Pub Date : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543329
Heng Zhang, Zhengbin Feng
Bias refers to a factor or factors that inherent within a test thus prevents the accession to the testing validity. Since college English adopted mixed teaching and testing, the shifting between online/offline mode demands systematical analysis, as a newly-added affecting factor for test bias and testing validity. To begin with a brief introduction about test bias and mixed teaching/testing mode in college English course, the article introduces the technical methods and online applications adopted in mixed teaching and testing and shows the comparison between online/offline testing scores and ranks. Two technique and designing- based hypothesis presented show that the technical development presents a possibility in test bias[1]. In the main part, proving process is explicitly presented, with clear steps followed and particular technical method used in testing practice, whether test validity and reliability defects or not will be clearly concluded. The diversity of samples and two hypothesis on bias attributes the research in this article a comprehensive and innovative one.
{"title":"The Application of Big Data in Bias Analysis on Mixed Teaching Mode","authors":"Heng Zhang, Zhengbin Feng","doi":"10.1109/CSAIEE54046.2021.9543329","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543329","url":null,"abstract":"Bias refers to a factor or factors that inherent within a test thus prevents the accession to the testing validity. Since college English adopted mixed teaching and testing, the shifting between online/offline mode demands systematical analysis, as a newly-added affecting factor for test bias and testing validity. To begin with a brief introduction about test bias and mixed teaching/testing mode in college English course, the article introduces the technical methods and online applications adopted in mixed teaching and testing and shows the comparison between online/offline testing scores and ranks. Two technique and designing- based hypothesis presented show that the technical development presents a possibility in test bias[1]. In the main part, proving process is explicitly presented, with clear steps followed and particular technical method used in testing practice, whether test validity and reliability defects or not will be clearly concluded. The diversity of samples and two hypothesis on bias attributes the research in this article a comprehensive and innovative one.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121225915","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}
The existing intelligent recognition methods have the problem of fuzzy characteristics of digital archives, resulting in slow recognition speed. A digital archives intelligent recognition method based on image recognition technology is designed. Optimize the application and installation in power business expanding, quantify the information flow, select the attribute with the maximum information gain rate as the new node of the decision tree, extract the digital file features, construct the signaling recognition layer of power supply enterprises with image recognition technology, find the port mapping table, and formulate the intelligent recognition mode according to the linear summation function attribute.
{"title":"Intelligent Recognition of Digital Archives of Application and Installation in Power Business Expanding Based On Image Recognition Technology","authors":"Ling Zeng, Haihong Liang, Linghan Meng, Yuqi Yang, Qian Guo","doi":"10.1109/CSAIEE54046.2021.9543456","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543456","url":null,"abstract":"The existing intelligent recognition methods have the problem of fuzzy characteristics of digital archives, resulting in slow recognition speed. A digital archives intelligent recognition method based on image recognition technology is designed. Optimize the application and installation in power business expanding, quantify the information flow, select the attribute with the maximum information gain rate as the new node of the decision tree, extract the digital file features, construct the signaling recognition layer of power supply enterprises with image recognition technology, find the port mapping table, and formulate the intelligent recognition mode according to the linear summation function attribute.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124383540","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543251
Haihong Liang, X. Cui, Ling Zeng, W. Zheng, Yang Dong
A large amount of data information is generated in the informatization construction of the application and installation in power business expanding of the power system. The traditional data analysis method of the application and installation in power business expanding only establishes a single analysis model for the data, and does not clarify the deep relationship of the data, which leads to the ineffective use of the archival data. For this reason, the mining analysis of the application and installation in power business expanding based on big data technology is proposed. Based on the establishment of the data warehouse of the application and installation in power business expanding, the data of the application and installation in power business expanding are processed by using the combined prediction model. After improving k-means clustering by genetic algorithm, data mining was performed to obtain the relationship between the archive data. The experimental results show that the studied analysis method not only has high data processing efficiency, but also can effectively shorten the application and installation in power business expanding process and improve the economic efficiency of enterprises when applied to actual power operation.
{"title":"Big data technology-based mining and analysis of application and installation in power business expanding","authors":"Haihong Liang, X. Cui, Ling Zeng, W. Zheng, Yang Dong","doi":"10.1109/CSAIEE54046.2021.9543251","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543251","url":null,"abstract":"A large amount of data information is generated in the informatization construction of the application and installation in power business expanding of the power system. The traditional data analysis method of the application and installation in power business expanding only establishes a single analysis model for the data, and does not clarify the deep relationship of the data, which leads to the ineffective use of the archival data. For this reason, the mining analysis of the application and installation in power business expanding based on big data technology is proposed. Based on the establishment of the data warehouse of the application and installation in power business expanding, the data of the application and installation in power business expanding are processed by using the combined prediction model. After improving k-means clustering by genetic algorithm, data mining was performed to obtain the relationship between the archive data. The experimental results show that the studied analysis method not only has high data processing efficiency, but also can effectively shorten the application and installation in power business expanding process and improve the economic efficiency of enterprises when applied to actual power operation.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114843505","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543374
Jiawei Zhang
In this paper, we introduce a novel multi-objective learning algorithm for related recommendations on industrial video sharing platforms. As an indispensable part in recommender system, the related video recommender system faces several realworld challenges, including maintaining high relevance between source item and target items, as well as achieving multiple competing ranking objectives. To solve these, we largely extended model-based collaborative filtering algorithm by adding related candidate generation stage, Two-tower DNN structure and a multi-task learning mechanism. Compared with typical baseline solutions, our proposed algorithm can capture both linear and non-linear relationships from user-item interactions, and live experiments demonstrate that it can significantly advance the state of the art on related recommendation quality.
{"title":"A Multi-objective Learning Algorithm for Related Recommendations","authors":"Jiawei Zhang","doi":"10.1109/CSAIEE54046.2021.9543374","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543374","url":null,"abstract":"In this paper, we introduce a novel multi-objective learning algorithm for related recommendations on industrial video sharing platforms. As an indispensable part in recommender system, the related video recommender system faces several realworld challenges, including maintaining high relevance between source item and target items, as well as achieving multiple competing ranking objectives. To solve these, we largely extended model-based collaborative filtering algorithm by adding related candidate generation stage, Two-tower DNN structure and a multi-task learning mechanism. Compared with typical baseline solutions, our proposed algorithm can capture both linear and non-linear relationships from user-item interactions, and live experiments demonstrate that it can significantly advance the state of the art on related recommendation quality.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115150736","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543197
Ziyi Guo
Because of the wide application of deep learning, there are more neural network structures in image recognition technology nowadays, but there are various differences in the accuracy of image recognition because of the various differences in network structures. For this reason, it is especially important to use different neural network structures for different forms of image data. This paper focuses on exploring the differences between LSTM networks, residual networks, and CNN networks in terms of the accuracy of cartoon character recognition.[1]Firstly, the web crawler acquires 14 different cartoon character images and manually screens the original data to remove the duplicate images and obtain the preliminary data. Then data enhancement was performed on the preliminary data, and the form of rotating the images was selected to complete the pre-processing of the data, which solved the problem of using different code forms for different forms of data importing into the neural network; the LSTM network, CNN network and CNN network with added residual function were used to recognize the pre-processed data. The experiments show that the CNN network structure with residual function can achieve higher accuracy compared to LSTM, with the final result of 76.08%.
{"title":"Cartoon Figure Recognition with The Deep Residual Network","authors":"Ziyi Guo","doi":"10.1109/CSAIEE54046.2021.9543197","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543197","url":null,"abstract":"Because of the wide application of deep learning, there are more neural network structures in image recognition technology nowadays, but there are various differences in the accuracy of image recognition because of the various differences in network structures. For this reason, it is especially important to use different neural network structures for different forms of image data. This paper focuses on exploring the differences between LSTM networks, residual networks, and CNN networks in terms of the accuracy of cartoon character recognition.[1]Firstly, the web crawler acquires 14 different cartoon character images and manually screens the original data to remove the duplicate images and obtain the preliminary data. Then data enhancement was performed on the preliminary data, and the form of rotating the images was selected to complete the pre-processing of the data, which solved the problem of using different code forms for different forms of data importing into the neural network; the LSTM network, CNN network and CNN network with added residual function were used to recognize the pre-processed data. The experiments show that the CNN network structure with residual function can achieve higher accuracy compared to LSTM, with the final result of 76.08%.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125506813","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543250
Yonghua Chen, Jia Chen
This paper uses computational linguistic statistics to analyze the novels of three Shaanxi-born authors of the Mao Dun Literature Prize, Lu Yao, Chen Zhongshi, and Jia Pingwa, to discover the differences in writing styles between the different authors from the perspectives of paragraph, sentence length, and vocabulary, to extract theme words from the works using the LDA topic model, and to analyze and compare the themes of concern among the works. A series of important research results were obtained on the differences or similarities between different authors in writing habits, content selection and word usage.
{"title":"Research on Literary Style Based on Statistical Analysis of Computational Language","authors":"Yonghua Chen, Jia Chen","doi":"10.1109/CSAIEE54046.2021.9543250","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543250","url":null,"abstract":"This paper uses computational linguistic statistics to analyze the novels of three Shaanxi-born authors of the Mao Dun Literature Prize, Lu Yao, Chen Zhongshi, and Jia Pingwa, to discover the differences in writing styles between the different authors from the perspectives of paragraph, sentence length, and vocabulary, to extract theme words from the works using the LDA topic model, and to analyze and compare the themes of concern among the works. A series of important research results were obtained on the differences or similarities between different authors in writing habits, content selection and word usage.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124644445","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543170
Qian Chen, Chao Ye
In recent years, a large number of scholars have been engaged in the research of target tracking algorithms, but target tracking is still a very challenging problem due to the variability of the observed target information in the tracking process, the mobility of the target and the complexity of the background. In this paper, relying on the theoretical basis of TLD tracking algorithm, implementation detection module, P-N learning module and synthesis module, the dynamic fusion features of the target in different states are used as target templates to take advantage of the different features of the target in different states and increase the tracking success rate of the algorithm. For the problem that the target motion background changes, when the target color is seriously affected by the background change or interfered by the similar target, Hog features are combined with color features to make the tracking algorithm track the target to the maximum extent. This study aims to set a new direction for research in this field, as a way to promote the update and iteration of the technology in this field.
{"title":"Research on target tracking technology based on machine learning","authors":"Qian Chen, Chao Ye","doi":"10.1109/CSAIEE54046.2021.9543170","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543170","url":null,"abstract":"In recent years, a large number of scholars have been engaged in the research of target tracking algorithms, but target tracking is still a very challenging problem due to the variability of the observed target information in the tracking process, the mobility of the target and the complexity of the background. In this paper, relying on the theoretical basis of TLD tracking algorithm, implementation detection module, P-N learning module and synthesis module, the dynamic fusion features of the target in different states are used as target templates to take advantage of the different features of the target in different states and increase the tracking success rate of the algorithm. For the problem that the target motion background changes, when the target color is seriously affected by the background change or interfered by the similar target, Hog features are combined with color features to make the tracking algorithm track the target to the maximum extent. This study aims to set a new direction for research in this field, as a way to promote the update and iteration of the technology in this field.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121361593","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543438
Yue Wu, Qi Wang
Nowadays, market volatility prediction is the most prominent terms you will hear in the trading market. Realized volatility is the representation of price movements, market's volatility and the trading risks. A little change happened in volatility will affect the expected return on all assets. In this article, we will use the dataset provided by Kaggle platform to predict the volatility. As a leading global electronic market maker, Optiver is dedicated to continuously improving financial markets, creating better access and prices for options, ETFs, cash equities, bonds and foreign currencies on numerous exchanges around the world. The prediction model we used in our paper is LightGBM, which is an iimproved version of XGBoost. We conclude some related work about the prediction of volatility. And we compute our model with others, the result shows that our model LightGBM has a lowest RMSPE score that is 0.211. And compared to it, the RMSPE of other models such as logistic regression, SVM and XGBoost are respectively 0.099. 0.076, 0.034 higher than LightGBM.
{"title":"LightGBM Based Optiver Realized Volatility Prediction","authors":"Yue Wu, Qi Wang","doi":"10.1109/CSAIEE54046.2021.9543438","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543438","url":null,"abstract":"Nowadays, market volatility prediction is the most prominent terms you will hear in the trading market. Realized volatility is the representation of price movements, market's volatility and the trading risks. A little change happened in volatility will affect the expected return on all assets. In this article, we will use the dataset provided by Kaggle platform to predict the volatility. As a leading global electronic market maker, Optiver is dedicated to continuously improving financial markets, creating better access and prices for options, ETFs, cash equities, bonds and foreign currencies on numerous exchanges around the world. The prediction model we used in our paper is LightGBM, which is an iimproved version of XGBoost. We conclude some related work about the prediction of volatility. And we compute our model with others, the result shows that our model LightGBM has a lowest RMSPE score that is 0.211. And compared to it, the RMSPE of other models such as logistic regression, SVM and XGBoost are respectively 0.099. 0.076, 0.034 higher than LightGBM.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116299501","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543352
Yingchao Ren, Xuefeng Yan, Haoming Guo
Aiming at the problem that the time stamp mechanism in the Kerberos protocol cannot effectively resist replay attacks, this paper proposes an improved kerberos protocol based on the sequence number and sliding window mechanism. The authentication server and the application server maintain a sliding window with a sequence number to determine the replay of the client's request message. Considering the impact of message reordering and long jump rearrangement, a fault-tolerant shift mechanism is added to the server to increase the window Flexibility. We give the specific process of the improved kerberos protocol, and use the BAN logic to formally analyze the improved protocol to verify the security and reliability of the protocol.
{"title":"Improved Kerberos protocol based on sliding window and its formal analysis","authors":"Yingchao Ren, Xuefeng Yan, Haoming Guo","doi":"10.1109/CSAIEE54046.2021.9543352","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543352","url":null,"abstract":"Aiming at the problem that the time stamp mechanism in the Kerberos protocol cannot effectively resist replay attacks, this paper proposes an improved kerberos protocol based on the sequence number and sliding window mechanism. The authentication server and the application server maintain a sliding window with a sequence number to determine the replay of the client's request message. Considering the impact of message reordering and long jump rearrangement, a fault-tolerant shift mechanism is added to the server to increase the window Flexibility. We give the specific process of the improved kerberos protocol, and use the BAN logic to formally analyze the improved protocol to verify the security and reliability of the protocol.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116487979","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 : 2021-08-20DOI: 10.1109/CSAIEE54046.2021.9543241
H. Ao
While the network brings great social and economic benefits to mankind, the security situation of the network is becoming increasingly severe, and various forms of network attacks occur frequently. This paper uses Python to train machine learning model to improve the processing efficiency of intrusion detection system. By comparing five machine learning models such as SGD Classifier, Ridge Classifier, Decision Tree classifier, Random Forest Classifier, Extra Tree Classifier, the best machine learning model suitable for intrusion detection system is found out. In the experiment, feature selection is used to filter the features of the data. The recursion method was used to eliminate the irrelevant features and the NSL-KDD data set was used to identify the relevant features, which greatly improved the accuracy and reliability of the model. The experimental results show that Random Forest Classifier and Extra Tree Classifier perform well, and the extra tree model can still guarantee high stability and accuracy when dealing with difficult problems. The application of these two models is helpful to build a better intrusion detection system.
{"title":"Using Machine Learning Models to Detect Different Intrusion on NSL-KDD","authors":"H. Ao","doi":"10.1109/CSAIEE54046.2021.9543241","DOIUrl":"https://doi.org/10.1109/CSAIEE54046.2021.9543241","url":null,"abstract":"While the network brings great social and economic benefits to mankind, the security situation of the network is becoming increasingly severe, and various forms of network attacks occur frequently. This paper uses Python to train machine learning model to improve the processing efficiency of intrusion detection system. By comparing five machine learning models such as SGD Classifier, Ridge Classifier, Decision Tree classifier, Random Forest Classifier, Extra Tree Classifier, the best machine learning model suitable for intrusion detection system is found out. In the experiment, feature selection is used to filter the features of the data. The recursion method was used to eliminate the irrelevant features and the NSL-KDD data set was used to identify the relevant features, which greatly improved the accuracy and reliability of the model. The experimental results show that Random Forest Classifier and Extra Tree Classifier perform well, and the extra tree model can still guarantee high stability and accuracy when dealing with difficult problems. The application of these two models is helpful to build a better intrusion detection system.","PeriodicalId":376014,"journal":{"name":"2021 IEEE International Conference on Computer Science, Artificial Intelligence and Electronic Engineering (CSAIEE)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133115560","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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