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学者姓名:郑庆华

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Explaining Regressions via Alignment Slicing and Mending EI SCIE
期刊论文 | 2021 , 47 (11) , 2421-2437 | IEEE TRANSACTIONS ON SOFTWARE ENGINEERING
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Abstract :

Regression faults, which make working code stop functioning, are often introduced when developers make changes to the software. Many regression fault localization techniques have been proposed. However, issues like inaccuracy and lack of explanation are still obstacles for their practical application. In this work, we propose a trace-based approach to identifying not only where the root cause of a regression bug lies, but also how the defect is propagated to its manifestation as the explanation. In our approach, we keep the trace of original correct version as reference and infer the faulty steps on the trace of regression version so that we can build a causality graph of how the defect is propagated. To this end, we overcomes two technical challenges. First, we align two traces derived from two program versions by extending state-of-the-art trace alignment technique for regression fault with novel relaxation technique. Second, we construct causality graph (i.e., explanation) by adopting a technique called alignment slicing and mending to isolate the failure-inducing changes and explain the failure. Our comparative experiment with the state-of-the-art techniques including dynamic slicing, delta-debugging, and symbolic execution on 24 real-world regressions shows that (1) our approach is more accurate on isolating the failure-inducing changes, (2) the generated explanation requires acceptable manual effort to inspect, and (3) our approach requires lower runtime overhead. In addition, we also conduct an applicability experiment based on Defects4J bug repository, showing the potential limitations of our trace-based approach and providing guidance for its practical use.

Keyword :

Runtime Debugging trace alignment Software Task analysis Java Semantics Regression bug Computer bugs alignment slicing and mending fault localization

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GB/T 7714 Wang, Haijun , Lin, Yun , Yang, Zijiang et al. Explaining Regressions via Alignment Slicing and Mending [J]. | IEEE TRANSACTIONS ON SOFTWARE ENGINEERING , 2021 , 47 (11) : 2421-2437 .
MLA Wang, Haijun et al. "Explaining Regressions via Alignment Slicing and Mending" . | IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 47 . 11 (2021) : 2421-2437 .
APA Wang, Haijun , Lin, Yun , Yang, Zijiang , Sun, Jun , Liu, Yang , Dong, Jinsong et al. Explaining Regressions via Alignment Slicing and Mending . | IEEE TRANSACTIONS ON SOFTWARE ENGINEERING , 2021 , 47 (11) , 2421-2437 .
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Fs-DSM: Few-Shot Diagram-Sentence Matching via Cross-Modal Attention Graph Model EI SCIE
期刊论文 | 2021 , 30 , 8102-8115 | IEEE TRANSACTIONS ON IMAGE PROCESSING
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Abstract :

Diagram-sentence matching is a valuable academic research because it can help learners effectively understand the diagrams with the assisted by sentences. However, there are many uncommon objects, i.e. few-shot contents in diagrams and sentences. The existing methods for image-sentence matching have great limitations when applied to diagrams. Because they focus on the high-frequency objects during training and ignore the uncommon objects. In addition, the specialty leads to the semantic non-intuition of the diagram itself. In this work, we propose a cross-modal attention graph model for the few-shot diagram-sentence matching task named Fs-DSM. Specifically, it is composed of three modules. The graph initialization module regards the region-level diagram features and word-level sentence features as the nodes of Fs-DSM, and edges are represented as similarity between nodes. The information propagation module is a key point of Fs-DSM, in which the few-shot contents are recognized by an uncommon object recognition strategy, and then the nodes are updated by a neighborhood aggregation procedure with cross-modal propagation between all visual and textual nodes, while the edges are recomputed based on the new node features. The global association module integrates the features of regions and words to represent the global diagrams and sentences. By conducting comprehensive experiments in terms of few-shot and conventional image-sentence matching, we demonstrate that Fs-DSM achieves superior performances over the competitors on the AI2D(#) diagram dataset and two public benchmark datasets with nature images.

Keyword :

few-shot learning Diagram understanding attention graph neural network

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GB/T 7714 Hu, Xin , Zhang, Lingling , Liu, Jun et al. Fs-DSM: Few-Shot Diagram-Sentence Matching via Cross-Modal Attention Graph Model [J]. | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2021 , 30 : 8102-8115 .
MLA Hu, Xin et al. "Fs-DSM: Few-Shot Diagram-Sentence Matching via Cross-Modal Attention Graph Model" . | IEEE TRANSACTIONS ON IMAGE PROCESSING 30 (2021) : 8102-8115 .
APA Hu, Xin , Zhang, Lingling , Liu, Jun , Zheng, Qinghua , Zhou, Jianlong . Fs-DSM: Few-Shot Diagram-Sentence Matching via Cross-Modal Attention Graph Model . | IEEE TRANSACTIONS ON IMAGE PROCESSING , 2021 , 30 , 8102-8115 .
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QoE-driven HAS Live Video Channel Placement in the Media Cloud EI SCIE
期刊论文 | 2021 , 23 , 1530-1541 | IEEE TRANSACTIONS ON MULTIMEDIA
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Abstract :

HTTP adaptive streaming (HAS) technology has been increasingly employed by video service providers (VSPs) due to its prominent benefits such as reducing interruptions of video playback and achieving higher bandwidth utilization and outstanding quality of experience (QoE). And many VSPs have deployed HAS applications in the media cloud to provide large-scale video streaming services. At present, research into the media cloud typically focuses on the management and optimization of cloud resources, such as the placement and migration of virtual machines in media cloud data centers. However, considering the HAS live video streaming service, existing related works have not adequately discussed the specific impact of the consumption of computing and bandwidth resources of media cloud servers on the user experience (QoE), particularly under the resource constraints in the media cloud. In this paper, we first investigate and formulate the computing and bandwidth resource consumption characteristics of HAS live video streaming with different frame rates and resolutions, and we further establish a resources-aware QoE model to quantify the user experience of live video channels (i.e., programs). Then, based on the model, we p rest nt a QoE-driven HAS live video channel placement approach (including a placement algorithm HCP and a rescheduling algorithm HCR) to optimize the channel allocation in media cloud servers, aiming to maximize the average user QoE. We abstract the maximization problem into an MMKP problem, and employ a heuristic solution to address this problem. The experimental results demonstrate the effectiveness of our proposed approach compared with benchmark solutions.

Keyword :

live video streaming HTTP adaptive streaming QoE-driven HAS channel placement media cloud

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GB/T 7714 Liu, Junquan , Zhang, Weizhan , Huang, Shouqin et al. QoE-driven HAS Live Video Channel Placement in the Media Cloud [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 : 1530-1541 .
MLA Liu, Junquan et al. "QoE-driven HAS Live Video Channel Placement in the Media Cloud" . | IEEE TRANSACTIONS ON MULTIMEDIA 23 (2021) : 1530-1541 .
APA Liu, Junquan , Zhang, Weizhan , Huang, Shouqin , Du, Haipeng , Zheng, Qinghua . QoE-driven HAS Live Video Channel Placement in the Media Cloud . | IEEE TRANSACTIONS ON MULTIMEDIA , 2021 , 23 , 1530-1541 .
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TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Groups EI SCIE SSCI
期刊论文 | 2021 , 27 (2) , 849-859 | IEEE Transactions on Visualization and Computer Graphics
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Abstract :

Tax evasion is a serious economic problem for many countries, as it can undermine the government's tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to support the analysis and exploration of the related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A taxpayer network is constructed and fused with the respective trade network to detect suspicious RPTTE groups. Rich visualizations are designed to facilitate the exploration and investigation of suspicious transactions between related taxpayers with profit and topological data analysis. Specifically, we propose a calendar heatmap with a carefully-designed encoding scheme to intuitively show the evidence of transferring revenue through related party transactions. We demonstrate the usefulness and effectiveness of TaxThemis through two case studies on real-world tax-related data and interviews with domain experts. © 2020 IEEE.

Keyword :

Visualization Taxation Competition Data Analytics

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GB/T 7714 Lin, Yating , Wong, Kamkwai , Wang, Yong et al. TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Groups [J]. | IEEE Transactions on Visualization and Computer Graphics , 2021 , 27 (2) : 849-859 .
MLA Lin, Yating et al. "TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Groups" . | IEEE Transactions on Visualization and Computer Graphics 27 . 2 (2021) : 849-859 .
APA Lin, Yating , Wong, Kamkwai , Wang, Yong , Zhang, Rong , Dong, Bo , Qu, Huamin et al. TaxThemis: Interactive Mining and Exploration of Suspicious Tax Evasion Groups . | IEEE Transactions on Visualization and Computer Graphics , 2021 , 27 (2) , 849-859 .
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Research on Chinese predicate head recognition based on Highway-BiLSTM network EI
期刊论文 | 2021 , 42 (1) , 100-107 | Journal on Communications
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Abstract :

Aiming at the problem of difficult recognition and uniqueness of Chinese predicate head, a Highway-BiLSTM model was proposed. Firstly, multi-layer BiLSTM networks were used to capture multi-granular semantic dependence in a sentence. Then, a Highway network was adopted to alleviate the problem of gradient disappearance. Finally, the output path was optimized by a constraint layer which was designed to guarantee the uniqueness of predicate head. The experimental results show that the proposed method effectively improves the performance of predicate head recognition. © 2021, Editorial Board of Journal on Communications. All right reserved.

Keyword :

Network layers Semantics

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GB/T 7714 Huang, Ruizhang , Jin, Wenfan , Chen, Yanping et al. Research on Chinese predicate head recognition based on Highway-BiLSTM network [J]. | Journal on Communications , 2021 , 42 (1) : 100-107 .
MLA Huang, Ruizhang et al. "Research on Chinese predicate head recognition based on Highway-BiLSTM network" . | Journal on Communications 42 . 1 (2021) : 100-107 .
APA Huang, Ruizhang , Jin, Wenfan , Chen, Yanping , Qin, Yongbin , Zheng, Qinghua . Research on Chinese predicate head recognition based on Highway-BiLSTM network . | Journal on Communications , 2021 , 42 (1) , 100-107 .
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A financial ticket image intelligent recognition system based on deep learning EI SCIE
期刊论文 | 2021 , 222 | KNOWLEDGE-BASED SYSTEMS
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Facing rapid growth in the issuance of financial tickets (e.g., bills, invoices), traditional manual invoice reimbursement and financial accounting systems are imposing an increasing burden on financial accountants and consuming excessive manpower. To solve this problem, we propose an iterative self-learning framework of Financial Ticket Intelligent Recognition System (FTIRS), which supports iteratively updating and extensibility of the algorithm model, which are the fundamental requirements for a practical financial accounting system. In addition, we designed a simple yet efficient Financial Ticket Faster Detection Network (FTFDNet) and an intelligent data warehouse of financial tickets to strengthen its efficiency and performance. Currently, the system can recognize 482 types of financial tickets and has an automatic iterative optimization mechanism. Thus, with increased application time, the types of tickets supported by the system will increase, and the accuracy of recognition will improve. Experimental results show that the average recognition accuracy of the system is 97.41%, and the average running time for a single ticket is 173.72 ms. The practical value of the system has been verified in business. It can greatly improve the efficiency of financial accounting and reduce the human cost of accounting staff. (C) 2021 Elsevier B.V. All rights reserved.

Keyword :

Intelligent system Financial tickets Image text recognition Deep learning

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GB/T 7714 Zhang, Hanning , Zheng, Qinghua , Dong, Bo et al. A financial ticket image intelligent recognition system based on deep learning [J]. | KNOWLEDGE-BASED SYSTEMS , 2021 , 222 .
MLA Zhang, Hanning et al. "A financial ticket image intelligent recognition system based on deep learning" . | KNOWLEDGE-BASED SYSTEMS 222 (2021) .
APA Zhang, Hanning , Zheng, Qinghua , Dong, Bo , Feng, Boqin . A financial ticket image intelligent recognition system based on deep learning . | KNOWLEDGE-BASED SYSTEMS , 2021 , 222 .
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Adaptive Video Streaming Using Dynamic Server Push over HTTP/2 EI CPCI-S
会议论文 | 2021 , 673-678 | 24th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2021
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Abstract :

With the increasing popularity of video services, HTTP adaptive streaming (HAS) has become the mainstream technology for media streaming distribution. In traditional HAS over HTTP/1.1, the HAS server responds to each request from the client individually. This process adds additional round-trip time, resulting in underestimation of available bandwidth. As a result, the HAS client chooses a lower bitrate, which reduces network utilization and the user's quality of experience. In recent years, the HTTP/2 protocol has emerged, which allows server to actively push multiple data segments to the client. Pushing multiple segments can reduce the negative impact of network latency on estimating available bandwidth, thereby increasing the user's request bitrate and video quality. However, when the network is unstable, the more video segments that are pushed by the server, the more challenges the client encounters in responding to network fluctuations i n time, causing playback stalling and poor user experience. Therefore, this paper proposes a dynamic server push algorithm over HTTP/2, which chooses a different number of segments for server push according to network fluctuations. For the evaluation results, relative to its benchmarks, the proposed approach improves the average video request bitrate while minimizing the probability of playback stalling. © 2021 IEEE.

Keyword :

Interactive computer systems HTTP Bandwidth Hypertext systems Video streaming User experience Quality of service

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GB/T 7714 Huang, Shouqin , Wang, Zhiwen , Zhang, Weizhan et al. Adaptive Video Streaming Using Dynamic Server Push over HTTP/2 [C] . 2021 : 673-678 .
MLA Huang, Shouqin et al. "Adaptive Video Streaming Using Dynamic Server Push over HTTP/2" . (2021) : 673-678 .
APA Huang, Shouqin , Wang, Zhiwen , Zhang, Weizhan , Du, Haipeng , Zheng, Qinghua . Adaptive Video Streaming Using Dynamic Server Push over HTTP/2 . (2021) : 673-678 .
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Interpretation-enabled Software Reuse Detection Based on a Multi-Level Birthmark Model EI CPCI-S
会议论文 | 2021 , 873-884 | 43rd IEEE/ACM International Conference on Software Engineering - Software Engineering in Practice (ICSE-SEIP) / 43rd ACM/IEEE International Conference on Software Engineering - New Ideas and Emerging Results (ICSE-NIER)
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Abstract :

Software reuse, especially partial reuse, poses legal and security threats to software development, Since its source codes are usually unavailable, software reuse is hard to be detected with interpretation. On the other hand. current approaches suffer from poor detection accuracy and efficiency, far from satisfying practical demands. To tackle these problems, in this paper, we propose ISRD, an interpretation-enabled software reuse detection approach based on a multi-level birthmark model that contains function level, basic block level, and instruction level. To overcome obfuscation caused by cross-compilation, we represent function semantics with Minimum Branch Path (MBP) and perform normalizatio, to extract core semantics of instructions. For efficiently detecting reused functions, a process for ''intent search bused on anchor recognition" is designed to speed up reuse detection. It uses strict instruction match and identical library call invocation check to find anchor functions (in short anchors) and then traverses neighbors of the anchors to explore potentially matched function pairs. Extensive experiments based on two real-world binary datasets reveal that ISRD is interpretable, effective, and efficient, which achieves 97.2% precision and 94.8% recall. Moreover, it is resilient to eruct-compilation, outperforming state-of-the-art approaches.

Keyword :

Software Reuse Detection interpretation Binary Similarity Analysis Multi-Level Software Birthmark

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GB/T 7714 Xu, Xi , Zheng, Qinghua , Yan, Zheng et al. Interpretation-enabled Software Reuse Detection Based on a Multi-Level Birthmark Model [C] . 2021 : 873-884 .
MLA Xu, Xi et al. "Interpretation-enabled Software Reuse Detection Based on a Multi-Level Birthmark Model" . (2021) : 873-884 .
APA Xu, Xi , Zheng, Qinghua , Yan, Zheng , Fan, Ming , Jia, Ang , Liu, Ting . Interpretation-enabled Software Reuse Detection Based on a Multi-Level Birthmark Model . (2021) : 873-884 .
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A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction SCIE
期刊论文 | 2021 , 13 (8) | SYMMETRY-BASEL
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The task to extract relations tries to identify relationships between two named entities in a sentence. Because a sentence usually contains several named entities, capturing structural information of a sentence is important to support this task. Currently, graph neural networks are widely implemented to support relation extraction, in which dependency trees are employed to generate adjacent matrices for encoding structural information of a sentence. Because parsing a sentence is error-prone, it influences the performance of a graph neural network. On the other hand, a sentence is structuralized by several named entities, which precisely segment a sentence into several parts. Different features can be combined by prior knowledge and experience, which are effective to initialize a symmetric adjacent matrix for a graph neural network. Based on this phenomenon, we proposed a feature combination-based graph convolutional neural network model (FC-GCN). It has the advantages of encoding structural information of a sentence, considering prior knowledge, and avoiding errors caused by parsing. In the experiments, the results show significant improvement, which outperform existing state-of-the-art performances.

Keyword :

nlp feature combination relation extraction graph neural network

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GB/T 7714 Xu, Jinling , Chen, Yanping , Qin, Yongbin et al. A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction [J]. | SYMMETRY-BASEL , 2021 , 13 (8) .
MLA Xu, Jinling et al. "A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction" . | SYMMETRY-BASEL 13 . 8 (2021) .
APA Xu, Jinling , Chen, Yanping , Qin, Yongbin , Huang, Ruizhang , Zheng, Qinghua . A Feature Combination-Based Graph Convolutional Neural Network Model for Relation Extraction . | SYMMETRY-BASEL , 2021 , 13 (8) .
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Knowledge forest: a novel model to organize knowledge fragments EI SCIE CSCD
期刊论文 | 2021 , 64 (7) | Science China Information Sciences
WoS CC Cited Count: 2
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GB/T 7714 Zheng, Qinghua , Liu, Jun , Zeng, Hongwei et al. Knowledge forest: a novel model to organize knowledge fragments [J]. | Science China Information Sciences , 2021 , 64 (7) .
MLA Zheng, Qinghua et al. "Knowledge forest: a novel model to organize knowledge fragments" . | Science China Information Sciences 64 . 7 (2021) .
APA Zheng, Qinghua , Liu, Jun , Zeng, Hongwei , Guo, Zhaotong , Wu, Bei , Wei, Bifan . Knowledge forest: a novel model to organize knowledge fragments . | Science China Information Sciences , 2021 , 64 (7) .
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