Pub Date : 2021-06-01DOI: 10.1109/ICAA53760.2021.00121
Liang Zhu, Peng Li, Yonggang Wei, Xin Song, Yu Wang
A KNN query over a relation is to find its $K$ nearest neighbors/tuples from a dataset/relation according to a distance function. In this paper, we discuss approximate KNN query processing based on the selection of many data sources with various dimensions. We propose algorithms to construct a UBR- Tree and a Centroid Base for selecting related data sources and retrieving $K$ NN tuples. For a $K$ NN query $Q$, (1) the related data sources are selected by using the Centroid Base, (2) these data sources are sorted according to their representative tuple in the Centroid Base, (3) local $K$ NN tuples in the related data sources are retrieved, and (4) a heap structure is used to merge the local $K$ NN tuples to form global $K$ NN tuples of $Q$. Extensive experiments over low-dimensional and high-dimensional datasets are conducted to demonstrate the performances of our proposed approaches.
{"title":"Processing Approximate KNN Query Based on Data Source Selection","authors":"Liang Zhu, Peng Li, Yonggang Wei, Xin Song, Yu Wang","doi":"10.1109/ICAA53760.2021.00121","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00121","url":null,"abstract":"A KNN query over a relation is to find its $K$ nearest neighbors/tuples from a dataset/relation according to a distance function. In this paper, we discuss approximate KNN query processing based on the selection of many data sources with various dimensions. We propose algorithms to construct a UBR- Tree and a Centroid Base for selecting related data sources and retrieving $K$ NN tuples. For a $K$ NN query $Q$, (1) the related data sources are selected by using the Centroid Base, (2) these data sources are sorted according to their representative tuple in the Centroid Base, (3) local $K$ NN tuples in the related data sources are retrieved, and (4) a heap structure is used to merge the local $K$ NN tuples to form global $K$ NN tuples of $Q$. Extensive experiments over low-dimensional and high-dimensional datasets are conducted to demonstrate the performances of our proposed approaches.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125120097","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}
With the rapid development of “Internet +”, mobile application and communication technology, e-commerce has increasingly become the main shopping way for consumers in China. Aiming at quality keywords, this paper designs a distributed data clustering system based on Hadoop and mahout. In order to overcome the randomness, low accuracy and many iterations of K-Means algorithm, Canopy and K-Means Clustering Algorithm based on Mahout is designed by using the advantages of Canopy algorithm, that is, no need to specify the number of clusters, high efficiency and conciseness Clustering keywords. Based on the algorithm, good results have been obtained.
{"title":"Study of Canopy and K-Means Clustering Algorithm Based on Mahout for E-commerce Product Quality Analysis","authors":"Peizhang Xie, Minming Mao, Xuguang Jin, Dong Chen, Mengyi Guo","doi":"10.1109/ICAA53760.2021.00090","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00090","url":null,"abstract":"With the rapid development of “Internet +”, mobile application and communication technology, e-commerce has increasingly become the main shopping way for consumers in China. Aiming at quality keywords, this paper designs a distributed data clustering system based on Hadoop and mahout. In order to overcome the randomness, low accuracy and many iterations of K-Means algorithm, Canopy and K-Means Clustering Algorithm based on Mahout is designed by using the advantages of Canopy algorithm, that is, no need to specify the number of clusters, high efficiency and conciseness Clustering keywords. Based on the algorithm, good results have been obtained.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127758441","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00161
Ming Lu, Miao Yu, Zhijun Qiao, Duanshuai Li, Junmin Peng
This literature investigates the output synchronization control of multiple strict-feedback systems with unknown parameter, uncertain control directions and disturbance under directed communication graph. We design a decentralized controller step by step for each agent such that their outputs are synchronized and the closed-loop system is guaranteed to be bounded. As an extension of our previous work, in this literature, disturbance is considered in the agent's dynamic. It is proved that the Nussbaum item is effective not only for seeking control direction automatically but also for guaranteeing the boundedness of the overall system without additional counteraction of the disturbance. The effectiveness of the proposed scheme has been verified by simulation results.
{"title":"Output Synchronization for Networked Strict-feedback Systems in the Presence of Uncertainties","authors":"Ming Lu, Miao Yu, Zhijun Qiao, Duanshuai Li, Junmin Peng","doi":"10.1109/ICAA53760.2021.00161","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00161","url":null,"abstract":"This literature investigates the output synchronization control of multiple strict-feedback systems with unknown parameter, uncertain control directions and disturbance under directed communication graph. We design a decentralized controller step by step for each agent such that their outputs are synchronized and the closed-loop system is guaranteed to be bounded. As an extension of our previous work, in this literature, disturbance is considered in the agent's dynamic. It is proved that the Nussbaum item is effective not only for seeking control direction automatically but also for guaranteeing the boundedness of the overall system without additional counteraction of the disturbance. The effectiveness of the proposed scheme has been verified by simulation results.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127791121","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00054
Xuyang Wang, Tongyan Wang
Local Binary Pattern is a kind of description of the texture within the scope of gray level, but under the influence of illumination and noise, the performance of classification decline rapidly. Therefore, a local feature extraction method is proposed, that is, Local Neighborhood Patterns (LNP). First of all, the image is divided into some local block. Then, treat every pixel and the pixel within the scope of neighborhood as a vector, calculate the distance of each local regional center vector and other pixels vector. Then take the ring extraction features in each local block, it means, using the center of the coded local image as the center of the different radius circle, from the center of the external circular in a certain sequence (clockwise or counterclockwise) extraction characteristics. Make the characteristics of the local image connect one by one as a whole image characteristic vector. At last use the nearest neighbor classifier. This paper also combines data fusion with the LNP, using D-S evidence theory for decision fusion. First of all, it used the gaussian filtering illumination pretreatment method to reduce the influence of the extreme imaging conditions on the face image. Secondly, the image convolution with sobel operator, then get horizontal and vertical edge image. Thirdly, extraction feature vector with LNP and calculate the distance between the test sample and all the classes, by means of the constructor function to realize the conversion of the Euclidean distance and the objective evidence. In the end, to make optimal decision Using D-S evidence theory to objective evidence for fusion. The result in the Extended Yale B database shown that this method can not only get high recognition rate, but also can effectively improve the robustness of illumination, posture, facial expression change.
{"title":"Research on Face Recognition Algorithm Based on D-S Evidence Theory and Local Domain Pattern","authors":"Xuyang Wang, Tongyan Wang","doi":"10.1109/ICAA53760.2021.00054","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00054","url":null,"abstract":"Local Binary Pattern is a kind of description of the texture within the scope of gray level, but under the influence of illumination and noise, the performance of classification decline rapidly. Therefore, a local feature extraction method is proposed, that is, Local Neighborhood Patterns (LNP). First of all, the image is divided into some local block. Then, treat every pixel and the pixel within the scope of neighborhood as a vector, calculate the distance of each local regional center vector and other pixels vector. Then take the ring extraction features in each local block, it means, using the center of the coded local image as the center of the different radius circle, from the center of the external circular in a certain sequence (clockwise or counterclockwise) extraction characteristics. Make the characteristics of the local image connect one by one as a whole image characteristic vector. At last use the nearest neighbor classifier. This paper also combines data fusion with the LNP, using D-S evidence theory for decision fusion. First of all, it used the gaussian filtering illumination pretreatment method to reduce the influence of the extreme imaging conditions on the face image. Secondly, the image convolution with sobel operator, then get horizontal and vertical edge image. Thirdly, extraction feature vector with LNP and calculate the distance between the test sample and all the classes, by means of the constructor function to realize the conversion of the Euclidean distance and the objective evidence. In the end, to make optimal decision Using D-S evidence theory to objective evidence for fusion. The result in the Extended Yale B database shown that this method can not only get high recognition rate, but also can effectively improve the robustness of illumination, posture, facial expression change.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133565508","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00182
T. Peng, Xiaobin Guo
In this paper a new computational scheme is presented for fuzzy linear system ${mathrm{A}}=tilde{{mathrm{b}}}$ where matrix A is a crisp one, and $tilde{{mathrm{x}}}$ and $tilde{{mathrm{b}}}$ are LR-trapezoidal fuzzy number vectors. By means of the basic operations of LR-trapezoidal fuzzy numbers, the original fuzzy equation is transformed into a crisp linear equation. Through solving the crisp linear equation, we find the solution of the LR-trapezoidal fuzzy linear equation. A directly sufficient condition for strong fuzzy solution is also investigated. An numerical example is put forth to show the method we constructed.
{"title":"Solving LR-trapezoidal Fuzzy Linear Systems","authors":"T. Peng, Xiaobin Guo","doi":"10.1109/ICAA53760.2021.00182","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00182","url":null,"abstract":"In this paper a new computational scheme is presented for fuzzy linear system ${mathrm{A}}=tilde{{mathrm{b}}}$ where matrix A is a crisp one, and $tilde{{mathrm{x}}}$ and $tilde{{mathrm{b}}}$ are LR-trapezoidal fuzzy number vectors. By means of the basic operations of LR-trapezoidal fuzzy numbers, the original fuzzy equation is transformed into a crisp linear equation. Through solving the crisp linear equation, we find the solution of the LR-trapezoidal fuzzy linear equation. A directly sufficient condition for strong fuzzy solution is also investigated. An numerical example is put forth to show the method we constructed.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133663201","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00132
Meili Zhang, Hongmei Pei, Weili Liu, Yue Yang
Let X be topological space. A vietoris-type topology, called the locally finite topology, is defined on the hyperspace $2^{X}$ of all closed, nonempty subsets of X. In this paper, we discuss compactness of the locally finite topology on hyperspace. And give the important conclusion, therefore this develops E.Micheal, J.Keesling some results.
{"title":"The Compactness of the Hyperspace 2X with the Locally Finite Topology","authors":"Meili Zhang, Hongmei Pei, Weili Liu, Yue Yang","doi":"10.1109/ICAA53760.2021.00132","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00132","url":null,"abstract":"Let X be topological space. A vietoris-type topology, called the locally finite topology, is defined on the hyperspace $2^{X}$ of all closed, nonempty subsets of X. In this paper, we discuss compactness of the locally finite topology on hyperspace. And give the important conclusion, therefore this develops E.Micheal, J.Keesling some results.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123897067","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00101
Shuguang Liu, L. Song, Yue Huang
The research is based on the undesirable super-efficiency EBM model to measure the GEE of 108 cities in the YREB from 2003 to 2018, uses Geographical Information System to characterize the temporal and spatial evolution of the GEE, and applies the geographic detector model to further reveal the spatial heterogeneity of its driving forces. The results show that: (1)The GEE of the YREB took 2013 as the inflection point, showing two phases of volatility decline period and rapid rise period, and reflecting the spatial differentiation characteristics of “upstream-midstream-downstream” urban agglomeration. (2)The core driving forces for the improvement of GEE in the YREB include urbanization, consumption level, financial industry development, technological innovation and Internet penetration rate.(3)The local scale of the driving forces for GEE improvement is significantly different. The core driving forces of the upstream are education investment, urbanization, Internet penetration rate and transportation infrastructure; and education investment, industrial structure and city scale in the midstream; and industrial structure, consumption, technological innovation, economic development level in the downstream. Therefore, upstream, midstream, and downstream must seek to adapt to the situation and local conditions to improve the GEE.
{"title":"Research on Spatial-temporal Differentiation and Driving Forces of Green Economic Efficiency in the Yangtze River Economic Belt Based on Geographic Detectors","authors":"Shuguang Liu, L. Song, Yue Huang","doi":"10.1109/ICAA53760.2021.00101","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00101","url":null,"abstract":"The research is based on the undesirable super-efficiency EBM model to measure the GEE of 108 cities in the YREB from 2003 to 2018, uses Geographical Information System to characterize the temporal and spatial evolution of the GEE, and applies the geographic detector model to further reveal the spatial heterogeneity of its driving forces. The results show that: (1)The GEE of the YREB took 2013 as the inflection point, showing two phases of volatility decline period and rapid rise period, and reflecting the spatial differentiation characteristics of “upstream-midstream-downstream” urban agglomeration. (2)The core driving forces for the improvement of GEE in the YREB include urbanization, consumption level, financial industry development, technological innovation and Internet penetration rate.(3)The local scale of the driving forces for GEE improvement is significantly different. The core driving forces of the upstream are education investment, urbanization, Internet penetration rate and transportation infrastructure; and education investment, industrial structure and city scale in the midstream; and industrial structure, consumption, technological innovation, economic development level in the downstream. Therefore, upstream, midstream, and downstream must seek to adapt to the situation and local conditions to improve the GEE.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"640 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124001363","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00073
Ning Wu, Hongying Zhao, Youlang Ji, Shaochen Sun
Power customer service intelligent Q&A system can greatly improve the efficiency of power customer service and reduce labor costs. In order to deal with the questions that need to be solved by reasoning, it is necessary to build the power customer service knowledge graph and accurately understand the questions. One of the key tasks is to implement a named entity recognizer using the historical log data of power customer service Q&A. Recently, lattice based neural networks have gained great advantages in Chinese named entity recognition. However, lattice based models rely heavily on an external predetermined dictionary, and the quality of the dictionary may interfere with entity boundary learning. As the power customer service Q&A is a form of oral conversation., it is difficult to build the specialized dictionary, which seriously restricts the application of the original menthod of lattice structure based neural network for Chinese named entity recognition in the field of power customer service. Therefore, this paper proposes a method of using entity boundary locally in lattice based neural networks for Chinese named entity recognition. Through joint learning of entity boundary and entity recognition, without any external dictionary, experiments on data sets in the field of power customer service show that this method has very good potential.
{"title":"Chinese Named Entity Recognition for a Power Customer Service Intelligent Q&A System","authors":"Ning Wu, Hongying Zhao, Youlang Ji, Shaochen Sun","doi":"10.1109/ICAA53760.2021.00073","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00073","url":null,"abstract":"Power customer service intelligent Q&A system can greatly improve the efficiency of power customer service and reduce labor costs. In order to deal with the questions that need to be solved by reasoning, it is necessary to build the power customer service knowledge graph and accurately understand the questions. One of the key tasks is to implement a named entity recognizer using the historical log data of power customer service Q&A. Recently, lattice based neural networks have gained great advantages in Chinese named entity recognition. However, lattice based models rely heavily on an external predetermined dictionary, and the quality of the dictionary may interfere with entity boundary learning. As the power customer service Q&A is a form of oral conversation., it is difficult to build the specialized dictionary, which seriously restricts the application of the original menthod of lattice structure based neural network for Chinese named entity recognition in the field of power customer service. Therefore, this paper proposes a method of using entity boundary locally in lattice based neural networks for Chinese named entity recognition. Through joint learning of entity boundary and entity recognition, without any external dictionary, experiments on data sets in the field of power customer service show that this method has very good potential.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129330662","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00025
Aidong Xu, Runhui Zhao, Zhongqi Mao, Hong Wen, Yixin Jiang, Lingzhi Fei, Peiyao Wang, Yunan Zhang
It is important to predict microgrid user's behaviour and the power generation quota of the microgrid for the optimal interaction of power distribution between microgrid generators and users. In this paper, PSO algorithm is used to get the optimal solution of the micro network transaction model, and the weight proportion of each cost attribute of the graph node is calculated through the normalization function and weight proportion formula by collection and analysis the producing electricity and demand data of microgrid system under the edge computing module. The experiment under a small distributed autonomous microgrid proves the effectiveness of the novel method.
{"title":"The Optimization of Microgrid Distribution Based on PSO","authors":"Aidong Xu, Runhui Zhao, Zhongqi Mao, Hong Wen, Yixin Jiang, Lingzhi Fei, Peiyao Wang, Yunan Zhang","doi":"10.1109/ICAA53760.2021.00025","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00025","url":null,"abstract":"It is important to predict microgrid user's behaviour and the power generation quota of the microgrid for the optimal interaction of power distribution between microgrid generators and users. In this paper, PSO algorithm is used to get the optimal solution of the micro network transaction model, and the weight proportion of each cost attribute of the graph node is calculated through the normalization function and weight proportion formula by collection and analysis the producing electricity and demand data of microgrid system under the edge computing module. The experiment under a small distributed autonomous microgrid proves the effectiveness of the novel method.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129395893","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 : 2021-06-01DOI: 10.1109/ICAA53760.2021.00053
Chuan Guo
Graph-structured data has been widely used. Graph neural network can be used to analyze graph-structured data well. However, the existence of adversarial samples indicates that the prediction results of graph neural networks can be deliberately manipulated. This affects the feasibility of applying deep learning methods to critical situations. Study on graph neural network adversarial sample attack methods and defense techniques can help to strengthen our understanding of graph neural network and build a more robust graph neural network model. It is of great significance to promote the feasibility and security of relevant algorithms in practical applications. This paper analyzes the current graph neural network adversarial sample attack and defense techniques, which has a guiding significance for future research work.
{"title":"An Overview of Adversarial Sample Attacks and Defenses for Graph Neural Networks","authors":"Chuan Guo","doi":"10.1109/ICAA53760.2021.00053","DOIUrl":"https://doi.org/10.1109/ICAA53760.2021.00053","url":null,"abstract":"Graph-structured data has been widely used. Graph neural network can be used to analyze graph-structured data well. However, the existence of adversarial samples indicates that the prediction results of graph neural networks can be deliberately manipulated. This affects the feasibility of applying deep learning methods to critical situations. Study on graph neural network adversarial sample attack methods and defense techniques can help to strengthen our understanding of graph neural network and build a more robust graph neural network model. It is of great significance to promote the feasibility and security of relevant algorithms in practical applications. This paper analyzes the current graph neural network adversarial sample attack and defense techniques, which has a guiding significance for future research work.","PeriodicalId":121879,"journal":{"name":"2021 International Conference on Intelligent Computing, Automation and Applications (ICAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129898876","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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