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A Clustering Method of Chinese Medicine Prescriptions Based on Modified Firefly Algorithm

Chinese Journal of Integrative Medicine 2016年第12期22卷 941-946页

作者：袁锋刘弘陈守强徐亮 School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Shandong Normal University School of Information Engineering Shandong Management University Heart Center Second Hospital Affiliated to Shandong University of Traditional Chinese Medicine School of Traditional Chinese Medicine Shandong University of Traditional Chinese Medicine

This paper is aimed to study the clustering method for Chinese medicine（CM） medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.

关键词： clustering firefly algorithm simulated annealing algorithm Chinese medicine prescriptions

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A characteristic-preserving steganographic method based on revision identifiers

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous Engineering 2016年第9期11卷 29-38页

作者： Xiang, Lingyun Sun, Caixia Liao, Niandong Wang, Weizheng Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology ChangshaHunan Province410114 China School of Computer and Communication Engineering Changsha University of Science and Technology ChangshaHunan Province410114 China

Since the majority of the available steganographic schemes in OOXML format documents suffered the disadvantages of unsatisfactory anti-detection capability and security level, a characteristic-preserving steganographic method with high security is proposed in this paper. The proposed method embeds secret information by replacing the last three bytes of the values of the revision identifiers in the main document body of the OOXML format document, while preserving the normal characteristics of the document. Meanwhile, position marks are added to track the locations of the embedded information. In order to keep the internal data consistency of the document, the newly created values are added into other related parts. Experimental results show that the method not only possesses good imperceptibility and anti-detection capability, but also has high security and large embedding capacity. © 2016 SERSC.

关键词： Anti-detection Characteristic-preserving OOXML Revision identifier Steganography

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Runtime model based approach to IoT application development

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Frontiers of computer Science 2015年第4期9卷 540-553页

作者： Xing CHEN College of Mathematics and Computer Science Fuzhou University Fuzhou 350116 China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou 350116 China Key Laboratory of High Confidence Software Technologies (Ministry of Education) Beijing 100871 China School of Electronics Engineering and Computer Science Peking University Beijing 100871 China

The internet of things （loT） attracts great interest in many application domains concerned with monitoring and ：ontrol of physical phenomena. However, application devel- opment is still one of the main hurdles to a wide adoption of IoT technology. Application development is done at a low level, very close to the operating system and requires pro- grammers to focus on low-level system issues. The under- lying APIs can be very complicated and the amount of data collected can be huge. This can be very hard to deal with as a developer. In this paper, we present a runtime model based approach to IoT application development. First, the manage- ability of sensor devices is abstracted as runtime models that are automatically connected with the corresponding systems. Second, a customized model is constructed according to a personalized application scenario and the synchronization be- tween the customized model and sensor device runtime mod- els is ensured through model transformation. Thus, all the application logic can be carried out by executing programs on the customized model. An experiment on a real-world ap- plication scenario demonstrates the feasibility, effectiveness, and benefits of the new approach to IoT application develop- ment.

关键词： IoT application development software architec-ture models at runtime

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A New Logistic Map Based Chaotic Biogeography-based Optimization Approach for Cluster Analysis

A New Logistic Map Based Chaotic Biogeography-based Optimiza...

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2016 IEEE International Conference on Progress in Informatics and Computing

作者： Shui-Hua Wang Hui-Min Lu Yu-Jie Li Yang Wang Zhi-Min Chen Yu-Min Wei Wen-Juan Jia Fang-Yuan Liu Yu-Dong Zhang School of Computer Science and Technology Nanjing Normal University Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Department of Electrical Engineering The city college of New YorkCUNY Department of Mechanical and Control Engineering Kyushu Institute of Technology School of Information Engineering Yangzhou University School of Physics and Electronic Engineering Taizhou University School of Electronic Information Shanghai Dianji University School of Energy and Mechanical Engineering Nanjing Normal University Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University

Cluster analysis is important in scientific and industrial fields. In this study, we proposed a novel chaotic biogeography-based optimization(CBBO) method, and applied it in centroid-based clustering methods. The results over three types of simulation data showed that this proposed CBBO method gave better performance than chaotic particle swarm optimization, genetic algorithm, firefly algorithm, and quantum-behaved particle swarm optimization. In all, our CBBO method is effective in centroid-based clustering.

关键词： chaotic biogeography-based optimization cluster analysis centroid-based clustering simulation particle swarm optimization

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Security improvement on a three party password based authenticated key exchange scheme using chaotic maps

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Journal of information Hiding and Multimedia Signal processing 2016年第6期7卷 1365-1372页

作者： Chen, Yu Chen, Chien-Ming Pan, Jeng-Shyang Wu, Tsu-Yang Liu, Shuai Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China Innovative Information Industry Research Center School of Computer Science and Technology Shenzhen Graduate School Harbin Institute of Technology Shenzhen518055 China

Recently, password based authenticated key exchange (or called PAKE for short) with chaotic maps has been received much attention for researchers. In 2013, Xie et al. proposed a three party PAKE scheme (based on chaotic maps). However, their scheme had been proved insecure by Lee et al. in 2015. In this paper, we first show that Xie et al.’s scheme is also vulnerable to a replay attack. To overcome this attack, we further propose an improvement based on their scheme. Finally, we demonstrate the security of our improvement with the BAN logic. © 2016.

关键词： Authentication

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A Parallel Algorithm to Construct BISTs on Parity Cubes

A Parallel Algorithm to Construct BISTs on Parity Cubes

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International Conference on information Science and Control Engineering (ICISCE)

作者： Yan Wang Jianxi Fan Wenjun Liu Yuejuan Han Provincial Key Laboratory for Computer Information Processing Technology Soochow University School of Computer Science and Technology Soochow University Suzhou China

ISBN: (纸本)9781467368513

Independent spanning trees(ISTs) have applications in interconnection networks such as reliable communication protocols, and secure message distribution, etc. In this paper, we use BIST to denote the IST isomorphic to binomial-like tree. We propose an algorithm to construct n BISTs and the algorithm can be parallelized on PQ n to run in O(N) time, where N = 2 n .

关键词： Broadcasting Hypercubes computer science Reliability computer network reliability Built-in self-test

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Longest Common Sub-sequence Computation and Retrieve for Encrypted Character Strings

Longest Common Sub-sequence Computation and Retrieve for Enc...

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International Conference on Network-Based information Systems (NBIS)

作者： Minghao Zhao Zhen Li Yilei Wang Qiuliang Xu School of Computer Science and Technology Shandong University Jinan China School of Computer Science and Technology Shandong University of Finance and Economics Jinan China Shandong Provincial Key Laboratory of Software Engineering Jinan China School of Information and Electric Engineering Ludong University Yantai Shandong

ISBN: (纸本)9781509009800

Longest Common Sub-sequence is a basic algorithm problem. It serves as a basic component for a variety of applications in information processing and bioinformatics. It is a NP-hard problem and often manipulated using dynamic programming, which is relatively fast but involves large memory space. Fortunately, cloud computing and outsourced computing provides a practical method for overload alleviation. However, for the security and privacy concern, clients hope to encrypt their data before upload them to the cloud, meanwhile maintain the ability for the cloud to process on the data. In this paper, we propose a method to computing Longest Common Sub-sequence using somewhat homomorphic encryption. Beyond that, we show how to use our achievement into searchable encryption to achieve rich expressiveness.

关键词： Encryption Protocols Pattern matching Cloud computing Indexes

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Predicting helix boundaries of α-helix transmembrane protein with feedback conditional random fields 11th

Predicting helix boundaries of α-helix transmembrane protei...

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11th International Conference on Intelligent Computing, ICIC 2015

作者： Wang, Kun Wu, Hongjie Lu, Weizhong Fu, Baochuan Lü, Qiang Huang, Xu School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China School of Computer Science and Technology Soochow University Suzhou215006 China Jiangsu Provincial Key Lab for Information Processing Technologies Suzhou215006 China School of Information Engineering Huzhou University Huzhou313000 China

ISBN: (纸本)9783319221793

Transmembrane proteins play an important role in cellular energy production, signal transmission, metabolism. Existing machine learning methods are difficult to model the global correlation of the membrane protein sequence, and they also can not improve the quality of the model from sophisticated sequence features. To address these problems, in this paper we proposed a novel method by a feedback conditional random fields (FCRF) to predict helix boundaries of α-helix transmembrane protein. A feedback mechanism was introduced into multi-level conditional random fields. The results of lower level model were used to calculate new feedback features to enhance the ability of basic conditional random fields. One wide-used dataset DB1 was used to validate the performance of the method. The method achieved 95% on helix location accuracy. Compared with the other predictors, FCRF ranks first on the accuracy of helix location. © Springer International Publishing Switzerland 2015.

关键词： Learning systems

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Semantic automatic image annotation based on Non-Abstract Visual keywords

Semantic automatic image annotation based on Non-Abstract Vi...

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2014 International Conference on information technology and Application, ITA2014

作者： Ke, Xiao Chen, Guolong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9781138026773

Automatic image annotation is a significant and challenging problem in pattern recognition and computer vision. Existing models did not describe the visual representations of corresponding keywords, which would lead to appearing plenty of irrelevant annotations in final annotation results. These annotations did not relate to any part of images considering visual contents. We propose a new automatic image annotation model (NAVK) based on relevant visual keywords to overcome above problems. Our model focuses on non-abstract words. First, we establish visual keyword seeds of each non-abstract word, and then a new method is proposed to extract visual keyword collections by using corresponding seeds. Second, we propose adaptive parameter method and fast solution algorithm to determine similarity thresholds of each keyword. Finally, the combinations of above methods are used to improve annotation performance. Experimental results verify the effectiveness of proposed image annotation model. © 2015 Taylor & Francis Group, London.

关键词： Image annotation

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Regression-based execution time prediction in hadoop environment

Regression-based execution time prediction in hadoop environ...

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International Conference on information technology and computer Application Engineering, ITCAE 2014

作者： Wu, Xiao Dong Zeng, Yu Zhu Zhao, C.T. Faculty of Mathematics and Computer Science Quanzhou Normal University China Fujian Provincial Key Laboratory of Data Intensive Computing China Key Laboratory of Intelligent Computing and Information Processing Fujian Province University China

ISBN: (纸本)9781138027176

Hadoop MapReduce has been proven an effective computing model to deal with big data for the last few years. However, one technical challenge facing this framework is how to predict the execution time of an individual job. In this paper, we propose a regression-based method to predict the execution times of Hadoop jobs. The proposed prediction method consists of three phases: learning data sampling, regression analysis and making prediction. Experimental results show that the predicted execution time obtained by our method is close to the actual execution time. © 2015 Taylor & Francis Group, London.

关键词： Regression analysis

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