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

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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A Sparsity-Based Clustering Framework for Radio Channel Impulse Responses

A Sparsity-Based Clustering Framework for Radio Channel Impu...

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IEEE Vehicular Technology Conference

作者： Ruisi He Wei Chen Bo Ai Andreas F. Molisch Wei Wang Zhangdui Zhong Jian Yu Seun Sangodoyin State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Department of Electrical Engineering University of Southern California Institute of Communications and Navigation German Aerospace Center Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University

ISBN: (纸本)9781509016990

In this paper, we propose a novel channel impulse response (CIR) clustering algorithm using a sparsity-based method, which is based on the model that the CIR consists of multiple groups of clusters, each of which shows a single-sided exponential power decay as a function of the delay. We first use a sparsity-based optimization to recover CIRs, which can be well solved by using reweighted l_1 minimization. Then a heuristic approach is provided to identify clusters in the recovered CIRs, which leads to improved clustering accuracy in comparison to identifying clusters directly in the raw CIRs. The proposed algorithm incorporates the standard model behaviors of MPCs into the clustering framework and enables applications with no prior knowledge of the clusters, such as number and initial locations of clusters. The results in this paper can be used to parameterize the CIR model of radio channels.

关键词： Channel impulse response Channel model Clustering Convex optimization Reweighted l_1 minimization Sparse representation sparse representation channel impulse response wireless channels Cluster Analysis Biomedical optical imaging Convex function Frameworks channel model power decay

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Search result diversification based on hierarchical intents 15

Search result diversification based on hierarchical intents

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24th ACM International Conference on Information and knowledge Management, CIKM 2015

作者： Hu, Sha Dou, Zhicheng Wang, Xiaojie Sakai, Tetsuya Wen, Ji-Rong Beijing Key Laboratory of Big Data Management and Analysis Methods China Key Laboratory of Data Engineering and Knowledge Engineering MOE China School of Information Renmin University of China China Waseda University Japan

ISBN: (纸本)9781450337946

A large percentage of queries issued to search engines are broad or ambiguous. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Most existing intent-aware search result diversification algorithms formulate user intents for a query as a flat list of subtopics. In this paper, we introduce a new hierarchical structure to represent user intents and propose two general hierarchical diversification models to leverage hierarchical intents. Experimental results show that our hierarchical diversification models outperform state-of-the-art diversification methods that use traditional flat subtopics. © 2015 ACM.

关键词： Search engines

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Load pattern window aware power supply device clustering

International Journal of Database Theory and Application

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International Journal of database Theory and Application 2016年第8期9卷 269-280页

作者： Sheng, Wanxing Liu, Ke-Yan Yu, Yixi An, Rungong Zhou, Ningnan Zhang, Xiao China Electric Power Research Institute Beijing100192 China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing100872 China School of Information Renmin University of China 100872 China

data-driven decision in big data era is becoming ubiquitous in electronic grid. In particular, daily collected power consumption records enable workload aware device clustering, which is crucial for critical domain applications such as device functionality identification. In this paper, we propose a load pattern window aware method for clustering power supply devices. Our approach overcomes the drawbacks in existing works, such as fuzzy based clustering, K-means based clustering and neutral network based clustering. After investigating the large scale records from power supply devices, our approach partitions device records into disjoint time intervals with parameterized window size, which indicate the load pattern feature for a period of time given a specific device. Devices are then decomposed into a mixture of these features, and those devices with similar dominating features are grouped together. The experimental results demonstrate the effectiveness and efficiency of our solution based on the real data collected from power grid in China. © 2016 SERSC.

关键词： Big data

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A direct fingerprint minutiae extraction approach based on convolutional neural networks

A direct fingerprint minutiae extraction approach based on c...

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International Joint Conference on Neural Networks (IJCNN)

作者： Lu Jiang Tong Zhao Chaochao Bai A Yong Min Wu School of Computer & Control University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing China Beijing Eastern Golden Finger Technology Co. Ltd Beijing China

Minutiae, as the essential features of fingerprints, play a significant role in fingerprint recognition systems. Most existing minutiae extraction methods are based on a series of hand-defined preprocesses such as binarization, thinning and enhancement. However, these preprocesses require strong prior knowledge and are always lossy operations. And that will lead to dropped or false extractions of minutiae. In this paper, a novel minutiae extraction approach based on deep convolutional neural networks is proposed, which directly extract minutiae on raw fingerprint images without any preprocess since we tactfully take the advantage of the strong representative capacity of deep convolutional neural networks. Minutiae can be effectively extracted due to the well designed architectures. Furthermore, the accuracy is guaranteed in that the comprehensive estimate is made to eliminate spurious minutiae. Moreover, a number of implement skills are employed both to avoid overfitting and to improve the robustness. This approach makes a good performance because it not only makes all use of information in fingerprint images but also learns the minutiae patterns from large amounts of data. Comparisons are made with previous works and a widely applied commercial fingerprint identification system. Results show that our approach performs better both in accuracy and robustness.

关键词： Neural networks Feature extraction Training data Training Convolution Electronic mail Computer architecture

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Publish me and protect me: Personalized and flexible location privacy protection in mobile social networks 23

Publish me and protect me: Personalized and flexible locatio...

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23rd IEEE International Symposium on Quality of Service, IWQoS 2015

作者： Wu, Yao Peng, Hui Zhang, Xiaoying Chen, Hong Li, Cuiping Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education Beijing China School of Information Renmin University of China Beijing China

ISBN: (纸本)9781467371131

With the increasing proliferation of the Mobile Social Networks (MSN) and the Location Based Service (LBS), location privacy has attracted broad attention in recent years. Most researches have been done with the assumption that the server is untrusted and a trusted third party is introduced to protect the user location privacy when a user sends queries for the location service. In this paper, we reconsider this assumption and propose a Personalized and Flexible location privacy protection Model (PFM) based on user relationship strength. We conduct researches in the situation that the server is trusted while malicious users in the MSN can disguise as a friend to break location privacy. We present an entropic TF-IDF based approach to measure the bi-directional relationship strength and propose probability distribution based cloaking model to protect user location privacy. We thoroughly evaluate our methods based on Quality of Privacy (QoP) via real and synthetic data. © 2015 IEEE.

关键词： Location based services

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Nonparallel Support Vector Machine Based on One Optimization Problem for Pattern Recognition

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Journal of the Operations Research Society of China 2015年第4期3卷 499-519页

作者： Ying-Jie Tian Xu-Chan Ju Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 101408China

In this paper,we present a novel nonparallel support vector machine based on one optimization problem(NSVMOOP)for binary *** NSVMOOP is formulated aiming to separate classes from the largest possible angle between the normal vectors and the decision hyperplanes in the feature space,at the same time implementing the structural risk minimization *** from other nonparallel classifiers,such as the representative twin support vector machine,it constructs two nonparallel hyperplanes simultaneously by solving a single quadratic programming problem,on which a modified sequential minimization optimization algorithm is *** NSVMOOP is analyzed theoretically and implemented *** results on both artificial and publicly available benchmark datasets show its feasibility and effectiveness.

关键词： Pattern recognition Support vector machines Nonparallel hyperplanes Sequential minimization optimization

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Building extraction from panchromatic high-resolution remotely sensed imagery based on potential histogram and neighborhood Total variation

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Earth Science Informatics 2016年第4期9卷 497-509页

作者： Wenzao Shi Zhengyuan Mao Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University Fuzhou China Key Lab of Spatial Data Mining & Information Sharing of Ministry of Education Fuzhou University Fuzhou China National Engineering Research Centre of Geospatial Information Technology Fuzhou University Fuzhou China Spatial Information Engineering Research Centre of Fujian Province Fuzhou University Fuzhou China

In order to extract buildings using only gray information, this article proposed an approach for recognizing and extracting buildings from panchromatic high-resolution remotely sensed imagery based on shadows and segmentation. First, shadows were detected by potential histogram function. Second, the value of neighborhood total variation for each pixel was calculated, and then binarization and annotation were implemented to generate lable regions whose centroids were used as the seeds of the region growing segmentation, candidate buildings were selected from the segmentation result with the constraint of aspect ratio and rectangularity. At last, shadows were processed with open, dilate and corrode operations respectively, buildings were extracted by computing the adjacency relationship of the processed shadows and candidate buildings, and the building boundaries were fitted with the minimum enclosing rectangle. For verifying the validity of the proposed method, eighteen representative sub-images were chosen from PLEIADES images covering Shenzhen, China. Experimental results show that the average precision and recall of the proposed method are 97.95 % and 79.40 % for the object-based evaluation, and are 98.75 % and 83.16 % for the area-based evaluation respectively, and it has more 10 % and 6 % increase in the overall performance for above two evaluation criterion comparing with two other similar methods.

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A secure access and associate deleting scheme for multi-replica in multi-cloud environment

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International Journal of High Performance Systems Architecture 2016年第3期6卷 143-152页

作者： Xiong, Jinbo Li, Suping Li, Xuan Zhang, Yuanyuan Jin, Biao Faculty of Software Fujian Normal University Fuzhou China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China Fujian Engineering Research Center Public Service Big Data Mining and Application Fuzhou China

With the rapid development of cloud services in multi-cloud environment, it is convenient to generate multi-replica for user's data stored in multi-cloud servers. On the one hand, multi-replica is able to improve data availability and users' experience. But on the other hand, it becomes increasingly easy to disclose sensitive data and brings serious challenges to security and privacy for user's data and its multi-replica. In order to tackle the above issues, in this paper, we propose a secure access and associate deleting scheme for multi-replica (MADS) in multi-cloud environment, which is based on symmetric encryption algorithm, attribute-based encryption and replica location technology. We construct a novel multi-layered key structure, called multi-way search key tree (MSKT) that ensures no key material will be revealed, yet the data owner is able to control the master key and manage other keys by performing some tree operations. We also propose a data replica associated model, which is able to associate all the data replicas in multi-cloud environment. Comprehensive comparison and security analysis demonstrate that the proposed MADS scheme is effective and secure. Copyright © 2016 Inderscience Enterprises Ltd.

关键词： Sensitive data

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Predicting strip thickness of hot rolled sheet based on the combination of correlation analysis and Extreme Learning Machine 10

Predicting strip thickness of hot rolled sheet based on the ...

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10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015

作者： Dezheng, Zhang Hongtao, Li Aziguli Linlin, Fan Fengbo, Yang Beijing China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing China Beijing China Anshan Group Mining Engineering Corporation China

ISBN: (纸本)9781467373173

Based on Extreme Learning Machine (ELM) an new hot-rolled strip thickness model prediction method is proposed. Firstly, the input variables is determined through the correlation analysis to ensure the effectiveness of the model;then use ELM network forecasting model. The network uses live production data for training and testing, and compared with the BP network prediction model. Simulation results show that the model can predict the thickness more quickly and accurately, and is able to meet the needs of actual rolling production. © 2015 IEEE.

关键词： Forecasting

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