Differential private federated transfer learning for mental health monitoring in everyday settings: A case study on stress detection

Z Wang, Z Yang, I Azimi… - 2024 46th Annual …, 2024 - ieeexplore.ieee.org
Mental health conditions, prevalent across various demographics, necessitate efficient
monitoring to mitigate their adverse impacts on life quality. The surge in data-driven …

Enhancing performance and user engagement in everyday stress monitoring: A context-aware active reinforcement learning approach

SAH Aqajari, Z Wang, A Tazarv, S Labbaf… - arxiv preprint arxiv …, 2024 - arxiv.org
In today's fast-paced world, accurately monitoring stress levels is crucial. Sensor-based
stress monitoring systems often need large datasets for training effective models. However …

Graph Cross Supervised Learning via Generalized Knowledge

X Yuan, Y Tian, C Zhang, Y Ye, NV Chawla… - Proceedings of the 30th …, 2024 - dl.acm.org
The success of GNNs highly relies on the accurate labeling of data. Existing methods of
ensuring accurate labels, such as weakly-supervised learning, mainly focus on the existing …

Ecg unveiled: Analysis of client re-identification risks in real-world ecg datasets

Z Wang, A Kanduri, SAH Aqajari… - 2024 IEEE 20th …, 2024 - ieeexplore.ieee.org
While ECG data is crucial for diagnosing and monitoring heart conditions, it also contains
unique biometric information that poses significant privacy risks. Existing ECG re …

F3: Fast and Flexible Network Telemetry with an FPGA coprocessor

W Feng, J Gao, X Chen, G Antichi, RB Basat… - Proceedings of the …, 2024 - dl.acm.org
Traffic monitoring in the dataplane is vital for reacting to network events such as microbursts,
incast, and attacks. However, current solutions are constrained by the limited resources …