• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.5
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• pp.1235-1241
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• 2016

Personalized App recommendation system is recently famous since the number of various apps that can be used in smart phones that increases exponentially. However, the site users using google play site with malwares have experienced severe damages of privacy exposure and extortion as well as a simple damage of satisfaction descent at the same time. In addition, Sybil attack (Sybil) manipulating the score (rating) of each app with falmay also present because of the social networks development. Up until now, the sybil detection studies and malicious apps studies have been conducted independently. But it is important to determine finally the existence of intelligent attack with Sybil and malware simultaneously when we consider the intelligent attack types in real-time. Therefore, in this paper we experimentally evaluate the relationship between malware and sybils based on real cralwed dataset of goodlplay. Through the extensive evaluations, the correlation between malware and sybils is low for malware providers to hide themselves from Anti-Virus (AV).

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.179-196
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• 2019

Recently, the "Smart Consumer" has been emerging. He or she is increasingly inclined to search for and purchase products by taking into account personal judgment or expert reviews rather than by relying on information delivered through manufacturers' advertising. This is especially true when purchasing cosmetics. Because cosmetics act directly on the skin, consumers respond seriously to dangerous chemical elements they contain or to skin problems they may cause. Above all, cosmetics should fit well with the purchaser's skin type. In addition, changes in global cosmetics consumer trends make it necessary to study this field. The desire to find one's own individualized cosmetics is being revealed to consumers around the world and is known as "Finding the Holy Grail." Many consumers show a deep interest in customized cosmetics with the cultural boom known as "K-Beauty" (an aspect of "Han-Ryu"), the growth of personal grooming, and the emergence of "self-culture" that includes "self-beauty" and "self-interior." These trends have led to the explosive popularity of cosmetics made in Korea in the Chinese and Southeast Asian markets. In order to meet the customized cosmetics needs of consumers, cosmetics manufacturers and related companies are responding by concentrating on delivering premium services through the convergence of ICT(Information, Communication and Technology). Despite the evolution of companies' responses regarding market trends toward customized cosmetics, there is no "Intelligent Data Platform" that deals holistically with consumers' skin condition experience and thus attaches emotions to products and services. To find the Holy Grail of customized cosmetics, it is important to acquire and analyze consumer data on what they want in order to address their experiences and emotions. The emotions consumers are addressing when purchasing cosmetics varies by their age, sex, skin type, and specific skin issues and influences what price is considered reasonable. Therefore, it is necessary to classify emotions regarding cosmetics by individual consumer. Because of its importance, consumer emotion analysis has been used for both services and products. Given the trends identified above, we judge that consumer emotion analysis can be used in our study. Therefore, we collected and indexed data on consumers' emotions regarding their cosmetics experiences focusing on consumers' language. We crawled the cosmetics emotion data from SNS (blog and Twitter) according to sales ranking ($1^{st}$ to $99^{th}$), focusing on the ample/serum category. A total of 357 emotional adjectives were collected, and we combined and abstracted similar or duplicate emotional adjectives. We conducted a "Consumer Sentiment Journey" workshop to build a "Consumer Sentiment Dictionary," and this resulted in a total of 76 emotional adjectives regarding cosmetics consumer experience. Using these 76 emotional adjectives, we performed clustering with the Self-Organizing Map (SOM) method. As a result of the analysis, we derived eight final clusters of cosmetics consumer sentiments. Using the vector values of each node for each cluster, the characteristics of each cluster were derived based on the top ten most frequently appearing consumer sentiments. Different characteristics were found in consumer sentiments in each cluster. We also developed a cosmetics experience pattern map. The study results confirmed that recommendation and classification systems that consider consumer emotions and sentiments are needed because each consumer differs in what he or she pursues and prefers. Furthermore, this study reaffirms that the application of emotion and sentiment analysis can be extended to various fields other than cosmetics, and it implies that consumer insights can be derived using these methods. They can be used not only to build a specialized sentiment dictionary using scientific processes and "Design Thinking Methodology," but we also expect that these methods can help us to understand consumers' psychological reactions and cognitive behaviors. If this study is further developed, we believe that it will be able to provide solutions based on consumer experience, and therefore that it can be developed as an aspect of marketing intelligence.

• Journal of Music and Human Behavior
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• v.6 no.1
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• pp.33-53
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• 2009

The purpose of this study is to examine the effects of a singing program on withdrawal behaviors of children in low-income families. To measure the effects of the singing program, the researcher compared the results of K-YSR and TRF withdrawal scales before and after the program. Finally, the researcher did research on the subjects' musical and non-musical behaviors related to withdrawal through a record of behavior observation. The results of this study were asfollows: First, after comparing the results of K-YSR and TRF withdrawal scales conducted before and after the singing program, the mean of the scores was decreased by 6.4 and 3.6 points respectively (p = .042). Second, an analysis of withdrawal-related behavioral changes in music activities after the program showed an increase in frequency of eye contact, as well as tone and volume of subjects' voices. The results of this study indicate that the singing program has positive effects on withdrawal behaviors of children in low-income familiesin addition, it is effective in alleviating withdrawal-related behaviors.

• Journal of Dental Rehabilitation and Applied Science
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• v.25 no.1
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• pp.13-22
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• 2009

The composite resin, due to its esthetic quality, is considered the material of choice for restoration of anterior teeth. To get a satisfactory result in the composite resin restorations, it is necessary to choose right shade. At present, most of the commercial composite resins are based on the Vita Lumin shade guides or shade guides that are provided by their company, but color differences among them might be expected even using the same shade in various materials. This study is to measure color differences between various light-cured composite resins and shade guides and to provide the clinicians with information which may aid in improved color match of esthetic restoration. Four kinds of light-cured composite resins (Gradia Direct (GD), Z250 (Z250), Clearfil AP-X (AP-X), Esthet X (E X)) and shade guides with A2 and A3 shade were used. Three specimens of each material and one specimen of each shade guide were made. Each composite resin was filled into the Teflon mold (1.35 mm depth, 8 mm diameter), followed by compression, polymerization and polishing with wet sandpaper. Shade guides were grinded with polishing stones and rubber points to a thickness of approximately 1.35 mm. Color characteristics were performed with a spectrophotometer(color i5, GretagMacbeth, USA). A computer-controlled spectrophotometer was used to determine CIELAB coordinates ($L^*$, $a^*$, $b^*$) of each specimen and shade guide. The CIELAB measurements made it possible to evaluate the amount of the color difference values (${\Delta}E^*ab$) between composite resins and shade guides. CIE standard D65 was used as the light source. The results were as follows : 1. Among the $L^*$, $a^*$, $b^*$ values of most of 4 kinds of composite resin specimens which are produced by same shade, there were significant differences(p<0.05). 2. Among all 4 kinds of composite resin specimens which are produced by same shade, there were color differences that is perceptible to human eye(${\Delta}E^*>3.3$). 3. Between most of composite resin specimens investigated and their corresponding shade guides, there were color differences that is perceptible to human eye(${\Delta}E^*>3.3$). 4. In the clinical environment, it is recommended that custom shade guides be made from resin material itself for better color matching. Shade guides supplied by manufacturers or Vita Lumin shade guide may not provide clinicians a accurate standard in matching color of composite resins, and there are perceptible color differences in most of products. Therefore, it is recommended that custom shade guides be made from resin material itself and used for better color matching.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.91-99
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• 2010

Recently, recognizing user resource for personalized service has been needed in digital content service fields. Especially, to analyze user taste, recommend music and service music related information need recognition of user music file in case of online music service. Music related information service is offered through recognizing user music based on tag information. Recognition error has grown by weak points like changing and removing of tag information. Techniques of content based user music recognition with music signal itself are researched for solving upper problems. In this paper, we propose user music recognition on the internet by extracted feature from music signal. Features are extracted after suitable preprocessing for structure of content based user music recognition. Recognizing on music server consist of feature form are progressed with extracted feature. Through this, user music can be recognized independently of tag data. 600 music was collected and converted to each 5 music qualities for proving of proposed recognition. Converted 3000 experiment music on this method is used for recognition experiment on music server including 300,000 music. Average of recognition ratio was 85%. Weak points of tag based music recognition were overcome through proposed content based music recognition. Recognition performance of proposed method show a possibility that can be adapt to online music service in practice.

• The Korean Journal of Applied Statistics
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• v.27 no.5
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• pp.667-679
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• 2014

National Health Insurance Service(NHIS) provide care-plans for beneficiaries in the long-term care insurance(LTCI) systems that help them use LTC services appropriately. The care-plan includes recommendations for the most adequate type of care (gold standard) for beneficiaries. This study develops a decision-support system to determine the appropriate type of care plan. To develop a model, we used a data set that well-trained assessors in the NHIS investigated as a gold standard for beneficiaries: nursing home care, home-visit care, home-visit bathing, home-visit nursing, or day and night care. The decision-support system was established through a decision-tree model, because it may be easy to explain the algorithm of a decision-support system to working groups and policy makers. Our results might be useful in evidence-based care planning in an LTCI system and contribute to the efficient use of LTC services.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.91-105
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• 2017

Korea is rapidly becoming a multicultural society in recent years, and the number of multicultural families in 2015 exceeds 3.5% and 800,000. Also, as international marriage rate exceeds 10% by 2016, the number of multicultural families is expected to steadily increase. This study is a design of a metadata application profile as part of the foundation for providing learning resources and content tailored to the needs and preferences of married immigrant women and multicultural family members who need to adapt to Korean society. In order to verify the necessity of the research, we conducted an in-depth interview by screening consumer groups, and analyzed the relevant international and Korean national standards as de-jure standards for the design of metadata standard profiles. Then, we analyzed the contents characteristics for multicultural members, and organized the necessary metadata elements into profiles. We defined the mandatory/optional conditions to reflect the needs of content providers. This study is meaningful in that the study analyzes the educational needs of married immigrant women and presents the necessary metadata standards to develop and service effective educational content, such as korean-to-korean conversion system, personalized learning contents recommendation service, and learning management system.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.13-23
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• 2022

In this paper, we propose a detailed component image classification algorithm by fashion item for unstructured data retrieval in the fashion field. Due to the COVID-19 environment, AI-based online shopping malls are increasing recently. However, there is a limit to accurate unstructured data search with existing keyword search and personalized style recommendations based on user surfing behavior. In this study, pre-processing using Mask R-CNN was conducted using images crawled from online shopping sites and then classified components for each fashion item through CNN. We obtain the accuaracy for collar of the shirt's as 93.28%, the pattern of the shirt as 98.10%, the 3 classese fit of the jeans as 91.73%, And, we further obtained one for the 4 classes fit of jeans as 81.59% and the color of the jeans as 93.91%. At the results for the decorated items, we also obtained the accuract of the washing of the jeans as 91.20% and the demage of jeans accuaracy as 92.96%.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.2
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• pp.317-342
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• 2024

The purpose of this study is to explore ways to improve accessibility to school library materials through analysis of elementary school students' information search behavior in DLS. Accordingly, the DLS search process was recorded for 26 students attempting a DLS search in the school library, and data was collected through a pre-search questionnaire on overall information needs and a post-search questionnaire on the search process and results. As a result of the analysis, satisfaction was found to be low when the main purpose of DLS use was simple leisure reading, when the search time and number of search words were long, and when there were too many search results. Accordingly, it was emphasized that curriculum subject-related metadata elements should be developed and a curriculum subject-specific thesaurus should be built and used to build lists and support user searches. In addition, it was suggested that the basic functions provided in external searches should be included, and a foundation should be laid in terms of resources and curriculum to systematically provide information utilization education to elementary school students who lack the ability to select search terms and judge the suitability of results after the search. It was proposed to provide an integrated search service with external resources and a personalized book recommendation service.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.