Energy consumption is an important issue in the design and use of networks. In this paper, we explore energy savings in networks via a rate adaptation model. This model can be represented by a cost-minimization networ...
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There exist noisy, unparallel sentences in parallel web pages. Web page structure is subjected to some limitation for sentences alignment task for web page text. The most straightforward way of aligning sentences is u...
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Image segmentation is still a crucial problem in image processing. It hasn yet been solved very well. In this study, we propose a novel multi-level thresholding image segmentation method based on PSNR using artificial...
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This research aims to evaluate the internal structure of concrete material configuration using an immersed ultrasonic computed tomography imaging technique. We propose a relative difference method of time of flight da...
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Answer Set Programming (ASP) is widely used in many areas of Artificial Intelligence. A parallel answer set solving algorithm based on multi-core processor technology is proposed in this paper. The parallel algorithm ...
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ISBN:
(纸本)9781849195379
Answer Set Programming (ASP) is widely used in many areas of Artificial Intelligence. A parallel answer set solving algorithm based on multi-core processor technology is proposed in this paper. The parallel algorithm is designed on the shared-memory parallel computing model which is the -abs model of multi-core processor. The algorithm can distribute the whole solving task to several threads separately run on different cores of the multi-core processor. To make this parallel algorithm more efficient, we implement load balancing among different threads by the technique of shared global queue. The experimental results show that the parallel algorithm can improve the solving efficiency by times with the growth of processor cores.
Recommending suitable routes to taxi drivers for picking up passengers is helpful to raise their incomes and reduce the gasoline consumption. In this paper, a pick-up tree based route recommender system is proposed to...
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PLSA(Probabilistic Latent Semantic Analysis) is a popular topic modeling technique for exploring document collections. Due to the increasing prevalence of large datasets, there is a need to improve the scalability of ...
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Prioritized preference based decision making is pervasive in real problems solving. We propose a new paradigm of logic programming to handle prioritized preference. The paradigm is interpreted based on answer set sema...
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ISBN:
(纸本)9781849195379
Prioritized preference based decision making is pervasive in real problems solving. We propose a new paradigm of logic programming to handle prioritized preference. The paradigm is interpreted based on answer set semantics. We introduce two semantics to optimize answer sets of the programs. Then, we present the properties of those semantics by investigating their order characterization. Finally, compared with related works, it shows our new paradigm has strong expressive power of preference representation and reasoning.
To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records...
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To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records. There are five types of semantic associations we intend to discover, including Disease-Pathogenesis-Drug set(DPaD), Disease-Symptoms-Drug set (DSyD), Disease-Drug set (DD), Disease-Physician-Drug set (DPhD) and Disease-Drug Category Set (DDC). To solve the synonymity problem and the data sparseness problem, we give a mapping strategy which maps pathogenesis to standardized forms and maps drugs to drug categories. With the mapping strategy the number of frequent drug sets rises from 287 to 1184. The experimental results indicate that our method with the mapping strategy is an effective way to acquire valuable semantic association rules.
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