Tootfinder

What's Behind the Magic? Audiences Seek Artistic Value in Generative AI's Contributions to a Live Dance Performance
Jacqueline Elise Bruen, Myounghoon Jeon
https://arxiv.org/abs/2508.00239

What's Behind the Magic? Audiences Seek Artistic Value in Generative AI's Contributions to a Live Dance Performance
With the development of generative artificial intelligence (GenAI) tools to create art, stakeholders cannot come to an agreement on the value of these works. In this study we uncovered the mixed opinions surrounding art made by AI. We developed two versions of a dance performance augmented by technology either with or without GenAI. For each version we informed audiences of the performance's development either before or after a survey on their perceptions of the performance. There were thirty-n…

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
Shruthi Chari, Oshani Seneviratne, Prithwish Chakraborty, Pablo Meyer, Deborah L. McGuinness
https://arxiv.org/abs/2508.00300

MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
Explanations are crucial for building trustworthy AI systems, but a gap often exists between the explanations provided by models and those needed by users. To address this gap, we introduce MetaExplainer, a neuro-symbolic framework designed to generate user-centered explanations. Our approach employs a three-stage process: first, we decompose user questions into machine-readable formats using state-of-the-art large language models (LLM); second, we delegate the task of generating system recomme…

Breaking the mould of Social Mixed Reality -- State-of-the-Art and Glossary
Marta Bie\'nkiewicz, Julia Ayache, Panayiotis Charalambous, Cristina Becchio, Marco Corragio, Bertram Taetz, Francesco De Lellis, Antonio Grotta, Anna Server, Daniel Rammer, Richard Kulpa, Franck Multon, Azucena Garcia-Palacios, Jessica Sutherland, Kathleen Bryson, St\'ephane Donikian, Didier Stricker, Beno\^it Bardy

Breaking the mould of Social Mixed Reality -- State-of-the-Art and Glossary
This article explores a critical gap in Mixed Reality (MR) technology: while advances have been made, MR still struggles to authentically replicate human embodiment and socio-motor interaction. For MR to enable truly meaningful social experiences, it needs to incorporate multi-modal data streams and multi-agent interaction capabilities. To address this challenge, we present a comprehensive glossary covering key topics such as Virtual Characters and Autonomisation, Responsible AI, Ethics by Desi…

Unlimited Editions: Documenting Human Style in AI Art Generation
Alex Leitch, Celia Chen
https://arxiv.org/abs/2507.19497 https://arxiv.org/pdf/2507.19497

Unlimited Editions: Documenting Human Style in AI Art Generation
As AI art generation becomes increasingly sophisticated, HCI research has focused primarily on questions of detection, authenticity, and automation. This paper argues that such approaches fundamentally misunderstand how artistic value emerges from the concerns that drive human image production. Through examination of historical precedents, we demonstrate that artistic style is not only visual appearance but the resolution of creative struggle, as artists wrestle with influence and technical con…

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Lillian Asiala, James E. McCarthy
https://arxiv.org/abs/2507.18878

Improving the State of the Art for Training Human-AI Teams: Technical Report #5 -- Individual Differences and Team Qualities to Measure in a Human-AI Teaming Testbed
Sonalysts, Inc. (Sonalysts) is working on an initiative to expand our expertise in teaming to include Human-Artificial Intelligence (AI) teams. The first step of this process is to develop a Synthetic Task Environment (STE) to support our original research. Prior knowledge elicitation efforts within the Human-AI teaming research stakeholder community revealed a desire to support data collection using pre- and post-performance surveys. In this technical report, we review a number of constructs t…

Evaluating Personalized Beneficial Interventions in the Daily Lives of Older Adults Using a Camera
Longfei Chen, Christopher Lochhead, Robert B. Fisher, Nusa Faric, Jacques Fleuriot, Subramanian Ramamoorthy
https://arxiv.org/abs/2507.19494

Evaluating Personalized Beneficial Interventions in the Daily Lives of Older Adults Using a Camera
Beneficial daily activity interventions have been shown to improve both the physical and mental health of older adults. However, there is a lack of robust objective metrics and personalized strategies to measure their impact. In this study, two older adults aged over 65, living in Edinburgh, UK, selected their preferred daily interventions (mindful meals and art crafts), which are then assessed for effectiveness. The total monitoring period across both participants was 8 weeks. Their physical b…

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Eden Wu, Dishita G Turakhia, Guande Wu, Christos Koutras, Sarah Keegan, Wenke Liu, Beata Szeitz, David Fenyo, Cl\'audio T. Silva, Juliana Freire
https://arxiv.org/abs/2507.16117

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Biomedical data harmonization is essential for enabling exploratory analyses and meta-studies, but the process of schema matching - identifying semantic correspondences between elements of disparate datasets (schemas) - remains a labor-intensive and error-prone task. Even state-of-the-art automated methods often yield low accuracy when applied to biomedical schemas due to the large number of attributes and nuanced semantic differences between them. We present BDIViz, a novel visual analytics sy…