Cu Nguyen Giap, Nguyen Nhu Son, Long Giang Nguyen, Hoang Thi Minh Chau, Tran Manh Tuan, Le Hoang Son
It has been witnessed in recent years for the rising of Group recommender systems (GRSs) in most e-commerce and tourism applications like Booking.com, Traveloka.com, Amazon, etc. One of the most concerned problems in GRSs is to guarantee the fairness between users in a group so-called the consensus-driven group recommender system. This paper proposes a new flexible alternative that embeds a fuzzy measure to aggregation operators of consensus process to improve fairness of group recommendation and deals with group member interaction. Choquet integral is used to build a fuzzy measure based on group member interactions and to seek a better fairness recommendation. The empirical results on the benchmark datasets show the incremental advances of the proposal for dealing with group member interactions and the issue of fairness in Consensus-driven GRS.
{"title":"A New Approach for Fairness Increment of Consensus-Driven Group Recommender Systems Based on Choquet Integral","authors":"Cu Nguyen Giap, Nguyen Nhu Son, Long Giang Nguyen, Hoang Thi Minh Chau, Tran Manh Tuan, Le Hoang Son","doi":"10.4018/ijdwm.290891","DOIUrl":"https://doi.org/10.4018/ijdwm.290891","url":null,"abstract":"It has been witnessed in recent years for the rising of Group recommender systems (GRSs) in most e-commerce and tourism applications like Booking.com, Traveloka.com, Amazon, etc. One of the most concerned problems in GRSs is to guarantee the fairness between users in a group so-called the consensus-driven group recommender system. This paper proposes a new flexible alternative that embeds a fuzzy measure to aggregation operators of consensus process to improve fairness of group recommendation and deals with group member interaction. Choquet integral is used to build a fuzzy measure based on group member interactions and to seek a better fairness recommendation. The empirical results on the benchmark datasets show the incremental advances of the proposal for dealing with group member interactions and the issue of fairness in Consensus-driven GRS.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"18 1","pages":"1-22"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86618244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning method, has been widely used in sentiment classification due to its describing sample relation in a graph-based pattern. Whereas, current graph developing strategies fail to use the global distribution and cannot handle the issues of polysemy and synonymy properly. In this paper, a semi-supervised learning methodology, integrating the tripartite graph and the clustering, is proposed for graph construction. Experiments on E-commerce reviews demonstrate the proposed method outperform baseline methods on the whole, which enables precise sentiment classification with few labeled samples.
{"title":"Semi-Supervised Sentiment Classification on E-Commerce Reviews Using Tripartite Graph and Clustering","authors":"Xin Lu, Donghong Gu, Haolan Zhang, Zhengxin Song, Qianhua Cai, Hongya Zhao, Haiming Wu","doi":"10.4018/ijdwm.307904","DOIUrl":"https://doi.org/10.4018/ijdwm.307904","url":null,"abstract":"Sentiment classification constitutes an important topic in the field of Natural Language Processing, whose main purpose is to extract the sentiment polarity from unstructured texts. The label propagation algorithm, as a semi-supervised learning method, has been widely used in sentiment classification due to its describing sample relation in a graph-based pattern. Whereas, current graph developing strategies fail to use the global distribution and cannot handle the issues of polysemy and synonymy properly. In this paper, a semi-supervised learning methodology, integrating the tripartite graph and the clustering, is proposed for graph construction. Experiments on E-commerce reviews demonstrate the proposed method outperform baseline methods on the whole, which enables precise sentiment classification with few labeled samples.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"3 1","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81757483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Wu, Lingyun Sun, Gautam Srivastava, Vicente García Díaz, Jerry Chun‐wei Lin
This article uses a new convolutional neural network framework, which has good performance for time series feature extraction and stock price prediction. This method is called the stock sequence array convolutional neural network, or SSACNN for short. SSACNN collects data on leading indicators including historical prices and their futures and options, and uses arrays as the input map of the CNN framework. In the financial market, every number has its logic behind it. Leading indicators such as futures and options can reflect changes in many markets, such as the industry's prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. This study takes the stock markets of the United States and Taiwan as the research objects and uses historical data, futures, and options as data sets to predict the stock prices of these two markets, and then uses genetic algorithms to find trading signals, so as to get a stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.
{"title":"A Stock Trading Expert System Established by the CNN-GA-Based Collaborative System","authors":"J. Wu, Lingyun Sun, Gautam Srivastava, Vicente García Díaz, Jerry Chun‐wei Lin","doi":"10.4018/ijdwm.309957","DOIUrl":"https://doi.org/10.4018/ijdwm.309957","url":null,"abstract":"This article uses a new convolutional neural network framework, which has good performance for time series feature extraction and stock price prediction. This method is called the stock sequence array convolutional neural network, or SSACNN for short. SSACNN collects data on leading indicators including historical prices and their futures and options, and uses arrays as the input map of the CNN framework. In the financial market, every number has its logic behind it. Leading indicators such as futures and options can reflect changes in many markets, such as the industry's prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. This study takes the stock markets of the United States and Taiwan as the research objects and uses historical data, futures, and options as data sets to predict the stock prices of these two markets, and then uses genetic algorithms to find trading signals, so as to get a stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46827513","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sarah Nait Bahloul, Oussama Abderrahim, Aya Ichrak Benhadj Amar, Mohammed Yacine Bouhedadja
The classification of data streams has become a significant and active research area. The principal characteristics of data streams are a large amount of arrival data, the high speed and rate of its arrival, and the change of their nature and distribution over time. Hoeffding Tree is a method to, incrementally, build decision trees. Since its proposition in the literature, it has become one of the most popular tools of data stream classification. Several improvements have since emerged. Hoeffding Anytime Tree was recently introduced and is considered one of the most promising algorithms. It offers a higher accuracy compared to the Hoeffding Tree in most scenarios, at a small additional computational cost. In this work, the authors contribute by proposing three improvements to the Hoeffding Anytime Tree. The improvements are tested on known benchmark datasets. The experimental results show that two of the proposed variants make better usage of Hoeffding Anytime Tree’s properties. They learn faster while providing the same desired accuracy.
{"title":"Improvement of Data Stream Decision Trees","authors":"Sarah Nait Bahloul, Oussama Abderrahim, Aya Ichrak Benhadj Amar, Mohammed Yacine Bouhedadja","doi":"10.4018/ijdwm.290889","DOIUrl":"https://doi.org/10.4018/ijdwm.290889","url":null,"abstract":"The classification of data streams has become a significant and active research area. The principal characteristics of data streams are a large amount of arrival data, the high speed and rate of its arrival, and the change of their nature and distribution over time. Hoeffding Tree is a method to, incrementally, build decision trees. Since its proposition in the literature, it has become one of the most popular tools of data stream classification. Several improvements have since emerged. Hoeffding Anytime Tree was recently introduced and is considered one of the most promising algorithms. It offers a higher accuracy compared to the Hoeffding Tree in most scenarios, at a small additional computational cost. In this work, the authors contribute by proposing three improvements to the Hoeffding Anytime Tree. The improvements are tested on known benchmark datasets. The experimental results show that two of the proposed variants make better usage of Hoeffding Anytime Tree’s properties. They learn faster while providing the same desired accuracy.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"44 1","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78748795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thanu Dayara, F. Thabtah, Hussein Abdel-jaber, S. Zeidan
One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago.
{"title":"Crime Analyses Using Data Analytics","authors":"Thanu Dayara, F. Thabtah, Hussein Abdel-jaber, S. Zeidan","doi":"10.4018/ijdwm.299014","DOIUrl":"https://doi.org/10.4018/ijdwm.299014","url":null,"abstract":"One potential approach for crime analysis that has shown promising results is data analytics, particularly descriptive and predictive techniques. Data analytics can explore former criminal incidents seeking hidden correlations and patterns, which potentially could be used in crime prevention and resource management. The purpose of this research is to build a crime analysis model using supervised techniques to predict the arrest status of serious crimes in Chicago. This is based on specific indicators, such as timeframe, location in terms of district, community, and beat, and crime type among others. We used time series and clustering techniques to help us identify influential features. Supervised machine learning algorithms then modelled the subset of features against incidents related to battery and assaults in specific timeframes and locations to predict the arrest status response variable. The models derived from Naïve Bayes, Decision Tree, and Support Vector Machine (SVM) algorithms reveal a high predictive accuracy rate at certain times in some communities within Chicago.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"19 1","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88401411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The focus of this paper is to identify anomalous spatial windows using clustering-based methods. Spatial Anomalous windows are the contiguous groupings of spatial nodes which are unusual with respect to the rest of the data. Many scan statistics based approaches have been proposed for the identification of spatial anomalous windows. To identify similarly behaving groups of points, clustering techniques have been proposed. There are parallels between both types of approaches but these approaches have not been used interchangeably. Thus, the focus of our work is to bridge this gap and identify anomalous spatial windows using clustering based methods. Specifically, we use the circular scan statistic based approach and DBSCAN- Density based Spatial Clustering of Applications with Noise, to bridge the gap between clustering and scan statistics based approach. We present experimental results in US crime data Our results show that our approach is effective in identifying spatial anomalous windows and performs equal or better than existing techniques and does better than pure clustering.
{"title":"Density-Based Spatial Anomalous Window Discovery","authors":"Prerna Mohod, V. Janeja","doi":"10.4018/ijdwm.299015","DOIUrl":"https://doi.org/10.4018/ijdwm.299015","url":null,"abstract":"The focus of this paper is to identify anomalous spatial windows using clustering-based methods. Spatial Anomalous windows are the contiguous groupings of spatial nodes which are unusual with respect to the rest of the data. Many scan statistics based approaches have been proposed for the identification of spatial anomalous windows. To identify similarly behaving groups of points, clustering techniques have been proposed. There are parallels between both types of approaches but these approaches have not been used interchangeably. Thus, the focus of our work is to bridge this gap and identify anomalous spatial windows using clustering based methods. Specifically, we use the circular scan statistic based approach and DBSCAN- Density based Spatial Clustering of Applications with Noise, to bridge the gap between clustering and scan statistics based approach. We present experimental results in US crime data Our results show that our approach is effective in identifying spatial anomalous windows and performs equal or better than existing techniques and does better than pure clustering.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"9 1","pages":"1-23"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86493401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
User-generated contents in social media are not verified before being posted. They could bring many problems if they were misused. Among various types of rumors, the authors focus on the type in which there's mismatch between images and their surrounding texts. They can be detected by multimodal feature fusion in RNNs with attention mechanism, but the relations between images and texts are not well-addressed. In this paper, the authors propose to improve rumor detection by image captioning and RNNs with self-attention. Firstly, they utilize the idea of image captioning to translate images into the corresponding text descriptions. Secondly, these caption words are represented by word embedding models and aggregated with surrounding texts using early fusion. Finally, multi-cell bi-directional RNNs with self-attention are used to learn important features to identify rumors. From the experimental results, the best F-measure of 0.882 can be obtained, which shows the potential of our proposed approach to rumor detection. Further investigation is needed for data in larger scale.
{"title":"Improving Rumor Detection by Image Captioning and Multi-Cell Bi-RNN With Self-Attention in Social Networks","authors":"Jenq-Haur Wang, Chin-Wei Huang, M. Norouzi","doi":"10.4018/ijdwm.313189","DOIUrl":"https://doi.org/10.4018/ijdwm.313189","url":null,"abstract":"User-generated contents in social media are not verified before being posted. They could bring many problems if they were misused. Among various types of rumors, the authors focus on the type in which there's mismatch between images and their surrounding texts. They can be detected by multimodal feature fusion in RNNs with attention mechanism, but the relations between images and texts are not well-addressed. In this paper, the authors propose to improve rumor detection by image captioning and RNNs with self-attention. Firstly, they utilize the idea of image captioning to translate images into the corresponding text descriptions. Secondly, these caption words are represented by word embedding models and aggregated with surrounding texts using early fusion. Finally, multi-cell bi-directional RNNs with self-attention are used to learn important features to identify rumors. From the experimental results, the best F-measure of 0.882 can be obtained, which shows the potential of our proposed approach to rumor detection. Further investigation is needed for data in larger scale.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42579490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A common model used in addressing today's overwhelming amounts of data is the OLAP Cube. The OLAP community has proposed several cube algebras, although a standard has still not been nominated. This study focuses on a recent addition to the cube algebras: the user-centric Cube Algebra Query Language (CAQL). The study aims to explore the optimization potential of this algebra by applying logical rewriting inspired by classic relational algebra and parallelism. The lack of standard algebra is often cited as a problem in such discussions. Thus, the significance of this work is that of strengthening the position of this algebra within the OLAP algebras by addressing implementation details. The modern open-source PostgreSQL relational engine is used to encode the CAQL abstraction. A query workload based on a well-known dataset is adopted, and CAQL and SQL implementations are compared. Finally, the quality of the query created is evaluated through the observed performance characteristics of the query. Results show strong improvements over the baseline case of the unoptimized query.
{"title":"Initial Optimization Techniques for the Cube Algebra Query Language: The Relational Model as a Target","authors":"Thomas Mercieca, J. Vella, K. Vella","doi":"10.4018/ijdwm.299016","DOIUrl":"https://doi.org/10.4018/ijdwm.299016","url":null,"abstract":"A common model used in addressing today's overwhelming amounts of data is the OLAP Cube. The OLAP community has proposed several cube algebras, although a standard has still not been nominated. This study focuses on a recent addition to the cube algebras: the user-centric Cube Algebra Query Language (CAQL). The study aims to explore the optimization potential of this algebra by applying logical rewriting inspired by classic relational algebra and parallelism. The lack of standard algebra is often cited as a problem in such discussions. Thus, the significance of this work is that of strengthening the position of this algebra within the OLAP algebras by addressing implementation details. The modern open-source PostgreSQL relational engine is used to encode the CAQL abstraction. A query workload based on a well-known dataset is adopted, and CAQL and SQL implementations are compared. Finally, the quality of the query created is evaluated through the observed performance characteristics of the query. Results show strong improvements over the baseline case of the unoptimized query.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":"81 1","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76108926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiaoyin Ge, Mingshu Zhang, Xu An Wang, Jia Liu, Bin Wei
Fake news has brought significant challenges to the healthy development of social media. Although current fake news detection methods are advanced, many models directly utilize unselected user comments and do not consider the emotional connection between news content and user comments. The authors propose an emotion-driven explainable fake news detection model (EDI) to solve this problem. The model can select valuable user comments by using sentiment value, obtain the emotional correlation representation between news content and user comments by using collaborative annotation, and obtain the weighted representation of user comments by using the attention mechanism. Experimental results on Twitter and Weibo show that the detection model significantly outperforms the state-of-the-art models and provides reasonable interpretation.
{"title":"Emotion-Drive Interpretable Fake News Detection","authors":"Xiaoyin Ge, Mingshu Zhang, Xu An Wang, Jia Liu, Bin Wei","doi":"10.4018/ijdwm.314585","DOIUrl":"https://doi.org/10.4018/ijdwm.314585","url":null,"abstract":"Fake news has brought significant challenges to the healthy development of social media. Although current fake news detection methods are advanced, many models directly utilize unselected user comments and do not consider the emotional connection between news content and user comments. The authors propose an emotion-driven explainable fake news detection model (EDI) to solve this problem. The model can select valuable user comments by using sentiment value, obtain the emotional correlation representation between news content and user comments by using collaborative annotation, and obtain the weighted representation of user comments by using the attention mechanism. Experimental results on Twitter and Weibo show that the detection model significantly outperforms the state-of-the-art models and provides reasonable interpretation.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42693737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nguyen Thi Kim Son, Nguyen Van Bien, Nguyen Huu Quynh, C. Thơ
In this paper, the authors explore the factors to improve the accuracy of predicting student learning outcomes. The method can remove redundant and irrelevant factors to get a “clean” data set without having to solve the NP-Hard problem. The method can improve the graduation outcome prediction accuracy through logistic regression machine learning method for “clean” data set. They empirically evaluate the training and university admission data of Hanoi Metropolitan University from 2016 to 2020. From data processing results and the support from the machine learning techniques application program, they analyze, evaluate, and forecast students' learning outcomes based on admission data, first-year, and second-year academic performance data. They then submit proposals of training and admission policies and methods of radically and quantitatively solving problems in university admissions.
{"title":"Machine Learning Based Admission Data Processing for Early Forecasting Students' Learning Outcomes","authors":"Nguyen Thi Kim Son, Nguyen Van Bien, Nguyen Huu Quynh, C. Thơ","doi":"10.4018/ijdwm.313585","DOIUrl":"https://doi.org/10.4018/ijdwm.313585","url":null,"abstract":"In this paper, the authors explore the factors to improve the accuracy of predicting student learning outcomes. The method can remove redundant and irrelevant factors to get a “clean” data set without having to solve the NP-Hard problem. The method can improve the graduation outcome prediction accuracy through logistic regression machine learning method for “clean” data set. They empirically evaluate the training and university admission data of Hanoi Metropolitan University from 2016 to 2020. From data processing results and the support from the machine learning techniques application program, they analyze, evaluate, and forecast students' learning outcomes based on admission data, first-year, and second-year academic performance data. They then submit proposals of training and admission policies and methods of radically and quantitatively solving problems in university admissions.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49537984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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