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Health Data Availability Protection:Delta-XOR-Relay Data Update in Erasure-Coded Cloud Storage Systems

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Computer Modeling in engineering & Sciences 2023年第4期135卷 169-185页

作者： Yifei Xiao Shijie Zhou School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China

To achieve the high availability of health data in erasure-coded cloud storage systems,the data update performance in erasure coding should be continuously optimized.However,the data update performance is often bottlenecked by the constrained cross-rack bandwidth.Various techniques have been proposed in the literature to improve network bandwidth efficiency,including delta transmission,relay,and batch update.These techniques were largely proposed individually previously,and in this work,we seek to use them jointly.To mitigate the cross-rack update traffic,we propose DXR-DU which builds on four valuable techniques:(i)delta transmission,(ii)XOR-based data update,(iii)relay,and(iv)batch update.Meanwhile,we offer two selective update approaches:1)data-deltabased update,and 2)parity-delta-based update.The proposed DXR-DU is evaluated via trace-driven local testbed experiments.Comprehensive experiments show that DXR-DU can significantly improve data update throughput while mitigating the cross-rack update traffic.

关键词： Data availability health data data update cloud storage IoT

SE-FCA: A Model of software Evolution with Formal Concept Analysis

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Chinese Journal of Electronics 2015年第1期24卷 13-19页

作者： SUN Xiaobing LI Bixin LI Bin CHEN Ying school of information engineering Yangzhou University State Key Laboratory for Novel software Technology Nanjing University school of Computer Science and engineering Southeast University

softwares are naturally evolved to cope with various changing system requirements. software evolution includes a series of activities to analyze, assess, and validate the changes. This paper proposes an integrated software evolution model, namely SE-FCA, to support four core software evolution activities: program comprehension,change impact analysis, regression testing, and fault localization. These four activities are integrated and supported under the formal concept analysis technique, which is efficient to deal with the relation between entities and entity properties to provide a remarkable insight into the structure of the original relation. These activities are evaluated in a unified empirical environment. The empirical study shows the effectiveness of these activities under the SEFCA model.

关键词： Formal concept analysis software evolution software change

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Chinese Journal of Electronics 2021年第1期30卷 55-63页

作者： BO Lili ZHU Xuanrui SUN Xiaobing NI Zhen LI Bin school of information engineering Yangzhou University Jiangsu engineering Research Center of Knowledge Management and Intelligent Service State Key Laboratory for Novel software Technology Nanjing University

Fine-grained change operations can help software developers fix software bugs more accurately and efficiently. However, the current fine-grained change operations are only used in specific fixing process, such as fixing of If statement. In this paper, we conducted an empirical study to explore the fine-grained change operations for bug fixing. Based on the Mozilla bug data,we examined whether similar bugs are fixed with similar change operations. The results show that: First, for bug reports with similar descriptions or bug-fix commits with similar descriptions, their corresponding fine-grained change operations are not related; Second, in the case where the descriptions of both bug reports and bug-fix commits are similar, the fine-grained change operations in patch code are not related; Third, by classifying bug reports, we find that the change operations in the same bug report category are similar; Finally, by analyzing the fine-grained change operations for each bug, we present some combined patterns that are often used together.

关键词： Automatic bug fixing Change operation Bug classification Empirical study

A Survey about Algorithms Utilized by Focused Web Crawler

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Journal of Electronic Science and Technology 2018年第2期16卷 129-138页

作者： Yong-Bin Yu Shi-Lei Huang Nyima Tashi Huan Zhang Fei Lei Lin-Yang Wu School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054 school of information Science and Technology Tibet UniversityLhasa 850000 school of Electronic information and engineering Xi'an Jiaotong UniversityXi'an 710049 Zhejiang University Hangzhou 310058

Abstract—Focused crawlers （also known as subjectoriented crawlers）, as the core part of vertical search engine, collect topic-specific web pages as many as they can to form a subject-oriented corpus for the latter data analyzing or user querying. This paper demonstrates that the popular algorithms utilized at the process of focused web crawling, basically refer to webpage analyzing algorithms and crawling strategies （prioritize the uniform resource locator （URLs） in the queue）. Advantages and disadvantages of three crawling strategies are shown in the first experiment, which indicates that the best-first search with an appropriate heuristics is a smart choice for topic-oriented crawlingwhile the depth-first search is helpless in focused crawling. Besides, another experiment on comparison of improved ones （with a webpage analyzing algorithm added） is carried out to verify that crawling strategies alone are not quite efficient for focused crawling and in most cases their mutual efforts are taken into consideration. In light of the experiment results and recent researches, some points on the research tendency of focused crawler algorithms are suggested.

关键词： Crawling strategies focused crawler harvest rate uniform resource locator(URL) prioritizing webpage analyzing

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Leveraging Auxiliary Knowledge for Web Service Clustering

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Chinese Journal of Electronics 2016年第5期25卷 858-865页

作者： TIAN Gang WANG Jian HE Keqing SUN Cheng'ai State Key Laboratory of software engineering School of Computer Wuhan University College of information and Science engineering Shandong University of Science and Technology

By grouping Web services that share similar functionalities, Web service clustering can greatly enhance Web service discovery and selection. Most existing clustering techniques are designed to handle long text documents. However, the descriptions of most publicly available Web services are in the form of short text, which impairs the quality of service clustering due to the sparseness of useful information. Towards this issue, we propose a new service clustering approach based on transfer learning from auxiliary long text data obtained from Wikipedia.To handle the inconsistencies in semantics and topics between service descriptions and auxiliary data, we introduce a novel topic model – Tag aided dual Author topical model(TD-ATM), which jointly learns two sets of topics on the two data sets and automatically couples the topic parameters to avoid the potential inconsistencies between these two data sets. Experimental results show the proposed approach outperforms several existing Web service clustering approaches.

关键词： Web service clustering Tags Topic model Transfer learning

Building a domain-specific compiler for emerging processors with a reusable approach

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Science China(information Sciences) 2024年第1期67卷 20-38页

作者： Mingzhen LI Yi LIU Bangduo CHEN Hailong YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of software Development Environment school of Computer Science and engineering Beihang University

High-performance computing and deep learning domains have been motivating the design of domain-specific processors.Although these processors can provide promising computation capability,they are notorious for exotic programming paradigms.To improve programming productivity and fully exploit the performance potential of these processors,domain-specific compilers(DSCs) have been proposed.However,building DSCs for emerging processors requires tremendous engineering efforts because the commonly used compilation stack is difficult to be reused.Owing to the advent of multilevel intermediate representation(MLIR),DSC developers can leverage reusable infrastructure to extend their customized functionalities without rebuilding the entire compilation stack.In this paper,we further demonstrate the effectiveness of MLIR by extending its reusable infrastructure to embrace a heterogeneous many-core processor(Sunway processor).In particular,we design a new Sunway dialect and corresponding backend for the Sunway processor,fully exploiting its architectural advantage and hiding its programming complexity.To show the ease of building a DSC,we leverage the Sunway dialect and existing MLIR dialects to build a stencil compiler for the Sunway processor.The experimental results show that our stencil compiler,built with a reusable approach,can even perform better than state-of-the-art stencil compilers.

关键词： domain-specific compiler emerging processor reusable dialect performance optimization MLIR

Intelligent analysis for software data:research and applications

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Frontiers of information Technology & Electronic engineering 2022年第5期23卷 661-663页

作者： Tao ZHANG Xiaobing SUN Zibin ZHENG Ge LI school of Computer Science and engineering Macao University of Science and TechnologyMacaoChina school of information engineering Yangzhou UniversityYangzhou 225127China school of software engineering Sun Yat-sen UniversityZhuhai 519082China school of Computer Science Peking UniversityBeijing 100871China

Over the last few decades,software has been one of the primary drivers of economic growth in the world.Human life depends on reliable software;therefore,the software production process(i.e.,software design,development,testing,and maintenance)becomes one of the most important factors to ensure the quality of software.During the production process,large amounts of software data(e.g.,source code,bug reports,logs,and user reviews)are generated.

关键词： software bug testing

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LECF: recommendation via learnable edge collaborative filtering

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Science China(information Sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI school of Computer Science (National Pilot software engineering school) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications software and Multimedia Beijing University of Posts and Telecommunications school of information Technology and Electrical engineering The University of Queensland Department of Computer Science and engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

Pre-course student performance prediction with multi-instance multi-label learning

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Science China(information Sciences) 2019年第2期62卷 200-205页

作者： Yuling MA Chaoran CUI Xiushan NIE Gongping YANG Kashif SHAHEED Yilong YIN software College Shandong University school of information engineering Shandong Yingcai College school of Computer Science and Technology Shandong University of Finance and Economics

Dear editor,Studying courses is one of the most basic and important tasks for college students. For each new course, the initial period of learning is crucial for students, and seriously influences subsequent learning activities. However, given a large number of classes in universities, it has become impossible for teachers to keep track of the individual performance of each student. In these circumstances, it is desirable to predict each student’s performance on a certain course prior to its commencement.

关键词： KNN DT Pre-course student performance prediction with multi-instance multi-label learning SVM Figure BCS