Q-PIPE is a quantum phase encoding for images that achieves O(qN) gate complexity, supports native finite-difference operations, and shows low error in edge-detection tests on benchmark data.
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Eye-tracking shows visual attention to contextual cues during password creation correlates with higher entropy, even as users favor self-generated passwords over stronger AI-generated alternatives.
ALPINE deploys an offline-trained TD3 policy on terminal devices to map multi-dimensional risk states to adaptive privacy budgets for local differential privacy in mobile edge crowdsensing, with edge feedback closing the loop.
A systematic literature review categorizing 32 papers on threat and attack modelling for CPS and noting that current models fail to address dynamic, multi-layer, multi-path, and multi-agent attack characteristics.
citing papers explorer
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Q-PIPE A Practical Quantum Phase Encoding Method
Q-PIPE is a quantum phase encoding for images that achieves O(qN) gate complexity, supports native finite-difference operations, and shows low error in edge-detection tests on benchmark data.
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Understanding Password Preferences, Memorability, and Security through a Human-Centered Lens
Eye-tracking shows visual attention to contextual cues during password creation correlates with higher entropy, even as users favor self-generated passwords over stronger AI-generated alternatives.
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ALPINE: Closed-Loop Adaptive Privacy Budget Allocation for Mobile Edge Crowdsensing
ALPINE deploys an offline-trained TD3 policy on terminal devices to map multi-dimensional risk states to adaptive privacy budgets for local differential privacy in mobile edge crowdsensing, with edge feedback closing the loop.
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Security Modelling for Cyber-Physical Systems: A Systematic Literature Review
A systematic literature review categorizing 32 papers on threat and attack modelling for CPS and noting that current models fail to address dynamic, multi-layer, multi-path, and multi-agent attack characteristics.