Edge computing has gained significant traction in recent years, promising enhanced
efficiency by integrating artificial intelligence capabilities at the edge. While the focus has …

Increased mass shootings and terrorist activities severely impact society mentally and
physically. Development of real-time and cost-effective automated weapon detection …

Conventional centralized deep learning paradigms are not feasible when data from different
sources cannot be shared due to data privacy or transmission limitation. To resolve this …

Deep learning has recently attracted great attention in many application fields, especially for
big data analysis in the field of edge computing. Federated learning, as a promising …

Zero-shot audio classification aims to recognize and classify a sound class that the model
has never seen during training. This paper presents a novel approach for zero-shot audio …

Federated Learning (FL) is a flexible and efficient approach for leveraging distributed data
through parameter upload and aggregation. However, the practical applicability of current …

Human Sensing, a field that leverages technology to monitor human activities, psycho-
physiological states, and interactions with the environment, enhances our understanding of …

Zero-shot learning is a learning regime that recognizes unseen classes by generalizing the
visual-semantic relationship learned from the seen classes. To obtain an effective ZSL …

Audio classification equips a machine with the feature of recognising the source of an audio
sample. Different from the conventional setting, by using zero-shot learning, an audio …

Sound anomaly detection in industrial applications has to cope with limited and diverse
training data as well as domain shifts. Because of privacy and security considerations, the …