• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.177-192
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• 2012

This is a study of match-making systems that considers the mutual satisfaction of matching partners. Recently, recommendation systems have been applied to people recommendation, such as recommending new friends, employees, or dating partners. One of the prominent domain areas is match-making systems that recommend suitable dating partners to customers. A match-making system, however, is different from a product recommender system. First, a match-making system needs to satisfy the recommended partners as well as the customer, whereas a product recommender system only needs to satisfy the customer. Second, match-making systems need to include as many participants in a matching pool as possible for their recommendation results, even with unpopular customers. In other words, recommendations should not be focused only on a limited number of popular people; unpopular people should also be listed on someone else's matching results. In product recommender systems, it is acceptable to recommend the same popular items to many customers, since these items can easily be additionally supplied. However, in match-making systems, there are only a few popular people, and they may become overburdened with too many recommendations. Also, a successful match could cause a customer to drop out of the matching pool. Thus, match-making systems should provide recommendation services equally to all customers without favoring popular customers. The suggested match-making system, called Mutually Beneficial Matching (MBM), considers the reciprocal satisfaction of both the customer and the matched partner and also considers the number of customers who are excluded in the matching. A brief outline of the MBM method is as follows: First, it collects a customer's profile information, his/her preferable dating partner's profile information and the weights that he/she considers important when selecting dating partners. Then, it calculates the preference score of a customer to certain potential dating partners on the basis of the difference between them. The preference score of a certain partner to a customer is also calculated in this way. After that, the mutual preference score is produced by the two preference values calculated in the previous step using the proposed formula in this study. The proposed formula reflects the symmetry of preferences as well as their quantities. Finally, the MBM method recommends the top N partners having high mutual preference scores to a customer. The prototype of the suggested MBM system is implemented by JAVA and applied to an artificial dataset that is based on real survey results from major match-making companies in Korea. The results of the MBM method are compared with those of the other two conventional methods: Preference-Based Matching (PBM), which only considers a customer's preferences, and Arithmetic Mean-Based Matching (AMM), which considers the preferences of both the customer and the partner (although it does not reflect their symmetry in the matching results). We perform the comparisons in terms of criteria such as average preference of the matching partners, average symmetry, and the number of people who are excluded from the matching results by changing the number of recommendations to 5, 10, 15, 20, and 25. The results show that in many cases, the suggested MBM method produces average preferences and symmetries that are significantly higher than those of the PBM and AMM methods. Moreover, in every case, MBM produces a smaller pool of excluded people than those of the PBM method.

• Journal of the Korean Society of Radiology
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• v.15 no.5
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• pp.667-673
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• 2021

As we enter the era of the 4th industrial revolution, it is judged that the scope of work of radiologists will be further expanded according to the innovation and advancement of radiation medical technology development. In this study, the current status of medical equipment and radiology technicians was identified, and basic data were provided for the plan for nurturing talents in the field of radiation medical technology in the era of the 4th industrial revolution, as well as career and employment counseling. Data from the second quarter of 2020 and the second quarter of 2021 were analyzed using health and medical big data. As a result of comparing the status of medical equipment by type in 2021 compared to 2020, C-Arm X-ray examination equipment increased by 5.83% to 6,638 units, followed by MRI examination equipment 1,811 units 5.29%, and angiography equipment 725 units 5.22% , general X-ray examination equipment 21,557 units increased 3.99%, CT examination equipment 2,136 units 3.03%, and breast examination equipment 3,425 units increased 3.00%. As a result of a comparison of the total number of radiologists in 2021 compared to 2020, the number was 29,038, an increase of 2.73%. As a result of comparing the status of radiographers by region, the increase was highest in the Gyeonggi region with 5.96%, followed by the Gangwon region with a 5.66% increase and the Chungnam region with a 3.81% increase. In a situation where the number of medical equipment and radiologist manpower is increasing, universities are developing specialized knowledge and practical competency through subject development related to the understanding and utilization of customized artificial intelligence and big data that can be applied in the medical radiation technology field in the era of the 4th industrial revolution. It is necessary to nurture qualified radiographers, and at the level of the association, it is thought that active policies are needed to create new jobs and improve employment.

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.33-53
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• 2021

In the crossroads of social change caused by the spread of the Fourth Industrial Revolution and the prolonged COVID-19, the Korean government announced the Digital New Deal policy on July 14, 2020. The Digital New Deal policy's primary goal is to create new businesses by accelerating digital transformation in the public sector and industries around data, networks, and artificial intelligence technologies. However, in a rapidly changing social environment, information asymmetry of the future benefits of technology can cause differences in the public's ability to analyze the direction and effectiveness of policies, resulting in uncertainty about the practical effects of policies. On the other hand, the media leads the formation of discourse through communicators' role to disseminate government policies to the public and provides knowledge about specific issues through the news. In other words, as the media coverage of a particular policy increases, the issue concentration increases, which also affects public decision-making. Therefore, the purpose of this study is to verify the dynamic relationship between the media coverage and the stock market on the Korean government's digital New Deal policy using Granger causality, impulse response functions, and variance decomposition analysis. To this end, the daily stock turnover ratio, daily price-earnings ratio, and EWMA volatility of digital technology-based companies related to the digital new deal policy among KOSDAQ listed companies were set as variables. As a result, keyword search volume, daily stock turnover ratio, EWMA volatility have a bi-directional Granger causal relationship with media coverage. And an increase in media coverage has a high impact on keyword search volume on digital new deal policies. Also, the impulse response analysis on media coverage showed a sharp drop in EWMA volatility. The influence gradually increased over time and played a role in mitigating stock market volatility. Based on this study's findings, the amount of media coverage of digital new deals policy has a significant dynamic relationship with the stock market.

• The Korean Society of Law and Medicine
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• v.21 no.2
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• pp.75-103
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• 2020

There is a growing voice that medical information should be shared because it can prepare for genetic diseases or cancer by analyzing and utilizing medical information in big data or artificial intelligence to develop medical technology and improve patient care. The utilization and protection of patients' personal information are the same as two sides of the same coin. Medical institutions or medical personnel should take extra caution in handling personal information with high environmental distinct characteristics and sensitivity, which is different from general information processors. In general, the patient's personal information is processed by medical personnel or medical institutions through the processes of collection, creation, and destruction. Still, the use of terms related to personal information in the Medical Service Act is jumbled, or the scope of application is unclear, so it relies on the interpretation of precedents. For the medical personnel or the founder of the medical institution, in the case of infringement of Article 24(4), it cannot be regarded that it means only medical treatment information among personal information, whether or not it should be treated the same as the personal information under Article 23, because the sensitive information of patients is recorded, saved, and stored in electronic medical records. Although the prohibition of information leakage under Article 19 of the Medical Service Act has a revision; 'secret' that was learned in business was revised to 'information', but only the name was changed, and the benefit and protection of the law is the same as the 'secret' of the criminal law, such that the patient's right to self-determination of personal information is not protected. The Privacy Law and the Local Health Act consider the benefit and protection of the law in 'information learned in business' as the right to self-determination of personal information and stipulate the same penalties for personal information infringement such as leakage, forgery, alteration, and damage. The privacy regulations of the Medical Service Act require that the terms be adjusted uniformly because the jumbled use of terms can confuse information subjects, information processors, and shows certain limitations on the protection of personal information because the contents or scope of the regulations of the Medical Service Law for special corporations and the Privacy Law may cause confusion in interpretation. The patient's personal information is sensitive and must be safely protected in its use and processing. Personal information must be processed in accordance with the protection principle of Privacy Law, and the rights such as privacy, freedom, personal rights, and the right to self-determination of personal information of patients or guardians, the information subject, must be guaranteed.

• Journal of the korean academy of Pediatric Dentistry
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• v.48 no.2
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• pp.221-228
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• 2021

The aim of this study was to evaluate the use of easily accessible machine learning application to identify mesiodens, and to compare the ability to identify mesiodens between trained model and human. A total of 1604 panoramic images (805 images with mesiodens, 799 images without mesiodens) of patients aged 5 - 7 years were used for this study. The model used for machine learning was Google's teachable machine. Data set 1 was used to train model and to verify the model. Data set 2 was used to compare the ability between the learning model and human group. As a result of data set 1, the average accuracy of the model was 0.82. After testing data set 2, the accuracy of the model was 0.78. From the resident group and the student group, the accuracy was 0.82, 0.69. This study developed a model for identifying mesiodens using panoramic radiographs of children in primary and early mixed dentition. The classification accuracy of the model was lower than that of the resident group. However, the classification accuracy (0.78) was higher than that of dental students (0.69), so it could be used to assist the diagnosis of mesiodens for non-expert students or general dentists.

• 기호학연구
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• no.56
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• pp.45-70
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• 2018

Criticism of the postmodern philosophy of the system of representation, which has continued since the Renaissance, is based on a critique of the dichotomy that separates the subjects and objects and the environment from the human being. Interactivity, highlighted in a series of works emerging as postmodern trends in the 1960s, was transmitted to an interactive aspect of digital art in the late 1990s. The key feature of digital art is the possibility of infinite variations reflecting unpredictable changes based on public participation on the spot. In this process, the importance of computer programs is highlighted. Instead of using the existing program as it is, more and more artists are creating and programming their own algorithms or creating unique algorithms through collaborations with programmers. We live in an era of paradigm shift in which programming itself must be considered as a creative act. Simulation technology and VR technology draw attention as a technique to represent the meaning of reality. Simulation technology helps artists create experimental works. In fact, Baudrillard's concept of Simulation defines the other reality that has nothing to do with our reality, rather than a reality that is extremely representative of our reality. His book Simulacra and Simulation refers to the existence of a reality entirely different from the traditional concept of reality. His argument does not concern the problems of right and wrong. There is no metaphysical meaning. Applying the concept of simulation to algorithmic art, the artist models the complex attributes of reality in the digital system. And it aims to build and integrate internal laws that structure and activate the world (specific or individual), that is to say, simulate the world. If the images of the traditional order correspond to the reproduction of the real world, the synthesized images of algorithmic art and simulated space-time are the forms of art that facilitate the experience. The moment of seeing and listening to the work of Ian Cheng presented in this article is a moment of personal experience and the perception is made at that time. It is not a complete and closed process, but a continuous and changing process. It is this active and situational awareness that is required to the audience for the comprehension of post-representation's forms.

• Food Science and Industry
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• v.51 no.4
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• pp.278-301
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• 2018

Although more than 80% of living organisms are found in marine ecosystems, only less than 10% of marine resources have been utilized for human food consumptions and other usages. It is well known that marine resources (fish, shellfish and algae) have exceptional nutritional properties; however, their functional characteristic has not been completely discovered. It is believed that metabolites (organic compounds, proteins, peptides, lipids, minerals, etc.) play an important role to show its biological properties. Marine proteins and peptides are considered to be future drugs due to their excellent biological activities with a fewer adverse side effect. Marine peptides show several biological activities, including antimicrobial, antioxidant, anti-inflammatory, anti-cancer, anti-viral, anti-tumor, anti-diabetic, anti-hypertensive, anti-coagulant, immunomodulatory, appetite suppressing and neuroprotective effects. Therefore, the pharmaceutical, nutraceutical, and cosmeceutical companies have been paid attention to the marine peptides to commercialize into products. This current review mainly focused on the above mentioned biological activities of marine peptides and protein hydrolysates as a functional food and pharmaceutical applications. To commercialize these materials in industrial level required large quantity in high-purity level, and it is complicated to produce huge quantity from the marine resources due to insufficient raw materials, unavailability of raw materials through a year, hinder the growth with geographical variations, and availability of compounds in extreme small quantities. The best solution for these issues is to introduce new modern technologies such as artificial intelligence robots, drones, submersibles and automated raw material harvesting vessels in farming industries instead of man power, which will lead to 4th industrial revolution.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Phonetics and Speech Sciences
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• v.11 no.1
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• pp.29-40
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• 2019

The paper investigates the minimal pair of Korean phonemes quantitatively. To achieve this goal, I calculated the number of consonant minimal pairs in the syllable-initial position as both raw counts and relative counts, and analyzed the part of speech relations of the two words in the minimal pair. "Urimalsaem" was chosen as the object of this study because it was judged that the minimal pair analysis should be done through a dictionary and it is the largest among Korean dictionaries. The results of the study are summarized as follows. First, there were 153 types of minimal pairs out of 337,135 examples. The ranking of phoneme pairs from highest to lowest was 'ㅅ-ㅈ, ㄱ-ㅅ, ㄱ-ㅈ, ㄱ-ㅂ, ㄱ-ㅎ, ${\ldots}$, ㅆ-ㅋ, ㄸ-ㅋ, ㅉ-ㅋ, ㄹ-ㅃ, ㅃ-ㅋ'. The phonemes that played a major role in the formation of the minimal pair were /ㄱ, ㅅ, ㅈ, ㅂ, ㅊ/, in that order, which showed a high proportion of palatals. The correlation between the raw count of minimal pairs and the relative count of minimal pairs was found to be quite high r=0.937. Second, 87.91% of the minimal pairs shared the part of speech (same syntactic category). The most frequently observed type has been 'noun-noun' pair (70.25%), and 'vowel-vowel' pair (14.77%) was the next ranking. It can be indicated that the minimal pair could be grouped into similar categories in terms of semantics. The results of this study can be useful for various research in Korean linguistics, speech-language pathology, language education, language acquisition, speech synthesis, and artificial intelligence-machine learning as basic data related to Korean phonemes.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.1
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• pp.54-69
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• 2021

In 2020, civilized society's overall lifestyle showed a distinct change from consumable analog media, such as paper, to digital media with the increased penetration of cloud computing, and from wired media to wireless media. Based on these social changes, this work examines whether the use of computer media in the field of landscape architecture is appropriately applied. This study will give directions for new computer media classes in landscape architectural education in the 4th Industrial Revolution era. Landscape architecture is a field that directly proposes the realization of a positive lifestyle and the creation of a living environment and is closely connected with social change. However, there is no clear evidence that landscape architectural education is making any visible change, while the digital infrastructure of the 4th Industrial Revolution, such as Artificial Intelligence (AI), Big Data, autonomous vehicles, cloud networks, and the Internet of Things, is changing the contemporary society in terms of technology, culture, and economy among other aspects. Therefore, it is necessary to review the current state of the use of computer technology and media in landscape architectural education, and also to examine the alternative direction of the curriculum for the new digital era. First, the basis for discussion was made by studying the trends of computational design in modern landscape architecture. Next, the changes and current status of computer media classes in domestic and overseas landscape education were analyzed based on prior research and curriculum. As a result, the number and the types of computer media classes increased significantly between the study in 1994 and the current situation in 2020 in the foreign landscape department, whereas there were no obvious changes in the domestic landscape department. This shows that the domestic landscape education is passively coping with the changes in the digital era. Lastly, based on the discussions, this study examined alternatives to the new curriculum that landscape architecture department should pursue in a new degital world.