Afflict
发表于 2025-3-25 03:53:58
Declarative Aspects in Explicative Data Mining for Computational Sensemakings as well as models and patterns captured therein. Here, . helps to include guiding knowledge structures into the process, while . provides interpretability, transparency, and explainability. This paper provides an overview of the key points and important developments in these areas, and outlines future potential and challenges.
SUE
发表于 2025-3-25 10:32:00
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先兆
发表于 2025-3-25 14:00:53
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发酵剂
发表于 2025-3-25 18:14:05
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Range-Of-Motion
发表于 2025-3-25 20:26:05
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占线
发表于 2025-3-26 01:34:40
https://doi.org/10.1007/978-3-658-34633-1Prolog’s backtracking and unification capabilities. To ensure the compliance to the . standard and to support the test–driven development, we have created a test framework based on the Test Anything Protocol and .–Prolog’s quasi–quotations.
补助
发表于 2025-3-26 07:34:03
Aktuelle Probleme der Demokratie of declarative languages. In this work, we discuss the application of concolic testing to the functional logic language Curry. More precisely, we present ., a concolic interpreter which is adapted for the automated generation of test cases for both purely functional and non-deterministic programs.
他姓手中拿着
发表于 2025-3-26 09:21:52
Concolic Testing of Functional Logic Programs of declarative languages. In this work, we discuss the application of concolic testing to the functional logic language Curry. More precisely, we present ., a concolic interpreter which is adapted for the automated generation of test cases for both purely functional and non-deterministic programs.
bypass
发表于 2025-3-26 15:07:09
Conference proceedings 2018ent, INAP 2017, the 31st Workshop on Logic Programming, WLP 2017, and the 25th Workshop on Functional and (Constraint) Logic Programming, WFLP 2017. The 12 full papers presented in this volume were carefully reviewed and selected from 26 submissions. The contributions were organized in topical secti
Default
发表于 2025-3-26 20:45:11
https://doi.org/10.1007/978-3-663-09578-1they have naturally drawn the attention of researchers in parallel constraint solving..In this paper, we address challenges faced when using multiple devices for constraint solving, especially GPUs, such as deciding on the appropriate level of parallelism to employ, load balancing and inter-device communication, and present our current solutions.