An empirical study of security DSLs and code analyzers finds few common concepts, overly general weakness descriptions, and that even experts are overwhelmed by the complexity of potential mappings.
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A graph autoencoder model using foundation model features achieves high retrieval accuracy (mAP 96.7-97.6%, mMV 91.5-94.2%) on BreakHis and BACH breast cancer histopathology datasets.
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Can I Check What I Designed? Mapping Security Design DSLs to Code Analyzers
An empirical study of security DSLs and code analyzers finds few common concepts, overly general weakness descriptions, and that even experts are overwhelmed by the complexity of potential mappings.
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Leveraging Medical Foundation Model Features in Graph Neural Network-Based Retrieval of Breast Histopathology Images
A graph autoencoder model using foundation model features achieves high retrieval accuracy (mAP 96.7-97.6%, mMV 91.5-94.2%) on BreakHis and BACH breast cancer histopathology datasets.