Gabriele Iob
Nuclear Security: A Natural Language Processing Generative Approach.
Rel. Raffaella Testoni. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Energetica E Nucleare, 2024
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Abstract
This thesis aims at investigating and evaluating the use of natural language gen- erative models, specifically in the framework of generating training scenarios for personnel working in critical infrastructures. Safety and security of an infrastruc- ture containing radioactive material should be addressed with emergency planning, which requires training scenarios for personnel involved. Such scenarios are tra- ditionally developed by human experts, although this is a process that is subject to several drawbacks. First of all, it requires a considerable amount of time for producing a qualitative output. Second, it must deal with multiple repetitive steps, thus leading to critical bottlenecks. Third, a final training scenario can at first glance seem reproducible and on-point, but when it comes to put it in practice, its plausibility might be insufficient.
These aspects can ultimately lead to considerable decrease in training quality, with severe consequences on overall safety and security
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