perception, with implications for psychology, human-computer interaction, and affective computing, and pave the way for developing precise emotion recognition systems. </p> </div> </dd> <dt> <a name='item46'>[46]</a> <a href ="/abs/2503.15936" title="Abstract" id="2503.15936"> arXiv:2503.15936 </a> [<a href="/pdf/2503.15936" title="Download PDF" id="pdf-2503.15936" aria-labelledby="pdf-2503.15936">pdf</a>, <a href="https://arxiv.org/html/2503.15936v1" title="View HTML" id="html-2503.15936" aria-labelledby="html-2503.15936" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15936" title="Other formats" id="oth-2503.15936" aria-labelledby="oth-2503.15936">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> No More Head-Turning: Exploring Passthrough Techniques for Addressing Rear Interruptions from the Front in VR </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+Z">Zixuan Guo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Shi,+Y">Yuekai Shi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ye,+T">Tiantian Ye</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wan,+T">Tingjie Wan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liang,+H">Hai-Ning Liang</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Virtual reality (VR) users often encounter interruptions, posing challenges to maintaining real-world awareness during immersive experiences. The Passthrough feature in VR headsets allows users to view their physical surroundings without removing the headset. However, when interruptions come from the rear, users need to turn their heads to see the real world, which can lead to negative experiences in VR. Study 1, conducted through semi-structured interviews involving 13 participants, found that users are less likely to use Passthrough for rear interruptions due to large head-turning movements, which cause inconvenience, increase the risk of motion sickness, and reduce the experience. Building on these findings, we introduced three Passthrough techniques in Study 2 for displaying the rear view in front of the user: Full Rear Passthrough + Pause (FRPP), Rear Passthrough Window (RPW), and Rear Passthrough AR (RPAR). Compared to the Baseline method that requires head-turning, all three systems reduced physical and temporal demands, alleviated disorientation caused by motion sickness, and provided a better user experience for managing rear interruptions. Among these, FRPP and RPAR were the most preferred. These findings provide valuable insights for future VR design, emphasizing the need for solutions that effectively manage rear interruptions while maintaining user comfort and experience. </p> </div> </dd> <dt> <a name='item47'>[47]</a> <a href ="/abs/2503.15942" title="Abstract" id="2503.15942"> arXiv:2503.15942 </a> [<a href="/pdf/2503.15942" title="Download PDF" id="pdf-2503.15942" aria-labelledby="pdf-2503.15942">pdf</a>, <a href="https://arxiv.org/html/2503.15942v1" title="View HTML" id="html-2503.15942" aria-labelledby="html-2503.15942" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15942" title="Other formats" id="oth-2503.15942" aria-labelledby="oth-2503.15942">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Social Media for Activists: Reimagining Safety, Content Presentation, and Workflows </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Luther,+A+R">Anna Ricarda Luther</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Heuer,+H">Hendrik Heuer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Geise,+S">Stephanie Geise</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Haunss,+S">Sebastian Haunss</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Breiter,+A">Andreas Breiter</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 18 pages, 4 figures, CHI 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Social media is central to activists, who use it internally for coordination and externally to reach supporters and the public. To date, the HCI community has not explored activists' perspectives on future social media platforms. In interviews with 14 activists from an environmental and a queer-feminist movement in Germany, we identify activists' needs and feature requests for future social media platforms. The key finding is that on- and offline safety is their main need. Based on this, we make concrete proposals to improve safety measures. Increased control over content presentation and tools to streamline activist workflows are also central to activists. We make concrete design and research recommendations on how social media platforms and the HCI community can contribute to improved safety and content presentation, and how activists themselves can reduce their workload. </p> </div> </dd> <dt> <a name='item48'>[48]</a> <a href ="/abs/2503.16011" title="Abstract" id="2503.16011"> arXiv:2503.16011 </a> [<a href="/pdf/2503.16011" title="Download PDF" id="pdf-2503.16011" aria-labelledby="pdf-2503.16011">pdf</a>, <a href="https://arxiv.org/html/2503.16011v1" title="View HTML" id="html-2503.16011" aria-labelledby="html-2503.16011" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.16011" title="Other formats" id="oth-2503.16011" aria-labelledby="oth-2503.16011">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> "This could save us months of work" -- Use Cases of AI and Automation Support in Investigative Journalism </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Cifliku,+B">Besjon Cifliku</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Heuer,+H">Hendrik Heuer</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To be published in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA '25) </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> As the capabilities of Large Language Models (LLMs) expand, more researchers are studying their adoption in newsrooms. However, much of the research focus remains broad and does not address the specific technical needs of investigative journalists. Therefore, this paper presents several applied use cases where automation and AI intersect with investigative journalism. We conducted a within-subjects user study with eight investigative journalists. In interviews, we elicited practical use cases using a speculative design approach by having journalists react to a prototype of a system that combines LLMs and Programming-by-Demonstration (PbD) to simplify data collection on numerous websites. Based on user reports, we classified the journalistic processes into data collecting and reporting. Participants indicated they utilize automation to handle repetitive tasks like content monitoring, web scraping, summarization, and preliminary data exploration. Following these insights, we provide guidelines on how investigative journalism can benefit from AI and automation. </p> </div> </dd> <dt> <a name='item49'>[49]</a> <a href ="/abs/2503.16114" title="Abstract" id="2503.16114"> arXiv:2503.16114 </a> [<a href="/pdf/2503.16114" title="Download PDF" id="pdf-2503.16114" aria-labelledby="pdf-2503.16114">pdf</a>, <a href="https://arxiv.org/html/2503.16114v1" title="View HTML" id="html-2503.16114" aria-labelledby="html-2503.16114" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.16114" title="Other formats" id="oth-2503.16114" aria-labelledby="oth-2503.16114">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Impact of Revealing Large Language Model Stochasticity on Trust, Reliability, and Anthropomorphization </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Swoopes,+C">Chelse Swoopes</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Holloway,+T">Tyler Holloway</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Glassman,+E+L">Elena L. Glassman</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted and presented at Trust and Reliance in Evolving Human-AI Workflows (TREW) Workshop, CHI 2024 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Interfaces for interacting with large language models (LLMs) are often designed to mimic human conversations, typically presenting a single response to user queries. This design choice can obscure the probabilistic and predictive nature of these models, potentially fostering undue trust and over-anthropomorphization of the underlying model. In this paper, we investigate (i) the effect of displaying multiple responses simultaneously as a countermeasure to these issues, and (ii) how a cognitive support mechanism-highlighting structural and semantic similarities across responses-helps users deal with the increased cognitive load of that intervention. We conducted a within-subjects study in which participants inspected responses generated by an LLM under three conditions: one response, ten responses with cognitive support, and ten responses without cognitive support. Participants then answered questions about workload, trust and reliance, and anthropomorphization. We conclude by reporting the results of these studies and discussing future work and design opportunities for future LLM interfaces. </p> </div> </dd> <dt> <a name='item50'>[50]</a> <a href ="/abs/2503.16135" title="Abstract" id="2503.16135"> arXiv:2503.16135 </a> [<a href="/pdf/2503.16135" title="Download PDF" id="pdf-2503.16135" aria-labelledby="pdf-2503.16135">pdf</a>, <a href="https://arxiv.org/html/2503.16135v1" title="View HTML" id="html-2503.16135" aria-labelledby="html-2503.16135" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.16135" title="Other formats" id="oth-2503.16135" aria-labelledby="oth-2503.16135">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> The Malleable Glyph (Challenge) </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Herout,+A">Adam Herout</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bartl,+V">Vojt臎ch Bartl</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gaens,+M">Martin Gaens</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tvr%C4%8Foch,+O">Oskar Tvr膹och</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Graphics (cs.GR) </div> <p class='mathjax'> Malleable Glyph is a new visualization problem and a public challenge. It originated from UX research (namely from research on card sorting UX), but its applications can be diverse (UI, gaming, information presentation, maps, and others). Its essence is: carrying as much information in a defined planar and static area as possible. The information should allow human observers to evaluate a pair of glyphs into three possible sortings: the first is "greater", or the second is "greater", or both are equal. The glyphs should adhere to the Illiteracy Rule, in other words, the observer should ask themselves the question "how much?" rather than "how many?". This article motivates the technique, explains its details, and presents the public challenge, including the evaluation protocol. <br>The article aims to call for ideas from other visualization and graphics researchers and practitioners and to invite everyone to participate in the challenge and, by doing so, move scientific knowledge forward. </p> </div> </dd> <dt> <a name='item51'>[51]</a> <a href ="/abs/2503.16227" title="Abstract" id="2503.16227"> arXiv:2503.16227 </a> [<a href="/pdf/2503.16227" title="Download PDF" id="pdf-2503.16227" aria-labelledby="pdf-2503.16227">pdf</a>, <a href="https://arxiv.org/html/2503.16227v1" title="View HTML" id="html-2503.16227" aria-labelledby="html-2503.16227" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.16227" title="Other formats" id="oth-2503.16227" aria-labelledby="oth-2503.16227">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Flight Testing an Optionally Piloted Aircraft: a Case Study on Trust Dynamics in Human-Autonomy Teaming </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+J+C">Jeremy C.-H. Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hou,+M">Ming Hou</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dunwoody,+D">David Dunwoody</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ilievski,+M">Marko Ilievski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tomasi,+J">Justin Tomasi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chao,+E">Edward Chao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pigeon,+C">Carl Pigeon</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> IEEE International Conference on Human-Machine Systems 2025, keywords: trust, human factors, aviation, safety-critical, human-autonomy teaming </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); Machine Learning (cs.LG); Systems and Control (eess.SY) </div> <p class='mathjax'> This paper examines how trust is formed, maintained, or diminished over time in the context of human-autonomy teaming with an optionally piloted aircraft. Whereas traditional factor-based trust models offer a static representation of human confidence in technology, here we discuss how variations in the underlying factors lead to variations in trust, trust thresholds, and human behaviours. Over 200 hours of flight test data collected over a multi-year test campaign from 2021 to 2023 were reviewed. The dispositional-situational-learned, process-performance-purpose, and IMPACTS homeostasis trust models are applied to illuminate trust trends during nominal autonomous flight operations. The results offer promising directions for future studies on trust dynamics and design-for-trust in human-autonomy teaming. </p> </div> </dd> </dl> <dl id='articles'> <h3>Cross submissions (showing 4 of 4 entries)</h3> <dt> <a name='item52'>[52]</a> <a href ="/abs/2502.12454" title="Abstract" id="2502.12454"> arXiv:2502.12454 </a> (cross-list from cs.CV) [<a href="/pdf/2502.12454" title="Download PDF" id="pdf-2502.12454" aria-labelledby="pdf-2502.12454">pdf</a>, <a href="https://arxiv.org/html/2502.12454v1" title="View HTML" id="html-2502.12454" aria-labelledby="html-2502.12454" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.12454" title="Other formats" id="oth-2502.12454" aria-labelledby="oth-2502.12454">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Benchmarking Zero-Shot Facial Emotion Annotation with Large Language Models: A Multi-Class and Multi-Frame Approach in DailyLife </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+H">He Zhang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Fu,+X">Xinyi Fu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 10 pages </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computer Vision and Pattern Recognition (cs.CV)</span>; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) </div> <p class='mathjax'> This study investigates the feasibility and performance of using large language models (LLMs) to automatically annotate human emotions in everyday scenarios. We conducted experiments on the DailyLife subset of the publicly available FERV39k dataset, employing the GPT-4o-mini model for rapid, zero-shot labeling of key frames extracted from video segments. Under a seven-class emotion taxonomy ("Angry," "Disgust," "Fear," "Happy," "Neutral," "Sad," "Surprise"), the LLM achieved an average precision of approximately 50%. In contrast, when limited to ternary emotion classification (negative/neutral/positive), the average precision increased to approximately 64%. Additionally, we explored a strategy that integrates multiple frames within 1-2 second video clips to enhance labeling performance and reduce costs. The results indicate that this approach can slightly improve annotation accuracy. Overall, our preliminary findings highlight the potential application of zero-shot LLMs in human facial emotion annotation tasks, offering new avenues for reducing labeling costs and broadening the applicability of LLMs in complex multimodal environments. </p> </div> </dd> <dt> <a name='item53'>[53]</a> <a href ="/abs/2503.15549" title="Abstract" id="2503.15549"> arXiv:2503.15549 </a> (cross-list from cs.CY) [<a href="/pdf/2503.15549" title="Download PDF" id="pdf-2503.15549" aria-labelledby="pdf-2503.15549">pdf</a>, <a href="https://arxiv.org/html/2503.15549v1" title="View HTML" id="html-2503.15549" aria-labelledby="html-2503.15549" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15549" title="Other formats" id="oth-2503.15549" aria-labelledby="oth-2503.15549">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Rendering Transparency to Ranking in Educational Assessment via Bayesian Comparative Judgement </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Gray,+A">Andy Gray</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Rahat,+A">Alma Rahat</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lindsay,+S">Stephen Lindsay</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Pearson,+J">Jen Pearson</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Crick,+T">Tom Crick</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computers and Society (cs.CY)</span>; Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Information Retrieval (cs.IR) </div> <p class='mathjax'> Ensuring transparency in educational assessment is increasingly critical, particularly post-pandemic, as demand grows for fairer and more reliable evaluation methods. Comparative Judgement (CJ) offers a promising alternative to traditional assessments, yet concerns remain about its perceived opacity. This paper examines how Bayesian Comparative Judgement (BCJ) enhances transparency by integrating prior information into the judgement process, providing a structured, data-driven approach that improves interpretability and accountability. <br>BCJ assigns probabilities to judgement outcomes, offering quantifiable measures of uncertainty and deeper insights into decision confidence. By systematically tracking how prior data and successive judgements inform final rankings, BCJ clarifies the assessment process and helps identify assessor disagreements. Multi-criteria BCJ extends this by evaluating multiple learning outcomes (LOs) independently, preserving the richness of CJ while producing transparent, granular rankings aligned with specific assessment goals. It also enables a holistic ranking derived from individual LOs, ensuring comprehensive evaluations without compromising detailed feedback. <br>Using a real higher education dataset with professional markers in the UK, we demonstrate BCJ's quantitative rigour and ability to clarify ranking rationales. Through qualitative analysis and discussions with experienced CJ practitioners, we explore its effectiveness in contexts where transparency is crucial, such as high-stakes national assessments. We highlight the benefits and limitations of BCJ, offering insights into its real-world application across various educational settings. </p> </div> </dd> <dt> <a name='item54'>[54]</a> <a href ="/abs/2503.15569" title="Abstract" id="2503.15569"> arXiv:2503.15569 </a> (cross-list from cs.LG) [<a href="/pdf/2503.15569" title="Download PDF" id="pdf-2503.15569" aria-labelledby="pdf-2503.15569">pdf</a>, <a href="https://arxiv.org/html/2503.15569v1" title="View HTML" id="html-2503.15569" aria-labelledby="html-2503.15569" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.15569" title="Other formats" id="oth-2503.15569" aria-labelledby="oth-2503.15569">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> RAG-based User Profiling for Precision Planning in Mixed-precision Over-the-Air Federated Learning </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Yuan,+J">Jinsheng Yuan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+Y">Yun Tang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Guo,+W">Weisi Guo</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 5 pages, 4 figures, 2 tables, submitted to IEEE VTC 2025 fall for possible publication </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Human-Computer Interaction (cs.HC) </div> <p class='mathjax'> Mixed-precision computing, a widely applied technique in AI, offers a larger trade-off space between accuracy and efficiency. The recent purposed Mixed-Precision Over-the-Air Federated Learning (MP-OTA-FL) enables clients to operate at appropriate precision levels based on their heterogeneous hardware, taking advantages of the larger trade-off space while covering the quantization overheads in the mixed-precision modulation scheme for the OTA aggregation process. A key to further exploring the potential of the MP-OTA-FL framework is the optimization of client precision levels. The choice of precision level hinges on multifaceted factors including hardware capability, potential client contribution, and user satisfaction, among which factors can be difficult to define or quantify. <br>In this paper, we propose a RAG-based User Profiling for precision planning framework that integrates retrieval-augmented LLMs and dynamic client profiling to optimize satisfaction and contributions. This includes a hybrid interface for gathering device/user insights and an RAG database storing historical quantization decisions with feedback. Experiments show that our method boosts satisfaction, energy savings, and global model accuracy in MP-OTA-FL systems. </p> </div> </dd> <dt> <a name='item55'>[55]</a> <a href ="/abs/2503.16191" title="Abstract" id="2503.16191"> arXiv:2503.16191 </a> (cross-list from cs.AI) [<a href="/pdf/2503.16191" title="Download PDF" id="pdf-2503.16191" aria-labelledby="pdf-2503.16191">pdf</a>, <a href="/format/2503.16191" title="Other formats" id="oth-2503.16191" aria-labelledby="oth-2503.16191">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Large Language Models for Water Distribution Systems Modeling and Decision-Making </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Goldshtein,+Y">Yinon Goldshtein</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Perelman,+G">Gal Perelman</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schuster,+A">Assaf Schuster</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ostfeld,+A">Avi Ostfeld</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted to EWRI Congress 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Artificial Intelligence (cs.AI)</span>; Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) </div> <p class='mathjax'> The design, operations, and management of water distribution systems (WDS) involve complex mathematical models. These models are continually improving due to computational advancements, leading to better decision-making and more efficient WDS management. However, the significant time and effort required for modeling, programming, and analyzing results remain substantial challenges. Another issue is the professional burden, which confines the interaction with models, databases, and other sophisticated tools to a small group of experts, thereby causing non-technical stakeholders to depend on these experts or make decisions without modeling support. Furthermore, explaining model results is challenging even for experts, as it is often unclear which conditions cause the model to reach a certain state or recommend a specific policy. The recent advancements in Large Language Models (LLMs) open doors for a new stage in human-model interaction. This study proposes a framework of plain language interactions with hydraulic and water quality models based on LLM-EPANET architecture. This framework is tested with increasing levels of complexity of queries to study the ability of LLMs to interact with WDS models, run complex simulations, and report simulation results. The performance of the proposed framework is evaluated across several categories of queries and hyper-parameter configurations, demonstrating its potential to enhance decision-making processes in WDS management. </p> </div> </dd> </dl> <dl id='articles'> <h3>Replacement submissions (showing 10 of 10 entries)</h3> <dt> <a name='item56'>[56]</a> <a href ="/abs/2304.08366" title="Abstract" id="2304.08366"> arXiv:2304.08366 </a> (replaced) [<a href="/pdf/2304.08366" title="Download PDF" id="pdf-2304.08366" aria-labelledby="pdf-2304.08366">pdf</a>, <a href="https://arxiv.org/html/2304.08366v2" title="View HTML" id="html-2304.08366" aria-labelledby="html-2304.08366" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2304.08366" title="Other formats" id="oth-2304.08366" aria-labelledby="oth-2304.08366">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Why is AI not a Panacea for Data Workers? An Interview Study on Human-AI Collaboration in Data Storytelling </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+H">Haotian Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+Y">Yun Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Liao,+Q+V">Q. Vera Liao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Qu,+H">Huamin Qu</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> To be published in IEEE Transactions on Visualization and Computer Graphics </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Artificial Intelligence (cs.AI) </div> <p class='mathjax'> Data storytelling plays an important role in data workers' daily jobs since it boosts team collaboration and public communication. However, to make an appealing data story, data workers spend tremendous efforts on various tasks, including outlining and styling the story. Recently, a growing research trend has been exploring how to assist data storytelling with advanced artificial intelligence (AI). However, existing studies may focus on individual tasks in the workflow of data storytelling and do not reveal a complete picture of humans' preference for collaborating with AI. To better understand real-world needs, we interviewed eighteen data workers from both industry and academia to learn where and how they would like to collaborate with AI. Surprisingly, though the participants showed excitement about collaborating with AI, many of them also expressed reluctance and pointed out nuanced reasons. Based on their responses, we first characterize stages and tasks in the practical data storytelling workflows and the desired roles of AI. Then the preferred collaboration patterns in different tasks are identified. Next, we summarize the interviewees' reasons why and why not they would like to collaborate with AI. Finally, we provide suggestions for human-AI collaborative data storytelling to hopefully shed light on future related research. </p> </div> </dd> <dt> <a name='item57'>[57]</a> <a href ="/abs/2401.10941" title="Abstract" id="2401.10941"> arXiv:2401.10941 </a> (replaced) [<a href="/pdf/2401.10941" title="Download PDF" id="pdf-2401.10941" aria-labelledby="pdf-2401.10941">pdf</a>, <a href="https://arxiv.org/html/2401.10941v2" title="View HTML" id="html-2401.10941" aria-labelledby="html-2401.10941" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2401.10941" title="Other formats" id="oth-2401.10941" aria-labelledby="oth-2401.10941">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Crowd-PrefRL: Preference-Based Reward Learning from Crowds </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Chhan,+D">David Chhan</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Novoseller,+E">Ellen Novoseller</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Lawhern,+V+J">Vernon J. Lawhern</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span>; Machine Learning (cs.LG); Social and Information Networks (cs.SI) </div> <p class='mathjax'> Preference-based reinforcement learning (RL) provides a framework to train AI agents using human feedback through preferences over pairs of behaviors, enabling agents to learn desired behaviors when it is difficult to specify a numerical reward function. While this paradigm leverages human feedback, it typically treats the feedback as given by a single human user. However, different users may desire multiple AI behaviors and modes of interaction. Meanwhile, incorporating preference feedback from crowds (i.e. ensembles of users) in a robust manner remains a challenge, and the problem of training RL agents using feedback from multiple human users remains understudied. In this work, we introduce a conceptual framework, Crowd-PrefRL, that integrates preference-based RL approaches with techniques from unsupervised crowdsourcing to enable training of autonomous system behaviors from crowdsourced feedback. We show preliminary results suggesting that Crowd-PrefRL can learn reward functions and agent policies from preference feedback provided by crowds of unknown expertise and reliability. We also show that in most cases, agents trained with Crowd-PrefRL outperform agents trained with majority-vote preferences or preferences from any individual user, especially when the spread of user error rates among the crowd is large. Results further suggest that our method can identify the presence of minority viewpoints within the crowd in an unsupervised manner. </p> </div> </dd> <dt> <a name='item58'>[58]</a> <a href ="/abs/2405.06061" title="Abstract" id="2405.06061"> arXiv:2405.06061 </a> (replaced) [<a href="/pdf/2405.06061" title="Download PDF" id="pdf-2405.06061" aria-labelledby="pdf-2405.06061">pdf</a>, <a href="https://arxiv.org/html/2405.06061v2" title="View HTML" id="html-2405.06061" aria-labelledby="html-2405.06061" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2405.06061" title="Other formats" id="oth-2405.06061" aria-labelledby="oth-2405.06061">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> GPTCoach: Towards LLM-Based Physical Activity Coaching </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=J%C3%B6rke,+M">Matthew J枚rke</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Sapkota,+S">Shardul Sapkota</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Warkenthien,+L">Lyndsea Warkenthien</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Vainio,+N">Niklas Vainio</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schmiedmayer,+P">Paul Schmiedmayer</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Brunskill,+E">Emma Brunskill</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Landay,+J+A">James A. Landay</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Please note that the title has been updated from a previous pre-print (previously: "Supporting Physical Activity Behavior Change with LLM-Based Conversational Agents") </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Mobile health applications show promise for scalable physical activity promotion but are often insufficiently personalized. In contrast, health coaching offers highly personalized support but can be prohibitively expensive and inaccessible. This study draws inspiration from health coaching to explore how large language models (LLMs) might address personalization challenges in mobile health. We conduct formative interviews with 12 health professionals and 10 potential coaching recipients to develop design principles for an LLM-based health coach. We then built GPTCoach, a chatbot that implements the onboarding conversation from an evidence-based coaching program, uses conversational strategies from motivational interviewing, and incorporates wearable data to create personalized physical activity plans. In a lab study with 16 participants using three months of historical data, we find promising evidence that GPTCoach gathers rich qualitative information to offer personalized support, with users feeling comfortable sharing concerns. We conclude with implications for future research on LLM-based physical activity support. </p> </div> </dd> <dt> <a name='item59'>[59]</a> <a href ="/abs/2410.00906" title="Abstract" id="2410.00906"> arXiv:2410.00906 </a> (replaced) [<a href="/pdf/2410.00906" title="Download PDF" id="pdf-2410.00906" aria-labelledby="pdf-2410.00906">pdf</a>, <a href="https://arxiv.org/html/2410.00906v3" title="View HTML" id="html-2410.00906" aria-labelledby="html-2410.00906" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.00906" title="Other formats" id="oth-2410.00906" aria-labelledby="oth-2410.00906">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Generative AI and Perceptual Harms: Who's Suspected of using LLMs? </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Kadoma,+K">Kowe Kadoma</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Metaxa,+D">Dana茅 Metaxa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Naaman,+M">Mor Naaman</a></div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Large language models (LLMs) are increasingly integrated into a variety of writing tasks. While these tools can help people by generating ideas or producing higher quality work, like many other AI tools they may risk causing a variety of harms, disproportionately burdening historically marginalized groups. In this work, we introduce and evaluate perceptual harm, a term for the harm caused to users when others perceive or suspect them of using AI. We examined perceptual harms in three online experiments, each of which entailed human participants evaluating the profiles for fictional freelance writers. We asked participants whether they suspected the freelancers of using AI, the quality of their writing, and whether they should be hired. We found some support for perceptual harms against for certain demographic groups, but that perceptions of AI use negatively impacted writing evaluations and hiring outcomes across the board. </p> </div> </dd> <dt> <a name='item60'>[60]</a> <a href ="/abs/2503.14103" title="Abstract" id="2503.14103"> arXiv:2503.14103 </a> (replaced) [<a href="/pdf/2503.14103" title="Download PDF" id="pdf-2503.14103" aria-labelledby="pdf-2503.14103">pdf</a>, <a href="https://arxiv.org/html/2503.14103v2" title="View HTML" id="html-2503.14103" aria-labelledby="html-2503.14103" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2503.14103" title="Other formats" id="oth-2503.14103" aria-labelledby="oth-2503.14103">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> DangerMaps: Personalized Safety Advice for Travel in Urban Environments using a Retrieval-Augmented Language Model </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Oppenlaender,+J">Jonas Oppenlaender</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 17 pages, 7 figures, 1 table </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Human-Computer Interaction (cs.HC)</span> </div> <p class='mathjax'> Planning a trip into a potentially unsafe area is a difficult task. We conducted a formative study on travelers' information needs, finding that most of them turn to search engines for trip planning. Search engines, however, fail to provide easily interpretable results adapted to the context and personal information needs of a traveler. Large language models (LLMs) create new possibilities for providing personalized travel safety advice. To explore this idea, we developed DangerMaps, a mapping system that assists its users in researching the safety of an urban travel destination, whether it is pre-travel or on-location. DangerMaps plots safety ratings onto a map and provides explanations on demand. This late breaking work specifically emphasizes the challenges of designing real-world applications with large language models. We provide a detailed description of our approach to prompt design and highlight future areas of research. </p> </div> </dd> <dt> <a name='item61'>[61]</a> <a href ="/abs/2409.00101" title="Abstract" id="2409.00101"> arXiv:2409.00101 </a> (replaced) [<a href="/pdf/2409.00101" title="Download PDF" id="pdf-2409.00101" aria-labelledby="pdf-2409.00101">pdf</a>, <a href="https://arxiv.org/html/2409.00101v3" title="View HTML" id="html-2409.00101" aria-labelledby="html-2409.00101" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2409.00101" title="Other formats" id="oth-2409.00101" aria-labelledby="oth-2409.00101">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signals </div> <div class='list-authors'><a href="https://arxiv.org/search/eess?searchtype=author&query=Jiang,+W">Wei-Bang Jiang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Wang,+Y">Yansen Wang</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Lu,+B">Bao-Liang Lu</a>, <a href="https://arxiv.org/search/eess?searchtype=author&query=Li,+D">Dongsheng Li</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> The Thirteenth International Conference on Learning Representations </div> <div class='list-journal-ref'><span class='descriptor'>Journal-ref:</span> The Thirteenth International Conference on Learning Representations, 2025 </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Signal Processing (eess.SP)</span>; Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) </div> <p class='mathjax'> Recent advancements for large-scale pre-training with neural signals such as electroencephalogram (EEG) have shown promising results, significantly boosting the development of brain-computer interfaces (BCIs) and healthcare. However, these pre-trained models often require full fine-tuning on each downstream task to achieve substantial improvements, limiting their versatility and usability, and leading to considerable resource wastage. To tackle these challenges, we propose NeuroLM, the first multi-task foundation model that leverages the capabilities of Large Language Models (LLMs) by regarding EEG signals as a foreign language, endowing the model with multi-task learning and inference capabilities. Our approach begins with learning a text-aligned neural tokenizer through vector-quantized temporal-frequency prediction, which encodes EEG signals into discrete neural tokens. These EEG tokens, generated by the frozen vector-quantized (VQ) encoder, are then fed into an LLM that learns causal EEG information via multi-channel autoregression. Consequently, NeuroLM can understand both EEG and language modalities. Finally, multi-task instruction tuning adapts NeuroLM to various downstream tasks. We are the first to demonstrate that, by specific incorporation with LLMs, NeuroLM unifies diverse EEG tasks within a single model through instruction tuning. The largest variant NeuroLM-XL has record-breaking 1.7B parameters for EEG signal processing, and is pre-trained on a large-scale corpus comprising approximately 25,000-hour EEG data. When evaluated on six diverse downstream datasets, NeuroLM showcases the huge potential of this multi-task learning paradigm. </p> </div> </dd> <dt> <a name='item62'>[62]</a> <a href ="/abs/2410.20081" title="Abstract" id="2410.20081"> arXiv:2410.20081 </a> (replaced) [<a href="/pdf/2410.20081" title="Download PDF" id="pdf-2410.20081" aria-labelledby="pdf-2410.20081">pdf</a>, <a href="https://arxiv.org/html/2410.20081v3" title="View HTML" id="html-2410.20081" aria-labelledby="html-2410.20081" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2410.20081" title="Other formats" id="oth-2410.20081" aria-labelledby="oth-2410.20081">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> emg2qwerty: A Large Dataset with Baselines for Touch Typing using Surface Electromyography </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Sivakumar,+V">Viswanath Sivakumar</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Seely,+J">Jeffrey Seely</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Du,+A">Alan Du</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bittner,+S+R">Sean R Bittner</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Berenzweig,+A">Adam Berenzweig</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Bolarinwa,+A">Anuoluwapo Bolarinwa</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gramfort,+A">Alexandre Gramfort</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mandel,+M+I">Michael I Mandel</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Published at NeurIPS 2024 Datasets and Benchmarks Track </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Machine Learning (cs.LG)</span>; Human-Computer Interaction (cs.HC); Audio and Speech Processing (eess.AS) </div> <p class='mathjax'> Surface electromyography (sEMG) non-invasively measures signals generated by muscle activity with sufficient sensitivity to detect individual spinal neurons and richness to identify dozens of gestures and their nuances. Wearable wrist-based sEMG sensors have the potential to offer low friction, subtle, information rich, always available human-computer inputs. To this end, we introduce emg2qwerty, a large-scale dataset of non-invasive electromyographic signals recorded at the wrists while touch typing on a QWERTY keyboard, together with ground-truth annotations and reproducible baselines. With 1,135 sessions spanning 108 users and 346 hours of recording, this is the largest such public dataset to date. These data demonstrate non-trivial, but well defined hierarchical relationships both in terms of the generative process, from neurons to muscles and muscle combinations, as well as in terms of domain shift across users and user sessions. Applying standard modeling techniques from the closely related field of Automatic Speech Recognition (ASR), we show strong baseline performance on predicting key-presses using sEMG signals alone. We believe the richness of this task and dataset will facilitate progress in several problems of interest to both the machine learning and neuroscientific communities. Dataset and code can be accessed at <a href="https://github.com/facebookresearch/emg2qwerty" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item63'>[63]</a> <a href ="/abs/2502.07058" title="Abstract" id="2502.07058"> arXiv:2502.07058 </a> (replaced) [<a href="/pdf/2502.07058" title="Download PDF" id="pdf-2502.07058" aria-labelledby="pdf-2502.07058">pdf</a>, <a href="https://arxiv.org/html/2502.07058v3" title="View HTML" id="html-2502.07058" aria-labelledby="html-2502.07058" rel="noopener noreferrer" target="_blank">html</a>, <a href="/format/2502.07058" title="Other formats" id="oth-2502.07058" aria-labelledby="oth-2502.07058">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Using Contextually Aligned Online Reviews to Measure LLMs' Performance Disparities Across Language Varieties </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Tang,+Z">Zixin Tang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+C">Chieh-Yang Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+T">Tsung-Che Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Ng,+H+Y+S">Ho Yin Sam Ng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+H">Hen-Hsen Huang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Huang,+T+'">Ting-Hao 'Kenneth' Huang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Accepted by 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), theme track </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computation and Language (cs.CL)</span>; Human-Computer Interaction (cs.HC) </div> <p class='mathjax'> A language can have different varieties. These varieties can affect the performance of natural language processing (NLP) models, including large language models (LLMs), which are often trained on data from widely spoken varieties. This paper introduces a novel and cost-effective approach to benchmark model performance across language varieties. We argue that international online review platforms, such as <a href="http://Booking.com" rel="external noopener nofollow" class="link-external link-http">this http URL</a>, can serve as effective data sources for constructing datasets that capture comments in different language varieties from similar real-world scenarios, like reviews for the same hotel with the same rating using the same language (e.g., Mandarin Chinese) but different language varieties (e.g., Taiwan Mandarin, Mainland Mandarin). To prove this concept, we constructed a contextually aligned dataset comprising reviews in Taiwan Mandarin and Mainland Mandarin and tested six LLMs in a sentiment analysis task. Our results show that LLMs consistently underperform in Taiwan Mandarin. </p> </div> </dd> <dt> <a name='item64'>[64]</a> <a href ="/abs/2502.11190" title="Abstract" id="2502.11190"> arXiv:2502.11190 </a> (replaced) [<a href="/pdf/2502.11190" title="Download PDF" id="pdf-2502.11190" aria-labelledby="pdf-2502.11190">pdf</a>, <a href="/format/2502.11190" title="Other formats" id="oth-2502.11190" aria-labelledby="oth-2502.11190">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> ReLearn: Unlearning via Learning for Large Language Models </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Xu,+H">Haoming Xu</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+N">Ningyuan Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Yang,+L">Liming Yang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhao,+S">Sendong Zhao</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Deng,+S">Shumin Deng</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Wang,+M">Mengru Wang</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Hooi,+B">Bryan Hooi</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Oo,+N">Nay Oo</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Chen,+H">Huajun Chen</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Zhang,+N">Ningyu Zhang</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> Work in progress </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Computation and Language (cs.CL)</span>; Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG) </div> <p class='mathjax'> Current unlearning methods for large language models usually rely on reverse optimization to reduce target token probabilities. However, this paradigm disrupts the subsequent tokens prediction, degrading model performance and linguistic coherence. Moreover, existing evaluation metrics overemphasize contextual forgetting while inadequately assessing response fluency and relevance. To address these challenges, we propose ReLearn, a data augmentation and fine-tuning pipeline for effective unlearning, along with a comprehensive evaluation framework. This framework introduces Knowledge Forgetting Rate (KFR) and Knowledge Retention Rate (KRR) to measure knowledge-level preservation, and Linguistic Score (LS) to evaluate generation quality. Our experiments show that ReLearn successfully achieves targeted forgetting while preserving high-quality output. Through mechanistic analysis, we further demonstrate how reverse optimization disrupts coherent text generation, while ReLearn preserves this essential capability. Code is available at <a href="https://github.com/zjunlp/unlearn" rel="external noopener nofollow" class="link-external link-https">this https URL</a>. </p> </div> </dd> <dt> <a name='item65'>[65]</a> <a href ="/abs/2503.15370" title="Abstract" id="2503.15370"> arXiv:2503.15370 </a> (replaced) [<a href="/pdf/2503.15370" title="Download PDF" id="pdf-2503.15370" aria-labelledby="pdf-2503.15370">pdf</a>, <a href="/format/2503.15370" title="Other formats" id="oth-2503.15370" aria-labelledby="oth-2503.15370">other</a>] </dt> <dd> <div class='meta'> <div class='list-title mathjax'><span class='descriptor'>Title:</span> Tangles: Unpacking Extended Collision Experiences with Soma Trajectories </div> <div class='list-authors'><a href="https://arxiv.org/search/cs?searchtype=author&query=Benford,+S">Steve Benford</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Garrett,+R">Rachael Garrett</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Li,+C">Christine Li</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tennent,+P">Paul Tennent</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=N%C3%BA%C3%B1ez-Pacheco,+C">Claudia N煤帽ez-Pacheco</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Kucukyilmaz,+A">Ayse Kucukyilmaz</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tsaknaki,+V">Vasiliki Tsaknaki</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=H%C3%B6%C3%B6k,+K">Kristina H枚枚k</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Caleb-Solly,+P">Praminda Caleb-Solly</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Marshall,+J">Joe Marshall</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Schneiders,+E">Eike Schneiders</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Popova,+K">Kristina Popova</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Afana,+J">Jude Afana</a></div> <div class='list-comments mathjax'><span class='descriptor'>Comments:</span> 32 pages, 13 figures </div> <div class='list-subjects'><span class='descriptor'>Subjects:</span> <span class="primary-subject">Robotics (cs.RO)</span>; Human-Computer Interaction (cs.HC) </div> <p class='mathjax'> We reappraise the idea of colliding with robots, moving from a position that tries to avoid or mitigate collisions to one that considers them an important facet of human interaction. We report on a soma design workshop that explored how our bodies could collide with telepresence robots, mobility aids, and a quadruped robot. Based on our findings, we employed soma trajectories to analyse collisions as extended experiences that negotiate key transitions of consent, preparation, launch, contact, ripple, sting, untangle, debris and reflect. We then employed these ideas to analyse two collision experiences, an accidental collision between a person and a drone, and the deliberate design of a robot to play with cats, revealing how real-world collisions involve the complex and ongoing entanglement of soma trajectories. We discuss how viewing collisions as entangled trajectories, or tangles, can be used analytically, as a design approach, and as a lens to broach ethical complexity. </p> </div> </dd> </dl> <div class='paging'>Total of 65 entries </div> <div class='morefewer'>Showing up to 2000 entries per page: <a href=/list/cs.HC/new?skip=0&show=1000 rel="nofollow"> fewer</a> | <span style="color: #454545">more</span> | <span style="color: #454545">all</span> </div> </div> </div> </div> </main> <footer style="clear: both;"> <div class="columns is-desktop" role="navigation" aria-label="Secondary" style="margin: -0.75em -0.75em 0.75em -0.75em">  <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul style="list-style: none; line-height: 2;"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 40.7 32.4 173.4 128.7 16.8 12.2 50.2 41.8 73.4 41.4z"/></svg> <a href="https://info.arxiv.org/help/contact.html"> Contact</a> </li> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>subscribe to arXiv mailings</title><desc>Click here to subscribe</desc><path d="M476 3.2L12.5 270.6c-18.1 10.4-15.8 35.6 2.2 43.2L121 358.4l287.3-253.2c5.5-4.9 13.3 2.6 8.6 8.3L176 407v80.5c0 23.6 28.5 32.9 42.5 15.8L282 426l124.6 52.2c14.2 6 30.4-2.9 33-18.2l72-432C515 7.8 493.3-6.8 476 3.2z"/></svg> <a href="https://info.arxiv.org/help/subscribe"> Subscribe</a> </li> </ul> </div> </div> </div>   <div class="column" style="padding: 0;"> <div class="columns"> <div class="column"> <ul style="list-style: none; 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