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Automatic extraction of foreground objects from Mars images

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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High-Quality Noise Detection for knowledge Graph Embedding with Rule-Based Triple Confidence 18th

High-Quality Noise Detection for Knowledge Graph Embedding w...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Hong, Yan Bu, Chenyang Wu, Xindong Ministry of Education Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China

ISBN: (纸本)9783030891879

knowledge representation learning is usually used in knowledge reasoning and other related fields. Its goal is to use low-dimensional vectors to represent the entities and relations in a knowledge graph. In the process of automatic knowledge graph construction, the complexity of unstructured text and the incorrect text may make automatic construction tools unable to accurately obtain the semantic information in the text. This leads to high-quality noise with matched entity types but semantic errors. Currently knowledge representation learning methods assume that the knowledge in knowledge graphs is completely correct, and ignore the noise data generated in the process of automatic construction of knowledge graphs, resulting in errors in the vector representation of entities and relations. In order to reduce the negative impact of noise data on the construction of a representation learning model, in this study, a high-quality noise detection method with rule information is proposed. Based on the semantic association between triples in the same rule, we propose the concept of rule-based triple confidence. The calculation strategy of triple confidence is designed inspired by probabilistic soft logic (PSL). The influence of high-quality noise data in the training process of the model can be weakened by this confidence. Experiments show the effectiveness of the proposed method in dealing with high-quality noise. © 2021, Springer Nature Switzerland AG.

关键词： knowledge graph

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Fast & scalable distributed set similarity joins for big data analytics 33

Fast & scalable distributed set similarity joins for big dat...

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33rd IEEE International Conference on data engineering, ICDE 2017

作者： Rong, Chuitian Lin, Chunbin Silva, Yasin N. Wang, Jianguo Lu, Wei Du, Xiaoyong Tianjin Polytechnic University China Arizona State University United States University of California San Diego United States Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China School of Information Renmin University of China China

ISBN: (纸本)9781509065431

Set similarity join is an essential operation in big data analytics, e.g., data integration and data cleaning, that finds similar pairs from two collections of sets. To cope with the increasing scale of the data, distributed algorithms are called for to support large-scale set similarity joins. Multiple techniques have been proposed to perform similarity joins using MapReduce in recent years. These techniques, however, usually produce huge amounts of duplicates in order to perform parallel processing successfully as MapReduce is a shared-nothing framework. The large number of duplicates incurs on both large shuffle cost and unnecessary computation cost, which significantly decrease the performance. Moreover, these approaches do not provide a load balancing guarantee, which results in a skewness problem and negatively affects the scalability properties of these techniques. To address these problems, in this paper, we propose a duplicate free framework, called FS-Join, to perform set similarity joins efficiently by utilizing an innovative vertical partitioning technique. FS-Join employs three powerful filtering methods to prune dissimilar string pairs without computing their similarity scores. To further improve the performance and scalability, FS-Join integrates horizontal partitioning. Experimental results on three real datasets show that FS-Join outperforms the state-of-Theart methods by one order of magnitude on average, which demonstrates the good scalability and performance qualities of the proposed technique. © 2017 IEEE.

关键词： MapReduce

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Vulnerabilities static detection for Web applications with false positive suppression

Vulnerabilities static detection for Web applications with f...

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2010 IEEE International Conference on Information Theory and Information Security, ICITIS 2010

作者： Huang, Jianjun Liang, Bin Zhong, Jiagui Wang, Qianqian Cai, Jingjing Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China MOE Beijing 100872 China School of Information Renmin University of China Beijing 100872 China Beijing Venustech Cybervision Co. Ltd Beijing 100094 China

ISBN: (纸本)9781424469406

Web applications become more and more important, and the corresponding security problems have been concerned about. This paper presents TASA, an ASP static analyzer, which employs a path-sensitive, inter-procedural and context-sensitive data flow analysis, mainly concerning the taint propagation and sanitization. This paper also discusses some techniques used in TASA, such as sanitization routines modeling, ASP specific features, alias analysis and path-related routines modeling, to prune false positives. Experiments on four open source applications show that TASA has a rate of false positive of 4.98% and it can avoid certain false warnings owing to the proposed approaches. © 2010 IEEE.

关键词： data flow analysis

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Scientific Value Weights more than Being Open or Toll Access:An analysis of the OA advantage in Nature and Science

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Journal of data and Information Science 2021年第4期6卷 62-75页

作者： Howell Y.Wang Shelia X.Wei Cong Cao Xianwen Wang Fred Y.Ye International Joint Informatics Laboratory&Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information ManagementNanjing UniversityNanjing 210023China Nottingham University Business School China University of Nottingham Ningbo ChinaNingbo315100China WISE Lab. Dalian University of TechnologyDalian 116024China

Purpose:We attempt to find out whether OA or TA really affects the dissemination of scientific ***/methodology/approach:We design the indicators,hot-degree,and R-index to indicate a topic OA or TA ***,according to the OA classification of the Web of Science(WoS),we collect data from the WoS by downloading OA and TA articles,letters,and reviews published in Nature and Science during 2010–*** papers are divided into three broad disciplines,namely biomedicine,physics,and ***,taking a discipline in a journal and using the classical Latent Dirichlet Allocation(LDA)to cluster 100 topics of OA and TA papers respectively,we apply the Pearson correlation coefficient to match the topics of OA and TA,and calculate the hot-degree and R-index of every OA-TA topic ***,characteristics of the discipline can be *** qualitative comparison,we choose some high-quality papers which belong to Nature remarkable papers or Science breakthroughs,and analyze the relations between OA/TA and citation ***:The result shows that OA hot-degree in biomedicine is significantly greater than that of TA,but significantly less than that of TA in *** on the R-index,it is found that OA advantages exist in biomedicine and TA advantages do in ***,the dissemination of average scientific discoveries in all fields is not necessarily affected by OA or ***,OA promotes the spread of important scientific discoveries in high-quality *** limitations:We lost some citations by ignoring other open sources such as arXiv and *** limitation came from that Nature employs some strong measures for access-promoting subscription-based articles,on which the boundary between OA and TA became *** implications:It is useful to select hot topics in a set of publications by the hotdegree *** finding comprehensively reflects the differences of OA and TA in different disciplines,which is a u

关键词： Open access Toll access Scientific discovery Academic dissemination data analysis

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Greedy-Algorithm of KSORD Supporting Multi-language Phrase Recognition

Greedy-Algorithm of KSORD Supporting Multi-language Phrase R...

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International Conference on Advanced Computer Theory and engineering, ICACTE

作者： Peng Li Qing Zhu Chao Tian Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing China

With the rapid development of information retrieval technology and daily increasing information in the Internet, common users can retrieve many text-based database and get part of the information through the search engines such as Google, and Baidu. However, there is a great amount of data contained in the background relational database of web pages. So there are many researches focusing on the search in these relational database with keywords, compared with these researches, our algorithms are mainly based on bags using the greedy algorithms and supporting the phrase recognition by utilizing multiple dictionaries. We make a comparison between our algorithm and the existing ones. The experiment results shows that our algorithm owns not only the feature of effectiveness but also the feature of efficiency.

关键词： Relational databases Internet Information retrieval data engineering knowledge engineering Search engines Dictionaries data structures Chaos Laboratories

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Selection of multimedia data source based on user feedback

Selection of multimedia data source based on user feedback

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2011 International Conference on Management of e-Commerce and e-Government, ICMeCG 2011

作者： Deng, Song Wan, Changxuan Liu, Xiping School of Information Technology Jiangxi University of Finance and Ecomomics Nanchang China School of Software Jiangxi Agricultural University Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

There are hundreds or thousands of web data sources providing data of relevance to a particular domain on the Web, so how to find a suitable set of sources quickly to integrate from a number of sources is becoming more and more important. Traditional data sources selection methods that are based on data quality do not consider quality characteristics of data sources under the different domains, but select the same quality dimensions for quality evaluation models. As a result, accuracies of a traditional method under different domains are quite different. In light of this, we propose a Web multimedia source selection method based on user feedback. We gain accurate core quality dimensions of data sources under the multimedia domain based on user feedback, and then achieve a quality estimation model of the multimedia data sources. The experiments on hundreds of real-world multimedia data sources have proved that our approach is efficient and accurate. © 2011 IEEE.

关键词： Accuracy Computers data Source data models databases Equations Mathematical model Multimedia Multimedia communication Quality Dimensions User Feedback Web data source data quality estimation model quality evaluation model quality multimedia computing mult

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SWEE: Approximately searching web service with keywords effectively and efficiently

SWEE: Approximately searching web service with keywords effe...

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The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

作者： Zuoyan Qin Peng Li Qing Zhu Chao Tian Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872

ISBN: (纸本)9781424458455;9781424458486;9781424458479;9781424458462

With the wide use of web services over the world, an effective and efficient search engine becomes necessary. In this paper, we analyzed the limitation of search method using traditional UDDI, and make it in LR-sryle and take advantage of approximate string matching. Motivated by the feature of WSDL files which define the interfaces of web services, we make operations defined in WSDL files which compose web services as the base units for searching and organize all information of operations and corresponding components as documents, which will facilitate IRStyle keyword searching. In order to improve the precision of searching, we enable users to specify weights in their queries. By defining our ranking function, we sort all the results and present top n ones to users. We also build inverted indexes on operations and corresponding documents to improve query efficiency. In addition, we carry out simple semantic analysis on WSDL files. to acquire better accuracy. At last, not only have we tested the efficiency of query after introducing approximate string matching, but also have we verified the effectiveness of the query results.

关键词： Web Service Search engines String matching fundamental units Consumers Search methods Searches keyWORD Surgery

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Enable Privacy Preservation for k-NN Query in Two-tiered Wireless Sensor Networks

Enable Privacy Preservation for k-NN Query in Two-tiered Wir...

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IEEE International Conference on Communications

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

ISBN: (纸本)9781467364300

Wireless sensor network is an important part of the Internet of Things. Preservation of privacy and integrity in wireless sensor networks is extremely urgent and challenging. To address this problem, we propose PPKN, an efficient and privacy-preserving k-NN query protocol in two-tiered wireless sensor networks. Our proposal prevents adversaries from gaining sensitive information of both queries issued by users and data collected by sensor nodes while allows the sink to verify whether results are valid. It offers confidentiality of queries and data by constructing a special code, provides integrity verification by the correlation among data. Moreover, by the implementation of KNQ query framework, PPKN also achieves high efficient in query response and energy consumption. Finally, the theoretical analysis and experiment results show the high performance of PPKN in terms of privacy preservation and query efficiency.

关键词： Wireless sensor networks Energy consumption data privacy Privacy Protocols Cryptography Storage management

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Research on access control decision model of Internet of Things based on attribute exploration

Research on access control decision model of Internet of Thi...

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2021 International Conference on Signal Processing and Communication Technology, SPCT 2021

作者： Shen, XiaJiong Zhang, Peng Zhang, YanFeng Henan Key Laboratory of Big Data Analysis and Processing Henan Univ. HA Kaifeng475004 China School of Computer and Information Engineering Henan Univ. HA Kaifeng475004 China Institute of Data and Knowledge Engineering Henan Univ. HA Kaifeng475004 China

ISBN: (数字)9781510653344

ISBN: (纸本)9781510653337

With the rapid development of the Internet of things, the problem of access control has become increasingly prominent. In order to solve the problems of high redundancy and low integrity caused by the traditional Role-Based Access Control model, this paper proposes an access control model of Internet of things based on attribute exploration. Firstly, the decision model is established based on attribute exploration algorithm, and finally merged into a complete decision model. The model has the function of dynamically modifying access rights in the process of building decision model. It is a new exploration in the field of access control model. © 2022 SPIE.

关键词： Internet of things

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