As the performance and complexity of machine learning models have grown significantly
over the last years, there has been an increasing need to develop methodologies to …

This survey presents an overview of integrating prior knowledge into machine learning
systems in order to improve explainability. The complexity of machine learning models has …

Labeling data is often a tedious and error-prone activity. However, organizing the labeling
experience as a human-machine collaboration has the potential to improve label quality and …

With advances in reinforcement learning (RL), agents are now being developed in high-
stakes application domains such as healthcare and transportation. Explaining the behavior …

With face-to-face music collaboration being severely limited during the recent pandemic,
mixed reality technologies and their potential to provide musicians a feeling of" being there" …

In this article, we introduce a next-generation annotation tool called NOVA for emotional
behaviour analysis, which implements a workflow that interactively incorporates the 'human …

Objective Given the importance of emotions in psychotherapy, valid measures are essential
for research and practice. As emotions are expressed at different levels, multimodal …

Background Emotions play a key role in psychotherapy. However, a problem with examining
emotional states via self-report questionnaires is that the assessment usually takes place …

The application of complex machine learning models has elicited research to make them
more explainable. However, most explainability methods cannot provide insight beyond the …

Understanding emotions is key to Affective Computing. Emotion recognition focuses on the
communicative component of emotions encoded in social signals. This view alone is …