• International Journal of Computer Science & Network Security
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• v.22 no.11
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• pp.43-50
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• 2022

A comparative analysis of the state and results of the functioning of the education system of Ukraine with the national educational systems of the USA and Great Britain was carried out. Based on which we found out similar and different in the process of developing the system of training specialists in higher education institutions of the USA, Great Britain, and Ukraine. Describing the main quantitative indicators of education in the UK, USA and Ukraine, we found common features and distinctive features. Consideration in the comparative aspect of trends in the development of higher teacher education in the United States, Great Britain and in Ukraine gives grounds for conclusion. For these countries, such groups of norms as types of educational institutions, forms of Education; introduction of a unified system of credit units in order to create conditions for broad mobility of students; availability of different levels of training; study of the best experience of educational activities of other states and its introduction into the educational process in combination with the cultural traditions of Ukraine coincide. Describing the main quantitative indicators of education in the analyzed countries, we found distinctive features.Teacher development systems in the UK, USA and Ukraine are compared. It was found out that the use of methods of Great Britain and the United States on the organization of independent work in the process of professional development of teachers will have a positive impact on training in the system of advanced training of teachers in Ukraine. The article examines the information culture of future specialists, which is based on knowledge about the information environment, the laws of its functioning and development, and the perfect ability to navigate the limitless modern world of information.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.112-123
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• 2014

This Study aims to establish better sound fiscal plan by investigating perception of local government officials. Local government fiscal consolidation is affected by a combination of factors, including social, economic, demographic, political financial health of local governments. We derived the financial situation of the government-related indicators, financial health-related indicators, the indicators to improve the financial health on the basis of this study are an existing discussion. To ensure the financial soundness of the Cheonan, it is necessary to increase the efficiency of financial management including financial monitoring and control devices provided the locals, investment screening analysis system to enable it. In addition, fiscal controls should be strengthened in order to effectively autonomous government debt management. You must cuts expense of local government to prepare for expansion of local government finance, it is necessary to realize that the fee rates. It should be made through a blend of autonomy and control in the central government, network of local government and the development of local financial operations. You should also to be distributed to the residents welfare and community development funds are invested substantially to establish a systematic planning, resource allocation, evaluation, and reflux system.

• International Commerce and Information Review
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• v.19 no.3
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• pp.3-23
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• 2017

With the globalization of the world economy, international trade networks are expanding beyond geographical proximity, and the expansion of such trade networks is playing a role in promoting globalization. Korea has established itself as a strong FTA for the past 13 years, starting with the Korea-Chile FTA. Successful establishment of a short-term FTA network has shown positive economic effects such as increased trade volume with partner countries and market share in overseas markets. Other countries are also turning to the paradigm of economic development through the formation of a regional economic integration and a bilateral trade agreement network, and it is time to investigate new opportunities through understanding the entire RTA and FTA network. In this study, we analyzed the status of RTA and FTA from the 1960s to 2010s, analyzed network structure and centrality through SNA(social network analysis). The results of the study show that the structure of the FTA network is gradually expanding, and the FTA network, which has been expanding to the center of the early European countries, is changing toward the Asian countries such as Korea, China and Japan. As a result of the analysis of the degree of centrality, Korea was ranked as the top in all the degree of centrality(Degree, Betweenness, Closeness and Eigenvector) indicators for a short period of time and it means that Korea's FTA strategy was evaluated as very successful. This study examines the FTA among the global RTAs, assesses the structure of the FTAs and evaluates Korea's FTA strategies and the FTA network from a network perspective.

• Knowledge Management Research
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• v.24 no.2
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• pp.241-262
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• 2023

The phenomena and game records that occur in sports matches are being analyzed in the field of sports game analysis, utilizing advanced technologies and various scientific analysis methods. Among these methods, social network analysis is actively employed in analyzing pass networks. As football is a representative sport in which the game unfolds through player interactions, efforts are being made to provide new insights into the game using social network analysis, which were previously unattainable. Consequently, this study aims to analyze the changes in pass networks over time for a specific football team and compare them in different scenarios, including variations in the game's nature (Qatar World Cup games vs. A match games) and alterations in the opposing team (higher FIFA rankers vs. lower FIFA rankers). To elaborate, we selected ten matches from the games of the Korean national football team following Coach Bento's appointment, extracted network indicators for these matches, and applied four indicators (efficiency, cohesion, vulnerability, and activity/leadership) from a football team's performance evaluation model to the extracted data for analysis under different circumstances. The research findings revealed a significant increase in cohesion and a substantial decrease in vulnerability during the analysis of game performance over time. In the comparative analysis based on changes in the game's nature, Qatar World Cup matches exhibited superior performance across all aspects of the evaluation model compared to A matches. Lastly, in the comparative analysis considering the variations in the opposing team, matches against lower FIFA rankers displayed superior performance in all aspects of the evaluation model in comparison to matches against top FIFA rankers. We hope that the outcomes of this study can serve as essential foundational data for the selection of football team coaches and the development of game strategies, thereby contributing to the enhancement of the team's performance.

• 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.

• 한국노년학
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• v.37 no.2
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• pp.329-347
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• 2017

The purpose of this study is to see if we can predict the health of seniors of community by analyzing the connection between social network degree and mental and physical health of old people who live in the areas of Gangwha Island. The subjects of the study were men and women aged 65 or over, a total of 643 that resided in Ganghwa A-county. The survey was conducted on Korean Social Life, Health and Aging Project from the year 2011 to 2012. Regression analysis was carried out using the data. The analysis results were as follows. First, it showed the relationships between income, gender, age out of demographic variables used as control variable and old persons'physical health. The research results showed that physical health was better in case of the higher incomes, men, and lower age. Second, out of demographic variables, educational background, income, age was shown to correlate with mental health. The research results showed that mental health was better in case of the higher incomes, higher educational background, and lower age. Third, in social network including direction, both out-degree and in-degree were shown to predict old people's physical and mental health. The results of this study suggest that not only out-degree but also in-degree should be considered in predicting the health of elderly persons by a person's human relationship. Also, two indicators of degree are meaningful in the dimension of health promotion and welfare of the old in that they can be used for finding isolated individuals that can be physically and mentally vulnerable.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.101-124
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• 2018

Recently, most of the technologies have been developed in various forms through the advancement of single technology or interaction with other technologies. Particularly, these technologies have the characteristic of the convergence caused by the interaction between two or more techniques. In addition, efforts in responding to technological changes by advance are continuously increasing through forecasting promising convergence technologies that will emerge in the near future. According to this phenomenon, many researchers are attempting to perform various analyses about forecasting promising convergence technologies. A convergence technology has characteristics of various technologies according to the principle of generation. Therefore, forecasting promising convergence technologies is much more difficult than forecasting general technologies with high growth potential. Nevertheless, some achievements have been confirmed in an attempt to forecasting promising technologies using big data analysis and social network analysis. Studies of convergence technology through data analysis are actively conducted with the theme of discovering new convergence technologies and analyzing their trends. According that, information about new convergence technologies is being provided more abundantly than in the past. However, existing methods in analyzing convergence technology have some limitations. Firstly, most studies deal with convergence technology analyze data through predefined technology classifications. The technologies appearing recently tend to have characteristics of convergence and thus consist of technologies from various fields. In other words, the new convergence technologies may not belong to the defined classification. Therefore, the existing method does not properly reflect the dynamic change of the convergence phenomenon. Secondly, in order to forecast the promising convergence technologies, most of the existing analysis method use the general purpose indicators in process. This method does not fully utilize the specificity of convergence phenomenon. The new convergence technology is highly dependent on the existing technology, which is the origin of that technology. Based on that, it can grow into the independent field or disappear rapidly, according to the change of the dependent technology. In the existing analysis, the potential growth of convergence technology is judged through the traditional indicators designed from the general purpose. However, these indicators do not reflect the principle of convergence. In other words, these indicators do not reflect the characteristics of convergence technology, which brings the meaning of new technologies emerge through two or more mature technologies and grown technologies affect the creation of another technology. Thirdly, previous studies do not provide objective methods for evaluating the accuracy of models in forecasting promising convergence technologies. In the studies of convergence technology, the subject of forecasting promising technologies was relatively insufficient due to the complexity of the field. Therefore, it is difficult to find a method to evaluate the accuracy of the model that forecasting promising convergence technologies. In order to activate the field of forecasting promising convergence technology, it is important to establish a method for objectively verifying and evaluating the accuracy of the model proposed by each study. To overcome these limitations, we propose a new method for analysis of convergence technologies. First of all, through topic modeling, we derive a new technology classification in terms of text content. It reflects the dynamic change of the actual technology market, not the existing fixed classification standard. In addition, we identify the influence relationships between technologies through the topic correspondence weights of each document, and structuralize them into a network. In addition, we devise a centrality indicator (PGC, potential growth centrality) to forecast the future growth of technology by utilizing the centrality information of each technology. It reflects the convergence characteristics of each technology, according to technology maturity and interdependence between technologies. Along with this, we propose a method to evaluate the accuracy of forecasting model by measuring the growth rate of promising technology. It is based on the variation of potential growth centrality by period. In this paper, we conduct experiments with 13,477 patent documents dealing with technical contents to evaluate the performance and practical applicability of the proposed method. As a result, it is confirmed that the forecast model based on a centrality indicator of the proposed method has a maximum forecast accuracy of about 2.88 times higher than the accuracy of the forecast model based on the currently used network indicators.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.129-138
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• 2021

Several studies have revealed that information on borrower characteristics plays an important factor in approving their credit requests. Though the extent to which such characteritics are also applicable to the case of fintech lending remain uncertain. The aim of this study is, thus, to investigate the determinant factors that influence MSEs in obtaining credit through fintech lending. Here, we emphasize virtual trust in fintech lending encompasing the dimension of social network, economic attributes, and risk perception based on several indicators that are used as proxies. Primary data used in the study was gathered from an online survey to the respondents of MSEs in Java. The result of the study indicates that determinants of MSEs in obtaining credit from lender through fintech lending are statistically influenced by internet usage activities, borrowing history, loan utilization, annuity payment system, completeness of credit requirement documents and compatibility of loan size with the business need. These factors have a significant effect on credit approval because they can generate virtual trust of fintech lender to MSEs as potential borrowers. It concludes that the probability of obtaining fintech loans in accordance with their expectations are influenced by the dimensions of social network, economic attributes and risk perception.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.25-52
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• 2018

This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.

• Journal of the Korean Society for Library and Information Science
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• v.55 no.2
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• pp.91-110
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• 2021

In the face of the prolonged Covid-19, this study conducted a network analysis to propose the policy direction for the Korean National Assembly to go forward. Using COVID-19 news articles, various types of networks were created and analyzed for the parliamentary activities of the Korean National Assembly related to Covid-19. Specifically, we utilize the co-occurrence and keyword information to generate two types of parliamentary networks: co-occurrence-based network and content-based network. In addition, a topic keyword-driven parliamentary network was constructed by using topic modeling. The results of the study are as follows. First, lawmakers in the ruling party had a wide range of topics regarding Covid-19, while lawmakers from other political parties had a limited number of issues covered. Next, a few representative legislators were identified as influential actors in most of the centrality indicators. Based on the research results, cooperation on diverse agendas related to Covid-19 should be promoted between lawmakers from various political parties. And representative legislators from both major parties should play a crucial role as intermediaries to increase communication between them.