Titlebook: Logical Foundations of Computer Science; Third International Anil Nerode,Yu. V. Matiyasevich Conference proceedings 1994 Springer-Verlag B

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書目名稱Logical Foundations of Computer Science影響因子(影響力)




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書目名稱Logical Foundations of Computer Science讀者反饋學(xué)科排名

用肘

轎車

Henrik Reif Andersentely infer tags for new software objects by learning the relationships between historical software objects and their corresponding tags. Given a new software object, TagDeepRec is able to compute the confidence probability of each tag and then recommend top-k tags by ranking the probabilities. We us

寄生蟲

Dmitry A. Archangelsky,Mikhail I. Dekhtyar,Evgeny Kruglov,Ilmir Kh. Musikaev,Mikhail A. Taitslinproaches, we propose a shallow semantic parsing framework to extract event arguments with the event trigger as the predicate and the event arguments as the predicate arguments. In specific, we design and compare different features for the proposed model. The experimental results show that our approa

脫水

Sergei Art?mov,Vladimir Krupskitely infer tags for new software objects by learning the relationships between historical software objects and their corresponding tags. Given a new software object, TagDeepRec is able to compute the confidence probability of each tag and then recommend top-k tags by ranking the probabilities. We us

Ptosis

Andrea Asperti,Giovanna Dored agency” is being developed principally by John Debenham [UTS, Australia] and Carles Sierra [IIIA, Spain]. Published work to date has considered argumentation frameworks between a set of (often just two) agents, and has not taken into account the relationships that the various agents have with othe

tackle

S. A. Berezin,N. V. Shiloves. The key insight of . is to extract the variance features of the fine-grained time-series CSI, which is sensitively affected by customer activity, to recognize what is the customer doing. First, we conduct . (PCA) to smooth the preprocessed CSI values since general denoising method is insufficien

pulse-pressure

Carolyn Brown,Alan Jeffreyes. The key insight of . is to extract the variance features of the fine-grained time-series CSI, which is sensitively affected by customer activity, to recognize what is the customer doing. First, we conduct . (PCA) to smooth the preprocessed CSI values since general denoising method is insufficien

Misnomer

monogamy