In multiple industries, such as metal forming and additive manufacturing, the discrepancy between the desired shape and the shape really obtained is significant, which hinders the development of these manufacturing techniques. This is largely due to the complexity of the thermal and mechanical processes involved, resulting in a high computational simulation time. The aim of...

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Video Anomaly Detection (VAD) aims to automatically identify unusual events in video that deviate from normal patterns. Existing methods often rely on One-Class or Weakly Supervised learning: the former uses only normal data for training, while the latter leverages video-level labels. Recent advances in Vision-Language Models (VLMs) and Large Language Models (LLMs) have improved both...

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Cryptography plays a fundamental role in securing modern communication systems by ensuring confidentiality, integrity, and authenticity. Public-key cryptography, in particular, has become indispensable for secure data exchange and authentication processes. However, the advent of quantum computing poses an existential threat to many of the traditional public-key cryptographic algorithms, such as RSA, DSA, and ECC, which...

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Recent Vision-Language Models (VLMs) like BLIP, LLaVA, and Qwen-VL have achieved impressive results in multimodal tasks but still face limitations in true spatial and temporal reasoning. Many current benchmarks conflate visual reasoning with general knowledge and involve shallow reasoning tasks. Furthermore, these models often struggle with understanding complex spatial relations and dynamic scenes due to...

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Critical systems must simultaneously meet the requirements of both Safety (preventing unintentional failures that could lead to damage) and Security (protecting against malicious attacks). Traditionally, these two areas are treated separately, whereas they are interdependent: An attack (Security) can trigger a failure (Safety), and a functional flaw can be exploited as an attack vector. MBSE...

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Federated Learning (FL) allows multiple clients to collaboratively train a global model without sharing their raw data. While this decentralized setup is appealing for privacy-sensitive domains like healthcare and finance, it is not inherently secure: model updates can leak private information, and malicious clients can corrupt training. To tackle these challenges, two main strategies are...

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The rise of Large Language Models (LLMs) and agentic AI systems is transforming how complex workflows are designed and managed. Unlike traditional centralized orchestration, modern workflows must support distributed, autonomous agents operating across cloud, edge, and on-premise environments. These agents collaborate with humans and other systems, adapt to evolving goals, and cross organizational and trust...

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The aim of this thesis is to design new 3D model generators based on Generative Artificial Intelligence (GenAI), capable of producing faithful, coherent and physically viable shapes. While 3D generation has become essential in many fields, current automatic generation approaches suffer from limitations in terms of respecting geometric, structural and physical constraints. The goal is...

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Formal verification of neural networks is facing a double-faceted issue. The expressiveness of specifications (as in: compact and close to human understanding) is apparently clashing with their efficient translation to state-of-the-art prover, who only support a fragment of arithmetic without quantifiers. This thesis will investigate quot;globalquot; properties. Such class of properties describe generic behaviours of...

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