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 …

Recent rapid progress in machine learning (ML), particularly so‐called 'deep learning', has
led to a resurgence in interest in explainability of artificial intelligence (AI) systems, reviving …

Abstract Analysis of learning behavior of MOOC enthusiasts has become a posed challenge
in the Learning Analytics field, which is especially related to video lecture data, since most …

The current evolution in multidisciplinary learning analytics research poses significant
challenges for the exploitation of behavior analysis by fusing data streams toward advanced …

Recent advances in Machine Learning (ML) have profoundly changed many detection,
classification, recognition and inference tasks. Given the complexity of the battlespace, ML …

Future military coalition operations will increasingly rely on machine learning (ML) methods
to improve situational awareness. The coalition context presents unique challenges for ML …

For many types of data and information fusion, input from human users is essential, both in
terms of defining or adjusting the processing steps, as well as in interacting with …

Key concerns for the deployment of information fusion systems is conformance to standards,
methods for certifiability, and assessment of machine techniques to enhance human …

Knowledge graphs capture complex domain-relevant information and relationships between
different entities, often based on detailed ontologies. Knowledge graph embedding (KGE) …

In military modeling and simulation (M&S) there is an increasing need for Computer
Generated Forces (CGFs) with machine learning capabilities for use in training or decision …