• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.101-116
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• 2015

Recently, due to the development of smart devices and social media, vast amounts of information with the various forms were accumulated. Particularly, considerable research efforts are being directed towards analyzing unstructured big data to resolve various social problems. Accordingly, focus of data-driven decision-making is being moved from structured data analysis to unstructured one. Also, in the field of recommendation system, which is the typical area of data-driven decision-making, the need of using unstructured data has been steadily increased to improve system performance. Approaches to improve the performance of recommendation systems can be found in two aspects- improving algorithms and acquiring useful data with high quality. Traditionally, most efforts to improve the performance of recommendation system were made by the former approach, while the latter approach has not attracted much attention relatively. In this sense, efforts to utilize unstructured data from variable sources are very timely and necessary. Particularly, as the interests of users are directly connected with their needs, identifying the interests of the user through unstructured big data analysis can be a crew for improving performance of recommendation systems. In this sense, this study proposes the methodology of improving recommendation system by measuring interests of the user. Specially, this study proposes the method to quantify interests of the user by analyzing user's internet usage patterns, and to predict user's repurchase based upon the discovered preferences. There are two important modules in this study. The first module predicts repurchase probability of each category through analyzing users' purchase history. We include the first module to our research scope for comparing the accuracy of traditional purchase-based prediction model to our new model presented in the second module. This procedure extracts purchase history of users. The core part of our methodology is in the second module. This module extracts users' interests by analyzing news articles the users have read. The second module constructs a correspondence matrix between topics and news articles by performing topic modeling on real world news articles. And then, the module analyzes users' news access patterns and then constructs a correspondence matrix between articles and users. After that, by merging the results of the previous processes in the second module, we can obtain a correspondence matrix between users and topics. This matrix describes users' interests in a structured manner. Finally, by using the matrix, the second module builds a model for predicting repurchase probability of each category. In this paper, we also provide experimental results of our performance evaluation. The outline of data used our experiments is as follows. We acquired web transaction data of 5,000 panels from a company that is specialized to analyzing ranks of internet sites. At first we extracted 15,000 URLs of news articles published from July 2012 to June 2013 from the original data and we crawled main contents of the news articles. After that we selected 2,615 users who have read at least one of the extracted news articles. Among the 2,615 users, we discovered that the number of target users who purchase at least one items from our target shopping mall 'G' is 359. In the experiments, we analyzed purchase history and news access records of the 359 internet users. From the performance evaluation, we found that our prediction model using both users' interests and purchase history outperforms a prediction model using only users' purchase history from a view point of misclassification ratio. In detail, our model outperformed the traditional one in appliance, beauty, computer, culture, digital, fashion, and sports categories when artificial neural network based models were used. Similarly, our model outperformed the traditional one in beauty, computer, digital, fashion, food, and furniture categories when decision tree based models were used although the improvement is very small.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.67-88
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• 2019

Webtoon is a Korean-style digital comics platform that distributes comics content produced using the characteristic elements of the Internet in a form that can be consumed online. With the recent rapid growth of the webtoon industry and the exponential increase in the supply of webtoon content, the need for effective webtoon content recommendation measures is growing. Webtoons are digital content products that combine pictorial, literary and digital elements. Therefore, webtoons stimulate consumer sentiment by making readers have fun and engaging and empathizing with the situations in which webtoons are produced. In this context, it can be expected that the sentiment that webtoons evoke to consumers will serve as an important criterion for consumers' choice of webtoons. However, there is a lack of research to improve webtoons' recommendation performance by utilizing consumer sentiment. This study is aimed at developing consumer sentiment pattern maps that can support effective recommendations of webtoon content, focusing on consumer sentiments that have not been fully discussed previously. Metadata and consumer sentiments data were collected for 200 works serviced on the Korean webtoon platform 'Naver Webtoon' to conduct this study. 488 sentiment terms were collected for 127 works, excluding those that did not meet the purpose of the analysis. Next, similar or duplicate terms were combined or abstracted in accordance with the bottom-up approach. As a result, we have built webtoons specialized sentiment-index, which are reduced to a total of 63 emotive adjectives. By performing exploratory factor analysis on the constructed sentiment-index, we have derived three important dimensions for classifying webtoon types. The exploratory factor analysis was performed through the Principal Component Analysis (PCA) using varimax factor rotation. The three dimensions were named 'Immersion', 'Touch' and 'Irritant' respectively. Based on this, K-Means clustering was performed and the entire webtoons were classified into four types. Each type was named 'Snack', 'Drama', 'Irritant', and 'Romance'. For each type of webtoon, we wrote webtoon-sentiment 2-Mode network graphs and looked at the characteristics of the sentiment pattern appearing for each type. In addition, through profiling analysis, we were able to derive meaningful strategic implications for each type of webtoon. First, The 'Snack' cluster is a collection of webtoons that are fast-paced and highly entertaining. Many consumers are interested in these webtoons, but they don't rate them well. Also, consumers mostly use simple expressions of sentiment when talking about these webtoons. Webtoons belonging to 'Snack' are expected to appeal to modern people who want to consume content easily and quickly during short travel time, such as commuting time. Secondly, webtoons belonging to 'Drama' are expected to evoke realistic and everyday sentiments rather than exaggerated and light comic ones. When consumers talk about webtoons belonging to a 'Drama' cluster in online, they are found to express a variety of sentiments. It is appropriate to establish an OSMU(One source multi-use) strategy to extend these webtoons to other content such as movies and TV series. Third, the sentiment pattern map of 'Irritant' shows the sentiments that discourage customer interest by stimulating discomfort. Webtoons that evoke these sentiments are hard to get public attention. Artists should pay attention to these sentiments that cause inconvenience to consumers in creating webtoons. Finally, Webtoons belonging to 'Romance' do not evoke a variety of consumer sentiments, but they are interpreted as touching consumers. They are expected to be consumed as 'healing content' targeted at consumers with high levels of stress or mental fatigue in their lives. The results of this study are meaningful in that it identifies the applicability of consumer sentiment in the areas of recommendation and classification of webtoons, and provides guidelines to help members of webtoons' ecosystem better understand consumers and formulate strategies.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.105-122
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• 2019

Dimensionality reduction is one of the methods to handle big data in text mining. For dimensionality reduction, we should consider the density of data, which has a significant influence on the performance of sentence classification. It requires lots of computations for data of higher dimensions. Eventually, it can cause lots of computational cost and overfitting in the model. Thus, the dimension reduction process is necessary to improve the performance of the model. Diverse methods have been proposed from only lessening the noise of data like misspelling or informal text to including semantic and syntactic information. On top of it, the expression and selection of the text features have impacts on the performance of the classifier for sentence classification, which is one of the fields of Natural Language Processing. The common goal of dimension reduction is to find latent space that is representative of raw data from observation space. Existing methods utilize various algorithms for dimensionality reduction, such as feature extraction and feature selection. In addition to these algorithms, word embeddings, learning low-dimensional vector space representations of words, that can capture semantic and syntactic information from data are also utilized. For improving performance, recent studies have suggested methods that the word dictionary is modified according to the positive and negative score of pre-defined words. The basic idea of this study is that similar words have similar vector representations. Once the feature selection algorithm selects the words that are not important, we thought the words that are similar to the selected words also have no impacts on sentence classification. This study proposes two ways to achieve more accurate classification that conduct selective word elimination under specific regulations and construct word embedding based on Word2Vec embedding. To select words having low importance from the text, we use information gain algorithm to measure the importance and cosine similarity to search for similar words. First, we eliminate words that have comparatively low information gain values from the raw text and form word embedding. Second, we select words additionally that are similar to the words that have a low level of information gain values and make word embedding. In the end, these filtered text and word embedding apply to the deep learning models; Convolutional Neural Network and Attention-Based Bidirectional LSTM. This study uses customer reviews on Kindle in Amazon.com, IMDB, and Yelp as datasets, and classify each data using the deep learning models. The reviews got more than five helpful votes, and the ratio of helpful votes was over 70% classified as helpful reviews. Also, Yelp only shows the number of helpful votes. We extracted 100,000 reviews which got more than five helpful votes using a random sampling method among 750,000 reviews. The minimal preprocessing was executed to each dataset, such as removing numbers and special characters from text data. To evaluate the proposed methods, we compared the performances of Word2Vec and GloVe word embeddings, which used all the words. We showed that one of the proposed methods is better than the embeddings with all the words. By removing unimportant words, we can get better performance. However, if we removed too many words, it showed that the performance was lowered. For future research, it is required to consider diverse ways of preprocessing and the in-depth analysis for the co-occurrence of words to measure similarity values among words. Also, we only applied the proposed method with Word2Vec. Other embedding methods such as GloVe, fastText, ELMo can be applied with the proposed methods, and it is possible to identify the possible combinations between word embedding methods and elimination methods.

• Journal of the Korean Institute of Landscape Architecture
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• v.43 no.2
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• pp.1-12
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• 2015

This study explores urban gardening and garden culture in residential area as an everydayness that has been overlooked during the modern period urbanization and investigates the meaning and value of urban gardening from the perspective of urban formations and growth in spontaneous urban residential area, Haebangchon. The result identified that urban gardening as a meaning of contemporary culture is a new clue to improving the urban physical environment and changing the lives and community network of residents. Haebangchon is one of the few remaining spontaneous habitations in Seoul, and was created as a temporary unlicensed shantytown in 1940s. It became the representative habitation for common people in downtown Seoul through the revitalization of the 60s and the local reform through self-sustaining redevelopment projects during the 70s through the 90s. This area still contains the image of times during the 50s to the 60s, the 70s to the 80s and present, with the percentage of long-term stay residents high. Within this context, the site is divided into third quarters, and the research undertaken by observation and investigation to determine characteristics of urban gardening as an everydayness. It can be said that urban gardening and garden culture in Haebangchon is a unique location culture that has accumulated in the crevices of the physical condition and culture of life. These places are an expression of resident's desires that seeking out nature and gardening as revealed in densely-populated areas and the grounds of practical acting and participating in care and cultivation. It forms a unique, indigenous local landscape as an accumulation of everyday life of residents. Urban gardens in detached home has retained the original function of the dwelling and the garden, or 'madang', and takes on the characteristic of public space through the sharing of a public nature as well as semi-private spatial characteristic. Also, urban gardens including small kitchen garden and flowerpots that appear in the narrow streets provide pleasure as a part of nature that blossoms in narrow alley and functions as a public garden for exchanging with neighbors by sharing produce. This paper provides the concept of redefining the relationship between the private-public area that occurs between outside spaces that are cut off in a modern city.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6099-6111
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• 2015

This study was a systematic review and meta-analysis comparing the effects of aquatic exercise and land-based exercise in the treatment of knee osteoarthritis. 7 studies (n=449) met selection and exclusion criteria out of 287 potential studies obtained from the literature search via Ovid-Medline, Cochrane Library CENTRAL, CINAHL, RISS and KISS. The overall risk of bias of selected studies using SIGN (Scottish Intercollegiate Guidelines Network) checklist for randomized controlled trials (RCT) was regarded as low. As a result of meta analysis, Standardized Mean Difference (SMD) for pain was -0.26(95% CI -0.49, -0.03, p=0.03, $I^2=14%$), which implies that aquatic exercise groups had significant less pain than land-based exercise groups. On the other hand, there was no significant difference between aquatic exercise groups and land based exercise groups for flexion Range of Motion (ROM) (-0.12, 95% CI -0.51, 0.27, p=0.53, $I^2=0%$), extension ROM (-0.04, 95% CI -0.55, 0.48, p=0.89, $I^2=43%$), physical function (-0.12, 95% CI -0.44, 0.19, p=0.44, $I^2=0%$), Quality of Life (QOL) (-0.15, 95% CI -0.54, 0.24, p=0.46, $I^2=0%$). This study has some limitations due to few RCTs comparing aquatic exercise groups and land-based exercise groups in the treatment of knee osteoarthritis. Therefore, further RCTs should be conducted along with long-term outcomes.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.1
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• pp.301-311
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• 2013

This study aims to analyze characteristics, health behaviors and health management level related to private health examination recipients in one general hospital. To achieve this, we analyzed 150,501 cases of private health examination data for 11 years from 2001 to 2011 for 20,696 participants in 2011 in a Dae-Jeon general hospital health examination center. The cluster analysis for classify private health examination group is used z-score standardization of K-means clustering method. The logistic regression analysis, decision tree and neural network analysis are used to periodic/non-periodic private health examination classification model. 1,000 people were selected as a customer management business group that has high probability to be non-periodic private health examination patients in new private health examination. According to results of this study, private health examination group was categorized by new, periodic and non-periodic group. New participants in private health examination were more 30~39 years old person than other age groups and more patients suspected of having renal disease. Periodic participants in private health examination were more male participants and more patients suspected of having hyperlipidemia. Non-periodic participants in private health examination were more smoking and sitting person and more patients suspected of having anemia and diabetes mellitus. As a result of decision tree, variables related to non-periodic participants in private health examination were sex, age, residence, exercise, anemia, hyperlipidemia, diabetes mellitus, obesity and liver disease. In particular, 71.4% of non-periodic participants were female, non-anemic, non-exercise, and suspicious obesity person. To operation of customized customer management business for private health examination will contribute to efficiency in health examination center.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.1
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• pp.88-95
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• 2002

In recent years, the security of hardware and software systems is one of the most essential factor of our safe network community. As elliptic Curve Cryptosystems proposed by N. Koblitz and V. Miller independently in 1985, require fewer bits for the same security as the existing cryptosystems, for example RSA, there is a net reduction in cost size, and time. In this thesis, we propose an efficient hardware architecture of underlying field arithmetic processor for Elliptic Curve Cryptosystems, and a very useful method for implementing the architecture, especially multiplicative inverse operator over GF$GF (2^m)$ onto FPGA and futhermore VLSI, where the method is based on optimized unit operation components. We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed We optimize the arithmetic processor for speed so that it has a resonable number of gates to implement. The proposed architecture could be applied to any finite field $F_{2m}$. According to the simulation result, though the number of gates are increased by a factor of 8.8, the multiplication speed and inversion speed has been improved 150 times, 480 times respectively compared with the thesis presented by Sarwono Sutikno et al. [7]. The designed underlying arithmetic processor can be also applied for implementing other crypto-processor and various finite field applications.

• The KIPS Transactions:PartC
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• v.9C no.2
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• pp.293-296
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• 2002

UP Link is One of the commercial product which converts HTML to HDML convertor in order to show the internet www contents in the mobile environments. When UP browser accesses HTML pages, the agent in the UP Link controls the converter to change the HTML to the HDML, I-Mode, which is developed by NTT-Docomo of Japan, has many contents through the long and stable commercial service. Micro Explorer, which is developed by Stinger project, also has many additional function. In this paper, we designed and implemented WAP convertor which can accept C-HTML contents and mHTML contents. C-HTML format by I-Mode is a subset of HTML format, mHTML format by ME is similar to C-HTML, So the content provides can easily develop C-HTML contents compared with WAP and the other case. Since C-HTML, mHTML and WML are used under the mobile environment, the limited transmission capacity of one page is also similar. In order to make a match table. After that, we apply conversion algorithm on it. If we can not find matched element, we arrange some tags which only can be supported by WML to display in the best shape. By the result, we can convert over 90% contents.

• Korea journal of population studies
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• v.31 no.3
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• pp.57-84
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• 2008

This study investigates the patterns of leisure in Korea and the effects of social class on the objective and subjective dimension of leisure activities and life satisfaction. A data set of 1376 Korean men and women over 18 years old is analyzed to yield five main results. First, Korean prefers domestic entertainment to outdoor activities as is exemplified by domestic audio-visual entertainment(TV/DVD/VCR) which ranks the highest in the favored leisure activity. Leisure activities are divided into four types; "activity-based", "relationship-based", "alcohol-based", "relaxation". Second, the function of leisure activity is to strengthen relationships. The main purpose of leisure activity is to relax and revitalize, while creating prospective social network ranks next to relax. But the effect of leisure time is often compromised by recurring thoughts related to work. Third, respondents with high educational and economic backgrounds are more likely to engage in "relationship-based," "activity-based", "alcohol-based" leisure type. However, such factors do not influence on "relaxation" type of leisure. While students and housewives rank highest in number of respondents, respondents with managerial/professional or white-collar/semi-professional occupations enjoy more diverse activities. Fourth, the effort to discern the significance of social class with respect to the leisure-activity-index revealed followings; the index scores elevate with higher education, younger age and higher income. Fifth, leisure-activity-index is the most important variable predicting leisure satisfaction. Leisure satisfaction is influenced by gender, age, income and occupation. The younger the age and higher the income, the higher it is the leisure satisfaction. Men are more satisfied with leisure activities than women. Students experience the highest satisfaction with leisure activities while service/sales workers, industrial/technical/blue-collar workers shows the least satisfaction. Also, the number of family members decreases significantly the leisure satisfaction. While "activity-based" leisure induces the highest satisfaction, "alcohol-based" leisure produces the least satisfaction. The frequency and diversity of leisure activities, and "activity-based" leisure incur the most positive effects on the life satisfaction.

• Journal of Korean Society of Transportation
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• v.19 no.1
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• pp.101-114
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• 2001

Transportation enterprises should maintain constant and qualitative operation. Thus, in short period, transportation enterprises don't change supply in accordance with demand. In the result, transportation enterprises don't reduce operation in spite of management deficit at will. In freight transportation type, less-than-truckload(LTL) has more relation with above transportation feature than truckload(TL) does. Because freight transportation supply of TL is more flexible than that of LTL in correspondence of freight transportation demand. Relating to above mention, it appears that shortage of road and freight terminal of LTL is larger than that of TL. Especially in road and freight terminal comparison, shortage of freight terminal is larger than that of road. Shortage of road is the largest in 1990, and improved after-ward. But shortage of freight terminal is serious lately. So freight terminal needs more expansion than road, and shows better investment condition than road. Freight terminal expansion brings road expansion in LTL, on the contrary, freight terminal expansion substitutes freight terminal for road in TL. In transportation revenue, freight terminal's contribution to LTL is larger than that to TL. However, when we adjust quasi-fixed factor - road and freight terminal - to optimal level in the long run, in TL, diseconomies of scale becomes large, but in LTL, economies of scale becomes large. Consequently, it is necessary for TL to make counterplans to activate management of small size enterprises and owner drivers. And LTL should make use of economies of scale by solving the problem, such as nonprofit route, excess of rental freight handling of office, insufficiency of freight terminal, shortage of driver, and unpreparedness of freight insurance.