Home > Avvisi > Tesi di Laurea Magistrale (NUOVE: 18/9/2024)
Tesi di Laurea Magistrale (NUOVE: 18/9/2024)
1) TECHNIQUES BASED ON MACHINE LEARNING FOR DERIVING CODE SIMILARITIES. Code is extremely fragile: e.g. swapping two lines of code changes completely its semantics. I want to devise machine learning techniques that are trained with abstract models of the codes and are able to derive similarities (and therefore detect plagiarism, bugs, malicious snippets). Then I want to study how much robust the technique is by perturbing codes in input (with automatic tools) and analysing results with statistical techniques. [WITH UNIVERSITY OF VENICE]
2) ALGORITHMS FOR SUSTAINABLE SUPPLY CHAINS. Shareholders, consumers, and employees have increasingly high expectations of the businesses they invest in, buy from, and are employed by. As a result, business leaders face more pressure from regulators and the market to prove that their organizations are acting responsibly and sustainably. I want to study techniques for, e.g., decarbonizing supply chains acting proactively on logistics or identifying opportunities for reducing waste or looking for alternative raw material with a lower environmental impact. The technologies should also create transparency of the performance of Environmental, Social and Governance (ESG) commitments. [WITH Prof. GIANCARLO SUCCI]
3) WATERMARKING TECHNIQUES ENFORCING OBJECT CODE AUTHORSHIP. Identifying authorship of software is essential to prosecute the usage of pirated copies. The current trend is to copy object code, rather than source code, which is almost never available. The goal of this thesis is to to study/find clever techniques that enhance/extend the object code with authorship informations (watermarking techniques). The techniques must be robust attackers that may decompile the code, remove authorship informations and recompile it. [WITH Prof. DANILO MONTESI]
Pubblicato il: 19 settembre 2023
