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Diffie-Hellman key exchange protocol based on ring-LWE

Open Cybernetics and Systemics Journal 2015年第1期9卷 1033-1037页

作者： Zhi-Min, Yu Zheng-Jun, Jing Shi-Cun, Li Key Laboratory of Cloud Computing and Intelligent Information Processing of Changzhou City Jiangsu University of Technology ChangzhouJiangsu213001 China

In this paper, we first construct a one-round Diffie-Hellman key exchange protocol based on Ring-LWE. The security of our construction is based on the hardness of the Ring-LWE problem. Second, we adaptively extend our construction from Ring-LWE to LWE. Finally, we efficiently implement our key exchange protocols based on Ring-LWE and LWE. © Zhi-Min et al.

关键词： Diffie-Hellman key exchange LWE Ring-LWE Security

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Aimed information quantity in text

Aimed information quantity in text

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作者： Chen, Jingqiang Zhuge, Hai Nanjing University of Posts and Telecommunications No.9 Wenyuan RoadYadong New District Nanjing210023 China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China

People often read with aims and reading process significantly influences understanding. This paper defines a new measure of information in text named Aimed information Quantity in Text by simulating human reading process with reading aims. Aimed information Quantity in Text reflects not only human memory of words but also relevancy between words emerging in aimed reading process. We demonstrate its application in recommendation. © 2015 John Wiley & Sons, Ltd.

关键词： Behavioral research

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LENSELET IMAGE COMPRESSION SCHEME BASED ON SUBAPERTURE IMAGES STREAMING

LENSELET IMAGE COMPRESSION SCHEME BASED ON SUBAPERTURE IMAGE...

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IEEE International Conference on Image processing

作者： Feng Dai Jun Zhang Yike Ma Yongdong Zhang Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (纸本)9781479983407

Plenoptic cameras capture the light field in a scene with a single shot and produce lenselet images. From a lenselet image, light field can be reconstructed, with which we can render images with different viewpoints and focal length. Because of large volume data, high efficient image compression scheme for storage and transmission is urgent. Containing 4D light field information, lenselet images have much more redundant information than traditional 2D images. In this paper, we propose a subaperture images streaming scheme to compress lenselet images, in which rotation scan mapping is adopted to further improve compression efficiency. The experiment results show our approach can efficient compress the redundancy in lenselet images and outperform traditional image compression method.

关键词： Plenoptic camera Lenselet image Light field Image compression Subaperture images streaming Light fields Image compression Data compression images image compression method single-shot two dimentional images FOCAL LENGTH redundancy

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A fast classification method using SVM based on affinity propagation clustering algorithm

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Journal of Computational information Systems 2015年第16期11卷 6073-6080页

作者： Xu, Li Ding, Shifei Huang, Huajuan School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

The classification speed of SVM is inversely proportional to the number of Support Vectors (SVs). Therefore, the less SVs mean the more sparseness and the higher classification speed. In order to reduce the number of SVs but without lossing of generalization performance, a new algorithm called Fast Classification Method Using SVM Based on Affinity Propagation clustering algorithm (FCSVM-AP) is proposed, which employs Affinity Propagation (AP) clustering algorithm to cluster the original SVs and the cluster centers are used as the new SVs, then aiming to minimize the classification gap between SVM and FCSVM-AP, a quadratic programming model is built for obtaining the optimal coefficients of the new SVs. Meanwhile, it is proven that when clustering the original SVs, the minimal upper bound of the error between the original decision function and the fast decision function can be achieved by AP. Finally, experiments show that compared with original SVs, the number of SVs decreases and the speed of classification increases using FCSVM-AP, while the loss of accuracy is in the acceptable level. © 2015 by Binary information Press

关键词： Quadratic programming

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A hybrid deep learning CNN-ELM model and its application in handwritten numeral recognition

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Journal of Computational information Systems 2015年第7期11卷 2673-2680页

作者： Guo, Lili Ding, Shifei School of Computer Science and Technology China University of Mining and Technology Xuzhou China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Convolutional Neural Network (CNN) is a typical algorithm structure of deep learning and it has applied in image recognition field widely. Based on CNN, this paper puts forward a novel hybrid deep learning model CNN-ELM. In this model, CNN is in charge of feature extraction and Extreme Learning Machine (ELM) performs as a classifier to complete the final classification so as to integrate the synergy of two classifiers. We have took experiments on MNIST digit database. Comparisons with CNN and ELM on the same database, CNN-ELM achieved an error rate of 0.67%, which indicate that CNN-ELM has achieved better generalization performance. ©, 2015, Binary information Press. All right reserved.

关键词： Character recognition

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Topic discovery of clusters from documents with geographical location

Topic discovery of clusters from documents with geographical...

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作者： Zhang, Li Sun, Xiaoping Zhuge, Hai Knowledge Grid Lab Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Nanjing University of Posts and Telecommunications Nanjing Jiangsu China State Key Laboratory of Software Engineering Wuhan University Wuhan Hubei China University of Chinese Academy of Sciences Beijing China

As smart phones with GPS become popular, more and more textual documents with geographical locations are published on the Web. keyword-based location services like vehicle navigation, tour planning, nearby object querying, and location pattern discovering of spatial objects are becoming popular and important. However, processing both text and geographical locations brings more challenges to information-retrieval techniques. In this paper, we focus on the problem of finding textual topics of clusters containing spatial objects with text descriptions. The key is how to combine clustering techniques with topic-retrieval models to integrate both geo-location information and text information. We investigated methods that combine clustering methods with the Latent Dirichlet Allocation model to discover topics of clusters of documents with geo-locations. Six different methods of combination are investigated, each having outputs with different meanings, which can be further leveraged to answer different types of queries over spatial documents. Experiments are conducted on both synthetic and real data. The results show that the combination of the probabilistic topic model with clustering algorithms is an efficient and effective way to discover meaningful clusters in different facets and levels of documents with textual and geographical information. © 2015 John Wiley & Sons, Ltd.

关键词： Cluster analysis

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A co-evolutionary particle swarm optimization with dynamic topology for solving multi-objective optimization problems

Advances in Modelling and Analysis A

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Advances in Modelling and Analysis A 2016年第1期53卷 145-159页

作者： Wu, Daqing Tang, Lixiang Li, Haiyan Ouyang, LiJun Computer Science and Technology Institute University of South China HangyangHunan China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200240 China Zigong643000 China Key Laboratory of Guangxi High Schools for Complex System and Computational Intelligence Guangxi University for Nationalities Nanning530006 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University HefeiAnhui Province230039 China Department of Business Administration Hunan University of Finance and Economics Hunan410205 China

This paper proposes a multi-objective with dynamic topology particle swarm optimization (PSO) algorithm for solving multi-objective problems, named DTPSO. One of the main drawbacks of classical multi-objective particle swarm optimization algorithm is low diversity. To overcome this disadvantage, DTPSO uses two dynamic local best particles to lead the search particles with multiple populations to deal with multiple objectives, and maintains diversity of new found non-dominated solutions via partitioned the searching space into fixed number of cells. The proposed DTPSO is validated through comparisons with other two multi-objective algorithms using established benchmarks and metrics. Simulation results demonstrated that DTPSO shows competitive, if not better, performance as compared to the other algorithms. © 2016, AMSE Press. All rights reserved.

关键词： Multiobjective optimization

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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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IEEE International Conference on Progress in Informatics and computing (PIC)

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 Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Policy Academy Changsha Hunan China School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China Department of Electrical Engineering CUNY New York NY USA Department of Mechanical and Control Engineering Kyushu Institute of Technology Fukuoka Prefecture Japan School of Information Engineering Yangzhou University Yangzhou Jiangsu China School of Physics and Electronic Engineering Taizhou University Taizhou Zhejiang China School of Electronic Information Shanghai Dianji University Shanghai China School of Energy and Mechanical Engineering Nanjing Normal University Nanjing Jiangsu China Key Laboratory of Intelligent Computing and Information Processing in Fujian Provincial University Quanzhou Normal University Quanzhou Fujian China

ISBN: (纸本)9781509034857

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： Genetic algorithms Optimization Clustering algorithms Particle swarm optimization Algorithm design and analysis Logistics Mathematical model

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An efficient MRF embedded level set method for image segmentation

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IEEE Transactions on Image processing 2015年第1期24卷 9-21页

作者： Yang, Xi Gao, Xinbo Tao, Dacheng Li, Xuelong Li, Jie State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an710071 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 235 Jones Street UltimoNSW2007 Australia State Key Laboratory of Transient Optics and Photonics Xi'An Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an710119 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi'an710071 China

This paper presents a fast and robust level set method for image segmentation. To enhance the robustness against noise, we embed a Markov random field (MRF) energy function to the conventional level set energy function. This MRF energy function builds the correlation of a pixel with its neighbors and encourages them to fall into the same region. To obtain a fast implementation of the MRF embedded level set model, we explore algebraic multigrid (AMG) and sparse field method (SFM) to increase the time step and decrease the computation domain, respectively. Both AMG and SFM can be conducted in a parallel fashion, which facilitates the processing of our method for big image databases. By comparing the proposed fast and robust level set method with the standard level set method and its popular variants on noisy synthetic images, synthetic aperture radar (SAR) images, medical images, and natural images, we comprehensively demonstrate the new method is robust against various kinds of noises. In particular, the new level set method can segment an image of size 500 x 500 within 3 s on MATLAB R2010b installed in a computer with 3.30-GHz CPU and 4-GB memory. © 2014 IEEE.

关键词： Image segmentation

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Motivation Learning in Mind Model CAM

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International Journal of Intelligence Science 2015年第2期5卷 63-71页

作者： Zhongzhi Shi Gang Ma Xi Yang Chengxiang Lu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Information Science and Engineering Qufu Normal University Rizhao China

Motivation learning aims to create abstract motivations and related goals. It is one of the high-level cognitive functions in Consciousness And Memory model (CAM). This paper proposes a new motivation learning algorithm which allows an agent to create motivations or goals based on introspective process. The simulation of cyborg rat maze search shows that the motivation learning algorithm can adapt agents’ behavior in response to dynamic environment.

关键词： Motivation Learning Motivation processing Consciousness and Memory Model Cyborg Rat

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