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学者姓名:郑庆华

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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
SCOPUS Cited Count: 1
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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
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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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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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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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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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Diverse fuzzy c-means for image clustering EI Scopus
期刊论文 | 2018 | Pattern Recognition Letters
SCOPUS Cited Count: 1
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Abstract :

Image clustering is a key technique for better accomplishing image annotation and searching in large image repositories. Fuzzy c-means and its variations have achieved excellent performance on image clustering because they allow each image to belong to more than one cluster. However, these methods neglect the relations between different image clusters, and hence often suffer from the “cluster one-sidedness” problem that redundant centers are learned to characterize the same or similar image clusters. To this issue, we propose a diverse fuzzy c-means for image clustering via introducing a novel diversity regularization into the traditional fuzzy c-means objective. This diversity regularization guarantees the learned image cluster centers to be different from each other and to fill the image data space as much as possible. An efficient optimization algorithm is exploited to address the diverse fuzzy c-means objective, which is proved to converge to local optimal solutions and has a satisfactory time complexity. Experiments on synthetic and six image datasets demonstrate the effectiveness of the proposed method as well as the necessity of the diversity regularization. © 2018

Keyword :

Cluster one-sidedness Diversity regularization Fuzzy C mean Image clustering Image clusters Local optimal solution Optimization algorithms Time complexity

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GB/T 7714 Zhang, Lingling , Luo, Minnan , Liu, Jun et al. Diverse fuzzy c-means for image clustering [J]. | Pattern Recognition Letters , 2018 .
MLA Zhang, Lingling et al. "Diverse fuzzy c-means for image clustering" . | Pattern Recognition Letters (2018) .
APA Zhang, Lingling , Luo, Minnan , Liu, Jun , Li, Zhihui , Zheng, Qinghua . Diverse fuzzy c-means for image clustering . | Pattern Recognition Letters , 2018 .
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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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Node2defect: Using network embedding to improve software defect prediction EI Scopus
会议论文 | 2018 , 844-849
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Abstract :

© 2018 Association for Computing Machinery. Network measures have been proved to be useful in predicting software defects. Leveraging the dependency relationships between software modules, network measures can capture various structural features of software systems. However, existing studies have relied on user-defined network measures (e.g., degree statistics or centrality metrics), which are inflexible and require high computation cost, to describe the structural features. In this paper, we propose a new method called node2defect which uses a newly proposed network embedding technique, node2vec, to automatically learn to encode dependency network structure into low-dimensional vector spaces to improve software defect prediction. Specifically, we firstly construct a program's Class Dependency Network. Then node2vec is used to automatically learn structural features of the network. After that, we combine the learned features with traditional software engineering features, for accurate defect prediction. We evaluate our method on 15 open source programs. The experimental results show that in average, node2defect improves the state-of-the-art approach by 9.15% in terms of F-measure.

Keyword :

Defect prediction Network embedding Software defect Software metrics

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GB/T 7714 Qu, Yu , Jin, Yangxu , Liu, Ting et al. Node2defect: Using network embedding to improve software defect prediction [C] . 2018 : 844-849 .
MLA Qu, Yu et al. "Node2defect: Using network embedding to improve software defect prediction" . (2018) : 844-849 .
APA Qu, Yu , Jin, Yangxu , Liu, Ting , Cui, Di , Zheng, Qinghua , Chi, Jianlei et al. Node2defect: Using network embedding to improve software defect prediction . (2018) : 844-849 .
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Deep Semisupervised Zero-Shot Learning with Maximum Mean Discrepancy. PubMed
期刊论文 | 2018 , 30 (5) , 1426-1447 | Neural computation
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Abstract :

Due to the difficulty of collecting labeled images for hundreds of thousands of visual categories, zero-shot learning, where unseen categories do not have any labeled images in training stage, has attracted more attention. In the past, many studies focused on transferring knowledge from seen to unseen categories by projecting all category labels into a semantic space. However, the label embeddings could not adequately express the semantics of categories. Furthermore, the common semantics of seen and unseen instances cannot be captured accurately because the distribution of these instances may be quite different. For these issues, we propose a novel deep semisupervised method by jointly considering the heterogeneity gap between different modalities and the correlation among unimodal instances. This method replaces the original labels with the corresponding textual descriptions to better capture the category semantics. This method also overcomes the problem of distribution difference by minimizing the maximum mean discrepancy between seen and unseen instance distributions. Extensive experimental results on two benchmark data sets, CU200-Birds and Oxford Flowers-102, indicate that our method achieves significant improvements over previous methods.

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GB/T 7714 Zhang Lingling , Liu Jun , Luo Minnan et al. Deep Semisupervised Zero-Shot Learning with Maximum Mean Discrepancy. [J]. | Neural computation , 2018 , 30 (5) : 1426-1447 .
MLA Zhang Lingling et al. "Deep Semisupervised Zero-Shot Learning with Maximum Mean Discrepancy." . | Neural computation 30 . 5 (2018) : 1426-1447 .
APA Zhang Lingling , Liu Jun , Luo Minnan , Chang Xiaojun , Zheng Qinghua . Deep Semisupervised Zero-Shot Learning with Maximum Mean Discrepancy. . | Neural computation , 2018 , 30 (5) , 1426-1447 .
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Experimental Study of Video Fusion for Multi-View Video Streaming in Mobile Media Cloud EI Scopus
会议论文 | 2018 , 2018-January , 79-86 | 6th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, MobileCloud 2018
SCOPUS Cited Count: 1
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Abstract :

Nowadays, mobile users are not just content with a quick access to Internet when they are watching various types of videos, but also expecting a better video viewing experience. The reality is that decoding videos will consume very large resources of mobile devices. However, the resources of mobile devices are limited in terms of CPU, the main memory and so on, which impedes the performance enhancement. Fortunately, the emergence of mobile media cloud makes it possible to lighten decoding load on mobile devices. Cloud computing has an outstanding performance in rendering diverse services, strong computation ability, and great storage capacity in a relatively lower cost, which provides an effective channel out for the present dilemma. In this paper, we focus on the experimental study of video fusion method for multi-view video streaming under mobile media cloud environment. Firstly, we conduct a large quantity of collection experiments and find that decoding load of videos follows the power function model, which demonstrates that video fusion of multi-view videos at cloud side may effectively reduce the CPU usage of decoding at mobile side. Secondly, we further quantify the condition when the video fusion method can reduce the CPU usage of mobile devices along with its corresponding reduction rates. © 2018 IEEE.

Keyword :

Computation ability Mobile media Multiview video Optimization conditions Performance enhancements Power functions Storage capacity Video fusion

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GB/T 7714 Wang, Jie , Yang, Shuquan , Zhang, Weizhan et al. Experimental Study of Video Fusion for Multi-View Video Streaming in Mobile Media Cloud [C] . 2018 : 79-86 .
MLA Wang, Jie et al. "Experimental Study of Video Fusion for Multi-View Video Streaming in Mobile Media Cloud" . (2018) : 79-86 .
APA Wang, Jie , Yang, Shuquan , Zhang, Weizhan , Liu, Junquan , Kong, Xie , Zheng, Qinghua . Experimental Study of Video Fusion for Multi-View Video Streaming in Mobile Media Cloud . (2018) : 79-86 .
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