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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 1293-1304页

作者： Zhu, Yi Wu, Xindong Li, Yun Qiang, Jipeng Yuan, Yunhao Yangzhou University School of Information Engineering Yangzhou225012 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China Hefei230002 China

Domain adaptation aims to transfer knowledge between different domains to develop an effective hypothesis in the target domain with scarce labeled data, which is an effective method for remedying the problem of labeled data requirement in deep learning. In reality, it is unavoidable that the dataset has a large gap in the number of positive and negative instances across different categories in source and target domains, which is the imbalanced domain adaptation problem. However, since the imbalanced degree always varies greatly in different source- and target-domain datasets, most of the existing imbalanced domain adaptation models fix the imbalanced parameters, which cannot adapt to the change of the proportion between positive and negative instances in different domains. To address this problem, in this article, we propose a self-adaptive imbalanced domain adaptation method via a deep sparse autoencoder, which can adjust the model automatically according to the imbalanced extent for bridging the chasm of domains. More specifically, the self-adaptive imbalanced cross-entropy loss is designed for emphasizing more on minority categories and compensating the bias of training loss automatically. In addition, to alleviate the deficient problem of labeled data, we further propose the unlabeled information incorporating method by minimizing the distribution discrepancy of high-level representation space between the source and target domains. Experiments on several real-world datasets demonstrate the effectiveness of our method compared to other state-of-the-art methods. © 2020 IEEE.

关键词： Noise abatement

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Random analysis of statistical rough set

Random analysis of statistical rough set

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2016 International Conference on Machine Learning and Cybernetics, ICMLC 2016

作者： Tsang, Eric C.C. Zhao, Su-Yun Faculty of Information Technology Macau University of Science and Technology C-Macau China Key Laboratory of Data Engineering and Knowledge Engineering MOE Renmin University of China Beijing China

ISBN: (纸本)9781509003891

Attribute reduction is an inevitable problem in machine learning and statistical learning. To improve the traditional rough set reduction, statistical rough sets is then proposed by introducing random sampling into the rough approximation. Random sampling is the main contribution of statistical rough sets. As a result, it is necessary to analyze the randomness of statistical rough sets. In this paper, we analyze and demonstrate the influence of the randomness in the process of attribute reduction by a large number of experiments to test the effectiveness and stability of the random sampling. © 2016 IEEE.

关键词： Sampling

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Efficient Distributed Skyline Queries for Mobile Applications

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计算机科学技术学报（英文版） 2010年第3期25卷 523-536页

作者： Ying-Yuan Xiao Yue-Guo Chen Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300191China Key Laboratory of Computer Vision and System Tianjin University of TechnologyTianjin 300191China School of Computing National University of Singapore117417Singapore Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijing 100872China

In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queries are issued by mobile units(laptop,cell phone,etc.)which access the data objects of database servers by wireless *** inherent properties of mobile computing environment such as mobility,limited wireless bandwidth,frequent disconnection,make skyline queries more *** show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach,called efficient distributed skyline based on mobile computing(EDS-MC).In EDS-MC,a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication,network delay and query response *** conduct extensive experiments in a simulated mobile database system,and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.

关键词： skyline distributed query processing mobile database system

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information Sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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Moment+: Mining closed frequent itemsets over data stream

Moment+: Mining closed frequent itemsets over data stream

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4th International Conference on Advanced data Mining and Applications, ADMA 2008

作者： Li, Haifeng Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)3540881913

Closed frequent itemsets(CFI) mining uses less memory to store the entire information of frequent itemsets thus is much suitable for mining stream. In this paper, we discuss recent CFI mining methods over stream and presents an improved algorithm Moment+ based on the existent one Moment. Moment+ focuses on the problem of mining CFI over data stream sliding window and proposes a new structure Extended Closed Enumeration Tree(ECET) to store the CFIs and nodes' BPN which is introduced to reduce the search space, with which new mining method is designed to mine more rapidly with little memory cost sacrifice. The experimental results show that this method is effective and efficient. © 2008 Springer-Verlag Berlin Heidelberg.

关键词： Mining

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A framework for personalized management of domain ontologies

A framework for personalized management of domain ontologies

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2012 IEEE/ACIS 11th International Conference on Computer and Information Science, ICIS 2012

作者： Liu, Runnan Wang, Puwei Zheng, Shixian Key Laboratory of Data Engineering and Knowledge Engineering Renmin University of China Ministry of Education Beijing 100872 China

ISBN: (纸本)9780769546940

As the sharable and reusable domain knowledge, domain ontology increasingly serves as a foundation for semantic Web. Personalized management of domain ontologies is to provide personalized views of domain ontologies to users during run time according to user preferences. It helps that users can focus on their interested parts, instead of the whole. It increases the efficiency of ontology-based application. This paper proposes a framework for personalized management of domain ontologies. In this framework, a user model firstly is introduced to describe user preferences. Secondly, domain ontologies are decomposed into moderate-scale modules with high cohesion and low coupling. During run time, modules that a user is interested in are selected out based on the user's preferences. Finally, the selected modules are combined to construct personalized views of domain ontologies for the user. © 2012 IEEE.

关键词： Ontology

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ETD-cache: An expiration-time driven cache scheme to make SSD-based read cache endurable and cost-efficient 15

ETD-cache: An expiration-time driven cache scheme to make SS...

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12th ACM International Conference on Computing Frontiers, CF 2015

作者： Dai, Ningwei Chai, Yunpeng Liang, Yushi Wang, Chunling Key Laboratory of Data Engineering and Knowledge Engineering Ministry of Education Renmin University of China Beijing100872 China

ISBN: (纸本)9781450333580

Recently flash-based solid-state drives (SSDs) have been widely deployed as cache devices to boost system performance. However, classical SSD cache algorithms (e.g. LRU) replace the cached data frequently to maintain high hit rates. Such aggressive data updating strategies result in too many writing operations on SSDs and make them wear out quickly, which finally leads to high costs of SSDs for enterprise applications. In this paper, we propose a novel Expiration-Time Driven Cache (ETD-Cache) method to solve this problem. In ETD-Cache, an active data eviction mechanism is adopted. An already cached block leaves the SSD cache if and only if there is no access to it for a time longer than a specified expiration time. This mechanism gives more time for the cached contents to wait for their following accesses and limits the admission of newly arrived blocks to generate less SSD writes. In addition, a low-overhead candidate management module is designed to maintain the most popular data in the system for the potential cache replacement. The simulations driven by a series of typical real-world traces indicate that due to the great reduction on data updating frequency, ETD-Cache lowers the total SSD costs by 98.45% compared with LRU under the same cache hit rate. Copyright 2015 ACM.

关键词： Costs

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Evolving sequential patterns mining model over click stream with Levenshtein-Automata

Evolving sequential patterns mining model over click stream ...

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3rd International Conference on Innovative Computing Information and Control, ICICIC'08

作者： Li, Haifeng Chen, Hong Key Laboratory of Data Engineering and Knowledge Engineering School of Information Renmin University of China Beijing 100872 China

ISBN: (纸本)9780769531618

Sequential pattern mining is an important problem in continuous, fast, dynamic and unlimited stream mining. Recently approximate mining algorithms are proposed which spend too many system resources and can only obtain the partial feature of stream. In this paper, a multi-level evolving sequential pattern mining model ESPMM is presented to address this problem thus the mostly entire stream feature is obtained. Furthermore, because of the smaller support of sequential patterns in each level, a mining method BMLA based on Levenshtein-Automata is proposed which builds state conversion model to compute sequences' similarity in linear time. The experiment results show this model is effective and efficient. © 2008 IEEE.

关键词： Automata theory

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Preventing Recommendation Attack in Trust-Based Recommender Systems

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Journal of Computer Science & Technology 2011年第5期26卷 823-828页

作者：张富国 School of Information and Technology Jiangxi University of Finance and Economics Institute of Information Resource Management Jiangxi University of Finance and Economics Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics

Despite its success,similarity-based collaborative filtering suffers from some limitations,such as scalability,sparsity and recommendation *** work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these *** argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender *** the best of our knowledge,there has not any prior study on recommendation attack in a trust-based recommender *** analyze the attack problem,and find that ＂victim＂ nodes play a significant role in the ***,we propose a data provenance method to trace malicious users and identify the ＂victim＂ nodes as distrust users of recommender *** study of the defend method is done with the dataset crawled from Epinions website.

关键词： data lineage "victim" node attack trust propagation

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Polynomial-Time Assignment-Based Cell Association with Generic Utility Functions

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China Communications 2022年第9期19卷 214-228页

作者： Lusheng Wang Chao Fang Hai Lin Min Peng Caihong Kai Key Laboratory of Knowledge Engineering with Big Data Ministry of Education.School of Computer Science and Information EngineeringHefei University of TechnologyHefei 230601China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education.School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Cell association is a significant research issue in future mobile communication systems due to the unacceptably large computational time of traditional *** article proposes a polynomial-time cell association scheme which not only completes the association in polynomial time but also fits for a generic optimization objective *** the one hand,traditional cell association as a non-deterministic polynomial(NP)hard problem with a generic utility function is heuristically transformed into a 2-dimensional assignment optimization and solved by a certain polynomial-time algorithm,which significantly saves computational *** the other hand,the scheme jointly considers utility maximization and load balancing among multiple base stations(BSs)by maintaining an experience pool storing a set of weighting factor values and their corresponding *** an association optimization is required,a suitable weighting factor value is taken from the pool to calculate a long square utility matrix and a certain polynomial-time algorithm will be applied for the *** with several representative schemes,the proposed scheme achieves large system capacity and high fairness within a relatively short computational time.

关键词： mobile communication system 2-dimensional assignment problem hungarian algorithm fairness cell association

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