Pub Date : 2021-12-01DOI: 10.1109/CSCI54926.2021.00218
Muhittin Yilmaz
This study presents a hands-on research experience for undergraduate senior-level computer architecture course students. The students have investigated scientific research process, literature review approaches, technical writing as well as blind-review principles, and conducted hands-on research on three different computer systems, namely, a supercomputer, an office desktop, and an autonomous vehicle artificial intelligence computer systems, for a budget-constrained final computer configuration of an office desktop computer.The final student team outcomes, relevant feedback, and the corresponding surveys, evaluated by the project administrators, strongly imply the success of the project for an effective research component inclusion in an undergraduate course.
{"title":"Undergraduate In-class Research Experience for Computer Architecture Students","authors":"Muhittin Yilmaz","doi":"10.1109/CSCI54926.2021.00218","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00218","url":null,"abstract":"This study presents a hands-on research experience for undergraduate senior-level computer architecture course students. The students have investigated scientific research process, literature review approaches, technical writing as well as blind-review principles, and conducted hands-on research on three different computer systems, namely, a supercomputer, an office desktop, and an autonomous vehicle artificial intelligence computer systems, for a budget-constrained final computer configuration of an office desktop computer.The final student team outcomes, relevant feedback, and the corresponding surveys, evaluated by the project administrators, strongly imply the success of the project for an effective research component inclusion in an undergraduate course.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"207 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":"116509889","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/CSCI54926.2021.00137
Noah Oghenefego Ogwara, K. Petrova, M. Yang
This paper presents an ensemble machine learning (ML) based system for the detection of malicious applications in the Mobile Cloud Computing (MCC) Environment. The proposed system named MOBDroid2 applies a static feature analysis approach using the permissions and intents demanded by Android apps. The experiments conducted showed that the proposed system was able to effectively detect malicious and benign apps, achieving a classification accuracy rate of 98.16%, a precision rate of 98.95%, a recall rate of 98.20%, and a false alarm rate of 1.85%. The results obtained in our experiment compared well with other results reported in extant literature.
{"title":"MOBDroid2: An Improved Feature Selection Method for Detecting Malicious Applications in a Mobile Cloud Computing Environment","authors":"Noah Oghenefego Ogwara, K. Petrova, M. Yang","doi":"10.1109/CSCI54926.2021.00137","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00137","url":null,"abstract":"This paper presents an ensemble machine learning (ML) based system for the detection of malicious applications in the Mobile Cloud Computing (MCC) Environment. The proposed system named MOBDroid2 applies a static feature analysis approach using the permissions and intents demanded by Android apps. The experiments conducted showed that the proposed system was able to effectively detect malicious and benign apps, achieving a classification accuracy rate of 98.16%, a precision rate of 98.95%, a recall rate of 98.20%, and a false alarm rate of 1.85%. The results obtained in our experiment compared well with other results reported in extant literature.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"6 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":"115396675","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/CSCI54926.2021.00042
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/CSCI54926.2021.00159
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/CSCI54926.2021.00324
Lucas Mendes Lima, Victor Calebe Cavalcante, Mariana Guimarães de Sousa, Cláudio Afonso Fleury, D. Oliveira, Eduardo Noronha de Andrade Freitas
Context: Although agribusiness corresponded to more than 20% of Brazil’s Gross Domestic Product (GDP), most livestock is under manual control and manual monitoring. Additionally, alternative technologies are either uncomfortable and stressful, or expensive. Now, despite the great scientific advances in the area, there is still a pressing need for an automated robust, inexpensive and (sub)optimal technology to monitor animal behavior in a cost-effective, contact-less and stress-free fashion. Overall, this niche can leverage the benefits of Deep Learning schemes.Objective: This review aims to provide a systematic overview of most current projects in the area of comfort monitoring dairy cattle, as well as their corresponding image recognition-based techniques and technologies.Methods: First, a systematic review planning was carried out, and objectives, research questions, search strings, among others, were defined. Subsequently,a broad survey was conducted to extract, analyze and compile the data, to generate a easy-to-read visual source of information (tables and graphics).Results: Information was extracted from the reviewed papers. Among this data collected from the papers are techniques utilized, target behaviors, cow bodyparts identified in visual computational, besides their paper source font, the publication date, and localization. For example, the papers present are mostly recent. China has had a larger number of relevant papers in the area. The back was the body region most analyzed by the papers and the behaviors most analyzed were body condition score, lameness, cow’s body position and feeding/drinking behavior. Among the methods used is RCNN Inception V3 with the best accuracy for cow’s back region.Conclusion: The aim of this work is to present some of the papers that are being carried out in the area of dairy cow behavior monitoring, using techniques of Artifical Intelligence. It is expected that the information collected and presented in the present systematic review paper contribute to the future researches and projects of the area and the application of new techniques.
{"title":"Artificial Intelligence in Support of Welfare Monitoring of Dairy Cattle: A Systematic Literature Review","authors":"Lucas Mendes Lima, Victor Calebe Cavalcante, Mariana Guimarães de Sousa, Cláudio Afonso Fleury, D. Oliveira, Eduardo Noronha de Andrade Freitas","doi":"10.1109/CSCI54926.2021.00324","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00324","url":null,"abstract":"Context: Although agribusiness corresponded to more than 20% of Brazil’s Gross Domestic Product (GDP), most livestock is under manual control and manual monitoring. Additionally, alternative technologies are either uncomfortable and stressful, or expensive. Now, despite the great scientific advances in the area, there is still a pressing need for an automated robust, inexpensive and (sub)optimal technology to monitor animal behavior in a cost-effective, contact-less and stress-free fashion. Overall, this niche can leverage the benefits of Deep Learning schemes.Objective: This review aims to provide a systematic overview of most current projects in the area of comfort monitoring dairy cattle, as well as their corresponding image recognition-based techniques and technologies.Methods: First, a systematic review planning was carried out, and objectives, research questions, search strings, among others, were defined. Subsequently,a broad survey was conducted to extract, analyze and compile the data, to generate a easy-to-read visual source of information (tables and graphics).Results: Information was extracted from the reviewed papers. Among this data collected from the papers are techniques utilized, target behaviors, cow bodyparts identified in visual computational, besides their paper source font, the publication date, and localization. For example, the papers present are mostly recent. China has had a larger number of relevant papers in the area. The back was the body region most analyzed by the papers and the behaviors most analyzed were body condition score, lameness, cow’s body position and feeding/drinking behavior. Among the methods used is RCNN Inception V3 with the best accuracy for cow’s back region.Conclusion: The aim of this work is to present some of the papers that are being carried out in the area of dairy cow behavior monitoring, using techniques of Artifical Intelligence. It is expected that the information collected and presented in the present systematic review paper contribute to the future researches and projects of the area and the application of new techniques.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"17 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":"115660002","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/CSCI54926.2021.00165
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/CSCI54926.2021.00278
Marcel S. Stolz
This paper defines platform neutrality as a concept for large technology companies, most notably, social media platform providers. It is deduced from the concept of state neutrality, and acknowledges societal and political functions as well as state-like structures these companies have put into place. The paper argues that recent developments demonstrate a convergence of social media towards platform neutrality. It explains the benefit of platform neutrality both for businesses as well as societies.
{"title":"Platform Neutrality and the Global Balance of Powers","authors":"Marcel S. Stolz","doi":"10.1109/CSCI54926.2021.00278","DOIUrl":"https://doi.org/10.1109/CSCI54926.2021.00278","url":null,"abstract":"This paper defines platform neutrality as a concept for large technology companies, most notably, social media platform providers. It is deduced from the concept of state neutrality, and acknowledges societal and political functions as well as state-like structures these companies have put into place. The paper argues that recent developments demonstrate a convergence of social media towards platform neutrality. It explains the benefit of platform neutrality both for businesses as well as societies.","PeriodicalId":206881,"journal":{"name":"2021 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"85 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":"128700846","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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