Applies Halpern-Pearl actual causality to fault trees, providing a complete classification of causality notions via graph and logical structure and linking them to minimal cut sets.
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Large-scale study on 60k firmware shows vulnerable function versions, search space, function sizes and compilation toolchains affect BCSD performance; build-aware queries raise MRR from 0.818 to 0.981 and TPL-aware two-stage search improves it by 18.5%.
CodeXHug is a curated dataset of 7,325 HuggingFace PTMs and 20,545 Python files from GitHub, demonstrated via statistical analysis and clustering to extract code usage patterns.
Interviews in a semiconductor company reveal 16 collaboration and communication challenges in ML engineering teams, with unclear roles and responsibilities as the top issue, and list effective mitigation practices under hardware-driven constraints.
A new toolkit with cards and maps enables AI designers to juxtapose values and harms in early concept stages, shown valuable in designer surveys and interviews.
Designers using generative AI for concept envisioning engage in reciprocal reflection-in-action that surfaces multi-level value tensions and prioritizes harm recognition over positive value articulation.
Insider action research in an AI startup identifies three patterns of how practitioners view regulatory requirements and proposes internal expert collaboration as a way to turn external governance rules into shared, practical ownership.
citing papers explorer
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Actual causality in fault trees
Applies Halpern-Pearl actual causality to fault trees, providing a complete classification of causality notions via graph and logical structure and linking them to minimal cut sets.
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Understanding Binary Code Similarity for Real-World Vulnerability Detection: A Large-Scale Empirical Study
Large-scale study on 60k firmware shows vulnerable function versions, search space, function sizes and compilation toolchains affect BCSD performance; build-aware queries raise MRR from 0.818 to 0.981 and TPL-aware two-stage search improves it by 18.5%.
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Generate with CodeXHug: A Dataset to Enhance Model Cards with Code Usage Patterns
CodeXHug is a curated dataset of 7,325 HuggingFace PTMs and 20,545 Python files from GitHub, demonstrated via statistical analysis and clustering to extract code usage patterns.
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Exploring CoCo Challenges in ML Engineering Teams: Insights From the Semiconductor Industry
Interviews in a semiconductor company reveal 16 collaboration and communication challenges in ML engineering teams, with unclear roles and responsibilities as the top issue, and list effective mitigation practices under hardware-driven constraints.
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How Designers Envision Value-Oriented AI Design Concepts with Generative AI
Designers using generative AI for concept envisioning engage in reciprocal reflection-in-action that surfaces multi-level value tensions and prioritizes harm recognition over positive value articulation.
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Engaged AI Governance: Addressing the Last Mile Challenge Through Internal Expert Collaboration
Insider action research in an AI startup identifies three patterns of how practitioners view regulatory requirements and proposes internal expert collaboration as a way to turn external governance rules into shared, practical ownership.