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pro vyhledávání: '"Saletta, Martina"'
Autor:
Anselmi, Fabio, Castelli, Mauro, d'Onofrio, Alberto, Manzoni, Luca, Mariot, Luca, Saletta, Martina
Geometric Semantic Geometric Programming (GSGP) is one of the most prominent Genetic Programming (GP) variants, thanks to its solid theoretical background, the excellent performance achieved, and the execution time significantly smaller than standard
Externí odkaz:
http://arxiv.org/abs/2305.16956
An interesting thread in the research of Boolean functions for cryptography and coding theory is the study of secondary constructions: given a known function with a good cryptographic profile, the aim is to extend it to a (usually larger) function po
Externí odkaz:
http://arxiv.org/abs/2111.13248
Autor:
Saletta, Martina, Ferretti, Claudio
In this paper, we propose a novel approach for mining different program features by analysing the internal behaviour of a deep neural network trained on source code. Using an unlabelled dataset of Java programs and three different embedding strategie
Externí odkaz:
http://arxiv.org/abs/2103.05442
Semi-bent Boolean functions are interesting from a cryptographic standpoint, since they possess several desirable properties such as having a low and flat Walsh spectrum, which is useful to resist linear cryptanalysis. In this paper, we consider the
Externí odkaz:
http://arxiv.org/abs/2005.08300
Autor:
Mercuri, Valeria1 (AUTHOR) v.mercuri4@campus.unimib.it, Saletta, Martina1 (AUTHOR) martina.saletta@unimib.it, Ferretti, Claudio1 (AUTHOR) martina.saletta@unimib.it
Publikováno v:
Algorithms. Oct2023, Vol. 16 Issue 10, p478. 15p.
Autor:
Saletta, Martina1,2 (AUTHOR) martina.saletta@unimib.it, Ferretti, Claudio2 (AUTHOR) martina.saletta@unimib.it
Publikováno v:
Information (2078-2489). Apr2023, Vol. 14 Issue 4, p251. 17p.
Autor:
Ferretti, Claudio1 (AUTHOR) claudio.ferretti@unimib.it, Saletta, Martina1 (AUTHOR) claudio.ferretti@unimib.it
Publikováno v:
Algorithms. Dec2022, Vol. 15 Issue 12, p449. 15p.
Autor:
Saletta, Martina, Ferretti, Claudio
Publikováno v:
2022 IEEE Congress on Evolutionary Computation (CEC).
Neural networks for source code processing have proven to be effective for solving multiple tasks, such as locating bugs or detecting vulnerabilities. In this paper, we propose an evolutionary approach for probing the behaviour of a deep neural sourc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::38b759845742064236fa05639b4a9b85
https://doi.org/10.1109/cec55065.2022.9870317
https://doi.org/10.1109/cec55065.2022.9870317
Akademický článek
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Publikováno v:
Progress in Artificial Intelligence ISBN: 9783030302405
EPIA (1)
EPIA (1)
Castelli, M., Manzoni, L., Mariot, L., & Saletta, M. (2019). Extending local search in geometric semantic genetic programming. In P. Moura Oliveira, P. Novais, & L. P. Reis (Eds.), Progress in Artificial Intelligence : 19th EPIA Conference on Artific
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cfb675d14a9b610949987ebf579f8124
https://doi.org/10.1007/978-3-030-30241-2_64
https://doi.org/10.1007/978-3-030-30241-2_64