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New aspect-oriented constructs for security hardening concerns
Published 2009“…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
Published 2007“…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
Published 2013“…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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Nouveaux points de coupure et primitives pour les préoccupations de renforcement de sécurité
Published 2009“…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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StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features
Published 2024“…Next, principal component analysis (PCA) is used to select the best subset of attributes. …”
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Exploring the Dynamic Interplay of Deleterious Variants on the RAF1–RAP1A Binding in Cancer: Conformational Analysis, Binding Free Energy, and Essential Dynamics
Published 2024“…Principal component analysis (PCA) and free energy landscape (FEL) evaluation further determined dynamical variations in the wild‐type and mutant complexes. …”
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DeepRaman: Implementing surface-enhanced Raman scattering together with cutting-edge machine learning for the differentiation and classification of bacterial endotoxins
Published 2025“…Unlike standard machine learning approaches such as PCA, LDA, SVM, RF, GBM etc, DeepRaman functions independently, requiring no human interaction, and can be used to much smaller datasets than traditional CNNs. …”
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