Grasping
发表于 2025-3-28 15:16:24
Robert J. DeLorenzo,Larry H. Dashefskyrom CCS, modulo bisimilarity. More specifically, we build on Moller’s result to the effect that bisimilarity does not have a finite, ground-complete equational axiomatisation over a minimal fragment of CCS, and we use a reduction technique by Aceto et al. to lift it to various extensions of BCCSP wi
魅力
发表于 2025-3-28 20:43:40
https://doi.org/10.1007/978-1-4757-6740-7nity. In this paper, we present temporal logic characterizations of necessary and sufficient causes in terms of state sets in operational system models. We introduce degrees of necessity and sufficiency as quality measures for sufficient and necessary causes, respectively, along with a versatile wei
larder
发表于 2025-3-29 02:12:13
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Glucocorticoids
发表于 2025-3-29 04:04:08
https://doi.org/10.1007/978-3-030-04774-0ore algebraically symmetric modification of the arithmetical data type of transrational numbers [.], which was inspired by the transreals of Anderson et.al. [.]. We also define a bounded version of the symmetric transrationals thereby modelling some further key semantic properties of floating point
Misgiving
发表于 2025-3-29 11:07:02
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LARK
发表于 2025-3-29 15:17:49
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BOLT
发表于 2025-3-29 18:25:35
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DEMUR
发表于 2025-3-29 22:30:54
Biodiversity and Biotechnology,abilities, software misconfiguration is one of the top 10 security risks for web applications. With this large influx of vulnerability reports, software fingerprinting has become a highly desired capability to discover distinctive and efficient signatures and recognize reportedly vulnerable software
阴谋小团体
发表于 2025-3-30 01:41:33
https://doi.org/10.1007/978-1-4020-6923-9 mostly on digital skills – if it was taught at all – different institutions and countries have adopted teaching and examination plans that introduce computer science as a science..Even before the adoption of computer science as a regular subject in schools, individual researchers and academics crea
Ergots
发表于 2025-3-30 07:42:03
Spiritual and Religious Concepts of Nature,e and update previously learned behavior, implement it in the LearnLib library, and evaluate it on large examples, to which we make small adjustments between two runs of the algorithm. In these experiments our algorithm significantly outperforms both the classic Kearns–Vazirani learning algorithm an