Pub Date : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-101
Jo Hagikura, Ryo Nakamura, H. Ohsaki
In recent years, Information-Centric Networking (ICN) that mainly focuses on contents that are transmitted and received instead on end hosts that transmit and receive contents has been under the spotlight. In the literature, there have been several studies on contents caching, which is one of the notable features in ICN. Furthermore, in recent years, the solution of the cache allocation problem has been studied with mathematical approaches as well as simulation experiments However, it is not well understood how the optimal cache allocation is affected by several factors such as the network topology and the total cache size. In this paper, by combining our performance analysis of ICN on an arbitrary network topology and conventional heuristic for optimization problems (i.e., generic algorithm), we investigate how the optimal cache allocation to routers is affected by several factors. Furthermore, we validate our experimental findings using a simplified model of an ICN network in the parking-lot configuration.
{"title":"On the Optimal Cache Allocation in Information-Centric Networking","authors":"Jo Hagikura, Ryo Nakamura, H. Ohsaki","doi":"10.1109/COMPSAC48688.2020.0-101","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-101","url":null,"abstract":"In recent years, Information-Centric Networking (ICN) that mainly focuses on contents that are transmitted and received instead on end hosts that transmit and receive contents has been under the spotlight. In the literature, there have been several studies on contents caching, which is one of the notable features in ICN. Furthermore, in recent years, the solution of the cache allocation problem has been studied with mathematical approaches as well as simulation experiments However, it is not well understood how the optimal cache allocation is affected by several factors such as the network topology and the total cache size. In this paper, by combining our performance analysis of ICN on an arbitrary network topology and conventional heuristic for optimization problems (i.e., generic algorithm), we investigate how the optimal cache allocation to routers is affected by several factors. Furthermore, we validate our experimental findings using a simplified model of an ICN network in the parking-lot configuration.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131315097","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-106
S. Reddivari, Jason Smith
With the emergence of sophisticated head-mounted displays (HMDs), virtual reality (VR) is gaining much interest in the field of medical science and diagnosis. There exist many software tools that support MRI imaging in 3D, however, limited attention has been paid to the VR domain. In this paper, we present a VR tool called VRvisu++ which attempts to bring the spatial advantage that VR has to offer to MRI imaging. This tool allows doctors and medical practitioners to directly interact with MRI images in a VR environment thereby supporting surgical training and clinical decision making.
{"title":"VRvisu++: A Tool for Virtual Reality-Based Visualization of MRI Images","authors":"S. Reddivari, Jason Smith","doi":"10.1109/COMPSAC48688.2020.0-106","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-106","url":null,"abstract":"With the emergence of sophisticated head-mounted displays (HMDs), virtual reality (VR) is gaining much interest in the field of medical science and diagnosis. There exist many software tools that support MRI imaging in 3D, however, limited attention has been paid to the VR domain. In this paper, we present a VR tool called VRvisu++ which attempts to bring the spatial advantage that VR has to offer to MRI imaging. This tool allows doctors and medical practitioners to directly interact with MRI images in a VR environment thereby supporting surgical training and clinical decision making.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131703904","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-50
F. Akhtar, Jianqiang Li, Pei Yan, A. Imran, G. Shaikh, Chun Xu
Large for gestational (LGA) means the fetus having an abnormal birth weight. It adheres severe complications during and after the maternal period. Therefore, this research presents an ensemble classification scheme using Chinese National Pre-Pregnancy Examination Program dataset to classify a fetus as an LGA or non-LGA based on provided Chinese LGA classification guidelines. Moreover, the proposed scheme is comprised of data cleansing and ensemble classification schemes that have drastically improved the LGA classification process with improved performance results compared to present published studies. Therefore, the recommended scheme can be utilized by healthcare professionals to build an enhanced and reliable LGA classification system.
{"title":"Exploiting Ensemble Classification Schemes to Improve Prognosis Process for Large for Gestational Age Fetus Classification","authors":"F. Akhtar, Jianqiang Li, Pei Yan, A. Imran, G. Shaikh, Chun Xu","doi":"10.1109/COMPSAC48688.2020.00-50","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-50","url":null,"abstract":"Large for gestational (LGA) means the fetus having an abnormal birth weight. It adheres severe complications during and after the maternal period. Therefore, this research presents an ensemble classification scheme using Chinese National Pre-Pregnancy Examination Program dataset to classify a fetus as an LGA or non-LGA based on provided Chinese LGA classification guidelines. Moreover, the proposed scheme is comprised of data cleansing and ensemble classification schemes that have drastically improved the LGA classification process with improved performance results compared to present published studies. Therefore, the recommended scheme can be utilized by healthcare professionals to build an enhanced and reliable LGA classification system.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126470156","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00018
Tomohisa Aoshima, K. Yoshida
Cloud computing is being increasingly employed to build systems. Rapid cost estimation for such systems is necessary to start new business. Here, the "Pay-Per-Use" billing model has been established, and providers offer various strategic pricing. There are many cost structures, so the users need to consider many pricing options when estimating system costs. Although companies often want to estimate the cost of their systems, it is difficult to obtain a comprehensive estimation that takes into account all of the components. Previously, researchers have implemented simulation-based and analytical approaches to solve this problem. In this study, we developed a cost-estimation method that employs directed acyclic graph (DAG)-based representation and matrix operations. Our method emphasizes simplicity through the use of a systematic procedure that can estimate the costs of new services at the pre-design stage.
{"title":"Pre-Design Stage Cost Estimation for Cloud Services","authors":"Tomohisa Aoshima, K. Yoshida","doi":"10.1109/COMPSAC48688.2020.00018","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00018","url":null,"abstract":"Cloud computing is being increasingly employed to build systems. Rapid cost estimation for such systems is necessary to start new business. Here, the \"Pay-Per-Use\" billing model has been established, and providers offer various strategic pricing. There are many cost structures, so the users need to consider many pricing options when estimating system costs. Although companies often want to estimate the cost of their systems, it is difficult to obtain a comprehensive estimation that takes into account all of the components. Previously, researchers have implemented simulation-based and analytical approaches to solve this problem. In this study, we developed a cost-estimation method that employs directed acyclic graph (DAG)-based representation and matrix operations. Our method emphasizes simplicity through the use of a systematic procedure that can estimate the costs of new services at the pre-design stage.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121393124","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-197
Yankan Yang, Baoqi Huang, Zhendong Xu, Runze Yang
In order to improve the performance of the indoor localization system, the fusion of multi-source data is a common approach. For example, one can improve the WiFi localization accuracy on smartphones by combining pedestrian dead reckoning (PDR) results obtained through inertial sensors embedded in smartphones. Though obvious improvement in localization can be achieved, the existing methods do not sufficiently exploit the advantages of two data sources. To be specific, the existing studies directly fuse WiFi localization results and PDR results at a high level, i.e., the final coordinates of the WiFi localization system integrate with the final coordinates of PDR by certain algorithms, but ignores their relationship at a low level, i.e, the heading of the PDR , not its location results, is improved by the help of the WiFi localization. In addition, it is acknowledged that the pedestrian heading is the major source determining the performance of PDR. Therefore, this paper proposes to design a novel pedestrian heading estimation by fusing PDR and WiFi at a low level. Different from the traditional method, which employs a magnetometer to eliminate the drifts of a gyroscope, the method utilizes only the gyroscope of a smartphone for the heading estimation and relies on the WiFi localization trajectory in the fusion to compensate for the drift errors of the gyroscope-based heading estimation. In our algorithm, firstly, a pedestrian's activities trajectory is segmented into several straight paths with the help of the gyroscope of a smartphone. Secondly, the WiFi fingerprint localization coordinates falling into the time window of each straight path are fitted by the least-squares linear regression method. Lastly, the deviations of the gyroscope heading estimation of the smartphone when pedestrians walk in a straight direction are mitigated using the fitting slope obtained by the WiFi localization. Extensive experimental results demonstrate that our proposed algorithm can efficiently estimate the heading of pedestrians, and effectively reduce the cumulative errors of the gyroscope-based heading estimation using smartphones. In our experiments, the average error of the heading for pedestrians in 294 steps was reduced from 24.3 degrees to 1.22 degrees. Not requiring a magnetometer, our algorithm can reduce the drift errors of the heading estimation of pedestrians, achieve the deeper fusion of multi-source data in the fusion of WiFi and PDR, and potentially improves the endurance of smartphones.
{"title":"A WiFi Assisted Pedestrian Heading Estimation Method Using Gyroscope","authors":"Yankan Yang, Baoqi Huang, Zhendong Xu, Runze Yang","doi":"10.1109/COMPSAC48688.2020.0-197","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-197","url":null,"abstract":"In order to improve the performance of the indoor localization system, the fusion of multi-source data is a common approach. For example, one can improve the WiFi localization accuracy on smartphones by combining pedestrian dead reckoning (PDR) results obtained through inertial sensors embedded in smartphones. Though obvious improvement in localization can be achieved, the existing methods do not sufficiently exploit the advantages of two data sources. To be specific, the existing studies directly fuse WiFi localization results and PDR results at a high level, i.e., the final coordinates of the WiFi localization system integrate with the final coordinates of PDR by certain algorithms, but ignores their relationship at a low level, i.e, the heading of the PDR , not its location results, is improved by the help of the WiFi localization. In addition, it is acknowledged that the pedestrian heading is the major source determining the performance of PDR. Therefore, this paper proposes to design a novel pedestrian heading estimation by fusing PDR and WiFi at a low level. Different from the traditional method, which employs a magnetometer to eliminate the drifts of a gyroscope, the method utilizes only the gyroscope of a smartphone for the heading estimation and relies on the WiFi localization trajectory in the fusion to compensate for the drift errors of the gyroscope-based heading estimation. In our algorithm, firstly, a pedestrian's activities trajectory is segmented into several straight paths with the help of the gyroscope of a smartphone. Secondly, the WiFi fingerprint localization coordinates falling into the time window of each straight path are fitted by the least-squares linear regression method. Lastly, the deviations of the gyroscope heading estimation of the smartphone when pedestrians walk in a straight direction are mitigated using the fitting slope obtained by the WiFi localization. Extensive experimental results demonstrate that our proposed algorithm can efficiently estimate the heading of pedestrians, and effectively reduce the cumulative errors of the gyroscope-based heading estimation using smartphones. In our experiments, the average error of the heading for pedestrians in 294 steps was reduced from 24.3 degrees to 1.22 degrees. Not requiring a magnetometer, our algorithm can reduce the drift errors of the heading estimation of pedestrians, achieve the deeper fusion of multi-source data in the fusion of WiFi and PDR, and potentially improves the endurance of smartphones.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122321487","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-96
A. Short, H. Leligou, M. Papoutsidakis, Efstathios Theocharis
The potential of Federated Learning (FL) deployment increases rapidly as the number of connected devices increases, the value of artificial intelligence is recognized and networking technologies and edge computing evolves. However, as in any distributed system, a set of security issues arise in FL systems. In this paper, we discuss the use of blockchain technology to address diverse security aspects of FL systems and focus on the model poisoning attack for which we propose a novel Blockchain-based defense scheme. An assessment using data from the MNIST database has shown that the proposed approach, which has been designed to be implemented on blockchain technology, offers significant protection against adversaries attempting model poisoning attacks. The approach adopts a novel algorithm for evaluating the model updates, by verifying each model update separately against a verification dataset, without requiring information about the training dataset size, which is often unavailable or easily falsified.
{"title":"Using Blockchain Technologies to Improve Security in Federated Learning Systems","authors":"A. Short, H. Leligou, M. Papoutsidakis, Efstathios Theocharis","doi":"10.1109/COMPSAC48688.2020.00-96","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-96","url":null,"abstract":"The potential of Federated Learning (FL) deployment increases rapidly as the number of connected devices increases, the value of artificial intelligence is recognized and networking technologies and edge computing evolves. However, as in any distributed system, a set of security issues arise in FL systems. In this paper, we discuss the use of blockchain technology to address diverse security aspects of FL systems and focus on the model poisoning attack for which we propose a novel Blockchain-based defense scheme. An assessment using data from the MNIST database has shown that the proposed approach, which has been designed to be implemented on blockchain technology, offers significant protection against adversaries attempting model poisoning attacks. The approach adopts a novel algorithm for evaluating the model updates, by verifying each model update separately against a verification dataset, without requiring information about the training dataset size, which is often unavailable or easily falsified.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126784570","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-168
Chihhsiong Shih, Youchen Lai, Cheng-hsu Chen, W. Chu
According to the 2018 annual report of US Department of Kidney Data System (USRDS), Taiwan's dialysis rate and prevalence rate are the highest in the world due to population aging, diabetes and progresses in cardiovascular care. With the rise of artificial intelligence deep learning in recent years, various analytical software resources have gradually become easier to obtain. At the same time, wearable cyber physical sensors are becoming more and more popular. Measurements on vital signs such as heartbeat, electrocardiogram, and blood oxygenation blood pressure values are ubiquitous. We propose an integrated system that combines dialysis big data deep learning analysis with cross platform physiological sensing. We specifically tackle the early warning of dialysis discomfort such as hypotension, hypertension, cramps, etc., this requires a large amount of data collection, related training, data sources including dialysis treatment process and home physiological data. Although the Dialysis machine is able to produce huge amount of IoT data, the usable data for early warning system training is not as huge due to the limited physician labors devoted for labeling questionable samples. This generally leads to low accuracy for regular CNN training methods. We enhance the AI training performance via a transfer learning technique. The AI training accuracy reaches the value of 99% with the help of transfer learning, while that of an original CNN process on the HD data bears a low 60% accuracy. Given the high prediction accuracy of our AI engine, we are able to integrate the real time measurements from Dialysis machine with wearable devices such as ECG sensors and wrist health watches, and make precision prediction of incoming discomfort during the HD treatments. The ECG signal of the same group patients are also analyzed with the same technique. The same accuracy enhancement are also observed.
{"title":"An Early Warning System for Hemodialysis Complications Utilizing Transfer Learning from HD IoT Dataset","authors":"Chihhsiong Shih, Youchen Lai, Cheng-hsu Chen, W. Chu","doi":"10.1109/COMPSAC48688.2020.0-168","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-168","url":null,"abstract":"According to the 2018 annual report of US Department of Kidney Data System (USRDS), Taiwan's dialysis rate and prevalence rate are the highest in the world due to population aging, diabetes and progresses in cardiovascular care. With the rise of artificial intelligence deep learning in recent years, various analytical software resources have gradually become easier to obtain. At the same time, wearable cyber physical sensors are becoming more and more popular. Measurements on vital signs such as heartbeat, electrocardiogram, and blood oxygenation blood pressure values are ubiquitous. We propose an integrated system that combines dialysis big data deep learning analysis with cross platform physiological sensing. We specifically tackle the early warning of dialysis discomfort such as hypotension, hypertension, cramps, etc., this requires a large amount of data collection, related training, data sources including dialysis treatment process and home physiological data. Although the Dialysis machine is able to produce huge amount of IoT data, the usable data for early warning system training is not as huge due to the limited physician labors devoted for labeling questionable samples. This generally leads to low accuracy for regular CNN training methods. We enhance the AI training performance via a transfer learning technique. The AI training accuracy reaches the value of 99% with the help of transfer learning, while that of an original CNN process on the HD data bears a low 60% accuracy. Given the high prediction accuracy of our AI engine, we are able to integrate the real time measurements from Dialysis machine with wearable devices such as ECG sensors and wrist health watches, and make precision prediction of incoming discomfort during the HD treatments. The ECG signal of the same group patients are also analyzed with the same technique. The same accuracy enhancement are also observed.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114176694","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.0-195
N. Soundararajan, R. Karne, A. Wijesinha, Navid Ordouie, Hojin Chang
We consider the design and implementation of a bare PC Web server with no OS or kernel running on a multicore architecture. Previous work has demonstrated initialization, loading and running of a 32-bit web server on a single core in a multicore configured system. The main design issues that need to be addressed are balancing the load, designing re-entrant code, enforcing concurrency control, partitioning network logic, sharing the network interface and designing multi-tasking execution. We describe a novel bare PC Web server architecture and design for addressing these issues. We also provide initial performance measurements that demonstrate the feasibility of this architecture and its implementation. It is shown that with this design and implementation, the main bottleneck impeding multicore parallelism is using a single Ethernet card in the system to handle multiple cores. This work serves as a basis for identifying issues that may exist in other networking and multicore configurations for a bare PC Web server
{"title":"Design Issues in Running a Web Server on Bare PC Multi-Core Architecture","authors":"N. Soundararajan, R. Karne, A. Wijesinha, Navid Ordouie, Hojin Chang","doi":"10.1109/COMPSAC48688.2020.0-195","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.0-195","url":null,"abstract":"We consider the design and implementation of a bare PC Web server with no OS or kernel running on a multicore architecture. Previous work has demonstrated initialization, loading and running of a 32-bit web server on a single core in a multicore configured system. The main design issues that need to be addressed are balancing the load, designing re-entrant code, enforcing concurrency control, partitioning network logic, sharing the network interface and designing multi-tasking execution. We describe a novel bare PC Web server architecture and design for addressing these issues. We also provide initial performance measurements that demonstrate the feasibility of this architecture and its implementation. It is shown that with this design and implementation, the main bottleneck impeding multicore parallelism is using a single Ethernet card in the system to handle multiple cores. This work serves as a basis for identifying issues that may exist in other networking and multicore configurations for a bare PC Web server","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"205 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116188813","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-38
Denis Stepanov, D. Towey, T. Chen, Z. Zhou
This paper describes an on-going project to develop a Virtual Reality platform to deliver phobia-inspired experiences. These experiences could induce a reaction in the user that may help the user overcome, or alleviate, the phobia. The platform includes monitoring sensors that could be used to measure how much impact the experience is having. The project development has been taking place at a Sino-foreign Higher Education Institution in Mainland China, University of Nottingham Ningbo China (UNNC). UNNC has already been host to a number of OER (Open Educational Resource) development projects, and the current project is also anticipated to eventually be released to the OER community. This paper presents the background, development, and current state of the project. Challenges to project completion, and future work are also outlined.
{"title":"A Virtual Reality OER Platform to Deliver Phobia-Motivated Experiences","authors":"Denis Stepanov, D. Towey, T. Chen, Z. Zhou","doi":"10.1109/COMPSAC48688.2020.00-38","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-38","url":null,"abstract":"This paper describes an on-going project to develop a Virtual Reality platform to deliver phobia-inspired experiences. These experiences could induce a reaction in the user that may help the user overcome, or alleviate, the phobia. The platform includes monitoring sensors that could be used to measure how much impact the experience is having. The project development has been taking place at a Sino-foreign Higher Education Institution in Mainland China, University of Nottingham Ningbo China (UNNC). UNNC has already been host to a number of OER (Open Educational Resource) development projects, and the current project is also anticipated to eventually be released to the OER community. This paper presents the background, development, and current state of the project. Challenges to project completion, and future work are also outlined.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123769728","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 : 2020-07-01DOI: 10.1109/COMPSAC48688.2020.00-16
A. Tagami, Zhishu Shen
With the rapid development of IoT (Internet of Things) technology, numerous sensors are being deployed to the smart society with the integration of IoT services. Since the collected sensor data reflect the current status of the surrounding environment, determining accurate positions for sensors is crucial from the stage of setting the sensors to realize meaningful sensor data analysis. In this paper, we propose LESAR, a localization system for environmental sensing using augmented reality. LESAR uses a smartphone camera with the AR (Augmented Reality) function to measure the distances between sensors, while the ID of each sensor is identified simultaneously by analyzing the collected Bluetooth signals. The vision-based approach used can enable three-dimensional localization through the simple use of a smartphone.
{"title":"LESAR: Localization System for Environmental Sensors using Augmented Reality","authors":"A. Tagami, Zhishu Shen","doi":"10.1109/COMPSAC48688.2020.00-16","DOIUrl":"https://doi.org/10.1109/COMPSAC48688.2020.00-16","url":null,"abstract":"With the rapid development of IoT (Internet of Things) technology, numerous sensors are being deployed to the smart society with the integration of IoT services. Since the collected sensor data reflect the current status of the surrounding environment, determining accurate positions for sensors is crucial from the stage of setting the sensors to realize meaningful sensor data analysis. In this paper, we propose LESAR, a localization system for environmental sensing using augmented reality. LESAR uses a smartphone camera with the AR (Augmented Reality) function to measure the distances between sensors, while the ID of each sensor is identified simultaneously by analyzing the collected Bluetooth signals. The vision-based approach used can enable three-dimensional localization through the simple use of a smartphone.","PeriodicalId":430098,"journal":{"name":"2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121469574","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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