Table of Content

    25 October 2022, Volume 42 Issue 10
    Research on the Identification of Key Developer Groups in the Open Source Community Collaboration Network
    LIU Peng, MA Jianan
    2022, 42(10):  2566-2581.  DOI: 10.12341/jssms22492KSS
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    The withdrawal of key developers in the open source community will directly threaten the sustainability of open source projects.Therefore,effectively identifying key developers who significantly affect the development work and taking preventive measures can promote the development of community collective wisdom.This paper analyzes Vue and Angular open source projects,focuses on the collaborative behavior of developers,proposes a connection coefficient index to measure the difference of collaborative behavior of developers,and divides developers into three groups and identifies key groups according to the evaluation index system.The results show that the index proposed in this paper is obviously superior to the existing evaluation methods,and the cooperative network suffers greater damage when the cooperative behaviors of the key developer groups detected by this index fail.In addition,key developer types in collaborative networks include nodes with low degree values and non-central locations.This provides a new perspective for further study of cooperative networks.
    Health Rumor Detection based on Pre-Trained Language Model
    XU Nuo, ZHAO Wei, SHANG Keyuan, CHEN Haoyu
    2022, 42(10):  2582-2589.  DOI: 10.12341/jssms22646KSS
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    Currently,most studies on rumor detection mainly focus on social media data and the length of text sequence is short.We argue that existing methods could not capture effective features from health rumors with long texts and then affect the validity of methods.To solve this,we propose an improved BERT-BiLSTM model (I-BERT-BiLSTM),which leverages effective information extracted from texts with long sequences for the health rumor detection.We first conduct text summarization from document-level text.The results are regarded as the input of the deep network model with multi-layer self-attention mechanisms for feature extraction.Finally,we feed the output into BiLSTM for rumor classification.The experimental results show that the model we proposed in this paper achieves 97.75% and 91.15% accuracy on the self-built health rumor data and public data.
    Exploring the Transfer of Event Risk Based on Dynamic Networks
    YAN Zhihua, TANG Xijin
    2022, 42(10):  2590-2601.  DOI: 10.12341/jssms22497KSS
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    In order to identify risk events from Internet media,describe the evolution structures of events and perceive the evolution patterns of event risk,this paper proposes an analysis framework of event risk evolution based on a dynamic network.We construct a time-series network to represent the dynamic development of events,use the Louvain algorithm to identify events,and employ the event transfer metric to construct relation graph between events.Based on the identification of event evolution structure,this paper identifies the main evolutionary paths of events and summarizes the relationship between event risk and event life cycle.The research results show that there are structures of event evolution such as event birth,event merge,and so on.The main paths of event evolution consist of evolution structure.Event risks vary at different stages of the event lifecycle.
    The Situation and Suggestions of Quality and Safety in Electronic and Electrical Industry
    KONG Lingyun, CEHN Jindong
    2022, 42(10):  2602-2615.  DOI: 10.12341/jssms22510KSS
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    Aiming at promoting the high-quality development of the electronic and electrical industry,the status and development trend research of product quality and safety in the electronic and electrical industry is implemented.Selecting the sampling inspection notices issued by the state and 31 provincial and municipal market supervision and administration bureaus from 2018 to 2021 as the data source,this paper applies TextRank algorithm to extract keywords of unqualified items from the published sampling inspection notices of power supply,mobile phone and induction cooker products with a high unqualified rate,and analyzes the key unqualified items;explores the hidden dangers of product safety from three aspects:Overall,time domain and region,and expounds the quality and safety problems of electronic and electrical products in China.The results show that the overall unqualified rate of electronic and electrical products is high,but shows a declining trend,and the coverage of the sampling products is still low,more sampling batches are in southeast coastal regions.Regulatory authorities should strengthen supervision and punishment,carry out the comprehensive sampling inspection of electronic and electrical products;Production enterprises need to strictly control the production process and strictly according to the standard production.
    Quality and Safety Risks Analysis of Food Industry Based on BiLSTM-CRF
    ZHANG Haihang, CHEN Jindong, ZHANG Jian
    2022, 42(10):  2616-2633.  DOI: 10.12341/jssms22508KSS
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    Collecting the food safety supervision and inspection reports issued by the national and provincial market supervision and administration bureaus from 2018 to 2020,this paper analyzes the current situation and evolution trend of China's food industry safety based on entity recognition,text mining and statistical analysis.Firstly,based on BiLSTM-CRF model,the entity names of the inspection category and inspection item in the reports are extracted,and the extracted entity names are combined for rule matching to mine the quality and safety risk information of food industry;Secondly,based on the collected information,this paper studies the causes of high-frequency quality and safety risks in China's food industry with the perspective of time and region,and explores the current situation and evolution trend of food industry safety.The results show that China's food industry is still in a good state with low safety risks;The causes and solutions to part of the quality and safety risks need to grasp the essence of the problems,and the problems exposed in the process of production,storage and sales need to be resolved urgently;Industry supervision needs to be further strengthened,the overall quality of food industry practitioners still has room for improvement,and part of the safety risks should be proceeded from reality.
    Macro-Structural Emotion Analysis of Social Risk Events in Weibo
    LENG Jie, TANG Xijin
    2022, 42(10):  2634-2646.  DOI: 10.12341/jssms22610KSS
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    It has been more than a decade since people got used to engaging in hot social discussions on social media platforms.Fine-grained sentiment analysis of public commons towards social risk events will be helpful for relevant departments to deal with hot issues in time and respond effectively.This paper introduces British sociologist J.M.Barbalet's theory of macrostructural emotion,which affects social structure,order and harmony.And then we construct a related macro-structured emotion lexicon using pre-trained word vector models,combining with psychological human emotion classifications and various emotion lexicons.By taking the"helmet incident"in April 2019 as a case,this paper identifies the main emotion types in Weibo comments under this event,and extracts entities and descriptions highly relevant with the macro structure emotions through point mutual information (PMI) and 2-order dependence distance,so as to obtain the main views and attitudes of the public.
    Research on Influencing Factors of New Energy Vehicle Sales Based on Online Reviews
    DING Pei, MA Tieju, MA Ye
    2022, 42(10):  2647-2664.  DOI: 10.12341/jssms22507KSS
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    The promotion of new energy vehicles is of positive significance for China to maintain energy security and achieve goals of carbon peaking and carbon neutrality under the new development philosophy.However,fuel vehicles still hold a top post in China's automobile market.New energy vehicles still need to be further promoted,and the influencing factors of new energy vehicle sales remain to be determined.This study uses web crawler technology to obtain online review data about new energy vehicles.It takes 129 new energy vehicles on sale in 2021 as research samples to reveal the internal mechanism of the impact of specific content in online reviews on the promotion of new energy vehicles through text mining and empirical analysis.The study's main conclusions are as follows:1) The number of comments and the emotional polarity of comments will have a positive impact on vehicle sales.2) The contents of "noise control","acceleration performance", "fuel vehicles","battery performance and charging infrastructure"and "weather" in the online reviews of new energy vehicles will have a significant impact on energy vehicle sales.3) The influence of specific content in comments on vehicle sales is heterogeneous in vehicles of different manufacturers and energy types,and the impact of specific content in comments with different degrees of emotional polarity on vehicle sales is also heterogeneous.This study shows that the specific content in online reviews of new energy vehicles will significantly impact new energy vehicle sales.Manufacturers should improve their services according to the user feedback reflected in the related content in the online reviews while conducting online word-of-mouth management.
    Brand Value Evaluation of Small and Medium-Sized Food Enterprise by Considering Online Comments
    ZHANG Jiamin, WANG Ying
    2022, 42(10):  2665-2679.  DOI: 10.12341/jssms22487KSS
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    Aiming at the issues of the high cost of brand value evaluation and the difficulty of collecting consumer comments,this paper proposes a brand value evaluation method for small and medium-sized enterprises by considering online comments.First,based on the framework of the national standard "multi-cycle excess return method for brand evaluation",a brand strength evaluation index system including four dimensions of capital strength,consumers,innovation and social responsibility is built.The indicators of different dimensions are evaluated according to online comments and other data,and the weight of different dimension is determined by AHP method;the brand excess return is calculated by grey forecasting model,and the brand value of small and medium enterprises is finally calculated.Taking 14 small and medium-sized enterprises in food industry as examples,the brand value and the ranking of each enterprise are estimated,and the key factors that affect the ranking of enterprise brand value are explored.The empirical results show that the model has strong applicability to the brand value evaluation of small and medium-sized food enterprises,and also has certain reference value for small and medium-sized enterprises in other industry.
    Consistency-Eigenvector-Driven Personalized Individual Semantics and Its Application in Rice Evaluation
    LIU Ying, WEI Haiyan, WEI Cuiping
    2022, 42(10):  2680-2697.  DOI: 10.12341/jssms22478KSS
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    In the process of linguistic decision-making,different decision-makers have different understandings of the same linguistic term,so it is of great significance to consider the personalized individual semantics of decision-makers for the rationality of decision-making results.In this paper,under the environment of linguistic distribution assessments decision matrix and fuzzy preference relation,we construct model to derive personalized individual semantics.A pairwise comparison matrix that satisfies multiplicative consistency is constructed from the comprehensive ranking vector derived from the decision matrix.Then,based on the multiplicative consistency of the fuzzy judgment matrix and the properties of the eigenvectors,starting from the idea of integrating the objective information of the decision matrix and the subjective information of the preference relationship,two methods are proposed to derive the decision maker's personalized individual semantic function:Multiplicative consistency method and eigenvector method.Finally,the individual evaluation information is aggregated to obtain the final ranking of the alternatives.combined the sensory evaluation method,the proposed method are applied to the actual decision-making problem of rice evaluation,and compared with the existing personalized individual semantic derivation methods to show its rationality and validity.
    Credit Risk Prediction of Small and Medium-Sized Enterprises Based on LSTM-CNN
    WANG Xin, WANG Ying
    2022, 42(10):  2698-2711.  DOI: 10.12341/jssms22488KSS
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    Aiming at the credit risk prediction of small and medium-sized enterprises,this paper proposes a credit risk prediction method based on Long Short-Term Memory (LSTM)-Convolutional Neural Network (CNN) of small and medium-sized enterprises.Firstly,according to the national standard "Enterprise Credit Evaluation Index" and the characteristics of small and medium-sized enterprises,this paper proposes a credit risk prediction index system of small and medium-sized enterprises.The index system includes three kinds of financial and non-financial indicators:Credit intention,credit ability and credit performance.Then,this paper optimizes the network structure and parameters of LSTM-CNN,and applies Dropout and Batch Normalization methods to prevent over fitting.Finally,collecting the information of the listed small and medium-sized enterprises,and after missing value processing,standardization and oversampling,LSTM-CNN is applied to automatically extract features and predict credit risk.The experimental results show that the index system of this paper comprehensively reflect the credit risk situation.The credit risk prediction effect of small and medium-sized enterprises based on LSTM-CNN is better than the comparative models,which overcomes the limitations of traditional methods that cannot dynamically predict the time series data,and ignore the development potential and time continuity of small and medium-sized enterprises.
    Credit Risk Prediction of the Listed Companies Based on SMOTETomek-RFE-MLP Algorithm
    LU Zhe, ZHANG Jian
    2022, 42(10):  2712-2726.  DOI: 10.12341/jssms22493KSS
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    It is of great significance for regulators,banks and other financial institutions to accurately grasp the credit risk status of listed companies.In this paper,financial indicators and non-financial indicators are integrated to construct a set of credit risk prediction indicators,meanwhile,a combination algorithm SMOTETomekRFE-MLP is proposed for the credit risk prediction of listed companies.The hybrid sampling algorithm SMOTETomek solves the problem of unbalanced sample classification by over-sampling a few samples and under-sampling a majority of samples.Through adding features into the model one by one,recursive feature elimination (RFE) algorithm selects the optimal feature sets based on classification accuracy.Multi-layer Perceptron (MLP) is applied as the binary classifier to predict the credit risk of listed companies.To verify the effectiveness of the algorithm,the base model comparison experiment and ablation experiment are designed to test the algorithm with 3797 A-share listed companies in 2019 as the research object.The results show that SMOTETomek-RFE-MLP credit risk prediction algorithm outperforms the baseline models such as Adaboost,and solves the classification disorder and feature selection problems due to data imbalance,which has certain guiding significance for financial institutions to evaluate the default risk of listed companies.
    Analysis on Factor Production Elasticity and Production Efficiency of China's Power Industry in the Context of Technological Innovation
    YANG Xu, LU Yan, MA Yixiang, YU Lean
    2022, 42(10):  2727-2739.  DOI: 10.12341/jssms22384
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    In order to explore the effect of technological innovation on the supply of the electricity market,this paper investigates the changing trend of the production efficiency of China's power industry during the "New Normal " period by constructing a panel stochastic frontier model,and analyzes the impact and direction of different input factors on the supply of the electricity market.Furthermore,by comparing the differences in production efficiency between different regions and provinces before and after the "New Normal" period,the changing trend of technological innovation in the power industry in various regions of China is analyzed.Through a series of hypothesis tests,the applicability of the panel stochastic frontier model is proved.By using this model,the technical efficiency of China's power industry can be analyzed,and the set form of the transcendent logarithmic production function with time variables can be determined.Combined with the results of parameter estimation,we prove that the output elasticity of capital is positive,indicating that the power industry is still a capital-intensive industry,and the increase in capital input at this stage can greatly improve the supply of the electricity market.At the same time,this study proves that the overall technical efficiency of China's power industry is relatively high.In particular,since entering the "New Normal" period,the average technical efficiency of the power industry in the western region has increased significantly relative to other regions,the provinces with high technical efficiency in the power industry in the western region have increased significantly,and the technical efficiency of the power industry in Ningxia,Yunnan and Xinjiang provinces has improved significantly compared with before the "New Normal" period,indicating that the application of power innovation technologies including high voltage/ultra-high voltage transmission technology,energy saving technology,new energy technology,energy storage technology and other power innovation technologies will help improve the technical efficiency of the power industry.
    An Evolutionary Analysis on Decision-Making Behaviors of Stakeholders in the Green Consumption Scenario——Efficient Stability Control Mechanism Derived from Supervision Based on Big Data and Mobile Internet
    JIN Jie, ZHAO Qiuhong
    2022, 42(10):  2740-2755.  DOI: 10.12341/jssms22420
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    Focusing on the bottlenecks in the green consumption field,we use the theory and method of evolutionary game,build an evolutionary game model which contains the government agency,the green enterprise (short for those enterprises who produce green products and provide green service) and the consumer which are three major stakeholders in the green consumption.Next,we apply system dynamics simulation method to analyze the stability of equilibrium strategy solutions of the game.The simulation results show that no stable equilibrium strategy solution exists in the current interactions among three players.Motivated by the development of mobile consumption and application of new information technologies like real-time location and mobile payment,we propose a dynamic incentive-penalty mechanism framework and verify its effectiveness to achieve the evolution stable state in which with less supervisions from the government agency side,the green enterprises implement the standards strictly and consumers choose to buy eco-labelled products.
    Research on Recycling Channel of Dual Channel Closed Loop Supply Chain Under Government Subsidy
    WANG Shanshan, QIN Jiangtao
    2022, 42(10):  2756-2773.  DOI: 10.12341/jssms21189
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    In the dual channel supply chain dominated by manufacturers,under the premise of government subsidies to manufacturers,three different recycling models are established:Manufacturer recycling,retailer recycling and mixed competitive recycling between manufacturers and retailers.The optimal decisions of different models are studied,and the conclusions are drawn.Finally,the simulation is carried out to verify them.The research shows that when the manufacturer monopolizes recycling,the income of each member of the supply chain is an increasing function of the price sensitivity coefficient of recycling;When retailers monopolize recycling,the profits of manufacturers and retailers are reduced.At this time,the recycling volume of the whole supply chain is half of that of manufacturers;In the competitive recovery mode,when the recovery price sensitivity coefficient increases,the manufacturer's profit decreases,the retailer's profit increases,the supply chain profit first increases and then decreases,and the manufacturer's recovery is greater than that of the retailer.Under the three models,the manufacturer's recycling mode is the best when the recycling price sensitivity coefficient is small;When the recovery price sensitivity coefficient is large,the competitive hybrid recovery model is the best.
    Study of H Control Problem for 2-D Discrete-Time Nonlinear Switched Systems with Mixed Time-Varying Delays
    LIU Mengjie, PENG Dan
    2022, 42(10):  2774-2793.  DOI: 10.12341/jssms22251
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    In this paper,we study the H control problem of two-dimensional (2-D) discrete-time nonlinear switched systems with mixed time-varying delays based on the Roesser model.Firstly,an improved Lyapunov function is implemented by introducing some sum of state vectors containing both single and double forms,so as to strive to obtain additional information related to the time delays.Secondly,a 2-D admissible edge-dependent average dwell time (2AED-ADT) method for 2-D discrete-time nonlinear switched systems is introduced.Using this method,sufficient conditions are given by the linear matrix inequalities (LMIs) to guarantee the exponential stability and H performance of the 2-D discrete-time nonlinear switched systems.Further,based on the above stability results,a dynamic output feedback (DOF) controller is designed to stabilize the nonlinear closed-loop switched systems with H performance index γ.Finally,the superiority and validity of the obtained results are verified by two numerical calculation examples.
    Robust Steady-State Estimation for Uncertain Multichannel Signal
    TAO Guili, LI Shuang, WANG Xuemei, LIU Wenqiang
    2022, 42(10):  2794-2816.  DOI: 10.12341/jssms22192
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    This paper studies the robust steady-state Kalman filtering problem for a class of multichannel autoregressive (AR) signal with random parameter matrices,uncertain noise variances,one-step random delay,packet dropouts,and missing measurements.Using the state space method,augmented method,and the fictitious noise technique,the mixed uncertain AR signal model under study is converted into a state space model only with uncertain noise variances and same process and measurement noises.In the light of the minimax robust estimation principle,based on the worstcase system with conservative upper bounds of uncertain noise variances,the robust steady-state Kalman one-step and multi-step signal predictors are proposed.The robustness of the proposed signal predictors is proved,such that for all admissible uncertainties,the actual steady-state estimation error variances of signal predictors are guaranteed to have the corresponding minimal upper bounds.A simulation example verifies the correctness and effectiveness of the proposed methods.
    Ordered Data Classification Based on Proportional Odds Model
    RUAN Tengfei, ZHANG Sanguo, SHEN Liyong
    2022, 42(10):  2817-2833.  DOI: 10.12341/jssms22051
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    According to whether the categories are ordered,the classification task can be divided into ordered data classification and unordered data classification.Traditional proportional odds model is popular and assumes that different categories of coefficient variables are the same,but this assumption is not always the suitable in practice.This article improves the proportional odds model,does not require the coefficient variables of different categories to be the same,and the fused-LASSO or fused-MCP regularization penalty is combined.We use MM algorithm to solve the model and select regularization parameters based on minimal BIC criterion.Both simulation studies and real data analysis demonstrate that POM-LASSO (Improved proportional odds model with fused-Lasso penalty) and POM-MCP (Improved proportional odds model with fused-MCP penalty) have better results than traditional proportional odds model when dealing with ordered multi-classification tasks.
    Estimation of the Average Treatment Effect on the Treated with Error-prone Covariates and Misclassified Outcomes
    WEI Shaojie, XIE Tianfa, ZHANG Zhongzhan
    2022, 42(10):  2834-2846.  DOI: 10.12341/jssms22080
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    The classic inverse probability weighting method relies on the precise measurement of variables.However,in practice,this condition is often violated.This paper focuses on the estimation of the average treatment effect on the treated with error-prone covariates and misclassified outcomes.Based on correction and conditional score methods,the weighting function that can guarantee a consistent inverse probability weighting estimator is discussed,and the consistent estimator of the average treatment effect on the treated is further given.Simulation studies and data analysis demonstrate the superiority of the proposed method.
    Event-Based Output Tracking of Probabilistic Boolean Networks with Time Delay
    CHEN Haodong, LI Lulu, HU Bosen
    2022, 42(10):  2847-2858.  DOI: 10.12341/jssms22131
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    This paper mainly studies the output tracking problem of probabilistic Boolean networks (PBNs) with time delay under event-triggered control.Firstly,PBNs with time delay is transformed into its algebraic form by using the method of semi-tensor product of matrices.Secondly,the delayed PBNs without control input are discussed,respectively.Based on the algebraic expression of delayed PBNs and a series of construed reachable sets,the necessary and sufficient conditions for the system achieving output tracking in finite time with probability one are given.Thirdly,the algorithm of designing event-triggered controller is proposed in this paper.Finally,an example is given to verify the correctness of the theoretical results and the effectiveness of the algorithm.