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Achieving lightweight and fine-grained access control in mobile cloud computing 3

Achieving lightweight and fine-grained access control in mob...

引用

3rd information Science and Management Engineering Conference, ISME 2015

作者： He, Heng Tian, Chuan Jin, Yu Xia, Wei Wang, Yadan College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9789897581632

Mobile cloud computing has emerged as one of the most promising technologies, which can greatly enlarge the application range of mobile devices by moving data storage and processing to powerful and centralized computing platforms located in clouds. However, how to keep data security and data privacy is an important and challenging issue when users outsource their sensitive data to mobile cloud for sharing. In this paper, we present a novel lightweight and fine-grained data access control scheme based on attribute-based encryption for mobile cloud computing. The analysis shows that the proposed scheme can meet the security requirement of mobile cloud computing, and provide flexible and expressive data access control policy. Furthermore, it has less computation cost compared with other schemes.

关键词： Access control

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Implementation of 3-D RDPAD algorithm on follicle images based CUDA 1

引用

11th International Conference on Intelligent Computing, ICIC 2015

作者： Jiang, Hongwei Liu, Jun Luo, keyang Hu, Wei Liu, Xiaoming College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9783319221861

ISBN: (纸本)9783319221854

The 3-D Robust Detail Preserving Anisotropic Diffusion (3-D RDPAD) is an anisotropic diffusion which has many advantages on 3-D ultrasound image processing, especially in 3-D cattle follicle ultrasound Images. The 3-D RDPAD algorithm has good performance both in noise suppression and edge preserving. However, the computation of 3-D RDPAD algorithm requires a huge amount of time to complete because it directly operates on the 3-D image and it has logical judgment in every iteration. In this paper, we proposed a 3-D Parallel RDPAD algorithm using CUDA to solve this problem. By optimizing the logical judgment in diffusion stop condition, the speed of 3-D RDPAD algorithm has a notable improvement. We performed our algorithm on 3-D cattle follicle ultrasound Images and the result of the experiments showed that it revealed a great speedup over the original 3-D RDPAD. © Springer International Publishing Switzerland 2015.

关键词： Diffusion

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Sparse learning for robust background subtraction of video sequences 11th

Sparse learning for robust background subtraction of video s...

引用

11th International Conference on Intelligent Computing, ICIC 2015

作者： Luo, Yuhan Zhang, Hong College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9783319221793

Sparse representation has been applied to background detecting by finding the best candidate with minimal reconstruction error using target templates. However most sparse representation based methods only consider the holistic representation and do not make full use of the sparse coefficients to discriminate between the foreground and the background. Learning overcomplete dictionaries that facilitate a sparse representation of the data as a liner combination of a few atoms from such dictionary leads to state-of-the-art results in image and video restoration and classification. To take these challenges, this paper proposes a new method for robust background detecting via sparse representation. Our method explores both the strength of the well-patch adaptive dictionary learning technique to video frame structure analysis and the robustness background detection by the l1-norm data-fidelity term. By using linear sparse combinations of dictionary atom, the proposed method learns the sparse representations of video frame regions corresponding to candidate particles. The experiments show that the proposed method is able to tolerate the background clutter and video frame deterioration, and improves the existing detecting performance. © Springer International Publishing Switzerland 2015.

关键词： Image representation

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Improved dendritic cell algorithm with false positives and false negatives adjustable 1

引用

11th International Conference on Intelligent Computing, ICIC 2015

作者： Yuan, Song Xu, Xin College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (数字)9783319221809

ISBN: (纸本)9783319221793

In order to overcome the blindness of the evaluation on contexts in the classical Dendritic Cell Algorithm (DCA), how weight matrixes influence detection results is analyzed, and two kinds of DCA which can adjust false positives and false negatives are proposed. The first one is the improved voting DCA, the Tendency Factor (TF) is involved in the Dendritic Cell (DC) state transition to assess contexts fairly, and through the fine adjustment of TF false positives and false negatives of detection results are controlled;the other one is the scoring DCA, in the DC state transition phase the evaluation of contexts is ignored, instead, the antigen is directly given a score, then according to the distribution of average scores of antigens the anomaly threshold value can be adjusted to control false positives and false negatives. Experiments show that the two algorithms can both effectively realize results controlled, comparatively the scoring DCA is more intuitive. © Springer International Publishing Switzerland 2015.

关键词： Data fusion

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A 3-D robust detail preserving anisotropic diffusion for speckle reduction in 3-D cattle follicle ultrasound image

A 3-D robust detail preserving anisotropic diffusion for spe...

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作者： Liu, Jun Jiang, Hongwei Liu, Xiaoming School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China

In this paper, an anisotropic diffusion filtering algorithm is proposed by modifying the diffusion coefficient and the instantaneous coefficient of variation (ICOV). This algorithm can reduce the speckle in the ultrasound image effectively and well preserve an image's edges and details compared with the 3-D speckle reducing anisotropic diffusion filtering (3DSRAD). We applied this algorithm on a real 3-D ultrasound image and the result shows that the modified algorithm is less sensitive to the number of iterations than conventional 3D speckle, thus reducing anisotropic diffusion filtering. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： Speckle

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An improved mixed-type data based kernel clustering algorithm

An improved mixed-type data based kernel clustering algorith...

引用

International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Min Ren Peiyu Liu Zhihao Wang Xiao Pan School of Mathematic and Quantitative Economics Shandong University of Finance and Economics Jinan Shandong province China School of Information Science and Engineering Shandong Normal University Jinan Shandong province China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan Shandong province China

ISBN: (纸本)9781509040940

Clustering algorithm is often used to analyze the communication data for network intrusion detection system. However, network communication data are mixed, e.g., numerical and categorical data. So, at first, this paper put forward a method for representing the cluster center (prototype) of mixed-type data. Then respectively in combination with the continuity characteristic of the numerical attributes and the semantic feature of the categorical attributes, the dissimilarity measurement formula was improved by use of the Gauss kernel function, on the base of which, defined the objective function. After that this paper further put forward an Improved Mixed-type Data based Kernel Clustering Algorithm (IKCA-MD), which showed a stable clustering result because the initial cluster centers are obtained by Maximum Density and Distance method (MDD). Finally the feasibility and effectiveness of the method for the network intrusion detection were verified by experiments.

关键词： Clustering algorithms Kernel Prototypes Intrusion detection Linear programming Training Testing

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Microblog Orientation Analysis Based on Connnectives 10

Microblog Orientation Analysis Based on Connnectives

引用

10th International Conference on Broadband and Wireless Computing, Communication and Applications, BWCCA 2015

作者： Zhao, Jun Wang, Hong School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014 China

ISBN: (纸本)9781467383158

At present, a larger number of researchers analyzed Micro-blog orientation and they concentrated their energy on emotional words, adverb and negative words without considering the impact of other emotional factors, such as the coordinative relation, progressive relation and adversative relation. So a method studying the sentiment polarity of associated words was proposed. The structure characteristics of associated words were analyzed sufficiently, and a turning words dictionary, a Chinese emotion dictionary and a negative words dictionary were founded based on the existing dictionary. This paper considers the combination laws of negative words, adversative words and conjunctions into Micro-blog. In the experiments, we verify this method can analyze the sentiment polarity of transition sentences efficiently and also has higher classification accuracy. © 2015 IEEE.

关键词： Chinese Microblog Connectives Emotional Dictionary Orientation Analysis SVM

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Locally linear representation manifolds margin 11th

Locally linear representation manifolds margin

引用

11th International Conference on Intelligent Computing, ICIC 2015

作者： Li, Bo Wang, Yun-Qing Lei, Lei Fan, Zhang-Tao School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China

ISBN: (纸本)9783319221793

In this paper, a novel supervised multiple manifolds learning method is presented for dimensionality reduction, which is titled locally linear representation manifold margin (LLRMM). In the proposed LLRMM, both an inter-manifold graph and intra-manifold graph are constructed, where any point in the inter-manifold graph must select neighbors from other manifolds while the neighborhood in the intra-manifold graph are composed of samples from the same manifold. Then the least locally linear representation technique is introduced to optimize the reconstruction weights as well as the corresponding inter-manifold scatter and intra-manifold scatter, based on which manifolds margin can be reasoned. At last, a discriminant subspace is explored. Experiments on some benchmark face data sets have been conducted and experimental results show that the proposed method outperforms some related state-of-the-art dimensionality reduction methods. © Springer International Publishing Switzerland 2015.

关键词： computers

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A new microcalcification detection method in full field digital mammogram images 11th

A new microcalcification detection method in full field digi...

引用

11th International Conference on Intelligent Computing, ICIC 2015

作者： Liu, Xiaoming Mei, Ming Sun, Weiwei Liu, Jun College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing Real-time Industrial System Wuhan China

ISBN: (纸本)9783319221793

Breast cancer is a great threat for women around the world. Mammography is the main approach for early detection and diagnosis. Microcalcification (MC) in mammograms is one of the important early signs of breast cancer. Their accurate detection is important in computer-aided detection (CADe). In this paper, we proposed a new Microcalcification detection method for full field digital mammograms (FFDM) by integrating Possibilistic Fuzzy c-Means (PFCM) clustering algorithm and weighted support vector machine (WSVM). The method includes a training process and a testing process. In the training process, possible microcalcification regions are located and extracted. Extracted features are selected with mutual information based technique. Positive and negative samples are weighted with PFCM and used to train a weighted SVM. A similar procedure is performed on test images. The proposed method is evaluated on a database of 410 clinical mammograms and compared with a standard unweighted support vector machine classifier. © Springer International Publishing Switzerland 2015.

关键词： Mammography

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Supervised sparse coding with local geometrical constraints 40

Supervised sparse coding with local geometrical constraints

引用

40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Zhang, Hanchao Xu, Jinhua Department of Computer Science and Technology East China Normal University Shanghai China Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China

ISBN: (纸本)9781467369978

Sparse coding algorithms with geometrical constraints have received much attention recently. However, these methods are unsupervised and might lead to less discriminative representations. In this paper, we propose a supervised localityconstrained sparse coding method for classification. Two graphs are constructed, a labeled graph and an unlabeled graph. Sparse codes with a labeled geometrical constraint will be more discriminative, however we cannot embed test samples with unknown label into a labeled graph. By coupling the two graphs, we aim to make the difference between sparse codes with labeled and unlabeled geometrical constraints as small as possible. As a result, sparse codes of test data can be obtained with the unlabeled geometrical constraint and the discrimination of the labeled geometrical constraint is maintained. Experiments on some benchmark datasets demonstrate the effectiveness of the proposed method. © 2015 IEEE.

关键词： geometrical constraint manifold embedding manifold learning sparse coding supervised

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