Pub Date : 2021-12-17DOI: 10.1109/PIC53636.2021.9687014
Taocun Yang, Xin Liu, Guohua Li, Ming-rui Dai, Lei Tian, Yan Xie
With the rapid development of China High-Speed Rail, massive text data related to railway safety has been accumulated. When analyzing and understanding this data, classifying railway accident report text is essential and tedious work. Usually, such classification tasks are manually done by experts and workers in the railway safety department. Traditional data mining algorithms have been applied in these tasks to classify the text automatically. However, due to the complexity of the text data, classification algorithms sometimes fail and have insufficient learning ability. Meanwhile, the rise of machine learning enables us to deal with these complex problems effectively. In this paper, we propose an end-to-end multi-layer convolutional neural networks model to classify the railway safety-related text. We update the CNN part of the traditional model by increasing layers and adding a multi-height convolutional kernel. Additionally, we develop a data-preprocessing strategy to obtain the neat input data and reduce the complexity of the task. Experiments show that our proposed method achieves competitive performance and is suitable for railway safety-related text classification problems.
{"title":"Exploring Multi-Layer Convolutional Neural Networks for Railway Safety Text Classification","authors":"Taocun Yang, Xin Liu, Guohua Li, Ming-rui Dai, Lei Tian, Yan Xie","doi":"10.1109/PIC53636.2021.9687014","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687014","url":null,"abstract":"With the rapid development of China High-Speed Rail, massive text data related to railway safety has been accumulated. When analyzing and understanding this data, classifying railway accident report text is essential and tedious work. Usually, such classification tasks are manually done by experts and workers in the railway safety department. Traditional data mining algorithms have been applied in these tasks to classify the text automatically. However, due to the complexity of the text data, classification algorithms sometimes fail and have insufficient learning ability. Meanwhile, the rise of machine learning enables us to deal with these complex problems effectively. In this paper, we propose an end-to-end multi-layer convolutional neural networks model to classify the railway safety-related text. We update the CNN part of the traditional model by increasing layers and adding a multi-height convolutional kernel. Additionally, we develop a data-preprocessing strategy to obtain the neat input data and reduce the complexity of the task. Experiments show that our proposed method achieves competitive performance and is suitable for railway safety-related text classification problems.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124976624","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687020
Hanxiao Shi, Liqiang You, Mimi Ren, Xiaojun Li
This paper proposes a sentiment analysis model based on two-channel attention-driven convolutional neural networks and long short term memory neural networks for financial text. Firstly, this paper uses two different word vector initialization methods to construct classification model by selecting different feature representations and taking full account of the relationship between words. Secondly, this paper adds Attention mechanism based on the context structure to analyze the text to obtain more hidden information. Finally, the experimental results show that our approach is feasible and effective.
{"title":"Research on an Two-Channel ACNN-LSTM Model for Financial Text Sentiment Analysis","authors":"Hanxiao Shi, Liqiang You, Mimi Ren, Xiaojun Li","doi":"10.1109/PIC53636.2021.9687020","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687020","url":null,"abstract":"This paper proposes a sentiment analysis model based on two-channel attention-driven convolutional neural networks and long short term memory neural networks for financial text. Firstly, this paper uses two different word vector initialization methods to construct classification model by selecting different feature representations and taking full account of the relationship between words. Secondly, this paper adds Attention mechanism based on the context structure to analyze the text to obtain more hidden information. Finally, the experimental results show that our approach is feasible and effective.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125907094","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687091
M. Shi, Fangxin Lin, Ying Qian, Liang Dou
With the rapid development of science, the quantity of data is increasing exponentially. And unprecedented opportunities are provided by machine learning and data mining. While data classification is commonly used as a primary data processing method, the diversity of data is also a great challenge. Among those, problems caused by class imbalance are attracting more attention, and there are also a number of strategies and improvement of original algorithms are proposed. Gcforest is a new integrated learning algorithm proposed by Professor Zhou Zhihua in 2017. It has the advantages of few super parameters, suitable for small-scale data sets and strong model expression ability. However, the algorithm does not optimize the unbalanced data classification. Inspired by the improvement of other ensemble learning algorithms for unbalanced data classification, this paper applies a variety of under sampling strategies to the cascaded forest of gcforest. Through experimental comparison, it has achieved better or similar performance than the current advanced learning algorithms for unbalanced data sets on a variety of typical unbalanced data sets.
{"title":"Research of Imbalanced Classification Based on Cascade Forest","authors":"M. Shi, Fangxin Lin, Ying Qian, Liang Dou","doi":"10.1109/PIC53636.2021.9687091","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687091","url":null,"abstract":"With the rapid development of science, the quantity of data is increasing exponentially. And unprecedented opportunities are provided by machine learning and data mining. While data classification is commonly used as a primary data processing method, the diversity of data is also a great challenge. Among those, problems caused by class imbalance are attracting more attention, and there are also a number of strategies and improvement of original algorithms are proposed. Gcforest is a new integrated learning algorithm proposed by Professor Zhou Zhihua in 2017. It has the advantages of few super parameters, suitable for small-scale data sets and strong model expression ability. However, the algorithm does not optimize the unbalanced data classification. Inspired by the improvement of other ensemble learning algorithms for unbalanced data classification, this paper applies a variety of under sampling strategies to the cascaded forest of gcforest. Through experimental comparison, it has achieved better or similar performance than the current advanced learning algorithms for unbalanced data sets on a variety of typical unbalanced data sets.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114906714","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687029
Mingyue Han, Yinglin Wang
Causality is the basis for humans to make rational decisions and is widely mentioned in different fields. In the natural language processing (NLP) community, the problem of causality is complex and challenging. This paper serves as an effort to briefly discuss the causal relation identification in texts, from the existing causal resources, research methodology, and the robustness problems. First, we introduce relevant causal datasets and resources. Second, the existing typical approaches that have been used in causal relation identification are categorized into unsupervised and supervised methods. In addition, the robustness of causality identification models is discussed succinctly. Finally, we try to list the research challenges at present and raise the future research directions in this field.
{"title":"A Survey on the Identification of Causal Relation in Texts","authors":"Mingyue Han, Yinglin Wang","doi":"10.1109/PIC53636.2021.9687029","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687029","url":null,"abstract":"Causality is the basis for humans to make rational decisions and is widely mentioned in different fields. In the natural language processing (NLP) community, the problem of causality is complex and challenging. This paper serves as an effort to briefly discuss the causal relation identification in texts, from the existing causal resources, research methodology, and the robustness problems. First, we introduce relevant causal datasets and resources. Second, the existing typical approaches that have been used in causal relation identification are categorized into unsupervised and supervised methods. In addition, the robustness of causality identification models is discussed succinctly. Finally, we try to list the research challenges at present and raise the future research directions in this field.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116107833","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687021
Bin Yang, R. Xiao
Big data has become viable as cost-effective approaches have emerged to tame the Volume, Velocity, Variety, Value (4Vs) of massive data. Within Big Data lies valuable patterns and information, previously hidden because of the tremendous amount of work required to extract them. Data-driven product design can guide designers making proper and accurate decisions, this design method is effective and useful, it becomes more and more popular today. Axiomatic design method is equally based on two design axioms of functional independency and information minimum, through "zigzag" decomposition, assigning design task to different design domains to complete design. This paper investigates how to apply Axiomatic design method in data-driven product design in order to bring new opportunities to enhance the production efficiency and product competitiveness.
{"title":"Data-Driven Product Design and Axiomatic Design","authors":"Bin Yang, R. Xiao","doi":"10.1109/PIC53636.2021.9687021","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687021","url":null,"abstract":"Big data has become viable as cost-effective approaches have emerged to tame the Volume, Velocity, Variety, Value (4Vs) of massive data. Within Big Data lies valuable patterns and information, previously hidden because of the tremendous amount of work required to extract them. Data-driven product design can guide designers making proper and accurate decisions, this design method is effective and useful, it becomes more and more popular today. Axiomatic design method is equally based on two design axioms of functional independency and information minimum, through \"zigzag\" decomposition, assigning design task to different design domains to complete design. This paper investigates how to apply Axiomatic design method in data-driven product design in order to bring new opportunities to enhance the production efficiency and product competitiveness.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122068412","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}
The Algorithms of object detection are usually difficult to deploy on low-end devices due to the large amount of computation, but knowledge distillation can solve this problem by training small models to learn the already trained complex network models, realizing model compression, and effectively reducing the amount of computation. How to transfer rich knowledge from teachers to students is a key step in the knowledge distillation. To solve this problem, this paper uses the knowledge of the teacher to guide the student network training in feature extraction, target classification and frame prediction, and proposes a distillation algorithm based on multi-scale attention mechanism, which uses attention mechanism to integrate different scale features. The correlation of features between different channels is learned by assigning weights to the features of each channel. The distillation algorithm proposed in this paper is based on YOLOv4, so it can strengthen the student network to learn the key knowledge of the teacher network, and make the knowledge of the teacher network How to the student network better. Experimental analysis shows that it can effectively improve the detection accuracy of the student network. The size of the model is only 6.4% of the teacher network, but the speed is increased by 3 times, and mAP is 5.7% higher than the original student network and 2.1% lower than the teacher network.
{"title":"Research on Knowledge Distillation Algorithm of Object Detection","authors":"Xue-fang Wang, Wenbin Zhang, Yuchun Chu, Peishun Liu, Qilin Yin, Qi Li","doi":"10.1109/PIC53636.2021.9687066","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687066","url":null,"abstract":"The Algorithms of object detection are usually difficult to deploy on low-end devices due to the large amount of computation, but knowledge distillation can solve this problem by training small models to learn the already trained complex network models, realizing model compression, and effectively reducing the amount of computation. How to transfer rich knowledge from teachers to students is a key step in the knowledge distillation. To solve this problem, this paper uses the knowledge of the teacher to guide the student network training in feature extraction, target classification and frame prediction, and proposes a distillation algorithm based on multi-scale attention mechanism, which uses attention mechanism to integrate different scale features. The correlation of features between different channels is learned by assigning weights to the features of each channel. The distillation algorithm proposed in this paper is based on YOLOv4, so it can strengthen the student network to learn the key knowledge of the teacher network, and make the knowledge of the teacher network How to the student network better. Experimental analysis shows that it can effectively improve the detection accuracy of the student network. The size of the model is only 6.4% of the teacher network, but the speed is increased by 3 times, and mAP is 5.7% higher than the original student network and 2.1% lower than the teacher network.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130404409","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687037
Jingmin Yang, Zhensen Chen, Li Xu
The video super-resolution (VSR) task refers to the use of corresponding low-resolution (LR) frames and multiple neighboring frames to generate high-resolution (HR) frames. An important step in VSR is to fuse the features of the reference frame with the features of the supporting frame. The existing VSR method does not make full use of the information provided by the distant neighboring frame, and usually fuses in a one-stage manner. In this paper, we propose a deep fusion video super-resolution network based on temporal grouping. We divide the input sequence into groups according to different frame rates to provide more accurate supplementary information, and the method aggregates temporal and spatial information at different stages of fusion.
{"title":"Deeply Feature Fused Video Super-resolution Network","authors":"Jingmin Yang, Zhensen Chen, Li Xu","doi":"10.1109/PIC53636.2021.9687037","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687037","url":null,"abstract":"The video super-resolution (VSR) task refers to the use of corresponding low-resolution (LR) frames and multiple neighboring frames to generate high-resolution (HR) frames. An important step in VSR is to fuse the features of the reference frame with the features of the supporting frame. The existing VSR method does not make full use of the information provided by the distant neighboring frame, and usually fuses in a one-stage manner. In this paper, we propose a deep fusion video super-resolution network based on temporal grouping. We divide the input sequence into groups according to different frame rates to provide more accurate supplementary information, and the method aggregates temporal and spatial information at different stages of fusion.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116184060","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687013
Li Yan, Chao Ma, Cong Wang, Ziqing Zhu, Gaozhou Wang, Huijian Wang
Traditional energy consumption data collection is usually deployed with weak security and privacy protection measures, resulting in high risks of data leakage and unauthorized access. To alleviate this problem, we propose a secure and privacy-preserving data transmission scheme (called DPDT) for advanced metering infrastructures based on local differential privacy protection and SM4 symmetric encryption algorithm. Specifically, we first protect the privacy of each client’s energy consumption data via the local differential privacy mechanism. Second, we employ the standard SM4 symmetric encryption algorithm to encrypt the aggregated data, in purpose of ensuring the data transmission. Further, we strictly prove the security of the proposed DPDT scheme, and verify the efficacy via extensive experiments.
{"title":"A Differentially Private Data Transmission Scheme for Advanced Metering Infrastructures","authors":"Li Yan, Chao Ma, Cong Wang, Ziqing Zhu, Gaozhou Wang, Huijian Wang","doi":"10.1109/PIC53636.2021.9687013","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687013","url":null,"abstract":"Traditional energy consumption data collection is usually deployed with weak security and privacy protection measures, resulting in high risks of data leakage and unauthorized access. To alleviate this problem, we propose a secure and privacy-preserving data transmission scheme (called DPDT) for advanced metering infrastructures based on local differential privacy protection and SM4 symmetric encryption algorithm. Specifically, we first protect the privacy of each client’s energy consumption data via the local differential privacy mechanism. Second, we employ the standard SM4 symmetric encryption algorithm to encrypt the aggregated data, in purpose of ensuring the data transmission. Further, we strictly prove the security of the proposed DPDT scheme, and verify the efficacy via extensive experiments.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115887493","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687000
Qiong Mou, Yunlong Cheng
In the real world, objects are usually measured at different scales and information is often incomplete. The main objective of this study is how to quickly obtain the optimal scale combinations of incomplete generalized multi-scale decision systems (IGMDSs). First, the concept of IGMDSs is introduced, and the sequential three-way decision model of scale space is developed. Second, a stepwise optimal scale selection algorithm is proposed to obtain an optimal scale combination of IGMDS quickly. Finally, to describe the relationships among the scale combinations, the adjacency matrix of the Hasse diagram and updating method for the adjacency matrix are proposed. Accordingly, an efficient optimal scale combinations selection algorithm based on sequential three-way decision is proposed to obtain all optimal scale combinations of IGMDS. Experimental results demonstrate that the proposed algorithms can significantly reduce computational time.
{"title":"Optimal Scale Combinations Selection for Incomplete Generalized Multi-scale Decision Systems","authors":"Qiong Mou, Yunlong Cheng","doi":"10.1109/PIC53636.2021.9687000","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687000","url":null,"abstract":"In the real world, objects are usually measured at different scales and information is often incomplete. The main objective of this study is how to quickly obtain the optimal scale combinations of incomplete generalized multi-scale decision systems (IGMDSs). First, the concept of IGMDSs is introduced, and the sequential three-way decision model of scale space is developed. Second, a stepwise optimal scale selection algorithm is proposed to obtain an optimal scale combination of IGMDS quickly. Finally, to describe the relationships among the scale combinations, the adjacency matrix of the Hasse diagram and updating method for the adjacency matrix are proposed. Accordingly, an efficient optimal scale combinations selection algorithm based on sequential three-way decision is proposed to obtain all optimal scale combinations of IGMDS. Experimental results demonstrate that the proposed algorithms can significantly reduce computational time.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114328262","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 : 2021-12-17DOI: 10.1109/PIC53636.2021.9687027
Zheng Liu, Huiqun Yu, Guisheng Fan, Liqiong Chen
In recent years, modern hospital has a large scale, complex relationship, and hospital information system (HIS) due to rapid development of computer networks. But there is still a big gap for reliable use of clinical information and management system, especially in terms of fault prevention. The microservice architecture has great advantages for development and delivery of complex system. This paper proposes a novel microservice reliability model (MSRM) for HIS based on the formalism of Predicate Petri net (PrT net). First, microservice reliability requirement design is given and PrT net is used to model the reliability of microservice, and the corresponding syntax and semantics are also presented. Then the redundancy and circuit breaker is designed by using PrT net, a composition strategy is proposed and the reliability of microservices is analyzed qualitatively and quantitatively. Based on the constructed MSRM, the correctness of PrT net modeling and effectiveness of the strategies have been proven theoretically. Finally, a public healthcare case is used to explain modeling process, and verify the effectiveness of proposed method. Experimental results show that the strategy for HIS microservice reliability is effective.
{"title":"Reliability Modeling and Analysis of Hospital Information System Based on Microservices","authors":"Zheng Liu, Huiqun Yu, Guisheng Fan, Liqiong Chen","doi":"10.1109/PIC53636.2021.9687027","DOIUrl":"https://doi.org/10.1109/PIC53636.2021.9687027","url":null,"abstract":"In recent years, modern hospital has a large scale, complex relationship, and hospital information system (HIS) due to rapid development of computer networks. But there is still a big gap for reliable use of clinical information and management system, especially in terms of fault prevention. The microservice architecture has great advantages for development and delivery of complex system. This paper proposes a novel microservice reliability model (MSRM) for HIS based on the formalism of Predicate Petri net (PrT net). First, microservice reliability requirement design is given and PrT net is used to model the reliability of microservice, and the corresponding syntax and semantics are also presented. Then the redundancy and circuit breaker is designed by using PrT net, a composition strategy is proposed and the reliability of microservices is analyzed qualitatively and quantitatively. Based on the constructed MSRM, the correctness of PrT net modeling and effectiveness of the strategies have been proven theoretically. Finally, a public healthcare case is used to explain modeling process, and verify the effectiveness of proposed method. Experimental results show that the strategy for HIS microservice reliability is effective.","PeriodicalId":297239,"journal":{"name":"2021 IEEE International Conference on Progress in Informatics and Computing (PIC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116013270","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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