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学者姓名:郑庆华

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< Page ,Total 42 >
Domain Adaption via Feature Selection on Explicit Feature Map EI Scopus SCIE
期刊论文 | 2019 , 30 (4) , 1180-1190 | IEEE Transactions on Neural Networks and Learning Systems
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Abstract :

In most domain adaption approaches, all features are used for domain adaption. However, often, not every feature is beneficial for domain adaption. In such cases, incorrectly involving all features might cause the performance to degrade. In other words, to make the model trained on the source domain work well on the target domain, it is desirable to find invariant features for domain adaption rather than using all features. However, invariant features across domains may lie in a higher order space, instead of in the original feature space. Moreover, the discriminative ability of some invariant features such as shared background information is weak, and needs to be further filtered. Therefore, in this paper, we propose a novel domain adaption algorithm based on an explicit feature map and feature selection. The data are first represented by a kernel-induced explicit feature map, such that high-order invariant features can be revealed. Then, by minimizing the marginal distribution difference, conditional distribution difference, and the model error, the invariant discriminative features are effectively selected. This problem is NP-hard to be solved, and we propose to relax it and solve it by a cutting plane algorithm. Experimental results on six real-world benchmarks have demonstrated the effectiveness and efficiency of the proposed algorithm, which outperforms many state-of-the-art domain adaption approaches. IEEE

Keyword :

Adaptation models Distribution distance Domain adaptions Kernel Transfer learning

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GB/T 7714 Deng, Wan-Yu , Lendasse, Amaury , Ong, Yew-Soon et al. Domain Adaption via Feature Selection on Explicit Feature Map [J]. | IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 (4) : 1180-1190 .
MLA Deng, Wan-Yu et al. "Domain Adaption via Feature Selection on Explicit Feature Map" . | IEEE Transactions on Neural Networks and Learning Systems 30 . 4 (2019) : 1180-1190 .
APA Deng, Wan-Yu , Lendasse, Amaury , Ong, Yew-Soon , Tsang, Ivor Wai-Hung , Chen, Lin , Zheng, Qing-Hua . Domain Adaption via Feature Selection on Explicit Feature Map . | IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 (4) , 1180-1190 .
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Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud SCIE
期刊论文 | 2019 , 14 (4) | PLOS ONE
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Abstract :

The cloud-based media streaming service is a promising paradigm for multimedia applications. It is attractive to media streaming service providers, who wish to deploy their media server clusters in a media cloud at reduced cost. Since the real-time live streaming service is both a bandwidth-intensive and quality-sensitive application, how to optimize the internal bandwidth utilization of a data center network (DCN) as well as guarantee the external bandwidth of the real-time live streaming application, is a key issue of deploying virtual machine (VM)-hosted media server cluster in a media cloud. Therefore, in this study, we propose an external-bandwidth-guaranteed media server cluster deployment scheme in media cloud. The approach simultaneously considers the outside bandwidth requirement of a tree-based media server cluster for live streaming and the intra-bandwidth consumption of a DCN. The proposed scheme models the optimal problem as a new terminal-Steiner-tree-like problem and provides an approximate algorithm for placing the media servers. Our evaluation results show that the proposed scheme guarantees the external bandwidth requirement of a real-time live streaming application, at the same time, greatly reduces the intra-bandwidth utilization of a media cloud with different DCN structures.

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GB/T 7714 Zhang, Weizhan , He, Zhichao , Du, Biao et al. Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud [J]. | PLOS ONE , 2019 , 14 (4) .
MLA Zhang, Weizhan et al. "Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud" . | PLOS ONE 14 . 4 (2019) .
APA Zhang, Weizhan , He, Zhichao , Du, Biao , Luo, Minnan , Zheng, Qinghua . Deploying external bandwidth guaranteed media server clusters for real-time live streaming in media cloud . | PLOS ONE , 2019 , 14 (4) .
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Implementation of National Health Informatization in China: Survey About the Status Quo SCIE
期刊论文 | 2019 , 7 (1) | JMIR MEDICAL INFORMATICS
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Abstract :

Background: The National Health and Family Planning Commission (NHFPC) in China organized a nationwide survey to investigate the informatization in hospitals and regional Health and Family Planning Commissions (HFPCs) in 2017. The survey obtained valid results from 79.69% (2021/2536) of major hospitals and 81% (26/32) of provincial and 73.1% (307/420) of municipal HFPCs. The investigated topics covered hardware infrastructure, information resources, applications, systems, and organizations in health informatics. Objective: This study aimed to provide evidence collected from the survey regarding China's health informatization and assist policy making regarding health informatics in the 13th Five-Year Plan of China. Methods: Based on the survey, the paper presented the status quo of China's health informatization and analyzed the progress and potential problems in terms of the country's health information development policies. Results: Related policies have helped to construct 4-level information platforms and start converging the regional data to the 3 centralized databases. The principle of informatics has been transiting from finance-centered to people-centered. Alternatively, the quality, usability, and interoperability of the data still need to be improved. Conclusions: The nationwide survey shows that China's health informatization is rapidly developing. Current information platforms and databases technically support data exchanging between all provinces and cities. As China is continuing to improve the infrastructure, more advanced applications are being developed upon it.

Keyword :

health informatization health policy electronic health record

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GB/T 7714 Li, Chen , Xu, Xiangdong , Zhou, Guanghua et al. Implementation of National Health Informatization in China: Survey About the Status Quo [J]. | JMIR MEDICAL INFORMATICS , 2019 , 7 (1) .
MLA Li, Chen et al. "Implementation of National Health Informatization in China: Survey About the Status Quo" . | JMIR MEDICAL INFORMATICS 7 . 1 (2019) .
APA Li, Chen , Xu, Xiangdong , Zhou, Guanghua , He, Kai , Qi, Tianliang , Zhang, Wei et al. Implementation of National Health Informatization in China: Survey About the Status Quo . | JMIR MEDICAL INFORMATICS , 2019 , 7 (1) .
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Large-Scale Robust Semisupervised Classification EI Scopus SCIE
期刊论文 | 2019 , 49 (3) , 907-917 | IEEE Transactions on Cybernetics
WoS CC Cited Count: 2 SCOPUS Cited Count: 3
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Abstract :

Semisupervised learning aims to leverage both labeled and unlabeled data to improve performance, where most of them are graph-based methods. However, the graph-based semisupervised methods are not capable for large-scale data since the computational consumption on the construction of graph Laplacian matrix is huge. On the other hand, the substantial unlabeled data in training stage of semisupervised learning could cause large uncertainties and potential threats. Therefore, it is crucial to enhance the robustness of semisupervised classification. In this paper, a novel large-scale robust semisupervised learning method is proposed in the framework of capped &#x2113;2,p-norm. This strategy is superior not only in computational cost because it makes the graph Laplacian matrix unnecessary, but also in robustness to outliers since the capped &#x2113;2,p-norm used for loss measurement. An efficient optimization algorithm is exploited to solve the nonconvex and nonsmooth challenging problem. The complexity of the proposed algorithm is analyzed and discussed in theory detailedly. Finally, extensive experiments are conducted over six benchmark data sets to demonstrate the effectiveness and superiority of the proposed method. IEEE

Keyword :

Computational consumption Labeled and unlabeled data Optimization algorithms Ridge regression Semi- supervised learning Semi-supervised classification Semi-supervised learning methods Semi-supervised method

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GB/T 7714 Zhang, Lingling , Luo, Minnan , Li, Zhihui et al. Large-Scale Robust Semisupervised Classification [J]. | IEEE Transactions on Cybernetics , 2019 , 49 (3) : 907-917 .
MLA Zhang, Lingling et al. "Large-Scale Robust Semisupervised Classification" . | IEEE Transactions on Cybernetics 49 . 3 (2019) : 907-917 .
APA Zhang, Lingling , Luo, Minnan , Li, Zhihui , Nie, Feiping , Zhang, Huaxiang , Liu, Jun et al. Large-Scale Robust Semisupervised Classification . | IEEE Transactions on Cybernetics , 2019 , 49 (3) , 907-917 .
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Identifying suspicious groups of affiliated-transaction-based tax evasion in big data EI Scopus SSCI SCIE
期刊论文 | 2019 , 477 , 508-532 | Information Sciences
WoS CC Cited Count: 1 SCOPUS Cited Count: 2
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Abstract :

© 2018 Elsevier Inc. Affiliated-transaction-based tax evasion (ATTE) is a new strategy in tax evasion that is carried out via legal-like transactions between a group of companies that have heterogeneous, complex and covert interactive relationships to evade taxes. Existing studies cannot effectively detect ATTE behaviors since (i) they perform well only for determining the abnormal financial status of individuals and ineffectively address the interactive relationships among companies, (ii) they aim at detecting ATTE from the perspective of structural characteristics, which leads to a poor false-positive rate, and (iii) few of them perform well in most sectors of companies. Effectively detecting suspicious groups according to both structural characteristics of ATTE groups and business characteristics of ATTE means (BC-ATTEM) remains an open issue. In this paper, we propose an affiliated-parties interest-related network (APIRN) for modeling affiliated parties, interest-related relationships, and their properties for identifying ATTE. Then, we identify the behavioral patterns of ATTE via topological pattern abstraction from APIRN and theoretical inference of BC-ATTEM. Based on the above, we further propose a hybrid method, namely, 3TI, for identifying ATTE suspicious groups via three steps: tax rate differential detection, topological pattern matching and tax burden abnormality identification. Experimental tests that are based on two years of real-world tax data from a province in China demonstrate that 3TI can identify ATTE suspicious groups with higher accuracy and better generality than existing works. Moreover, we identify various interesting implications and provide useful guidance for ATTE inspection based on an analysis of our experimental results.

Keyword :

Affiliated transaction Big data Graph mining Tax evasion

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GB/T 7714 Ruan, Jianfei , Yan, Zheng , Dong, Bo et al. Identifying suspicious groups of affiliated-transaction-based tax evasion in big data [J]. | Information Sciences , 2019 , 477 : 508-532 .
MLA Ruan, Jianfei et al. "Identifying suspicious groups of affiliated-transaction-based tax evasion in big data" . | Information Sciences 477 (2019) : 508-532 .
APA Ruan, Jianfei , Yan, Zheng , Dong, Bo , Zheng, Qinghua , Qian, Buyue . Identifying suspicious groups of affiliated-transaction-based tax evasion in big data . | Information Sciences , 2019 , 477 , 508-532 .
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Anomalous: A joint modeling approach for anomaly detection on attributed networks EI Scopus
会议论文 | 2018 , 2018-July , 3513-3519 | 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
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Abstract :

The key point of anomaly detection on attributed networks lies in the seamless integration of network structure information and attribute information. A vast majority of existing works are mainly based on the Homophily assumption that implies the nodal attribute similarity of connected nodes. Nonetheless, this assumption is untenable in practice as the existence of noisy and structurally irrelevant attributes may adversely affect the anomaly detection performance. Despite the fact that recent attempts perform subspace selection to address this issue, these algorithms treat subspace selection and anomaly detection as two separate steps which often leads to suboptimal solutions. In this paper, we investigate how to fuse attribute and network structure information more synergistically to avoid the adverse effects brought by noisy and structurally irrelevant attributes. Methodologically, we propose a novel joint framework to conduct attribute selection and anomaly detection as a whole based on CUR decomposition and residual analysis. By filtering out noisy and irrelevant node attributes, we perform anomaly detection with the remaining representative attributes. Experimental results on both synthetic and real-world datasets corroborate the effectiveness of the proposed framework. © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.

Keyword :

Attribute information Attribute selection Attribute similarity Network structures Real-world datasets Seamless integration Suboptimal solution Subspace selection

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GB/T 7714 Peng, Zhen , Luo, Minnan , Li, Jundong et al. Anomalous: A joint modeling approach for anomaly detection on attributed networks [C] . 2018 : 3513-3519 .
MLA Peng, Zhen et al. "Anomalous: A joint modeling approach for anomaly detection on attributed networks" . (2018) : 3513-3519 .
APA Peng, Zhen , Luo, Minnan , Li, Jundong , Liu, Huan , Zheng, Qinghua . Anomalous: A joint modeling approach for anomaly detection on attributed networks . (2018) : 3513-3519 .
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Measuring student's utilization of video resources and its effect on academic performance EI Scopus
会议论文 | 2018 , 196-198 | 18th IEEE International Conference on Advanced Learning Technologies, ICALT 2018
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Abstract :

Massive video resources were produced to meet the needs of learning knowledge and skills anytime and anywhere through internet. Therefore, whether these video resources were fully utilized by students is an important issue for schools and teachers. This paper proposes three indicators based on student's log data and course's video information to measure the utilization of video resources. In addition, the proposed indicators are applied in a case study to analyze how different utilization patterns affect students' academic performance in a large-scale online distance education context. © 2018 IEEE.

Keyword :

Academic performance Evaluation indicators Log data Online distance education Utilization patterns Video information

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GB/T 7714 He, Huan , Zheng, Qinghua , Dong, Bo et al. Measuring student's utilization of video resources and its effect on academic performance [C] . 2018 : 196-198 .
MLA He, Huan et al. "Measuring student's utilization of video resources and its effect on academic performance" . (2018) : 196-198 .
APA He, Huan , Zheng, Qinghua , Dong, Bo , Yu, Hongchao . Measuring student's utilization of video resources and its effect on academic performance . (2018) : 196-198 .
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Test Case Prioritization Based on Method Call Sequences EI Scopus
会议论文 | 2018 , 1 , 251-256 | 42nd IEEE Computer Software and Applications Conference, COMPSAC 2018
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Abstract :

Test case prioritization is widely used in testing with the purpose of detecting faults as early as possible. Most existing techniques exploit coverage to prioritize test cases based on the hypothesis that a test case with higher coverage is more likely to catch bugs. Statement coverage and function coverage are the two widely used coverage granularity. The former typically achieves better test case prioritization in terms of fault detection capability, while the latter is more efficient because it incurs less overhead. In this paper we argue that static information such as statement and function coverage may not be the best criteria for guiding dynamic executions. Executions that cover the same set of statements /functions can may exhibit very different behavior. Therefore, the abstraction that reduces program behavior to statement/function coverage can be too simplistic to predicate fault detection capability. We propose a new approach that exploits function call sequences to prioritize test cases. This is based on the observation that the function call sequences rather than the set of executed functions is a better indicator of program behavior. Test cases that reveal unique function call sequences may have better chance to encounter faults. We choose function instead of statement sequences due to the consideration of efficiency. We have developed and implemented a new prioritization strategy AGC (Additional Greedy method Call sequence), that exploit function call sequences. We compare AGC against existing test case prioritization techniques on eight real-world open source Java projects. Our experiments show that our approach outperforms existing techniques on large programs (but not on small programs) in terms of bug detection capability. The performance shows a growth trend when the size of program increases. © 2018 IEEE.

Keyword :

Behavior graphs Detection capability Dynamic execution Function coverage Program behavior Statement coverage Static information Test case prioritization

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GB/T 7714 Chi, Jianlei , Qu, Yu , Zheng, Qinghua et al. Test Case Prioritization Based on Method Call Sequences [C] . 2018 : 251-256 .
MLA Chi, Jianlei et al. "Test Case Prioritization Based on Method Call Sequences" . (2018) : 251-256 .
APA Chi, Jianlei , Qu, Yu , Zheng, Qinghua , Yang, Zijiang , Jin, Wuxia , Cui, Di et al. Test Case Prioritization Based on Method Call Sequences . (2018) : 251-256 .
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A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map EI Scopus CSSCI-E SSCI SCIE
期刊论文 | 2018 , 6 , 57562-57571 | IEEE Access
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Abstract :

Learning resource recommendation, such as curriculum video recommendation, is an effective way to reduce cognitive overload in online learning. The existing curriculum video recommendation systems are generally limited to one course, ignoring the knowledge correlation between courses. In this paper, we propose a two-stage cross-curriculum video recommendation algorithm that considers both the learners' implicit feedback and the knowledge association between course videos. First, we use collaborative filtering to generate a video seed set, which is based on the learner's implicit video feedback, such as video learning frequencies, video learning duration, and video pausing and dragging frequencies. Second, we construct a cross-curriculum video-associated knowledge map and use a random walk algorithm to measure the relevance of the course videos. The relevance is based on each video seed as a starting node and is extended to a video subgraph. Then, several cross-curricular video-oriented subgraphs are recommended for the learners. The experimental results indicate that our cross-curriculum video recommendation algorithm performs better than the traditional collaborative filtering-based recommendation algorithms in terms of accuracy, recall rate, and knowledge relevance. © 2013 IEEE.

Keyword :

Cognitive overload Implicit feedback Knowledge map Learning resource Online learning Random walk algorithms Recommendation algorithms Video feedback

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GB/T 7714 Zhu, Haiping , Liu, Yu , Tian, Feng et al. A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map [J]. | IEEE Access , 2018 , 6 : 57562-57571 .
MLA Zhu, Haiping et al. "A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map" . | IEEE Access 6 (2018) : 57562-57571 .
APA Zhu, Haiping , Liu, Yu , Tian, Feng , Ni, Yifu , Wu, Ke , Chen, Yan et al. A Cross-Curriculum Video Recommendation Algorithm Based on a Video-Associated Knowledge Map . | IEEE Access , 2018 , 6 , 57562-57571 .
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IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning EI Scopus
会议论文 | 2018 , 1224-1235 | 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications and 12th IEEE International Conference on Big Data Science and Engineering, Trustcom/BigDataSE 2018
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Abstract :

Tax evasion detection plays a crucial role in addressing tax revenue loss. Many efforts have been made to develop tax evasion detection models by leveraging machine learning techniques, but they have not constructed a uniform model for different geographical regions because an ample supply of training examples is a fundamental prerequisite for an effective detection model. When sufficient tax data are not readily available, the development of a representative detection model is more difficult due to unequal feature distributions in different regions. Existing methods face a challenge in explaining and tracing derived results. To overcome these challenges, we propose an Inter-Region Tax Evasion Detection method based on Transfer Learning (IRTED-TL), which is optimized to simultaneously augment training data and induce interpretability into the detection model. We exploit evasion-related knowledge in one region and leverage transfer learning techniques to reinforce the tax evasion detection tasks of other regions in which training examples are lacking. We provide a unified framework that takes advantage of auxiliary data using a transfer learning mechanism and builds an interpretable classifier for inter-region tax evasion detection. Experimental tests based on real-world tax data demonstrate that the IRTED-TL can detect tax evaders with higher accuracy and better interpretability than existing methods. © 2018 IEEE.

Keyword :

Experimental test Feature distribution Interpretability Machine learning techniques Region detection Tax evasions Transfer learning Unified framework

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GB/T 7714 Zhu, Xulyu , Yan, Zheng , Ruan, Jianfei et al. IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning [C] . 2018 : 1224-1235 .
MLA Zhu, Xulyu et al. "IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning" . (2018) : 1224-1235 .
APA Zhu, Xulyu , Yan, Zheng , Ruan, Jianfei , Zheng, Qinghua , Dong, Bo . IRTED-TL: An Inter-Region Tax Evasion Detection Method Based on Transfer Learning . (2018) : 1224-1235 .
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