2021
DOI: 10.5114/aoms/141164
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COVID-19: Access to Information, Health Service, Daily Life Facility and Risk Perception of Foreigners during Coronavirus pandemic in South Korea

Abstract: IntroductionCoronavirus disease outbreak has become a top global challenge. Critical issues have emerged regarding access to information, health services, and daily necessities. Effective access to such components is expected to promote public safety and survival as well as to help combat social fear and risk perception. This study aims to fill this gap by exploring public perceptions of access, risk, and government response during the COVID-19 outbreak.Material and methodsA quantitative field survey using a s… Show more

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Cited by 9 publications
(6 citation statements)
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“…We investigate the setting for the adversary network for the poison attack, the assumption, the first case, taken as perfect knowledge gained by the adversary on the target classifier ( P TC ) and known feature space t ( x ).The second case, is that adversaries gained the less or limited knowledge ( L TC ), target classifier. We assumed that attacker may have knowledge of features representation, but not the training dataset (Rathore et al, 2022; Poongodi, Bourouis et al, 2022; Ramesh, Lihore et al, 2022; Poongodi, Malviya, Hamdi et al, 2022; Poongodi, Malviya, Kumar et al, 2022; Poongodi, Hamdi, & Wang 2022; Poongodi et al, 2021; Ramesh, Vijayaragavan et al, 2022; Hamdi et al, 2022; Poongodi, Hamdi, Malviya et al, 2022; Kamruzzaman 2021; Hossain et al, 2022; Chen et al, 2019; Kamruzzaman 2013, 2014; Zhang et al, 2021; Hossain, Kamruzzaman et al, 2022; Sarker et al, 2021; Shi et al, 2020; Chen et al, 2020).…”
Section: Attack Modelsmentioning
confidence: 99%
“…We investigate the setting for the adversary network for the poison attack, the assumption, the first case, taken as perfect knowledge gained by the adversary on the target classifier ( P TC ) and known feature space t ( x ).The second case, is that adversaries gained the less or limited knowledge ( L TC ), target classifier. We assumed that attacker may have knowledge of features representation, but not the training dataset (Rathore et al, 2022; Poongodi, Bourouis et al, 2022; Ramesh, Lihore et al, 2022; Poongodi, Malviya, Hamdi et al, 2022; Poongodi, Malviya, Kumar et al, 2022; Poongodi, Hamdi, & Wang 2022; Poongodi et al, 2021; Ramesh, Vijayaragavan et al, 2022; Hamdi et al, 2022; Poongodi, Hamdi, Malviya et al, 2022; Kamruzzaman 2021; Hossain et al, 2022; Chen et al, 2019; Kamruzzaman 2013, 2014; Zhang et al, 2021; Hossain, Kamruzzaman et al, 2022; Sarker et al, 2021; Shi et al, 2020; Chen et al, 2020).…”
Section: Attack Modelsmentioning
confidence: 99%
“…This knowledge can include actionable information in terms of indigent support. This tends to lower the perception risks [ 93 ]. However, the intrinsic knowledge gained by staying in area for long periods and the formal and informal relationships built, can reduce efficiencies in implementing the indigent support program.…”
Section: Discussionmentioning
confidence: 99%
“…al (2020, Yan Zhang et.al (2020), Md Hossain et. al (2021), Md Nazirul Islam Sarker et. al (2021) ,Y. Shi et.…”
Section: Figure 3 Analysis Of Network Visualization In Scopusmentioning
confidence: 99%