Towards for Designing Educational System Using Role-Based Access Control

During the COVID-19 pandemic, online electronic educational systems have been used in most schools and universities as they were forced to move their operations from classrooms to online settings. However, these systems face a serious security issue. Access control considers the core of data security for any implemented system. This paper presents the well-known role-based access control (RBAC) approach to enhance system security and improve user role and system privilege. This study also addresses the issues faced by extant schemes, such as security risk tolerance, by proposing a privacy-preserving educational system that utilizes RBAC and smart multifactor authentication. This approach uses an asymmetric cryptosystem based on the Elgamal digital signature operation to provide multi-factor authentication while relying on low-complexity cryptographic hash functions. RBAC manages system security via the "user classification, role authorization, and unified management” approach. By limiting the amount of data that users can access, RBAC is particularly suited for multi-level applications. This approach also uses informal analysis and the Scyther tool to conduct extensive formal security proofs. RBAC offers many benefits, including mutual authentication, identity anonymity, forward secrecy, key management, and high resistance to well-known attacks, such as phishing, replay, Man-In-The-Middle (MITM), and insider attacks. Compared with other schemes, RBAC offers more security features and boasts higher cost effectiveness in processing and communication. Furthermore, our work achieves a good balance between performance and security complexity when compared to the state-of-the-art. So, we get good results at a cost of 0.253 ms for computing and 1326 bits for communication © 2023, International Journal of Intelligent Engineering and Systems.All Rights Reserved.

Distribution of credit-risk concentration in particular sectors of the economy, and economic capital before and during the COVID-19 pandemic

The aim of the work underpinning this paper has been to track the evolution of tail risk in banks' NPL portfolios present under normal and worst conditions (before and during the pandemic of COVID-19), and to estimate the impact of sector concentration risk on amounts of economic capital. Results further allowed for analysis of different sectors with a view to determining which is riskiest. The study makes use of a multi-factor structural model, given that each sector is affected by a different systematic risk factor, with the assets of borrowers from the same sector thus correlated markedly, even as correlations between sectors are low. The research has in fact sought the further development of methodology proposed by Düllmann and Masschelein in 2006—in the direction of improved accuracy of economic-capital estimates, thanks to alternate means of mapping out the sectoral factor correlation matrix. The empirical analysis was based on individual data from Prudential Reporting under the National Bank of Poland, as well as market data. Results reveal an increase in tail risk through the 2015–2017 period, as followed by the onset of a decline. Where the paper's second aim is concerned, there is found to be support for the idea that economic capital may be increased where sector concentration in the portfolio of a bank is accounted for. Tail risk is found to be concentrated in the sectors of construction and real estate, with accommodation and food services becoming more volatile during the pandemic. A channel for risk transfer between the financial and corporate sectors is thus found to exist. Thanks to the work done we have a better understanding of the impact of sectoral concentration of individual banks' lending activities on level of risk, with the possibility of this gaining application as stress tests are conducted, and as supervisory recommendations from Poland's Financial Supervision Authority are formulated.

Online based Viva Voce Control System

Viva voce is an assessment method carried out by academic staff to assess the knowledge capacity of candidates. The assessment is usually held physically. With the Covid-19 pandemic, the whole process has been shifted onto an online context. There are several difficulties that come across when conducting online, such as marking of answers, guaranteeing the honesty of the candidate, and the manageability of the whole viva session. This research paper discusses the solution to the problem of conducting and managing online viva voce assessments. The proposed solution consists of mechanisms such as, a sandboxed environment to isolate the application, an advanced authenticating system to identify the intended candidate, a comprehensive monitoring system to monitor the candidate during the assessment, an answer validating system to provide a percentage mark to the answers provided by the candidate against a set marking scheme and finally a process to coordinate the viva voce session. © 2022 IEEE.

GCN-based stock relations analysis for stock market prediction.

Most stock price predictive models merely rely on the target stock's historical information to forecast future prices, where the linkage effects between stocks are neglected. However, a group of prior studies has shown that the leverage of correlations between stocks could significantly improve the predictions. This article proposes a unified time-series relational multi-factor model (TRMF), which composes a self-generating relations (SGR) algorithm that can extract relational features automatically. In addition, the TRMF model integrates stock relations with other multiple dimensional features for the price prediction compared to extant works. Experimental validations are performed on the NYSE and NASDAQ data, where the model is compared with the popular methods such as attention Long Short-Term Memory network (Attn-LSTM), Support Vector Regression (SVR), and multi-factor framework (MF). Results show that compared with these extant methods, our model has a higher expected cumulative return rate and a lower risk of return volatility.

Study on Forecasting Model of Port Container Throughput in China

The new corona pneumonia (COVID-19) epidemic is still spreading globally. The critical role of ports in the global economy and logistics system are highlighted. More and more attention is paid to port development trend. Therefore, it is very important to establish a model to forecast the development trend of the port container throughput. This paper quantified the factors affecting container throughput such as economy and foreign trade, and predicted the container throughput of ports in China. In this paper, a multi-factor dynamic model is constructed, which considers macroeconomic growth, foreign trade import and export volume, containerization rate, single container weight, empty and heavy container ratio and other factors. With the data of 2020 as the benchmark, it is comprehensively predicted that by 2025. China's port container throughput will reach 320 million TEU. The container throughput growth will continue to decline. The average annual growth will be 4.0% in the 14th Five-Year Plan period. Further, this model can be used to predict the development trend of port container in 2050. At the same time, the development peak of port container throughput in China can also be analyzed. This conclusion can provide a basis for government departments and enterprises to make decisions. © 2022 SPIE

A Temporal-Spatial Model Based Short-Term Power Load Forecasting Method in COVID-19 Context

The worldwide coronavirus disease 2019 (COVID-19) pandemic has greatly affected the power system operations as a result of the great changes of socio-economic behaviours. This paper proposes a short-term load forecasting method in COVID-19 context based on temporal-spatial model. In the spatial scale, the cross-domain couplings analysis of multi-factor in COVID-19 dataset is performed by means of copula theory, while COVID-19 time-series data is decomposed via variational mode decomposition algorithm into different intrinsic mode functions in the temporal scale. The forecasting values of load demand can then be acquired by combining forecasted IMFs from light Gradient Boosting Machine (LightGBM) algorithm. The performance and superiority of the proposed temporal-spatial forecasting model are evaluated and verified through a comprehensive cross-domain dataset.

Distribution of credit-risk concentration in particular sectors of the economy, and economic capital before and during the COVID-19 pandemic

The aim of the work underpinning this paper has been to track the evolution of tail risk in banks' NPL portfolios present under normal and worst conditions (before and during the pandemic of COVID-19), and to estimate the impact of sector concentration risk on amounts of economic capital. Results further allowed for analysis of different sectors with a view to determining which is riskiest. The study makes use of a multi-factor structural model, given that each sector is affected by a different systematic risk factor, with the assets of borrowers from the same sector thus correlated markedly, even as correlations between sectors are low. The research has in fact sought the further development of methodology proposed by Dullmann and Masschelein in 2006-in the direction of improved accuracy of economic-capital estimates, thanks to alternate means of mapping out the sectoral factor correlation matrix. The empirical analysis was based on individual data from Prudential Reporting under the National Bank of Poland, as well as market data. Results reveal an increase in tail risk through the 2015-2017 period, as followed by the onset of a decline. Where the paper's second aim is concerned, there is found to be support for the idea that economic capital may be increased where sector concentration in the portfolio of a bank is accounted for. Tail risk is found to be concentrated in the sectors of construction and real estate, with accommodation and food services becoming more volatile during the pandemic. A channel for risk transfer between the financial and corporate sectors is thus found to exist. Thanks to the work done we have a better understanding of the impact of sectoral concentration of individual banks' lending activities on level of risk, with the possibility of this gaining application as stress tests are conducted, and as supervisory recommendations from Poland's Financial Supervision Authority are formulated.

Investing style and sensitivity analysis in U.S. stock market during Covid-19 based on multi-factor model

According to the unimaginable influence of Covid-19 and the essential of capital asset pricing in the market, this article analyzes the TV industry of the US stock market before and during the epidemic based on the Fama-French five-factor model. Fama-French five-factor model comprehensively considers the impact of market risk premium (Mkt-RF), market value scale factor, (SMB), book-to-market value ratio factor (HML), profit factor (RMW) and investment factor (CMA) on this industry. Meanwhile, it can conduct a comprehensive evaluation of the impact of Covid-19 on the TV industry. The data in this article was selected from Kenneth R. French's databases and used multiple linear regression to obtain the results. The performance of factors is different due to the outbreak of Covid-19. By analyzing the result, it found that Mkt-RF, SMB are not significant in the model, but HML, RMW, CMA have changed from insignificant to significant. It indicates that during the Covid-19, investors are recommended to pay more attention to the firms with high book-to-market ratios, stable profitability, and aggressive investment style in the USA TV industry. Therefore, research on the stock market of the TV industry plays an important role in the steady development of the economy, the creation of social wealth, and the improvement of people's living standards. © 2021 IEEE.

Short-term power load forecasting during the COVID-19 pandemic using XGBoost and Copula theory

The global coronavirus disease (COVID-19) has brought great challenges to the power systems due to its limitations on social, economic and productive activities. This paper proposes a short-term load forecasting method during COVID-19 pandemic based on copula theory and eXtreme Gradient Boosting (XGBoost). In this method, the coupling relationship among the cross-domain meteorological, public health, and mobility time-series data are fully analyzed based on copula theory, which is used for the short-term power load forecasting based on multi-factor fusion XGBoost algorithm. The proposed method has been fully evaluated and benchmarked on available cross-domain open-access United States data to demonstrate its effectiveness and superiority on short-term load forecasting of COVID-19. © 2021 IEEE

Multi-Factor Triage Algorithm (MUFTA): Quantitative and Qualitative Ethical Factors on Triage Decisions During COVID-19

Background: This study shows how multiple ethical criteria evaluations result in patient screening and ranking. Furthermore, as Omicron outbreaks increase, hospital emergency departments will become overburdened with critically ill patients. It is a one-of-a-kind global triage algorithm for infectious decreases of COVID-19 and Omicron. The algorithm is qualitative and quantitative, and adaptable to various bio-ethical and social factors. The measurement of the evaluation process eliminates any inconsistencies, which is an advantage of a decision-making algorithm. The proposed algorithm is unique because there are no similar algorithms in the literature that provide triage guidelines based on social ethics, bioethics, and human dignity. Objective: It's simple to evaluate a patient's potential benefits when ethical triage judgments are structured and transparent. Furthermore, decisions made primarily based on economic considerations in stressful situations overlook the socioeconomic realities of the underprivileged. This triage algorithm eliminates the need for ad hoc triage evaluations and facilitates criteria for inclusion, such as human dignity. It also takes into account patient comorbidities and social, ethical issues. Method: Healthcare professionals use predefined ethical criteria to assign relative rankings among patients based on treatment response and social circumstances. It is a Delphi method for evaluating patient illnesses with the help of medical professionals. For example, the admission to the intensive care unit and providing a ventilator depend entirely on hierarchical multidimensional triage scoring results. This algorithm can evaluate triage scores quickly. It is robust, accurate, and quick in assessment, evaluation, and reevaluation during an emergency. A team of three experts can implement this algorithm. Result: The Consistency Scores (CR) show how well clinical and non-clinical ethical criteria may be used to make triage judgments. As a result, all specialists have reported allogeneic reactions in the triage assessment. Furthermore, this system enables decision-makers to identify cognitive biases that may influence their decisions. A Group Consciousness Ratio (GCR) of over 85% indicates that the decision-making process is transparent. Patients with a high level of social dependency, a reasonable probability of recovery, a favorable weighted average comorbidity score, and those who are less fortunate are all considered in the overall triage decision. Conclusions: This algorithm differentiates patients who need ICU (Incentive Care Unit) care and do not immediately require critical resources. As a result, patients queue up on a waiting list when the ICU demand spikes due to the increased incidence of COVID-19 infection or its variants. This situation presents a dilemma for the triage policy. Therefore, a national emergency policy requires monetary and technical assistance to expand healthcare facilities. However, the clarity of this triage policymaking is at odds with decision-makers interested in manipulating results. It is challenging to deal with consistency issues in the Delphi process in group decision-making without professional moderators and valid evaluation metrics. Therefore, transparency, consistency, and strong judgment are essential elements of the presented algorithm. © 2022 by the authors. Licensee ESJ, Italy.

Our Design and Implementation of Multi-Factor Authentication Deployment for Microsoft 365 in Kyushu University

In Kyushu University, Information Infrastructure Initiative manages a Microsoft 365 tenant for our university members. We started offering Office 365 in 2016 and migrated our university-wide email service to Microsoft 365 Exchange Online in 2018. Due to the recent outbreak of COVID-19, off-campus uses of Microsoft 365 have increased, and concerns about account security arose. We discussed how to deploy Multi-Factor Authentication (MFA) to protect our users. Microsoft 365 comes with Azure Active Directory (Azure AD), and it includes built-in MFA functionality. With the basic Azure AD MFA, individual users can register MFA information anytime but have no control to enable or disable MFA. Tenant administrators need to enable MFA for each account. For a gradual deployment, we want to allow users to enroll in MFA and register information at their convenience. In addition to that, we want to prevent malicious attackers from registering their MFA information if an account should be already compromised. Such control was difficult with the basic Azure AD MFA. Since 2020 our tenant subscribes to Azure AD Premium P2 licenses, which provides Azure AD Conditional Access. Conditional Access enables fine controls of MFA and other user access behavior with security groups. We designed an MFA self-enrolling and configuration system, and implemented it with Microsoft Forms, Power Automate, Conditional Access, and in-house web applications. By design, this system prohibits MFA information registration until user's self-enrollment in MFA, and requests the user to register MFA information upon the next sign-in after the self-enrollment. This is supposed to reduce the possible unauthorized registration of MFA information. We extensively discussed implementation of various measures and preparation of documents to counter users' troubles and complaints. We started deploying MFA in April 2021, but we have not yet fully mandated MFA due to a push back from some executives expressing concern about the adverse effects of enforcing MFA too quickly. © 2022 ACM.

Adaptive Multi-Factor Quantitative Analysis and Prediction Models: Vaccination, Virus Mutation and Social Isolation on COVID-19.

Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.

Top Reported Data Security Risks in the Age of COVID-19

Data has been collected and stored for thousands of years. Securing data during the digital age has remained difficult. Research shows that in 2018 there was over 33 zettabytes of data, which is approximately an equivalent to 129 billion 256GB mobile devices of data. Risk management in recent years has made attempts at balancing data security risks with organizational business and budgetary requirements. This research examines high probability data security threats and mitigations. It then reports on the threats in connection with the top United States healthcare data breaches reported during the COVID outbreak to the Health and Human Services (HHS) between June 11, 2020 and June 11, 2021. The data analysis shows that there were nine breaches of over a million affected individuals reported to HHS affecting 15,936,679 individuals in total. Five-million individuals is approximately larger than the populations of Los Angeles, New York, and Chicago combined. We connect the common security risks with the reports of these incidents to gain insights into common network security concerns and inform future network architectures and risk mitigations. © 2021 IEEE.

Usable comprehensive-factor authentication for a secure time attendance system.

In information security, it is widely accepted that the more authentication factors are used, the higher the security level. However, more factors cannot guarantee usability in real usage because human and other non-technical factors are involved. This paper proposes the use of all possible authentication factors, called comprehensive-factor authentication, which can maintain the required security level and usability in real-world implementation. A case study of an implementation of a secure time attendance system that applies this approach is presented. The contribution of this paper is therefore to provide a security scheme seamlessly integrating all classical authentication factors plus a location factor into one single system in a real environment with a security and usability focus. Usability factors emerging from the study are related to a seamless process including the least number of actions required, the lowest amount of time taken, health safety during the pandemic, and data privacy compliance.