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cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Autonomous Heavy-Duty Mobile Machinery: A Multidisciplina...
Tyrone Machado, David Fassbender, Abdolreza Taheri, Daniel Eriks · 2021-12-06 · via cs.HC updates on arXiv.org

Heavy-duty mobile machines (HDMMs) are a wide range of machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortage, poor safety records, and harsh work environments. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMM are complex machines requiring continuous physical and cognitive inputs from human-operators. Thus, developing autonomous HDMM is a huge challenge, with current research and developments being performed in several independent research domains. Through this study, we use the bounded rationality concept to propose multidisciplinary collaborations for new autonomous HDMMs and apply the transaction cost economics framework to suggest future implications in the HDMM industry. Furthermore, we introduce a conceptual understanding of collaborations in the autonomous HDMM as a unified approach, while highlighting the practical implications and challenges of the complex nature of such multidisciplinary collaborations. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks.