ABSTRACT
As the coronavirus disease (COVID-19) broke out in late 2019, the electricity sector was significantly impacted. Hence, the effects of the pandemic and restricting measures in power system operation are investigated during pandemic circumstances. The secure operation of the power system is a fundamental requirement. Appropriate procedures should be taken to mitigate these effects and ensure the power system's security. Accordingly, in this study, the authors determine that the COVID-19 pandemic can change the system's operating conditions in the first stage. Since data-driven security assessment methods require the training database to learn about Security constraints, this paper proposes an efficient database generation strategy respecting the consequences of the COVID-19 outbreak. The proposed strategy provides a training set with high information content compatible with the operating conditions. To this end, the method consists of a characteristics extraction approach and updating scheme. The characteristics should be extracted to represent the operating conditions of the system. Further, the similarity of intervals is compared using characteristics in updating scheme. The copula-based sampling approach is provided to generate the random samples. The proposed strategy generates a database for data-driven methods. Therefore, it can be utilized in various applications of security assessment. Real-world data is mapped to the IEEE 39-bus system to illustrate the framework efficiency. The outcomes indicate that a classification using the proposed strategy outperforms conventional methods in terms of evaluation metrics. © 2017 Elsevier Inc. All rights reserved. © 2023 Elsevier Ltd
ABSTRACT
Introduction: Covid-19 was the cause of a pandemic that claimed thousands of human lives. The pandemic has caused health professionals mental health problems that influence emotional, psychological and social well-being, which affects the way they think, feel and act in daily life. Objective(s): To de-termine the quality of life of health personnel during the Co-vid-19 pandemic in public institutions in the city of Cuenca. Material(s) and Method(s): Descriptive, cross-sectional and observational study. The study sample was 338 health professionals belonging to the Ministry of Public Health of the Canton Cuenca, province of Azuay-Ecuador, doctors, nurses, dentists, psychologists, assistants and biochemists who work at differ-ent levels of care were included. Two WHOQOL surveys and the Lazarus and Folkman scale of coping modes were used, for the tabulation of the data the RStudio statistical program was used. Result(s): In the psychological domain, no significant destructuring was found in the personnel studied. In the field of health, it should be noted that the personnel studied reported the inability to carry out the activities they need and the diffi-culty in moving from one place to another. Conclusion(s): When investigating the relationship of health personnel with the envi-ronment, no statistically significant alteration was found, but it was evidenced that social support is a protective factor for the mental health of personnel.Copyright © 2022, Venezuelan Society of Pharmacology and Clinical and Therapeutic Pharmacology. All rights reserved.
ABSTRACT
Since the beginning of the Covid-19 Pandemic, Contact Tracing Apps have been implemented in many countries as a way to detect if someone has been in contact with a patient within a minimum amount of time. However, most existing solutions only consider users in pairs. Since many people meet at the same time in real-life scenarios, those applications aren't able to accurately reflect the situation. Moreover, extending current schemes to a multi-party setting could cause scaling problems and place a heavier load on the device. In this paper, we propose a new Contact Tracing protocol that works in a multi-party setting. We evaluate our scheme to show its efficiency. © 2022 Information Processing Society of Japan.
ABSTRACT
Face recognition is used in a wide variety of applications such as surveillance systems, human-computer interaction, automatic door access control systems, and network security. One of the policies of the smart university is to adopt technology to help with teaching and learning, especially during the Covid-19 pandemic. In this paper, a smart attendance system using face recognition algorithms with deep learning is proposed and used in the university's classroom. Instead of calling names to confirm the identity of students, our system does it automatically. The system was tested in 3 scenarios, namely, in online classes, in on-site classes, and in problematic cases using a standard dataset. The performances of the 3 scenarios were compared in the experiment in terms of precision, recall, F1 score, and percentage accuracy. Our result revealed that in online classes the recognition accuracy is as high as 100%. The implemented system is inexpensive and practical. The application can be used on any portable device such as tablets or smartphones. History viewing, multiple subjects handling, and file exporting features are also incorporated into the system. © 2022 IEEE.
ABSTRACT
Since the beginning of the Covid-19 Pandemic, many Countries adopted Contact Tracing Apps as an automatic way to trace if someone has been in contact with a Covid-19 patient. However, most existing solutions consider two party settings only. Since many people meet at the same time in real-life scenarios, one cannot extend current schemes to multi-party settings because they will not scale well and burden the device. In this paper, we propose a new contact tracing protocol that works in a multi-party setting. We evaluate our scheme to show its efficiency.
ABSTRACT
The emergence of the COVID-19 pandemic led several organizations around the world and in the most varied areas of activity, to move from the intention to implement a digital transformation in the medium/long-term, to an instant obligation to apply the digital transformation. The organizations’ ability to adapt immediately meant their survival and even in some cases a positive evolution of their business. The digital transformation applied in an abrupt way has uncovered some critical factors for its success. One of the most relevant factors will be information security. Many of the digital systems put into operation more intensively during the pandemic, have shown to be highly fragile on issues related to information security. One relevant problem of the organizations is the low effectiveness and efficiency of financial, human, and material resources, allocated to the reduction or mitigation of the risks identified in their information systems. This study aims to offer a new method for prioritizing security risks. The new proposed method directs the organizations resources to more effectively and efficiently actions to reduce or mitigate the identified vulnerabilities of the information system. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The voting mechanism is extremely important in a democratic country like India. And we all know that any flaw in the voting mechanism will raise serious concerns about the entire electoral process. Creating a crowd in the existing scenario of Covid-19 also adds a lot of complications. As a result, in such a situation, the online voting system will be a huge success in the election. However, the online system's security and transparency raise certain concerns. So incorporating blockchain into online E-voting will eliminate all of these flaws. The method allows voters to register and vote for any candidate. The vote will be saved in a secure block chain, but all other information, such as the voter's name, city, and whether they voted or not, will be accessible to anybody via the website. This system will provide security by denying duplication of votes. © 2022 IEEE.
ABSTRACT
This paper uses VMware virtual machine and ghost technology to design virtual simulation experiment teaching case. In the Vmware virtual machine environment, the approach of project-led and task-driven is used to carry out for related experiments such as computer system construction, BIOS system application and security, hard disk management, operating system installation, system backup and restoration, image and cloning of partition and disk and so on. With OBE as the experimental teaching concept, a comprehensive virtual simulation experimental teaching case is designed. The teaching case design implements CDIO thinking, emphasizes process education, and cultivates the ability to analyze and solve problems. In the teaching process, the ideas of case conception, case design, case implementation, case operation and result analysis and summary are implemented. This virtual simulation experiment teaching case relies on the private cloud server platform in the school, adopts B / S architecture, and is shared and opened through internet, which can facilitate the implementation of online and offline experimental teaching, as well as experimental exercises and assessments. During the COVID-19 epidemic, it provides online experimental teaching environment and student training environment for relevant experimental teaching in courses such as "Computer System Security", "Information Security", "Computer System Maintenance", "Operating System and Application", "Computer Assembly and Maintenance"and other courses. It effectively solves the problems of inconvenience to carry out relevant experimental teaching through physical machine and the inability to carry out experimental teaching and conducting experiments online. It helps to improve the quality and effect of experimental teaching, increase the rate of experimentation and save experimental cost. © 2021 ACM.
ABSTRACT
Since the advent of COVID-19, most schools and universities have adopted online courses to reduce the spread of the virus. However, they failed to address security issues adequately in a rush to embrace digital learning platforms. Unfortunately, the platforms used for online courses collect and generate data that attracts hackers. Also, cyberattacks have been increasing since the advent of COVID-19. Therefore, it is essential to outline digital learning spaces' security issues to find solutions to ensure learners' and teachers' safety. This paper discusses vulnerabilities and cyberattacks and their impacts on digital learning spaces. We used the classification of the most common web application vulnerabilities presented by the Open Web Application Security Project (OWASP) and Common Weakness Enumeration (CWE). In addition to the technical risks, this paper also highlights management and user operational risks caused by human errors and ignorance. Furthermore, we performed a comparative study on the vulnerabilities of the most used Learning management Systems (LMS) and video conferencing tools. The study revealed that Moodle is the most vulnerable to attacks, Blackboard is the most secure among LMSs, and Zoom is the most secure among video conferencing tool. © 2021 IEEE.
ABSTRACT
Election infrastructure includes socio-technical systems that are designated as United States critical infrastructure within the Government Facilities sector. Following the 2016 United States' General Election and during the 2020 Presidential Election cycle, election security and the integrity of election processes became a prevalent, national conversation. From the 2019 U.S. Senate Intelligence Committee report indicating that election systems in all 50 states had been targeted by foreign adversaries to the more recent broadened use of, and concern about, mail-based voting during the COVID-19 pandemic, election integrity is increasingly important. Furthermore, poll workers play a crucial role in elections and election equipment, as they are one of the first lines of defense in systems security. This paper contributes to improving the security of election infrastructure through intentional, targeted, cyber, physical, and insider threat training for poll workers. Specifically, this paper details the engineering design, including pedagogical approach, and deployment of online, election-specific, threat training modules. Results of a System Usability Scale assessment from 44 poll workers indicate the content and online platform are easy to interact with and use. Further, the developed modules were piloted and then deployed in a mid-Atlantic state;participating counties include over 1,900 poll workers who serve nearly 750,000 voters. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.