Pub Date : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00032
Chao Wang, Zhenggao Pan, Lin Cui, Xiaoying Yang
This paper mainly introduces the main problems and solutions of digital circuit, the basic course of software engineering, under the background of advocating engineering education in China. In order to solve the problems of low classroom learning enthusiasm and weak classroom participation, the teaching concept of "teacher led and student-centered" digital circuit course is adopted. In view of the problems faced by the teaching of digital electronics courses, such as single teaching methods, lack of real-time guidance and weak supervision, this paper puts forward a systematic solution to organically combine classroom teaching with extracurricular innovation, cultivate students' innovative thinking and improve their practical ability. In order to solve the problem of disconnection between efficient classroom education content and enterprise requirements, enterprises are introduced to participate in curriculum teaching, reflecting the seamless connection with the industry. Take competition as the starting point, cultivate students' cooperation and competition awareness, and improve their innovation ability.
{"title":"Research on Digital Circuit Teaching Reform and Innovation Practice of Software Engineering Specialty under Engineering Education","authors":"Chao Wang, Zhenggao Pan, Lin Cui, Xiaoying Yang","doi":"10.1109/ISSSR53171.2021.00032","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00032","url":null,"abstract":"This paper mainly introduces the main problems and solutions of digital circuit, the basic course of software engineering, under the background of advocating engineering education in China. In order to solve the problems of low classroom learning enthusiasm and weak classroom participation, the teaching concept of \"teacher led and student-centered\" digital circuit course is adopted. In view of the problems faced by the teaching of digital electronics courses, such as single teaching methods, lack of real-time guidance and weak supervision, this paper puts forward a systematic solution to organically combine classroom teaching with extracurricular innovation, cultivate students' innovative thinking and improve their practical ability. In order to solve the problem of disconnection between efficient classroom education content and enterprise requirements, enterprises are introduced to participate in curriculum teaching, reflecting the seamless connection with the industry. Take competition as the starting point, cultivate students' cooperation and competition awareness, and improve their innovation ability.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114183095","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}
Named entity recognition plays a very important role in the field of natural language processing. Aiming at the special semantic morphology and scarcity of data in Uyghur named entity recognition, a neural network model based on BIGRU_IDCNN_ATT_CRF is proposed. First, extract the long-dependent semantic information of the Uyghur language context through the bidirectional gated recurrent neural network (BIGRU), and then uses the word vector through iterated dilated convolutional neural network (IDCNN) to increase the perception field to reduce the number of neurons and training parameters. Then use the self-attention mechanism to weight the features extracted from BIGRU_IDCNN to strengthen key features and weaken useless features. Finally, Conditional Random Field (CRF) is used for label prediction. It is concluded through experiments that the accuracy, recall and F1 value of this model on the Uyghur language data set are 85.0%, 84.3% and 84.58%, respectively, which can significantly improve the Uyghur language recognition task compared with the existing models.
{"title":"Uyghur Language Recognition Method based on BIGRU_IDCNN_ATT_CRF","authors":"Yifei Ge, Azragul, Degang Chen, Ke Li, Zongli Fu, Jincheng Guo","doi":"10.1109/ISSSR53171.2021.00033","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00033","url":null,"abstract":"Named entity recognition plays a very important role in the field of natural language processing. Aiming at the special semantic morphology and scarcity of data in Uyghur named entity recognition, a neural network model based on BIGRU_IDCNN_ATT_CRF is proposed. First, extract the long-dependent semantic information of the Uyghur language context through the bidirectional gated recurrent neural network (BIGRU), and then uses the word vector through iterated dilated convolutional neural network (IDCNN) to increase the perception field to reduce the number of neurons and training parameters. Then use the self-attention mechanism to weight the features extracted from BIGRU_IDCNN to strengthen key features and weaken useless features. Finally, Conditional Random Field (CRF) is used for label prediction. It is concluded through experiments that the accuracy, recall and F1 value of this model on the Uyghur language data set are 85.0%, 84.3% and 84.58%, respectively, which can significantly improve the Uyghur language recognition task compared with the existing models.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122064199","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 : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00031
Cheng Liu, Lei Luo, Mengmeng Li, Pinyuan Lei, Lirong Chen, Kun Xiao
With the coming of the Internet of things(IoT) era and the development of semiconductor equipment, multicore processors have begun to be widely used in IoT devices to meet their requirements for powerful processing capabilities. Unlike desktop or server operating systems such as Linux, current embedded operating systems often do not support multi-core processors well. Tasks on different cores often require information exchange, known as inter-core communication, which significantly impacts the processing performance of multi-core operation systems. In this paper, we proposed an inter-core communication method based on signal transmission and shared memory, which is flexible and various types of data can be transferred efficiently. We have implemented and experimented with it on our own microkernel operating system named Mginkgo. The experimental results show that the average time to trigger an inter-core interrupt is about 0.093 microseconds. The average inter-core interrupt processing time is about 3.986 microseconds. And the communication time of the system for multi-core Inter-Process Communication(IPC) is about 18us, which is the same as that of single-core IPC. The inter-core communication method proposed in this paper achieves very low latency with almost no performance consumption and maintains the high performance of the whole system.
{"title":"Inter-Core Communication Mechanisms for Microkernel Operating System based on Signal Transmission and Shared Memory","authors":"Cheng Liu, Lei Luo, Mengmeng Li, Pinyuan Lei, Lirong Chen, Kun Xiao","doi":"10.1109/ISSSR53171.2021.00031","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00031","url":null,"abstract":"With the coming of the Internet of things(IoT) era and the development of semiconductor equipment, multicore processors have begun to be widely used in IoT devices to meet their requirements for powerful processing capabilities. Unlike desktop or server operating systems such as Linux, current embedded operating systems often do not support multi-core processors well. Tasks on different cores often require information exchange, known as inter-core communication, which significantly impacts the processing performance of multi-core operation systems. In this paper, we proposed an inter-core communication method based on signal transmission and shared memory, which is flexible and various types of data can be transferred efficiently. We have implemented and experimented with it on our own microkernel operating system named Mginkgo. The experimental results show that the average time to trigger an inter-core interrupt is about 0.093 microseconds. The average inter-core interrupt processing time is about 3.986 microseconds. And the communication time of the system for multi-core Inter-Process Communication(IPC) is about 18us, which is the same as that of single-core IPC. The inter-core communication method proposed in this paper achieves very low latency with almost no performance consumption and maintains the high performance of the whole system.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117247816","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 : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00013
Yihao Li, Pan Liu, Xiao Zhao, Jiaqi Yan, Xiaoyu Song
To locate multiple bugs in parallel, one common practice is to generate fault-focused clusters where failed test cases that are likely caused by the same bug are grouped together. With respect to the fault-focused clustering performance, a critical impact factor is the distance metric used to measure the similarity between two rankings. This paper proposes a method to evaluate the fault-focused clustering performance of distance metrics from an omniscient perspective where the fault-focused information for each failed test case is already given. Case studies are conducted using the proposed method to evaluate Jaccard and Kendall tau distance on three programs with multiple bugs. The findings seem to challenge previous perceptions regarding the performance of these two distance metrics in generating fault-focused clusters.
{"title":"Evaluating the Fault-Focused Clustering Performance of Distance Metrics in Parallel Fault Localization: From an Omniscient Perspective","authors":"Yihao Li, Pan Liu, Xiao Zhao, Jiaqi Yan, Xiaoyu Song","doi":"10.1109/ISSSR53171.2021.00013","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00013","url":null,"abstract":"To locate multiple bugs in parallel, one common practice is to generate fault-focused clusters where failed test cases that are likely caused by the same bug are grouped together. With respect to the fault-focused clustering performance, a critical impact factor is the distance metric used to measure the similarity between two rankings. This paper proposes a method to evaluate the fault-focused clustering performance of distance metrics from an omniscient perspective where the fault-focused information for each failed test case is already given. Case studies are conducted using the proposed method to evaluate Jaccard and Kendall tau distance on three programs with multiple bugs. The findings seem to challenge previous perceptions regarding the performance of these two distance metrics in generating fault-focused clusters.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128304222","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 : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00030
Hua-wen Chang, Cheng-Yang Du, Xiao-Dong Bi, Ming-hui Wang
In the field of image quality evaluation, visual saliency and gradient information are very effective features for quality evaluation models. Visual saliency is often used to study which areas of an image are most attractive to the human visual system. Moreover, the degradation of gradient information can reflect the degree of structure distortion of images. Considering these two points, we propose a simple but very effective quality evaluation metric for color images. After obtaining the local gradient similarity information, the similarity of visual saliency and color information are also calculated, and then we calculate the standard deviations of the three components to obtain the final quality score. The experimental results from five benchmark databases (LIVE, IVC, TID2008, TID2013 and CSIQ) show that our model performs better than other methods in the correlation with human visual quality judgment.
{"title":"Color Image Quality Evaluation based on Visual Saliency and Gradient Information","authors":"Hua-wen Chang, Cheng-Yang Du, Xiao-Dong Bi, Ming-hui Wang","doi":"10.1109/ISSSR53171.2021.00030","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00030","url":null,"abstract":"In the field of image quality evaluation, visual saliency and gradient information are very effective features for quality evaluation models. Visual saliency is often used to study which areas of an image are most attractive to the human visual system. Moreover, the degradation of gradient information can reflect the degree of structure distortion of images. Considering these two points, we propose a simple but very effective quality evaluation metric for color images. After obtaining the local gradient similarity information, the similarity of visual saliency and color information are also calculated, and then we calculate the standard deviations of the three components to obtain the final quality score. The experimental results from five benchmark databases (LIVE, IVC, TID2008, TID2013 and CSIQ) show that our model performs better than other methods in the correlation with human visual quality judgment.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132099869","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 : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00019
Hua-wen Chang, Xiao-Dong Bi, Cheng-Yang Du, Ming-hui Wang
In this paper, a new neural network structure, which is called fast quality assessment network (FQA-Net), is proposed for fast blind image quality assessment (BIQA). FQA-Net is a very simple neural network, which mainly includes convolution layer, standard deviation measurement layer and regression layer. In order to improve the efficiency of the network, a group of visual filters (VFs) are obtained by simulating the neurons in the cerebral cortex. The VFs are used as the convolution kernels in the convolution layer, then the outputs of the convolutional layer are a set of feature maps. After that the standard deviation of each feature map is calculated directly. Finally, the regression function is used for the mapping between the standard deviation values and the quality scores. FQA-Net not only reduces the number of parameters and the output dimensions in the training process, but also prevents network overfitting effectively. The experiment results show that FQA-Net has relatively low computational complexity and high competitiveness compared with the leading BIQA methods.
{"title":"Blind Image Quality Assessment by Fast Quality Assessment Network","authors":"Hua-wen Chang, Xiao-Dong Bi, Cheng-Yang Du, Ming-hui Wang","doi":"10.1109/ISSSR53171.2021.00019","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00019","url":null,"abstract":"In this paper, a new neural network structure, which is called fast quality assessment network (FQA-Net), is proposed for fast blind image quality assessment (BIQA). FQA-Net is a very simple neural network, which mainly includes convolution layer, standard deviation measurement layer and regression layer. In order to improve the efficiency of the network, a group of visual filters (VFs) are obtained by simulating the neurons in the cerebral cortex. The VFs are used as the convolution kernels in the convolution layer, then the outputs of the convolutional layer are a set of feature maps. After that the standard deviation of each feature map is calculated directly. Finally, the regression function is used for the mapping between the standard deviation values and the quality scores. FQA-Net not only reduces the number of parameters and the output dimensions in the training process, but also prevents network overfitting effectively. The experiment results show that FQA-Net has relatively low computational complexity and high competitiveness compared with the leading BIQA methods.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130852701","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 : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00012
Pan Liu, Yihao Li, Lian Zeng, Xuankui Zheng, Sihao Huang
With the rise of the applications of the Internet of Things (IoT) in human society, how to ensure the reliability of IoT systems has become a research hotspot. Generally, there are complex interactions between multiple systems in IoT. Therefore, even if a single system can pass rigorous tests, it may not be able to guarantee that the system runs reliably in a complex IoT environment. With the operation of the IoT system, a large amount of data will be generated to record sensor data, system operations, user’s operations, and other information. Therefore, software faults or software design defects can be discovered if we use appropriate big data technology to mine the massive amount of data. The paper states the characteristics of big data-based testing and compares this test method with traditional software test methods in the software life cycle. Then, the paper discusses the challenges of applying big data-based testing to IoT systems. Finally, some future research directions of big data-based testing are given in the paper.
{"title":"Big Data-based Testing: Characteristics, Challenges, and Future Directions","authors":"Pan Liu, Yihao Li, Lian Zeng, Xuankui Zheng, Sihao Huang","doi":"10.1109/ISSSR53171.2021.00012","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00012","url":null,"abstract":"With the rise of the applications of the Internet of Things (IoT) in human society, how to ensure the reliability of IoT systems has become a research hotspot. Generally, there are complex interactions between multiple systems in IoT. Therefore, even if a single system can pass rigorous tests, it may not be able to guarantee that the system runs reliably in a complex IoT environment. With the operation of the IoT system, a large amount of data will be generated to record sensor data, system operations, user’s operations, and other information. Therefore, software faults or software design defects can be discovered if we use appropriate big data technology to mine the massive amount of data. The paper states the characteristics of big data-based testing and compares this test method with traditional software test methods in the software life cycle. Then, the paper discusses the challenges of applying big data-based testing to IoT systems. Finally, some future research directions of big data-based testing are given in the paper.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133262604","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}
As computer information science technology and software technology advances, the acquisition and use of soft-ware have become very convenient and available, but software piracy has become commonplace. 66% of software piracy was used in China alone in 2017, resulting in a loss of at least $6.8 billion to enterprises, according to the BSA. So how to protect our intellectual property while enjoying the convenience of software is a problem for every developer. One way to alleviate this problem from a technical perspective is to use software protection techniques, especially the obfuscation of code. The most common and prominent of the obfuscation techniques is control flow obfuscation. There are many studies of source code and bytecode obfuscation. However, research on Micropython bytecode obfuscation is quiet rare. In this paper, we propose a Micropython bytecode obfuscator based on control flow obfuscation, which has the advantage of being efficient and convenient, and we have implemented and experimented on the STM32L4 platform. The test results prove that the obfuscator can greatly increase the difficulty of cracking Micropython bytecode.
{"title":"An Efficient Control-flow based Obfuscator for Micropython Bytecode","authors":"Lantao Wang, Yun Li, Haitao Zhang, Qigu Han, Lirong Chen","doi":"10.1109/ISSSR53171.2021.00028","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00028","url":null,"abstract":"As computer information science technology and software technology advances, the acquisition and use of soft-ware have become very convenient and available, but software piracy has become commonplace. 66% of software piracy was used in China alone in 2017, resulting in a loss of at least $6.8 billion to enterprises, according to the BSA. So how to protect our intellectual property while enjoying the convenience of software is a problem for every developer. One way to alleviate this problem from a technical perspective is to use software protection techniques, especially the obfuscation of code. The most common and prominent of the obfuscation techniques is control flow obfuscation. There are many studies of source code and bytecode obfuscation. However, research on Micropython bytecode obfuscation is quiet rare. In this paper, we propose a Micropython bytecode obfuscator based on control flow obfuscation, which has the advantage of being efficient and convenient, and we have implemented and experimented on the STM32L4 platform. The test results prove that the obfuscator can greatly increase the difficulty of cracking Micropython bytecode.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132191384","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 : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00034
Sa Meng, Liang Luo, Xiwei Qiu, Peng Sun
With the advancement of IoT and Smart City, public cloud computing systems are required to be powerful in data processing and be dependable as a service provider. Thus, reliability analysis of cloud computing systems has been widely investigated but far from being solved. Reliability of the public cloud computing system is indeed affected by many factors, such as service performance, system energy consumption. Researchers can analysis such important correlation to find correlation factors that can cause significant changes in the correlation, and further optimize those correlation factors dynamically and intelligently. This would be an effective approach to improve the reliability of the public cloud system. This paper tries to establish a Reliability analysis framework covering four levels, i.e., component, system, mission and data, by using of Markov process and hierarchical correlation modelling. Numerical results indicate that the proposed methods improve the reliability by reliability planning, optimizes energy utilization, and uses stand-by policies.
{"title":"A Reliability Optimization Framework for Public Cloud Services based on Markov Process and Hierarchical Correlation Modelling","authors":"Sa Meng, Liang Luo, Xiwei Qiu, Peng Sun","doi":"10.1109/ISSSR53171.2021.00034","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00034","url":null,"abstract":"With the advancement of IoT and Smart City, public cloud computing systems are required to be powerful in data processing and be dependable as a service provider. Thus, reliability analysis of cloud computing systems has been widely investigated but far from being solved. Reliability of the public cloud computing system is indeed affected by many factors, such as service performance, system energy consumption. Researchers can analysis such important correlation to find correlation factors that can cause significant changes in the correlation, and further optimize those correlation factors dynamically and intelligently. This would be an effective approach to improve the reliability of the public cloud system. This paper tries to establish a Reliability analysis framework covering four levels, i.e., component, system, mission and data, by using of Markov process and hierarchical correlation modelling. Numerical results indicate that the proposed methods improve the reliability by reliability planning, optimizes energy utilization, and uses stand-by policies.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128643060","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 : 2021-09-01DOI: 10.1109/ISSSR53171.2021.00022
Baohua Jin, Songtao Shang, Miaomiao Qin, Zuhe Li
Relationship extraction is a fundamental component of various information extraction systems. Traditional relationship extraction methods are mainly rule-based methods and machine learning methods. Rule-based methods require induction and analysis of the corpus, followed by extraction of relationship extraction rules and finally pattern matching. The machine learning approach requires a large amount of manually annotated train data and manual extraction of features. However, these methods require a lot of statics and higher time costs. Considering these issues in the traditional relationship extraction methods and the linguistic characteristics of Chinese text, this paper proposes a new deep neural network structure. Firstly, the dependency relationships between sentence components are analyzed by using dependency parsing, which reveals the syntactic structure of the sentence and enhance the potential semantic information. Secondly, the important semantic information in the sentences is captured by using the sentence-level attention mechanism. Finally, the Bidirectional Gating Recurrent Unit model is used to simultaneously capture the contextual information of the text, and to improve the performance of relation extraction. The experimental results show that the model proposed in this paper is more effective than existing methods.
{"title":"Inter-personal Relation Extraction Model based on Dependency Parsing and Bidirectional Gating Recurrent Unit","authors":"Baohua Jin, Songtao Shang, Miaomiao Qin, Zuhe Li","doi":"10.1109/ISSSR53171.2021.00022","DOIUrl":"https://doi.org/10.1109/ISSSR53171.2021.00022","url":null,"abstract":"Relationship extraction is a fundamental component of various information extraction systems. Traditional relationship extraction methods are mainly rule-based methods and machine learning methods. Rule-based methods require induction and analysis of the corpus, followed by extraction of relationship extraction rules and finally pattern matching. The machine learning approach requires a large amount of manually annotated train data and manual extraction of features. However, these methods require a lot of statics and higher time costs. Considering these issues in the traditional relationship extraction methods and the linguistic characteristics of Chinese text, this paper proposes a new deep neural network structure. Firstly, the dependency relationships between sentence components are analyzed by using dependency parsing, which reveals the syntactic structure of the sentence and enhance the potential semantic information. Secondly, the important semantic information in the sentences is captured by using the sentence-level attention mechanism. Finally, the Bidirectional Gating Recurrent Unit model is used to simultaneously capture the contextual information of the text, and to improve the performance of relation extraction. The experimental results show that the model proposed in this paper is more effective than existing methods.","PeriodicalId":211012,"journal":{"name":"2021 7th International Symposium on System and Software Reliability (ISSSR)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125012422","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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