Pub Date : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00009
Tao Sun, Lan Luo, Guang-yu Chen
Under the trend of digitalization and informatization of teaching systems, the design and application of human-computer interaction technology reflects the psychology of human as a social being. The design of the human-computer interaction process in the informatization and digital teaching system collects information and data by establishing usage feedback, enhances the user experience in the human-computer interaction process through computer vision technology, visualizes the information with Focus+Context technology, and deeply integrates the user with the digital teaching system by establishing a model of the user’s demand for the teaching system. In addition, according to the user’s psychology when maintaining attention to the system, the network feature model of CSP+CBAM is constructed by using Yolov4-Tiny network, which integrates spatial attention and channel attention into the network of CBAM.
{"title":"The Role of Social Psychology in Human-computer Interaction in Teaching Systems","authors":"Tao Sun, Lan Luo, Guang-yu Chen","doi":"10.1109/icise-ie58127.2022.00009","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00009","url":null,"abstract":"Under the trend of digitalization and informatization of teaching systems, the design and application of human-computer interaction technology reflects the psychology of human as a social being. The design of the human-computer interaction process in the informatization and digital teaching system collects information and data by establishing usage feedback, enhances the user experience in the human-computer interaction process through computer vision technology, visualizes the information with Focus+Context technology, and deeply integrates the user with the digital teaching system by establishing a model of the user’s demand for the teaching system. In addition, according to the user’s psychology when maintaining attention to the system, the network feature model of CSP+CBAM is constructed by using Yolov4-Tiny network, which integrates spatial attention and channel attention into the network of CBAM.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124745138","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00025
Juanyu Yang, Yang Chen, Xiao-jun Liu, Xiuchao Luo
The construction of intelligent campus in colleges and universities can provide more humanized services to teachers and students through rational distribution of data resources, which can better promote the progress of education. Therefore, this paper studies the design and practice of the integrated platform of smart campus in colleges and universities under the background of big data. In this paper, hadoop distributed storage and spark computing components are used, and javaweb technology is used to develop this platform. In terms of system performance, the average data response time is 53 m s, the bit error rate can reach below 0.3%, the average database size is 122.7 T B, and the number of queries can reach more than 10 000 times. This paper makes an in-depth study on the process of data sharing and exchange, and on this basis, puts forward a concrete construction scheme for the integration of intelligent campus in colleges and universities, and sums up the intelligent application and service mode of campus data. The experiment shows that this research is conducive to improving the accuracy of campus data governance, at the same time, improving the collaboration of campus management services, and meeting the long-term development needs of intelligent campus.
{"title":"Design and Practice of University Smart Campus Integration Platform under the Background of Big Data","authors":"Juanyu Yang, Yang Chen, Xiao-jun Liu, Xiuchao Luo","doi":"10.1109/icise-ie58127.2022.00025","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00025","url":null,"abstract":"The construction of intelligent campus in colleges and universities can provide more humanized services to teachers and students through rational distribution of data resources, which can better promote the progress of education. Therefore, this paper studies the design and practice of the integrated platform of smart campus in colleges and universities under the background of big data. In this paper, hadoop distributed storage and spark computing components are used, and javaweb technology is used to develop this platform. In terms of system performance, the average data response time is 53 m s, the bit error rate can reach below 0.3%, the average database size is 122.7 T B, and the number of queries can reach more than 10 000 times. This paper makes an in-depth study on the process of data sharing and exchange, and on this basis, puts forward a concrete construction scheme for the integration of intelligent campus in colleges and universities, and sums up the intelligent application and service mode of campus data. The experiment shows that this research is conducive to improving the accuracy of campus data governance, at the same time, improving the collaboration of campus management services, and meeting the long-term development needs of intelligent campus.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124088073","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00018
Ken Cheng, Shixian Wang, Yu Zhu
In the process of essay scoring, the item-specific index is an important factor affecting the final score. In previous studies, the item-specific index was not included in the characteristic system of objective essay scoring. Therefore, it is easy to produce the phenomenon that the deviation from the topic with high language standardization, rich vocabulary and correct grammar is judged high, resulting in the decrease of the accuracy of the score prediction model, which is quite different from the results of manual evaluation. In this paper, the LDA topic model is used to model the article, and the topic probability distribution distance and word vector are used to calculate the topic degree index of the article. The index is added to the composition automatic scoring system to improve the accuracy of the machine scoring.
{"title":"Research on Question Severity Detection for Automatic Essay Scoring","authors":"Ken Cheng, Shixian Wang, Yu Zhu","doi":"10.1109/icise-ie58127.2022.00018","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00018","url":null,"abstract":"In the process of essay scoring, the item-specific index is an important factor affecting the final score. In previous studies, the item-specific index was not included in the characteristic system of objective essay scoring. Therefore, it is easy to produce the phenomenon that the deviation from the topic with high language standardization, rich vocabulary and correct grammar is judged high, resulting in the decrease of the accuracy of the score prediction model, which is quite different from the results of manual evaluation. In this paper, the LDA topic model is used to model the article, and the topic probability distribution distance and word vector are used to calculate the topic degree index of the article. The index is added to the composition automatic scoring system to improve the accuracy of the machine scoring.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132379305","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00043
F. Zhao, Yi Wang
The Practical Byzantine Fault Tolerant (PBFT) algorithm is a common consensus algorithm for coalition chain, but it has some problems such as low efficiency, poor scalability and high communication complexity. To solve these problems, this paper proposes a PBFT consensus algorithm based on reward and punishment mechanism (RP-PBFT). Based on the PBFT algorithm, the algorithm rewards and penalises the performance of each node in the consensus process, and divides the nodes into three categories according to their reputation value. The two categories of nodes with high reputation value can participate in the consensus, reduce the scale of nodes participating in the consensus, improve the security and simplify the three-stage consensus to improve the efficiency. The experimental results show that the communication complexity of RP-PBFT algorithm is significantly reduced, the throughput is increased, the delay is reduced, and the system efficiency is improved.
{"title":"PBFT consensus algorithm based on reward and punishment mechanism","authors":"F. Zhao, Yi Wang","doi":"10.1109/icise-ie58127.2022.00043","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00043","url":null,"abstract":"The Practical Byzantine Fault Tolerant (PBFT) algorithm is a common consensus algorithm for coalition chain, but it has some problems such as low efficiency, poor scalability and high communication complexity. To solve these problems, this paper proposes a PBFT consensus algorithm based on reward and punishment mechanism (RP-PBFT). Based on the PBFT algorithm, the algorithm rewards and penalises the performance of each node in the consensus process, and divides the nodes into three categories according to their reputation value. The two categories of nodes with high reputation value can participate in the consensus, reduce the scale of nodes participating in the consensus, improve the security and simplify the three-stage consensus to improve the efficiency. The experimental results show that the communication complexity of RP-PBFT algorithm is significantly reduced, the throughput is increased, the delay is reduced, and the system efficiency is improved.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133897225","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00019
Xin Shao, Jinghan Zhang
In order to realize the informatization of university digital library management and establish a real multimedia knowledge center, this paper upgrades and improves the university digital library system. This paper takes big data technology as the core, uses hadoop framework to build a distributed data processing model, and combines Web technology to build a digital library system in colleges and universities in JAVA environment. The system adopts B/S architecture, guided by MVC design idea, and introduces SSM framework to complete the design and deployment of Web Server, which is convenient for users to quickly search and view all kinds of book information and electronic documents through simple interactive operations. It not only meets students’ learning needs, but also provides necessary help for teachers’ teaching and scientific research.
{"title":"Design and Implementation of University Digital Library System Based on Hadoop Framework","authors":"Xin Shao, Jinghan Zhang","doi":"10.1109/icise-ie58127.2022.00019","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00019","url":null,"abstract":"In order to realize the informatization of university digital library management and establish a real multimedia knowledge center, this paper upgrades and improves the university digital library system. This paper takes big data technology as the core, uses hadoop framework to build a distributed data processing model, and combines Web technology to build a digital library system in colleges and universities in JAVA environment. The system adopts B/S architecture, guided by MVC design idea, and introduces SSM framework to complete the design and deployment of Web Server, which is convenient for users to quickly search and view all kinds of book information and electronic documents through simple interactive operations. It not only meets students’ learning needs, but also provides necessary help for teachers’ teaching and scientific research.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128949386","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00044
Jingjing Shi, S. Narasuman, Huichun Ning, Fang Yue
In the era of internet+, hybrid learning is bound to become a trend, while the Covid-19 pandemic acts as an accelerator of hybrid learning. This research selected 1773 articles from WOS and opted SATI as the major platform to do the bibliographic information processing. The results of word frequency analysis and co-word analysis were then interpreted to identify the current trend and hotspots of research in the fields of hybrid learning in ESL/EFL. The purpose is to seek more effective teaching mode reform and innovation in the future and make new technology really serve teaching.
{"title":"Research Trend Analysis with SATI on Hybrid-learning in ESL/EFL Since COVID-19","authors":"Jingjing Shi, S. Narasuman, Huichun Ning, Fang Yue","doi":"10.1109/icise-ie58127.2022.00044","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00044","url":null,"abstract":"In the era of internet+, hybrid learning is bound to become a trend, while the Covid-19 pandemic acts as an accelerator of hybrid learning. This research selected 1773 articles from WOS and opted SATI as the major platform to do the bibliographic information processing. The results of word frequency analysis and co-word analysis were then interpreted to identify the current trend and hotspots of research in the fields of hybrid learning in ESL/EFL. The purpose is to seek more effective teaching mode reform and innovation in the future and make new technology really serve teaching.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133451679","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00036
Xiaokang Si, Jian Wang
The man-machine interaction technology based on gesture recognition has some problems, such as slow speed and low precision of static gesture recognition, and poor expansibility of gesture action. Yolov4-Tiny algorithm based on attention mechanism was proposed, and action semantics was designed by combining basic gestures with gesture state change, and the application function was called according to action semantics, which realize efficient human-computer interaction. By comparing the methods involved in each process, it can be seen that deep learning has strong fault tolerance, robust-ness, high parallelism, anti-interference, etc., which has achieved great achievements above the traditional learning algorithm in the field of gesture identification.
{"title":"Gesture recognition based on human - computer interaction","authors":"Xiaokang Si, Jian Wang","doi":"10.1109/icise-ie58127.2022.00036","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00036","url":null,"abstract":"The man-machine interaction technology based on gesture recognition has some problems, such as slow speed and low precision of static gesture recognition, and poor expansibility of gesture action. Yolov4-Tiny algorithm based on attention mechanism was proposed, and action semantics was designed by combining basic gestures with gesture state change, and the application function was called according to action semantics, which realize efficient human-computer interaction. By comparing the methods involved in each process, it can be seen that deep learning has strong fault tolerance, robust-ness, high parallelism, anti-interference, etc., which has achieved great achievements above the traditional learning algorithm in the field of gesture identification.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131682805","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00037
Bin Wang, Guilei Zhu, Zhu Zeng
Due to the universality and rapidity of information dissemination on social media, it is of guiding significance for automobile manufacturers to improve product design and optimize quality management to timely discover the defect information of automobiles from social media. At present, the research on social media defect recognition has mined less defect information and mostly takes negative comments as product defects. To solve this problem, we put forward a comment representation model based on sentiment-dependent linguistic features, which effectively uses the domain context. In reality, the distribution of the data set is biased in some way. To avoid the major defect, we use the clustering-based under-sampling method. The experimental results show that the model can effectively identify car defects in Chinese social media, and has a high accuracy and recall rate.
{"title":"Mining Sentiment-Dependent Linguistic Patterns from Automotive Reviews for Product Defects","authors":"Bin Wang, Guilei Zhu, Zhu Zeng","doi":"10.1109/icise-ie58127.2022.00037","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00037","url":null,"abstract":"Due to the universality and rapidity of information dissemination on social media, it is of guiding significance for automobile manufacturers to improve product design and optimize quality management to timely discover the defect information of automobiles from social media. At present, the research on social media defect recognition has mined less defect information and mostly takes negative comments as product defects. To solve this problem, we put forward a comment representation model based on sentiment-dependent linguistic features, which effectively uses the domain context. In reality, the distribution of the data set is biased in some way. To avoid the major defect, we use the clustering-based under-sampling method. The experimental results show that the model can effectively identify car defects in Chinese social media, and has a high accuracy and recall rate.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115422457","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00040
Si-Lin Liu, Mengzhen Xia
With the rapid development of maker education in China, more and more science and technology enterprises, publishing units, science popularization venues, and educational institutions have been involved in the upsurge of resource development for maker education. Research on the development trend of maker education also shows an upward trend. However, most of the existing studies on trend prediction give the future development trend of maker education in the way of literature statistics and subjective judgment, which makes the prediction results strongly subjective. In order to solve this problem, we drew on the advantages of machine learning in data prediction and proposed a research method for maker education based on regression models. The core idea of the proposed method is to use the characteristics of regression models to predict future maker education without adding subjective factors. Specifically, we built regression models based on the collected historical data, and then predicted future development based on these regression models. In the experiment, we verified the effectiveness of the model based on the research literature on maker education in China from 2013 to 2019.
{"title":"Research Method of Maker Education Based on Regression Models","authors":"Si-Lin Liu, Mengzhen Xia","doi":"10.1109/icise-ie58127.2022.00040","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00040","url":null,"abstract":"With the rapid development of maker education in China, more and more science and technology enterprises, publishing units, science popularization venues, and educational institutions have been involved in the upsurge of resource development for maker education. Research on the development trend of maker education also shows an upward trend. However, most of the existing studies on trend prediction give the future development trend of maker education in the way of literature statistics and subjective judgment, which makes the prediction results strongly subjective. In order to solve this problem, we drew on the advantages of machine learning in data prediction and proposed a research method for maker education based on regression models. The core idea of the proposed method is to use the characteristics of regression models to predict future maker education without adding subjective factors. Specifically, we built regression models based on the collected historical data, and then predicted future development based on these regression models. In the experiment, we verified the effectiveness of the model based on the research literature on maker education in China from 2013 to 2019.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125040893","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 : 2022-11-01DOI: 10.1109/icise-ie58127.2022.00038
Feng Zhang, Yaxin Qin, Jingjing Chen
Similarity measure of MOOC learners is a hot topic in the current research of educational data mining, and it is also the basis of learners clustering and grouping. In the online education environment based on MOOC, learning MOOC videos is one of the most basic behaviors of learners. The degree and ability of learners to master the videos’ content can be implicitly obtained through their video learning behavior, thus providing a basis for the measure of learners’ ability similarity. Most existing researches on the similarity of learners focus on the similarity of learners’ interests or behavior patterns, and the similarity measure of ability is ignored. Meanwhile, most existing works only use video related data as a dimension of learners’ similarity measure, and there are still shortcomings in judging the ability similarity of learners. This paper proposes an approach to measure learners’ ability similarity based on MOOC video learning data. Based on the videos and their learning times of learners, a bipartite graph model is constructed, and the ability similarity between all learners is measured iteratively through SimRank++ algorithm. The experiments based on the real data set show that the proposed approach has better accuracy than the cosine similarity that is widely used in related works, and the NDCG value is increased by 34% on average.
{"title":"Ability Similarity Measure of MOOC Learners Based on Video Learning Data","authors":"Feng Zhang, Yaxin Qin, Jingjing Chen","doi":"10.1109/icise-ie58127.2022.00038","DOIUrl":"https://doi.org/10.1109/icise-ie58127.2022.00038","url":null,"abstract":"Similarity measure of MOOC learners is a hot topic in the current research of educational data mining, and it is also the basis of learners clustering and grouping. In the online education environment based on MOOC, learning MOOC videos is one of the most basic behaviors of learners. The degree and ability of learners to master the videos’ content can be implicitly obtained through their video learning behavior, thus providing a basis for the measure of learners’ ability similarity. Most existing researches on the similarity of learners focus on the similarity of learners’ interests or behavior patterns, and the similarity measure of ability is ignored. Meanwhile, most existing works only use video related data as a dimension of learners’ similarity measure, and there are still shortcomings in judging the ability similarity of learners. This paper proposes an approach to measure learners’ ability similarity based on MOOC video learning data. Based on the videos and their learning times of learners, a bipartite graph model is constructed, and the ability similarity between all learners is measured iteratively through SimRank++ algorithm. The experiments based on the real data set show that the proposed approach has better accuracy than the cosine similarity that is widely used in related works, and the NDCG value is increased by 34% on average.","PeriodicalId":376815,"journal":{"name":"2022 3rd International Conference on Information Science and Education (ICISE-IE)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132045654","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：