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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1411-1424页

作者： Zhang, Yuhong Shi, Congmei Li, Xinzheng Zhang, Zan Hu, Xuegang School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education Hefei230009 China

Cross-network node classification aims to train a classifier for an unlabeled target network using a source network with rich labels. In applications, the degree of nodes mostly conforms to the long-tail distribution, i.e., most nodes in the network are tail nodes with sparse neighborhoods. The established methods focus on either the discrepancy cross network or the long tail in a single network. As for the cross-network node classification under long tail, the coexistence of sparsity of tail nodes and the discrepancy cross-network challenges existing methods for long tail or methods for the cross-network node classification. To this end, a multicomponent similarity graphs for cross-network node classification (MS-CNC) is proposed in this article. Specifically, in order to address the sparsity of the tail nodes, multiple component similarity graphs, including attribute and structure similarity graphs, are constructed for each network to enrich the neighborhoods of the tail nodes and alleviate the long-tail phenomenon. Then, multiple representations are learned from the multiple similarity graphs separately. Based on the multicomponent representations, a two-level adversarial model is designed to address the distribution difference across networks. One level is used to learn the invariant representations cross network in view of structure and attribute components separately, and the other level is used to learn the invariant representations in view of the fused structure and attribute graphs. Extensive experimental results show that the MS-CNC outperforms the state-of-the-art methods. Impact Statement-Node classification is an important task in graph mining. With the unavailability of labels, some researchers propose cross-network node classification, using one labeled network to assist the node classification of another unlabeled network. However, the long-tail of nodes leads to unsatisfactory performance and challenges the recent cross-network node classification m

关键词： Graphic methods

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DPListCF: A Differentially Private Approach for Listwise Collaborative Filtering

DPListCF: A Differentially Private Approach for Listwise Col...

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IEEE Symposium on Computers and Communications

作者： Yuncheng Wu Juru Zeng Hong Chen Yao Wu Wenjuan Liang Hui Peng Cuiping Li Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education China

ISBN: (纸本)9781509006809

Recently, listwise ranking-oriented collaborative filtering (CF) algorithms have gained great success in recommender systems. However, the ranked preference list may compromise the privacy of individuals. A notable paradigm for offering strong privacy guarantee is differential privacy. In this paper, we propose DPListCF, a differentially private algorithm based on ListCF (a state-of-art listwise CF algorithm). The main idea of DPListCF is to make both of the similarity calculation phase and rank prediction phase of ListCF satisfy differential privacy, by using input perturbation method and output perturbation method in the two phases respectively. Extensive experiments using two real datasets evaluate the performance of DPListCF, and demonstrate that the proposed algorithm outperforms state-of-art approaches.

关键词： Open area test sites Privacy collaborative filtering Recommender systems Perturbation methods Arts CONTAINMENT FAILURE

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Fuzzy associative classifier for probabilistic numerical data 7

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7th International Conference on Intelligent Systems and knowledge engineering, ISKE 2012 with 1st International Conference on Cognitive Systems and Information Processing, CSIP 2012

作者： Pei, Bin Zhao, Tingting Zhao, Suyun Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China School of Information Renmin University of China Beijing China New Star Research Institute of Applied Tech Hefei China

ISBN: (纸本)9783642378287

Recently, a number of advanced data collection and processing methodologies have led to the proliferation of uncertain data. When discovering from such uncertain data, we should handle these uncertainties with caution, because classical mining algorithms may not be appropriate for uncertain tasks. This paper proposes a generic framework of fuzzy associative classifier for probabilistic numerical data, which is prevalent in the real-world applications, such as sensor networks and GPS-based location. In this paper, we first introduce an Aprioribased algorithm for mining fuzzy association rules from a probabilistic numerical dataset based on novel support and confidence measures suitable for such dataset. Then, we give fuzzy rules redundancy pruning strategy and database coverage method to build a compact fuzzy associative classifier in removing redundant rules and thus improving the accuracy of the classifier. We also redefine multiple fuzzy rules classification method for classifying new instances. Extensive experimental results show the effectiveness and efficiency of our algorithm. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Fuzzy rules

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Research on XML element search results clustering

Research on XML element search results clustering

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6th International Conference on Management of e-Commerce and e-Government, ICMeCG 2012

作者： Zhong, Min-Juan Wan, Chang-Xuan Liu, De-Xi Jiao, Xian-Pei School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9781467329439

Clustering XML search results is an effective way to improve performance. However, the key problem is how to measure similarity between XML documents. This paper studies XML search results clustering based on element granularity and proposes one similarity measurement method. The method firstly uses latent semantic indexing technology(LSI) to obtain term semantics and then combines the XML element node content and semantic structure properties(CASS). To evaluate clustering performance, two new performance evaluation methodologies, namely R-ClusterRatio and R-DocuRatio are introduced. It is motivated by the observations of relevant documents distribution and the fact that the experiment data collection, IEEE CS corpus, do not provide classification information. Experiment results show that proposed similarity method combining term semantics with content and structure semantics integration(LSI-CASS) is feasible, and it produces better clustering quality than LSI-CAS. © 2012 IEEE.

关键词： Semantics

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Sentimental analysis of web financial reviews opportunities and challenges 6

Sentimental analysis of web financial reviews opportunities ...

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6th International Conference on knowledge Discovery and Information Retrieval, KDIR 2014

作者： Wan, Changxuan Jiang, Tengjiao Liu, Dexi Liao, Guoqiong School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9789897580482

Web financial reviews are real-time, comprehensive and authentic. The construction and quantification of Web financial indexes based on Web financial reviews is of great significance for the financial early warning for enterprises. Comparing with product reviews and news commentaries, in Web financial reviews, the opinion targets have more diverse compositions, the frequencies of opinion targets' occurrence vary greatly, and the sentiment words' have more diverse parts of speech. These characteristics make the extraction of opinion targets, the construction of Web financial indexes, and opinion targets-based sentimental analysis all more complicated, posing new challenges to natural language processing. Copyright © 2014 SCITEPRESS - Science and Technology Publications All rights reserved.

关键词： Sentiment analysis

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Clustering XML search results based on content and structure similarity

Clustering XML search results based on content and structure...

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

作者： Min-Juan, Zhong Chang-Xuan, Wan De-Xi, Liu Xian-Pei, Jiao School of Information and Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9780769545448

Clustering XML search results is an effective way to improve performance. However, the key problem is how to measure similarity between XML documents. In this paper, we propose a semantic similarity measure method combining content with structure, in which a variety of XML document features, including term element frequency, term inverse element frequency, semantic weight of tag label and level information of the term, are analyzed and applied for computing the similarity between XML documents. In addition, two new performance evaluation methodology, namely Cluster Ratio Relevant and Docu Ratio Relevant, for clustering quality are introduced motivated by the observations of relevant documents distribution and the fact that collection has no classification information. Experiment results show that proposed similarity method(CAS measure)outperforms traditional document clustering(CO measure) in Cluster Ratio Relevant and Docu Ratio Relevant and produces better clustering quality. © 2011 IEEE.

关键词： XML

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An effective XML identifying feedback documents method based on two-stage ranking model for pseudo-relevance feedback

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2013 3rd International Symposium on Chemical engineering and Material Properties, ISCEMP 2013

作者： Zhong, Min Juan School of Information Technology Jiangxi University of Finance and Economics Nanchang China JiangXi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783037858363

Although pseudo relevant feedback is an effective query expansion method, query drift away from the topic has been occurred frequently. Therefore, the first important problem is how to identify relevant documents in the top retrieved set and form the good feedback source. In this paper, an effective XML identifying feedback documents method is proposed, in which a two-stage ranking model is presented and the relevant XML documents are found. The experiment results show that the proposed method is reasonable and the quality of feedback source is ensured. © (2013) Trans Tech Publications, Switzerland.

关键词： XML

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Swot analysis of Chinese rural information society policies

Swot analysis of Chinese rural information society policies

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2nd International Conference on Mechatronics and Industrial Informatics, ICMII 2014

作者： Zhou, Ping School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9783038351764

This paper presents an analysis of strengths, weaknesses, opportunities, and threats (SWOT) for Chinese rural information society development (CRISD) policies in terms of its missions and strategies. After briefly describing the institutional and information society development contexts, the paper draws on literature reviews and questionnaires survey to list of SWOT factors for CRISD. These findings could be used to help adjust the next step work to support decision making for rural information society construction and to provide a valuable reference for other investors who are planning to invest in information society programs in China's rural regions. This article presents valuable missions and strategic goals through the SWOT analysis for realizing information society development in Chinese rural areas. © (2014) Trans Tech Publications, Switzerland.

关键词： Rural areas

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A Parallel Recovery Scheme for Update Intensive Main Memory database Systems

A Parallel Recovery Scheme for Update Intensive Main Memory ...

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IEEE International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

作者： Xiongpai Qin Yanqin Xiao Wei Cao Shan Wang Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing P.R. China

In update intensive applications, main memory database systems produce large volume of log records, it is critical to write out the log records efficiently to speedup transaction processing. We propose a parallel recovery scheme based on XOR differential logging for main memory database systems in such environments. Some NVRAM is used to temporarily hold log records and decouple transaction committing from disk writes, inherited parallelism properties of differential logging are exploited to accelerate log flushing by using multiple log disks. During recovery, log records are loaded from multiple log disks and applied to data partition in time without the need of reordering according to serialization order, total recovery time is cut down. The scheme employs a data partition based consistent checkpointing method. The log records are classified according to IDs of data partitions accessed. data partitions are recovered according to loading priorities computed from update frequencies and transaction waiting times, data access demands of new transactions coming after failure recovery are given attention immediately, thus the scheme provides system availability during recovery, which is of importance for large scale main memory database systems.

关键词： database systems data engineering Large-scale systems Random access memory Checkpointing Communication industry Defense industry Distributed computing Laboratories knowledge engineering

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VID Join： Mapping Trajectories to Points of Interest to Support Location-Based Services

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Journal of Computer Science & Technology 2015年第4期30卷 725-744页

作者：商烁谢珂心郑凯刘家俊文继荣 Department of Computer Science China University of Petroleum Beijing 102249 China School of Information Technology and Electrical Engineering The University of Queensland Brisbane QLD 4072 Australia School of Computer Science and Technology Soochow University Suzhou 215006 China Commonwealth Scientific and Industrial Research Organisation Kenmore QLD 4069 Australia Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100080 China

Variable influence duration （VID） join is a novel spatio-temporal join operation between a set T of trajectories and a set P of spatial points. Here, trajectories are traveling histories of moving objects （e.g., travelers）, and spatial points are points of interest （POIs, e.g., restaurants）. VID join returns all pairs of （τs, p） if τs is spatially close to p for a long period of time, where τs is a segment of trajectory τ ∈ T and p ∈ P. Each returned （τs, p） implies that the moving object associated with τs stayed at p （e.g., having dinner at a restaurant）. Such information is useful in many aspects, such as targeted advertising, social security, and social activity analysis. The concepts of influence and influence duration are introduced to measure the spatial closeness between τ and p, and the time spanned, respectively. Compared to the conventional spatio-temporal join, the VID join is more challenging since the join condition varies for different POIs, and the additional temporal requirement cannot be indexed effectively. To process the VID join e？ciently, three algorithms are developed and several optimization techniques are applied, including spatial duplication reuse and time duration based pruning. The performance of the developed algorithms is verified by extensive experiments on real spatial data.

关键词： trajectory spatial database spatial join spatio-temporal join

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