All Outputs (4)

Addressing Complexity and Intelligence in Systems Dependability Evaluation (2023)
Thesis
Aslansefat, K. Addressing Complexity and Intelligence in Systems Dependability Evaluation. (Thesis). University of Hull. https://hull-repository.worktribe.com/output/4500562

Engineering and computing systems are increasingly complex, intelligent, and open adaptive. When it comes to the dependability evaluation of such systems, there are certain challenges posed by the characteristics of “complexity” and “intelligence”. T... Read More about Addressing Complexity and Intelligence in Systems Dependability Evaluation.

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.