PROVFUSION fuses three complementary views of provenance data with lightweight schemes and voting to achieve higher detection accuracy and lower false positives than node- or edge-only baselines on nine benchmarks.
Billion-scale similarity search with GPUs
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
DL-VINS-Factory integrates learned extractors (ALIKED, RaCo, SuperPoint, XFeat) with LK or LightGlue tracking in a shared Ceres VI-SLAM back-end and reports modest ATE reductions plus real-time FPS on EuRoC, NTU-VIRAL, Botanic Garden and SubT-MRS.
An LLM-supported framework maps natural-language commands to distinguishable Apollo lane-change parameters for three driving styles via clustering and RAG, with experiments showing improved interpretation of implicit preferences.
citing papers explorer
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Beyond Nodes vs. Edges: A Multi-View Fusion Framework for Provenance-Based Intrusion Detection
PROVFUSION fuses three complementary views of provenance data with lightweight schemes and voting to achieve higher detection accuracy and lower false positives than node- or edge-only baselines on nine benchmarks.
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DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM
DL-VINS-Factory integrates learned extractors (ALIKED, RaCo, SuperPoint, XFeat) with LK or LightGlue tracking in a shared Ceres VI-SLAM back-end and reports modest ATE reductions plus real-time FPS on EuRoC, NTU-VIRAL, Botanic Garden and SubT-MRS.
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A Large-Language-Model Supported Personalized Driving Framework for Lane Change in Highway Scenarios
An LLM-supported framework maps natural-language commands to distinguishable Apollo lane-change parameters for three driving styles via clustering and RAG, with experiments showing improved interpretation of implicit preferences.