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Cooperative provable data possession with stateless verification in multicloud storage

Journal of Computational Information Systems 2014年第8期10卷 3403-3411页

作者： Sun, Xingming Chen, Lihong Xia, Zhihua Zhu, Yanling Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

Provable Data Possession (PDP) is a probabilistic proof technique for checking the availability and integrity of files on untrusted storage servers without downloading the whole data files. Recently, Zhu et al. proposed a cooperative PDP (CPDP) scheme for multicloud storage, where multiple cloud storage providers (CSPs) can cooperatively store and maintain the users' data. However, their scheme does not conform to the property of knowledge soundness. Moreover, the verifier needs to maintain and update state in their scheme, which is a serious drawback for proof-of-storage systems with public verifiability. In this paper, we design a secure CPDP scheme based on a sequence-enforced Merkle Hash Tree (sMHT) construction and Homomorphic Verifiable Response (HVR) with random masking. The proposed scheme achieves knowledge soundness, full stateless verification and dynamic data updates. Our analysis shows that the new solution resolves the issue of Zhu's scheme with only a little of computation and communication overheads. © 2014 Binary Information Press.

关键词： Cloud storage

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Nonsubsampled contourlet transform based illumination invariant extracting method

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Advances in Information Sciences and Service Sciences 2012年第17期4卷 47-55页

作者： Fan, Chun-nian Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

Face recognition research in recent years has made great progress, yet many issues still remain to be addressed. Illumination changes are one of the key issues. In order to eliminate the effect of varying illumination on face recognition, a novel illumination invariant method based on nonsubsampled Contourlet transform is proposed. Firstly, we perform illumination normalization on images under varying illumination, which can reduce the effect of varying illumination to some extent. Secondly, the logarithmic transformation and the nonsubsampled Contourlet transform is used to decompose the images into its low frequency and high frequency directional subband components. Thirdly, adaptive NormalShrink is applied to each directional subband to eliminate noise, and the histogram equalization is applied to the low frequency components aimed at weakening the illumination affects further. Lastly, the illumination invariant is obtained by inverse nonsubsampled Contourlet transform using the modified low frequency components and high frequency directional subband components. Experimental results on the Yale face database B and CMU PIE database show that the proposed method can effectively eliminate the effect of varying illumination on face recognition and the obtained illumination invariant is robust.

关键词： Face recognition

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An elastic meteorological cloud platform based on Xen

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Information Technology Journal 2013年第15期12卷 3174-3178页

作者： Wei, Fang Zhi, Wen Xue Xu, Jiang Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology 210044 Nanjing China School of Computer and Software Nanjing University of Information Science and Technology 210044 Nanjing China

In the atmospheric sciences, the scales of atmospheric data are massive and growing rapidly. For complex weather forecast, such as the typhoon track prediction requires the computing of very large of computers. In order to reduce the computing cost and protect meteorological resources, we use cloud computing technologies to design an elastic meteorological cloud platform (EMCP). It can share and manage meteorological resources flexibly and efficiently. The experimental results show the EMCP has better performance and efficiency. © 2013 Asian network tor Scientitic Intormation.

关键词： Cloud computing

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Multi-Path Attention Inverse Discrimination network for Offline Signature Verification

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Intelligent Automation & Soft Computing 2023年第6期36卷 3057-3071页

作者： Xiaorui Zhang Yingying Wang Wei Sun Qi Cui Xindong Wei School of Computer and Software Nanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of WindsorWindsorN9B 3P4Canada School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China

Signature verification,which is a method to distinguish the authenticity of signature images,is a biometric verification technique that can effectively reduce the risk of forged signatures in financial,legal,and other business ***,compared with ordinary images,signature images have the following characteristics:First,the strokes are slim,i.e.,there is less effective ***,the signature changes slightly with the time,place,and mood of the signer,i.e.,it has high intraclass *** challenges lead to the low accuracy of the existing methods based on convolutional neural net-works(CNN).This study proposes an end-to-end multi-path attention inverse dis-crimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images,which effectively solves the problem of little effective *** solve the problem of high intraclass variability of signature images,we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature ***,a multi-path discrimination loss function is proposed,which does not require the feature representation of the samples with the same class label to be infinitely close,as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold,which radically resolves the problem of high intra-class difference of signature *** addition,this loss can also spur the network to explore the detailed infor-mation on the stroke parts,such as the crossing,thickness,and connection of *** respectively tested on CEDAR,BHSig-Bengali,BHSig-Hindi,and GPDS Synthetic datasets with accuracies of 100%,96.24%,93.86%,and 83.72%,which are more accurate than existing signature verification *** is more helpful to the task of signature authentication in justice and finance.

关键词： Offline signatures biometric verification multi-path discrimination loss attention mechanisms inverse discrimination

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Energy-landscape paving for prediction of face-centered-cubic hydrophobic-hydrophilic lattice model proteins

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Physical Review E 2013年第5期88卷 052704-052704页

作者： Jingfa Liu Beibei Song Zhaoxia Liu Weibo Huang Yuanyuan Sun Wenjie Liu Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China Network Information Center Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China

Protein structure prediction (PSP) is a classical NP-hard problem in computational biology. The energy-landscape paving (ELP) method is a class of heuristic global optimization algorithm, and has been successfully applied to solving many optimization problems with complex energy landscapes in the continuous space. By putting forward a new update mechanism of the histogram function in ELP and incorporating the generation of initial conformation based on the greedy strategy and the neighborhood search strategy based on pull moves into ELP, an improved energy-landscape paving (ELP+) method is put forward. Twelve general benchmark instances are first tested on both two-dimensional and three-dimensional (3D) face-centered-cubic (fcc) hydrophobic-hydrophilic (HP) lattice models. The lowest energies by ELP+ are as good as or better than those of other methods in the literature for all instances. Then, five sets of larger-scale instances, denoted by S, R, F90, F180, and CASP target instances on the 3D FCC HP lattice model are tested. The proposed algorithm finds lower energies than those by the five other methods in literature. Not unexpectedly, this is particularly pronounced for the longer sequences considered. Computational results show that ELP+ is an effective method for PSP on the fcc HP lattice model.

关键词： HYDROPHILIC compounds PROTEIN structure COMPUTATIONAL biology CONFORMATIONAL analysis AMINO acid sequence

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Wang-Landau sampling in face-centered-cubic hydrophobic-hydrophilic lattice model proteins

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Physical Review E 2014年第4期90卷 042715-042715页

作者： Jingfa Liu Beibei Song Yonglei Yao Yu Xue Wenjie Liu Zhaoxia Liu Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing 210044 China School of Computer & Software Nanjing University of Information Science & Technology Nanjing 210044 China Network Information Center Nanjing University of Information Science & Technology Nanjing 210044 China

Finding the global minimum-energy structure is one of the main problems of protein structure prediction. The face-centered-cubic (fcc) hydrophobic-hydrophilic (HP) lattice model can reach high approximation ratios of real protein structures, so the fcc lattice model is a good choice to predict the protein structures. The lacking of an effective global optimization method is the key obstacle in solving this problem. The Wang-Landau sampling method is especially useful for complex systems with a rough energy landscape and has been successfully applied to solving many optimization problems. We apply the improved Wang-Landau (IWL) sampling method, which incorporates the generation of an initial conformation based on the greedy strategy and the neighborhood strategy based on pull moves into the Wang-Landau sampling method to predict the protein structures on the fcc HP lattice model. Unlike conventional Monte Carlo simulations that generate a probability distribution at a given temperature, the Wang-Landau sampling method can estimate the density of states accurately via a random walk, which produces a flat histogram in energy space. We test 12 general benchmark instances on both two-dimensional and three-dimensional (3D) fcc HP lattice models. The lowest energies by the IWL sampling method are as good as or better than those of other methods in the literature for all instances. We then test five sets of larger-scale instances, denoted by the S, R, F90, F180, and CASP target instances on the 3D fcc HP lattice model. The numerical results show that our algorithm performs better than the other five methods in the literature on both the lowest energies and the average lowest energies in all runs. The IWL sampling method turns out to be a powerful tool to study the structure prediction of the fcc HP lattice model proteins.

关键词： WANG-Landau algorithm FACE centered cubic structure HYDROPHOBIC interactions LATTICE models (Statistical physics) PROTEIN structure

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Combiningwavelet transform and LBP related features for fingerprint liveness detection

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2016年第3期43卷 290-298页

作者： Xia, Zhihua Yuan, Chengsheng Sun, Xingming Sun, Decai Rui, L. Jiangsu Engineering Center of Network Monitoring Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology School of Computer and SoftwareNanjing University of Information Science and Technology Nanjing China College of Information Science and Technology Bohai University China

Fingerprint authentication system may verify the identity of the user according to the features of fingerprint. It has been widely used in government departments and national security departments. However, there are some security and privacy problems: Fingerprint authentication system may be easily tricked by fake fingers which are produced through using common artificial materials, such as silicon, wood glue and latex. Thus, designing a fingerprint liveness detection module is necessary for authentication system. In this paper, in order to obtain the optimal set of features and recognize fake fingerprints, a new software-based liveness detection approach using a novel fingerprint parameterization based on wavelet transform and local binary pattern (LBP) related features is applied. The performance of our proposed approach has been evaluated through a comparison with several state-of-the-art techniques for fingerprint liveness detection. In addition, the liveness detection method presented in this paper has an extra advantage over previously studied techniques, since only a fingerprint image is needed to judge whether it is real or fake. Experiments have been carried out by adopting standard databases which are taken from the Liveness Detection Competition 2011 and 2013. Besides that, we have also analyzed the performance of our method for the different combination of wavelet decomposition coefficients during the process of training. Finally, classification accuracy of feature vectors is predicted based on a SVM classifier. Experimental results demonstrate that our method can detect the fingerprint liveness with higher classification accuracy. In addition, this study also confirms that multi- resolution analysis is a useful tool for the extraction of texture feature during fingerprint images processing.

关键词： Support vector machines

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Survey on the Application of Deep Reinforcement Learning in Image Processing

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Journal on Artificial Intelligence 2020年第1期2卷 39-58页

作者： Wei Fang Lin Pang Weinan Yi School of Computer&Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science&TechnologyNanjingChina State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingChina Jiangsu Key Laboratory of Computer Information Process Technology Soochow UniversitySuzhouChina

feature representations from a large amount of data,and use reinforcement learning to learn the best strategy to complete the *** the combination of deep learning and reinforcement learning,end-to-end input and output can be achieved,and substantial breakthroughs have been made in many planning and decision-making systems with infinite states,such as games,in particular,AlphaGo,robotics,natural language processing,dialogue systems,machine translation,and computer *** this paper we have summarized the main techniques of deep reinforcement learning and its applications in image processing.

关键词： Reinforcement learning image processing

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Mixed Noise Removal by Residual Learning of Deep CNN

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Journal of New Media 2020年第1期2卷 1-10页

作者： Kang Yang Jielin Jiang Zhaoqing Pan School of Computer and Software Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjing210044China Country Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and TechnologyNanjing210044China

Due to the huge difference of noise distribution,the result of a mixture of multiple noises becomes very *** normal circumstances,the most common type of mixed noise is to add impulse noise(IN)and then white Gaussian noise(AWGN).From the reduction of cascaded IN and AWGN to the latest sparse representation,a great deal of methods has been proposed to reduce this form of mixed ***,when the mixed noise is very strong,most methods often produce a lot of *** order to solve the above problems,we propose a method based on residual learning for the removal of AWGN-IN noise in this *** training,our model can obtain stable nonlinear mapping from the images with mixed noise to the clean *** a series of experiments under different noise settings,the results show that our method is obviously better than the traditional sparse representation and patch based ***,the time of model training and image denoising is greatly reduced.

关键词： Mixed noise denoising residual learning deep convolutional neural

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Efficient and secure multi-keyword ranked search over encrypted office open XML data

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Journal of Computational Information Systems 2015年第7期11卷 2701-2708页

作者： Xi, Jie Sun, Xingming Fu, Zhangjie Zhang, Youdong Xin, Lin Cao, Xinyue School of Computer & Software Nanjing University of Information Science & Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science & Technology Nanjing China Department of Computer Engineering Huaiyin Institute of Technology Huaian China

As cloud computing goes increasingly mature, searchable encryption schemes have been widely applied to the real world. Those not only provide high efficiency but also protect users' privacy well. However, the existing schemes mainly study plain text documents without any format information. In this paper, for the first time, we study and propose a secure and efficient multi-keyword search scheme over encrypted Office Open XML data (OOXML) documents. Specifically, a keyword red-black (KRB) tree is improved to adapt to format attribute-based keywords search. By recording format attribute (FA) of keywords, the returned results conform to users' search purpose well and own high relevancy. Extensive experiments on real-world dataset demonstrate the effectiveness of our scheme. ©, 2015, Binary Information Press. All right reserved.

关键词： Cryptography

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