Pub Date : 2022-10-26DOI: 10.4108/eetsis.v10i1.2577
Haiyan Peng, Min Zhang
INTRODUCTION: In the online English learning platform, noise interference makes people unable to hear the content of English teaching clearly, which leads to a great reduction in the efficiency of English learning. In order to improve the voice quality of online English learning platform, the speech enhancement method of the online English learning platform based on deep neural network is studied.OBJECTIVES: This paper proposes a deep neural network-based speech enhancement method for online English learning platform in order to obtain more desirable results in the application of speech quality optimization.METHODS: The optimized VMD (Variable Modal Decomposition) algorithm is combined with the Moth-flame optimization algorithm to find the optimal solution to obtain the optimal value of the decomposition mode number and the penalty factor of the variational modal decomposition algorithm, and then the optimized variational modal decomposition algorithm is used to filter the noise information in the speech signal; Through the network speech enhancement method based on deep neural network learning, the denoised speech signal is taken as the enhancement target to achieve speech enhancement.RESULTS: The research results show that the method not only has significant denoising ability for speech signal, but also after this method is used, PESQ value of speech quality perception evaluation of speech signal is greater than 4.0dB, the spectral features are prominent, and the speech quality is improved.CONCLUSION: Through experiments from three perspectives: speech signal denoising, speech quality enhancement and speech spectrum information, the usability of the method in this paper is confirmed.
{"title":"Application of Deep Neural Network Algorithm in Speech Enhancement of Online English Learning Platform","authors":"Haiyan Peng, Min Zhang","doi":"10.4108/eetsis.v10i1.2577","DOIUrl":"https://doi.org/10.4108/eetsis.v10i1.2577","url":null,"abstract":"INTRODUCTION: In the online English learning platform, noise interference makes people unable to hear the content of English teaching clearly, which leads to a great reduction in the efficiency of English learning. In order to improve the voice quality of online English learning platform, the speech enhancement method of the online English learning platform based on deep neural network is studied.OBJECTIVES: This paper proposes a deep neural network-based speech enhancement method for online English learning platform in order to obtain more desirable results in the application of speech quality optimization.METHODS: The optimized VMD (Variable Modal Decomposition) algorithm is combined with the Moth-flame optimization algorithm to find the optimal solution to obtain the optimal value of the decomposition mode number and the penalty factor of the variational modal decomposition algorithm, and then the optimized variational modal decomposition algorithm is used to filter the noise information in the speech signal; Through the network speech enhancement method based on deep neural network learning, the denoised speech signal is taken as the enhancement target to achieve speech enhancement.RESULTS: The research results show that the method not only has significant denoising ability for speech signal, but also after this method is used, PESQ value of speech quality perception evaluation of speech signal is greater than 4.0dB, the spectral features are prominent, and the speech quality is improved.CONCLUSION: Through experiments from three perspectives: speech signal denoising, speech quality enhancement and speech spectrum information, the usability of the method in this paper is confirmed. ","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"217 1","pages":"e10"},"PeriodicalIF":1.3,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76430709","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 : 2022-10-18DOI: 10.4108/eetsis.v9i6.2591
Yong Gong, Gautam Srivastava
INTRODUCTION: There is occlusion interference in the multi-target visual tracking process of basketball video images, which leads to poor accuracy of multi-target trajectory tracking. This paper studies the multi-target trajectory tracking method in multi-frame video images of basketball sports based on deep learning. OBJECTIVES: Aiming at the problem of target occlusion in the tracking process and the problem of trajectory tracking anomaly caused by target occlusion, a modified algorithm is proposed. METHODS: The method is divided into two parts: detection and tracking. In the detection part, the YOLOv3 algorithm in deep learning technology is used to detect each target in the video, and the original YOLOv3 backbone network Darknet-53 is replaced by the lightweight backbone network MobileNetV2 to extract the target features. RESULTS: Based on the target detection results, the Kalman filter is used to predict the next position and bounding box size of the target to obtain the target trajectory prediction results according to the current target position, then a hierarchical data association algorithm is designed, and multi-target tracking of the same category is completed based on the target appearance feature similarity and feature similarity. CONCLUSION: The experimental results show that the method can accurately detect the targets in multi-frame video images in basketball sports and obtain high-precision target trajectory tracking results.
{"title":"Multi-target trajectory tracking in multi-frame video images of basketball sports based on deep learning","authors":"Yong Gong, Gautam Srivastava","doi":"10.4108/eetsis.v9i6.2591","DOIUrl":"https://doi.org/10.4108/eetsis.v9i6.2591","url":null,"abstract":"INTRODUCTION: There is occlusion interference in the multi-target visual tracking process of basketball video images, which leads to poor accuracy of multi-target trajectory tracking. This paper studies the multi-target trajectory tracking method in multi-frame video images of basketball sports based on deep learning.\u0000OBJECTIVES: Aiming at the problem of target occlusion in the tracking process and the problem of trajectory tracking anomaly caused by target occlusion, a modified algorithm is proposed.\u0000METHODS: The method is divided into two parts: detection and tracking. In the detection part, the YOLOv3 algorithm in deep learning technology is used to detect each target in the video, and the original YOLOv3 backbone network Darknet-53 is replaced by the lightweight backbone network MobileNetV2 to extract the target features.\u0000RESULTS: Based on the target detection results, the Kalman filter is used to predict the next position and bounding box size of the target to obtain the target trajectory prediction results according to the current target position, then a hierarchical data association algorithm is designed, and multi-target tracking of the same category is completed based on the target appearance feature similarity and feature similarity.\u0000CONCLUSION: The experimental results show that the method can accurately detect the targets in multi-frame video images in basketball sports and obtain high-precision target trajectory tracking results.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"129 1","pages":"e9"},"PeriodicalIF":1.3,"publicationDate":"2022-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75702285","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 : 2022-10-12DOI: 10.4108/eetsis.v9i6.2642
Yuzhong Zhou, Zhèng-Hóng Lin, Yuan La, Junkai Huang, Xin Wang
The transformer can convert one kind of electric energy such as AC current and AC voltage into another kind of electric energy with the same frequency. Knowledge graph (KG) can describe various entities and concepts in the real world and their relationships, and it can be considered as a semantic network for power system transformer. Hence, it is of vital importance to analyze and design the power system transformer standard based on the knowledge graph. To this end, we firstly examine the power system transformer with one KG node and one eavesdropper E, where the eavesdropper E can overhear the network from the source, which may cause physical-layer secure issue and an outage probability event. To deal with the issue, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. We further investigate the power system transformer with multiple KG nodes which can help strengthen the system security and reliability. For such a system, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. Finally, we give some simulations to analyze the impact of secure transformer standard on the power system, and verify the accuracy of our proposed analytical expression for the the power system transformer standard based on the knowledge graph.
{"title":"Analysis and Design of Power System Transformer Standard Based on Knowledge Graph","authors":"Yuzhong Zhou, Zhèng-Hóng Lin, Yuan La, Junkai Huang, Xin Wang","doi":"10.4108/eetsis.v9i6.2642","DOIUrl":"https://doi.org/10.4108/eetsis.v9i6.2642","url":null,"abstract":"The transformer can convert one kind of electric energy such as AC current and AC voltage into another kind of electric energy with the same frequency. Knowledge graph (KG) can describe various entities and concepts in the real world and their relationships, and it can be considered as a semantic network for power system transformer. Hence, it is of vital importance to analyze and design the power system transformer standard based on the knowledge graph. To this end, we firstly examine the power system transformer with one KG node and one eavesdropper E, where the eavesdropper E can overhear the network from the source, which may cause physical-layer secure issue and an outage probability event. To deal with the issue, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. We further investigate the power system transformer with multiple KG nodes which can help strengthen the system security and reliability. For such a system, we analyze and design the system secure performance under the eavesdropper and define the outage probability for system security, by providing analytical expression of outage probability. Finally, we give some simulations to analyze the impact of secure transformer standard on the power system, and verify the accuracy of our proposed analytical expression for the the power system transformer standard based on the knowledge graph.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"3 1","pages":"e6"},"PeriodicalIF":1.3,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90986748","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 : 2022-10-12DOI: 10.4108/eetsis.v9i6.2634
Zhèng-Hóng Lin, Jiaxin Lin
With the rapid development of information technology, power system has been developed and applied rapidly. In the power system, fault detection is very important and is one of the key means to ensure the operation of power system. How to effectively improve the ability of fault detection is the most important issue in the research of power system. Traditional fault detection mainly relies on manual daily inspection, and power must be cut off during maintenance, which affects the normal operation of the power grid. In case of emergency, the equipment can not be powered off, which may lead to missed test and bury potential safety hazards. To solve these issues, in this paper, we study the knowledge management based power system by employing the deep learning technique. Specifically, we firstly introduce the data augmentation in the knowledge management based power system and the associated activated functions. We then develop the deep network architecture to extract the local spatial features among the data of the knowledge management based power system. We further provide several training strategies for the data classification in the knowledge management based power system, where the cross entropy based loss function is used. Finally, some experimental results are demonstrated to show the effectiveness of the proposed studies for the knowledge management based power system.
{"title":"Research on Knowledge Management of Novel Power System Based on Deep Learning","authors":"Zhèng-Hóng Lin, Jiaxin Lin","doi":"10.4108/eetsis.v9i6.2634","DOIUrl":"https://doi.org/10.4108/eetsis.v9i6.2634","url":null,"abstract":"With the rapid development of information technology, power system has been developed and applied rapidly. In the power system, fault detection is very important and is one of the key means to ensure the operation of power system. How to effectively improve the ability of fault detection is the most important issue in the research of power system. Traditional fault detection mainly relies on manual daily inspection, and power must be cut off during maintenance, which affects the normal operation of the power grid. In case of emergency, the equipment can not be powered off, which may lead to missed test and bury potential safety hazards. To solve these issues, in this paper, we study the knowledge management based power system by employing the deep learning technique. Specifically, we firstly introduce the data augmentation in the knowledge management based power system and the associated activated functions. We then develop the deep network architecture to extract the local spatial features among the data of the knowledge management based power system. We further provide several training strategies for the data classification in the knowledge management based power system, where the cross entropy based loss function is used. Finally, some experimental results are demonstrated to show the effectiveness of the proposed studies for the knowledge management based power system.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"56 1","pages":"e7"},"PeriodicalIF":1.3,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88890015","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 development of information technology has changed the mode of communication of social information, and this change has put forward new requirements on the contents, methods and even objects of information science research. Knowledge service in the information service process can extract knowledge and information content from various explicit and implicit knowledge resources according to people’s needs, build knowledge networks, and provide knowledge content or solutions for users’ problems. Hence, it is very important to investigate how to analyze and design the advanced standard knowledge service system based on deep learning. To this end, we firstly introduce the typical deep learning networks of convolutional neural network (CNN) for the knowledge service system, and then employ the CNN to implement the knowledge classification based on deep learning. Finally, some simulation results on the knowledge service system are presented to validate the proposed studies in this paper.
{"title":"Analysis and Design of Standard Knowledge Service System based on Deep Learning","authors":"Yuzhong Zhou, Zhèng-Hóng Lin, Liang-Jung Tu, Junkai Huang, Zifeng Zhang","doi":"10.4108/eetsis.v9i6.2637","DOIUrl":"https://doi.org/10.4108/eetsis.v9i6.2637","url":null,"abstract":"The development of information technology has changed the mode of communication of social information, and this change has put forward new requirements on the contents, methods and even objects of information science research. Knowledge service in the information service process can extract knowledge and information content from various explicit and implicit knowledge resources according to people’s needs, build knowledge networks, and provide knowledge content or solutions for users’ problems. Hence, it is very important to investigate how to analyze and design the advanced standard knowledge service system based on deep learning. To this end, we firstly introduce the typical deep learning networks of convolutional neural network (CNN) for the knowledge service system, and then employ the CNN to implement the knowledge classification based on deep learning. Finally, some simulation results on the knowledge service system are presented to validate the proposed studies in this paper.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"14 1","pages":"e8"},"PeriodicalIF":1.3,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82170121","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 : 2022-10-05DOI: 10.4108/eetsis.v9i5.1567
Shuhui Bi, Zhihao Li, Mackenzie Brown, Lei Wang, Yuan Xu
Smart storage is widely used for its efficient storage and applications. For making dynamic decisions when robots conflict and eliminating robot conflicts and improving efficiency from a global perspective, path-planning Algorithm will be analyzed and improved by integrating dynamic weighted and heat-map algorithm based on the scalable information of multi-robot in this paper. Firstly, a small storage grid model applicable to a variety of storage modes is established. Second, in order to solve the frontal collision problem of robots, an improved reservation table is established, which greatly reduces the storage space occupied by the reservation table while improving the operation efficiency; the A* algorithm is improved to achieve the purpose of avoiding vertex conflict and edge conflict at the same time; dynamic weighting table is added to solve the multi-robot driving strategy of intersection conflict and ensure that the most urgent goods are out of the warehouse firstly; the heat map algorithm is appended to reasonably allocate tasks, avoiding congested areas and realizing the dynamic assignment of tasks. Finally, the simulation was done by the proposed path planning method, the average transportation time was reduced by 14.97% comparing with the traditional path algorithm.
{"title":"Dynamic Weighted and Heat-map Integrated Scalable Information Path-planning Algorithm","authors":"Shuhui Bi, Zhihao Li, Mackenzie Brown, Lei Wang, Yuan Xu","doi":"10.4108/eetsis.v9i5.1567","DOIUrl":"https://doi.org/10.4108/eetsis.v9i5.1567","url":null,"abstract":"Smart storage is widely used for its efficient storage and applications. For making dynamic decisions when robots conflict and eliminating robot conflicts and improving efficiency from a global perspective, path-planning Algorithm will be analyzed and improved by integrating dynamic weighted and heat-map algorithm based on the scalable information of multi-robot in this paper. Firstly, a small storage grid model applicable to a variety of storage modes is established. Second, in order to solve the frontal collision problem of robots, an improved reservation table is established, which greatly reduces the storage space occupied by the reservation table while improving the operation efficiency; the A* algorithm is improved to achieve the purpose of avoiding vertex conflict and edge conflict at the same time; dynamic weighting table is added to solve the multi-robot driving strategy of intersection conflict and ensure that the most urgent goods are out of the warehouse firstly; the heat map algorithm is appended to reasonably allocate tasks, avoiding congested areas and realizing the dynamic assignment of tasks. Finally, the simulation was done by the proposed path planning method, the average transportation time was reduced by 14.97% comparing with the traditional path algorithm.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"36 1","pages":"e5"},"PeriodicalIF":1.3,"publicationDate":"2022-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76437124","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 : 2022-09-29DOI: 10.4108/eetsis.v9i6.1747
Ziquan Zhu, Shuihua Wang
INTRODUCTION: Breast cancer is one of the most common malignant tumors in women, and the incidence rate is increasing year by year. Women in every country in the world may develop breast cancer at any age after puberty. The cause of breast cancer is not fully understood. At present, the main methods of breast cancer detection are inefficient. Researchers are trying to use computer technology to detect breast cancer. But there are some still limitations. METHODS: We propose a network (ODET) to detect breast cancer based on ultrasound images. In this paper, we use ResNet50 as the backbone model. We make some modifications to the backbone model by deep ELM-based transfer learning. After these modifications, the network is named DET. However, DET still has some shortcomings because the parameters in DET are randomly assigned and will not change in the experiment. In this case, we select BA to optimize DET. The optimized DET is named ODET. RESULTS: The proposed ODET gets the F1-score (F1), precision (PRE), specificity (SPE), sensitivity (SEN), and accuracy (ACC) are 93.16%±1.12%, 93.28%±1.36%, 98.63%±0.31%, 93.96%±1.85%, and 97.84%±0.37%, respectively. CONCLUSION: It proves that the proposed ODET is an effective method for breast cancer detection.
{"title":"ODET: Optimized Deep ELM-based Transfer Learning for Breast Cancer Explainable Detection","authors":"Ziquan Zhu, Shuihua Wang","doi":"10.4108/eetsis.v9i6.1747","DOIUrl":"https://doi.org/10.4108/eetsis.v9i6.1747","url":null,"abstract":"INTRODUCTION: Breast cancer is one of the most common malignant tumors in women, and the incidence rate is increasing year by year. Women in every country in the world may develop breast cancer at any age after puberty. The cause of breast cancer is not fully understood. At present, the main methods of breast cancer detection are inefficient. Researchers are trying to use computer technology to detect breast cancer. But there are some still limitations.\u0000METHODS: We propose a network (ODET) to detect breast cancer based on ultrasound images. In this paper, we use ResNet50 as the backbone model. We make some modifications to the backbone model by deep ELM-based transfer learning. After these modifications, the network is named DET. However, DET still has some shortcomings because the parameters in DET are randomly assigned and will not change in the experiment. In this case, we select BA to optimize DET. The optimized DET is named ODET.\u0000RESULTS: The proposed ODET gets the F1-score (F1), precision (PRE), specificity (SPE), sensitivity (SEN), and accuracy (ACC) are 93.16%±1.12%, 93.28%±1.36%, 98.63%±0.31%, 93.96%±1.85%, and 97.84%±0.37%, respectively.\u0000CONCLUSION: It proves that the proposed ODET is an effective method for breast cancer detection.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"36 1","pages":"e4"},"PeriodicalIF":1.3,"publicationDate":"2022-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83259896","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 : 2022-09-20DOI: 10.4108/eetsis.v9i6.2713
Soumaya El Emrani, M. Palomo-duarte, José Miguel Mota, J. Dodero
This paper describes the types of MOOC considered by researchers, and highlights the latter’s focus on Connectivist MOOC. In addition, it analyses MOOC methodologies, and learners’ interest in MOOC based on the concepts of adaptability, connectivism, and socio-constructivism. This is to address the high dropout rate issue on MOOC platforms. The main objective of this work is to review the empirical results reported in these studies. To reach this goal, a Systematic Literature Review of 798 papers was carried out from 2013 until April 2021, where 446 papers were selected as primary studies. The results obtained from the classification and the analysis of the collected data confirmed the importance of continuing research in the field. Based on the concepts of socio-constructivism and adaptability, the objective is to provide an adaptive cMOOC for the profile and the needs of each learner; blending learning styles and pedagogical models with machine learning technologies.
{"title":"E-Learning through an Adaptive cMOOC: Is it Worthy of Further Research?","authors":"Soumaya El Emrani, M. Palomo-duarte, José Miguel Mota, J. Dodero","doi":"10.4108/eetsis.v9i6.2713","DOIUrl":"https://doi.org/10.4108/eetsis.v9i6.2713","url":null,"abstract":"This paper describes the types of MOOC considered by researchers, and highlights the latter’s focus on Connectivist MOOC. In addition, it analyses MOOC methodologies, and learners’ interest in MOOC based on the concepts of adaptability, connectivism, and socio-constructivism. This is to address the high dropout rate issue on MOOC platforms. The main objective of this work is to review the empirical results reported in these studies. To reach this goal, a Systematic Literature Review of 798 papers was carried out from 2013 until April 2021, where 446 papers were selected as primary studies. The results obtained from the classification and the analysis of the collected data confirmed the importance of continuing research in the field. Based on the concepts of socio-constructivism and adaptability, the objective is to provide an adaptive cMOOC for the profile and the needs of each learner; blending learning styles and pedagogical models with machine learning technologies.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"10 1","pages":"e10"},"PeriodicalIF":1.3,"publicationDate":"2022-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84654583","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 : 2022-09-13DOI: 10.4108/eetsis.v9i6.2419
Yajuan Tang, Shiwei Lai, Zichao Zhao, Yanyi Rao, Wen Zhou, Fusheng Zhu, Liming Chen, Dan Deng, J. Wang, Tao Cui, Yuwei Zhang, Jun Liu, Di Wu, Huan-guang Huang, Xuan-Yue Zhou, Weishi Zhou, Zhao Wang, Kai Chen, C. Li, Yun Li, Kaimeno Dube, Abbarbas Muazu, Nakilavai Rono, Suili Feng, J. Qin, Haige Xiang, Zhigang Cao, Lieguang Zeng, Zhixing Yang, Zhi Wang, Yan Xu, Xiaosheng Lin, Zizhi Wang, Yu Zhang, B. Lu, Wanxin Zou
Currently, massive data communication and computing pose a severe challenge on existing wireless network architecture, from various aspects such as data rate, latency, energy consumption and pricing. Hence, it is of vital importance to investigate active wireless transmission for wireless networks. To this end, we first overview the data rate of wireless active transmission. We then overview the latency of wireless active transmission, which is particularly important for the applications of monitoring services. We further overview the spectral efficiency of the active transmission, which is particularly important for the battery-limited Internet of Things (IoT) networks. After these overviews, we give several critical challenges on the active transmission, and we finally present feasible solutions to meet these challenges. The work in this paper can serve as an important reference to the wireless networks and IoT networks.
{"title":"An Overview on Active Transmission Techniques for Wireless Scalable Networks","authors":"Yajuan Tang, Shiwei Lai, Zichao Zhao, Yanyi Rao, Wen Zhou, Fusheng Zhu, Liming Chen, Dan Deng, J. Wang, Tao Cui, Yuwei Zhang, Jun Liu, Di Wu, Huan-guang Huang, Xuan-Yue Zhou, Weishi Zhou, Zhao Wang, Kai Chen, C. Li, Yun Li, Kaimeno Dube, Abbarbas Muazu, Nakilavai Rono, Suili Feng, J. Qin, Haige Xiang, Zhigang Cao, Lieguang Zeng, Zhixing Yang, Zhi Wang, Yan Xu, Xiaosheng Lin, Zizhi Wang, Yu Zhang, B. Lu, Wanxin Zou","doi":"10.4108/eetsis.v9i6.2419","DOIUrl":"https://doi.org/10.4108/eetsis.v9i6.2419","url":null,"abstract":"Currently, massive data communication and computing pose a severe challenge on existing wireless network architecture, from various aspects such as data rate, latency, energy consumption and pricing. Hence, it is of vital importance to investigate active wireless transmission for wireless networks. To this end, we first overview the data rate of wireless active transmission. We then overview the latency of wireless active transmission, which is particularly important for the applications of monitoring services. We further overview the spectral efficiency of the active transmission, which is particularly important for the battery-limited Internet of Things (IoT) networks. After these overviews, we give several critical challenges on the active transmission, and we finally present feasible solutions to meet these challenges. The work in this paper can serve as an important reference to the wireless networks and IoT networks.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"33 1","pages":"e3"},"PeriodicalIF":1.3,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82847546","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 : 2022-09-07DOI: 10.4108/eetsis.v5i16.1560
G. Sahani, Chirag S. Thaker, Sanjay M. Shah
Attribute-Based Access Control (ABAC) is an emerging access control model. It is the more flexible, scalable, and most suitable access control model for today’s large-scale, distributed, and open application environments. It has become an emerging research area nowadays. However, Role-Based Access Control (RBAC) has been the most widely used and general access control model so far. It is simple in administration and policy definition. But user-to-role assignment process of RBAC makes it non-scalable for large-scale organizations with a large number of users. To scale up the growing organization, RBAC needs to be transformed into ABAC. Transforming existing RBAC systems into ABAC is complicated and time-consuming. In this paper, we present a supervised machine learning-based approach to extract attribute-based conditions from the existing RBAC system to construct ABAC rules at the primary level and simplify the process of the transforming RBAC system to ABAC.
{"title":"Supervised Learning-Based Approach Mining ABAC Rules from Existing RBAC Enabled Systems","authors":"G. Sahani, Chirag S. Thaker, Sanjay M. Shah","doi":"10.4108/eetsis.v5i16.1560","DOIUrl":"https://doi.org/10.4108/eetsis.v5i16.1560","url":null,"abstract":"Attribute-Based Access Control (ABAC) is an emerging access control model. It is the more flexible, scalable, and most suitable access control model for today’s large-scale, distributed, and open application environments. It has become an emerging research area nowadays. However, Role-Based Access Control (RBAC) has been the most widely used and general access control model so far. It is simple in administration and policy definition. But user-to-role assignment process of RBAC makes it non-scalable for large-scale organizations with a large number of users. To scale up the growing organization, RBAC needs to be transformed into ABAC. Transforming existing RBAC systems into ABAC is complicated and time-consuming. In this paper, we present a supervised machine learning-based approach to extract attribute-based conditions from the existing RBAC system to construct ABAC rules at the primary level and simplify the process of the transforming RBAC system to ABAC.","PeriodicalId":43034,"journal":{"name":"EAI Endorsed Transactions on Scalable Information Systems","volume":"10 1","pages":"e9"},"PeriodicalIF":1.3,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91368362","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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