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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
Detox Browser -- Towards Filtering Sensitive Content On t...
Noble Saji Mathews, Sridhar Chimalakonda · 2021-06-18 · via cs.HC updates on arXiv.org

The annual consumption of web-based resources is increasing at a very fast rate, mainly due to an increase in affordability and accessibility of the internet. Many are relying on the web to get diverse perspectives, but at the same time, it can expose them to content that is harmful to their mental well-being. Catchy headlines and emotionally charged articles increase the number of readers which in turn increases ad revenue for websites. When a user consumes a large quantity of negative content, it adversely impacts the user's happiness and has a significant impact on his/her mood and state of mind. Many studies carried out during the COVID-19 pandemic has shown that people across the globe irrespective of their country of origin have experienced higher levels of anxiety and depression. Web filters can help in constructing a digital environment that is more suitable for people prone to depression, anxiety and stress. A significant amount of work has been done in the field of web filtering, but there has been limited focus on helping Highly Sensitive Persons (HSP's) or those with stress disorders induced by trauma. Through this paper, we propose detox Browser, a simple tool that enables end-users to tune out of or control their exposure to topics that can affect their mental well being. The extension makes use of sentiment analysis and keywords to filter out flagged content from google search results and warns users if any blacklisted topics are detected when navigating across websites