Select for better learning: Identifying high-quality training data for a multimodal cyclic transformer.

Tonic-clonic seizures (TCSs), which present a significant risk for sudden unexpected death in epilepsy (SUDEP), require accurate detection to enable effective long-term monitoring. Previous studies have demonstrated the advantages of multimodal seizure detection systems in reliably detecting TCSs over extended periods. However, the effectiveness of these data-driven systems depends heavily on the availability of reliable training data.

Gestational weight gain prediction using privacy preserving federated learning.

Gestational weight gain prediction in expecting women is associated with multiple risks. Manageable interventions can be devised if the weight gain can be predicted as early as possible. However, training the model to predict such weight gain requires access to centrally stored privacy sensitive weight data.

Trusted Operations on Sensor Data .

The widespread use of mobile devices has allowed the development of participatory sensing systems that capture various types of data using the existing or external sensors attached to mobile devices. Gathering data from such anonymous sources requires a mechanism to establish the integrity of sensor readings. In many cases, sensor data need to be preprocessed on the device itself before being uploaded to the target server while ensuring the chain of trust from capture to the delivery of the data.