Transactions on Computational Science XL [electronic resource] /

Trustworthy Technologies for Autonomous Human-Machine Systems -- Challenges in Understanding Trust and Trust Modeling: Quenching the Thirst for AI Trust Management -- Stress Contagion Protocols for Human-Autonomy Teaming -- AVCA: Autonomous Virtual Cognitive Assessment -- Light-weighted CNN-Attention based Architecture Trained with a Hybrid Objective Function for EMG-based Human Machine Interfaces -- Fairness, Bias and Trust in the Context of Biometric-enabled Autonomous Decision Support -- An Autonomous Fake News Recognition System by Semantic Learning in Cognitive Computing -- Addressing Dataset Shift for Trustworthy Deep Learning Diagnostic Ultrasound Decision Support.

The LNCS journal Transactions on Computational Science reflects recent developments in the field of Computational Science, conceiving the field not as a mere ancillary science but rather as an innovative approach supporting many other scientific disciplines. The journal focuses on original high-quality research in the realm of computational science in parallel and distributed environments, encompassing the facilitating theoretical foundations and the applications of large-scale computations and massive data processing. It addresses researchers and practitioners in areas ranging from aerospace to biochemistry, from electronics to geosciences, from mathematics to software architecture, presenting verifiable computational methods, findings, and solutions, and enabling industrial users to apply techniques of leading-edge, large-scale, high performance computational methods.This, the 40th issue of the Transactions on Computational Science, is a special issue, comprised of seven papers, anddevoted to the developing and novel techniques for Trustworthy Technologies for Autonomous Human-Machine Systems. They include emerging and innovative applications of computer security-based applications, as well as theoretical contributions that are relevant to Trustworthy Technologies for Autonomous Human-Machine Systems.