Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must
be able to identify and understand their inevitable mistakes if they are to fulfill this potential …

Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …

In a real-world scenario, human actions are typically out of the distribution from training data,
which requires a model to both recognize the known actions and reject the unknown …

Thanks to graph neural networks (GNNs), semi-supervised node classification has shown
the state-of-the-art performance in graph data. However, GNNs have not considered …

Multi-view learning aims to combine multiple features to achieve more comprehensive
descriptions of data. Most previous works assume that multiple views are strictly aligned …

Deep models have the risk of utilizing spurious clues to make predictions, eg, recognizing
actions via classifying the background scene. This problem severely degrades the open-set …

Driven by the rapid development of the Internet of Things (IoT) technology, the issue of trust
has become increasingly apparent and received considerable scholarly attention in recent …

An automatic vision-based sewer inspection plays a vital role of sewage system in a modern
city. Recent advances focus on modeling a deep learning-based method to realize the …

This work describes the architecture and prototype implementation of a novel trust-aware
continuous authorization technology that targets consumer Internet of Things (IoT), eg, Smart …