• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.272-282
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• 2022

Between 2014 and 2019, the US lost more than 2.1 billion USD to phishing attacks, according to the FBI's Internet Crime Complaint Center, and COVID-19 scam complaints totaled more than 1,200. Phishing attacks reflect these awful effects. Phishing websites (PWs) detection appear in the literature. Previous methods included maintaining a centralized blacklist that is manually updated, but newly created pseudonyms cannot be detected. Several recent studies utilized supervised machine learning (SML) algorithms and schemes to manipulate the PWs detection problem. URL extraction-based algorithms and schemes. These studies demonstrate that some classification algorithms are more effective on different data sets. However, for the phishing site detection problem, no widely known classifier has been developed. This study is aimed at identifying the features and schemes of SML that work best in the face of PWs across all publicly available phishing data sets. The Scikit Learn library has eight widely used classification algorithms configured for assessment on the public phishing datasets. Eight was tested. Later, classification algorithms were used to measure accuracy on three different datasets for statistically significant differences, along with the Welch t-test. Assemblies and neural networks outclass classical algorithms in this study. On three publicly accessible phishing datasets, eight traditional SML algorithms were evaluated, and the results were calculated in terms of classification accuracy and classifier ranking as shown in tables 4 and 8. Eventually, on severely unbalanced datasets, classifiers that obtained higher than 99.0 percent classification accuracy. Finally, the results show that this could also be adapted and outperforms conventional techniques with good precision.

• Journal of Preventive Medicine and Public Health
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• v.55 no.4
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• pp.313-320
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• 2022

The KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD) was launched in 2011 with the support of the Korea Disease Control and Prevention Agency. The study was designed with the aim of exploring the various clinical features and characteristics of chronic kidney disease (CKD) in Koreans, and elucidating the risk factors for CKD progression and adverse outcomes of CKD. For the cohort study, nephrologists at 9 tertiary university-affiliated hospitals participated in patient recruitment and follow-up. Biostatisticians and epidemiologists also participated in the basic design and structuring of the study. From 2011 until 2016, the KNOW-CKD Phase I recruited 2238 adult patients with CKD from stages G1 to G5, who were not receiving renal replacement therapy. The KNOW-CKD Phase II recruitment was started in 2019, with an enrollment target of 1500 subjects, focused on diabetic nephropathy and hypertensive kidney diseases in patients with reduced kidney function who are presumed to be at a higher risk of adverse outcomes. As of 2021, the KNOW-CKD investigators have published articles in the fields of socioeconomics, quality of life, nutrition, physical activity, renal progression, cardiovascular disease and outcomes, anemia, mineral bone disease, serum and urine biomarkers, and international and inter-ethnic comparisons. The KNOW-CKD researchers will elaborate a prediction model for various outcomes of CKD such as the development of end-stage kidney disease, major adverse cardiovascular events, and death.

• Journal of The Korean Association For Science Education
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• v.42 no.2
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• pp.177-184
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• 2022

This article aims to explain the concept and characteristics of the 'Intelligent Science Lab', which is being promoted nationwide in Korea since 2021. The Korean Ministry of Education creates a master plan containing a vision for science education every five years. The most recently announced '4th Master plan for science education (2020-2024)' emphasizes the policy of setting up an 'intelligent science lab' in all elementary and secondary schools as an online and offline space for scientific inquiry using advanced technologies, such as Internet of Things and Augmented and Virtual Reality. The 'Intelligent Science Lab' project is being pursued in two main directions: (1) developing an online platform named 'Intelligent Science Lab-ON' that supports science inquiry classes, and (2) building a science lab space in schools that encourages active student participation while utilizing the online platform. This article presents the key features of the 'Intelligent Science Lab-ON' and the characteristics of intelligent science lab spaces newly built in schools. Furthermore, it introduces inquiry-based science learning programs developed for intelligent science labs. These programs include scientific inquiry activities in which students generate and collect data ('data generation' type), utilize datasets provided by the online platform ('data utilization' type), or utilize open and public data sources ('open data source' type). The Intelligent Science Lab project is expected to not only encourage students to engage in scientific inquiry that solves individual and social problems based on real data, but also contribute to presenting a model of online and offline linked scientific inquiry lessons required in the post-COVID-19 era.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.299-303
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• 2022

The article examines the stage of verification of a crime report from the standpoint of the need for its legislative regulation. Moreover, it investigates the international experience in this field. The existing procedural models are described in detail on the example of the neighboring and faraway countries. An analysis of the provisions of the current criminal procedure law of Russia and foreign experience allowed the authors to identify existing problems in the implementation of departmental control and prosecutorial supervision at the stage of verifying a crime report. The aim of the study is to develop theoretical provisions and recommendations regarding the implementation of departmental procedural control and prosecutorial supervision over the activities of the investigator during the verification of reports of crimes, based on the study of experience, both in Russia and in a number of countries of the near and far abroad, which could find their reflection in law enforcement practice, as well as aimed at improving the current criminal procedure legislation. The authors substantiated the theory that a detailed examination of the foreign procedural foundations of checking a crime report will allow us to form the most suitable model for checking a crime report for our state, taking into account all possible features and successfully implement it into the current criminal procedural law of the Russian Federation.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.327-331
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• 2022

The article aims at studying the institution of human rights in an ever-evolving world in the context of the interdisciplinary approach. The main scientific method was deduction that allowed examining the specific interdisciplinary approach in relation to the institution of human rights on the global scale. To solve the issue set, it is necessary to study legal foundations and features of the interdisciplinary approach to the institution of human rights in the modern world. The article proves there is no theoretical anthropological understanding of the institution of human rights. It has been concluded that the appeal to anthropological jurisprudence requires the identification of the initial theoretical and methodological principles, parameters and axioms of cognition, the integration of a person into the subject field of legal science, linking jurisprudence with the chosen external environment (philosophy, sociology, theology, etc.), predetermining the existence (understanding) of a person, causing qualitative differences and the structure of subject-methodological phenomena. In addition to the identification of such hypotheses, prerequisites and axioms, the basic method (principle) of cognition and its heuristic potential are also being searched (defined). The terminological designation of the formed subject-methodological phenomenon (legal anthropology, anthropology of law, anthropological approach, etc.) reveals its role in the system of interdisciplinary relations of legal science.

• Architectural research
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• v.24 no.4
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• pp.105-111
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• 2022

During pandemic period, college students lost lots of such academic opportunities as extra-curriculum social activities, contact classes, and friendly socializing in university campus area, etc. Previous many studies have shown that physical environment has certain relevance on the well-being of human-beings. Recent public statistics on mental health had shown an increase in psychological distress and a decrease in college students and people's well-being during the lockdown in response to the Covid-19 pandemic. However, there were little evidence on what the college students in dormitory suffered from COVID-19 incident in relation with their physical environment. The purpose of this study is to investigate the relationship between environmental factors and psychological well-being of dormitory students in university campus. In order to explore the impact of physical environment on students' psychological well-being, survey instrumentation consisted of 25 indices were employed to measure the level of awareness to each index. A Chi-square analysis on individual characteristics of 200 students found that number of students living in single dwelling unit was statistically significant to maintain their psychological well-being, except for number of students living in each dwelling unit (χ² =128.92, p= .004). Pearson correlation analysis also found that there exists statistically significant relationship between psychological well-being of students and environmental factors. Further, stepwise multiple regression analysis revealed that the most prime predictor for psychological well-being of students residing in dorm was "use of furniture" (β= .281), implying careful design, lay-out and easy-access to interior furniture by facility planner. The study also demonstrated that as the level of positive perception of physical environmental features rose, overall psychological well-being of students also responded positively at specified rate. Finally, the findings reinforce a solid evidence that carefully well-coordinated physical environments play an important role in maintaining emotional stability of college students in dorm even in pandemic period.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• Knowledge Management Research
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• v.23 no.2
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• pp.1-13
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• 2022

Metaverse has been encountering in our daily lives, and it has dynamically changed people's way of working, educating, and entertaining. During the Covid-19, people has been more immersed with virtual world. For example, the virtual environments have created new form and features of our remote work, online education, entertainment, and so on. Also, some people make a strong tie with their avatar to live in a virtual world. Indeed, it became a new normal life now. With this radical social and technological change, the metaverse has become a core issue for the communities of researchers and practitioners as well, and a variety of meaningful research and products have been conducted so far. Nevertheless, it still is lack of diverse theoretical, empirical, and practical studies, dealing with this huge socio-technical shift with the metaverse. Therefore, in this special issue commentary, we suggest potential metaverse research issues and topics, which highlight how management, organization, and information systems researchers could dance with the ongoing metaverse ecosystem for creating more productive research performances.

• Health Policy and Management
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• v.2 no.2
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• pp.131-151
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• 1992

The contents of prescription service were comparatively analysed between health centers(HC) and private clinics(PC). Medical chart review was done for 330 otu-patients diagnosed with upper respiratory tract infection(UR) of 120 adults and 90 children, and gastritis or duodenitis of 120 adults. Emphasis on comparison was the prime cost of medication which used in prescription service. The results were as follows; 1. The prime costs fro the medication per visit of HC group were significantly higher than PC group in all three diseases, and the out of pocket payments of patients per visit were significantly lower in the HC group than PC group. 2. The reason for high prime costs of medication per visit of HC in adult case of URI were due to the idverse use of medication and long prescription period per visit. And high medication costs in children cases of URI in HC group were due to the longer prescription day. In cases of gastritis, the prime cost of medication was also higher because of longer prescription period and the higher prime cost of medication. The proportions of medications for injection in the HC and PC groups showed similar features. 3. In depth analysis of the prescription services showed the differences of the contents of medication. In adults cases of URI, the averaged cost of oral medication was significantly lower in HC group, but that of medication for injection was higher in HC group. In children cases of URI, the averaged cost of oral medication and medication for injection was lower in HC group than in PC group. But in the cases of gastritis it was was higher in HC group than in PC group. The prescription periods were longer in HC group than in PC group in all three diseases. As a conclusion prime medication cost and quality of prescription services of HC group were higher than PC group. In terms of health care the cost containment and quality assurance in physician visit for common disease, public sector utilization is good option for those perspectives. But it should not be generalized unless future study about structure and outcome research for quality assurance.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.