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Unit 42

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
The MIT Voice Name System
Brian Subirana, Harry Levinson, Ferran Hueto, Prithvi Rajasekara · 2022-03-29 · via cs.HC updates on arXiv.org

This RFC white Paper summarizes our progress on the MIT Voice Name System (VNS) and Huey. The VNS, similar in name and function to the DNS, is a system to reserve and use "wake words" to activate Artificial Intelligence (AI) devices. Just like you can say "Hey Siri" to activate Apple's personal assistant, we propose using the VNS in smart speakers and other devices to route wake requests based on commands such as "turn off", "open grocery shopping list" or "271, start flash card review of my computer vision class". We also introduce Huey, an unambiguous Natural Language to interact with AI devices. We aim to standardize voice interactions to a universal reach similar to that of other systems such as phone numbering, with an agreed world-wide approach to assign and use numbers, or the Internet's DNS, with a standard naming system, that has helped flourish popular services including the World-Wide-Web, FTP, and email. Just like these standards are "neutral", we also aim to endow the VNS with "wake neutrality" so that each participant can develop its own digital voice. We focus on voice as a starting point to talk to any IoT object and explain briefly how the VNS may be expanded to other AI technologies enabling person-to-machine conversations (really machine-to-machine), including computer vision or neural interfaces. We also describe briefly considerations for a broader set of standards, MIT Open AI (MOA), including a reference architecture to serve as a starting point for the development of a general conversational commerce infrastructure that has standard "Wake Words", NLP commands such as "Shopping Lists" or "Flash Card Reviews", and personalities such as Pi or 271. Privacy and security are key elements considered because of speech-to-text errors and the amount of personal information contained in a voice sample.