With the rapid development of Internet technology, various network attack methods come out one after the other. SQL injection has become one of the most severe threats to Web applications and seriously threatens vario...
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The connotation of the cloud resources have been extended to be multi-scale resources, which includes central resources as presented by data center, edge resources as presented by Content Delivery Network (CDN) and en...
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The connotation of the cloud resources have been extended to be multi-scale resources, which includes central resources as presented by data center, edge resources as presented by Content Delivery Network (CDN) and end resources as presented by Peer-to-Peer (P2P). Under the development situation of the scale of the cloud services, it is difficult to provide services (e.g. streaming distribution) with guaranteed QoS only relying on single type of resource (e.g. central resources) to geo-distributed users. Therefore, making multi-resources cooperative to provide reliable services is necessary. However, it is a great challenge to realize Federated Management of Multi-scale Resources (FMMR). In this research, we propose the idea of prediction-based FMMR, and present the problem formulation introducing economic profit from the perspective of CDN operators. Then, we present the method of Time-series Prediction based on Wavelet Analysis (TPWA) to predict the resource requirements of streaming cloud services in CDN. Finally, the predictability of the resource requirement pattern of the streaming cloud service and the effectiveness of our proposed method have been verified, based on the traces collected from a real CDN entity.
Mining of repeated patterns from HTML documents is the key step towards Web-based data mining and knowledge extraction. Many web crawling applications need efficient repeated patterns mining techniques to generate the...
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Static data-race detection is a powerful tool by providing clues for dynamic approaches to only instrument certain memory accesses. However, static data-race analysis suffers from high false positive rate. A key reaso...
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There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "wo...
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ISBN:
(纸本)9781577355120
There are two key issues for information diffusion in blogosphere: (1) blog posts are usually short, noisy and contain multiple themes, (2) information diffusion through blogosphere is primarily driven by the "word-of- mouth" effect, thus making topics evolve very fast. This paper presents a novel topic tracking approach to deal with these issues by modeling a topic as a semantic graph, in which the semantic relatedness between terms are learned from Wikipedia. For a given topic/post, the name entities, Wikipedia concepts, and the semantic relatedness are extracted to generate the graph model. Noises are filtered out through the graph clustering algorithm. To handle topic evolution, the topic model is enriched by using Wikipedia as background knowledge. Furthermore, graph edit distance is used to measure the similarity between a topic and its posts. The proposed method is tested by using the real-world blog data. Experimental results show the advantage of the proposed method on tracking the topic in short, noisy texts.
Meteorology Grid Computing aims to provide scientist with seamless, reliable, secure and inexpensive access to meteorological resources. In this paper, we presented a semantic-based meteorology grid service registry, ...
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OONS is a new Object Oriented Neural Simulator. The goal creating it is making the construction of neural model as quickly and easily as possible for the users, and can run in shorter time than other simulators. OONS ...
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Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy...
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Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent (LI) with lightweight speaker-dependent (SD) automatic speech recognition (ASR) is a convenient option to solve tile problem. The dynamic time warping (DTW) algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping (MWDTW) algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model (HMM) significantly, and is also six times faster than DTW overall.
MapReduce is commonly used as a parallel massive data processing model. When deploying it as a service over the open systems, the computational integrity of the participants is becoming an important issue due to the u...
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High baud rate optical transceiver based on time division multiplexing technology is proposed. A communication channel at 80GBoud with 4 bit streams at 20Gbps is realized by 4-stage cascaded high speed switches with s...
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