Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

The dynamicity of real-world systems poses a significant challenge to deployed predictive
machine learning (ML) models. Changes in the system on which the ML model has been …

Recommender systems have become prosperous nowadays, designed to predict users'
potential interests in items by learning embeddings. Recent developments of the Graph …

Encoding a driving scene into vector representations has been an essential task for
autonomous driving that can benefit downstream tasks eg, trajectory prediction. The driving …

Generative self-supervised learning (SSL), especially masked autoencoders, has become
one of the most exciting learning paradigms and has shown great potential in handling …

Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

Events are happening in real world and real time, which can be planned and organized for
occasions, such as social gatherings, festival celebrations, influential meetings, or sports …

Vehicular ad hoc networks (VANETs) and the services they support are an essential part of
intelligent transportation. Through physical technologies, applications, protocols, and …