Pub Date : 2020-12-01DOI: 10.1109/ITCA52113.2020.00128
Yizhuo Rao, Chengyuan Duan, Xiao Wei
With the repaid development of the Internet, multimedia data such as image, text, video, audio is increasing, which brings opportunities and challenges to the development of the economy and science. Cross-modal data entity resolution aims to find different objective descriptions of the semantically similar items from objects in different modalities. However, different modality data have the features with underlying heterogeneity and high-level semantic related. Starting from the problem of modality gap between cross-modal data, this paper introduces how to use the idea of confrontational learning to solve the cross-modal data entity resolution problem between images and text from the aspects of feature extraction and emotional state association.
{"title":"Review on Deep Adversarial Learning of Entity Resolution for Cross-Modal Data","authors":"Yizhuo Rao, Chengyuan Duan, Xiao Wei","doi":"10.1109/ITCA52113.2020.00128","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00128","url":null,"abstract":"With the repaid development of the Internet, multimedia data such as image, text, video, audio is increasing, which brings opportunities and challenges to the development of the economy and science. Cross-modal data entity resolution aims to find different objective descriptions of the semantically similar items from objects in different modalities. However, different modality data have the features with underlying heterogeneity and high-level semantic related. Starting from the problem of modality gap between cross-modal data, this paper introduces how to use the idea of confrontational learning to solve the cross-modal data entity resolution problem between images and text from the aspects of feature extraction and emotional state association.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114731766","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 : 2020-12-01DOI: 10.1109/ITCA52113.2020.00061
Quan Nie, Yingfeng Zhao, Li Xu, Bin Li
Continuous collision detection (CCD) is a key technology in the field of virtual surgery, cloth simulation and robot motion planning. It can accurately detect the first time of contact between objects and returns collision information such as penetration depth, friction and repulsive force, etc., have a wide range of application and important research value. By analyzing the processing framework of continuous collision detection algorithm in detail, the current research status of continuous collision detection is systematically reviewed from perspectives of two phases respectively. In broad-phase, the recent achievements of space decomposition and sweep and prune are introduced. In narrow-phase, the research status of intelligent optimization based algorithm and image-space based algorithm is illustrated. Besides, the development of bounding volume hierarchy (BVH) is analyzed and discussed. After that, the performance and innovative achievements of self-collision detection in deformable objects are summarized and analyzed. Finally, the challenges and future trends of algorithm research are pointed out.
{"title":"A Survey of Continuous Collision Detection","authors":"Quan Nie, Yingfeng Zhao, Li Xu, Bin Li","doi":"10.1109/ITCA52113.2020.00061","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00061","url":null,"abstract":"Continuous collision detection (CCD) is a key technology in the field of virtual surgery, cloth simulation and robot motion planning. It can accurately detect the first time of contact between objects and returns collision information such as penetration depth, friction and repulsive force, etc., have a wide range of application and important research value. By analyzing the processing framework of continuous collision detection algorithm in detail, the current research status of continuous collision detection is systematically reviewed from perspectives of two phases respectively. In broad-phase, the recent achievements of space decomposition and sweep and prune are introduced. In narrow-phase, the research status of intelligent optimization based algorithm and image-space based algorithm is illustrated. Besides, the development of bounding volume hierarchy (BVH) is analyzed and discussed. After that, the performance and innovative achievements of self-collision detection in deformable objects are summarized and analyzed. Finally, the challenges and future trends of algorithm research are pointed out.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130424226","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}
In power applications, it is often difficult for equipment to keep time independently due to channel congestion, equipment disconnection, isolated network operation and other reasons. The long-term failure to obtain accurate time information will lead to the event SOE not having rigorous reference significance when the power terminal fails, and it often takes the data acquisition time of the dispatching master station as the main reference basis, and fails to get a good collaborative judgment effect. In order to meet the urgent needs of time, space and security in power production and management business, eliminate the major hidden dangers of GPS for China's power safety, develop the application of GPS and Beidou dual system timing and positioning, and transition to the application of Beidou III navigation satellite which can cover the world and has higher reliability.
{"title":"Power spatiotemporal data sensing application based on Beidou","authors":"You Li, Baoquan Liao, Xiaoou Wu, Chuanfu Xia, Yonghui Zhang, Shuai Huang, Zhixi Yu","doi":"10.1109/ITCA52113.2020.00065","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00065","url":null,"abstract":"In power applications, it is often difficult for equipment to keep time independently due to channel congestion, equipment disconnection, isolated network operation and other reasons. The long-term failure to obtain accurate time information will lead to the event SOE not having rigorous reference significance when the power terminal fails, and it often takes the data acquisition time of the dispatching master station as the main reference basis, and fails to get a good collaborative judgment effect. In order to meet the urgent needs of time, space and security in power production and management business, eliminate the major hidden dangers of GPS for China's power safety, develop the application of GPS and Beidou dual system timing and positioning, and transition to the application of Beidou III navigation satellite which can cover the world and has higher reliability.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122972909","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 : 2020-12-01DOI: 10.1109/ITCA52113.2020.00060
Qiang Lin, Xiaohan Gao, Yang Guo, Xilin Zhang
The system designed in the paper uses the method of collecting Twitter tweets to test the collection system. The specific collection content is tweet collection based on keywords, testing the collection rate of tweets and system stability. In the test, the bandwidth of each collection node is limited by using bandwidth limiting software to simulate different network agent environments. The collection system in different environments is tested by changing the collection task and the bandwidth limit of each collection node. The results prove that the algorithm proposed in the paper can significantly improve the evaluation accuracy of media data, and has a significant effect on analyzing the collected objects.
{"title":"Research on Media Data Analysis System Based on Big Data Technology","authors":"Qiang Lin, Xiaohan Gao, Yang Guo, Xilin Zhang","doi":"10.1109/ITCA52113.2020.00060","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00060","url":null,"abstract":"The system designed in the paper uses the method of collecting Twitter tweets to test the collection system. The specific collection content is tweet collection based on keywords, testing the collection rate of tweets and system stability. In the test, the bandwidth of each collection node is limited by using bandwidth limiting software to simulate different network agent environments. The collection system in different environments is tested by changing the collection task and the bandwidth limit of each collection node. The results prove that the algorithm proposed in the paper can significantly improve the evaluation accuracy of media data, and has a significant effect on analyzing the collected objects.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127102238","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 : 2020-12-01DOI: 10.1109/ITCA52113.2020.00063
Xizhi Wu, Rongzhe Liu, Han-Ni Yang, Zizhao Chen
Over the past decade, image classification, which can provide assistance to address complex tasks such as planetary exploration and unmanned driving, has become a hot topic. As a subproblem of image classification, scene image classification has received increasing attention. Based on previous studies, the Xception model achieved superior performance on image classification tasks in comparison with the original Inception model. The Xception model is advantageous at processing image classification, yet it has not been used for scene image classification. To tackle this issue, this paper proposed an Xception based transfer learning, and analyzed the model performance by comparing it with the Inception-V3 model. We found that the Xception based transfer learning significantly outperforms other methods such as Inception-V3, which is nicely demonstrated by the experimental results on the Intel Image Classification Challenge dataset. Furthermore, the Xception has shown greater robustness and ability in generalization with less overfitting problems.
{"title":"An Xception Based Convolutional Neural Network for Scene Image Classification with Transfer Learning","authors":"Xizhi Wu, Rongzhe Liu, Han-Ni Yang, Zizhao Chen","doi":"10.1109/ITCA52113.2020.00063","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00063","url":null,"abstract":"Over the past decade, image classification, which can provide assistance to address complex tasks such as planetary exploration and unmanned driving, has become a hot topic. As a subproblem of image classification, scene image classification has received increasing attention. Based on previous studies, the Xception model achieved superior performance on image classification tasks in comparison with the original Inception model. The Xception model is advantageous at processing image classification, yet it has not been used for scene image classification. To tackle this issue, this paper proposed an Xception based transfer learning, and analyzed the model performance by comparing it with the Inception-V3 model. We found that the Xception based transfer learning significantly outperforms other methods such as Inception-V3, which is nicely demonstrated by the experimental results on the Intel Image Classification Challenge dataset. Furthermore, the Xception has shown greater robustness and ability in generalization with less overfitting problems.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121724733","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 : 2020-12-01DOI: 10.1109/ITCA52113.2020.00123
Fajia Lin
With the emergence and development of high-tech such as the Internet, the Internet has become an important part of people’s lives. The use of Internet technology to carry out education and teaching reform is conducive to promoting the development of the education industry in the direction of informatization. In the context of the Internet, foreign language learning is no longer limited to classroom teaching, and mobile foreign language learning using big data technology has become one of the main educational methods for foreign language learning. This article will discuss the background of mobile foreign language learning in the era of educational informationization from the perspective of theoretical research and the feasibility and role of mobile foreign language learning. The author analyzed the current situation and existing problems of mobile foreign language learning and proposed corresponding improvement measures.
{"title":"Mobile Foreign Language Learning in the Era of Educational Information","authors":"Fajia Lin","doi":"10.1109/ITCA52113.2020.00123","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00123","url":null,"abstract":"With the emergence and development of high-tech such as the Internet, the Internet has become an important part of people’s lives. The use of Internet technology to carry out education and teaching reform is conducive to promoting the development of the education industry in the direction of informatization. In the context of the Internet, foreign language learning is no longer limited to classroom teaching, and mobile foreign language learning using big data technology has become one of the main educational methods for foreign language learning. This article will discuss the background of mobile foreign language learning in the era of educational informationization from the perspective of theoretical research and the feasibility and role of mobile foreign language learning. The author analyzed the current situation and existing problems of mobile foreign language learning and proposed corresponding improvement measures.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127805873","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 : 2020-12-01DOI: 10.1109/ITCA52113.2020.00036
Gang Ji
This article mainly introduces the use of the Java Web Enterprise Framework Struts2+Hibernate+Spring to develop productive management platform. It focuses on the design, implementation and years of application to demenstrate the productive training management platform based on the MVC model. In short, the productive training in vocational colleges is not a new concept, but a challenge to meet the real process control standards of enterprises. The enterprise-level work processes and the training practice in teaching are combined to be integrated into the platform workflow, so as to construct the multifunctional integration mode of building, teaching, learning, practising and testing, which is designed to make the trainees in productive training practice improve their professional skills and professional performance. Besides designing the framework, this paper also proposes the data based evaluation of the framework. The machine learning model is integrated to conduct the numerical overview.
{"title":"Development and Implementation of Project-Driven Productive Training Platform Based on Enterprise Framework","authors":"Gang Ji","doi":"10.1109/ITCA52113.2020.00036","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00036","url":null,"abstract":"This article mainly introduces the use of the Java Web Enterprise Framework Struts2+Hibernate+Spring to develop productive management platform. It focuses on the design, implementation and years of application to demenstrate the productive training management platform based on the MVC model. In short, the productive training in vocational colleges is not a new concept, but a challenge to meet the real process control standards of enterprises. The enterprise-level work processes and the training practice in teaching are combined to be integrated into the platform workflow, so as to construct the multifunctional integration mode of building, teaching, learning, practising and testing, which is designed to make the trainees in productive training practice improve their professional skills and professional performance. Besides designing the framework, this paper also proposes the data based evaluation of the framework. The machine learning model is integrated to conduct the numerical overview.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122908697","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 : 2020-12-01DOI: 10.1109/itca52113.2020.00013
Xianghui Miao
With the continuous and in-depth development of the domestic digital economy, the actual development prospects of various small and medium-sized companies have also reached a new height. If you want to gradually guide small and mediumsized companies to grasp the many opportunities in the new era and have a vast world of their own, companies need to use computer technology to assist companies in the internal management system, target planning, and cost management in the current digital economy. As well as the quality of personnel, all aspects have begun to be improved, which really pave the way for the future development of small and medium-sized companies. Based on this, the author combined his own experience to analyze the role of computer technology in small and medium-sized enterprises under the digital economy, hoping to provide certain reference and help to relevant people.
{"title":"Research on the Role of Computer Technology in Small and Medium-sized Enterprises in the Digital Economy","authors":"Xianghui Miao","doi":"10.1109/itca52113.2020.00013","DOIUrl":"https://doi.org/10.1109/itca52113.2020.00013","url":null,"abstract":"With the continuous and in-depth development of the domestic digital economy, the actual development prospects of various small and medium-sized companies have also reached a new height. If you want to gradually guide small and mediumsized companies to grasp the many opportunities in the new era and have a vast world of their own, companies need to use computer technology to assist companies in the internal management system, target planning, and cost management in the current digital economy. As well as the quality of personnel, all aspects have begun to be improved, which really pave the way for the future development of small and medium-sized companies. Based on this, the author combined his own experience to analyze the role of computer technology in small and medium-sized enterprises under the digital economy, hoping to provide certain reference and help to relevant people.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123947258","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 : 2020-12-01DOI: 10.1109/ITCA52113.2020.00130
Haiyan Lv, Zhiqiang Li, Baoqiang Wen, Chauan Wan
The best tourist attractions is the most concerned issue of consumers for our choice with development of information technology and the application of big data technology have solved this demand of consumers. The best tourist attractions recommendation system developed by Internet platform, MySQL database, JAVA JSP technology, B/S design mode and other technologies are widely used in the selection and decision-making process of the best tourist attractions for the optimal Economy Recommendation System.
{"title":"A Recommendation System Design and Development for the best Tourist Attraction","authors":"Haiyan Lv, Zhiqiang Li, Baoqiang Wen, Chauan Wan","doi":"10.1109/ITCA52113.2020.00130","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00130","url":null,"abstract":"The best tourist attractions is the most concerned issue of consumers for our choice with development of information technology and the application of big data technology have solved this demand of consumers. The best tourist attractions recommendation system developed by Internet platform, MySQL database, JAVA JSP technology, B/S design mode and other technologies are widely used in the selection and decision-making process of the best tourist attractions for the optimal Economy Recommendation System.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125322373","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 : 2020-12-01DOI: 10.1109/ITCA52113.2020.00096
Wang Hao, Yizhou Wang, Lou Yaqin, Song Zhili
We all know that the purpose of introducing activation function is to give neural network nonlinear expression ability, so that it can better fit the results, so as to improve the accuracy. However, different activation functions have different performance in different neural networks. In this paper, several activation functions commonly used by researchers are compared one by one, and qualitative comparison results are given by combining with specific neural network models. For example, when using the MNIST dataset in LeNet, PReLU achieved the highest accuracy of 98.724%, followed by Swish at 98.708%. When cifar-10 data set was used, the highest accuracy rate of ELU was 64.580%, followed by Mish at 64.455%. When Using VGG16, ReLU reached the highest accuracy of 90.226%, followed by PReLU at 90.197%. When using ResNet50, ELU achieved the highest accuracy of 89.943%, followed by Mish at 89.780%.
{"title":"The Role of Activation Function in CNN","authors":"Wang Hao, Yizhou Wang, Lou Yaqin, Song Zhili","doi":"10.1109/ITCA52113.2020.00096","DOIUrl":"https://doi.org/10.1109/ITCA52113.2020.00096","url":null,"abstract":"We all know that the purpose of introducing activation function is to give neural network nonlinear expression ability, so that it can better fit the results, so as to improve the accuracy. However, different activation functions have different performance in different neural networks. In this paper, several activation functions commonly used by researchers are compared one by one, and qualitative comparison results are given by combining with specific neural network models. For example, when using the MNIST dataset in LeNet, PReLU achieved the highest accuracy of 98.724%, followed by Swish at 98.708%. When cifar-10 data set was used, the highest accuracy rate of ELU was 64.580%, followed by Mish at 64.455%. When Using VGG16, ReLU reached the highest accuracy of 90.226%, followed by PReLU at 90.197%. When using ResNet50, ELU achieved the highest accuracy of 89.943%, followed by Mish at 89.780%.","PeriodicalId":103309,"journal":{"name":"2020 2nd International Conference on Information Technology and Computer Application (ITCA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125336537","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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