• Korean Journal of Culture and Social Issue
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• v.12 no.3
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• pp.85-127
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• 2006

This study examines the formation and change of relational trust among Korean adolescents using indigenous psychological perspective. Among adolescents, we examine the basis of trust of their parents, friends and teacher and also their trust in people and institutions. A total of 968 adolescents (227 elementary, 284 middle school, 213 high school, and 244 university students) completed a questionnaire that contains the Relational Trust Scale developed by Kim and Park (2004a), Generalized Trust Scale developed by Yamagishi (1998) and a scale measuring trust of people and institutions (Kim, Helgesen & Ahn, 2002). The results are as follows. First, adolescents trust their parents due to their sacrifice, followed by their counsel and advice, dependability, consanguinity and respects. They trust their teachers due to their counsel and advice, followed by respect, sacrifice, and mutual trust. They trust their friends due to their advice, followed by empathy, mutual trust, dependability, friendship and sacrifice. Second, trust of their parents are highest among the elementary school students and lowest for the middle school students and lower for older age groups. The trust in teachers are highest among the elementary school students and lowest among the middle school and university students. In contrast, trust in friends increase from elementary school students to older age groups. Third, among trust in people and institutions, they are highest for parents and family and lowest for politicians and political parties. Except for trust in friends, trust of significant others and institutions (family, teachers, neighbors, company executives and politicians) is highest among the youngest age group and it decreases with older age group. Fourth, Yamagishi's generalized trust was highest among the older age groups (high school and university students). The patterns of results for relational trust and generalized trust is different and it points to the importance of understanding trust from the indigenous psychological perspective.

• Land and Housing Review
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• v.15 no.1
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• pp.1-22
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• 2024

This research explores the household and housing characteristics of young renters aged between 19 and 34 living independently in rental housing of non-Seoul Metropolitan Area (non-SMA) and to determine the factors of their housing affordability and residential environment qualities in two districts of non-SMA - metropolises and non-metropolises. Using the 2020 Korean Housing Survey (KHS), this study identified 1,191 unmarried young renters, and most were single adults in mid-twenties who were salaried workers with a bachelor's degree or higher. Also, many lived in single-room occupancy of non-APT housing for less than 2 years and rarely relied on social services. The findings showed that the distinction of local housing market between metropolises and non-metropolises forced the former to spend more housing expense (tenancy deposit and rental fees) than the latter. With regard to housing affordability indices (Schwabe index, housing expense ratio and rent to income ratio), most were housing cost-burdened and nearly one quarter were severely rent-burdened. The regression analysis indicated that housing affordability in both districts was positively affected by income increase and social services, and housing satisfaction in non-metropolises was added to its determinants. Further, residential environment qualities were largely divided into two groups of livelihood and urban infrastructure, and the two factors influenced residential assessment in both districts. Since young renters interdependently living had suffered with housing affordability, both income growth and housing assistance are critically required to enable them not just to reduce the burden but to ensure livability.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.49-95
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• 2024

Polygraph examination, statement validity analysis and P300-based concealed information test are major three examination tools, which are use to determine a person's truthfulness and credibility in criminal procedure. Although polygraph examination is most common in criminal procedure, but it has little admissibility of evidence due to the weakness of scientific basis. In 1990s to support the weakness of scientific basis about polygraph, Farwell and Donchin proposed the P300-based concealed information test technique. The P300-based concealed information test has two strong points. First, the P300-based concealed information test is easy to conduct with polygraph. Second, the P300-based concealed information test has plentiful scientific basis. Nevertheless, the utilization of P300-based concealed information test is infrequent, because of the quantity of probe stimulus. The probe stimulus contains closed information that is relevant to the crime or other investigated situation. In tradition P300-based concealed information test protocol, three or more probe stimuli are necessarily needed. But it is hard to acquire three or more probe stimuli, because most of the crime relevant information is opened in investigative situation. In addition, P300-based concealed information test uses oddball paradigm, and oddball paradigm makes imbalance between the number of probe and irrelevant stimulus. Thus, there is a possibility that the unbalanced number of probe and irrelevant stimulus caused systematic underestimation of P300 amplitude of irrelevant stimuli. To overcome the these two limitation of P300-based concealed information test, one-probe P300-based concealed information test protocol is explored with various machine learning algorithms. According to this study, parameters of the modified one-probe protocol are as follows. In the condition of female and male face stimuli, the duration of stimuli are encouraged 400ms, the repetition of stimuli are encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. In the condition of two-syllable word stimulus, the duration of stimulus is encouraged 300ms, the repetition of stimulus is encouraged 60 times, the analysis method of P300 amplitude is encouraged peak to peak method, the cut-off of guilty condition is encouraged 90% and the cut-off of innocent condition is encouraged 30%. It was also conformed that the logistic regression (LR), linear discriminant analysis (LDA), K Neighbors (KNN) algorithms were probable methods for analysis of P300 amplitude. The one-probe P300-based concealed information test with machine learning protocol is helpful to increase utilization of P300-based concealed information test, and supports to determine a person's truthfulness and credibility with the polygraph examination in criminal procedure.

• Journal of Korean Home Economics Education Association
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• v.25 no.1
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• pp.95-118
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• 2013

The purpose of this study was to analyze the contents of housing related to characters, objectives, and contents of FCS curricular from the 1st to 2009 revised curriculum of secondary school. The data were downloaded from the NCIC homepage(http://www.ncic.re.kr/2012. 04. 08) from the 1st(1955. 08) to 2009 revised curriculum(2012. 03) of secondary school. After examining the characters and objectives of each curriculum, contents of housing was analyzed by units and context elements of middle and high school separately. The titles of the subject, the objectives, the instructions, the leaning spheres, weekly hours, grade and gender of candidates, the emphasis of the instruction, etc. have been changed through the curriculum revision. The 6th curriculum was the main period to open to both genders, the $7^{th}$ was the period to combine with technology, the 2007 version was to change the structure of contents of home economics, and the 2009 version switched technology home economics from mandatory to optional in high school. The character of the courses was presented at the 1st curriculum, but it was left out from the $2^{nd}$ to $5^{th}$ curriculum. From the $6^{th}$ curriculum, the characters were separately given to middle and high school. The character of housing area started to appear only in high school home economics from the $7^{th}$ curriculum. The course objectives were described in all curriculum of both middle and high school. This applies to housing area as well. The course objectives have been modified in order to reflect value changes due to social issues. During each curriculum, contents of housing continued to change in context, course load, and candidates. Reflection of housing trends and social needs were the main causes of the change. 2009 version emphasizes on eco-life and sense of community.

• Korean Journal of Heritage: History & Science
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• v.37
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• pp.65-89
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• 2004

Up to now, the studies for a pond of ancient palace of Korea are mainly achieved in a landscape architectural field. In fact, we can't grasp the general aspects but we are only heard about the fragmentary ruins and remains by the people who are in charge of an archaeological excavation. In this thesis, therefore, I attempt to grasp the relational categories of the ponds of an ancient palace of Korea, and find out the ideological backgrounds of the ponds of a palace construction through classify them excavated so far. The ancient ponds of Korea are divided to the class of square ponds(I) and curved ponds(II) according a shapes of planes. The class of square ponds(I) are subdivided to the form IA of the class of square ponds and form IB of the class of square ponds by whether it has an island, artificial hill and ornamental stone or not. And the class of the curved ponds(II) are divided to the form IIC that is only composed of curves in shore and the form IID that is composed of curves and straight lines. According the size, it is divided to a small size that is below the maximal diameter, 20m, and a large size that is more than 45m, after all, the ponds of the ancient palaces are devided to IAa, IAb, IBa, II Ca, IICb, IIDa. The square ponds and the curved ponds are co-exist from the initial stage when a pond of a place was found in our country and are succeeded or changed after Silla unified the three Kingdoms. In other words, we can infer a continuity from the earlier stage from the fact that there is a flat figure ground mainly constituted by the ponds of a palace mixed up of a straight line and a curved line in United Silla Kingdom while it succeeds the ponds of a palace that has a square form of Goguryo in Balhai. Different from the successional relation of the flat figure grounds, in an aspect of the elements of the construction, the site of the arbor at the top of the island and the bridge facilities in a field of a palace those are not exist in three Kingdoms period are appeared in United Silla Kingdom. The point that this aspect is simultaneously appeared in a neighboring country, or Japan, allows us to infer that there may be some motivations cause the changes in a construction of the ponds of a palace of Korea, China and Japan from the latter half of the 7th century to the first half of the 8th century. The ideological backgrounds of the ponds of a palace construction are divided roughly into Taoism and Buddhism. We can recognize that the ponds of a palace made up of the islands, the artificial hills and the garden rocks reflect Taiosm, considering the records of the ponds a palace of Korea and China are all use the term, Taoism, or the concrete statement represents that the islands, the artificial hills and the garden rocks are used in the description of the ponds of a palace of Korea. Both two are, therefore, obviously differentiated from the ponds of a palace that doesn't include them. We can conclude that the ponds of a palace that doesn't include them are colored by Buddhism since they are overtly distinguished from the class of curved ponds that reflect Taoism at the same period and they are identical with the site of an ancient temples in an aspect of their type and construction.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.19-42
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• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.