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WT-FCTGN:A wavelet-enhanced fully connected time-gated neural network for complex noisy traffic flow modeling

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Chinese Physics B 2024年第7期33卷 652-664页

作者：廖志芳孙轲刘文龙余志武刘承光宋禹成 School of Computer Science and Engineering Central South UniversityChangsha 410083China National Engineering Research Center of High-speed Railway Construction Technology Changsha 410075China School of Civil Engineering Central South UniversityChangsha 410075China School of Traffic&Transportation Engineering Central South UniversityChangsha 410083China

Accurate forecasting of traffic flow provides a powerful traffic decision-making basis for an intelligent transportation system. However, the traffic data's complexity and fluctuation, as well as the noise produced during collecting information and summarizing original data of traffic flow, cause large errors in the traffic flow forecasting results. This article suggests a solution to the above mentioned issues and proposes a fully connected time-gated neural network based on wavelet reconstruction(WT-FCTGN). To eliminate the potential noise and strengthen the potential traffic trend in the data, we adopt the methods of wavelet reconstruction and periodic data introduction to preprocess the data. The model introduces fully connected time-series blocks to model all the information including time sequence information and fluctuation information in the flow of traffic, and establishes the time gate block to comprehend the periodic characteristics of the flow of traffic and predict its flow. The performance of the WT-FCTGN model is validated on the public Pe MS data set. The experimental results show that the WT-FCTGN model has higher accuracy, and its mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) are obviously lower than those of the other algorithms. The robust experimental results prove that the WT-FCTGN model has good anti-noise ability.

关键词： traffic flow modeling time-series wavelet reconstruction

MEIM:A Multi-Source Software Knowledge Entity Extraction Integration Model

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Computers, Materials & Continua 2021年第1期66卷 1027-1042页

作者： Wuqian Lv zhifang liao Shengzong Liu Yan Zhang School of Computer Science and Engineering Central South UniversityChangsha410075China School of Information Technology and Management Hunan University of Finance and EconomicsChangsha410205China School of Computing Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK

Entity recognition and extraction are the foundations of knowledge graph *** data in the field of software engineering come from different platforms and communities,and have different *** paper divides multi-source software knowledge entities into unstructured data,semi-structured data and code *** these different types of data,Bi-directional Long Short-Term Memory(Bi-LSTM)with Conditional Random Field(CRF),template matching,and abstract syntax tree are used and integrated into a multi-source software knowledge entity extraction integration model(MEIM)to extract software *** model can be updated continuously based on user’s feedbacks to improve the *** deal with the shortage of entity annotation datasets,keyword extraction methods based on Term Frequency–Inverse Document Frequency(TF-IDF),TextRank,and K-Means are applied to annotate *** proposed MEIM model is applied to the Spring Boot framework,which demonstrates good *** extracted entities are used to construct a knowledge graph,which is applied to association retrieval and association visualization.

关键词： Entity extraction software knowledge graph software data

Code Reviewer Intelligent Prediction in Open Source Industrial Software Project

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 687-704页

作者： zhifang liao Bolin Zhang Xuechun Huang Song Yu Yan Zhang School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Computing School of ComputingEngineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK

Currently, open-source software is gradually being integrated into industrial software, while industry protocolsin industrial software are also gradually transferred to open-source community development. Industrial protocolstandardization organizations are confronted with fragmented and numerous code PR (Pull Request) and informalproposals, and differentworkflowswill lead to increased operating costs. The open-source community maintenanceteam needs software that is more intelligent to guide the identification and classification of these issues. To solvethe above problems, this paper proposes a PR review prediction model based on multi-dimensional features. Weextract 43 features of PR and divide them into five dimensions: contributor, reviewer, software project, PR, andsocial network of developers. The model integrates the above five-dimensional features, and a prediction model isbuilt based on a Random Forest Classifier to predict the review results of PR. On the other hand, to improve thequality of rejected PRs, we focus on problems raised in the review process and review comments of similar *** a PR revision recommendation model based on the PR review knowledge graph. Entity information andrelationships between entities are extracted from text and code information of PRs, historical review comments,and related issues. PR revisions will be recommended to code contributors by graph-based similarity *** experimental results illustrate that the above twomodels are effective and robust in PR review result predictionand PR revision recommendation.

关键词： Open source software pull request random forest knowledge graph

P&T-Inf: A Result Inference Method for Context-Sensitive Tasks in Crowdsourcing

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Intelligent Automation & Soft Computing 2023年第7期37卷 599-618页

作者： zhifang liao Hao Gu Shichao Zhang Ronghui Mo Yan Zhang School of Computer Science and Engineering Central South UniversityChangsha410075China Glasgow Caledonian University GlasgowG40BAUK

Context-Sensitive Task(CST)is a complex task type in crowdsourc-ing,such as handwriting recognition,route plan,and audio *** current result inference algorithms can perform well in simple crowd-sourcing tasks,but cannot obtain high-quality inference results for *** conventional method to solve CSTs is to divide a CST into multiple independent simple subtasks for crowdsourcing,but this method ignores the context correlation among subtasks and reduces the quality of result *** solve this problem,we propose a result inference algorithm based on the Partially ordered set and Tree augmented naive Bayes Infer(P&T-Inf)for ***,we screen the candidate results of context-sensitive tasks based on the partially ordered *** there are parallel candidate sets,the conditional mutual information among subtasks containing context infor-mation in external knowledge(such as Google n-gram corpus,American Contemporary English corpus,etc.)will be *** with the tree augmented naive(TAN)Bayes model,the maximum weighted spanning tree is used to model the dependencies among subtasks in each *** collect two crowdsourcing datasets of handwriting recognition tasks and audio transcription tasks from the real crowdsourcing *** experimental results show that our approach improves the quality of result inference in CSTs and reduces the time cost compared with the latest methods.

关键词： Crowdsourcing result inference tree augmented naive Bayes context-sensitive

The Measurement of the Software Ecosystem’s Productivity with GitHub

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Computer Systems Science & Engineering 2021年第1期36卷 239-258页

作者： zhifang liao Yiqi Zhao Shengzong Liu Yan Zhang Limin Liu Jun Long School of Computer Science and Engineering Central South UniversityChangsha410075China School of Information Technology and Management Hunan University of Finance and EconomicsChangsha410075China Department of Computing School of ComputingEngineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK

Software productivity has always been one of the most critical metrics for measuring software ***,with the open-source community(e.g.,GitHub),new software development models are *** traditional productivity metrics do not provide a comprehensive measure of the new software development ***,it is necessary to build a productivity measurement model of open source software ecosystem suitable for the open-source community’s production *** on the natural ecosystem,this paper proposes concepts related to the productivity of open source software ecosystems,analyses influencing factors of open source software ecosystem productivity,and constructs a measurement model using these *** validation experiments show that the model is compatible with a large portion of open source software ecosystems in *** study can provide references for participants of the open-source software ecosystem to choose proper types of *** study also provides a basis for ecosystem health assessment for researchers interested in ecosystem quality.

关键词： Software ecosystem open-source software ecosystem OSSECO productivity productivity model

A Crowdsourcing Recommendation that Considers the Influence of Workers

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Computers, Materials & Continua 2021年第2期66卷 1379-1396页

作者： zhifang liao Xin Xu Peng Lan Liu Yang Yan Zhang Xiaoping Fan School of Computer Science and Engineering Central South UniversityChangsha410075China Glasgow Caledonian University GlasgowG40BAUK Hunan University of Finance and Economics Changsha410205China

In the context of the continuous development of the Internet,crowdsourcing has received continuous attention as a new cooperation model based on the relationship between enterprises,the public and *** them,a reasonably designed recommendation algorithm can recommend a batch of suitable workers for crowdsourcing tasks to improve the final task completion ***,this paper proposes a crowdsourcing recommendation framework based on workers’influence(CRBI).This crowdsourcing framework completes the entire process design from task distribution,worker recommendation,and result return through processes such as worker behavior analysis,task characteristics construction,and cost *** this paper,a calculation model of workers’influence characteristics based on the ablation method is designed to evaluate the comprehensive performance of *** the same time,the CRBI framework combines the traditional open-call task selection mode,builds a new task characteristics model by sensing the influence of the requesting worker and its task *** the end,accurate worker recommendation and task cost optimization are carried out by calculating model *** addition,for recommending workers to submit task answers,this paper also proposes an aggregation algorithm based on weighted influence to ensure the accuracy of task *** paper conducts simulation experiments on some public datasets of AMT,and the experimental results show that the CRBI framework proposed in this paper has a high comprehensive ***,CRBI has better usability,more in line with commercial needs,and can well reflect the wisdom of group intelligence.

关键词： Crowdsourcing recommendation framework workers’ influence worker recommendation weighted voting

BDGOA:A bot detection approach for GitHub OAuth Apps

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Intelligent and Converged Networks 2023年第3期4卷 181-197页

作者： zhifang liao Xuechun Huang Bolin Zhang Jinsong Wu Yu Cheng the School of Computer Science and Engineering Central South UniversityChangsha 410083China the School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 541004China the Department of Electrical Engineering University of ChileSantiago 8320000 Hunan Glozeal Science and Technology Co. Ltd.Changsha 410083China

As various software bots are widely used in open source software repositories,some drawbacks are coming to light,such as giving newcomers non-positive feedback and misleading empirical studies of software engineering *** techniques have been proposed by researchers to perform bot detection,but most of them are limited to identifying bots performing specific activities,let alone distinguishing between GitHub App and OAuth *** this paper,we propose a bot detection technique for OAuth App,named BDGOA.24 features are used in BDGOA,which can be divided into three dimensions:account information,account activity,and text *** better explore the behavioral features,we define a fine-grained classification of behavioral events and introduce self-similarity to quantify the repeatability of behavioral *** leverage five machine learning classifiers on the benchmark dataset to conduct bot detection,and finally choose random forest as the classifier,which achieves the highest F1-score of 95.83%.The experimental results comparing with the state-of-the-art approaches also demonstrate the superiority of BDGOA.

关键词： Github DevBots machine learning text similarity

Graph Convolutional Network-Based Repository Recommendation System

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Computer Modeling in Engineering & Sciences 2023年第10期137卷 175-196页

作者： zhifang liao Shuyuan Cao Bin Li Shengzong Liu Yan Zhang Song Yu School of Computer Science and Engineering Central South UniversityChangshaChina School of Information Technology and Management Hunan University of Finance and EconomicsChangshaChina School of Engineering and Built Environment Glasgow Caledonian UniversityGlasgowUK

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

关键词： Repository recommendation graph convolutional network open-source software GitHub

Friction between Strongly Compressed Polymer Brushes

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Chinese Journal of Polymer Science 2024年第9期42卷 1368-1374,I0008页

作者： Qi liao Institute of Chemistry Chinese Academy of SciencesBejing 100190China

We present the results of molecular dynamics simulations of steady shear between a pair of neutral polymer brushes,as well as a pair of charged polymer brushes in the strongly compressed *** results of the molecular dynamic simulations of neutral and polyelectrolyte brushes in implicit solvent including normal forces,shear forces,viscosities and friction coefficients as a function of separation between brushes,are presented in the *** comparison of the simulation results of neutral and charged brushes shows that the charged brushes is in the quasi-neutral regime,and the dependence of viscosity on the separation distance show the similar power law of neutral *** simulation results confirm that the implicit solvent simulations of polyelectrolyte brushes that ignore hydrodynamics interaction are in agreement with the scaling predictions qualitatively because of screening of hydrodynamic interaction and long-range electrostatic interactions on the correlation length *** of neutral and charged brushes show the lubrication properties that the friction coefficient decreases with the separation decreases at enough large ***,a maximum of friction coefficients is observed for polyelectrolyte brushes,which is in contrast to the neutral brushes with monotonical dependence.

关键词： Polymer brush Frictions coefficient Polyelectrolyte Lubrication