• Journal of the Korean Society for information Management
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• v.38 no.3
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• pp.23-39
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• 2021

This study aims to explore research trends in Blockchain studies in South Korea using dynamic topic modeling and network analysis. To achieve this goal, we conducted the university & institute collaboration network analysis, the keyword co-occurrence network analysis, and times series topic analysis using dynamic topic modeling. Through the university & institute collaboration network analysis, we found major universities such as Soongsil University, Soonchunhyang University, Korea University, Korea Advanced Institute of Science and Technology (KAIST) and major institutes such as Ministry of National Defense, Korea Railroad Research Institute, Samil PricewaterhouseCoopers, Electronics and Telecommunications Research Institute that led collaborative research. Next, through the analysis of the keyword co-occurrence network, we found major research keywords including virtual assets (Cryptocurrency, Bitcoin, Ethereum, Virtual currency), blockchain technology (Distributed ledger, Distributed ledger technology), finance (Smart contract), and information security (Security, privacy, Personal information). Smart contracts showed the highest scores in all network centrality measures showing its importance in the field. Finally, through the time series topic analysis, we identified five major topics including blockchain technology, blockchain ecosystem, blockchain application 1 (trade, online voting, real estate), blockchain application 2 (food, tourism, distribution, media), and blockchain application 3 (economy, finance). Changes of topics were also investigated by exploring proportions of representative keywords for each topic. The study is the first of its kind to attempt to conduct university & institute collaboration networks analysis and dynamic topic modeling-based times series topic analysis for exploring research trends in Blockchain studies in South Korea. Our results can be used by government agencies, universities, and research institutes to develop effective strategies of promoting university & institutes collaboration and interdisciplinary research in the field.

• Journal of Christian Education in Korea
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• v.77
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• pp.63-84
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• 2024

Research Objective : The study aimed to investigate the satisfaction with the operation of theological university curricula from the perspective of learners experiencing the theological education curriculum in the field. The goal is to provide a basis for reflective introspection on the current theological education curriculum and for restructuring it to have influential impacts within the church and society. Content and Methodology : A survey was conducted with 80 learners currently enrolled in undergraduate, graduate, master's, and doctoral programs at a theological university to analyze satisfaction with current theological education programs. To interpret the survey results progressively, in-depth interviews were conducted with a randomly selected group of 6 participants. Survey Results : First, the satisfaction with the current theological education programs was found to be 60%, indicating a high level of satisfaction. Second, while 77.5% recognized the need for practical pastoral education, only 45.5% reported that practical pastoral education is currently provided in theological education programs, indicating a lower percentage than the perceived need. Third, 73.7% responded negatively regarding whether the current theological education programs can enhance pastoral competence for future society. Lastly, the areas identified as urgently requiring change for the restructuring of theological education programs were theological education content, methodology, and objectives, in that order. Conclusion and Recommendations : In an era of great transformation, our society is changing rapidly. In the face of this wave of change, the theological education curriculum also requires adaptation to suit the new era. Traditional theological education courses have primarily focused on imparting theory-centered knowledge. However, theological education in the new era necessitates a curriculum that enhances the pastoral capacity of churches and pastors to dynamically navigate through this era of significant transition. To achieve this, it is imperative to restructure the curriculum to one that is more closely related to the pastoral field. This involves offering a variety of constructivist-based, learner-centered teaching and learning methods within a theory-centered curriculum and methodology. Additionally, it entails establishing a practice-oriented theological school that can actively address the evolving pastoral landscape in this era of great transition. Restructuring of the process is essential to meet these goals.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.137-152
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• 2012

In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.

• Radiation Oncology Journal
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• v.25 no.1
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• pp.7-15
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• 2007

[ $\underline{Purpose}$ ]: To determine the patterns of evaluation and treatment in patients with breast cancer after mastectomy and treated with radiotherapy. A nationwide study was performed with the goal of improving radiotherapy treatment. $\underline{Materials\;and\;Methods}$: A web- based database system for the Korean Patterns of Care Study (PCS) for 6 common cancers was developed. Randomly selected records of 286 eligible patients treated between 1998 and 1999 from 17 hospitals were reviewed. $\underline{Results}$: The ages of the study patients ranged from 20 to 80 years (median age 44 years). The pathologic T stage by the AJCC was T1 in 9.7% of the cases, T2 in 59.2% of the cases, T3 in 25.6% of the cases, and T4 in 5.3% of the cases. For analysis of nodal involvement, N0 was 7.3%, N1 was 14%, N2 was 38.8%, and N3 was 38.5% of the cases. The AJCC stage was stage I in 0.7% of the cases, stage IIa in 3.8% of the cases, stage IIb in 9.8% of the cases, stage IIIa in 43% of the cases, stage IIIb in 2.8% of the cases, and IIIc in 38.5% of the cases. There were various sequences of chemotherapy and radiotherapy after mastectomy. Mastectomy and chemotherapy followed by radiotherapy was the most commonly performed sequence in 47% of the cases. Mastectomy, chemotherapy, and radiotherapy followed by additional chemotherapy was performed in 35% of the cases, and neoadjuvant chemoradiotherapy was performed in 12.5% of the cases. The radiotherapy volume was chest wall only in 5.6% of the cases. The volume was chest wall and supraclavicular fossa (SCL) in 20.3% of the cases; chest wall, SCL and internal mammary lymph node (IMN) in 27.6% of the cases; chest wall, SCL and posterior axillary lymph node in 25.9% of the cases; chest wall, SCL, IMN, and posterior axillary lymph node in 19.9% of the cases. Two patients received IMN only. The method of chest wall irradiation was tangential field in 57.3% of the cases and electron beam in 42% of the cases. A bolus for the chest wall was used in 54.8% of the tangential field cases and 52.5% of the electron beam cases. The radiation dose to the chest wall was $45{\sim}59.4\;Gy$ (median 50.4 Gy), to the SCL was $45{\sim}59.4\;Gy$ (median 50.4 Gy), and to the PAB was $4.8{\sim}38.8\;Gy$, (median 9 Gy) $\underline{Conclusion}$: Different and various treatment methods were used for radiotherapy of the breast cancer patients after mastectomy in each hospital. Most of treatment methods varied in the irradiation of the chest wall. A separate analysis for the details of radiotherapy planning also needs to be followed and the outcome of treatment is needed in order to evaluate the different processes.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Animal Science and Technology
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• v.49 no.5
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• pp.677-688
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• 2007

돼지를 포함한 대부분의 동물은 일정한 발정주기를 가지고 일정한 시기에 배란을 하는 자연배란동물이지만, 토끼, 고양이, 밍크 등의 암놈은 교미자극에 의해 배란이 일어나는 유기배란동물이다. 또한 1년에 한 번만 발정하는 단발정동물과 1년에 수차례 발정하는 다발정동물이 있다. 이 중에서 모돈은 1년에 수차례 발정하는 다발정 동물로서 발정기에 들면 비발정기와는 다른 행동을 나타낸다(Diehl 등, 2001). 양돈가의 수익을 최대화하기 위해서는 비생산일수를 최소로 줄여야 한다. 모돈의 비생산일수를 줄일 수 있는 한 가지 방법은 성공적으로 교배를 시키는 것이다. 이처럼 성공적으로 교배를 시키기 위해서는 수정적기를 정확히 예측해야 한다. 만약 수정적기를 정확히 판단하지 못하여 수태가 되지 않으면, 비생산일수가 늘어나 손실을 입게 된다. 따라서 수정적기를 정확히 판단하는 것은 모돈의 성공적인 인공수정에 있어서 중요한 요소이다. 수정적기는 배란이 일어나기 전 10시간에서 12시간 사이이며, 발정이 시작되는 시점을 기준으로 하였을 때 경산돈의 경우 26시간에서 34시간 사이이고 미경산돈의 경우는 18시간에서 26시간 사이이다(Evans 등, 2001). 현재 하루에 두 번 모돈의 발정을 확인하는 것이 일반화되어 있으며, 이 때 웅돈을 접촉시키거나 육안관찰을 통하여 발정 유무를 판단한다. 이러한 방법에는 숙련된 기술과 풍부한 경험이 요구될 뿐만 아니라 총 소요노동력의 30% 정도가 요구된다(Perez 등, 1986). 하루에 두 번밖에 발정을 감지하지 않기 때문에 발정이 언제 시작되었는지를 정확히 알 수 없으며, 또한 발정의 대부분이 새벽에 시작되므로 수정적기를 정확히 판단하기란 매우 어렵다. 만약 발정을 감지했더라도 적기에 인공수정을 하지 못한다면, 수태율이 낮아지므로 경제적 손실이 초래된다. 현재 이러한 문제점 때문에 2회에서 3회에 걸쳐 인공수정을 하고 있으나 이에 따른 소요비용과 소요노동력 등은 양돈가의 부담을 가중시키는 요인이 되고 있다. 돼지는 발정기가 되면 비발정기에 나타내지 않던 외음부의 냄새를 맡는 행동, 귀를 세우는 행동 및 승가허용 행동 등을 나타낸다(Diehl 등, 2001). 또한 돼지는 비발정기에 비하여 발정기에 더 많은 활동량을 나타낸다(Altman, 1941; Erez and Hartsock, 1990). Freson 등(1998)은 스톨에서 개별적으로 사육되고 있는 모돈의 활동량을 적외선센서를 이용하여 측정함으로써 발정을 86%까지 감지하였다고 보고하였다. 그러나 이 연구는 단지 모돈의 발정을 감지하였을 뿐 번식관리에 있어서 가장 중요한 수정적기의 판단 기준을 제시하지 못하였다. 따라서, 본 연구는 스톨에서 사육되는 모돈의 활동량을 측정함으로써 발정시작시각을 감지하고 이를 기준으로 인공수정적기를 예측할 수 있는 인공수정적기 예측 장치를 개발한 후 이의 성능을 농장실증실험을 통하여 시험하고자 수행되었다.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.53-74
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• 2012

A challenge in fostering virtual communities is the continuous supply of knowledge, namely members' willingness to contribute knowledge to their communities. Previous research argues that giving away knowledge eventually causes the possessors of that knowledge to lose their unique value to others, benefiting all except the contributor. Furthermore, communication within virtual communities involves a large number of participants with different social backgrounds and perspectives. The establishment of mutual understanding to comprehend conversations and foster knowledge contribution in virtual communities is inevitably more difficult than face-to-face communication in a small group. In spite of these arguments, evidence suggests that individuals in virtual communities do engage in social behaviors such as knowledge contribution. It is important to understand why individuals provide their valuable knowledge to other community members without a guarantee of returns. In virtual communities, knowledge is inherently rooted in individual members' experiences and expertise. This personal nature of knowledge requires social interactions between virtual community members for knowledge transfer. This study employs the social capital theory in order to account for interpersonal relationship factors and identity theory for individual and group factors that may affect knowledge contribution. First, social capital is the relationship capital which is embedded within the relationships among the participants in a network and available for use when it is needed. Social capital is a productive resource, facilitating individuals' actions for attainment. Nahapiet and Ghoshal (1997) identify three dimensions of social capital and explain theoretically how these dimensions affect the exchange of knowledge. Thus, social capital would be relevant to knowledge contribution in virtual communities. Second, existing research has addressed the importance of identity in facilitating knowledge contribution in a virtual context. Identity in virtual communities has been described as playing a vital role in the establishment of personal reputations and in the recognition of others. For instance, reputation systems that rate participants in terms of the quality of their contributions provide a readily available inventory of experts to knowledge seekers. Despite the growing interest in identities, however, there is little empirical research about how identities in the communities influence knowledge contribution. Therefore, the goal of this study is to better understand knowledge contribution by examining the roles of social capital and identity in virtual communities. Based on a theoretical framework of social capital and identity theory, we develop and test a theoretical model and evaluate our hypotheses. Specifically, we propose three variables such as cohesiveness, reciprocity, and commitment, referring to the social capital theory, as antecedents of knowledge contribution in virtual communities. We further posit that members with a strong identity (self-presentation and group identification) contribute more knowledge to virtual communities. We conducted a field study in order to validate our research model. We collected data from 192 members of virtual communities and used the PLS method to analyse the data. The tests of the measurement model confirm that our data set has appropriate discriminant and convergent validity. The results of testing the structural model show that cohesion, reciprocity, and self-presentation significantly influence knowledge contribution, while commitment and group identification do not significantly influence knowledge contribution. Our findings on cohesion and reciprocity are consistent with the previous literature. Contrary to our expectations, commitment did not significantly affect knowledge contribution in virtual communities. This result may be due to the fact that knowledge contribution was voluntary in the virtual communities in our sample. Another plausible explanation for this result may be the self-selection bias for the survey respondents, who are more likely to contribute their knowledge to virtual communities. The relationship between self-presentation and knowledge contribution was found to be significant in virtual communities, supporting the results of prior literature. Group identification did not significantly affect knowledge contribution in this study, inconsistent with the wealth of research that identifies group identification as an important factor for knowledge sharing. This conflicting result calls for future research that examines the role of group identification in knowledge contribution in virtual communities. This study makes a contribution to theory development in the area of knowledge management in general and virtual communities in particular. For practice, the results of this study identify the circumstances under which individual factors would be effective for motivating knowledge contribution to virtual communities.

• Korean Journal of Childcare and Education
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• v.11 no.2
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• pp.177-200
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• 2015

The purpose of this study is to analyze physical activities in 'Teachers' Guidance Books for the Nuri Curriculum of 4-year-old children' through simulated instruction of pre-service teachers and, through this, to help them better perform physical activities in their field education for early-aged. The subjects of the study were 30 sophomore students in the early-aged children's Education Department in their 2ndsemester of K University located in Gyeonggi-province. For the analysis of physical activities in 'Teachers' Guidance Books for Nuri Curriculum of 4-year-old children', a qualitative study was conducted and data were collected through informal interviews, reflective journals of pre-service teachers and 30 sessions of education assessment sports. The results of the analysis on the physical activities in 'Teachers' Guidance Books for Nuri Curriculum of 4-year-old children' are as follows; first, preliminary teachers of early-aged children understood the major goal of physical activities in 'Teachers' Guidance Books for Nuri Curriculum of 4-year-old children' as 'expressing.' Second, the teachers thought careful analysis is required on media such as 'video, illustration books, sounds, picture materials' presented together with physical activities in 'Teachers' Guidance Books for Nuri Curriculum of 4-year-old children.' Third, teachers pointed out 'activities that were difficult to understand for pre-service early childhood teachers' and 'improperly presented activities different from the title' as errors and problems in the performance of the Nuri Curriculum. Fourth, as for 'points to make improvement on', pre-service early childhood teachers' requested basic physical activities before the actual activities, the provision of proper actual materials, the necessity of active demonstrations of teachers and making a regulation for the situation of physical activities by early-aged children and teachers together. The results of the study illustrate that deep contemplation and judgment is required of the teachers before conducting physical activities of the Nuri Curriculum.

• Management & Information Systems Review
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• v.36 no.1
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• pp.59-80
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• 2017

Due to the recent deepening youth unemployment aftereffect, government, companies and universities seek a youth unemployment resolution method and jobs creating measures. But there are indications that the Good Job the university students prefer are limited and sudden rise of the youth unemployment rate mirrors the situation in Japan 20 years ago. Thus, based on the preceding research, this research attempted to perform comparative analysis on Korean and Japanese university students' employment preparation activities and perceived organizational support affect their Awareness of Good Job. To achieve the goal, 2013 GOMS 5,380 copies provided by Korea Employment Information Service are used in the case of Korea and total 5,636 copies within 256 questionnaires targeted to Japanese university students are used in the case of Japan. The results of analysis are as follows. The effect relationship between the perception of organizational support and awareness of Good Job showed a positive influence both in Korea and Japan. The effect relationship between employment preparation activities and awareness of Good Job showed a meaningful effect in Korea whereas it showed no effect in Japan. In the relationship between activities of employment preparation and awareness of Good Job, moderating effect of gender and major field of study didn't show any effect either in Korea or Japan. The results of this research are as follows. First, because it is verified that the support of university has positive influence on the university students' awareness of Good Job, it seems that universities need to intensify the support for the students' welfare enhancement, education satisfaction and the structural support system. Second, the gap of attitude of employment preparation activities and awareness of Good Job between Korea and Japan occurred due to the levels of social structure, welfare and wage differences in the two countries. Therefore, if measures of policy to resolve the welfare and wage gaps between conglomerates and smaller enterprises are enacted, the awareness of younger generations to the Good Job will show a corresponding effect.