Pub Date : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377432
Jaeyeon Jang, Byungjun Kim, Kveo Re Lee, J. Kim
This essay explores significant cultural differences on startup discourses between United States and China by demonstrating a big data analysis through Structural Topic Modeling. Results and findings were interpreted with Hofstede”s cultural dimensions, indicating that the conventional theory of social science was applicable when it comes to explaining technological subjects
{"title":"A Cross-Cultural Comparative Study on the Startup Discourse in 2000–2019 between United States and China","authors":"Jaeyeon Jang, Byungjun Kim, Kveo Re Lee, J. Kim","doi":"10.1109/IMCOM51814.2021.9377432","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377432","url":null,"abstract":"This essay explores significant cultural differences on startup discourses between United States and China by demonstrating a big data analysis through Structural Topic Modeling. Results and findings were interpreted with Hofstede”s cultural dimensions, indicating that the conventional theory of social science was applicable when it comes to explaining technological subjects","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130794983","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377369
Seiya Kobayashi, T. Miyazaki
This study proposes a network protocol that enables connecting Internet-of-Things (IoT) devices to IoT servers without modifying the original IoT service applications. Access proxies, introduced at the edge of an IoT network cloud, intercept packets flowing between the IoT devices and servers, and examine the credibility of each loT device in real time. After registering a new IoT device using an access proxy, the data produced by the IoT device or the server were significantly protected. Intended attacks and spoofing were also blocked at the access proxies. Therefore, IoT services are safe for use in the proposed network environment.
{"title":"Authentication and Trustful Communication Protocol for IoT Devices","authors":"Seiya Kobayashi, T. Miyazaki","doi":"10.1109/IMCOM51814.2021.9377369","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377369","url":null,"abstract":"This study proposes a network protocol that enables connecting Internet-of-Things (IoT) devices to IoT servers without modifying the original IoT service applications. Access proxies, introduced at the edge of an IoT network cloud, intercept packets flowing between the IoT devices and servers, and examine the credibility of each loT device in real time. After registering a new IoT device using an access proxy, the data produced by the IoT device or the server were significantly protected. Intended attacks and spoofing were also blocked at the access proxies. Therefore, IoT services are safe for use in the proposed network environment.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126656988","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377370
Sungjin Chun, C. Son, Hyunseung Choo
With the wide availability of historical data from baseball games, one of the most popular sports, high accurate winner prediction has become a significant target of statistical analysis and machine learning. However, existing techniques for a pre-game prediction yield poor accuracies due to the incomplete player lists given in starting lineups and substitutions occurring during the game. We exploit the capability of Long Short-Term Memory (LSTM) in identifying hidden patterns of time series data to propose inter-dependent LSTM baseball game prediction with only the starting lineup information. Particularly, we preprocess historical data to generate a pair of pre-game and post-game records for each baseball game. The pre-game record indicates the incomplete player lists given in starting lineups, and the post-game one contains the list of all players who participated in the game. The inter-dependent LSTM model exploits the dependencies of the pairs to predict a game result with only pre-game input. Our experiment results show that the proposed model achieves up to 12% higher accuracy than the existing ones.
{"title":"Inter-dependent LSTM: Baseball Game Prediction with Starting and Finishing Lineups","authors":"Sungjin Chun, C. Son, Hyunseung Choo","doi":"10.1109/IMCOM51814.2021.9377370","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377370","url":null,"abstract":"With the wide availability of historical data from baseball games, one of the most popular sports, high accurate winner prediction has become a significant target of statistical analysis and machine learning. However, existing techniques for a pre-game prediction yield poor accuracies due to the incomplete player lists given in starting lineups and substitutions occurring during the game. We exploit the capability of Long Short-Term Memory (LSTM) in identifying hidden patterns of time series data to propose inter-dependent LSTM baseball game prediction with only the starting lineup information. Particularly, we preprocess historical data to generate a pair of pre-game and post-game records for each baseball game. The pre-game record indicates the incomplete player lists given in starting lineups, and the post-game one contains the list of all players who participated in the game. The inter-dependent LSTM model exploits the dependencies of the pairs to predict a game result with only pre-game input. Our experiment results show that the proposed model achieves up to 12% higher accuracy than the existing ones.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126551830","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377417
Nader Bazyari, H. Sajedi
In this paper a new approach toward data processing is proposed that is inspired by all the prominent data clustering algorithms proposed by scholars. The main motif that drove this approach was to mix hierarchical clustering methods with Gaussian Estimators as to find a hidden structure in data that was not reachable using traditional bandwidth estimators. Instead the criteria for assessing similarity among data was the principles for Newtonian Physics.
{"title":"A Reconcile of Density Based and Hierarchical Clustering Based on the Laws of Physics","authors":"Nader Bazyari, H. Sajedi","doi":"10.1109/IMCOM51814.2021.9377417","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377417","url":null,"abstract":"In this paper a new approach toward data processing is proposed that is inspired by all the prominent data clustering algorithms proposed by scholars. The main motif that drove this approach was to mix hierarchical clustering methods with Gaussian Estimators as to find a hidden structure in data that was not reachable using traditional bandwidth estimators. Instead the criteria for assessing similarity among data was the principles for Newtonian Physics.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123053615","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377383
Jhilakshi Sharma, Donghyun Kim, Ahyoung Lee, Daehee Seo
During recent years, mobile edge computing is getting much attention from both academia and industry. However, many found that this emerging architecture needs a proper data privacy protection mechanism at mobile edge nodes against unintended data use by authorized data analysts. Due to the reason, the development of a proper lightweight privacy-preserving data analysis mechanism is of great urgency. Thus, we propose DP-FCNN, a light-weight Differential Privacy (DP) framework using Fuzzy Convolution Neural Network (FCNN) with Laplace Mechanism which injects noise into the personal data before uploading data from users generating the data into the storage so that the data is still useful but data privacy can be properly protected against unauthorized data analysis attempt. We implemented the proposed framework, and tested its performance in terms of scalability, processing time, and accuracy. The result shows that the proposed framework is very practical.
{"title":"Differential Privacy using Fuzzy Convolution Neural Network (DP-FCNN) with Laplace Mechanism and Authenticated Access in Edge Computing","authors":"Jhilakshi Sharma, Donghyun Kim, Ahyoung Lee, Daehee Seo","doi":"10.1109/IMCOM51814.2021.9377383","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377383","url":null,"abstract":"During recent years, mobile edge computing is getting much attention from both academia and industry. However, many found that this emerging architecture needs a proper data privacy protection mechanism at mobile edge nodes against unintended data use by authorized data analysts. Due to the reason, the development of a proper lightweight privacy-preserving data analysis mechanism is of great urgency. Thus, we propose DP-FCNN, a light-weight Differential Privacy (DP) framework using Fuzzy Convolution Neural Network (FCNN) with Laplace Mechanism which injects noise into the personal data before uploading data from users generating the data into the storage so that the data is still useful but data privacy can be properly protected against unauthorized data analysis attempt. We implemented the proposed framework, and tested its performance in terms of scalability, processing time, and accuracy. The result shows that the proposed framework is very practical.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116778646","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377395
Tao Wang, Yaokai Feng, K. Sakurai
In recent years, the DDoS (Distributed Denial of Service) attack continues to be one of the most dangerous threats even in the SDN (Software Defined Networking) environment. Many approaches have been proposed to deal with the DDoS attacks in the SDN environment. Among those approaches, the two-step detection, in which a trigger mechanism is added before the detection algorithm is called, is gaining more and more attention. In other words, it is the trigger, not the resource-consuming detection algorithm, that constantly monitors network traffic. Thus, the detection algorithm is only called when it is triggered. However, in the existing two-step methods, the trigger uses a static threshold to determine whether or not to start the detection process. In practice, it is difficult to determine an appropriate threshold, and the threshold has a decisive effect on the frequency of the detection process being called and ultimately, it impacts detection performance. In this paper, we propose a self-feedback dynamic thresholding system in which the threshold used in the trigger is dynamically adjusted based on the previous results of trigger and detection. Experimental results and our discussion show that our proposal significantly reduces the number of calls to the resource-consuming detection algorithm with no sacrifice of detection result.
{"title":"Improving the Two-stage Detection of Cyberattacks in SDN Environment Using Dynamic Thresholding","authors":"Tao Wang, Yaokai Feng, K. Sakurai","doi":"10.1109/IMCOM51814.2021.9377395","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377395","url":null,"abstract":"In recent years, the DDoS (Distributed Denial of Service) attack continues to be one of the most dangerous threats even in the SDN (Software Defined Networking) environment. Many approaches have been proposed to deal with the DDoS attacks in the SDN environment. Among those approaches, the two-step detection, in which a trigger mechanism is added before the detection algorithm is called, is gaining more and more attention. In other words, it is the trigger, not the resource-consuming detection algorithm, that constantly monitors network traffic. Thus, the detection algorithm is only called when it is triggered. However, in the existing two-step methods, the trigger uses a static threshold to determine whether or not to start the detection process. In practice, it is difficult to determine an appropriate threshold, and the threshold has a decisive effect on the frequency of the detection process being called and ultimately, it impacts detection performance. In this paper, we propose a self-feedback dynamic thresholding system in which the threshold used in the trigger is dynamically adjusted based on the previous results of trigger and detection. Experimental results and our discussion show that our proposal significantly reduces the number of calls to the resource-consuming detection algorithm with no sacrifice of detection result.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"357 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122806141","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377390
ShaoPeng Che, Dongyan Nan, Pim Kamphuis, Xinvu Jin, J. Kim
The COVID-19 pandemic has had a significant impact on tourism-driven industries worldwide. As East Asia is gaining control in the fight against the coronavirus, countries in this region started easing their travel restrictions. Tourist-oriented retail stores are slowly preparing to receive international visitors again, and to be well adjusted, it is crucial to understand foreign customers' perceptions. From the seven most popular tourism platforms in China, we collected comments about Lotte Young Plaza in South Korea and applied semantic network analysis to understand its perception among young Chinese customers. Based on this analysis, we divided the conclusions into 6 aspects: People whose main purpose is to buy clothes, people who have communication needs in Chinese, the Lotte Department Store's driving force on customer flow to Lotte Young Plaza, young people, features of Lotte Young Plaza (mouthwash) and cosmetics.
{"title":"A Cluster Analysis of Lotte Young Plaza Using Semantic Network Analysis Method","authors":"ShaoPeng Che, Dongyan Nan, Pim Kamphuis, Xinvu Jin, J. Kim","doi":"10.1109/IMCOM51814.2021.9377390","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377390","url":null,"abstract":"The COVID-19 pandemic has had a significant impact on tourism-driven industries worldwide. As East Asia is gaining control in the fight against the coronavirus, countries in this region started easing their travel restrictions. Tourist-oriented retail stores are slowly preparing to receive international visitors again, and to be well adjusted, it is crucial to understand foreign customers' perceptions. From the seven most popular tourism platforms in China, we collected comments about Lotte Young Plaza in South Korea and applied semantic network analysis to understand its perception among young Chinese customers. Based on this analysis, we divided the conclusions into 6 aspects: People whose main purpose is to buy clothes, people who have communication needs in Chinese, the Lotte Department Store's driving force on customer flow to Lotte Young Plaza, young people, features of Lotte Young Plaza (mouthwash) and cosmetics.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131123683","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377406
Mingue Song, Yanggon Kim
Medical image analysis is consistently being researched in the computer vision in that it captures potential symptoms and enables more delicate diagnosis of patients. Based on the development of medical equipment such as optical coherence tomography(OCT) and magnetic resonance imaging(MRI), it is possible to analyze medical data with clearer and higher resolution than before. However, there are still many data that have limitations in manually diagnosis by human. Moreover, identifying the extent of the damaged retinal layer also remains one of the most challenging tasks since the damaged layer not only contains too many invisible layers, but it is too small. Normal OCT data has smooth layers while age-related macular degeneration(AMD) or diabetic macular edema(DME), which are classified as abnormal, has layers that are damaged by bleeding. The precise regional classification is required for the diagnosis and prescription of the damaged layers and a new approach to effectively training an irregular layer of abnormal data is also needed. Hence, this paper proposes an OCT data manipulation method as a preprocessing step to improve training boundaries of regional layers. The preprocessed data were generated by manual range using the proposed method and applied to the encoder-decoder networks, SegNet and Unet. The experiment shows that the preprocessed datasets were trained much faster than the original and the optimized range was also confirmed through comparison the results of preprocessed dataset by each range.
{"title":"Manipulating Retinal OCT data for Image Segmentation based on Encoder-Decoder Network","authors":"Mingue Song, Yanggon Kim","doi":"10.1109/IMCOM51814.2021.9377406","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377406","url":null,"abstract":"Medical image analysis is consistently being researched in the computer vision in that it captures potential symptoms and enables more delicate diagnosis of patients. Based on the development of medical equipment such as optical coherence tomography(OCT) and magnetic resonance imaging(MRI), it is possible to analyze medical data with clearer and higher resolution than before. However, there are still many data that have limitations in manually diagnosis by human. Moreover, identifying the extent of the damaged retinal layer also remains one of the most challenging tasks since the damaged layer not only contains too many invisible layers, but it is too small. Normal OCT data has smooth layers while age-related macular degeneration(AMD) or diabetic macular edema(DME), which are classified as abnormal, has layers that are damaged by bleeding. The precise regional classification is required for the diagnosis and prescription of the damaged layers and a new approach to effectively training an irregular layer of abnormal data is also needed. Hence, this paper proposes an OCT data manipulation method as a preprocessing step to improve training boundaries of regional layers. The preprocessed data were generated by manual range using the proposed method and applied to the encoder-decoder networks, SegNet and Unet. The experiment shows that the preprocessed datasets were trained much faster than the original and the optimized range was also confirmed through comparison the results of preprocessed dataset by each range.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134584936","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377351
An Le Ha, Trinh Van Chien, T. Nguyen, Wan Choi, V. Nguyen
Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for 5G-and-beyond networks. In this paper, we propose a new channel estimation method with the assistance of deep learning in order to support the least squares estimation, which is a low-cost method but having relatively high channel estimation errors. This goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile used for simulations in the 5G networks under the severity of Doppler effects. Numerical results demonstrate the superiority of the proposed deep learning-assisted channel estimation method over the other channel estimation methods in previous works in terms of mean square errors.
{"title":"Deep Learning-Aided 5G Channel Estimation","authors":"An Le Ha, Trinh Van Chien, T. Nguyen, Wan Choi, V. Nguyen","doi":"10.1109/IMCOM51814.2021.9377351","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377351","url":null,"abstract":"Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for 5G-and-beyond networks. In this paper, we propose a new channel estimation method with the assistance of deep learning in order to support the least squares estimation, which is a low-cost method but having relatively high channel estimation errors. This goal is achieved by utilizing a MIMO (multiple-input multiple-output) system with a multi-path channel profile used for simulations in the 5G networks under the severity of Doppler effects. Numerical results demonstrate the superiority of the proposed deep learning-assisted channel estimation method over the other channel estimation methods in previous works in terms of mean square errors.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"35 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132151271","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 : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377367
Koki Ohtsuka, D. Kitayama, K. Sumiya, Kazushi Fujita, Goto Shin, Isshu Munemasa
There have been many studies on automatic generation of artificial maps, but few have focused on the objects displayed on artificial maps. Therefore, in this research, we analyzed an object extraction method for automatically generating artificial map. In this research, objects are extracted using Biased Page Rank. Biased Page Rank is a method for improving the quality of search results by changing the rank source according to the interests and interests of individual users. By using this method for object extraction, it is possible to display the objects with a high evaluation value among the objects existing in a specific area on artificial map.
{"title":"A Geographical Object Extraction Method Using User's Trajectories for Generating Artificial Maps","authors":"Koki Ohtsuka, D. Kitayama, K. Sumiya, Kazushi Fujita, Goto Shin, Isshu Munemasa","doi":"10.1109/IMCOM51814.2021.9377367","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377367","url":null,"abstract":"There have been many studies on automatic generation of artificial maps, but few have focused on the objects displayed on artificial maps. Therefore, in this research, we analyzed an object extraction method for automatically generating artificial map. In this research, objects are extracted using Biased Page Rank. Biased Page Rank is a method for improving the quality of search results by changing the rank source according to the interests and interests of individual users. By using this method for object extraction, it is possible to display the objects with a high evaluation value among the objects existing in a specific area on artificial map.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"136 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114998266","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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