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Differential private federated transfer learning for mental health monitoring in everyday settings: A case study on stress detection
Mental health conditions, prevalent across various demographics, necessitate efficient
monitoring to mitigate their adverse impacts on life quality. The surge in data-driven …
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
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 …
stress monitoring systems often need large datasets for training effective models. However …
Graph Cross Supervised Learning via Generalized Knowledge
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 …
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
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 …
unique biometric information that poses significant privacy risks. Existing ECG re …
F3: Fast and Flexible Network Telemetry with an FPGA coprocessor
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 …
incast, and attacks. However, current solutions are constrained by the limited resources …