New aspect-oriented constructs for security hardening concerns by Mourad, Azzam

“…They allow to analyze a program's call graph in order to determine how to change function signatures for passing the parameters associated with a given security hardening. …”
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New primitives to AOP weaving capabilities for security hardening concerns by Mourad, Azzam

“…They allow to analyze a program’s call graph in order to determine how to change function signatures for the passing of parameters associated with a given security hardening. …”
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Common weaving approach in mainstream languages for software security hardening by Alhadidi, Dima

“…In this paper, we propose a novel aspect-oriented approach based on GIMPLE, a language-independent and a tree-based representation generated by the GNU Compiler Collection (GCC), for the systemization of application security hardening. The security solutions are woven into GIMPLE representations in a systematic way, eliminating the need for manual hardening that might generate a considerable number of errors. …”
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Evolution Of Activation Functions for Neural Architecture Search by Nader, Andrew

“…However, to the best of our knowledge, the design of new activation functions has mostly been done by hand. In this work, we propose the use of a self-adaptive evolutionary algorithm that searches for new activation functions using a genetic programming approach, and we compare the performance of the obtained activation functions to ReLU. …”
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Nouveaux points de coupure et primitives pour les préoccupations de renforcement de sécurité by Mourad, Azzam

“…They allow to analyze a program’s call graph in order to determine how to change function signatures for the passing of parameters associated with a given security hardening. …”
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R-CONV++: uncovering privacy vulnerabilities through analytical gradient inversion attacks by Tamer Ahmed Eltaras (22565414)

“…The first algorithm presents a novel data leakage method that efficiently exploits convolutional layer gradients, demonstrating that even with non-fully invertible activation functions, such as ReLU, training samples can be analytically reconstructed directly from gradients without the need to reconstruct intermediate layer outputs. …”