Many existing image and text sentiment analysis methods only consider the interaction between image and text modalities, while ignoring the inconsistency and correlation of image and text data, to address this issue, an image and text aspect level multimodal sentiment analysis model using transformer and multi-layer attention interaction is proposed. Firstly, ResNet50 is used to extract image features, and RoBERTa-BiLSTM is used to extract text and aspect level features. Then, through the aspect direct interaction mechanism and deep attention interaction mechanism, multi-level fusion of aspect information and graphic information is carried out to remove text and images unrelated to the given aspect. The emotional representations of text data, image data, and aspect type sentiments are concatenated, fused, and fully connected. Finally, the designed sentiment classifier is used to achieve sentiment analysis in terms of images and texts. This effectively has improved the performance of sentiment discrimination in terms of graphics and text.
{"title":"Image and Text Aspect Level Multimodal Sentiment Classification Model Using Transformer and Multilayer Attention Interaction","authors":"Xiuye Yin, Liyong Chen","doi":"10.4018/ijdwm.333854","DOIUrl":"https://doi.org/10.4018/ijdwm.333854","url":null,"abstract":"Many existing image and text sentiment analysis methods only consider the interaction between image and text modalities, while ignoring the inconsistency and correlation of image and text data, to address this issue, an image and text aspect level multimodal sentiment analysis model using transformer and multi-layer attention interaction is proposed. Firstly, ResNet50 is used to extract image features, and RoBERTa-BiLSTM is used to extract text and aspect level features. Then, through the aspect direct interaction mechanism and deep attention interaction mechanism, multi-level fusion of aspect information and graphic information is carried out to remove text and images unrelated to the given aspect. The emotional representations of text data, image data, and aspect type sentiments are concatenated, fused, and fully connected. Finally, the designed sentiment classifier is used to achieve sentiment analysis in terms of images and texts. This effectively has improved the performance of sentiment discrimination in terms of graphics and text.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139275602","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 loading of Automatic Identification System equipment on low-orbiting satellites can adapt to the demand of exchanging data and information with greater “capacity” brought by the AIS data information of ships in deep waters that cannot be covered by land-based stations. The information in the satellite AIS data contains a large number of potential features of ship activities, and by selecting the ship satellite AIS data of typical months in the South China Sea in 2020. Data mining, geographic information system, and traffic flow theory are used to visualize and analyze the ship activities in the South China Sea. The study shows that the distribution of ship routes in the South China Sea is highly compatible with the recommended routes of merchant ships, and the width of the track belt is obviously characterized. The number of ships passing through the southern waters of the Taiwan Strait has increased significantly, and the focus of traffic safety in the South China Sea should also focus on major route belt and important straits.
{"title":"Mining and Analysis of the Traffic Information Situation in the South China Sea Based on Satellite AIS Data","authors":"Tianyu Pu","doi":"10.4018/ijdwm.332864","DOIUrl":"https://doi.org/10.4018/ijdwm.332864","url":null,"abstract":"The loading of Automatic Identification System equipment on low-orbiting satellites can adapt to the demand of exchanging data and information with greater “capacity” brought by the AIS data information of ships in deep waters that cannot be covered by land-based stations. The information in the satellite AIS data contains a large number of potential features of ship activities, and by selecting the ship satellite AIS data of typical months in the South China Sea in 2020. Data mining, geographic information system, and traffic flow theory are used to visualize and analyze the ship activities in the South China Sea. The study shows that the distribution of ship routes in the South China Sea is highly compatible with the recommended routes of merchant ships, and the width of the track belt is obviously characterized. The number of ships passing through the southern waters of the Taiwan Strait has increased significantly, and the focus of traffic safety in the South China Sea should also focus on major route belt and important straits.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136234799","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}
Brainstorming is a widely used problem-solving method that generates a large number of innovative ideas by guiding and stimulating intuitive and divergent thinking. However, in practice, the method is limited by the human brain's capacity or special capabilities, especially by the experience and knowledge they possess. How does our brain create ideas like storming? Based on the new discipline of Extenics, the authors propose a new model that explores the process of how ideas are created in our brain, with the goal of helping people think multi-dimensionally and getting more ideas. With the support of information technology and artificial intelligence, we can systematically collect more information and knowledge than ever before to form a basic-element information base and build human-computer interaction models, to make up for the lack of information and knowledge in the human brain. In addition, the authors provide a methodology to help people think positively in a multidimensional way based on the guidance of Extenics in the brainstorming process.
{"title":"An Integration Model on Brainstorming and Extenics for Intelligent Innovation in Big Data Environment","authors":"Xingsen Li, Haibin Pi, Junwen Sun, Hao Lan Zhang, Zhencheng Liang","doi":"10.4018/ijdwm.332413","DOIUrl":"https://doi.org/10.4018/ijdwm.332413","url":null,"abstract":"Brainstorming is a widely used problem-solving method that generates a large number of innovative ideas by guiding and stimulating intuitive and divergent thinking. However, in practice, the method is limited by the human brain's capacity or special capabilities, especially by the experience and knowledge they possess. How does our brain create ideas like storming? Based on the new discipline of Extenics, the authors propose a new model that explores the process of how ideas are created in our brain, with the goal of helping people think multi-dimensionally and getting more ideas. With the support of information technology and artificial intelligence, we can systematically collect more information and knowledge than ever before to form a basic-element information base and build human-computer interaction models, to make up for the lack of information and knowledge in the human brain. In addition, the authors provide a methodology to help people think positively in a multidimensional way based on the guidance of Extenics in the brainstorming process.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135168344","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}
As a new mobile communication technology in the era of the internet of things, 5G is characterized by high speed, low delay, and large connection. It is a network infrastructure to realize human-computer and internet of things in the era of the internet of things. Power quality data is the efficiency with which a power grid delivers electricity to users and expresses how well a piece of machinery uses the electricity it receives. The waveform at the nominal voltage and frequency is the goal of power quality research and improvement. The power internet of things (IoT) is an intelligent service platform that fully uses cutting-edge tech to enable user-machine interaction, data-driven decision-making, real-time analytics, and adaptive software design. The process by which plaintext is converted into cipher text is called an encryption algorithm. The cipher text may seem completely random, but it can be decrypted using the exact mechanism that created the encryption key.
{"title":"Secure Transmission Method of Power Quality Data in Power Internet of Things Based on the Encryption Algorithm","authors":"Xin Liu, Yingxian Chang, Honglei Yao, Bing Su","doi":"10.4018/ijdwm.330014","DOIUrl":"https://doi.org/10.4018/ijdwm.330014","url":null,"abstract":"As a new mobile communication technology in the era of the internet of things, 5G is characterized by high speed, low delay, and large connection. It is a network infrastructure to realize human-computer and internet of things in the era of the internet of things. Power quality data is the efficiency with which a power grid delivers electricity to users and expresses how well a piece of machinery uses the electricity it receives. The waveform at the nominal voltage and frequency is the goal of power quality research and improvement. The power internet of things (IoT) is an intelligent service platform that fully uses cutting-edge tech to enable user-machine interaction, data-driven decision-making, real-time analytics, and adaptive software design. The process by which plaintext is converted into cipher text is called an encryption algorithm. The cipher text may seem completely random, but it can be decrypted using the exact mechanism that created the encryption key.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44637794","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}
Viet-Thang Vu, T. T. Q. Bui, Tien Loi Nguyen, Doan-Vinh Tran, Quan Hong, V. Vu, S. Avdoshin
Clustering is a commonly used tool for discovering knowledge in data mining. Density peak clustering (DPC) has recently gained attention for its ability to detect clusters with various shapes and noise, using just one parameter. DPC has shown advantages over other methods, such as DBSCAN and K-means, but it struggles with datasets that have both high and low-density clusters. To overcome this limitation, the paper introduces a new semi-supervised DPC method that improves clustering results with a small set of constraints expressed as must-link and cannot-link. The proposed method combines constraints and a k-nearest neighbor graph to filter out peaks and find the center for each cluster. Constraints are also used to support label assignment during the clustering procedure. The efficacy of this method is demonstrated through experiments on well-known data sets from UCI and benchmarked against contemporary semi-supervised clustering techniques.
{"title":"Constrained Density Peak Clustering","authors":"Viet-Thang Vu, T. T. Q. Bui, Tien Loi Nguyen, Doan-Vinh Tran, Quan Hong, V. Vu, S. Avdoshin","doi":"10.4018/ijdwm.328776","DOIUrl":"https://doi.org/10.4018/ijdwm.328776","url":null,"abstract":"Clustering is a commonly used tool for discovering knowledge in data mining. Density peak clustering (DPC) has recently gained attention for its ability to detect clusters with various shapes and noise, using just one parameter. DPC has shown advantages over other methods, such as DBSCAN and K-means, but it struggles with datasets that have both high and low-density clusters. To overcome this limitation, the paper introduces a new semi-supervised DPC method that improves clustering results with a small set of constraints expressed as must-link and cannot-link. The proposed method combines constraints and a k-nearest neighbor graph to filter out peaks and find the center for each cluster. Constraints are also used to support label assignment during the clustering procedure. The efficacy of this method is demonstrated through experiments on well-known data sets from UCI and benchmarked against contemporary semi-supervised clustering techniques.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46687825","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}
Anomaly detection on sequence dataset typically focuses on the detection of collective anomalies, aiming to find anomalous patterns consisting of sequences of data with specific relationships rather than individual observations. In this survey, existing studies are summarized to align with temporal sequence dataset and spatial sequence dataset. For the first category, the detection can be subdivided into symbolic dataset based and time series dataset based, which include similarity, probabilistic, and trend approaches. For the second category, it can be subdivided into homogeneous datasets based heterogeneous datasets based, which include multi-dataset fusion and joint approaches. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of various representations of collective anomaly in different application field and their corresponding detection methods, representative techniques. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.
{"title":"A Survey of Collective Anomaly Detection on Sequence Dataset","authors":"Xiaodi Huang, Po Yun, Zhongfeng Hu","doi":"10.4018/ijdwm.327363","DOIUrl":"https://doi.org/10.4018/ijdwm.327363","url":null,"abstract":"Anomaly detection on sequence dataset typically focuses on the detection of collective anomalies, aiming to find anomalous patterns consisting of sequences of data with specific relationships rather than individual observations. In this survey, existing studies are summarized to align with temporal sequence dataset and spatial sequence dataset. For the first category, the detection can be subdivided into symbolic dataset based and time series dataset based, which include similarity, probabilistic, and trend approaches. For the second category, it can be subdivided into homogeneous datasets based heterogeneous datasets based, which include multi-dataset fusion and joint approaches. Compared to the state-of-the-art survey papers, the contribution of this paper lies in providing a deep analysis of various representations of collective anomaly in different application field and their corresponding detection methods, representative techniques. As a result, practitioners can receive some guidance for selecting the most suitable methods for their particular case.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43124738","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}
Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.
{"title":"Cybersecurity of Medical Data Based on Big Data and Privacy Protection Method","authors":"Jianhong Li, An Pan, Tongxing Zheng","doi":"10.4018/ijdwm.325222","DOIUrl":"https://doi.org/10.4018/ijdwm.325222","url":null,"abstract":"Big data brings new opportunities to discover the new value of healthcare industry, since it can help us understand the hidden value of data deeply. This also brings new challenges: how to effectively manage and organize these datasets. Throughout the whole life cycle of publishing, storing, mining, and using big data in health care, different users are involved, so there are corresponding privacy protection methods and technologies for different life cycles. Data usage is the last and most important part of the whole life cycle. Therefore, this article proposes a privacy protection method for large medical data: an access control based on credibility of the requesting user. This model evaluates and quantifies doctors' credibility from the perspective of behavioral trust. Comparative experiments show that under the background of linear, geometric and exponential distribution trends and mixed trends, the regression model in this article is better than the existing methods in predicting trust accuracy and trust trends.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46241194","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}
Skin cancer is affected by the uncommon evolution of skin cells and is a deadly type of cancer. In addition, skin lesion is affected by numerous factors, such as exposure to the sun, infections, allergies, etc. These skin illnesses have become a challenge in therapeutic diagnosis because of virtual resemblances, where image classification is vital to sufficiently diagnose dissimilar lesions. Therefore, early diagnosis is significant and can avert skin cancers like focal cell carcinoma and melanoma. A deep learning-based computer analyzing model can be an automatic solution in medical evaluations to overcome this issue. Hence, this paper suggests an improved chameleon swarm algorithm and convolutional neural networks (ICSA-CNN) for effective skin cancer identification and classification. The data are collected from the Kaggle dataset for classifying skin cancer. Chameleon swarm algorithm is a clustering technique utilized in data mining to the cluster dataset utilizing dynamic systems, and it can resolve constrained and global numerical optimization issues in skin cancer detection.
{"title":"Application of Improved Chameleon Swarm Algorithm and Improved Convolution Neural Network in Diagnosis of Skin Cancer","authors":"Wu Beibei, Nikolaj Jade","doi":"10.4018/ijdwm.325059","DOIUrl":"https://doi.org/10.4018/ijdwm.325059","url":null,"abstract":"Skin cancer is affected by the uncommon evolution of skin cells and is a deadly type of cancer. In addition, skin lesion is affected by numerous factors, such as exposure to the sun, infections, allergies, etc. These skin illnesses have become a challenge in therapeutic diagnosis because of virtual resemblances, where image classification is vital to sufficiently diagnose dissimilar lesions. Therefore, early diagnosis is significant and can avert skin cancers like focal cell carcinoma and melanoma. A deep learning-based computer analyzing model can be an automatic solution in medical evaluations to overcome this issue. Hence, this paper suggests an improved chameleon swarm algorithm and convolutional neural networks (ICSA-CNN) for effective skin cancer identification and classification. The data are collected from the Kaggle dataset for classifying skin cancer. Chameleon swarm algorithm is a clustering technique utilized in data mining to the cluster dataset utilizing dynamic systems, and it can resolve constrained and global numerical optimization issues in skin cancer detection.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136355362","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}
In this study, the authors devised a big data-driven evaluation model to measure the effect of college music education, aiming at filling the gaps of poor accuracy and time-consuming results of music education effect evaluation. Firstly, the authors determined the effect of an evaluation system of music for learning, and the evaluation of this effect. Then, they carried out a simulation experiment. The literature review evidenced that few domestic research reports considered college students' communication fear. Thus, combining the characteristics of current college students' psychological counseling and the theory of communication fear, the authors tried to apply the music system desensitization therapy to address college students' communication fear, from the intervention effect, feasibility, and therapy as a psychological counseling method. The results showed that music system desensitization therapy eliminates college students' fear of communication, reduces speech anxiety, reduces shyness, and improves interpersonal communication skills.
{"title":"Construction and Application of a Big Data Analysis Platform for College Music Education for College Students' Mental Health","authors":"Xiaochen Wang, Tao Wang","doi":"10.4018/ijdwm.324060","DOIUrl":"https://doi.org/10.4018/ijdwm.324060","url":null,"abstract":"In this study, the authors devised a big data-driven evaluation model to measure the effect of college music education, aiming at filling the gaps of poor accuracy and time-consuming results of music education effect evaluation. Firstly, the authors determined the effect of an evaluation system of music for learning, and the evaluation of this effect. Then, they carried out a simulation experiment. The literature review evidenced that few domestic research reports considered college students' communication fear. Thus, combining the characteristics of current college students' psychological counseling and the theory of communication fear, the authors tried to apply the music system desensitization therapy to address college students' communication fear, from the intervention effect, feasibility, and therapy as a psychological counseling method. The results showed that music system desensitization therapy eliminates college students' fear of communication, reduces speech anxiety, reduces shyness, and improves interpersonal communication skills.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45213567","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}
Since its birth, supply chain finance (SCF) has made contributions to the development of small and medium-sized enterprises, but it also faces many challenges in the development process. With the development and continuous progress of the internet and information technology, it has also opened up new ways for urban development and innovation. This article introduced the background of intelligent business model, conducted academic research and summary on the keywords of SCF and the internet of things (IOT), and then summarized urban analysis by combining AI and big data. Then it put forward the business model factor analysis of SCF and modern logistics enterprises. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average transaction cost of the new business model was 3.5 lower than that of the traditional business model. With the continuous development of artificial intelligence technology and big data technology, urban planning is also facing new opportunities and challenges.
{"title":"Assistance of Internet of Things to Intelligent Business Management Model of Supply Chain Finance and Modern Logistics Enterprises","authors":"Qing Li","doi":"10.4018/ijdwm.323189","DOIUrl":"https://doi.org/10.4018/ijdwm.323189","url":null,"abstract":"Since its birth, supply chain finance (SCF) has made contributions to the development of small and medium-sized enterprises, but it also faces many challenges in the development process. With the development and continuous progress of the internet and information technology, it has also opened up new ways for urban development and innovation. This article introduced the background of intelligent business model, conducted academic research and summary on the keywords of SCF and the internet of things (IOT), and then summarized urban analysis by combining AI and big data. Then it put forward the business model factor analysis of SCF and modern logistics enterprises. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average transaction cost of the new business model was 3.5 lower than that of the traditional business model. With the continuous development of artificial intelligence technology and big data technology, urban planning is also facing new opportunities and challenges.","PeriodicalId":54963,"journal":{"name":"International Journal of Data Warehousing and Mining","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2023-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44177922","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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