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cs.HC updates on arXiv.org

From Prompt to Service: An SLM-Based Agent Orchestration Gateway for AI-Driven Virtual Worlds What LLMs Must Forget to Teach Effectively: A DIY Approach to Premodern Japanese Language Pedagogy 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 MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis
Privacy Law Enforcement Under Centralized Governance: A Q...
Tao Jing, Yao Li, Jingzhou Ye, Jie Wang, Xueqiang Wang · 2025-03-11 · via cs.HC updates on arXiv.org

In recent years, major privacy laws like the GDPR have brought about positive changes. However, challenges remain in enforcing the laws, particularly due to under-resourced regulators facing a large number of potential privacy-violating software applications (apps) and the high costs of investigating them. Since 2019, China has launched a series of privacy enforcement campaigns known as Special Privacy Rectification Campaigns (SPRCs) to address widespread privacy violations in its mobile application (app) ecosystem. Unlike the enforcement of the GDPR, SPRCs are characterized by large-scale privacy reviews and strict sanctions, under the strong control of central authorities. In SPRCs, central government authorities issue administrative orders to mobilize various resources for market-wide privacy reviews of mobile apps. They enforce strict sanctions by requiring privacy-violating apps to rectify issues within a short timeframe or face removal from app stores. While there are a few reports on SPRCs, the effectiveness and potential problems of this campaign-style privacy enforcement approach remain unclear to the community. In this study, we conducted 18 semi-structured interviews with app-related engineers involved in SPRCs to better understand the campaign-style privacy enforcement. Based on the interviews, we reported our findings on a variety of aspects of SPRCs, such as the processes that app engineers regularly follow to achieve privacy compliance in SPRCs, the challenges they encounter, the solutions they adopt to address these challenges, and the impacts of SPRCs, etc. We found that app engineers face a series of challenges in achieving privacy compliance in their apps...