collaborators

7 papers

cs.HC2026

VisGuardian: A Lightweight Group-based Privacy Control Technique For Front Camera Data From AR Glasses in Home Environments

Shuning Zhang, Qucheng Zang, Yongquan `Owen' Hu +7

Always-on sensing of AI applications on AR glasses makes traditional permission techniques ill-suited for context-dependent visual data, especially within home environments. The ho…

cs.HC2025

Position: Human-Robot Interaction in Embodied Intelligence Demands a Shift From Static Privacy Controls to Dynamic Learning

Shuning Zhang, Hong Jia, Simin Li +4

The reasoning capabilities of embodied agents introduce a critical, under-explored inferential privacy challenge, where the risk of an agent generate sensitive conclusions from amb…

cs.HC2025

From Patient Burdens to User Agency: Designing for Real-Time Protection Support in Online Health Consultations

Shuning Zhang, Ying Ma, Yongquan `Owen' Hu +4

Online medical consultation platforms, while convenient, are undermined by significant privacy risks that erode user trust. We first conducted in-depth semi-structured interviews w…

cs.HC2025

Vision-Based Multimodal Interfaces: A Survey and Taxonomy for Enhanced Context-Aware System Design

Yongquan 'Owen' Hu, Jingyu Tang, Xinya Gong +6

The recent surge in artificial intelligence, particularly in multimodal processing technology, has advanced human-computer interaction, by altering how intelligent systems perceive…

cs.HC2025

Actual Achieved Gain and Optimal Perceived Gain: Modeling Human Take-over Decisions Towards Automated Vehicles' Suggestions

Shuning Zhang, Xin Yi, Shixuan Li +7

Driver decision quality in take-overs is critical for effective human-Autonomous Driving System (ADS) collaboration. However, current research lacks detailed analysis of its variat…

cs.HC2025

Adanonymizer: Interactively Navigating and Balancing the Duality of Privacy and Output Performance in Human-LLM Interaction

Shuning Zhang, Xin Yi, Haobin Xing +3

Current Large Language Models (LLMs) cannot support users to precisely balance privacy protection and output performance during individual consultations. We introduce Adanonymizer,…