Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00046
Ping Mu
In the information age, ERP financial software has been widely used in various industries, and its application effect is very significant. In order to give full play to the role of ERP financial software itself and further improve the level of corporate financial management, we need to strengthen the study of its specific application in the enterprise. Although ERP financial software is helpful to the financial management level of enterprises, because each enterprise has different situations, we need to apply ERP financial software flexibly in accordance with the actual situation. In view of the actual situation of corporate financial management, we should pay close attention to some issues to ensure that the software can healthly integrate corporate financial management.
{"title":"Research on the Application of ERP Financial Software in Enterprises","authors":"Ping Mu","doi":"10.1109/ICCSMT54525.2021.00046","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00046","url":null,"abstract":"In the information age, ERP financial software has been widely used in various industries, and its application effect is very significant. In order to give full play to the role of ERP financial software itself and further improve the level of corporate financial management, we need to strengthen the study of its specific application in the enterprise. Although ERP financial software is helpful to the financial management level of enterprises, because each enterprise has different situations, we need to apply ERP financial software flexibly in accordance with the actual situation. In view of the actual situation of corporate financial management, we should pay close attention to some issues to ensure that the software can healthly integrate corporate financial management.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126378802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00039
Wangchun Zhang
Enterprises are aware of the important value of social resources with the era of digital economy and begin to pay attention to the social commerce combining social networks with traditional e-commerce. However, social commerce faces the problem of social media and traditional e-commerce. Based on the Stimulus-Organism-Response (SOR) framework, this study explores how users' perceived overload (information, system feature and social overload) affects their continuance intention mediated by two perceived states (social support and perceived risk). The results show that only information overload and system feature overload significantly affect informational support and emotional support, while social overload and system feature overload significantly affect perceived risk. In addition, only emotional support and perceived risk affects users' continuance intention. Both the theoretical and practical implications are discussed.
{"title":"The Impact of User Perceived Overload on Continuance Intention to Use Social Commerce: —Based on Stimulus-Organism-Response Model","authors":"Wangchun Zhang","doi":"10.1109/ICCSMT54525.2021.00039","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00039","url":null,"abstract":"Enterprises are aware of the important value of social resources with the era of digital economy and begin to pay attention to the social commerce combining social networks with traditional e-commerce. However, social commerce faces the problem of social media and traditional e-commerce. Based on the Stimulus-Organism-Response (SOR) framework, this study explores how users' perceived overload (information, system feature and social overload) affects their continuance intention mediated by two perceived states (social support and perceived risk). The results show that only information overload and system feature overload significantly affect informational support and emotional support, while social overload and system feature overload significantly affect perceived risk. In addition, only emotional support and perceived risk affects users' continuance intention. Both the theoretical and practical implications are discussed.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125265993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Seismic exploration is an interdisciplinary subject. Combined with artificial intelligence, it can automatically identify seismic dips and distinguish faults. The application of Deep Neural Network can reduce the error of manual recognition and improve the efficiency of recognition. Most seismic exploration datasets lack labels, so the supervised learning algorithm cannot be used to extract image features in order to obtain better seismic facies classification effect. Due to the proposal of the F3 dataset which contains real labels in 2019, the supervised learning algorithm can be used on 3D seismic data to take less time and get better prediction results. It is an effective means of evaluation. However, the classification effect of some deep learning models is not satisfactory, especially the neglect of underlying features and the misclassification of small categories on the F3 dataset. Therefore, we apply firstly the TransUNET to the F3 dataset, and modify the input method to 3D volume data, the Transformer layers are added at the end of the CNN layers to collect deep and potential information. The output of the decoder needs to be integrated in the X, Y and Z directions to get the final result. Finally, we propose EarthTransNet, which is applied to the seismic dataset to obtain higher accuracy and better boundary characterization ability.
{"title":"Seismic Facies Classification Algorithm Based on the EarthTransNet","authors":"Haoran Liang, Yanxin Yang, Liang Shi, Qingqiang Wu","doi":"10.1109/ICCSMT54525.2021.00058","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00058","url":null,"abstract":"Seismic exploration is an interdisciplinary subject. Combined with artificial intelligence, it can automatically identify seismic dips and distinguish faults. The application of Deep Neural Network can reduce the error of manual recognition and improve the efficiency of recognition. Most seismic exploration datasets lack labels, so the supervised learning algorithm cannot be used to extract image features in order to obtain better seismic facies classification effect. Due to the proposal of the F3 dataset which contains real labels in 2019, the supervised learning algorithm can be used on 3D seismic data to take less time and get better prediction results. It is an effective means of evaluation. However, the classification effect of some deep learning models is not satisfactory, especially the neglect of underlying features and the misclassification of small categories on the F3 dataset. Therefore, we apply firstly the TransUNET to the F3 dataset, and modify the input method to 3D volume data, the Transformer layers are added at the end of the CNN layers to collect deep and potential information. The output of the decoder needs to be integrated in the X, Y and Z directions to get the final result. Finally, we propose EarthTransNet, which is applied to the seismic dataset to obtain higher accuracy and better boundary characterization ability.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121680158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00041
Shuguang Wang, Mengshan Li
All aspects of the construction of the smart city need to rely on the information management platforms to achieve sustainable expansion and intelligent integration. It also needs to rely on the information model to obtain reliable big data onto real-time sharing, so as to improve the efficiency of urban governance of multiple dimensions. The construction and application of emergency big data and intelligent security emergency management platform will help to improve emergency management efficiency and reduce losses caused by emergencies. This paper expounds the problems existing on the emergency management of public safety problems with the smart city, uses research methods such as data analysis, is committed to the collection, processing and analysis of big data of the emergency management system, scientifically forecasts the public emergency management needs of the smart city, and puts forward suggestions to improve the public safety emergency management in combination with the concept of the smart city.
{"title":"Research on public safety emergency management of “Smart city”","authors":"Shuguang Wang, Mengshan Li","doi":"10.1109/ICCSMT54525.2021.00041","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00041","url":null,"abstract":"All aspects of the construction of the smart city need to rely on the information management platforms to achieve sustainable expansion and intelligent integration. It also needs to rely on the information model to obtain reliable big data onto real-time sharing, so as to improve the efficiency of urban governance of multiple dimensions. The construction and application of emergency big data and intelligent security emergency management platform will help to improve emergency management efficiency and reduce losses caused by emergencies. This paper expounds the problems existing on the emergency management of public safety problems with the smart city, uses research methods such as data analysis, is committed to the collection, processing and analysis of big data of the emergency management system, scientifically forecasts the public emergency management needs of the smart city, and puts forward suggestions to improve the public safety emergency management in combination with the concept of the smart city.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"379 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129111598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00080
Honglin Chen, Chunting Li
Deep convolutional neural networks use their powerful feature representation capability to extract deep information of the targets, which is conducive to the improvement of model accuracy. However, its model is more complex, with a heavier computational burden and greater demand on computational and memory resources, which affects the real-time performance and lightness of the model. To address the above limitations of deep convolutional neural networks, we define a new metric for measuring the importance of convolutional kernels in conjunction with feature maps, introduce a non-linear mapping function that maps feature maps to important convolutional kernels, propose a continuous and smooth pruning strategy for deep convolutional neural networks, and obtain the Pruning deep feature networks using channel importance propagation model to reduce the complexity of the network and reduce the computational burden, and improve the accuracy and training efficiency of the model, while ensuring the feature network representation capability and the system performance loss is small. Our proposed model was tested on three datasets, CIFAR-10, CIFAR-100 and SVHN, and the test results demonstrated the validity of the model.
{"title":"Pruning Deep Feature Networks Using Channel Importance Propagation","authors":"Honglin Chen, Chunting Li","doi":"10.1109/ICCSMT54525.2021.00080","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00080","url":null,"abstract":"Deep convolutional neural networks use their powerful feature representation capability to extract deep information of the targets, which is conducive to the improvement of model accuracy. However, its model is more complex, with a heavier computational burden and greater demand on computational and memory resources, which affects the real-time performance and lightness of the model. To address the above limitations of deep convolutional neural networks, we define a new metric for measuring the importance of convolutional kernels in conjunction with feature maps, introduce a non-linear mapping function that maps feature maps to important convolutional kernels, propose a continuous and smooth pruning strategy for deep convolutional neural networks, and obtain the Pruning deep feature networks using channel importance propagation model to reduce the complexity of the network and reduce the computational burden, and improve the accuracy and training efficiency of the model, while ensuring the feature network representation capability and the system performance loss is small. Our proposed model was tested on three datasets, CIFAR-10, CIFAR-100 and SVHN, and the test results demonstrated the validity of the model.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129734081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00076
Chunting Li, Honglin Chen
Deep correlation filter tracking method based on the fusion of correlation filter and deep convolutional neural network is one of the research hot topics in the field of visual object tracking. But how to choose an effective decision-making mechanism for implementing the online updating of feature network to fully adapt to the changes of target and environment in the tracking process is one of the key problems in the research of deep correlation filter tracking. It is obvious that the decision-making mechanism that only considers single factor can hardly meet the complex situation of the changes of target and environment. To address such an issue, this paper proposes a “Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision”. In the process of tracking, the cosine similarity based on Siamese network and the SSIM similarity both for the predicting tracking targets in two consecutive frames are calculated in real time. And then these two kinds of the similarity are fused together into the final similarity of the predicting tracking targets by full use of the fuzzy decision, which is taken as the criterion to determine whether the feature network needs updating and whether the tracking fails. When the feature network needs to be updated, the model is updated online while the tracking continues. In the case of tracking failure, the target is searched again, and the tracking is resumed. We tested the model on the OTB data set, and the experiments show that the tracking model designed in this paper can improve the tracking accuracy under the conditions of real-time tracking.
{"title":"Research on Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision","authors":"Chunting Li, Honglin Chen","doi":"10.1109/ICCSMT54525.2021.00076","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00076","url":null,"abstract":"Deep correlation filter tracking method based on the fusion of correlation filter and deep convolutional neural network is one of the research hot topics in the field of visual object tracking. But how to choose an effective decision-making mechanism for implementing the online updating of feature network to fully adapt to the changes of target and environment in the tracking process is one of the key problems in the research of deep correlation filter tracking. It is obvious that the decision-making mechanism that only considers single factor can hardly meet the complex situation of the changes of target and environment. To address such an issue, this paper proposes a “Lightweight Deep Correlation Filter Tracking Algorithm Based on Fuzzy Decision”. In the process of tracking, the cosine similarity based on Siamese network and the SSIM similarity both for the predicting tracking targets in two consecutive frames are calculated in real time. And then these two kinds of the similarity are fused together into the final similarity of the predicting tracking targets by full use of the fuzzy decision, which is taken as the criterion to determine whether the feature network needs updating and whether the tracking fails. When the feature network needs to be updated, the model is updated online while the tracking continues. In the case of tracking failure, the target is searched again, and the tracking is resumed. We tested the model on the OTB data set, and the experiments show that the tracking model designed in this paper can improve the tracking accuracy under the conditions of real-time tracking.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127961971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00116
Nan Cheng, Hailiang Wu, Zhong Liu, Yamin Wang, Wuchen Zhang, Jizhi Su
In view of the current problems of substation fire protection, the risk structure decomposition method is used to identify risks, and the risk function is introduced to analyze the risk areas in the entire station area, build a risk evaluation matrix, and take risk measures by area to form a typical area intelligent fire protection Terminal layout plan, build a substation fire protection perception system. The typical regional intelligent fire terminal layout plan formed can provide a scientific basis for the construction and transformation of the substation fire protection system in the future.
{"title":"Research and application of Substation Fire Protection System based on big data","authors":"Nan Cheng, Hailiang Wu, Zhong Liu, Yamin Wang, Wuchen Zhang, Jizhi Su","doi":"10.1109/ICCSMT54525.2021.00116","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00116","url":null,"abstract":"In view of the current problems of substation fire protection, the risk structure decomposition method is used to identify risks, and the risk function is introduced to analyze the risk areas in the entire station area, build a risk evaluation matrix, and take risk measures by area to form a typical area intelligent fire protection Terminal layout plan, build a substation fire protection perception system. The typical regional intelligent fire terminal layout plan formed can provide a scientific basis for the construction and transformation of the substation fire protection system in the future.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133441015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00084
Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu
Lines matching is the significant image pre-processing technique, which plays a central role in 3D reconstruction, visual navigation and other research fields. However, traditional lines matching methods suffered due to issues, e.g., complex processes, low efficiency, and poor matching effect, while those drawbacks strongly hurt the performance as required in the V-SLAM. In this research, we propose a fast and effective lines matching method. Based on the previous research of the fast line detection, we make full use of depth information to construct line features candidate areas to eliminate invalid features and to reduce the computational cost. Then, we use LBD descriptor to inscribe line features, and thereby ensuring the proper lines matching. It is worth noting that, in searching the effectiveness as required by tasks of lines detection and matching, we introduce geometric constraints into our framework. Experiments show that the method proposed in this paper can effectively improve the effectiveness and efficiency of the lines matching in real V-SLAM tasks.
{"title":"A Fast and Efficient Lines Matching Method via Multi-depth-layer Strategy","authors":"Qiang Chen, Lingkun Luo, Jiyuan Cai, Shiqiang Hu","doi":"10.1109/ICCSMT54525.2021.00084","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00084","url":null,"abstract":"Lines matching is the significant image pre-processing technique, which plays a central role in 3D reconstruction, visual navigation and other research fields. However, traditional lines matching methods suffered due to issues, e.g., complex processes, low efficiency, and poor matching effect, while those drawbacks strongly hurt the performance as required in the V-SLAM. In this research, we propose a fast and effective lines matching method. Based on the previous research of the fast line detection, we make full use of depth information to construct line features candidate areas to eliminate invalid features and to reduce the computational cost. Then, we use LBD descriptor to inscribe line features, and thereby ensuring the proper lines matching. It is worth noting that, in searching the effectiveness as required by tasks of lines detection and matching, we introduce geometric constraints into our framework. Experiments show that the method proposed in this paper can effectively improve the effectiveness and efficiency of the lines matching in real V-SLAM tasks.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130024622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00018
Lianhong Ding, Shengchang Gao
Knowledge graphs connect different entities through relationships, Multi-relational knowledge graphs are the common graph form. There are many unexplored potential relationships in multi-relational knowledge graphs. Link prediction is commonly used for knowledge graph completion, The link prediction task can infer possible relationships based on existing entities. Inspired by the advances of graph convolutional networks the link prediction task, we proposed a relational relevance-based GCN framework called RC-CompGCN. Firstly, update the embedding of all low-dimensional relations using the relational correlation module. Secondly, combined embedding entities and relationships using the graph structure module and various entities in knowledge graph embedding techniques are utilized relationship combination operations. We use the relational correlation module and graph convolutional network for link prediction tasks for the first time.
{"title":"Multi-Relational Graph Convolutional Network Based on Relational Correlation for Link Prediction","authors":"Lianhong Ding, Shengchang Gao","doi":"10.1109/ICCSMT54525.2021.00018","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00018","url":null,"abstract":"Knowledge graphs connect different entities through relationships, Multi-relational knowledge graphs are the common graph form. There are many unexplored potential relationships in multi-relational knowledge graphs. Link prediction is commonly used for knowledge graph completion, The link prediction task can infer possible relationships based on existing entities. Inspired by the advances of graph convolutional networks the link prediction task, we proposed a relational relevance-based GCN framework called RC-CompGCN. Firstly, update the embedding of all low-dimensional relations using the relational correlation module. Secondly, combined embedding entities and relationships using the graph structure module and various entities in knowledge graph embedding techniques are utilized relationship combination operations. We use the relational correlation module and graph convolutional network for link prediction tasks for the first time.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126924206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-11-01DOI: 10.1109/ICCSMT54525.2021.00010
Hai-Feng Zhang, Yeqiu Wang
Based on the panel data of computer software and information technology service industry agglomeration in 28 provinces and cities in China from 2015 to 2019, this paper calculates the agglomeration and other indicators, and uses the threshold effect model to study the impact of computer software and information technology service industry agglomeration on manufacturing competitiveness. The results show that when agglomeration is taken as the threshold variable, there is a significant single threshold for manufacturing competitiveness. The degree of competition and the investment of per capita GDP can significantly promote the improvement of manufacturing competitiveness. The impact of regional economy and openness on manufacturing competitiveness is contrary, and the interaction needs to be improved.
{"title":"Threshold regression analysis is used to analyze the impact of computer software and information technology service industry agglomeration on manufacturing competitiveness","authors":"Hai-Feng Zhang, Yeqiu Wang","doi":"10.1109/ICCSMT54525.2021.00010","DOIUrl":"https://doi.org/10.1109/ICCSMT54525.2021.00010","url":null,"abstract":"Based on the panel data of computer software and information technology service industry agglomeration in 28 provinces and cities in China from 2015 to 2019, this paper calculates the agglomeration and other indicators, and uses the threshold effect model to study the impact of computer software and information technology service industry agglomeration on manufacturing competitiveness. The results show that when agglomeration is taken as the threshold variable, there is a significant single threshold for manufacturing competitiveness. The degree of competition and the investment of per capita GDP can significantly promote the improvement of manufacturing competitiveness. The impact of regional economy and openness on manufacturing competitiveness is contrary, and the interaction needs to be improved.","PeriodicalId":304337,"journal":{"name":"2021 2nd International Conference on Computer Science and Management Technology (ICCSMT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116492302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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