In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by
promoting two complementary activities—symbolic knowledge extraction (SKE) and …

Deep neural networks have achieved significant success across various fields, but their
intrinsic black-box nature hinders the further development. Addressing the interpretability …

Modern power grids are faced with a series of challenges, such as the ever-increasing
demand for renewable energy sources, extensive urbanization, climate and energy crisis …

Abstract Fuzzy Knowledge Graph (FKG) has recently been emerging as one of the key
techniques for supporting classification and decision-making problems. FKG is a novel …

Graph Neural Networks (GNNs) have shown remarkable performance in tackling complex
tasks. However, interpreting the decision-making process of GNNs remains a challenge. To …

The layered structure of deep neural networks hinders the use of numerous analysis tools
and thus the development of its interpretability. Inspired by the success of functional brain …

Leveraging generalized knowledge from multiple source domains with rich labels to the
target domain without labeled data is a more realistic and challenging issue compared with …

Various topological methods have emerged in recent years to investigate the inner workings
of deep neural networks (DNNs) based on the structural and weight information. However …

With the burgeoning advancements of computing and network communication technologies,
network infrastructures and their application environments have become increasingly …

Psychology has benefited from an enormous wealth of knowledge about processes of
cognition in relation to how the brain organizes information. Within the categorization …