Protein
发表于 2025-3-25 03:52:42
Bogdan Ghiț,Asser Tantawi Entlastung bei der Weitergabe von Informationen und Wis sen realisierbar ist. Die Entwicklung eines Benutzermodells für das individuelle Wissensmanagement basiert auf den Ergebnissen einer empirischen Studie mit mehreren hundert Teilnehmern. Dabei wird nicht nur herausgearbeitet, welche Informationen über d978-3-8244-0587-9978-3-322-83437-9
裁决
发表于 2025-3-25 10:59:36
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平常
发表于 2025-3-25 11:44:12
Conference proceedings 2021gust 2021..The 21 full papers and 2 short papers presented together with 2 keynote papers were carefully reviewed and selected from 47 submissions. The papers are organized in the following topics: probabilistic model checking; quantitative models and metamodels: analysis and validation; queueing sy
窃喜
发表于 2025-3-25 18:48:55
A Modest Approach to Dynamic Heuristic Search in Probabilistic Model Checking comparison to state-of-the-art model checkers in a large case study rooted in the well-established .. This study demonstrates that . is especially attractive to use on very large benchmark instances which are not solvable by any other tool.
喃喃诉苦
发表于 2025-3-25 22:43:36
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resilience
发表于 2025-3-26 02:49:36
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Basal-Ganglia
发表于 2025-3-26 07:15:26
0302-9743 nce, in August 2021..The 21 full papers and 2 short papers presented together with 2 keynote papers were carefully reviewed and selected from 47 submissions. The papers are organized in the following topics: probabilistic model checking; quantitative models and metamodels: analysis and validation; q
调整
发表于 2025-3-26 09:05:25
Quantifying Software Reliability via Model-Countingprogram translation, the reduction of C-program into SAT instances via software-bounded model-checker (.), and precise or approximate model-counting providing a reliable assessment. We evaluate our prototype implementation on over 24 examples with different model-counters. We show the feasibility of our pipeline and benefits against competitors.
Oscillate
发表于 2025-3-26 13:41:43
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FLAT
发表于 2025-3-26 18:24:53
Active and Sparse Methods in Smoothed Model Checkingsteer the model-checking to more informative areas of the parameter space and thus improve sample efficiency. We demonstrate that online extensions of sparse variational Gaussian process inference algorithms provide a scalable method for implementing active learning approaches for smoothed model checking.