Since the popularisation of the Internet in the 1990s, the cyberspace has kept evolving. We
have created various computer-mediated virtual environments including social networks …

Although running deep-learning (DL) algorithms is challenging due to resource constraints
on mobile and wearable devices, they provide performance improvements compared to …

Science and technology transform the frontiers of knowledge and have deep and powerful
impacts on society, demonstrating how social reality varies with each era of the world. As a …

Lifelong learning—an agent's ability to learn throughout its lifetime—is a hallmark of
biological learning systems and a central challenge for artificial intelligence (AI). The …

The multi-layer structures of Deep Learning facilitate the processing of higher-level
abstractions from data, thus leading to improved generalization and widespread …

Continual learning (CL) trains NN models incrementally from a continuous stream of tasks.
To remember previously learned knowledge, prior studies store old samples over a memory …

Internet of Things (IoT) systems provide large amounts of data on all aspects of human
behavior. Machine learning techniques, especially deep neural networks (DNN), have …

Continuous learning (CL) has recently been adopted into edge video analytics, gaining
huge success in maintaining high accuracy without constantly retraining DNN models by …

Real-world Facial Expression Recognition (FER) systems require models to constantly learn
and adapt with novel data. Traditional Machine Learning (ML) approaches struggle to adapt …

Modern advances in machine learning (ML) and wearable medical sensors (WMSs) in edge
devices have enabled ML-driven disease detection for smart healthcare. Conventional ML …