Hamm-grams are a new class of fixed-length regular expressions over bytes with single-character wildcards, mined efficiently with LSH and clustering to yield more robust features than n-grams for malware classification and detection.
2008.Introduction to information retrieval
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NeuroFlake integrates discriminative token mining into LLMs to classify flaky tests, raising F1-score to 69.34% on FlakeBench while showing greater robustness to semantic-preserving perturbations than prior methods.
Visualization retrieval systems can transform static collections of visualizations into dynamic, inquiry-based environments that support design exploration, data consumption, and resource management for data literacy education.
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Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes
Hamm-grams are a new class of fixed-length regular expressions over bytes with single-character wildcards, mined efficiently with LSH and clustering to yield more robust features than n-grams for malware classification and detection.
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NeuroFlake: A Neuro-Symbolic LLM Framework for Flaky Test Classification
NeuroFlake integrates discriminative token mining into LLMs to classify flaky tests, raising F1-score to 69.34% on FlakeBench while showing greater robustness to semantic-preserving perturbations than prior methods.