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3rd APWeb and WAIM Joint Conference on Web and Big data, APWeb-WAIM 2019

作者： Xiu, Yeliang Zhao, Suyun Chen, Hong Li, Cuiping Information of School Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Beijing China

ISBN: (纸本)9783030260743

An incremental method of feature selection based on mutual information, called incremental Max-Relevance, and Min-Redundancy (I-mRMR), is presented. I-mRMR is an incremental version of Max-Relevance, and Min-Redundancy feature selection (mRMR), which is used to handle streaming data or large-scale data. First, Incremental key Instance Set is proposed which composes of the non-distinguished instances by the historical selected features. Second, an incremental feature selection algorithm is designed in which the incremental key instance set, replacing of all the seen instances so far, is used in the process of adding representative features. Since the Incremental key Instance Set is far less than the whole instances, the incremental feature selection by using this key set avoids redundant computation and save computation time and space. Finally, the experimental results show that I-mRMR could significantly or even dramatically reduce the time of feature selection with an acceptable classification accuracy. © 2019, Springer Nature Switzerland AG.

关键词： Redundancy

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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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Efficient SPARQL query evaluation via automatic data partitioning

Efficient SPARQL query evaluation via automatic data partiti...

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18th International Conference on database Systems for Advanced Applications, DASFAA 2013

作者： Yang, Tao Chen, Jinchuan Wang, Xiaoyan Chen, Yueguo Du, Xiaoyong School of Information Renmin University of China China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE China

ISBN: (纸本)9783642374494

The volume of RDF data increases very fast within the last five years, e.g. the Linked Open data cloud grows from 2 billions to 50 billions of RDF triples. With its wonderful scalability, cloud computing platform like Hadoop is a good choice for processing queries over large data sets. Previous works on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect the performance. Specifically, a good partitioning will greatly reduce or even totally avoid cross-node joins and significantly reduce the cost of query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture where Map/Reduce takes charge of the computing tasks and an RDF query engine, RDF-3X, stores the data and evaluates join operations over local data. Based on analysis of query work-loads, we propose a novel algorithm for automatically partitioning RDF data. We also present an approximate solution to physically place the partitions in order to reduce data redundancy. All the proposed approaches are evaluated by extensive experiments over large RDF data sets. © Springer-Verlag 2013.

关键词： Resource Description Framework (RDF)

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Research on Visualization Algorithm of Association Rules Based on Concept Lattice

Research on Visualization Algorithm of Association Rules Bas...

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Shen, Xiajiong Bao, Lijie Zhang, Lei Institute of Data and Knowledge Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781450387828

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display. © 2020 ACM.

关键词： Visualization

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Research on Penetration Strategy of UAV

Research on Penetration Strategy of UAV

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Han, Daojun Xiang, Hongzhen Chen, Haolin Xie, Yi Institute of Data and Knowledge Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781450387828

Taking a military operation as an example, this paper makes an in-depth study of what tactics a single UAV should adopt when facing the interception of multiple enemy UAV groups. According to the analysis of the impact diagram, the aerial range of the UAV standoff defines a threat area, that is, when the UAV arrives at the threat area, it needs to adopt a strategy for penetration, or it will be intercepted. The strategy adopted by UAV in the threat area is analyzed, and the decision control method is made. © 2020 ACM.

关键词： Unmanned aerial vehicles (UAV)

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A dynamic key node learning algorithm in evolving financial network: case study during the period of coronavirus 9th

A dynamic key node learning algorithm in evolving financial ...

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9th International Conference on Information Technology and Quantitative Management, ITQM 2022

作者： Lu, Doudou Deng, Wei Xu, Yongyin Shi, Yong College of Big Data Statistics Guizhou University of Finance and Economics Guizhou550025 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China

Finding an influential set of nodes in the interconnected network, whose activities would maximally influence the development of overall network, is a fundamental topic in finance, biology, social science, epidemic control and other scenarios with respect to network science. In recent years, some works are proposed to find key nodes via network connectivity measures, these studies assume a static environment, and besides, key nodes are calculated through pairwise connectivity, the number of connected components and other measures from the perspective of graph theory. However, these studies leave out of consideration on the dynamic evolving environment and causal relationships in the network. In this paper, we introduce a key node learning algorithm in an evolving financial network. The intuition of the algorithm is to model the causality of financial entities over the time, and the metrics for cascading networks are proposed to grade the entities in the network. In the experiment, the worldwide financial activities before and during the period of coronavirus are analyzed. The proposed metrics can effectively capture the influential entities and quantitatively describe these influences on the whole financial system. © 2022 The Authors. Published by Elsevier B.V.

关键词： COVID-19

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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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Automatic extraction of foreground objects from Mars images

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Geo-Spatial Information Science 2012年第1期15卷 17-25页

作者： WANG Shuliang LIU Chang WU Shangru NIE Qianqian WANG Yongtao ZENG Shi ZHU Haifeng International School of Software Wuhan University129 Luoyu RoadWuhan 430079China Key Laboratory of Data Mining and Knowledge Engineering Inner Mongolia Autonomous RegionInner Mongolia UniversityHohhot 010021China Department of Geography East Michigan UniversityYpsilantiMI 48197USA

A novel method is proposed to automatically extract foreground objects from Martian surface *** characteristics of Mars images are distinct,*** illumination,low contrast between foreground and background,much noise in the background,and foreground objects with irregular *** the context of these characteristics,an image is divided into foreground objects and background *** filtering is first applied to rectify ***,wavelet transformation enhances contrast and denoises the ***,edge detection and active contour are combined to extract contours regardless of the shape of the *** results show that the method can extract foreground objects from Mars images automatically and accurately,and has many potential applications.

关键词： automatic object extraction Mars images homomorphic filtering wavelet transformation active contour edge detection

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An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

arXiv

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arXiv 2025年

作者： Lin, Meijin Guo, Lin Chen, Dicheng Chen, Jianshu Tu, Zhangren Huang, Xu Wang, Jianhua Qi, Ji Long, Yuan Huang, Zhiguo Guo, Di Qu, Xiaobo Han, Haiwei College of Ocean & Earth Sciences Xiamen University Xiamen361102 China Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China National Integrated Circuit Industry Education Integration Innovation Platform Xiamen University Xiamen361100 China Department of Electronic Science Xiamen University Xiamen361102 China Department of Radiology the First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361100 China Software Technology Company Limited Suzhou215163 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age ± standard deviation = 27.4 ± 3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively;Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel. © 2025, CC BY.

关键词： Metabolites

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Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

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