Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

In the last decade, there has been a significant surge of interest in machine learning,
primarily driven by advancements in deep learning (DL). DL has emerged as a powerful …

Self-supervised representation learning methods have achieved significant success in
computer vision and natural language processing (NLP), where data samples exhibit explicit …

The data produced by millions of connected devices and smart sensors in the Industrial
Internet of Things (IIoT) is highly dynamic, large-scale, heterogeneous, and time-stamped …

Abstract The Internet of Things (IoT) scenario places important challenges even for deep
learning-based intrusion detection systems. IoTs are highly heterogeneous networks in …

Large models have emerged as the most recent groundbreaking achievements in artificial
intelligence, and particularly machine learning. However, when it comes to graphs, large …

Video object detection needs to solve feature degradation situations that rarely happen in
the image domain. One solution is to use the temporal information and fuse the features from …

Human comfort studies are a complex topic given their interdisciplinary and multi-stimuli
nature. The integration of different sources of information from multi-domain experiments for …

Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …

Blockchains are becoming increasingly important in today's Internet, enabling large-scale
decentralized applications with strong security and transparency properties. In a blockchain …