• Journal of the Korean Society of Earth Science Education
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• v.16 no.1
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• pp.56-71
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• 2023

The purpose of this study is to explore the role and effectiveness of argumentation structure and the developmental characteristics of science PCK with Earth science preservice teachers who used argumentation structure as a pedagogical reasoning tool. Since teachers demonstrate PCK in a series of pedagogical reasoning processes using argumentation structures, we explored the characteristics of future-oriented family resemblance-PCK shown by preservice science teachers using argumentation structures. At the end of the semester, we conducted in-depth interviews with 15 earth science preservice teachers who had experienced lesson design and teaching practice using the argumentation structure. Qualitative analysis including a semantic network analysis was conducted based on the in-depth interview to analyze the characteristics of preservice teachers' family resemblance-PCK. Results include that preservice teachers organized their classes systematically by applying the argumentation structure, and structured classes by differentiating argumentation elements from facts to conclusions. Regarding the characteristics of each component of the argumentation structure, preservice teachers had difficulty finding warrant, rebuttal, and qualifier. The area of PCK most affected by the argumentation structure is the science teaching practice, and preservice teachers emphasized the selection of a instructional model suitable for lesson content, the use of various teaching methods and inquiry activities to persuade lesson content, and developing of data literacy and digital competency. Discussed in the conclusion are the potential and usability of argument structure as a pedagogical reasoning tool, the possibility of developing science inquiry and reasoning competency of secondary school students who experience science classes using argumentation structure, and the need for developing a teacher education protocol using argumentation structure as a pedagogical reasoning tool.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.34 no.3
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• pp.135-161
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• 2023

This study analyzed the research trends of COVID-19 research papers published in medical journals of Korea. Data were collected from 25 MEDLINE journals in 'Medicine and Pharmacy' studies and a total of 800 were selected. As a result of the study, authors from domestic affiliations made up 76.96% of the total, and the proportion of authors from foreign institutions decreased without significant change. The authors' majors were 'Internal Medicine' (32.85%), 'Preventive Medicine/Occupational and Environmental Medicine' (16.23%), 'Radiology' (5.74%), and 'Pediatrics' (5.50%), and 435 (54.38%) papers were collaborative research. As for author keywords, 'COVID19' (674), 'SARSCoV2' (245), 'Coronavirus' (81), and 'Vaccine' (80) were derived as top keywords. There were six words that appeared throughout the entire period: 'COVID19,' 'SARSCoV2,' 'Coronavirus,' 'Korea,' 'Pandemic,' and 'Mortality.' Co-occurrence network analysis was conducted on MeSH terms and author keywords, and common keywords such as 'covid-19,' 'sars-cov-2,' and 'public health' were derived. In topic modeling, five topics were identified, including 'Vaccination,' 'COVID-19 outbreak status,' 'Omicron variant,' 'Mental health, control measures,' and 'Transmission and control in Korea.' Through this study, it was possible to identify the research areas and major keywords by year of COVID-19 research papers published during the 'Public Health Emergency of International Concern (PHEIC).'

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.6
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• pp.42-57
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• 2022

This study aims to create a basic model for classifying the activity photos that urban park users shared on social media using Deep Learning through Artificial Intelligence. Regarding the social media data, photos related to urban parks were collected through a Naver search, were collected, and used for the classification model. Based on the indicators of Naturalness, Potential Attraction, and Activity, which can be used to evaluate the characteristics of urban parks, 21 classification categories were created. Urban park photos shared on Naver were collected by category, and annotated datasets were created. A custom CNN model and a transfer learning model utilizing a CNN pre-trained on the collected photo datasets were designed and subsequently analyzed. As a result of the study, the Xception transfer learning model, which demonstrated the best performance, was selected as the urban park user activity image classification model and evaluated through several evaluation indicators. This study is meaningful in that it has built AI as an index that can evaluate the characteristics of urban parks by using user-shared photos on social media. The classification model using Deep Learning mitigates the limitations of manual classification, and it can efficiently classify large amounts of urban park photos. So, it can be said to be a useful method that can be used for the monitoring and management of city parks in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.2
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• pp.295-303
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• 2023

As the cloud computing market grows, a variety of cloud services are now reliably delivered. Administrative agencies and public institutions of South Korea are transferring all their information systems to cloud systems. It is essential to develop security solutions in advance in order to safely operate cloud services, as protecting cloud services from misuse and malicious access by insiders and outsiders over the Internet is challenging. In this paper, we propose a zero trust model for cloud storage services that store sensitive data. We then verify the effectiveness of the proposed model by operating a cloud storage service. Memory, web, and network forensics are also performed to track access and usage of cloud users depending on the adoption of the zero trust model. As a cloud storage service, we use Amazon S3(Simple Storage Service) and deploy zero trust techniques such as access control lists and key management systems. In order to consider the different types of access to S3, furthermore, we generate service requests inside and outside AWS(Amazon Web Services) and then analyze the results of the zero trust techniques depending on the location of the service request.

• Journal of Korean Neurosurgical Society
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• v.65 no.1
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• pp.84-95
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• 2022

Objective : To investigate the clinical efficacy and safety of the controlled distraction-compression technique using an expandable titanium cage (ETC) in posttraumatic kyphosis (PTK). Methods : We retrospectively studied and collected data on 20 patients with PTK. From January 2014 to December 2017, the controlled distraction-compression technique using ETC was consecutively performed in 20 patients with PTK of the thoracolumbar zone (range, 36-82 years). Among them, nine were males and 11 were females and the mean age was 61.5 years. The patients were followed regularly at 1, 3, 6, and 12 months, and the last follow-up was more than 2 years after surgery. Results : The mean follow-up period was 27.3±7.3 months (range, 14-48). The average operation time was 286.8±33.1 minutes (range, 225-365). The preoperative regional kyphotic angle (RKA) ranged from 35.6° to 70.6° with an average of 47.5°±8.1°. The immediate postoperative mean RKA was 5.9°±3.8° (86.2% correction rate, p=0.000), and at the last follow-up more than 2 years later, the mean RKA was 9.2°±4.9° (80.2% correction rate, p=0.000). The preoperative mean thoracolumbar kyphosis was 49.1°±9.2° and was corrected to an average of 8.8°±5.3° immediately after surgery (p=0.000). At the last follow-up, a correction of 11.9°±6.3° was obtained (p=0.000). The preoperative mean back visual analog scale (VAS) score was 7.9±0.8 and at the last follow-up, the VAS score was improved to a mean of 2.3±1.0 with a 70.9% correction rate (p=0.000). The preoperative mean Oswestry disability index (ODI) score was 32.3±6.9 (64.6%) and the last follow-up ODI score was improved to a mean of 6.85±2.9 (3.7%) with a 78.8% correction rate (p=0.000). The overall complication was 15%, with two of distal junctional fractures and one of proximal junctional kyphosis and screw loosening. However, there were no complications directly related to the operation. Conclusion : Posterior vertebral column resection through the controlled distraction-compression technique using ETC showed safe and good results in terms of complications, and clinical and radiologic outcomes in PTK. However, to further evaluate the efficacy of this surgical procedure, more patients need long-term follow-up and there is a need to apply it to other diseases.

• Therapeutic Science for Rehabilitation
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• v.11 no.1
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• pp.7-20
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• 2022

Objective : This study aims to identify international academic trends regarding the effectiveness of telehealth for the elderly through bibliographic analysis and to secure the foundation for providing basic data and subsequent research to promote domestic research. Methods : This study collected bibliographic information on the effectiveness of telehealth for the elderly published in international academia from January 2010 to December 2020 and analyzed and visualized the relationships between information using VOS viewer software (version 1.6.16, CWTS, Netherlands, 2020). Results : First, the research trend analysis shows a 678% increase in the number of papers published over the past 10 years. Most of the research was conducted in 145 (45.89%) Health care science services, and the most papers were published in 39 Telemedicine and e-Health journals (9.11%). Second, the network analysis showed that Oxford University had a total of 168 connections in other countries and institutions in the U.K, indicating the strongest influence in international academic societies. Third, as a result of the keyword analysis, 'older adults (64 times)', 'care (62 times)', 'health (50 times)', 'technology (40 times)', and 'outcomes (41 times)' were used in the study. Conclusion : In this study, the trends and topics of international academia on the effectiveness of telehealth for the elderly were analyzed to form the basis for research activities and the institutional implementation of telehealth for the elderly in Korea.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.197-216
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• 2022

With the development of Internet and mobile technology and the spread of social media, a large amount of information is being generated and distributed online. Some of them are useful information for the public, but others are misleading information. The misleading information, so-called 'fake news', has been causing great harm to our society in recent years. Since the global spread of COVID-19 in 2020, much of fake news has been distributed online. Unlike other fake news, fake news related to COVID-19 can threaten people's health and even their lives. Therefore, intelligent technology that automatically detects and prevents fake news related to COVID-19 is a meaningful research topic to improve social health. Fake news related to COVID-19 has spread rapidly through social media, however, there have been few studies in Korea that proposed intelligent fake news detection using the information about how the fake news spreads through social media. Under this background, we propose a novel model that uses Graph2vec, one of the graph embedding methods, to effectively detect fake news related to COVID-19. The mainstream approaches of fake news detection have focused on news content, i.e., characteristics of the text, but the proposed model in this study can exploit information transmission relationships in social engagement networks when detecting fake news related to COVID-19. Experiments using a real-world data set have shown that our proposed model outperforms traditional models from the perspectives of prediction accuracy.

• Korea Trade Review
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• v.41 no.4
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• pp.131-155
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• 2016

Recently, the global trade environment has been composed of transactions in trade through integration of the global supply chain network. As FTAs are being signed between countries, the country of origin (COO) system on products has become an important issue. Companies are procuring raw materials through global sourcing and supplying to the retail markets. This research deducted major factors regarding the verification and utilization of the COO system through research on domestic and international literatures, and verified the mediating effects on the verification and utilization elements of the country image and the brand image of COO on the performance of the COO system through empirical study. For the purpose of this research, we conducted a survey implementing the COO system and analyzed the 152 data collected. The results of this research is as follows: First, the external verification level of the COO system has an impact on the performance of the COO system, and a mediating effect on the country image and the brand image of the COO. Second, the management capability of the COO has an impact on the performance of the COO system, and a mediating effect on the brand image of the COO. A research comparing and analyzing the difference in establishment of the verification system of the COO depending on the size of a corporation is necessary.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.3
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• pp.499-510
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• 2023

Recently, an intelligent and advanced cyber attack attacks a computer network of a public institution using a file containing malicious code or leaks information, and the damage is increasing. Even in public institutions with various information protection systems, known attacks can be detected, but unknown dynamic and encryption attacks can be detected when existing signature-based or static analysis-based malware and ransomware file detection methods are used. vulnerable to The detection method proposed in this study extracts the detection result data of the system that can detect malicious code and ransomware among the information protection systems actually used by public institutions, derives various attributes by combining them, and uses a machine learning classification algorithm. Results are derived through experiments on how the derived properties are classified and which properties have a significant effect on the classification result and accuracy improvement. In the experimental results of this paper, although it is different for each algorithm when a specific attribute is included or not, the learning with a specific attribute shows an increase in accuracy, and later detects malicious code and ransomware files and abnormal behavior in the information protection system. It is expected that it can be used for property selection when creating algorithms.