RWoDSN extracts feature points from point clouds via a novel DSN descriptor and random walk graph analysis, reporting 22% higher recall than prior state-of-the-art with 0.784 precision.
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Evidence-based taxonomy of security properties with first-order logic definitions and ProVerif/Tamarin executable examples derived from a 2022-2025 literature review of 53 studies.
Peer review reports in AI conferences have grown longer and more standardized after LLMs, with increased emphasis on surface-level clarity and summaries at the expense of deeper critiques on originality and replicability.
PAFER estimates statistical parity for differentially private decision trees using Laplacian noise, achieving low error while preserving privacy and favoring interpretable trees.
This survey defines the Federated Continual Learning problem, proposes a taxonomy for approaches, reviews applications and metrics, and identifies open challenges in lifelong privacy-preserving learning on non-stationary distributed data.
citing papers explorer
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Random Walk on Point Clouds for Feature Detection
RWoDSN extracts feature points from point clouds via a novel DSN descriptor and random walk graph analysis, reporting 22% higher recall than prior state-of-the-art with 0.784 precision.
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Bridging Theory and Practice: An Executable Taxonomy of Security Properties for ProVerif and Tamarin
Evidence-based taxonomy of security properties with first-order logic definitions and ProVerif/Tamarin executable examples derived from a 2022-2025 literature review of 53 studies.
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Impact of large language models on peer review opinions from a fine-grained perspective: Evidence from top conference proceedings in AI
Peer review reports in AI conferences have grown longer and more standardized after LLMs, with increased emphasis on surface-level clarity and summaries at the expense of deeper critiques on originality and replicability.
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Privacy Constrained Fairness Estimation for Decision Trees
PAFER estimates statistical parity for differentially private decision trees using Laplacian noise, achieving low error while preserving privacy and favoring interpretable trees.
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Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data
This survey defines the Federated Continual Learning problem, proposes a taxonomy for approaches, reviews applications and metrics, and identifies open challenges in lifelong privacy-preserving learning on non-stationary distributed data.