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Tianshu Zhou

Publications and source records attributed to Tianshu Zhou.

3 recordsLinked to original sources

Listen to the Voices of Everyday Users: Democratizing Privacy Ratings for Sensitive Data Access in Mobile Apps

Mobile apps frequently request excessive data access, raising significant privacy concerns. While regulations like GDPR emphasize data minimization, they provide limited guidance on concretely defining and enforcing necessary data access. Existing regulatory mechanisms primarily rely on expert-driven audits that face challenges in scalability, neutrality, and alignment with user expectations. In this paper, we propose a novel paradigm--democratizing privacy assessment, inspired by prior work on user-centric privacy perceptions--which repositions users as active evaluators in the privacy auditing process, recognizing that user perceptions of data usage play a crucial role in assessing the appropriateness and necessity of data access. To operationalize this paradigm, we introduce DePRa, a prototype system developed through participatory design, featuring contextual explanation provision, category-based representative selection, an intuitive rating interface, and preference-based rating adjustment. We evaluated DePRa with 200 everyday mobile app users, analyzing how effectively it captures user opinions on sensitive data access, comparing their privacy ratings with expert assessments, and exploring risk preference-based score calibration. Our findings show the feasibility and promise of democratized privacy assessment, highlighting its potential to complement expert auditing and support inclusive privacy evaluation.

cs.CR

The Lost-K and Shorter-J Phenomenon in Non-Standard Ballistocardiography Data

Non-standard ballistocardiogram(BCG) data generally do not have prominent J peaks. This paper introduces two phenomena that reduce the prominence of Jpeaks: the shorter-J phenomenon and the lost-K phenomenon, both of which are commonly observed in non-standard BCG signals . This paper also proposes three signal transformation methods that effectively improve the lost-K and shorter-J phenomena. The methods were evaluated on a time-aligned ECG-BCG dataset with 40 subjects. The results show that based on the transformed signal, simple J-peak-based methods using only the detection of local maxima or minima show better performance in locating J-peaks and extracting BCG cycles, especially for non-standard BCG data.

eess.SP

Streamlining evidence based clinical recommendations with large language models

Clinical evidence underpins informed healthcare decisions, yet integrating it into real-time practice remains challenging due to intensive workloads, complex procedures, and time constraints. This study presents Quicker, an LLM-powered system that automates evidence synthesis and generates clinical recommendations following standard guideline development workflows. Quicker delivers an end-to-end pipeline from clinical questions to recommendations and supports customized decision-making through integrated tools and interactive interfaces. To evaluate how closely Quicker can reproduce guideline development processes, we constructed Q2CRBench-3, a benchmark derived from guideline development records for three diseases. Experiments show that Quicker produces precise question decomposition, expert-aligned retrieval, and near-comprehensive screening. Quicker assistance improved the accuracy of extracted study data, and its recommendations were more comprehensive and coherent than clinician-written ones. In system-level testing, Quicker working with one participant reduced recommendation development to 20-40 min. Overall, the findings demonstrate Quicker's potential to enhance the speed and reliability of evidence-based clinical decision-making.

cs.CL