Dear editor,Self-adaptation is a promising approach to allocate resources for cloud-based software services [1, 2].Traditional self-adaptive resource-allocation methods are rule-driven, which leads to high administrat...
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Dear editor,Self-adaptation is a promising approach to allocate resources for cloud-based software services [1, 2].Traditional self-adaptive resource-allocation methods are rule-driven, which leads to high administrative cost and implementation complexity. Machine learning techniques and control theory are
NKI contains a multi-domain oriented and large scale knowledge base. Text corpus is an important knowledge source of it. This paper presents an ontology-driven and integrated multi-agent architecture (MAKAT) for achie...
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Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al- th...
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Analytical study of large-scale nonlinear neural circuits is a difficult task. Here we analyze the function of neural systems by probing the fuzzy logical framework of the neural cells' dynamical equations. Al- though there is a close relation between the theories of fuzzy logical systems and neural systems and many papers investigate this subject, most investigations focus on finding new functions of neural systems by hybridizing fuzzy logical and neural system. In this paper, the fuzzy logical framework of neural cells is used to understand the nonlinear dynamic attributes of a common neural system by abstracting the fuzzy logical framework of a neural cell. Our analysis enables the educated design of network models for classes of computation. As an example, a recurrent network model of the primary visual cortex has been built and tested using this approach.
In this paper, we propose a novel dependency-based bracketing transduction grammar for statistical machine translation, which converts a source sentence into a target dependency tree. Different from conventional brack...
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In this paper, we propose a novel dependency-based bracketing transduction grammar for statistical machine translation, which converts a source sentence into a target dependency tree. Different from conventional bracketing transduction grammar models, we encode target dependency information into our lexical rules directly, and then we employ two different maximum entropy models to determine the reordering and combination of partial dependency structures, when we merge two neighboring blocks. By incorporating dependency language model further, large-scale experiments on Chinese-English task show that our system achieves significant improvements over the baseline system on various test sets even with fewer phrases.
We propose a relaxed correspondence assumption for cross-lingual projection of constituent syntax, which allows a supposed constituent of the target sentence to correspond to an unrestricted treelet in the source pars...
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Tree-based translation models, which exploit the linguistic syntax of source language, usually separate decoding into two steps: parsing and translation. Although this separation makes tree-based decoding simple and e...
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Tree-based translation models, which exploit the linguistic syntax of source language, usually separate decoding into two steps: parsing and translation. Although this separation makes tree-based decoding simple and efficient, its translation performance is usually limited by the number of parse trees offered by parser. Alternatively, we propose to parse and translate jointly by casting tree-based translation as parsing. Given a source-language sentence, our joint decoder produces a parse tree on the source side and a translation on the target side simultaneously. By combining translation and parsing models in a discriminative framework, our approach significantly outperforms a forest based tree-to-string system by 1.1 absolute BLEU points on the NIST 2005 Chinese-English test set. As a parser, our joint decoder achieves an F1 score of 80.6% on the Penn Chinese Treebank.
Chiaroscuro in art is characterized by strong contrasts between light and dark. An object in a certain light condition has a certain chiaroscuro pattern in appearance;and this pattern is invariant to the changes of il...
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Although discriminative training guarantees to improve statistical machine translation by incorporating a large amount of overlapping features, it is hard to scale up to large data due to decoding complexity. We propo...
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One of the most challenging issues in visual information retrieval is retrieval by shape, due to a lack of mathematically rigorous definition of shape similarity. This paper presents a bipolar model for computing shap...
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An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for const...
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ISBN:
(纸本)158113844X
An important obstacle to the success of the Semantic Web is that the establishment of the semantic relationship is labor-intensive. This paper proposes an automatic semantic relationship discovering approach for constructing the semantic link network. The basic premise of this work is that the semantics of a web page can be reflected by a set of keywords, and the semantic relationship between two web pages can be determined by the semantic relationship between their keyword sets. The approach adopts the data mining algorithms to discover the semantic relationships between keyword sets, and then uses deductive and analogical reasoning to enrich the semantic relationships. The proposed algorithms have been implemented. Experiment shows that the approach is feasible.
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