Classification of Visual Attention Level During Target Gazing Using Microsaccades -- Multiscale Analysis of Textual Content Using Eyegaze -- In-Car eCall Device for Automatic Accident Detection, Passenger Counting, and Alarming -- Volumetric Density of Triangulated Range Images for Face Recognition -- Combining Merkle Hash Tree and Chaotic Cryptography for Secure Data Fusion in IoT -- A Deployment Framework for Ensuring Business Compliance Using Goal Models -- A Methodology for Root-Causing In-Field Attacks on Microfluidic Executions.

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 35th issue of the Transactions on Computational Science, focusses on signal processing and security in distributed systems. The topics covered include classification of visual attention levels using microsaccades; analysis of textual content using Eyegaze; automatic car-accident detection and passenger counting; face recognition; secure data fusion in IoT; business compliance using goal models; and microfluidic executions.