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8th IFIP International Conference on Intelligent information processing, IIP 2014

作者： Xia, Fei Cao, Cungen Key Laboratory of Intelligent Information Processing Institute of Computer Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783662449790

Automatic discovery of part-whole relations is a fundamental problem in the area of information extraction. In this paper, we present an unsupervised approach to learning lexical patterns from online encyclopedia for extracting part-whole relations. The only input is a few part-whole instances. To tackle the term recognition problem, terms from the domain of the seeds are extracted taking use of the semantic information contained in the online encyclopedia. Instead of collecting sentences that contain relation instances from the seeds, we introduce a novel process to select sentences that may indicate part-whole relations. Patterns are produced from these sentences with terms replaced by Part and Whole tags. A similarity measurement based on a new edit distance is used and an algorithm is described to cluster similar patterns. We rank the pattern clusters according to their frequencies, and patterns from the top-k clusters are chosen to be applied to identify the new part-whole relations. Experimental results show that our method can extract abundant part-whole relations and achieve a preferable precision compared to the other state-of-the-art approaches. © IFIP International Federation for information processing 2014.

关键词： Semantics

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Latency-aware routing with bandwidth assignment for Software Defined Networks

Latency-aware routing with bandwidth assignment for Software...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Qiongyu Zhang Liehuang Zhu Meng Shen Mingzhong Wang Fan Li Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education P. R. China Faculty of Arts and Business University of the Sunshine Coast Queensland Australia

Reducing the flow latency is of great importance in traffic management, which benefits both service providers and end users. Routing design and flow scheduling are typical ways to improve the flow transmission efficiency. However, existing studies usually consider them separately, due to the complexity of joint consideration. Here, we propose a latency-aware routing scheme with bandwidth assignment in the Software Defined Networks, which can efficiently reduce the flow latency with a moderate complexity, to combine the routing and scheduling together.

关键词： Routing Bandwidth Processor scheduling Complexity theory Bismuth Software Mathematical model

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Self-adaptive anonymous communication scheme under SDN architecture

Self-adaptive anonymous communication scheme under SDN archi...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Tingting Zeng Meng Shen Mingzhong Wang Liehuang Zhu Fan Li Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology P. R. China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education P. R. China Faculty of Arts and Business University of the Sunshine Coast Queensland Australia

Communication privacy and latency perceived by users have become great concerns for delay-sensitive Internet services. Existing anonymous communication systems either provide high anonymity at an expense of prolonged latency (e.g., mix-net), or offer better real-time performance by sacrificing the ability against traffic analysis attacks (e.g., Onion Routing). The emerging Software-Defined Networking (SDN) introduces additional challenges to communication anonymity, due to the existence of a centralized controller that has a global view of the entire network traffic. In this paper, we propose a new anonymous communication scheme for delay-sensitive services under SDN scenarios, which can simultaneously protect communication privacy and reduce the end-to-end latency. A self-adaptive method based on the mix-net framework is designed to dynamically modify the waiting threshold of mix nodes, which helps to reduce the communication latency. In order to preserve the degree of anonymity, the self-adaptive method is incorporated with a random walking strategy for packets forwarding. Both theoretical analysis and experimental results prove that our scheme provides a moderate degree of anonymity and effectively reduces the latency derived from mix-net by up to 50%.

关键词： Real-time systems Relays Entropy Lead Distance measurement Annealing Probabilistic logic

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Application and research of improved probability matrix factorization techniques in collaborative filtering

International Journal of Control and Automation

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International Journal of Control and Automation 2014年第8期7卷 79-92页

作者： Zhang, Zhijun Liu, Hong School of Information Science and Engineering Shandong Normal University Jinan Shandong 250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Jinan Shandong 250014 China School of Computer Science and Technology Shandong Jianzhu University Jinan Shandong 250101 China

The matrix factorization algorithms such as the matrix factorization technique (MF), singular value decomposition (SVD) and the probability matrix factorization (PMF) and so on, are summarized and compared. Based on the above research work, a kind of improved probability matrix factorization algorithm called MPMF is proposed in this paper. MPMF determines the optimal value of dimension D of both the user feature vector and the item feature vector through experiments. The complexity of the algorithm scales linearly with the number of observations, which can be applied to massive data and has very good scalability. Experimental results show that MPMF can not only achieve higher recommendation accuracy, but also improve the efficiency of the algorithm in sparse and unbalanced data sets compared with other related algorithms. © 2014 SERSC.

关键词： Singular value decomposition

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On YASS’s Non-monotonic Security Performance

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Circuits, Systems, and Signal processing 2014年第11期33卷 3527-3537页

作者： Yu, Lifang Zhao, Yao Ni, Rongrong Shi, Yun Qing Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Signal and Information Processing for High-end Printing Equipments Beijing Institute of Graphic Communication Beijing102600 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

Recently, researchers have discovered unexpected bumps in the detection rate curve of yet another steganographic scheme (YASS). We refer to this abnormal phenomenon as non-monotonic security performance. This paper first analyzes this abnormality and points out that it is caused by the non-uniformity in probability of coincidence of (Formula presented.) embedding blocks and (Formula presented.) JPEG blocks. Based on this observation, we propose that further randomizing data embedding positions can overcome the non-monotonic security performance. Experimental results have demonstrated a complete removal of bumps in the detection rate curve of YASS after further randomization. © 2014, Springer Science+Business Media New York.

关键词： Steganography

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Sub-pixel change detection for urban land-cover analysis via multi-temporal remote sensing images

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Geo-Spatial information Science 2014年第1期17卷 26-38页

作者： Peijun DU Sicong LIU Pei LIU Kun TAN Liang CHENG Key Laboratory for Satellite Mapping Technology and Application of National Administration of Surveying Mapping and GeoinformationNanjing UniversityNanjing CityJiangsu ProvinceP.R.China Department of Information Engineering and Computer Science University of TrentoTrentoItaly Jiangsu Provincial Key Laboratory for Resources and Environment Information Engineering China University of Mining and TechnologyXuzhouJiangsu ProvinceP.R.China

Conventional change detection approaches are mainly based on per-pixel processing,which ignore the sub-pixel spectral variation resulted from spectral *** for medium-resolution remote sensing images used in urban landcover change monitoring,land use/cover components within a single pixel are usually complicated and heterogeneous due to the limitation of the spatial ***,traditional hard detection methods based on pure pixel assumption may lead to a high level of omission and commission errors inevitably,degrading the overall accuracy of change *** order to address this issue and find a possible way to exploit the spectral variation in a sub-pixel level,a novel change detection scheme is designed based on the spectral mixture analysis and decision-level *** spectral mixture model is selected for spectral unmixing,and change detection is implemented in a sub-pixel level by investigating the inner-pixel subtle changes and combining multiple composition *** proposed method is tested on multi-temporal Landsat Thematic Mapper and China–Brazil Earth Resources Satellite remote sensing images for the land-cover change detection over urban *** effectiveness of the proposed approach is confirmed in terms of several accuracy indices in contrast with two pixel-based change detection methods(*** vector analysis and principal component analysis-based method).In particular,the proposed sub-pixel change detection approach not only provides the binary change information,but also obtains the characterization about change direction and intensity,which greatly extends the semantic meaning of the detected change targets.

关键词： change detection sub-pixel level processing multi-temporal images spectral mixture model back propagation neural network remote sensing

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Construction of extraction model for ontology concept of four diagnostics information

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Journal of Computational information Systems 2015年第7期11卷 2367-2376页

作者： Yuan, Feng Chen, Shouqiang Liu, Hong Xu, Liang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University Jinan China School of Information Engineering Shandong Management University Jinan China Center of Hear of the Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan China Shandong University of Traditional Chinese Medicine Jinan China

This paper is aimed at extraction of ontology concept from four diagnostics information. Due to the diversity and complexity of the four diagnostics information, there are still some difficulties when practicing ontology learning methods for TCM medical cases, so a new concept extraction method combining composite seed extraction and extended concept extraction is proposed for four diagnostics information corpus of TCM. Composite seed extraction refers to a method in which the mutual information is used to determine the appropriateness of word formation between seed and collocations by using seeds as headword to generate concepts. Extended concept extraction refers to a method in which the membership in the domain is used set the fitness function so as to extract extended concept with the assistance of artificial bee colony (ABC) algorithm. The candidate set of concepts is built by combining composite seed concept and extended concept. Through a confirmatory experiment on four diagnostics information corpus of TCM medical cases, this method is cautiously confirmed effective for concept extraction. Hence, this method has a certain reference value for ontology construction of four diagnostics information. ©, 2015, Binary information Press. All right reserved.

关键词： Extraction

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Hidden space discriminant neighborhood embedding

Hidden space discriminant neighborhood embedding

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International Joint Conference on Neural Networks (IJCNN)

作者： Chuntao Ding Li Zhang Bangjun Wang School of Computer Science and Technology & Provincial Key laboratory for Computer Information Processing Soochow University Suzhou Jiangsu China

Discriminant neighborhood embedding (DNE) algorithm is one of supervised linear dimensionality reduction methods. Its nonlinear version kernel discriminant neighborhood embedding (KDNE) is expected to behave well on classification tasks. However, since KDNE constructs an adjacent graph in the original space, the adjacency graph could not represent the adjacent information in the kernel mapping space. By introducing hidden space, this paper proposes a novel nonlinear method for DNE, called hidden space discriminant neighborhood embedding (HDNE). This algorithm first maps the data in the original space into a high dimensional hidden space by a set of nonlinear hidden functions, and then builds an adjacent graph incorporating neighborhood information of the dataset in the hidden space. Finally, DNE is used to find a transformation matrix which would map the data in the hidden space to a low-dimensional subspace. The proposed method is applied to ORL face and MNIST handwritten digit databases. Experimental results show that the proposed method is efficiency for classification tasks.

关键词： Joints Conferences Neural networks

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A programming related courses' E-learning platform based on online judge

A programming related courses' E-learning platform based on ...

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5th International Symposium on IT in Medicine and Education, ITME 2013

作者： Fang, Xiaonan Zhang, Huaxiang Sun, Yunchen School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China

ISBN: (纸本)9789400776173

Coding practice is the most efficient way in learning of programming related courses. In this paper, we propose a programming related courses' E-learning platform based on online judge. This platform is designed according to B/S structure, and page optimization techniques such as Gzip are applied to boost the access speed. Teaching achievements prove that the platform can markedly improve the learning effect of programming related courses. © Springer Science+Business Media Dordrecht 2014.

关键词： E-learning

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Research on distributed data stream mining in Internet of things

Research on distributed data stream mining in Internet of th...

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2014 International Conference on Logistics Engineering, Management and computer Science, LEMCS 2014

作者： Xu, Liancheng Xun, Jiao School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9789462520103

For mining useful data from mass data generated by Internet of things, analyses shortages of the traditional Apriori algorithm which has a lower mining efficiency and occupies the larger memory space. So, MapReduce model of cloud computing is introduced. In the mechanism of MapReduce, combine the architecture characteristics and key technology of Internet of Things, conduct distributedmining on data and information in environment of the Internet of Things, and the calculation model of distributed data stream mining is drawn. Performance analysis proves that the new data stream mining model overcomes difficulty of traditional data mining, and each link reflects the idea of distributed structure and improves mining efficiency obviously. © 2014. The authors - Published by Atlantis Press.

关键词： Cloud computing

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