Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674163
Ejiyi Chukwuebuka Joseph, O. Bamisile, Nneji Ugochi, Qin Zhen, Ndalahwa Ilakoze, Chikwendu A. Ijeoma
This paper explicates the systematic advancements that were observed from the inception of the YOLO (You Only Look Once) object detector to the most recent version 4. Since its introduction in late 2015, YOLO has recorded tremendous implementation as well as improvements and applications. In this work, a brief survey of the YOLO network is presented considering the introduction that was made to each version that succeeded each preceding version and the advancement on how the model performed with detection. We used the latest version of the network (YOLOv4) to train 50 classes of objects that we considered popular objects for real-time detection. The model trained obtained an mAP of 64.80% @IoU of 0.5 and when deployed for real-time detection, it achieved a 43FPS speed of detection.
{"title":"Systematic Advancement of Yolo Object Detector For Real-Time Detection of Objects","authors":"Ejiyi Chukwuebuka Joseph, O. Bamisile, Nneji Ugochi, Qin Zhen, Ndalahwa Ilakoze, Chikwendu A. Ijeoma","doi":"10.1109/ICCWAMTIP53232.2021.9674163","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674163","url":null,"abstract":"This paper explicates the systematic advancements that were observed from the inception of the YOLO (You Only Look Once) object detector to the most recent version 4. Since its introduction in late 2015, YOLO has recorded tremendous implementation as well as improvements and applications. In this work, a brief survey of the YOLO network is presented considering the introduction that was made to each version that succeeded each preceding version and the advancement on how the model performed with detection. We used the latest version of the network (YOLOv4) to train 50 classes of objects that we considered popular objects for real-time detection. The model trained obtained an mAP of 64.80% @IoU of 0.5 and when deployed for real-time detection, it achieved a 43FPS speed of detection.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"22 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":"121308080","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}
Pub Date : 2021-12-17DOI: 10.1109/ICCWAMTIP53232.2021.9674093
Liu Di, Kong Xinyue, Yong-Cheul Jun
With the development of machine learning, transfer learning has great development prospect and commercial value compared with the traditional supervised learning. As neural network developed, transfer learning based on metric learning is widely used in the field of Computer Vision and gradually applied to Natural Language Processing. This paper proposes to use BERT encoder and BiLSTM to improve the performance of intention detection especially in classification performance. SMP2017 data set shows that it can effectively improve the accuracy of intention detection when the sample size is small and uneven.
{"title":"Optimization of Intention Detection Based on Metric Learning","authors":"Liu Di, Kong Xinyue, Yong-Cheul Jun","doi":"10.1109/ICCWAMTIP53232.2021.9674093","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674093","url":null,"abstract":"With the development of machine learning, transfer learning has great development prospect and commercial value compared with the traditional supervised learning. As neural network developed, transfer learning based on metric learning is widely used in the field of Computer Vision and gradually applied to Natural Language Processing. This paper proposes to use BERT encoder and BiLSTM to improve the performance of intention detection especially in classification performance. SMP2017 data set shows that it can effectively improve the accuracy of intention detection when the sample size is small and uneven.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"23 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":"126687601","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/ICCWAMTIP53232.2021.9674175
Yang Yujun, Yang Yimei, Zhouqiong Wang, Xi Hongbo, Li Liyun
The quality and safety of agricultural products has been widely concerned by the whole society in recent years. Therefore, the traceability of agricultural products is a research hotspot of scholars. The quality and safety traceability system of agricultural products is an important method to monitor the quality and safety of agricultural products. The emergence and use of big data help to solve the problems of high cost, scattered information and incomplete industrial chain of quality and safety traceability of agricultural products and improve the efficiency and accuracy of the quality and safety traceability system of agricultural products. There are still some problems in the application of big data, such as weak pertinence. It is necessary to mine and use big data to realize the traceability of agricultural products.
{"title":"Research On The Construction of Agricultural Product Quality Maintenance And Quality Traceability System Based On Big Data","authors":"Yang Yujun, Yang Yimei, Zhouqiong Wang, Xi Hongbo, Li Liyun","doi":"10.1109/ICCWAMTIP53232.2021.9674175","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674175","url":null,"abstract":"The quality and safety of agricultural products has been widely concerned by the whole society in recent years. Therefore, the traceability of agricultural products is a research hotspot of scholars. The quality and safety traceability system of agricultural products is an important method to monitor the quality and safety of agricultural products. The emergence and use of big data help to solve the problems of high cost, scattered information and incomplete industrial chain of quality and safety traceability of agricultural products and improve the efficiency and accuracy of the quality and safety traceability system of agricultural products. There are still some problems in the application of big data, such as weak pertinence. It is necessary to mine and use big data to realize the traceability of agricultural products.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"3 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":"126788012","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/ICCWAMTIP53232.2021.9673712
Mou Jianhua, Zheng Qiaoyan, He Guotian
IoT devices constitute the key infrastructure to support various important IoT applications. To ensure the high reliability of these devices and their generated data, a dual verification framework based on a trusted execution environment and Blockchain was proposed to verify the device identity and data authenticity in this paper. In addition, the security of the framework is also analyzed. The scheme provides a data verification reference for the expansion of the ecological application of the IoT.
{"title":"Authenticity Verification Scheme Based On Tee and Blockchain","authors":"Mou Jianhua, Zheng Qiaoyan, He Guotian","doi":"10.1109/ICCWAMTIP53232.2021.9673712","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9673712","url":null,"abstract":"IoT devices constitute the key infrastructure to support various important IoT applications. To ensure the high reliability of these devices and their generated data, a dual verification framework based on a trusted execution environment and Blockchain was proposed to verify the device identity and data authenticity in this paper. In addition, the security of the framework is also analyzed. The scheme provides a data verification reference for the expansion of the ecological application of the IoT.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"71 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":"127165938","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/ICCWAMTIP53232.2021.9674104
Liu Yaning, Wang Juan, Li Liangxiao, Ma Quan, Gong Xuepeng
This paper decomposes the multi-element DXF format design file, extracts the most important layer, block, entity element and the number of entities according to the elements, and constitutes the characteristics of the design file; Then, the extracted features are taken as the underlying leaves, and the Merkel tree based on MD5 algorithm is used to get the tamper proof code. By comparing the tamper resistant code, we can detect whether tampering occurs and locate the tampering location, and store the tamper resistant code and tampering location information on the chain, using the characteristics of the blockchain to ensure that they are not tampered and traceable.
{"title":"Research on Design Document Tampering Detection and Location Based on Blockchain Technology","authors":"Liu Yaning, Wang Juan, Li Liangxiao, Ma Quan, Gong Xuepeng","doi":"10.1109/ICCWAMTIP53232.2021.9674104","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674104","url":null,"abstract":"This paper decomposes the multi-element DXF format design file, extracts the most important layer, block, entity element and the number of entities according to the elements, and constitutes the characteristics of the design file; Then, the extracted features are taken as the underlying leaves, and the Merkel tree based on MD5 algorithm is used to get the tamper proof code. By comparing the tamper resistant code, we can detect whether tampering occurs and locate the tampering location, and store the tamper resistant code and tampering location information on the chain, using the characteristics of the blockchain to ensure that they are not tampered and traceable.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"43 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":"123759100","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/ICCWAMTIP53232.2021.9674177
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/ICCWAMTIP53232.2021.9674143
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/ICCWAMTIP53232.2021.9674061
Wang Jiao, Liao Jianqing
Blind identification of modulation and channel coding parameters is a very important research topic in civil-military communication systems. The traditional algorithm is mainly implemented in the way of hierarchical recognition, that is, modulation recognition of the signal first, then demodulation of the signal, and finally coding type recognition and parameter estimation of the demodulated information stream, so as to realize the joint recognition of modulation and coding. In this paper, we propose a deep learning (DL)-based joint recognition algorithm for modulation and coding, which can achieve the recognition of modulation type and coding parameters simultaneously without using additional demodulation algorithms. Simulation results show that the proposed method performs well for the recognition of various modulation and coding types under high signal-to-noise ratio (SNR) conditions.
{"title":"Joint Modulation and Coding Recognition Using Deep Learning","authors":"Wang Jiao, Liao Jianqing","doi":"10.1109/ICCWAMTIP53232.2021.9674061","DOIUrl":"https://doi.org/10.1109/ICCWAMTIP53232.2021.9674061","url":null,"abstract":"Blind identification of modulation and channel coding parameters is a very important research topic in civil-military communication systems. The traditional algorithm is mainly implemented in the way of hierarchical recognition, that is, modulation recognition of the signal first, then demodulation of the signal, and finally coding type recognition and parameter estimation of the demodulated information stream, so as to realize the joint recognition of modulation and coding. In this paper, we propose a deep learning (DL)-based joint recognition algorithm for modulation and coding, which can achieve the recognition of modulation type and coding parameters simultaneously without using additional demodulation algorithms. Simulation results show that the proposed method performs well for the recognition of various modulation and coding types under high signal-to-noise ratio (SNR) conditions.","PeriodicalId":358772,"journal":{"name":"2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)","volume":"14 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114060502","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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