• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.61-67
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• 2024

Soldiers serving in military units and institutions are subject to strict security policies and technologies because they handle sensitive and confidential information related to national security, so they are likely to experience security stress. The purpose of this study is to recognize the need to manage the security stress of military personnel and to suggest management measures. To this end, a literature study was conducted on 12 KCI(Korean Journal Citation Index) journals dealing with security stress. Since 2016, research on security stress has been conducted mainly through empirical analysis through surveys. Studies related to security stress were divided into studies dealing with factors that affect stress, the relationship between security stress and security compliance intentions, and factors that reduce security stress. In particular, it was confirmed that factors such as organizational justice, organizational technical support, and security feedback can alleviate security stress. Next, by applying the results of this literature study to the defense security environment, we presented security stress management measures for military personnel in terms of improving security-related organizational justice awareness, technical support, and security feedback. The significance of this study is that we recognized the need to manage military personnel's security stress and reviewed practical measures related to this.

• Korean Journal of Heritage: History & Science
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• v.56 no.2
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• pp.80-98
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• 2023

In the late Joseon Dynasty, when the bride would ride a palanquin when she went to live with her in-laws, it was a custom to cover the palanquin with tiger skin to ward off misfortunes that may come her way. The higher classes used tiger skin or leopard skin for this purpose, but the common people had to substitute this expensive item with a tiger pattern painted on a blanket. Such blankets were called hotanja, hogu, hoguyok and the like. The term "hotanja" is a pure Korean word. It is not known when the cover for the bridal palanquin was first used, but it was popular from the end of the 19^th century and then gradually disappeared. This is due to the introduction of new Western style weddings that eliminated the need for a bridal palanquin. The tiger print blanket was used not only to cover the bride's palanquin but also to cover a table or floor during the wedding ceremony. This study ran a material analysis on nine pieces of tiger print blankets. All of the blanket artifacts examined in this study had an outer cover and a lining made of fabric that used cotton thread for the warp and wool thread for the weft. Two kinds of wool were found in the weft thread in the outer covers: fat-tailed sheep hair from China and goat hair for carpets from the Hebei province, China. Records show that "blankets with painted tiger patterns" were imported from Russia, and the imported blankets were from Russia and China. The outer cover can be categorized into six types, and the lining into three types depending on the weave and direction of the thread twist. The hem facing can be divided into four types. The lining and outer cover use the full width of the fabric, which was woven in wide widths of 135 cm or wider. The tiger pattern on the blanket was made by stenciling. The stencil design of the body and tail of the tiger were placed on a red blanket to be painted in white, and then the background color of the tiger, which is yellow, would be painted over the white, and then black stripes would be added. The pattern of the tiger varies, which shows that the blankets were made by various craftspeople. The pattern of the tiger print blanket is usually of a tiger lying down, but there were tiger print blankets with a tiger standing up. The pattern of the tiger grew smaller over time, and flower patterns were added in the background. Decorative elements were gradually added to the tiger print blanket patterns, but its function as a palanquin cover became lost. By taking the features of tiger print blankets into consideration, it can be assumed that there are imported pieces among the remaining pieces, and were produced in various places because it was popular at that time.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.1-21
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• 2020

With the rapid development of artificial intelligence technology, various techniques have been developed to extract meaningful information from unstructured text data which constitutes a large portion of big data. Over the past decades, text mining technologies have been utilized in various industries for practical applications. In the field of business intelligence, it has been employed to discover new market and/or technology opportunities and support rational decision making of business participants. The market information such as market size, market growth rate, and market share is essential for setting companies' business strategies. There has been a continuous demand in various fields for specific product level-market information. However, the information has been generally provided at industry level or broad categories based on classification standards, making it difficult to obtain specific and proper information. In this regard, we propose a new methodology that can estimate the market sizes of product groups at more detailed levels than that of previously offered. We applied Word2Vec algorithm, a neural network based semantic word embedding model, to enable automatic market size estimation from individual companies' product information in a bottom-up manner. The overall process is as follows: First, the data related to product information is collected, refined, and restructured into suitable form for applying Word2Vec model. Next, the preprocessed data is embedded into vector space by Word2Vec and then the product groups are derived by extracting similar products names based on cosine similarity calculation. Finally, the sales data on the extracted products is summated to estimate the market size of the product groups. As an experimental data, text data of product names from Statistics Korea's microdata (345,103 cases) were mapped in multidimensional vector space by Word2Vec training. We performed parameters optimization for training and then applied vector dimension of 300 and window size of 15 as optimized parameters for further experiments. We employed index words of Korean Standard Industry Classification (KSIC) as a product name dataset to more efficiently cluster product groups. The product names which are similar to KSIC indexes were extracted based on cosine similarity. The market size of extracted products as one product category was calculated from individual companies' sales data. The market sizes of 11,654 specific product lines were automatically estimated by the proposed model. For the performance verification, the results were compared with actual market size of some items. The Pearson's correlation coefficient was 0.513. Our approach has several advantages differing from the previous studies. First, text mining and machine learning techniques were applied for the first time on market size estimation, overcoming the limitations of traditional sampling based- or multiple assumption required-methods. In addition, the level of market category can be easily and efficiently adjusted according to the purpose of information use by changing cosine similarity threshold. Furthermore, it has a high potential of practical applications since it can resolve unmet needs for detailed market size information in public and private sectors. Specifically, it can be utilized in technology evaluation and technology commercialization support program conducted by governmental institutions, as well as business strategies consulting and market analysis report publishing by private firms. The limitation of our study is that the presented model needs to be improved in terms of accuracy and reliability. The semantic-based word embedding module can be advanced by giving a proper order in the preprocessed dataset or by combining another algorithm such as Jaccard similarity with Word2Vec. Also, the methods of product group clustering can be changed to other types of unsupervised machine learning algorithm. Our group is currently working on subsequent studies and we expect that it can further improve the performance of the conceptually proposed basic model in this study.