Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674109
Gan Tao, Zhang Heng, He Yanmin, Luo Yu
Self-supervised learning constructs supervised signals inside samples without relying on external labels, which is becoming a promising research direction. Recently, works on self-supervised learning by maximizing local-global mutual information on networks have achieved state-of-the-art performance comparable to semi-supervised graph neural networks (GNNs). However, these methods have not explored the collaborative relationship of multiple meta-path views, and the global representation is weakened by irrelevant nodes which participate in the average operation over all nodes. In this paper, a self-supervised approach based on mutual information for heterogeneous information network embedding is proposed. Specifically, it utilizes the contrast of multiple meta-path views to supervise each other, and positive samples are selected to obtain a robust global representation. Experimental results demonstrate the proposed method has competitive performance over the existing mutual-information-based ones and even outperforms some supervised learning methods.
{"title":"Contrastive Multi-View Self-Supervised Learning for Heterogeneous Information Network","authors":"Gan Tao, Zhang Heng, He Yanmin, Luo Yu","doi":"10.1109/ICCWAMTIP53232.2021.9674109","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674109","url":null,"abstract":"Self-supervised learning constructs supervised signals inside samples without relying on external labels, which is becoming a promising research direction. Recently, works on self-supervised learning by maximizing local-global mutual information on networks have achieved state-of-the-art performance comparable to semi-supervised graph neural networks (GNNs). However, these methods have not explored the collaborative relationship of multiple meta-path views, and the global representation is weakened by irrelevant nodes which participate in the average operation over all nodes. In this paper, a self-supervised approach based on mutual information for heterogeneous information network embedding is proposed. Specifically, it utilizes the contrast of multiple meta-path views to supervise each other, and positive samples are selected to obtain a robust global representation. Experimental results demonstrate the proposed method has competitive performance over the existing mutual-information-based ones and even outperforms some supervised learning methods.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"6 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":"129674159","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/ICCWAMTIP53232.2021.9674148
Liu Yuting, Ji Jing, Chen Wei
Intended to achieve a signal design with high spectrum utilization efficiency and high measurement accuracy within a limited bandwidth, in this paper, a modulation scheme of communication and navigation fusion signal is presented by combining continuous phase modulation and spectral overlay. The results show that the proposed signals perform well on the anti-multipath performance and ranging accuracy while theoretically possess good compatibility to other navigation service signals in S-band. This modulation scheme can generate flexible waveforms that provide reference to design fusion communication and navigation signals. It has a positive impact on the construction of location based services equipped with higher ranging accuracy and higher tracking sensitivity.
{"title":"Design and Performance Analysis of A Communication and Navigation Fusion Signal","authors":"Liu Yuting, Ji Jing, Chen Wei","doi":"10.1109/ICCWAMTIP53232.2021.9674148","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674148","url":null,"abstract":"Intended to achieve a signal design with high spectrum utilization efficiency and high measurement accuracy within a limited bandwidth, in this paper, a modulation scheme of communication and navigation fusion signal is presented by combining continuous phase modulation and spectral overlay. The results show that the proposed signals perform well on the anti-multipath performance and ranging accuracy while theoretically possess good compatibility to other navigation service signals in S-band. This modulation scheme can generate flexible waveforms that provide reference to design fusion communication and navigation signals. It has a positive impact on the construction of location based services equipped with higher ranging accuracy and higher tracking sensitivity.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"08 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":"130548390","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/ICCWAMTIP53232.2021.9674119
Zhouyi Wang
Unlimited dissemination of rumors in social media has a tremendous negative impact on our society. To address this issue, many rumor verification models have been proposed and achieved reasonable verification performance. However, the imbalanced data distribution between samples heavily limit the further prosperity of the deep learning-based models. To alleviate challenges, we propose a novel hierarchical data augmentation method for the rumor verification task (termed as HDA-RV), which consists two data augmentation methods (tweet-level and thread-level data augmentation). Tweet-level data augmentation simulates the noise of text information in social media and thread-level data augmentation corresponds to the noise of the propagation structure in social networks. Experiments on the PHEME dataset show that our method can effectively alleviate the problem of data imbalance.
{"title":"Hierarchical Data Augmentation for Rumor Verification on Twitter","authors":"Zhouyi Wang","doi":"10.1109/ICCWAMTIP53232.2021.9674119","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674119","url":null,"abstract":"Unlimited dissemination of rumors in social media has a tremendous negative impact on our society. To address this issue, many rumor verification models have been proposed and achieved reasonable verification performance. However, the imbalanced data distribution between samples heavily limit the further prosperity of the deep learning-based models. To alleviate challenges, we propose a novel hierarchical data augmentation method for the rumor verification task (termed as HDA-RV), which consists two data augmentation methods (tweet-level and thread-level data augmentation). Tweet-level data augmentation simulates the noise of text information in social media and thread-level data augmentation corresponds to the noise of the propagation structure in social networks. Experiments on the PHEME dataset show that our method can effectively alleviate the problem of data imbalance.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"28 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":"130741078","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/ICCWAMTIP53232.2021.9674089
Fadia Shah, Jianping Li, F. Shah, Y. Shah
Medical data is becoming more dense and complicated day by data. After COVID-19, the medical information is entirely expended from terabytes and petabytes. An accurate diagnosis needs a sophisticated mechanism and the support of information technology. Hadoop ecosystem is facilitating big data management for various health care applications. As dense patient history leads to better diagnosis; Hadoop architecture supports patient data accommodation, retrieval, update, and many similar functions like information assortment, information intricacy, information stockpiling, information investigation, information security, and protection.
{"title":"Hadoop with Wavelet Support for Medical Big Data","authors":"Fadia Shah, Jianping Li, F. Shah, Y. Shah","doi":"10.1109/ICCWAMTIP53232.2021.9674089","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674089","url":null,"abstract":"Medical data is becoming more dense and complicated day by data. After COVID-19, the medical information is entirely expended from terabytes and petabytes. An accurate diagnosis needs a sophisticated mechanism and the support of information technology. Hadoop ecosystem is facilitating big data management for various health care applications. As dense patient history leads to better diagnosis; Hadoop architecture supports patient data accommodation, retrieval, update, and many similar functions like information assortment, information intricacy, information stockpiling, information investigation, information security, and protection.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"16 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":"123177055","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/ICCWAMTIP53232.2021.9674063
Xiao Fei, Liao Jianping, Gao Yuan, Zhou Yue
Text classification is an important problem in natural language processing. The main task is to divide the text into different categories according to the content of the text. This article preprocesses the text in the SMS data set used to a certain extent, using the Tf-Idf model. The frequency of the text unit is counted as the feature value of the corresponding vector of the text, so that the text is converted into a vector, and then these vectors are fitted and predicted by the support vector machine algorithm.
{"title":"SMS Text Classification Model Based on Machine Learning","authors":"Xiao Fei, Liao Jianping, Gao Yuan, Zhou Yue","doi":"10.1109/ICCWAMTIP53232.2021.9674063","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674063","url":null,"abstract":"Text classification is an important problem in natural language processing. The main task is to divide the text into different categories according to the content of the text. This article preprocesses the text in the SMS data set used to a certain extent, using the Tf-Idf model. The frequency of the text unit is counted as the feature value of the corresponding vector of the text, so that the text is converted into a vector, and then these vectors are fitted and predicted by the support vector machine algorithm.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","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":"123101260","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/ICCWAMTIP53232.2021.9674143
Yang Yimei, Yang Yujun, Zhouyi Wang, Xi Hongbo, Li Wei
With the deepening application of big data technology in the field of health care, the potential risks such as personal privacy and security that may be brought by the collection, analysis and sharing of health data cannot be ignored. How to ensure the safety of health big data and conduct reasonable and compliant analysis and utilization of health big data is an urgent problem to be solved at present. Based on the characteristics of health big data, this paper focuses on the privacy connotation of health big data, puts forward the privacy protection framework of health big data around the privacy protection needs of various stakeholders in the life cycle of health big data, and combs the privacy protection technology system currently available in the field of health care, In order to provide support for each application link of health big data, a set of health data desensitization method based on XML is studied and designed. This method can dynamically add data desensitization strategy, meet the different needs of hospitals for medical record privacy data protection under different application scenarios, and promote the standardized and orderly development of health big data.
{"title":"A Privacy Protection Mechanism For Health Big Data Based On Xml","authors":"Yang Yimei, Yang Yujun, Zhouyi Wang, Xi Hongbo, Li Wei","doi":"10.1109/ICCWAMTIP53232.2021.9674143","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674143","url":null,"abstract":"With the deepening application of big data technology in the field of health care, the potential risks such as personal privacy and security that may be brought by the collection, analysis and sharing of health data cannot be ignored. How to ensure the safety of health big data and conduct reasonable and compliant analysis and utilization of health big data is an urgent problem to be solved at present. Based on the characteristics of health big data, this paper focuses on the privacy connotation of health big data, puts forward the privacy protection framework of health big data around the privacy protection needs of various stakeholders in the life cycle of health big data, and combs the privacy protection technology system currently available in the field of health care, In order to provide support for each application link of health big data, a set of health data desensitization method based on XML is studied and designed. This method can dynamically add data desensitization strategy, meet the different needs of hospitals for medical record privacy data protection under different application scenarios, and promote the standardized and orderly development of health big data.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","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":"134532065","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/ICCWAMTIP53232.2021.9674136
H. Tiantian, Su Sheng
With the advent of the era of sharing economy, shared travel mode has gradually entered the public's vision and attracted the public's attention and favor. The long-distance between different locations and the difficulty of route planning not only increase the difficulty of people sharing travel to a certain extent but also make the shared bus scheduling problem become a very hot topic. Aiming at this problem, this paper proposes a variable population evolution algorithm based on the pyramid model (PME). Firstly, due to the slow convergence speed of traditional evolutionary algorithms, the concept of variable population evolution and the random selection of weighted genes are introduced to generate a chromosome. Secondly, the crossover operation in the genetic algorithm is improved by crossing all chromosomes with excellent genes. In addition, the PME algorithm proposed in this paper can accurately predict the specific number of vehicles required for dispatch on the next day, and it can also realize the sharing of all vehicles when the route in the specified range is unknown. Experimental data show that the proposed method achieves better performance.
{"title":"A Variable Population Evolutionary Algorithm Based on Pyramid Model for Shared Bus Scheduling Problem","authors":"H. Tiantian, Su Sheng","doi":"10.1109/ICCWAMTIP53232.2021.9674136","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674136","url":null,"abstract":"With the advent of the era of sharing economy, shared travel mode has gradually entered the public's vision and attracted the public's attention and favor. The long-distance between different locations and the difficulty of route planning not only increase the difficulty of people sharing travel to a certain extent but also make the shared bus scheduling problem become a very hot topic. Aiming at this problem, this paper proposes a variable population evolution algorithm based on the pyramid model (PME). Firstly, due to the slow convergence speed of traditional evolutionary algorithms, the concept of variable population evolution and the random selection of weighted genes are introduced to generate a chromosome. Secondly, the crossover operation in the genetic algorithm is improved by crossing all chromosomes with excellent genes. In addition, the PME algorithm proposed in this paper can accurately predict the specific number of vehicles required for dispatch on the next day, and it can also realize the sharing of all vehicles when the route in the specified range is unknown. Experimental data show that the proposed method achieves better performance.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"15 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":"133668576","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/ICCWAMTIP53232.2021.9674123
Yang Xianhua, Yang Yi, Yang Juan, Yao Han, Wang Zheng, Long Shuquan
Image multi-label classification is a critical task in the field of computer vision. The primary difficulty is that multi-label classification relies on the complex information in the image to differentiate different labels, significantly increasing the classification difficulty. We proposed a method for modifying previous models. First, we use TResNet as the benchmark model, replacing ordinary convolution with pyramid convolution in the original model and the attention mechanism in the model with the split-attention method. Then the model was trained on the VOC2007 and MS-COCO data sets. The process of selecting the model's parameters and determining the optimal modification method was demonstrated through comparative experiments. Finally, by comparing the performance of the modified model with the performance of the unmodified model, it is proved that our two modification methods can effectively improve the performance of the model. On the VOC data set, the modified model by the two methods increased by 1% and 1.6%, respectively.
{"title":"Image Multi-Label Classification Based on Pyramid Convolution and Split-Attention Mechanism","authors":"Yang Xianhua, Yang Yi, Yang Juan, Yao Han, Wang Zheng, Long Shuquan","doi":"10.1109/ICCWAMTIP53232.2021.9674123","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674123","url":null,"abstract":"Image multi-label classification is a critical task in the field of computer vision. The primary difficulty is that multi-label classification relies on the complex information in the image to differentiate different labels, significantly increasing the classification difficulty. We proposed a method for modifying previous models. First, we use TResNet as the benchmark model, replacing ordinary convolution with pyramid convolution in the original model and the attention mechanism in the model with the split-attention method. Then the model was trained on the VOC2007 and MS-COCO data sets. The process of selecting the model's parameters and determining the optimal modification method was demonstrated through comparative experiments. Finally, by comparing the performance of the modified model with the performance of the unmodified model, it is proved that our two modification methods can effectively improve the performance of the model. On the VOC data set, the modified model by the two methods increased by 1% and 1.6%, respectively.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"74 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":"117305061","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/ICCWAMTIP53232.2021.9674177
Yan Qi, Cheng Baiyang, Luo Lan
Deep learning technology is an important new force in the emerging science and technology revolution and the revolution of the animal husbandry industry, and plays a crucial role in the process of being digitization, informatization and wisdom of the animal husbandry industry in China. The application of deep learning-based image recognition in the livestock industry provides a new solution to the problems of disease prevention, precise identification and biosafety prevention and control at the farming side, and will become a powerful booster to promote the livestock industry towards modernization. The use of convolutional neural network after extracting a feature to complete the link according to the type of feature classification, then complete the data pre-processing, and using super pixel-based image segmentation and SIFT algorithm to complete image segmentation and image feature extraction, and finally through the convolutional neural network and support vector machine to complete the classification and prediction of animal action, driving the overall management level of the livestock industry to improve, and become an effective way to promote the development of intelligent animal husbandry.
{"title":"Deep Learning Based Image Recognition In Animal Husbandry","authors":"Yan Qi, Cheng Baiyang, Luo Lan","doi":"10.1109/ICCWAMTIP53232.2021.9674177","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674177","url":null,"abstract":"Deep learning technology is an important new force in the emerging science and technology revolution and the revolution of the animal husbandry industry, and plays a crucial role in the process of being digitization, informatization and wisdom of the animal husbandry industry in China. The application of deep learning-based image recognition in the livestock industry provides a new solution to the problems of disease prevention, precise identification and biosafety prevention and control at the farming side, and will become a powerful booster to promote the livestock industry towards modernization. The use of convolutional neural network after extracting a feature to complete the link according to the type of feature classification, then complete the data pre-processing, and using super pixel-based image segmentation and SIFT algorithm to complete image segmentation and image feature extraction, and finally through the convolutional neural network and support vector machine to complete the classification and prediction of animal action, driving the overall management level of the livestock industry to improve, and become an effective way to promote the development of intelligent animal husbandry.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"44 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":"116892325","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/ICCWAMTIP53232.2021.9674115
Zhang Haitao, Chen Lirong, Luo Lei
A formal computational model is presented for the sequential kernel of an automotive embedded real-time operating system, which provides infrastructural mechanism to support the isolation between applications and the operating system, as well as the isolation between executive entities such as tasks and ISRs (Interrupt Service Routines) in applications. The target embedded system is modeled at the granularity of isolated memory regions and stacks. Tasks, nested ISRs and the preempt-able part of the operating system (i.e. system services) are concurrent entities executing on dedicated memory regions and stacks determined by the sequential kernel. States of these entities can be correctly saved and restored in isolated stacks and in the kernel data structures, such that the control flow changes among them can be correctly made. The implementation correctness theorem of the kernel is established along with the corresponding simulation relationship and implementation invariants. According to the features of the model and the related implementation languages, the kernel is formally verified with the theorem prover Isabelle/HOL.
{"title":"Formal Modeling and Verification of the Sequential Kernel of an Embedded Operating System","authors":"Zhang Haitao, Chen Lirong, Luo Lei","doi":"10.1109/ICCWAMTIP53232.2021.9674115","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674115","url":null,"abstract":"A formal computational model is presented for the sequential kernel of an automotive embedded real-time operating system, which provides infrastructural mechanism to support the isolation between applications and the operating system, as well as the isolation between executive entities such as tasks and ISRs (Interrupt Service Routines) in applications. The target embedded system is modeled at the granularity of isolated memory regions and stacks. Tasks, nested ISRs and the preempt-able part of the operating system (i.e. system services) are concurrent entities executing on dedicated memory regions and stacks determined by the sequential kernel. States of these entities can be correctly saved and restored in isolated stacks and in the kernel data structures, such that the control flow changes among them can be correctly made. The implementation correctness theorem of the kernel is established along with the corresponding simulation relationship and implementation invariants. According to the features of the model and the related implementation languages, the kernel is formally verified with the theorem prover Isabelle/HOL.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"97 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":"121177964","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：