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Titlebook: Database Systems for Advanced Applications. DASFAA 2020 International Workshops; BDMS, SeCoP, BDQM, G Yunmook Nah,Chulyun Kim,Steven Euijon

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樓主: DUBIT
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發(fā)表于 2025-3-23 13:36:36 | 只看該作者
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發(fā)表于 2025-3-23 16:54:17 | 只看該作者
https://doi.org/10.1007/978-1-4020-5920-9r use handcrafted features or complex neural network architectures. In this paper, we propose a simple Two-level CNN model to extract product aspects from customer reviews, namely Char- and Word-level CNN. The Char-level CNN learns char-level representation of each word (also named morphological inf
13#
發(fā)表于 2025-3-23 20:19:47 | 只看該作者
https://doi.org/10.1007/978-1-4684-2268-9t collections have various characteristics. Some frequently answered questions may have distinct statistical patterns, while some questions are syntactically different but semantically similar. To measure question similarity more adaptively to different kinds of question text, this paper proposes a
14#
發(fā)表于 2025-3-24 01:58:58 | 只看該作者
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發(fā)表于 2025-3-24 03:13:58 | 只看該作者
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發(fā)表于 2025-3-24 08:10:15 | 只看該作者
Business Ethics: Oxymoron or Good Business?age systems. The proposed approach consists of a file compaction tool and an access interface. The compaction tool merges a group (usually a directory) of small files into a set of “big files” to reduce the metadata required to be maintained in the on-chip memory. The data locality and tree structur
17#
發(fā)表于 2025-3-24 11:58:33 | 只看該作者
Business Ethics: Oxymoron or Good Business?ch other, and then meet the intelligent demand of multi-objective combustion optimization for coal-fired power stations, an intelligent historical database (IHDB), which is a combination of artificial intelligence and historical database, is proposed. The structures of file, index and module, which
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發(fā)表于 2025-3-24 15:16:01 | 只看該作者
19#
發(fā)表于 2025-3-24 19:19:15 | 只看該作者
20#
發(fā)表于 2025-3-25 00:47:28 | 只看該作者
https://doi.org/10.1007/978-1-4020-4984-2mpact on people’s production and life. Therefore, it is of great practical significance and social value to realize the prediction of air quality index. This paper takes the Tianjin air quality data and meteorological data from 2017 to 2019 as an example. Firstly, random forest interpolation was use
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