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1.
18th ACM Conference on Emerging Networking Experiment and Technologies, CoNEXT 2022 ; : 349-368, 2022.
Article in English | Scopus | ID: covidwho-2194085

ABSTRACT

Internet content providers often deliver content through bandwidth bottlenecks that are out of their control. Thus, despite often having massively over-provisioned upstream servers, the content providers still cannot control the end-to-end user experience. This paper explores remote traffic shaping, allowing the content provider to allocate its share of a remote bottleneck link across its users using a metric other than TCP fairness, while remaining TCP-friendly to cross traffic on the bottleneck link. To evaluate this approach, we designed FlowTele, the first system that shapes outbound traffic on an Internet-scale network to optimize provider-selected metrics, using source control with neither in-network support nor special client support. Our extensive evaluations over the Internet show that by strategically reallocating bandwidth among provider-owned co-bottlenecked flows, FlowTele improves the provider's total revenue by roughly 20% - 30% in various network settings, compared with both (i) status quo TCP fairshare and (ii) recent practice by content providers that proactively throttles video quality during the COVID-19 pandemic, while being TCP-friendly to cross-traffic. Besides revenue, we also study other metrics, such as QoE fairness, that a content provider may wish to optimize using FlowTele. © 2022 Owner/Author.

2.
SN Comput Sci ; 4(1), 2023.
Article in English | PubMed Central | ID: covidwho-2158268

ABSTRACT

In the paper, the authors investigated and predicted the future environmental circumstances of a COVID-19 to minimize its effects using artificial intelligence techniques. The experimental investigation of COVID-19 instances has been performed in ten countries, including India, the United States, Russia, Argentina, Brazil, Colombia, Italy, Turkey, Germany, and France using machine learning, deep learning, and time series models. The confirmed, deceased, and recovered datasets from January 22, 2020, to May 29, 2021, of Novel COVID-19 cases were considered from the Kaggle COVID dataset repository. The country-wise Exploratory Data Analysis visually represents the active, recovered, closed, and death cases from March 2020 to May 2021. The data are pre-processed and scaled using a MinMax scaler to extract and normalize the features to obtain an accurate prediction rate. The proposed methodology employs Random Forest Regressor, Decision Tree Regressor, K Nearest Regressor, Lasso Regression, Linear Regression, Bayesian Regression, Theilsen Regression, Kernel Ridge Regressor, RANSAC Regressor, XG Boost, Elastic Net Regressor, Facebook Prophet Model, Holt Model, Stacked Long Short-Term Memory, and Stacked Gated Recurrent Units to predict active COVID-19 confirmed, death, and recovered cases. Out of different machine learning, deep learning, and time series models, Random Forest Regressor, Facebook Prophet, and Stacked LSTM outperformed to predict the best results for COVID-19 instances with the lowest root-mean-square and highest R2 score values.

3.
Tourism Review ; 2022.
Article in English | Scopus | ID: covidwho-1909174

ABSTRACT

Purpose: Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set. Design/methodology/approach: Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set. Findings: The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM. Practical implications: With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting. Originality/value: To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests. © 2020, Emerald Publishing Limited.

4.
Ieee Access ; 10:59782-59791, 2022.
Article in English | Web of Science | ID: covidwho-1895882

ABSTRACT

E-learning is an evolutionary concept, not a revolutionary concept since it has been introduced and practiced in previous decades as well. The current research paper contributes to educational research based on the investigation of holistic perspectives of the ongoing online teaching activities during the lockdown period. The article is built on a literature review. The methodologies are described and the results are presented. There have been research on distant learning and digitalization, but mostly in terms of potential, obstacles, and student assessments, as well as their influence on education. There are no empirical research on the worldwide trend of how employees utilize e-learning resources to determine instructors' interest in and attitudes about using them. This chasm is also regarded a new angle of empirical research that uses the Chi-square test of independence to find patterns. The present research aims to build a realistic instrument that can assess instructors' attitudes about e-learning during COVID-19 with this purpose in mind.The paper consists of two parts: a literature review and statistical analysis. The research was conducted by means of a diagnostic opinion CAWI (Computer Assisted Web Interview) questionnaire and statistical analysis. This study was conducted among 342 teachers working in various universities located in the following countries: Poland, Pakistan, Iraq, USA, UK, Germany, and Austria. The questionnaire consisted of 20 questions. The researchers provided a detailed overview of e-learning practices during the pandemic and its future prospects from the teacher's point of view. The goals of this article and the research problems are to learn about the worldwide trend of how employees utilize e-learning resources, to identify teachers' interests in and attitudes towards using e-learning resources throughout the globe, and to recommend opportunities for workers to use e-learning resources around the world, which is a very important issue, especially in the era of globalization, society 5.0, and industry 4.0. The authors also proposed a Chi-square statistical analysis to check whether there exists a relationship between two variables based on which the hypotheses were formulated. The results of the study clearly show that the respondents approach to e-learning was very responsible because they realized that it was up to them to properly convey knowledge. The results of empirical research show that the digitalization of education is no longer a future trend, but today's trend or even a university standard in the education improvement direction.

5.
Tourism Review ; 2022.
Article in English | Scopus | ID: covidwho-1713957

ABSTRACT

Purpose: This study aims to address three important issues of combination forecasting in the tourism context: reducing the restrictions arising from requirements related to the statistical properties of the available data, assessing the weights of single models and considering nonlinear relationships among combinations of single-model forecasts. Design Methodology Approach: A three-stage multiple-attribute decision-making (MADM)-based methodological framework was proposed. Single-model forecasts were generated by grey prediction models for the first stage. Vlsekriterijumska Optimizacija I Kompromisno Resenje was adopted to develop a weighting scheme in the second stage, and the Choquet integral was used to combine forecasts nonlinearly in the third stage. Findings: The empirical results for inbound tourism in Taiwan showed that the proposed method can significantly improve accuracy to a greater extent than other combination methods. Along with scenario forecasting, a good forecasting practice can be further provided by estimating ex-ante forecasts post-COVID-19. Practical Implications: The private and public sectors in economies with high tourism dependency can benefit from the proposed method by using the forecasts to help them formulate tourism strategies. Originality Value: This study contributed to presenting a MADM-based framework that advances the development of a more accurate combination method for tourism forecasting. © 2022, Emerald Publishing Limited.

6.
European Review for Medical and Pharmacological Sciences ; 25(23):7585-7597, 2021.
Article in English | Web of Science | ID: covidwho-1576100

ABSTRACT

OBJECTIVE: Coronavirus disease 2019 (COVID-19) spread around the world in 2020. Abnormal pulmonary function and residual CT abnormalities were observed in COVID-19 patients during recovery. Appropriate rehabilitation training is around the corner. The correlation between spirometric impairment and residual CT abnormality remains largely unknown. PATIENTS AND METHODS: A cross-sectional study conducted on the pulmonary function of 101 convalescent COVID-19 patients before discharge. Multivariate analysis was used to establish a scoring system to evaluate the spirometric abnormality based on residual chest CT. RESULTS: Lung consolidation area >25% and severe-type COVID-19 were two independent risk factors for severe pulmonary dysfunction. Besides, a scoring system was established. People scoring more than 12 points have more chances (17 times) to get severe pulmonary function impairment before discharge. CONCLUSIONS: For the first time, a chest CT characteristics-based grading system was suggested to predict the pulmonary dysfunction of COVID-19 patients during convalescence in this study. This study may provide suggestions for pulmonary rehabilitation.

7.
Studies in Systems, Decision and Control ; 382:23-45, 2022.
Article in English | Scopus | ID: covidwho-1391726

ABSTRACT

As an essential and exciting topic in financial management, MCDM has been widely used in evaluating financial performance to improve the suitability and reliability of financial indicators with respect to the impacts of both qualitative and quantitative information. This chapter aims to present a hybrid MCDM approach to evaluate the Vietnamese banking sector's performance under COVID-19 impacts. The proposed method utilizes The Criteria Importance Through Intercriteria Correlation (CRITIC) technique to determine objective weights of financial ratios. Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is employed to obtain the cause-effect relationship and the subjective weights based on experts’ judgments. Bank alternatives’ ranking is estimated using the

8.
SenSys - Proc. ACM Conf. Embedded Networked Sens. Syst. ; : 770-771, 2020.
Article in English | Scopus | ID: covidwho-991891

ABSTRACT

Due to the COVID-19 pandemic, many researchers have proposed privacy-preserving smartphone proximity tracing. Current projects, based on ephemeral IDs, are vulnerable to DoS attacks. In this paper, we present BlindSignedIDs that can be verified in-place through a TESLA server. We will demonstrate our BlindSignedIDs can effectively mitigate such DoS attacks. © 2020 Owner/Author.

9.
Epidemiol Infect ; 148: e141, 2020 07 06.
Article in English | MEDLINE | ID: covidwho-633492

ABSTRACT

The pandemic of coronavirus disease 2019 (COVID-19) has posed serious challenges. It is vitally important to further clarify the epidemiological characteristics of the COVID-19 outbreak for future study and prevention and control measures. Epidemiological characteristics and spatial-temporal analysis were performed based on COVID-19 cases from 21 January 2020 to 1 March 2020 in Shandong Province, and close contacts were traced to construct transmission chains. A total of 758 laboratory-confirmed cases were reported in Shandong. The sex ratio was 1.27: 1 (M: F) and the median age was 42 (interquartile range: 32-55). The high-risk clusters were identified in the central, eastern and southern regions of Shandong from 25 January 2020 to 10 February 2020. We rebuilt 54 transmission chains involving 209 cases, of which 52.2% were family clusters, and three widespread infection chains were elaborated, occurring in Jining, Zaozhuang and Liaocheng, respectively. The geographical and temporal disparity may alert public health agencies to implement specific measures in regions with different risk, and should attach importance on how to avoid household and community transmission.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , Adult , COVID-19 , China/epidemiology , Contact Tracing , Female , Geographic Information Systems , Humans , Male , Middle Aged , Pandemics , Time Factors
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