ALOFT
发表于 2025-3-28 14:56:53
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有节制
发表于 2025-3-28 20:56:56
What’s to Come is Still Unsureate winning condition permits to specify desirable behavior. The classical model here is the positional game, where both players can (fully or partially) observe the current position in the game graph, which in turn is indicative of their mutual current states. In practice, neither sensing or actuat
emission
发表于 2025-3-29 01:48:11
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prodrome
发表于 2025-3-29 07:04:36
Robustness Testing of Intermediate Verifierse changes considerably with details of how the input program is expressed—details that should be irrelevant, such as the order of independent declarations. Such a lack of robustness frustrates users who have to spend considerable time figuring out a tool’s idiosyncrasies before they can use it effec
淡紫色花
发表于 2025-3-29 09:01:41
Simulation Algorithms for Symbolic Automatalabeled by predicates over alphabet symbols. In one approach, we build on the algorithm by Ilie, Navaro, and Yu proposed originally for classical finite automata, modifying it using the so-called mintermisation of the transition predicates. This solution, however, generates all Boolean combinations
档案
发表于 2025-3-29 13:29:23
Quantitative Projection Coverage for Testing ML-enabled Autonomous Systemsngineered using data-driven approach. We propose quantitative .-projection coverage as a metric to mediate combinatorial explosion while guiding the data sampling process. By assuming that domain experts propose largely independent environment conditions and by associating elements in each condition
Limerick
发表于 2025-3-29 18:11:25
Recursive Online Enumeration of All Minimal Unsatisfiable Subsetsdesirable to identify the core problems among them. Such cores are called minimal unsatisfiable subsets (MUSes). The more MUSes are identified, the more information about the conflicts among the constraints is obtained. However, a full enumeration of all MUSes is in general intractable due to the la
Vertebra
发表于 2025-3-29 21:33:46
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心胸开阔
发表于 2025-3-30 02:29:48
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组装
发表于 2025-3-30 04:02:51
Bisimilarity Distances for Approximate Differential Privacyn probability distributions. We study .-differential privacy in the setting of labelled Markov chains. While the exact differences relevant to .-differential privacy are not computable in this framework, we propose a computable bisimilarity distance that yields a sound technique for measuring ., the