• The Korean Journal of Archival Studies
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• no.79
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• pp.5-32
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• 2024

The Korean government ranked 3rd among 193 UN member countries in the UN's 2022 e-Government Development Index. Korea, which has consistently been evaluated as a top country, can clearly be said to be a leading country in the world of e-government. The lubricant of e-government is data. Data itself is neither information nor a record, but it is a source of information and records and a resource of knowledge. Since administrative actions through electronic systems have become widespread, the production and technology of data-based records have naturally expanded and evolved. Technology may seem value-neutral, but in fact, technology itself reflects a specific worldview. The digital order of new technologies, armed with hyper-connectivity and super-intelligence, not only has a profound influence on traditional power structures, but also has an a similar influence on existing information and knowledge transmission media. Moreover, new technologies and media, including data-based generative artificial intelligence, are by far the hot topic. It can be seen that the all-round growth and spread of digital technology has led to the augmentation of human capabilities and the outsourcing of thinking. This also involves a variety of problems, ranging from deep fakes and other fake images, auto profiling, AI lies hallucination that creates them as if they were real, and copyright infringement of machine learning data. Moreover, radical connectivity capabilities enable the instantaneous sharing of vast amounts of data and rely on the technological unconscious to generate actions without awareness. Another irony of the digital world and online network, which is based on immaterial distribution and logical existence, is that access and contact can only be made through physical tools. Digital information is a logical object, but digital resources cannot be read or utilized without some type of device to relay it. In that respect, machines in today's technological society have gone beyond the level of simple assistance, and there are points at which it is difficult to say that the entry of machines into human society is a natural change pattern due to advanced technological development. This is because perspectives on machines will change over time. Important is the social and cultural implications of changes in the way records are produced as a result of communication and actions through machines. Even in the archive field, what problems will a data-based archive society face due to technological changes toward a hyper-intelligence and hyper-connected society, and who will prove the continuous activity of records and data and what will be the main drivers of media change? It is time to research whether this will happen. This study began with the need to recognize that archives are not only records that are the result of actions, but also data as strategic assets. Through this, author considered how to expand traditional boundaries and achieves reterritorialization in a data-driven society.

• Korean Journal of Health Education and Promotion
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• v.1 no.1
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• pp.21-36
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• 1983

It is assumed that menarche is affected not only by the biological factors such as nutrition and genetic heritage, but also it is affected by other socio-cultural environmental factors including weather, geographic location, education and level of modernization. Also recent trend of menarche in Korea indicates that a lot of discussion are being generated to the need of sex education as a part of formal school education. The purpose of this study is to develop the school health education program by determine the age of menarche, the factors relavant to time of menarche and psycho-mental state of students at the time in menarche and investigate the present state of school health education relate to menarche of adolescents. The total number of 732 girls was drown from first, second and third grades of 4 middle schools in Seoul. For the data collection the survey was conducted during the period from May 1 to May 20, 1982 by using prepared questionair. The major results are summarized as follow; 1. Mean age at menarche and the percent distribution of menarche experienced. It was observed that about 68.7% of sampled students have been experienced menarche at the time interviewed. For the each group, age at menarche is revealed that among the students about 37.8% are experienced menarche for under 12 years old group, 62.1% for 13 year-old group, 80.6％ for 14 year-old group and 95.5% for over 15 years old. In sum it was found that the mean age at menarche was 12.3 years old, ranged from age at 10 as earlist the age at 15 as latest. 2. Variables associated with age at menarche. 1) There was tendency those student who belong to upper class economic status have had menarche earlier than those student who belong to lower class. Therefore, economic status is closely related to age at menarche. 2) In time of mother's education level, it is also found that those students whose mother's education levels from high school and college are experienced menarche earlier than those students whose mother's education levels from primary school and no-education. 3) However, in connection with home discipline, there was no significant relationship between age at menarche and home disciplines which are being treated "Rigid", "Moderated ", "Indifferent". 4) Degree of communication between parents and daughter about sex matters was found to be associated each others in determination of age at menarche. 5) It was found that high association between mother's menarche age and their daughter's menarche age was observed. Mother's age at menarche earlier trend to be shown also as earlier of their daughters. 6) Those students belong to "D & E" of physical substantiality index are trend to be earlier in menarche than those students in the index "A & B". 3. Psycho-mental state at the time of menarche. Out of the total students 68.2% had at least one or more than one of subjective symptoms. Shyness was shown as most higher prevalent symptom and others are fear, emotional instability, unpleasant feeling, depression, radical behavior, inferior complex and satisfaction appeared. Very few cases are appeared be guilty and stealing feeling. 4. The present status of school health education program related to menarche. As to the source of information about menarche, teacher was a main source with average index 5.88 and the other informants were mother & family member, friends, books and magagines, movies, television, and radio. For the problem solving at menarche, mother & family members were subject to discussion with an average index 6.02 as high. The others for discuss and knowledge about menarche were books, magagine, friends, teachers, and self-learning based on own experienced. The time of learning about menarche, it was learned as highest percentage with 43.2% at a 6 grades of primary school, middle school with 34.4%, 5 grade of primary school with 18.2%, and 4 grade of primary school with 4.0% respectively.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.