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The Second IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(ICSDM 2015)

作者： Taiping Lai Xianghan Zheng College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9781479977505

In view of the problems existing in traditional recommendation algorithm of low accuracy and low efficiency, this paper presents a machine learning based social media recommendation algorithm. The algorithm is based on the traditional personalized collaborative filtering algorithm, and combines with the correlation characteristics among users in a social network. Besides, the algorithm also considers the network rating factors and upgrade its efficiency by using clustering algorithm. At last, the algorithm is realized on the Hadoop cloud platform.

关键词： Social Network Recommendation Clustering Collaborative Filtering MapReduce

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A Novel Method for Spammer Detection in Social Networks

A Novel Method for Spammer Detection in Social Networks

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The Second IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services(ICSDM 2015)

作者： Xueying Zhang Xianghan Zheng College of Mathematics and Computer Science Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing

ISBN: (纸本)9781479977505

Online social networks have played an important role in people's common life. Most existing social network platforms,however, face the challenges of dealing with undesirable users and their malicious spam activities that disseminate content,malware, viruses, etc. to the legitimate users of the service. In this paper, an Extreme Learning Machine based supervised machine is proposed for effective spammer detection. The experiment and evaluation show that the proposed solution provides excellent performance with a true positive rate of spammers and non-spammers reaching 99% and 99.95%,respectively. As the results suggest, the proposed solution could achieve better reliability and feasibility compared with existing SVM based approaches.

关键词： Social network Spammer Machine learning Extreme learning machine

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A kind of binary image complete level authentication method based on hamming code and digital watermark

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Journal of information and Computational Science 2015年第1期12卷 181-190页

作者： Li, Min Deng, Shaobo Scholar of Information Engineering Nanchang Institute of Technology Nanchang China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Each pixel of the binary image can be viewed as a binary number. Base on this characteristic, we design a supervisory relationship. A detection image is gotten from the supervisory relationship and binary image. Based on this, a kind of pixel level digital watermark complete authentication method is put forward. This method is able to conduct binary image pixel authentication. When authenticating, only from the detection image, people can accurately identify whether a pixel point is falsified or not, furthermore, it has falsification localization and repair capacity.

关键词： Authentication

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Adaptive sparse recovery based on difference algorithm

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 2015年第6期27卷 1047-1052页

作者： Zhang, Fengzhen Zhao, Ruizhen Cen, Yigang Hu, Shaohai Zhang, Yongdong Institute of Information Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Advanced Information Science and Network Technology of Beijing Beijing100044 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

To improve the disadvantages that iterative reconstruction algorithms of compressed sensing need priori knowledge of the sparsity of original signal or iterative threshold, an adaptive sparse recovery based on difference algorithm is proposed. When the sparsity of original signal is unknown, the proposed algorithm takes advantage of unbalance of correlation coefficient between the measurement matrix and residual. With those properties, the proposed algorithm can select the support set of the original signal, and approach the sparsity of the original signal. Simulation results show that the proposed algorithm obtains better recovery results under the same conditions. Especially in the lower sampling rate, the advantage is more obvious. ©, 2015, Institute of computing Technology. All right reserved.

关键词： Recovery

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The B4-Modalized Propositional Logic

The B4-Modalized Propositional Logic

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2015 International Conference on Mechanical,Electronic and information Technology Engineering

作者： Cao Cungen Sui Yuefei Deng Shaobo Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Science University of Chinese Academy of Sciences

A modalized propositional Belnap-Dunn logic will be proposed in this paper which there are four modalities [t],[T],[⊥],[f]to represent the four values t,T,⊥,f, respectively, and a Gentzentyped deduction system will ... 详细信息

关键词： The Belnap-Dunn logic,modality,the soundness,the completeness

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Cloud removal of remote sensing image based on multi-output support vector regression

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Journal of Systems Engineering and Electronics 2014年第6期25卷 1082-1088页

作者： Gensheng Hu Xiaoqi Sun Dong Liang Yingying Sun Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University School of Electronics and Information Engineering Anhui University

Removal of cloud cover on the satellite remote sensing image can effectively improve the availability of remote sensing images. For thin cloud cover, support vector value contourlet transform is used to achieve multi-scale decomposition of the area of thin cloud cover on remote sensing images. Through enhancing coefficients of high frequency and suppressing coefficients of low frequency, the thin cloud is removed. For thick cloud cover, if the areas of thick cloud cover on multi-source or multi-temporal remote sensing images do not overlap, the multi-output support vector regression learning method is used to remove this kind of thick clouds. If the thick cloud cover areas overlap, by using the multi-output learning of the surrounding areas to predict the surface features of the overlapped thick cloud cover areas, this kind of thick cloud is removed. Experimental results show that the proposed cloud removal method can effectively solve the problems of the cloud overlapping and radiation difference among multi-source images. The cloud removal image is clear and smooth.

关键词： remote sensing image cloud removal support vector regression multi-output

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A MapReduce Optimization Method on Hadoop Cluster

A MapReduce Optimization Method on Hadoop Cluster

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International Conference on Industrial Informatics - computing Technology, intelligent Technology, Industrial information Integration

作者： Xiaodong Wu Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information Processing Fujian Province University

ISBN: (纸本)9781467383134

The MapReduce parallel and distributed computing framework has been widely applied in both academia and industry. MapReduce applications are divided into two steps: Map and Reduce. Then, the input data is divided into splits, which can be concurrently processed, and the amount of the splits determines the number of map tasks. In this paper, we present a regression-based method to compute the number of Map tasks as well as Reduce tasks such that the performance of the MapReduce application can be improved. The regression analysis is used to predict the executing time of MapReduce applications. Experimental results show that the proposed optimization method can effectively reduce the execution time of the applications.

关键词： MapReduce Hadoop Optimization Polynomial Regression

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Noisy image super-resolution reconstruction based on sparse representation

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Jisuanji Yanjiu yu Fazhan/Computer Research and Development 2015年第4期52卷 943-951页

作者： Dou, Nuo Zhao, Ruizhen Cen, Yigang Hu, Shaohai Zhang, Yongdong Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Institute of Information Science Beijing Jiaotong University Beijing100044 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

Denoising and super-resolution reconstruction are performed separately in traditional methods for noisy image super-resolution reconstruction, while in the noisy image super-resolution reconstruction method based on sparse representation and dictionary learning the two processes are compounded together. Since an image patch can be well represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary, two dictionaries are trained respectively from noisy low-and clean high-resolution image patches by enforcing the similarity of two sparse representations with respect to their own dictionary. Given a noisy low-resolution image, sparse representations of low-resolution patches via trained low-dictionary are computed, then the high-resolution image can be reconstructed from high-resolution patches with the help of the related low-resolution sparse representations and trained high-dictionary, after global optimization a clean high-resolution is obtained to accomplish the goal of image super-resolution and denosing simultaneously. The experiments show that zooming low-resolution image to a middle-resolution using locally adaptive zooming algorithm for extracting features can get a better reconstructed image than bicubic interpolation algorithm. By setting the parameter λ, we can obtain the best performance both in super-resolution and denoising with absolute advantages in image quality and visual effect, which demonstrates the validity and robustness of our algorithm. ©, 2015, Science Press. All right reserved.

关键词： Image denoising

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The Recognition of Maize seeds Based on Multi-scale Feature Fusion and Extreme Learning Machine

The Recognition of Maize seeds Based on Multi-scale Feature ...

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2015 3rd International Conference on Mechanical Engineering and intelligent Systems(ICMEIS 2015)

作者： Mingzhi Du Xiao Ke Mingke Zhou College of Mathematics and Computer Science Fuzhou University Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing (Fuzhou University)

In recognizing traditional crops seeds like maize seeds, we usually use electrophoresis assay method, fluorescence scanning method and chemical assay method. These methods are destructive methods. They take a long time to detect and are demanding of professional background knowledge and hardware conditions etc. What's more, these methods, based on BP neural network and support vector machine(SVM)while taking a long time to detect are less accurate in process of classification. In this paper, based on the computer vision technology, we proposed a new method for the classification of maize seeds, a method based on multi-scale feature fusion and extreme learning machine. First, we extract the multi-scale fusion feature of maize seeds. Second, based on extreme learning machine, we construct the classifier model of maize seed. Third, because of the window of image in the case of multi-scale detection has the problem of capturing the same object seed with many overlapping windows, we put forward a kind of window fusion algorithm to solve it. The simulation results show that: The method is able to identify the maize seeds accurately. Using this method the accuracy of classification of maize seeds can reached 97.66% and the error rate is less than 0.1%. Compared with the traditional methods, the method we proposed can improve the speed of detection and the accuracy of classification, and has no strict hardware requirements.

关键词： extreme learning machine feature fusion object recognition

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A Runtime Architecture Based Framework Managing Hybrid Clouds

A Runtime Architecture Based Framework Managing Hybrid Cloud...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Xuee Zeng Xingtu Lan Xing Chen Wenzhong Guo College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing

Cloud management becomes increasingly complex and brings high costs, especially with the advent of hybrid cloud. In a hybrid cloud, numerous resources like Virtual Machines (VMs) and Physical Machines in different clouds have to be managed together to make the whole hybrid cloud work cost-effectively. For controlling the management cost, in particular the manual management cost, many programs have been developed to take over manual management tasks or reduce their complexity and difficulty. These programs are usually hard-coded by languages like Java and C++, which bring enough capability and flexibility but also cause high programming effort and cost. As the architecture-based runtime model is causally connected with the corresponding running system automatically, constructing a hybrid cloud management system based on the architecture-based runtime models of clouds can benefit from the model-specific natures, and thus reduce the development workload. This paper proposes a runtime architecture based approach to developing the management programs in a simple but powerful enough manner. First of all, the manageability (such as APIs, configuration files and scripts) of different kinds of clouds, is abstracted as a runtime architecture based model of cloud software architecture, which can automatically and immediately propagate any observable runtime changes of the target platforms to the corresponding architecture models, and vice versa. Second, we provide a unified model of cloud software architecture, according to the domain knowledge of current cloud platforms, such as Cloud Stack, Open Stack and Eucalyptus. Third, the synchronization between the unified model and cloud runtime models is ensured through model transformation, thus, all the management tasks of the hybrid cloud, could be carried out through executing programs on the unified model, which decreases the complexity of use and management. The experiment on a real-world hybrid cloud demonstrates the feasibil

关键词： Computers Software Conferences

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