Deep learning has revolutionized many machine learning tasks in recent years, ranging
from image classification and video processing to speech recognition and natural language …

Suicide is a critical issue in modern society. Early detection and prevention of suicide
attempts should be addressed to save people's life. Current suicidal ideation detection (SID) …

Personalized decision-making can be implemented in a Federated learning (FL) framework
that can collaboratively train a decision model by extracting knowledge across intelligent …

Large language models (LLMs) have not only revolutionized the field of natural language
processing (NLP) but also have the potential to bring a paradigm shift in many other fields due …

Spatial-temporal graph modeling is an important task to analyze the spatial relations and
temporal trends of components in a system. Existing approaches mostly capture the spatial …

Modeling multivariate time series has long been a subject that has attracted researchers
from a diverse range of fields including economics, finance, and traffic. A basic assumption …

Graph embedding is an effective method to represent graph data in a low dimensional space
for graph analytics. Most existing embedding algorithms typically focus on preserving the …

Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP
tasks to capture the long-term and local dependencies, respectively. Attention mechanisms …

Heterogeneity across clients in federated learning (FL) usually hinders the optimization
convergence and generalization performance when the aggregation of clients' knowledge …

Graph clustering is a fundamental task which discovers communities or groups in networks.
Recent studies have mostly focused on developing deep learning approaches to learn a …