Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00042
Jiahua Wu, Hyo Jong Lee
The typical bottom-up human pose estimation methods can be divided into two steps, keypoint detection and grouping. The traditional keypoint regression-based methods exploit an effective backbone (like HRNet) and different prediction heads to acquire the body center and body joint. Then they utilize the offset between the body center and body joint to figure out the grouping task. In this paper, we first propose a body branch module and keypoint attention module to improve keypoint detection and keypoint regression. In body branch module, we exploit a multi-branch structure for keypoint detection and keypoint regression. Each branch represents a part of human body. In keypoint attention module, two simple yet reliable pooling layers are adopted to extract the attention areas of different kinds of keypoints. Combining these two modules, we propose a Partial Attention CenterNet for multi-person human pose estimation. The proposed method outperforms the traditional keypoint regression-based methods. Experiments have demonstrated the obvious performance improvements on COCO dataset brought by the introduced components.
{"title":"Partial Attention CenterNet for Bottom-Up Human Pose Estimation","authors":"Jiahua Wu, Hyo Jong Lee","doi":"10.1109/CSCI54926.2021.00042","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00042","url":null,"abstract":"The typical bottom-up human pose estimation methods can be divided into two steps, keypoint detection and grouping. The traditional keypoint regression-based methods exploit an effective backbone (like HRNet) and different prediction heads to acquire the body center and body joint. Then they utilize the offset between the body center and body joint to figure out the grouping task. In this paper, we first propose a body branch module and keypoint attention module to improve keypoint detection and keypoint regression. In body branch module, we exploit a multi-branch structure for keypoint detection and keypoint regression. Each branch represents a part of human body. In keypoint attention module, two simple yet reliable pooling layers are adopted to extract the attention areas of different kinds of keypoints. Combining these two modules, we propose a Partial Attention CenterNet for multi-person human pose estimation. The proposed method outperforms the traditional keypoint regression-based methods. Experiments have demonstrated the obvious performance improvements on COCO dataset brought by the introduced components.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121902837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00291
Arata Endo, Chun-Jae Lee, S. Date
Today’s Internet of Things (IoT) devices have a variety of security requirements and policies. While an access control is applied to such devices to meet the varieties of requirements and policies, the access control has rarely been used for network resources. Due to this situation, we have proposed a per-user access control framework, which realizes the access control for network links and bandwidth as network resources by using Software-Defined Networking, in our previous work. The proposed framework enables a network administrator to apply access control to network resources simply by giving the administrator’s policy as input to the proposed framework. However, there remains the concern that the proposed framework may cause a significant overhead for the data transfers when the number of IoT devices is increased. In this paper, we investigate how scalable the proposed framework is as infrastructure, by considering the actual and practical situation where lots of IoT devices are used. Our evaluation results imply that the overhead incurred by the proposed method is negligible, especially in the case where IoT devices transfer large-sized data. Also, the evaluation results show that the proposed framework reduces the exposure time of the IoT devices to a third party.
{"title":"Scalability Evaluation of a Per-User Access Control Framework","authors":"Arata Endo, Chun-Jae Lee, S. Date","doi":"10.1109/CSCI54926.2021.00291","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00291","url":null,"abstract":"Today’s Internet of Things (IoT) devices have a variety of security requirements and policies. While an access control is applied to such devices to meet the varieties of requirements and policies, the access control has rarely been used for network resources. Due to this situation, we have proposed a per-user access control framework, which realizes the access control for network links and bandwidth as network resources by using Software-Defined Networking, in our previous work. The proposed framework enables a network administrator to apply access control to network resources simply by giving the administrator’s policy as input to the proposed framework. However, there remains the concern that the proposed framework may cause a significant overhead for the data transfers when the number of IoT devices is increased. In this paper, we investigate how scalable the proposed framework is as infrastructure, by considering the actual and practical situation where lots of IoT devices are used. Our evaluation results imply that the overhead incurred by the proposed method is negligible, especially in the case where IoT devices transfer large-sized data. Also, the evaluation results show that the proposed framework reduces the exposure time of the IoT devices to a third party.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123879184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00266
J. Balen, Davor Damjanović, P. Maric, Krešimir Vdovjak
The future of mobile systems relies on big data and fast information processing to improve efficiency, user experience, and system autonomy. With the massive growth of intelligent and mobile devices, along with the development of computational and communication technologies, a vast amount of data is being generated which needs to be processed and distributed as fast as possible, while reducing costs. Therefore, gathering and data processing, as well as efficient distribution of messages across the network, is the key problem of future mobile systems. In this paper, we are proposing a cost-effective method that relies on fog and edge computing principles called FOGO (FOG Optimization). The method proposes a three layer architecture that utilizes an upgraded existing infrastructure where the mobile nodes will create a fog service layer that could provide processing of medium-sized messages and the distribution of the results across the network. Furthermore, cloud computing is also considered but only in a case of necessity for a large-sized data processing. The proposed system architecture is described along with the method flowcharts, operating algorithms, and proposed message structure. Furthermore, the metrics for the system performance evaluation are proposed, as well as possible application domains.
{"title":"Optimized Edge, Fog and Cloud Computing Method for Mobile Ad-hoc Networks","authors":"J. Balen, Davor Damjanović, P. Maric, Krešimir Vdovjak","doi":"10.1109/CSCI54926.2021.00266","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00266","url":null,"abstract":"The future of mobile systems relies on big data and fast information processing to improve efficiency, user experience, and system autonomy. With the massive growth of intelligent and mobile devices, along with the development of computational and communication technologies, a vast amount of data is being generated which needs to be processed and distributed as fast as possible, while reducing costs. Therefore, gathering and data processing, as well as efficient distribution of messages across the network, is the key problem of future mobile systems. In this paper, we are proposing a cost-effective method that relies on fog and edge computing principles called FOGO (FOG Optimization). The method proposes a three layer architecture that utilizes an upgraded existing infrastructure where the mobile nodes will create a fog service layer that could provide processing of medium-sized messages and the distribution of the results across the network. Furthermore, cloud computing is also considered but only in a case of necessity for a large-sized data processing. The proposed system architecture is described along with the method flowcharts, operating algorithms, and proposed message structure. Furthermore, the metrics for the system performance evaluation are proposed, as well as possible application domains.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126220561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00289
M. Anedda, M. Fadda, M. Farina, Roberto Girau, M. Sole, D. Giusto
Smart cities are characterized by smart heterogeneous devices that can interact and cooperate with each other by exchanging regularly big amounts of data with the big issue to treat sensitive data in a properly respectful manner, avoiding exposure to the risks that new technologies inevitably bring to the fore. This objective can only be pursued with adequate knowledge of the risks and methods of protection, for this reason in addition to producing materially functional results, it has been studied in depth the techniques of protection of personal data including the anonymization and pseud-anonymization of sensitive data. We provide the analysis of the state of the art that starts from the concepts of security and privacy and comes to an analysis of anonymization algorithms. This analysis tries to give an overview of the two fundamental issues in the field of data security: privacy, according to the European Regulation 2016 (GDPR) and the practical techniques with which it is preserved, with particular attention to anonymization algorithms: we analyze the advantages and disadvantages of the latter. The performance of the proposed solution is compared against that of a TraffictYpe-based DifferEntiated Reputation (TYDER) algorithm. This performance was evaluated in terms of QoS parameters such as delay, latency, packet loss and prediction error. The results show how MISSION outperforms TYDER in urban mobility scenario.
{"title":"Safe Social Internet of Thing for Urban Mobility Services","authors":"M. Anedda, M. Fadda, M. Farina, Roberto Girau, M. Sole, D. Giusto","doi":"10.1109/CSCI54926.2021.00289","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00289","url":null,"abstract":"Smart cities are characterized by smart heterogeneous devices that can interact and cooperate with each other by exchanging regularly big amounts of data with the big issue to treat sensitive data in a properly respectful manner, avoiding exposure to the risks that new technologies inevitably bring to the fore. This objective can only be pursued with adequate knowledge of the risks and methods of protection, for this reason in addition to producing materially functional results, it has been studied in depth the techniques of protection of personal data including the anonymization and pseud-anonymization of sensitive data. We provide the analysis of the state of the art that starts from the concepts of security and privacy and comes to an analysis of anonymization algorithms. This analysis tries to give an overview of the two fundamental issues in the field of data security: privacy, according to the European Regulation 2016 (GDPR) and the practical techniques with which it is preserved, with particular attention to anonymization algorithms: we analyze the advantages and disadvantages of the latter. The performance of the proposed solution is compared against that of a TraffictYpe-based DifferEntiated Reputation (TYDER) algorithm. This performance was evaluated in terms of QoS parameters such as delay, latency, packet loss and prediction error. The results show how MISSION outperforms TYDER in urban mobility scenario.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129491018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00165
Eman Azab, Nour El-Din Ali Said, Mohamed Nafea, Yassin Samaha, L. Shihata, M. Mashaly
In this paper, a comparative study between Genetic Algorithm and Discrete Event Simulation to solve the flexible jobshop scheduling problem is presented. Two different approaches are used to generate a flexible job-shop schedule for a pharmaceutical factory X with minimum make-span which is defined as the duration required to complete all jobs. The first approach uses Genetic Algorithm to find an optimal or near-optimal solution for the flexible job-shop problem. The second approach uses Discrete Event Simulation and predefined dispatching rules to solve the flexible job-shop problem by creating a model for the pharmaceutical factory X production line. The same case study is used to evaluate the two approaches results. The Genetic Algorithm approach showed better performance compared to the Discrete Event Simulation approach for the same case study while using different dispatching rules. Both approaches showed better performance compared to basic sequential schedule.
{"title":"Employing Genetic Algorithm and Discrete Event Simulation for Flexible Job-Shop Scheduling Problem","authors":"Eman Azab, Nour El-Din Ali Said, Mohamed Nafea, Yassin Samaha, L. Shihata, M. Mashaly","doi":"10.1109/CSCI54926.2021.00165","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00165","url":null,"abstract":"In this paper, a comparative study between Genetic Algorithm and Discrete Event Simulation to solve the flexible jobshop scheduling problem is presented. Two different approaches are used to generate a flexible job-shop schedule for a pharmaceutical factory X with minimum make-span which is defined as the duration required to complete all jobs. The first approach uses Genetic Algorithm to find an optimal or near-optimal solution for the flexible job-shop problem. The second approach uses Discrete Event Simulation and predefined dispatching rules to solve the flexible job-shop problem by creating a model for the pharmaceutical factory X production line. The same case study is used to evaluate the two approaches results. The Genetic Algorithm approach showed better performance compared to the Discrete Event Simulation approach for the same case study while using different dispatching rules. Both approaches showed better performance compared to basic sequential schedule.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128561651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00236
M. Al-Yahya, Rana Alkadhi, H. Alrasheed
Preparing graduates for the job market is a key objective of higher education. The Information Technology (IT) department at King Saud University has adopted a strategy of program alignment with industry to ensure that program outcomes are in line with the market needs and requirements. Graduates in the field of Information Technology should be equipped with software development skills needed by industry to drive business value and deliver high quality software products and services. To this end, the IT department undertook the decision to adopt an agile transformation strategy for the final year capstone project course converting it from a waterfall software development process model to an agile approach in response to the job market need. In this paper, we present the transformation strategy, the design of the course, and discuss opportunities and challenges. Reporting our transformation experience will provide insights and guidance to those who want to undergo a similar transformation.
{"title":"Agile Transformation for Capstone Projects: Preparing Graduates for the Job Market","authors":"M. Al-Yahya, Rana Alkadhi, H. Alrasheed","doi":"10.1109/CSCI54926.2021.00236","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00236","url":null,"abstract":"Preparing graduates for the job market is a key objective of higher education. The Information Technology (IT) department at King Saud University has adopted a strategy of program alignment with industry to ensure that program outcomes are in line with the market needs and requirements. Graduates in the field of Information Technology should be equipped with software development skills needed by industry to drive business value and deliver high quality software products and services. To this end, the IT department undertook the decision to adopt an agile transformation strategy for the final year capstone project course converting it from a waterfall software development process model to an agile approach in response to the job market need. In this paper, we present the transformation strategy, the design of the course, and discuss opportunities and challenges. Reporting our transformation experience will provide insights and guidance to those who want to undergo a similar transformation.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128586632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00159
Mansoureh Lord, Adam Kaplan
This paper explores machine learning on an embedded device to detect anomalies with sophisticated low-power neural networks. We leverage this deep learning approach to detect mechanical anomalies as they occur on a top-load washing machine. We collect normal data from balanced laundry loads and abnormal data from unbalanced laundry loads, as they are being washed by the machine. The normal data is then used to train two different neural network models: autoencoder and variational autoencoder. This model is ported to an Arduino Nano microcontroller mounted to the washing machine. Using the autoencoder model, the microcontroller detects unbalanced washing machine loads with 92% accuracy, 90% precision and 99% recall. The battery life for this autoencoder model is 20 hours on 5 V lithium batteries, which is only 14.9% less than the life of a basic LED-blink application on the same platform.
{"title":"Mechanical Anomaly Detection on an Embedded Microcontroller","authors":"Mansoureh Lord, Adam Kaplan","doi":"10.1109/CSCI54926.2021.00159","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00159","url":null,"abstract":"This paper explores machine learning on an embedded device to detect anomalies with sophisticated low-power neural networks. We leverage this deep learning approach to detect mechanical anomalies as they occur on a top-load washing machine. We collect normal data from balanced laundry loads and abnormal data from unbalanced laundry loads, as they are being washed by the machine. The normal data is then used to train two different neural network models: autoencoder and variational autoencoder. This model is ported to an Arduino Nano microcontroller mounted to the washing machine. Using the autoencoder model, the microcontroller detects unbalanced washing machine loads with 92% accuracy, 90% precision and 99% recall. The battery life for this autoencoder model is 20 hours on 5 V lithium batteries, which is only 14.9% less than the life of a basic LED-blink application on the same platform.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124645721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00097
Korn Sooksatra, P. Rivas, J. Orduz
Machine learning can thrust technological advances and benefit different application areas. Further, with the rise of quantum computing, machine learning algorithms have begun to be implemented in a quantum environment; this is now referred to as quantum machine learning. There are several attempts to implement deep learning in quantum computers. Nevertheless, they were not entirely successful. Then, a convolutional neural network (CNN) combined with an additional quanvolutional layer was discovered and called a quanvolutional neural network (QNN). A QNN has shown a higher performance over a classical CNN. As a result, QNNs could achieve better accuracy and loss values than the classical ones and show their robustness against adversarial examples generated from their classical versions. This work aims to evaluate the accuracy, loss values, and adversarial robustness of QNNs compared to CNNs.
{"title":"Evaluating Accuracy and Adversarial Robustness of Quanvolutional Neural Networks","authors":"Korn Sooksatra, P. Rivas, J. Orduz","doi":"10.1109/CSCI54926.2021.00097","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00097","url":null,"abstract":"Machine learning can thrust technological advances and benefit different application areas. Further, with the rise of quantum computing, machine learning algorithms have begun to be implemented in a quantum environment; this is now referred to as quantum machine learning. There are several attempts to implement deep learning in quantum computers. Nevertheless, they were not entirely successful. Then, a convolutional neural network (CNN) combined with an additional quanvolutional layer was discovered and called a quanvolutional neural network (QNN). A QNN has shown a higher performance over a classical CNN. As a result, QNNs could achieve better accuracy and loss values than the classical ones and show their robustness against adversarial examples generated from their classical versions. This work aims to evaluate the accuracy, loss values, and adversarial robustness of QNNs compared to CNNs.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125012892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00078
Choong-soo Han
Unfortunately, convenience is getting more overemphasized, the risk of smart world is getting more neglected. Smart worlds like smart cities, smart homes, smart factories, smart traffics are making web-based IT systems more and more. Many research papers tells us that web-based IT systems are fundamentally vulnerable. Truly, it is very difficult to defend against every cyber attack. Definitely, it is impossible to think about safe smart world without cybersecurity. It is really needed to reduce the risk of smart world. If access from overseas is not necessary, blocking cyber threats from abroad is the best way to reduce the risk of cyber infringements for smart world.
{"title":"Enhanced cybersecurity for safe smart world","authors":"Choong-soo Han","doi":"10.1109/CSCI54926.2021.00078","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00078","url":null,"abstract":"Unfortunately, convenience is getting more overemphasized, the risk of smart world is getting more neglected. Smart worlds like smart cities, smart homes, smart factories, smart traffics are making web-based IT systems more and more. Many research papers tells us that web-based IT systems are fundamentally vulnerable. Truly, it is very difficult to defend against every cyber attack. Definitely, it is impossible to think about safe smart world without cybersecurity. It is really needed to reduce the risk of smart world. If access from overseas is not necessary, blocking cyber threats from abroad is the best way to reduce the risk of cyber infringements for smart world.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130085403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00211
Sara Suleymanova, A. Gawanmeh, W. Mansoor
Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus. Governments had to close several sections, including educational institutes and universities, around the world suddenly in March 2020. Hence, emergency remote learning was adopted as alternative and as an immediate response to the ongoing situation using whatever available online tools. As a result, both students and instructors were forced to adapt to this new situation. While there were several studies that addressed several issues related to preparation, contents, course delivery, readiness, etc., there are few ones that were intended to address the mental challenges resulted from the shift to emergency remote learning. The main motivation behind this study is to understand the mental challenges and effects of the sudden transformation into emergency remote learning considering engineering students as case study and improve the delivery and experience of learning for both the instructors and the students.
{"title":"The Mental Challenges of Emergency Remote Learning: UAE Engineering Students Case Study","authors":"Sara Suleymanova, A. Gawanmeh, W. Mansoor","doi":"10.1109/CSCI54926.2021.00211","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00211","url":null,"abstract":"Student mental health in higher education has been an increasing concern. The COVID-19 pandemic situation has brought this vulnerable population into renewed focus. Governments had to close several sections, including educational institutes and universities, around the world suddenly in March 2020. Hence, emergency remote learning was adopted as alternative and as an immediate response to the ongoing situation using whatever available online tools. As a result, both students and instructors were forced to adapt to this new situation. While there were several studies that addressed several issues related to preparation, contents, course delivery, readiness, etc., there are few ones that were intended to address the mental challenges resulted from the shift to emergency remote learning. The main motivation behind this study is to understand the mental challenges and effects of the sudden transformation into emergency remote learning considering engineering students as case study and improve the delivery and experience of learning for both the instructors and the students.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130221151","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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