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学者姓名:郑庆华

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Identifying suspicious groups of affiliated-transaction-based tax evasion in big data EI Scopus
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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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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Semi-supervised clue fusion for spammer detection in Sina Weibo EI SCIE Scopus
期刊论文 | 2018 , 44 , 22-32 | INFORMATION FUSION
SCOPUS Cited Count: 2
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Abstract :

Microblog is a popular social network platform that facilitates users to collect and spread information on the Internet, but on the other side it stimulates new forms of spammers, who can hinder effective information dissemination. Spammers in Sina Weibo use various spamming strategies to evade protection mechanisms, which presents practical challenges in spammer detection. First, clues to identify spammers are usually hidden in multiple aspects, such as content, behavior, relationship, and interaction. Second, labeled training instances are lacking for learning. In this paper, a novel approach called Semi-Supervised Clue Fusion (SSCF) is proposed to conduct effective spammer detection in Sina Weibo. SSCF acquires a linear weighted function to fuse the comprehensive clues explored from multiple aspects to obtain final results. SSCF iteratively predicts the unlabeled instances based on a small size of primarily labeled instances in a semi-supervised fashion. SSCF is empirically validated on the real-world data from Sina Weibo. Results show that this approach significantly outperforms state-of-the-art baselines.

Keyword :

Spanner Sina Weibo Fusion Multiple views

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GB/T 7714 Chen, Hao , Liu, Jun , Lv, Yanzhang et al. Semi-supervised clue fusion for spammer detection in Sina Weibo [J]. | INFORMATION FUSION , 2018 , 44 : 22-32 .
MLA Chen, Hao et al. "Semi-supervised clue fusion for spammer detection in Sina Weibo" . | INFORMATION FUSION 44 (2018) : 22-32 .
APA Chen, Hao , Liu, Jun , Lv, Yanzhang , Li, Max Haifei , Liu, Mengyue , Zheng, Qinghua . Semi-supervised clue fusion for spammer detection in Sina Weibo . | INFORMATION FUSION , 2018 , 44 , 22-32 .
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Crowd Intelligence for Decision Making Based on Positive and Negative Comparing With Linguistic Scale CPCI-S
会议论文 | 2018 | IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
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Crowd intelligence opens up new ways for decision making in open environments, traditional decision making is unable to effectively make correct decisions in open environments. In this paper, positive and negative comparing method using linguistic scale is proposed to make decisions in the open environments with crowd intelligence. Firstly, the crowd participants compare the alternative with the corresponding positive and negative assessment points, and give their evaluations using linguistic scales form positive and negative views. The crowd participants' evaluations can be translated into Intuitionistic Fuzzy Numbers (IFNs). In the proposed methods, the evaluations given by the crowd participants do not depend on the pairwise comparisons of the alternatives, the consistent problem can be avoided. Secondly, the consensus measures between aggregating results and IFNs are proposed. Based on these concepts, the aggregating methods that without discarding any IFNs are proposed and studied. The studying results show that the proposed methods can improve the consensus measures between the aggregating result and evaluations given by crowd participants.

Keyword :

Consensus measure Linguistic scale Crowd Intelligence Outlier Intuitionistic Fuzzy Numbers

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GB/T 7714 Zhang, Hengshan , Zheng, Qinghua , Wang, Zhongmin et al. Crowd Intelligence for Decision Making Based on Positive and Negative Comparing With Linguistic Scale [C] . 2018 .
MLA Zhang, Hengshan et al. "Crowd Intelligence for Decision Making Based on Positive and Negative Comparing With Linguistic Scale" . (2018) .
APA Zhang, Hengshan , Zheng, Qinghua , Wang, Zhongmin , Chen, Yanping , Qu, Yu , Liu, Ting . Crowd Intelligence for Decision Making Based on Positive and Negative Comparing With Linguistic Scale . (2018) .
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Dynamic structure measurement for distributed software EI SCIE Scopus
期刊论文 | 2018 , 26 (3) , 1119-1145 | SOFTWARE QUALITY JOURNAL
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Abstract :

With the advent of network technologies and the ultra-fast increasing of computing ability, the distributed architecture has become a necessity for the majority of software systems. However, it is difficult for current architecture measurements to evaluate distributed systems, such as cohesion and coupling. Most current methods focus on the relations among various classes or packages but barely consider the structure at component level, which has a serious impact on change impact analysis, fault diagnosis, or other maintenance activities. In this paper, we propose a dynamic structure measurement for distributed software. The intra-component and inter-component dependencies are introduced into a Calling Network model to further represent distributed software. More importantly, based on the Kieker monitoring framework, the measurement methods are proposed and implemented for distributed software. Two structural quality attributes cohesion factor of component (CHC) and coupling factor of component (CPC) are measured. Finally, case studies are conducted on two open-source distributed systems: RSS Reader Recipes and the distributed version of iBATIS JPetStore. By applying the proposed methods and comparing with the existing ones, the features of CHC and CPC can be assessed and observed for distributed software.

Keyword :

Structure measurement Dynamic metric Calling network Distributed software

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GB/T 7714 Jin, Wuxia , Liu, Ting , Qu, Yu et al. Dynamic structure measurement for distributed software [J]. | SOFTWARE QUALITY JOURNAL , 2018 , 26 (3) : 1119-1145 .
MLA Jin, Wuxia et al. "Dynamic structure measurement for distributed software" . | SOFTWARE QUALITY JOURNAL 26 . 3 (2018) : 1119-1145 .
APA Jin, Wuxia , Liu, Ting , Qu, Yu , Zheng, Qinghua , Cui, Di , Chi, Jianlei . Dynamic structure measurement for distributed software . | SOFTWARE QUALITY JOURNAL , 2018 , 26 (3) , 1119-1145 .
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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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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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Debugging Multithreaded Programs as if They Were Sequential EI SCIE Scopus
期刊论文 | 2018 , 6 , 40024-40040 | IEEE ACCESS
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Debugging multithread programs is extremely difficult because the basic assumption that underlies sequential program debugging, that is, the program behavior is deterministic under a fixed input, is no longer valid due to the nondeterminism attributed to thread scheduling. It is because the programs behavior is non-deterministic due to the nondeterminism of parallel execution, which makes debugging or testing multithreaded programs extremely difficult. In this paper, we propose a proactive debugging method to restore this basic assumption so that programmers can debug multithreaded programs as if they were sequential. Our approach is based on the synergistic integration of symbolic analysis and dynamic analysis techniques. In particular, symbolic analysis investigates the program behavior under the same input to search whether there is a thread schedule would trigger a bug or a not-yet-explored path. Dynamic analysis is to execute these new paths with the guide of the generated thread schedules, thereby further guiding the symbolic analysis. The net effect of applying this feedback loop is a systematic and complete coverage of the program behavior under a fixed test input. We implement the proposed approach in a prototype tool called proactive-debugger. Our experiments show that proactive-debugger outperforms both ESBMC and Maple, which are two powerful and well-known testing tools for failure detection in multithreaded programs.

Keyword :

debugging constraint solving Testing symbolic analysis multithreaded programs

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GB/T 7714 Zhang, Xiaodong , Yang, Zijiang , Zheng, Qinghua et al. Debugging Multithreaded Programs as if They Were Sequential [J]. | IEEE ACCESS , 2018 , 6 : 40024-40040 .
MLA Zhang, Xiaodong et al. "Debugging Multithreaded Programs as if They Were Sequential" . | IEEE ACCESS 6 (2018) : 40024-40040 .
APA Zhang, Xiaodong , Yang, Zijiang , Zheng, Qinghua , Hao, Yu , Liu, Pei , Yu, Lechen et al. Debugging Multithreaded Programs as if They Were Sequential . | IEEE ACCESS , 2018 , 6 , 40024-40040 .
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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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Prediction-Based and Locality-Aware Task Scheduling for Parallelizing Video Transcoding Over Heterogeneous MapReduce Cluster EI SCIE Scopus
期刊论文 | 2018 , 28 (4) , 1009-1020 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
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MapReduce is a popular programming model in cloud computing to deal with the high computational task, such as video transcoding. It splits the video (task) into multiple segments (subtasks) and transcodes them in parallel in cluster. Due to the complexity of video transcoding and the poor performance of heterogeneous MapReduce cluster, scheduling these subtasks to minimize the total transcoding time is still a challenge. In this paper, we propose a prediction-based and locality-aware task scheduling (PLTS) method for parallelizing video transcoding over heterogeneous MapReduce cluster. First, we analyze video decoding and encoding technologies and predict the segment transcoding complexity, which can provide a foundational base for the following scheduling. Second, we attempt to schedule subtasks on machines that contain the related input data, which are referred to as data locality, so as to reduce large-scale data movement and data transfer during the mapping phase. Third, we formulate the scheduling as a job shop scheduling problem and propose a heuristic PLTS algorithm. It combines the benefits of two traditional heuristic scheduling algorithms, Max-Min and Min-Min, to make load balancing in cluster and short the total transcoding time. The experimental results also show the efficiency of our algorithm.

Keyword :

task scheduling Complexity prediction video transcoding locality-aware MapReduce

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GB/T 7714 Zhao, Hui , Zheng, Qinghua , Zhang, Weizhan et al. Prediction-Based and Locality-Aware Task Scheduling for Parallelizing Video Transcoding Over Heterogeneous MapReduce Cluster [J]. | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (4) : 1009-1020 .
MLA Zhao, Hui et al. "Prediction-Based and Locality-Aware Task Scheduling for Parallelizing Video Transcoding Over Heterogeneous MapReduce Cluster" . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 28 . 4 (2018) : 1009-1020 .
APA Zhao, Hui , Zheng, Qinghua , Zhang, Weizhan , Wang, Jing . Prediction-Based and Locality-Aware Task Scheduling for Parallelizing Video Transcoding Over Heterogeneous MapReduce Cluster . | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY , 2018 , 28 (4) , 1009-1020 .
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