All Outputs (3)

Safety Analysis Concept and Methodology for EDDI development (Initial Version) (2023)
Report
Aslansefat, K., Gerasimou, S., Michalodimi-trakis, E., Papoutsakis, M., Reich, J., Sorokos, I., Walker, M., & Papadopoulos, Y. (2023). Safety Analysis Concept and Methodology for EDDI development (Initial Version). European Comission

Executive Summary:
This deliverable describes the proposed safety analysis concept and accompanying methodology to be defined in the SESAME project. Three overarching challenges to the development of safe and secure multi-robot systems are identifie... Read More about Safety Analysis Concept and Methodology for EDDI development (Initial Version).

Safety-Security Co-Engineering Framework (2023)
Report
Aslansefat, K., Gerasimou, S., Hamibi, H., Matragkas, N., Michalodimitrakis, E., Papadopoulos, Y., Papoutsakis, M., & Walker, M. (2023). Safety-Security Co-Engineering Framework. European Commission

Executive Summary:
The advantages of a model-based approach for safety have been clear for many years now. However, security analysis is typically less formal and more ad-hoc; it may involve systematic processes but these are not generally tied into... Read More about Safety-Security Co-Engineering Framework.

Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations (2023)
Journal Article
Aslansefat, K., Hashemian, M., Walker, M., Akram, M. N., Sorokos, I., & Papadopoulos, Y. (2023). Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations. IEEE Software, https://doi.org/10.1109/MS.2023.3321282

Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black boxes, their... Read More about Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations.