Post-stroke anxiety (PSA) affects 20-30% of stroke survivors and significantly impacts quality of life (QoL) and rehabilitation outcomes. Traditional emotion assessment relies on subjective self-reports, limiting to capture real-time fluctuations in emotional states. This study proposes a self-supervised learning (SSL) framework combined with a transformer-based emotion classifier to enable continuous anxiety tracking in patients with stroke using smartwatch-derived photoplethysmography (PPG). The SSL model was pretrained on the VitalDB dataset using R-peak-to-peak intervals (RRIs) extracted from electrocardiography (ECG) signals. The pretrained encoder is then integrated into a transformer-based classifier trained on the Psychophysiology of Positive and Negative Emotions (POPANE) dataset with labeled emotional responses. The trained model was applied to smartwatch-derived pulse peak intervals (PPI) from patients with stroke, and evaluated against Generalized Anxiety Disorder-7 (GAD-7) scores and we evaluated it against GAD-7 scores using both group-level and 90-day longitudinal analyses, along with on-device feasibility on a Galaxy Watch6. Over the 30 days preceding the second survey, between-group differences were significant by Welch's t-test (p = 0.0086), and discrimination reached an AUC of 0.859 with a 95% confidence interval of 0.664-0.992. In 90-day monitoring, generalized estimating equations showed persistent divergence when groups were defined by the second survey, with significant differences across multiple weeks preceding the survey, consistent with the retrospective GAD-7 window. These findings indicate that ECG-pretrained cardiac representations can be translated to smartwatch PPG for unobtrusive, continuous anxiety tracking in stroke. The results provide feasibility-level evidence and motivate larger, multi-site prospective validation toward clinical deployment.

Ensuring fairness in blockchain-based data trading presents significant challenges, as the transparency of blockchain can expose sensitive details and compromise fairness. Fairness ensures that the seller receives payment only if they provide the correct data, and the buyer gains access to the data only after making the payment. Existing approaches face limitations in efficiency, particularly when applied to large-scale data. Moreover, preserving privacy has also been a significant challenge in blockchain. In this paper, we introduce zkMarket, a privacy-preserving fair trade system on the blockchain. We ensure fairness by integrating encryption with zk-SNARKs, enabling verifiable proofs for fair trading. However, applying zk-SNARKs directly can be computationally expensive for the prover. To address this, we improve efficiency by leveraging our novel matrix-formed PRG (MatPRG) and commit-and-prove SNARK (CP-SNARK), making the data registration process more concise and significantly reducing the seller's proving time. To ensure transaction privacy, zkMarket is built upon an anonymous transfer protocol. Experimental results demonstrate that zkMarket significantly reduces the computational overhead associated with traditional blockchain solutions while maintaining robust security and privacy. Specifically, our evaluation quantifies this high efficiency: the seller can register 1 MB of data in 2.8 seconds, the buyer can generate the trade transaction in 0.2 seconds, and the seller can finalize the trade within 0.4 seconds.

Online chat is an emerging channel for discussing community problems. It is common practice for communities to assign dedicated moderators to maintain a structured discussion and enhance the problem-solving experience. However, due to the synchronous nature of online chat, moderators face a high managerial overhead in tasks like discussion stage management, opinion summarization, and consensus-building support. To assist moderators with facilitating a structured discussion for community problem-solving, we introduce SolutionChat, a system that (1) visualizes discussion stages and featured opinions and (2) recommends contextually appropriate moderator messages. Results from a controlled lab study (n=55, 12 groups) suggest that participants' perceived discussion trackability was significantly higher with SolutionChat than without. Also, moderators provided better summarization with less effort and better managerial support using system-generated messages with SolutionChat than without. With SolutionChat, we envision untrained moderators to effectively facilitate chat-based discussions of important community matters.