This study focuses on specifying local models in federated learning (FL), which allows a
large number of clients to improve their corresponding models by training a shared global …

This paper focuses on the problem of learning discriminative representation for brain
images, which is a critical task toward understanding brain developments. Related studies …

Featured Application As students are usually characterized by more than one learning style,
multi-label classification methods may be applied for the diagnosis of a composite students' …

Wind-waves exhibit variations both in shape and steepness, and their asymmetrical nature
is a well-known feature. One of the important characteristics of the sea surface is the front …

In current research processes, mathematical learning has significantly impacted the brain's
plasticity and cognitive functions. While biochemical changes in brain have been …

This paper proposes a representation learning model to identify task-state fMRIs for
knowledge-concept recognition, which has the potential to model the human cognitive …

Federated learning (FL) allows a large number of clients to improve their respective models
through training a shared global model. However, passing the same global model is not …

The joint problem of imbalance samples and noisy labels challenges the current text
classifiers in real-world applications. Existing approaches are mostly devoted to handling …

Academic grades in assessments are predicted to determine if a student is at risk of failing a
course. Sequential models or graph neural networks that have been employed for grade …

This work intends to solve the problem that the traditional education system cannot
reasonably adjust the educational integration of children with the arrival of labor force in a …