Pub Date : 2021-01-04DOI: 10.1109/IMCOM51814.2021.9377327
M. Hossain, Tangina Sultana, Md. Alamgir Hossain, E. Huh
Multi-Access Edge Computing (MEC) is a promising candidate to handle the enormous computation demands of many emerging applications and the ever-growing user's quality-of-service (QoS) requirements. However, due to the limitation of computing resource capacity of a distinct edge server, most of the previous studies have proposed a collaboration approach. For collaboration, they considered vertical offloading between mobile with edge computing or edge with cloud computing for taking the advantages of both these technologies. Therefore, these approaches ignored the neighboring edge server having spare computing resources in the same tier. This paper thus proposes edge orchestration based computation peer offloading (EOPO) scheme among the edge servers in the same tier. The main objective is to share the computation resources among the edge servers. Our proposed approach selects the optimal computational node for task offloading based on fuzzy rules. Simulation results corroborate that fuzzy decision based computation peer offloading scheme significantly improves the performance of edge computing. Our proposed EOPO scheme outperformed the two reference schemes which can reduce the average task completion time and task failure rate at approximately 36% and 80.5% respectively when compared with the local edge offloading (LEO) scheme; and at approximately 25.4% and 67.2% respectively when compared with two-tier based offloading between edge with cloud (TTO) scheme.
{"title":"Edge Orchestration Based Computation Peer Offloading in MEC-Enabled Networks: A Fuzzy Logic Approach","authors":"M. Hossain, Tangina Sultana, Md. Alamgir Hossain, E. Huh","doi":"10.1109/IMCOM51814.2021.9377327","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377327","url":null,"abstract":"Multi-Access Edge Computing (MEC) is a promising candidate to handle the enormous computation demands of many emerging applications and the ever-growing user's quality-of-service (QoS) requirements. However, due to the limitation of computing resource capacity of a distinct edge server, most of the previous studies have proposed a collaboration approach. For collaboration, they considered vertical offloading between mobile with edge computing or edge with cloud computing for taking the advantages of both these technologies. Therefore, these approaches ignored the neighboring edge server having spare computing resources in the same tier. This paper thus proposes edge orchestration based computation peer offloading (EOPO) scheme among the edge servers in the same tier. The main objective is to share the computation resources among the edge servers. Our proposed approach selects the optimal computational node for task offloading based on fuzzy rules. Simulation results corroborate that fuzzy decision based computation peer offloading scheme significantly improves the performance of edge computing. Our proposed EOPO scheme outperformed the two reference schemes which can reduce the average task completion time and task failure rate at approximately 36% and 80.5% respectively when compared with the local edge offloading (LEO) scheme; and at approximately 25.4% and 67.2% respectively when compared with two-tier based offloading between edge with cloud (TTO) scheme.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"34 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":"124940602","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/IMCOM51814.2021.9377416
Kyu Min Yoo, R. Kil, H. Youn
This paper presents a new method of predicting the values of time series using recursive update Gaussian Kernel Function Networks. First, the input structure of time series prediction model is determined by the phase space analysis of time series. Then, the one step time series prediction model is trained using the Gaussian kernel function network. In the case of multiple step time series prediction, the estimated value is used along with previous input data to make a prediction model for the right next prediction step and the same process is recursively updated until it reaches the desired prediction step. In this model, the prediction model is trained in such a way that the accumulated error due to the recursive prediction method is reduced as much as possible. For the demonstration of the proposed method, the time series prediction of Kosdaq (one of the Korean composite index) data was performed. As a result, the proposed model outperforms other prediction models such as a simple recursive prediction model, direct prediction model and also other widely used regression methods, such as support vector machines and k-nearest neighbors.
{"title":"Time Series Prediction Based on Recursive Update Gaussian Kernel Function Networks","authors":"Kyu Min Yoo, R. Kil, H. Youn","doi":"10.1109/IMCOM51814.2021.9377416","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377416","url":null,"abstract":"This paper presents a new method of predicting the values of time series using recursive update Gaussian Kernel Function Networks. First, the input structure of time series prediction model is determined by the phase space analysis of time series. Then, the one step time series prediction model is trained using the Gaussian kernel function network. In the case of multiple step time series prediction, the estimated value is used along with previous input data to make a prediction model for the right next prediction step and the same process is recursively updated until it reaches the desired prediction step. In this model, the prediction model is trained in such a way that the accumulated error due to the recursive prediction method is reduced as much as possible. For the demonstration of the proposed method, the time series prediction of Kosdaq (one of the Korean composite index) data was performed. As a result, the proposed model outperforms other prediction models such as a simple recursive prediction model, direct prediction model and also other widely used regression methods, such as support vector machines and k-nearest neighbors.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"104 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":"121279876","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/IMCOM51814.2021.9377377
Jeon-Pyo Hong, Yoon-Yeol Lee, Jahwan Koo, U. Kim
Most people can easily find any place with enough portable devices and big data. Location information must already be known to someone, verified, and provided by a trusted provider. Therefore, Location Service Providers (LSP) may offer their clients biased information to use all of this information correctly and appropriately. But can clients are sure which LSP's approach is right for them? Therefore, it is very difficult to fit individuality into these tasks. We are attempting to solve this problem using collective intelligence to balance of information that is lacking in the Big Data industry. In our focus, Crowd Based System utilizes crowd wisdom to provide a variety of analytics. So using Worker Search Model (WSM) using learning techniques and Response Limit Model (RLM), which is a data selection set, we propose a strategy to optimize various interpretations to users. In addition, we challenge to find suitable locations by driving simulation. Simulation results show that our proposed system is about 1.5 times more likely to find a suitable worker compared to a simple conditional change approach.
{"title":"Crowd Worker Selection with Wide Learning and Narrow Evaluation","authors":"Jeon-Pyo Hong, Yoon-Yeol Lee, Jahwan Koo, U. Kim","doi":"10.1109/IMCOM51814.2021.9377377","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377377","url":null,"abstract":"Most people can easily find any place with enough portable devices and big data. Location information must already be known to someone, verified, and provided by a trusted provider. Therefore, Location Service Providers (LSP) may offer their clients biased information to use all of this information correctly and appropriately. But can clients are sure which LSP's approach is right for them? Therefore, it is very difficult to fit individuality into these tasks. We are attempting to solve this problem using collective intelligence to balance of information that is lacking in the Big Data industry. In our focus, Crowd Based System utilizes crowd wisdom to provide a variety of analytics. So using Worker Search Model (WSM) using learning techniques and Response Limit Model (RLM), which is a data selection set, we propose a strategy to optimize various interpretations to users. In addition, we challenge to find suitable locations by driving simulation. Simulation results show that our proposed system is about 1.5 times more likely to find a suitable worker compared to a simple conditional change approach.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"52 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":"127604301","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}
We present a chatbot system to offer medical consultation services to patients anytime, anywhere. Our chatbot deals with ophthalmologic diseases, currently focusing on macular degeneration. We built the system components and created QA datasets, working closely with an ophthalmologist to obtain and verify medical data.
{"title":"Developing a Ophthalmic Chatbot System","authors":"Jung-Hoon Lee, Min-Su Jeong, Jin-Uk Cho, Hyun-Kyu Jeon, Jong-Hyeok Park, Kyoung-Deok Shin, Su-Jeong Song, Yun-Gyung Cheong","doi":"10.1109/IMCOM51814.2021.9377398","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377398","url":null,"abstract":"We present a chatbot system to offer medical consultation services to patients anytime, anywhere. Our chatbot deals with ophthalmologic diseases, currently focusing on macular degeneration. We built the system components and created QA datasets, working closely with an ophthalmologist to obtain and verify medical data.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"36 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":"128400430","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/IMCOM51814.2021.9377374
SeongGyo Seo, J. Jeon
Infrared cameras require constant nonuniformity correction because image nonuniformity occurs with environmental changes. In this paper, we propose a nonuniformity correction algorithm using feature pattern matching that can correct nonuniformities in real time. The proposed algorithm consists of motion estimation and nonuniformity correction steps. The motion estimation algorithm consists of feature extraction, feature point simplification, and feature point pattern generation steps and is proposed to calculate the amount of motion between frames in real time using a field programmable gate array. The experimental results confirm that the proposed method is robust against ghost phenomenon, compared to a statistics-based nonuniformity correction, and improves the real-time performance while providing the same performance as the existing interframe registration-based nonuniformity correction algorithm.
{"title":"Real-time scene-based nonuniformity correction using feature pattern matching","authors":"SeongGyo Seo, J. Jeon","doi":"10.1109/IMCOM51814.2021.9377374","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377374","url":null,"abstract":"Infrared cameras require constant nonuniformity correction because image nonuniformity occurs with environmental changes. In this paper, we propose a nonuniformity correction algorithm using feature pattern matching that can correct nonuniformities in real time. The proposed algorithm consists of motion estimation and nonuniformity correction steps. The motion estimation algorithm consists of feature extraction, feature point simplification, and feature point pattern generation steps and is proposed to calculate the amount of motion between frames in real time using a field programmable gate array. The experimental results confirm that the proposed method is robust against ghost phenomenon, compared to a statistics-based nonuniformity correction, and improves the real-time performance while providing the same performance as the existing interframe registration-based nonuniformity correction algorithm.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"18 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":"124337555","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/IMCOM51814.2021.9377354
Jumpei Yamada, D. Kitayama
In recent years, due to the widespread use of the Internet, the number of opportunities to search the Web using search engines has been increasing. In conventional search engines, information retrieval is achieved by repeatedly entering a query and selecting and browsing each page in the search engine result pages (SERPs). The search engines present titles, snippets, and other information to help users select suitable Web pages. However, there are cases in which people view Web pages one by one due to lack of prior knowledge or failure of search strategies. To solve this problem, we present keywords from unvisited results in the SERPs, so that users can predict the content of the Web pages. We propose two kinds of feature words as extended snippets to be presented in each search result: a content word to indicate the central content of a Web page and known-topic and unknown-topic words to indicate the degree of knowledge that one would gain by browsing the Web page. The extraction of those is based on the clustering of words in snippet sentences using the distributed representation of the words and the clustering of words in the visited pages, respectively. We investigated the impact of the proposed extended snippet on user search behavior. The experimental findings indicate that our method was useful in certain types of search, as it decreased the time necessary to complete the search. Furthermore, the participants' ratings of the extended snippets were favorable, especially those of the unknown-topic words.
{"title":"The Analysis of Web Search Snippets Displaying User's Knowledge","authors":"Jumpei Yamada, D. Kitayama","doi":"10.1109/IMCOM51814.2021.9377354","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377354","url":null,"abstract":"In recent years, due to the widespread use of the Internet, the number of opportunities to search the Web using search engines has been increasing. In conventional search engines, information retrieval is achieved by repeatedly entering a query and selecting and browsing each page in the search engine result pages (SERPs). The search engines present titles, snippets, and other information to help users select suitable Web pages. However, there are cases in which people view Web pages one by one due to lack of prior knowledge or failure of search strategies. To solve this problem, we present keywords from unvisited results in the SERPs, so that users can predict the content of the Web pages. We propose two kinds of feature words as extended snippets to be presented in each search result: a content word to indicate the central content of a Web page and known-topic and unknown-topic words to indicate the degree of knowledge that one would gain by browsing the Web page. The extraction of those is based on the clustering of words in snippet sentences using the distributed representation of the words and the clustering of words in the visited pages, respectively. We investigated the impact of the proposed extended snippet on user search behavior. The experimental findings indicate that our method was useful in certain types of search, as it decreased the time necessary to complete the search. Furthermore, the participants' ratings of the extended snippets were favorable, especially those of the unknown-topic words.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"14 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":"114813043","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/IMCOM51814.2021.9377401
Tin Trung Duong, Huy-Hung Nguyen, J. Jeon
In this research, a multitask convolutional neural network that can do end-to-end road scene classification and semantic segmentation, which are the two crucial tasks for advanced driver assistance systems (ADAS), is proposed. We name the network TSS which means time-based semantic segmentation. The network contains three main modules: an image encoder, a scene classifier, and two time-based segmentation decoders. For each road scene image, the encoder extracts image features which will be used for classifier and decoders. Next, the image features are fed to the classifier to predict the scene type (in this case a day or a night scene). Then, based on the predicted scene type, the same extracted features are fed to a corresponding segmentation decoder to produce the final semantic segmentation result. By using this classification-driven decoder approach, we can improve the accuracy of the segmentation model, even when the model has been trained excessively earlier. Through the experiment, the validity of our proposed method has been proven. Our approach can be considered as stacking multiple segmentation modules on top of the classification module with all of them share the same image encoder. With this approach, we can utilize the result from classification to gain more accuracy in segmentation in one feed forward only.
{"title":"TSS-Net: Time-based Semantic Segmentation Neural Network for Road Scene Understanding","authors":"Tin Trung Duong, Huy-Hung Nguyen, J. Jeon","doi":"10.1109/IMCOM51814.2021.9377401","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377401","url":null,"abstract":"In this research, a multitask convolutional neural network that can do end-to-end road scene classification and semantic segmentation, which are the two crucial tasks for advanced driver assistance systems (ADAS), is proposed. We name the network TSS which means time-based semantic segmentation. The network contains three main modules: an image encoder, a scene classifier, and two time-based segmentation decoders. For each road scene image, the encoder extracts image features which will be used for classifier and decoders. Next, the image features are fed to the classifier to predict the scene type (in this case a day or a night scene). Then, based on the predicted scene type, the same extracted features are fed to a corresponding segmentation decoder to produce the final semantic segmentation result. By using this classification-driven decoder approach, we can improve the accuracy of the segmentation model, even when the model has been trained excessively earlier. Through the experiment, the validity of our proposed method has been proven. Our approach can be considered as stacking multiple segmentation modules on top of the classification module with all of them share the same image encoder. With this approach, we can utilize the result from classification to gain more accuracy in segmentation in one feed forward only.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"2013 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":"127319564","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/IMCOM51814.2021.9377361
V. Ponnaganti, M. Moh, Teng-Sheng Moh
This project evaluates the feasibility of utilizing popular Convolutional Neural Networks (CNNs) to detect objects present in LiDAR (Light Detection And Ranging) data, and the resulting neural network's performance. This work aims to further existing experimentation using raw LiDAR data that is analyzed and represented in a two-dimensional frame. Using this method, hundreds of frames were generated to create a dataset that was used for neural network training and validation on an existing CNN architecture. The LiDAR dataset was used to train YOLOv3, a popular CNN model, to detect vehicles. This research aims to test a smaller version of the network, YOLOv3-tiny, to measure the change in accuracy between using YOLOv3 and YOLOv3-tiny on the LiDAR dataset. The results are then compared to the loss typically found when going from YOLOv3 to YOLOV3-tiny on camera-based images. In prior experimentation, a preprocessing method was also introduced to attempt to isolate target objects in the frame. The method will be evaluated in this paper to measure its effect on the final accuracy metric of the network. Lastly, the runtime performance of these networks will be evaluated on two embedded platforms to understand if the frame rate that the networks perform on is usable for real-world applications, based on the frame rate the sensor is capable of outputting and the inference speed of the network on the embedded platforms.
{"title":"Utilizing CNNs for Object Detection with LiDAR Data for Autonomous Driving","authors":"V. Ponnaganti, M. Moh, Teng-Sheng Moh","doi":"10.1109/IMCOM51814.2021.9377361","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377361","url":null,"abstract":"This project evaluates the feasibility of utilizing popular Convolutional Neural Networks (CNNs) to detect objects present in LiDAR (Light Detection And Ranging) data, and the resulting neural network's performance. This work aims to further existing experimentation using raw LiDAR data that is analyzed and represented in a two-dimensional frame. Using this method, hundreds of frames were generated to create a dataset that was used for neural network training and validation on an existing CNN architecture. The LiDAR dataset was used to train YOLOv3, a popular CNN model, to detect vehicles. This research aims to test a smaller version of the network, YOLOv3-tiny, to measure the change in accuracy between using YOLOv3 and YOLOv3-tiny on the LiDAR dataset. The results are then compared to the loss typically found when going from YOLOv3 to YOLOV3-tiny on camera-based images. In prior experimentation, a preprocessing method was also introduced to attempt to isolate target objects in the frame. The method will be evaluated in this paper to measure its effect on the final accuracy metric of the network. Lastly, the runtime performance of these networks will be evaluated on two embedded platforms to understand if the frame rate that the networks perform on is usable for real-world applications, based on the frame rate the sensor is capable of outputting and the inference speed of the network on the embedded platforms.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"32 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":"126396156","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/IMCOM51814.2021.9377382
Stevanus Wisnu Wiiava, I. Handoko
Twitter becomes one of the most adopted social media platforms globally, and Indonesia is a country with a rapid growth of Twitter user number in recent years. This paper discusses about the examination of conversation network of Twitter hashtag related to Covid-19 in Indonesia by using a network perspective. During this pandemic situation, Twitter has been increasingly adopted as a medium of conversational interaction amongst people to express their opinion and feeling about the situation, or share information, among others. At the same time, the Indonesian Government has established an official hashtag (#) to coordinate and organize conversations related to a specific topic of Covid-19, namely #BersatuLawanCovid19. In this way, the Government would be able to reach the public interest due to the capability of the hashtag to become a trending topic. This study examines how the Twitter conversations emerged and developed within the Twitter community by using Social Network Analysis approach. We have collected 793 Twitter users and 4441 Twitter chats from the hashtag #BersatuLawanCovid19. We then visualized the relationship network and examined the community using Social Network Analysis metrics with NodeXL. This study found that there is no a mutual engagement amongst the community members in terms of conversational practices. Interestingly, although some members of the community received a high number of engagement efforts from others, they do not actively respond to the initiatives. This suggests that the official account of government who is in-charge of managing the conversation need to enhance their communication strategy to improve the conversation within the community.
{"title":"Examining a Covid-19 Twitter Hashtag Conversation in Indonesia: A Social Network Analysis Approach","authors":"Stevanus Wisnu Wiiava, I. Handoko","doi":"10.1109/IMCOM51814.2021.9377382","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377382","url":null,"abstract":"Twitter becomes one of the most adopted social media platforms globally, and Indonesia is a country with a rapid growth of Twitter user number in recent years. This paper discusses about the examination of conversation network of Twitter hashtag related to Covid-19 in Indonesia by using a network perspective. During this pandemic situation, Twitter has been increasingly adopted as a medium of conversational interaction amongst people to express their opinion and feeling about the situation, or share information, among others. At the same time, the Indonesian Government has established an official hashtag (#) to coordinate and organize conversations related to a specific topic of Covid-19, namely #BersatuLawanCovid19. In this way, the Government would be able to reach the public interest due to the capability of the hashtag to become a trending topic. This study examines how the Twitter conversations emerged and developed within the Twitter community by using Social Network Analysis approach. We have collected 793 Twitter users and 4441 Twitter chats from the hashtag #BersatuLawanCovid19. We then visualized the relationship network and examined the community using Social Network Analysis metrics with NodeXL. This study found that there is no a mutual engagement amongst the community members in terms of conversational practices. Interestingly, although some members of the community received a high number of engagement efforts from others, they do not actively respond to the initiatives. This suggests that the official account of government who is in-charge of managing the conversation need to enhance their communication strategy to improve the conversation within the community.","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"36 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":"126849627","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/IMCOM51814.2021.9377380
Fahad Ahmed Satti, Musarrat Hussain, Jamil Hussain, Tae-Seong Kim, Sungyoung Lee, T. Chung
The advent of digital era has brought great advances in the quality and accuracy of Bio medical sensors and other physiological devices. Similarly, digital games have also witnessed massive improvements in their scale, mechanics, graphics, and reach, which has led to a fierce debate on their human and societal impact, especially in terms of identifying the correlation, if any, between the gamer and violent transgressors. From a pure technological perspective, it is thus imperative that advances in sensory technologies and machine learning are then utilized to build a model for identifying the stress experienced by the gamer, during any game session. Galvanic Skin Response(GSR), can act as a good indicator of this experienced stress, by measuring the change in skin conductance and skin resistance of the user. However, GSR data, in its raw form, is very much user dependent, often biased, and is difficult to analyze, as it gives a long term measure of the user behavior changes, based on skin precipitation. In this research work, we have collected user's perceived notion of stress along with sensory data from a GSR device, which was then analyzed using various machine learning models, before creating a majority voting based ensemble model for stress modeling. Showing comparable values of accuracy(63.39%) and precision(51.22%), our model was able to substantially increase the class recall rate for identifying stress (27.08%), from the individual approaches (0-8.95%).
{"title":"User Stress Modeling through Galvanic Skin Response","authors":"Fahad Ahmed Satti, Musarrat Hussain, Jamil Hussain, Tae-Seong Kim, Sungyoung Lee, T. Chung","doi":"10.1109/IMCOM51814.2021.9377380","DOIUrl":"https://doi.org/10.1109/IMCOM51814.2021.9377380","url":null,"abstract":"The advent of digital era has brought great advances in the quality and accuracy of Bio medical sensors and other physiological devices. Similarly, digital games have also witnessed massive improvements in their scale, mechanics, graphics, and reach, which has led to a fierce debate on their human and societal impact, especially in terms of identifying the correlation, if any, between the gamer and violent transgressors. From a pure technological perspective, it is thus imperative that advances in sensory technologies and machine learning are then utilized to build a model for identifying the stress experienced by the gamer, during any game session. Galvanic Skin Response(GSR), can act as a good indicator of this experienced stress, by measuring the change in skin conductance and skin resistance of the user. However, GSR data, in its raw form, is very much user dependent, often biased, and is difficult to analyze, as it gives a long term measure of the user behavior changes, based on skin precipitation. In this research work, we have collected user's perceived notion of stress along with sensory data from a GSR device, which was then analyzed using various machine learning models, before creating a majority voting based ensemble model for stress modeling. Showing comparable values of accuracy(63.39%) and precision(51.22%), our model was able to substantially increase the class recall rate for identifying stress (27.08%), from the individual approaches (0-8.95%).","PeriodicalId":275121,"journal":{"name":"2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM)","volume":"173 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":"132113182","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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