The purpose of this study is to develop a method for establishing an educational system for World Heritage interpretation applicable to World Heritage sites and to design an education program for World Heritage interpretation by applying the developed education system in this study. This study first identified the definition of interpretation and the relationship between interpretation and education through previous research and analyzed the role and significance of heritage interpretation due to the paradigm transition in the capacity building of World Heritage sites. Next, the educational status of World Heritage interpretation was analyzed through educational institutions, educational subjects, and curriculum examples. Finally, this study argues that a method for establishing a World Heritage interpretation education system should be presented according to the composition of the curriculum, and the interpretation curriculum planning by interpreters should be proposed focusing on the Outstanding Universal Value (OUV) of World Heritage sites.

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald’s (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

본 연구는 미중 경쟁이 심화되고 있는 오늘날, 동남아시아를 포괄한 동아시아 지역에 있어 일본의 원조 이념 변화가 미친 정책적 영향이 무엇이고 어떠한 함의를 가지고 있는가에 대한 질문에서 출발했다. 이 에 대한 연구의 주요 결론은 다음과 같다. 2000년대 이후 일본은 '원 조 이념의 변화'를 추진했고 이를 통해 기존의 '비군사화 규범' 중심의 원조 이념에서 벗어나 안전보장 규범을 적극적으로 수용했으며, 특히 해상법집행에 중점을 두고 동남아시아 국가의 해양능력구축을 지원하 고 있다. 이제까지 방산수출을 통한 접근은 전무하다고 보아도 좋을 정 도로 제한적이었으나, 원조 이념의 변화와 함께 방위장비 해외이전이 자유로워지고 '원조화'하면서 장기적으로는 변화의 가능성이 보이고 있 다. 한국은 일본의 사례를 참고하면서, 동남아시아 역내 국가들의 수요 와 우려가 무엇인지를 상대국 입장에서 파악하는 해양능력구축 지원을 추진할 필요가 있다.

Recently, local studies looking at the 'local(region) as a whole' are gradually increasing. The study of local architectural history is important in that it provides specific information that encompasses the local and the entire and clues to three-dimensionalize the time and space in the local. To infer the 'presentness' of each era, reliable data in various fields are needed. Recently, as many databases (DB) and archives, from the 'National Archives of Korea' to other local archives, have been established and publicized, research resources in the local are growing rapidly in quantity and quality. Nevertheless, it is difficult to comprehensively check the data necessary to study the local architectural history(local architectural history research resources). Against this background, this study confirmed the trend of changes in the archive construction environment and the status and problems of local architectural history research resources in places that currently disclose local history research resources among generalized web-archives. Next, the relationship between the actual research on local architectural history was confirmed through the analysis of existing studies and the data used for Jeju. As a result, local studies, local archives, and local architectural history research agree with recent changes in local research trends, and the degree of archival construction has reached the same level as the available research resources except core data in local architectural history research. However, there is a problem that the density of information that can be used is low because the local architectural history research resources that can be obtained are fragmented because there are no archives and construction entities specialized in local architecture. As each archive has entered the stabilization and upgrading stage, the construction of new archives needs to be reconsidered, but it is time to find a detailed way to link related information quickly and accurately, such as private records, to reduce the gap in information needed in terms of research on local architecture and architecture history.

The management of algal bloom is essential for the proper management of water supply systems and to maintain the safety of drinking water. Chlorophyll-a(Chl-a) is a commonly used indicator to represent the algal concentration. In recent years, advanced machine learning models have been increasingly used to predict Chl-a in freshwater systems. Machine learning models show good performance in various fields, while the process of model development requires considerable labor and time by experts. Automated machine learning(auto ML) is an emerging field of machine learning study. Auto ML is used to develop machine learning models while minimizing the time and labor required in the model development process. This study developed an auto ML to predict Chl-a using auto sklearn, one of most widely used open source auto ML algorithms. The model performance was compared with other two popular ensemble machine learning models, random forest(RF) and XGBoost(XGB). The model performance was evaluated using three indices, root mean squared error, root mean squared error-observation standard deviation ratio(RSR) and Nash-Sutcliffe coefficient of efficiency. The RSR of auto ML, RF, and XGB were 0.659, 0.684 and 0.638, respectively. The results shows that auto ML outperforms RF, and XGB shows better prediction performance than auto ML, while the differences between model performances were not significant. Shapley value analysis, an explainable machine learning algorithm, was used to provide quantitative interpretation about the model prediction of auto ML developed in this study. The results of this study present the possible applicability of auto ML for the prediction of water quality.

본 글의 문제의식은 위성정당 출현의 재발을 막기 위해 선거법에 대한 여야합의처리의 중요성을 강조하는 것이다. 본 글의 목적은 선거법 개정 방향의 대안을 모색하기 위해 여야합의가 깨진 상황에서 민주당의 다수 결주의에 따른 졸속적인 준연동형 비례제 선거법의 강행처리가 어떻게 위성정당을 제도화하고 21대 총선에서 양당제 효과를 구축하였는지를 인 과론적으로 살펴보는 데 있다. 당연한 결과이지만 이런 위성정당의 출현 과 제도화는 소수정당의 정치적 진출을 활성화하여 다당체제 구축 효과 를 기대했던 준연동형 비례제 선거법의 취지를 무력화하였다는 점에서, 졸속적인 선거법 논의과정과 다수결주의에 따른 강행처리의 문제점을 근 본적으로 시정해야 한다는 것을 강조하였다. 본 글에서는 “독일식 연동형 비례대표제” 대신 “한국식 병립형 비례대표제”로의 전환을 제언하고 있다.