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学者姓名:郑庆华

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< Page ,Total 42 >
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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Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis EI CPCI-S SCIE Scopus
会议论文 | 2018 , 13 (8) , 1890-1905 | 26th IEEE International Symposium on Software Reliability Engineering (ISSRE)
WoS CC Cited Count: 2
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Abstract :

The rapid increase in the number of Android malware poses great challenges to anti-malware systems, because the sheer number of malware samples overwhelms malware analysis systems. The classification of malware samples into families, such that the common features shared by malware samples in the same family can be exploited in malware detection and inspection, is a promising approach for accelerating malware analysis. Furthermore, the selection of representative malware samples in each family can drastically decrease the number of malware to be analyzed. However, the existing classification solutions are limited because of the following reasons. First, the legitimate part of the malware may misguide the classification algorithms because the majority of Android malware are constructed by inserting malicious components into popular apps. Second, the polymorphic variants of Android malware can evade detection by employing transformation attacks. In this paper, we propose a novel approach that constructs frequent subgraphs (fregraphs) to represent the common behaviors of malware samples that belong to the same family. Moreover, we propose and develop FalDroid, a novel system that automatically classifies Android malware and selects representative malware samples in accordance with fregraphs. We apply it to 8407 malware samples from 36 families. Experimental results show that FalDroid can correctly classify 94.2% of malware samples into their families using approximately 4.6 sec per app. FalDroid can also dramatically reduce the cost of malware investigation by selecting only 8.5% to 22% representative samples that exhibit the most common malicious behavior among all samples.

Keyword :

frequent subgraph familial classification Android malware

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GB/T 7714 Fan, Ming , Liu, Jun , Luo, Xiapu et al. Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis [C] . 2018 : 1890-1905 .
MLA Fan, Ming et al. "Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis" . (2018) : 1890-1905 .
APA Fan, Ming , Liu, Jun , Luo, Xiapu , Chen, Kai , Tian, Zhenzhou , Zheng, Qinghua et al. Android Malware Familial Classification and Representative Sample Selection via Frequent Subgraph Analysis . (2018) : 1890-1905 .
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Exploring open information via event network EI AHCI SSCI SCIE Scopus
期刊论文 | 2018 , 24 (2) , 199-220 | NATURAL LANGUAGE ENGINEERING
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Abstract :

It is a challenging task to discover information from a large amount of data in an open domain.(1) In this paper, an event network framework is proposed to address this challenge. It is in fact an empirical construct for exploring open information, composed of three steps: document event detection, event network construction and event network analysis. First, documents are clustered into document events for reducing the impact of noisy and heterogeneous resources. Secondly, linguistic units (e.g., named entities or entity relations) are extracted from each document event and combined into an event network, which enables content-oriented retrieval. Then, in the final step, techniques such as social network or complex network can be applied to analyze the event network for exploring open information. In the implementation section, we provide examples of exploring open information via event network.

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GB/T 7714 Chen, Yanping , Zheng, Qinghua , Tian, Feng et al. Exploring open information via event network [J]. | NATURAL LANGUAGE ENGINEERING , 2018 , 24 (2) : 199-220 .
MLA Chen, Yanping et al. "Exploring open information via event network" . | NATURAL LANGUAGE ENGINEERING 24 . 2 (2018) : 199-220 .
APA Chen, Yanping , Zheng, Qinghua , Tian, Feng , Liu, Huan , Hao, Yazhou , Shah, Nazaraf . Exploring open information via event network . | NATURAL LANGUAGE ENGINEERING , 2018 , 24 (2) , 199-220 .
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A multi-constraint learning path recommendation algorithm based on knowledge map EI SSCI SCIE Scopus
期刊论文 | 2018 , 143 , 102-114 | KNOWLEDGE-BASED SYSTEMS
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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Abstract :

It is difficult for e-learners to make decisions on how to learn when they are facing with a large amount of learning resources, especially when they have to balance available limited learning time and multiple learning objectives in various learning scenarios. This research presented in this paper addresses this challenge by proposing a new multi-constraint learning path recommendation algorithm based on knowledge map. The main contributions of the paper are as follows. Firstly, two hypotheses on e-learners' different learning path preferences for four different learning scenarios (initial learning, usual review, pre-exam learning and pre-exam review) are verified through questionnaire-based statistical analysis. Secondly, according to learning behavior characteristics of four types of the learning scenarios, a multi constraint learning path recommendation model is proposed, in which the variables and their weighted coefficients considers different learning path preferences of the learners in different learning scenarios as well as learning resource organization and fragmented time. Thirdly, based on the proposed model and knowledge map, the design and implementation of a multi-constraint learning path recommendation algorithm is described. Finally, it is shown that the questionnaire results from over 110 e-learners verify the effectiveness of the proposed algorithm and show the similarity between the learners' self-organized learning paths and the recommended learning paths. (C) 2017 Elsevier B.V. All rights reserved.

Keyword :

E-learning Knowledge map Learning path recommendation Learning scenario

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GB/T 7714 Zhu, Haiping , Tian, Feng , Wu, Ke et al. A multi-constraint learning path recommendation algorithm based on knowledge map [J]. | KNOWLEDGE-BASED SYSTEMS , 2018 , 143 : 102-114 .
MLA Zhu, Haiping et al. "A multi-constraint learning path recommendation algorithm based on knowledge map" . | KNOWLEDGE-BASED SYSTEMS 143 (2018) : 102-114 .
APA Zhu, Haiping , Tian, Feng , Wu, Ke , Shah, Nazaraf , Chen, Yan , Ni, Yifu et al. A multi-constraint learning path recommendation algorithm based on knowledge map . | KNOWLEDGE-BASED SYSTEMS , 2018 , 143 , 102-114 .
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A Group-Oriented Recommendation Algorithm Based on Similarities of Personal Learning Generative Networks EI SCIE Scopus
期刊论文 | 2018 , 6 , 42729-42739 | IEEE ACCESS
SCOPUS Cited Count: 1
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Abstract :

To solve the lack of consideration of the learning time sequence and knowledge dependencies in group-based recommendation, we proposed a novel group-oriented recommendation algorithm which is characterized by mapping the user's learning log to a personal learning generative network (PLGN) based on a knowledge map. In this paper, we first provide calculation methods of similarity and temporal correlation between knowledge points, where we provide the construction method of the PLGN. Second, a method for measuring the similarities between any two PLGNs is proposed. According to the similarities, we perform the CURE clustering algorithm to obtain learning groups. Third, based on the group clustering, the group learning generative network using a graph overlay method is generated. We calculate the importance of the vertices on the different learning needs and propose a group-oriented recommendation algorithm. Finally, we compare the effect of the proposed recommendation to that of a group-based collaborative filtering recommendation for the aspects of precision rate, recall rate, normalized discounted cumulative gain, and the average accuracy of parameters (MAP). The experimental results show that the group-oriented learning recommendation based on the learning generated network outperforms the group recommendation-based collaborative filtering when the amount of data is large enough.

Keyword :

Graph similarity group recommendation knowledge map learning generative network

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GB/T 7714 Zhu, Haiping , Ni, Yifu , Tian, Feng et al. A Group-Oriented Recommendation Algorithm Based on Similarities of Personal Learning Generative Networks [J]. | IEEE ACCESS , 2018 , 6 : 42729-42739 .
MLA Zhu, Haiping et al. "A Group-Oriented Recommendation Algorithm Based on Similarities of Personal Learning Generative Networks" . | IEEE ACCESS 6 (2018) : 42729-42739 .
APA Zhu, Haiping , Ni, Yifu , Tian, Feng , Feng, Pei , Chen, Yan , Zheng, Qinghua . A Group-Oriented Recommendation Algorithm Based on Similarities of Personal Learning Generative Networks . | IEEE ACCESS , 2018 , 6 , 42729-42739 .
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Adaptive Unsupervised Feature Selection With Structure Regularization EI SCIE Scopus
期刊论文 | 2018 , 29 (4) , 944-956 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
WoS CC Cited Count: 4
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Abstract :

Feature selection is one of the most important dimension reduction techniques for its efficiency and interpretation. Since practical data in large scale are usually collected without labels, and labeling these data are dramatically expensive and time-consuming, unsupervised feature selection has become a ubiquitous and challenging problem. Without label information, the fundamental problem of unsupervised feature selection lies in how to characterize the geometry structure of original feature space and produce a faithful feature subset, which preserves the intrinsic structure accurately. In this paper, we characterize the intrinsic local structure by an adaptive reconstruction graph and simultaneously consider its multiconnected-components (multi-cluster) structure by imposing a rank constraint on the corresponding Laplacian matrix. To achieve a desirable feature subset, we learn the optimal reconstruction graph and selective matrix simultaneously, instead of using a predetermined graph. We exploit an efficient alternative optimization algorithm to solve the proposed challenging problem, together with the theoretical analyses on its convergence and computational complexity. Finally, extensive experiments on clustering task are conducted over several benchmark data sets to verify the effectiveness and superiority of the proposed unsupervised feature selection algorithm.

Keyword :

Adaptive neighbors dimension reduction local linear embedding unsupervised feature selection structure regularization

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GB/T 7714 Luo, Minnan , Nie, Feiping , Chang, Xiaojun et al. Adaptive Unsupervised Feature Selection With Structure Regularization [J]. | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2018 , 29 (4) : 944-956 .
MLA Luo, Minnan et al. "Adaptive Unsupervised Feature Selection With Structure Regularization" . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29 . 4 (2018) : 944-956 .
APA Luo, Minnan , Nie, Feiping , Chang, Xiaojun , Yang, Yi , Hauptmann, Alexander G. , Zheng, Qinghua . Adaptive Unsupervised Feature Selection With Structure Regularization . | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS , 2018 , 29 (4) , 944-956 .
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A Bandwidth Variation Pattern-Differentiated Rate Adaptation for HTTP Adaptive Streaming Over an LTE Cellular Network EI SCIE Scopus
期刊论文 | 2018 , 6 , 9554-9569 | IEEE ACCESS
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Abstract :

Currently, HTTP adaptive streaming (HAS) is the state-of-the-art technology for mobile video streaming. The rate adaptation of HAS has been designed to make a trade-off between two contrasting requirements, i.e., enhancing the quality of a video, and reducing the probability of video freezes, by adaptively switching between different video bitrates during a video playback session. This process becomes more challenging when moving onto an long term evolution (LTE) cellular network due to the unstable nature of the wireless channel. In this paper, we propose the bandwidth variation pattern-differentiated rate adaptation (BVPDRA) algorithm for LTE cellular networks. Unlike prior works, BVPDRA does not strike a balance between the stableness and responsiveness of bitrate switching in the case of bandwidth capacity variations. BVPDRA differentiates between bandwidth variation patterns of the LTE cellular network as either constant bandwidth fluctuations or instantaneous bandwidth hopping. Accordingly, BVPDRA operates with a dual character: for the constant bandwidth fluctuations, BVPDRA performs smoothed bandwidth prediction and conservative rate switching to minimize video quality version oscillations; for the instantaneous bandwidth hopping, BVPDRA performs positive bandwidth prediction and aggressive rate switching to maximize the bandwidth utilization and minimize the risk of playback stalling. We empirically evaluate the performance of BVPDRA on an LTE cellular network testbed. The results demonstrate that BVPDRA achieves a higher average bitrate, and lower rebuffering ratio with a reduced bitrate switching frequency.

Keyword :

HTTP adaptive streaming rate adaptation long term evolution (LTE) cellular network

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GB/T 7714 Du, Haipeng , Zheng, Qinghua , Zhang, Weizhan et al. A Bandwidth Variation Pattern-Differentiated Rate Adaptation for HTTP Adaptive Streaming Over an LTE Cellular Network [J]. | IEEE ACCESS , 2018 , 6 : 9554-9569 .
MLA Du, Haipeng et al. "A Bandwidth Variation Pattern-Differentiated Rate Adaptation for HTTP Adaptive Streaming Over an LTE Cellular Network" . | IEEE ACCESS 6 (2018) : 9554-9569 .
APA Du, Haipeng , Zheng, Qinghua , Zhang, Weizhan , Gao, Xiang . A Bandwidth Variation Pattern-Differentiated Rate Adaptation for HTTP Adaptive Streaming Over an LTE Cellular Network . | IEEE ACCESS , 2018 , 6 , 9554-9569 .
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Detecting global and local topics via mining twitter data EI SCIE Scopus
期刊论文 | 2018 , 273 , 120-132 | NEUROCOMPUTING
SCOPUS Cited Count: 3
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Abstract :

Detecting topics from Twitter has been widely studied for understanding social events. There are two types of topics, i.e., global topics attracting widespread tweets with larger volume and local topics drawing attention of limited tweets of somewhere. However, most of existent works neglect the difference between them and suffer from the Long Tail Effect, resulting in the inability to detect the local one. In this paper, we distinguish global and local topics by associating each tweet with both of them simultaneously. We propose a probabilistic graphical model to extract global and local topics related to social events in a unified framework at the same time. Our model learns global topics using tweets scattered around all locations, while studies local topics merely utilizing tweets within the corresponding location. We collect two tweet datasets on Twitter from several cities in USA and evaluate our model over them. The experimental results show significant improvement of our model compared to baseline methods. (C) 2017 Elsevier B.V. All rights reserved.

Keyword :

Global and local topic Twitter Probabilistic graphical model Social event

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GB/T 7714 Liu, Huan , Ge, Yong , Zheng, Qinghua et al. Detecting global and local topics via mining twitter data [J]. | NEUROCOMPUTING , 2018 , 273 : 120-132 .
MLA Liu, Huan et al. "Detecting global and local topics via mining twitter data" . | NEUROCOMPUTING 273 (2018) : 120-132 .
APA Liu, Huan , Ge, Yong , Zheng, Qinghua , Lin, Rongcheng , Li, Huayu . Detecting global and local topics via mining twitter data . | NEUROCOMPUTING , 2018 , 273 , 120-132 .
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Robust dictionary learning with graph regularization for unsupervised person re-identification EI SCIE Scopus
期刊论文 | 2018 , 77 (3) , 3553-3577 | MULTIMEDIA TOOLS AND APPLICATIONS
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Abstract :

Most existing approaches for person re-identification are designed in a supervised way, undergoing a prohibitively high labeling cost and poor scalability. Besides establishing effective similarity distance metrics, these supervised methods usually focus on constructing discriminative and robust features, which is extremely difficult due to the significant viewpoint variations. To overcome these challenges, we propose a novel unsupervised method, termed as Robust Dictionary Learning with Graph Regularization (RDLGR), which can guarantee view-invariance through learning a dictionary shared by all the camera views. To avoid the significant degradation of performance caused by outliers, we employ a capped l (2,1)-norm based loss to make our model more robust, addressing the problem that traditional quadratic loss is known to be easily dominated by outliers. Considering the lack of labeled cross-view discriminative information in our unsupervised method, we further introduce a cross-view graph Laplacian regularization term into the framework of dictionary learning. As a result, the geographical structure of original data space can be preserved in the learned latent subspace as discriminative information, making it possible to further boost the matching accuracy. Extensive experimental results over four widely used benchmark datasets demonstrate the superiority of the proposed model over the state-of-the-art methods.

Keyword :

Robust dictionary learning Graph regularization Unsupervised person re-identification

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GB/T 7714 Yan, Caixia , Luo, Minnan , Liu, Wenhe et al. Robust dictionary learning with graph regularization for unsupervised person re-identification [J]. | MULTIMEDIA TOOLS AND APPLICATIONS , 2018 , 77 (3) : 3553-3577 .
MLA Yan, Caixia et al. "Robust dictionary learning with graph regularization for unsupervised person re-identification" . | MULTIMEDIA TOOLS AND APPLICATIONS 77 . 3 (2018) : 3553-3577 .
APA Yan, Caixia , Luo, Minnan , Liu, Wenhe , Zheng, Qinghua . Robust dictionary learning with graph regularization for unsupervised person re-identification . | MULTIMEDIA TOOLS AND APPLICATIONS , 2018 , 77 (3) , 3553-3577 .
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