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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
Towards improved software visualisation of parameterised ...
Jaxon Kneipp, Alex Potanin, Michael Anenburg · 2024-05-13 · via cs.HC updates on arXiv.org

Modern geological studies and mineral exploration techniques rely heavily on being able to digitally visualise and interpret data. Rare earth elements (REEs) are vital for renewable energy technologies. REE concentrations, when normalised to a standard material, show unique geometric curves (or patterns) in geological samples due to their similar chemical properties. The lambda technique can be used to describe these patterns and turn them into points - making it easier to visualise and interpret larger datasets. Lambdas have the potential to help industry understand intricate sample relationships and the geological and economic importance of their data. This study explored the use of lambdas through the evaluation of various visualisation methods to determine their usefulness in mineral exploration. The 'REEkit' platform facilitated the evaluation of the different visualisation methods and gauged industry interest and acceptance of such a service. Qualitative data was gathered through contextual inquiry, utilising semi-structured interviews and an observational session with 10 participants. Conceptual thematic analysis was applied to extract key findings. This study found that two critical factors for successful lambda data visualisation in the mineral exploration industry are familiarity and clarity: visualisations that were familiar and commonplace for users allowed for better analysis and clear communication to non-technical audiences. This included visualisations such as the 3D scatter plot and scatter plot matrix. Furthermore, visualisations that complemented each other and seamlessly integrated into the same workflow provided diverse perspectives on the data. Important aspects included understanding population grouping versus data distribution, achieved through combinations such as scatter plot and density contour plot, or 3D scatter plot and violin plot.