Iatrogenic 发表于 2025-3-30 11:35:01

https://doi.org/10.1007/978-3-319-54951-4e principled use of data with a probabilistic model is expected to lead to a reduced number of experiments in the design and propose the fundamental part of the approach. A simple but a real example using two swarm robots is described as an application.

举止粗野的人 发表于 2025-3-30 13:34:36

https://doi.org/10.1007/978-1-4613-8436-6to retrieving documents and data that captures best of both the worlds. Specifically, it improves recall for legacy documents and provides keyword-based search capability for the Semantic Web. We specify the Hybrid Query Language that embodies this approach, and the prototype system SITAR that implements it. We conclude with areas of future work.

Cupidity 发表于 2025-3-30 20:33:50

https://doi.org/10.1007/978-94-015-2850-4he Web can be examined from various perspectives. There are two purposes of the this chapter. One is to present an overview of the triarchic theory of granular computing, and the other is to examine granular computing perspectives on Web Intelligence (WI).

Rejuvenate 发表于 2025-3-30 23:14:32

https://doi.org/10.1007/978-3-030-42435-0ate sequence data and present solutions for automatically converting between various formats, e.g., between horizontal and vertical presentations but also from state sequences into event sequences and reciprocally. Special attention is also drawn to the handling of missing values in these conversion processes.

gerontocracy 发表于 2025-3-31 03:54:27

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一瞥 发表于 2025-3-31 07:09:49

History, Voyages, Ethnography, Geography,d monitoring which can be used by privacy analyst to observe the flow and capture privacy vulnerabilities that may lead to non-compliance. The model is built on top of (i) properties and timed-related privacy requirements to be monitored that are specified using LTL (Linear Temporal Logic) (ii) a set of identified privacy misuses.

BARB 发表于 2025-3-31 09:12:45

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查看完整版本: Titlebook: Advances in Data Management; Zbigniew W. Ras,Agnieszka Dardzinska Book 2009 Springer-Verlag Berlin Heidelberg 2009 Bayesian network.DBMS.d