All Outputs (5)

Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance (2023)
Presentation / Conference Contribution
Walker, C., Rothon, C., Aslansefat, K., Papadopoulos, Y., & Dethlefs, N. (2023). Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance. Safe AI Systems: Proceedings of the 32nd Safety-Critical Systems Symposium (SSS’24). Safety Critical Systems Club. https://scsc.uk/scsc-188

It has been forecasted that a quarter of the world's energy usage will be supplied from Offshore Wind (OSW) by 2050 (Smith 2023). Given that up to one third of Levelised Cost of Energy (LCOE) arises from Operations and Maintenance (O&M), the motive f... Read More about Safety Monitoring for Large Language Models: A Case Study of Offshore Wind Maintenance.

Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring (2022)
Presentation / Conference Contribution
Farhad, A. H., Sorokos, I., Schmidt, A., Akram, M. N., Aslansefat, K., & Schneider, D. (2022). Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring. In C. Seguin, M. Zeller, & T. Prosvirnova (Eds.), Model-Based Safety and Assessment 8th International Symposium, IMBSA 2022, Proceedings. Lecture Notes in Computer Science (LNCS, volume 13525) (13525, pp. 219-234). Springer (part of Springer Nature). https://doi.org/10.1007/978-3-031-15842-1_16

Machine Learning (ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety need to be ensured. To this end, one important aspect is to determine... Read More about Keep Your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring.

SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures (2020)
Presentation / Conference Contribution
Aslansefat, K., Sorokos, I., Whiting, D., Tavakoli Kolagari, R., & Papadopoulos, Y. (2020). SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures. Lecture notes in computer science, 12297, 197-211. https://doi.org/10.1007/978-3-030-58920-2_13

Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety standards that are not satisfied with... Read More about SafeML: Safety Monitoring of Machine Learning Classifiers Through Statistical Difference Measures.

Model-Based Safety and Assessment: 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16–18, 2019, Proceedings (2019)
Presentation / Conference Contribution
Papadopoulos, Y., Aslansefat, K., Katsaros, P., & Bozzano, M. (Ed.). (2019). Model-Based Safety and Assessment: 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16–18, 2019, Proceedings [Edited Proceedings]. 6th International Symposium, IMBSA: International Symposium on Model-Based Safety and Assessment, Thessaloniki, Greece. https://doi.org/10.1007/978-3-030-32872-6

This book constitutes the proceedings of the 6th International Symposium on Model-Based Safety and Assessment, IMBSA 2019, held in Thessaloniki, Greece, in October 2019.

The 24 revised full papers presented were carefully reviewed and selected fro... Read More about Model-Based Safety and Assessment: 6th International Symposium, IMBSA 2019, Thessaloniki, Greece, October 16–18, 2019, Proceedings.

A runtime safety analysis concept for open adaptive systems (2019)
Presentation / Conference Contribution
Kabir, S., Sorokos, I., Aslansefat, K., Papadopoulos, Y., Gheraibia, Y., Reich, J., Saimler, M., & Wei, R. (2019). A runtime safety analysis concept for open adaptive systems. Lecture notes in computer science, 11842, 332-346. https://doi.org/10.1007/978-3-030-32872-6_22

© Springer Nature Switzerland AG 2019. In the automotive industry, modern cyber-physical systems feature cooperation and autonomy. Such systems share information to enable collaborative functions, allowing dynamic component integration and architectu... Read More about A runtime safety analysis concept for open adaptive systems.