• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.25-44
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• 2013

Accessing movie contents has become easier and increased with the advent of smart TV, IPTV and web services that are able to be used to search and watch movies. In this situation, there are increasing search for preference movie contents of users. However, since the amount of provided movie contents is too large, the user needs more effort and time for searching the movie contents. Hence, there are a lot of researches for recommendations of personalized item through analysis and clustering of the user preferences and user profiles. In this study, we propose recommendation system which uses ontology based knowledge base. Our ontology can represent not only relations between metadata of movies but also relations between metadata and profile of user. The relation of each metadata can show similarity between movies. In order to build, the knowledge base our ontology model is considered two aspects which are the movie metadata model and the user model. On the part of build the movie metadata model based on ontology, we decide main metadata that are genre, actor/actress, keywords and synopsis. Those affect that users choose the interested movie. And there are demographic information of user and relation between user and movie metadata in user model. In our model, movie ontology model consists of seven concepts (Movie, Genre, Keywords, Synopsis Keywords, Character, and Person), eight attributes (title, rating, limit, description, character name, character description, person job, person name) and ten relations between concepts. For our knowledge base, we input individual data of 14,374 movies for each concept in contents ontology model. This movie metadata knowledge base is used to search the movie that is related to interesting metadata of user. And it can search the similar movie through relations between concepts. We also propose the architecture for movie recommendation. The proposed architecture consists of four components. The first component search candidate movies based the demographic information of the user. In this component, we decide the group of users according to demographic information to recommend the movie for each group and define the rule to decide the group of users. We generate the query that be used to search the candidate movie for recommendation in this component. The second component search candidate movies based user preference. When users choose the movie, users consider metadata such as genre, actor/actress, synopsis, keywords. Users input their preference and then in this component, system search the movie based on users preferences. The proposed system can search the similar movie through relation between concepts, unlike existing movie recommendation systems. Each metadata of recommended candidate movies have weight that will be used for deciding recommendation order. The third component the merges results of first component and second component. In this step, we calculate the weight of movies using the weight value of metadata for each movie. Then we sort movies order by the weight value. The fourth component analyzes result of third component, and then it decides level of the contribution of metadata. And we apply contribution weight to metadata. Finally, we use the result of this step as recommendation for users. We test the usability of the proposed scheme by using web application. We implement that web application for experimental process by using JSP, Java Script and prot$\acute{e}$g$\acute{e}$ API. In our experiment, we collect results of 20 men and woman, ranging in age from 20 to 29. And we use 7,418 movies with rating that is not fewer than 7.0. In order to experiment, we provide Top-5, Top-10 and Top-20 recommended movies to user, and then users choose interested movies. The result of experiment is that average number of to choose interested movie are 2.1 in Top-5, 3.35 in Top-10, 6.35 in Top-20. It is better than results that are yielded by for each metadata.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• Korean Journal of Clinical Laboratory Science
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• v.49 no.3
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• pp.285-293
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• 2017

Globally, 1.3 million people develop colon cancer every year, and 600,000 people die each year it. In Korea, colorectal carcinoma was associated with the highest death rate, accounting for 8,380 people, among solid cancers in 2015. Among the various methods for the diagnosis and study of colorectal carcinoma, the results obtained by cytogenetic and molecular genetic methods were compared. Detection rate was 47% in 18q, 40% in 17p, 27% in 22q, and 17% in 10q via CGH; detection rate was 57% in D18S59, 50% in D18S68, 50% in TP53CA, 47% in D18S6940% in D22S274, 37% in D22S283, 27% in D10S187, and 23% in D10S541 with LOH. Microsatellite marker matching rates were 100% in D22S274, 100% in D22S283, 100% in D10S186, 100% in D10S187, 100% in D10S541, 93% in D18S69, 93% in D18S68, 92% in TP53CA, and 89% in D18S59. The agreement rate between the two methods was 94.4% based on positive results using CGH. Based on the advantages of CGH, which was the ability to obtain information regarding the entire tumor genome at once, this experiment could identify the region with significant deletion using CGH and the more limited region LOH, with a completely different approach. LOH in the recurrent high-risk group, 18q21, was helpful in the selection of treatment modalities and in prognostic estimation as well as making the most appropriate decision for treatment. Therefore, it is suggested that LOH with surgical site tissues could be one of the treatment methods for recurrent high-risk group among patients with colorectal carcinoma.

• Journal of Conservation Science
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• v.32 no.2
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• pp.167-178
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• 2016

Despite the size layer is an important part for conserving the artworks in the configuration of oil painting, the conservation scientific approaches of that have not been made yet. Therefore, this study produced standard samples on the basis of the analysis results of oil painting works, and carried out the evaluation of functions of the size layer materials. As a result of literature material, traditionally, animal glue was used for the size layer, whereas synthetic resin have been used in combination with animal glue since the modern age, in particular, it was identified that Polyvinyl Acetate(PVAc) was in general use. As a result of analysis of oil painting works, size layer was detected on the support and it was identified as animal glue. As a result of analysis based on Funaoka canvas for ground, it showed that the lead oxide and the titanium dioxide were the main constituents. On the basis of these results, standard samples were produced. As a result of evaluation on the functions of the size layer materials, in the case of the animal glue, stable result was observed in the shrinkag expansion rate, whereas slight weakness was observed in moisture proofing, color, and tensile strength, and dense cracks were found on surface. As for PVAc(A), moisture proofing, color, and the tensile strength exhibited stable results. Higher shrinkage rate was observed and the cracks with wide gaps were found on surface. As for PVAc(B), tensile strength, shrinkage expansion rate, and surface observation showed stable results, whereas moisture proofing property showed poor results. Different aspects were observed in each experiment, and this phenomena were considered to be due to the density and the adhesion properties between the hydrophilic and hydrophobic molecules in the size layer materials. The results are expected to be used as materials for the oil painting work conservation henceforth.

• 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.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.32 no.3
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• pp.218-228
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• 2010

Components of dental resin-based restorative materials are reported to leach from the filling materials even after polymerization. Hydroquinone (HQ) is one of the major monomers used in the dental resin and is known as a carcinogen. Thus, carcinogenic risk of HQ leaching from the dental resin becomes a public health concern. The present study attempted to examine the carcinogenic potentials of HQ on the human epithelial cell, which is the target cell origin of the most of oral cancers. Cytotoxicity of HQ was observed above 50${\mu}M$ as measured by LDH assay, indicating a relatively low toxicity of this substance in human epithelial cells. The parameters of neoplastic cellular transformation such as cell saturation density, soft agar colony formation and cell aggregation were analyzed to examine the carcinogenic potential of HQ. The study showed that 2-week exposure of HQ showed the tendency of increase in the saturation density and the significant enhancement of soft agar colony formation at the highest dose, 50 ${\mu}M$ only. It is suggested that HQ has a weak potential of carcinogenicity. When cells were treated with HQ and TPA, a well-known tumor promoter, the parameters of neoplastic cellular transformation was significantly increased. This result indicates that the potential risk of carcinogenicity from HQ is largely dependent upon the presence of promoter. Exposure of 50 ${\mu}M$ HQ increased the time-dependent apoptosis as measured by the ELISA kit. This concentration coincides with a dose of neoplastic transformation, indicating a possible link between apoptosis and HQ-induced cellular transformation. Hydroquinone generated Reactive Oxygen Species (ROS) which was evidenced by the treatment of antioxidants such as trolox and N-acetyl cysteine and the GSH depleting agent, BSO. Antioxidants blocked the generation of ROS and the GSH depleting agent, BSO dramatically increased the ROS production. Since HQ is known to increase ROS production thru activation of transcriptional factor such as c-Myb and Pim-1, it is speculated that ROS generation by HQ plays a role in the activation of oncogene, which may lead to neoplastic transformation. In addition, ROS is involved in the alteration of signal transduction, which regulates the apoptosis in many cellular systems. Thus, ROS-mediated apoptosis may be involved in the HQ-induced carcinogenic processes. Protein kinase C (PKC) is known to play pivotal roles in neoplastic transformation of cells and its high expression is often found in a variety of types of tumors including oral cancer. PKC translocation of PKC-${\alpha}$ was observed following HQ exposure. Altered signaling system may also play a role in the transformation process. Taken together, HQ leached from the dental resin does not pose a significant threat as a cancer causing agent, but its carcinogenic potential can be significantly elevated in the presence of promoter. The mechanism of HQ-induced carcinogenesis involved ROS generation, apoptosis and altered signaling pathway. The present study will provide a valuable data to estimate the potential risk of HQ as a carcinogen and understand mechanism of HQ-induced carcinogenesis in human epithelial cells.

• Journal of the Korean Society of Food Science and Nutrition
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• v.44 no.6
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• pp.904-911
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• 2015

The objective of this study was to investigate the quality characteristics of traditional wine using an radio-frequency identification (RFID) system annexed to a fermenter. In this study, we proposed an RFID-based data transmission scheme for monitoring fermentation of traditional alcoholic beverages. The pH, total acidity, total sugar, soluble sugar, free sugar, alcohol content, and organic acids of were investigated and subjected to fermentation of traditional alcoholic beverages three times. The pH ranged from 7.98, 7.95, and 7.68 at day 0, decreased drastically to 3.31~2.96 at day 2, and then slowly increased to the end point, finally reaching 3.34 at day 20. Acidity tended to increase quickly with time, especially for all samples after day 2. The fermentation environment induced a sudden increase acidity in reactants and indicated a low pH. The total sugars during fermentation quickly decreased to the range of 20.3, 22.43, and 19.2% at day 2, and the slope of reduction steadily decreased to 5.1, 6.1, and 4.8% at day 10. On the other hand, the alcohol content showed the reverse trend as total sugars. The alcohol content also showed the same pattern as total acids, showing the highest alcohol content of 17.3% (v/v) on day 20. In this study on traditional wine fermentation using an RFID system, we showed that pH, soluble sugar, and alcohol content can be adopted as key indicators for quality control and standardization of traditional wine manufacturing.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.69-94
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• 2017

Recently, increase of demand for big data analysis has been driving the vigorous development of related technologies and tools. In addition, development of IT and increased penetration rate of smart devices are producing a large amount of data. According to this phenomenon, data analysis technology is rapidly becoming popular. Also, attempts to acquire insights through data analysis have been continuously increasing. It means that the big data analysis will be more important in various industries for the foreseeable future. Big data analysis is generally performed by a small number of experts and delivered to each demander of analysis. However, increase of interest about big data analysis arouses activation of computer programming education and development of many programs for data analysis. Accordingly, the entry barriers of big data analysis are gradually lowering and data analysis technology being spread out. As the result, big data analysis is expected to be performed by demanders of analysis themselves. Along with this, interest about various unstructured data is continually increasing. Especially, a lot of attention is focused on using text data. Emergence of new platforms and techniques using the web bring about mass production of text data and active attempt to analyze text data. Furthermore, result of text analysis has been utilized in various fields. Text mining is a concept that embraces various theories and techniques for text analysis. Many text mining techniques are utilized in this field for various research purposes, topic modeling is one of the most widely used and studied. Topic modeling is a technique that extracts the major issues from a lot of documents, identifies the documents that correspond to each issue and provides identified documents as a cluster. It is evaluated as a very useful technique in that reflect the semantic elements of the document. Traditional topic modeling is based on the distribution of key terms across the entire document. Thus, it is essential to analyze the entire document at once to identify topic of each document. This condition causes a long time in analysis process when topic modeling is applied to a lot of documents. In addition, it has a scalability problem that is an exponential increase in the processing time with the increase of analysis objects. This problem is particularly noticeable when the documents are distributed across multiple systems or regions. To overcome these problems, divide and conquer approach can be applied to topic modeling. It means dividing a large number of documents into sub-units and deriving topics through repetition of topic modeling to each unit. This method can be used for topic modeling on a large number of documents with limited system resources, and can improve processing speed of topic modeling. It also can significantly reduce analysis time and cost through ability to analyze documents in each location or place without combining analysis object documents. However, despite many advantages, this method has two major problems. First, the relationship between local topics derived from each unit and global topics derived from entire document is unclear. It means that in each document, local topics can be identified, but global topics cannot be identified. Second, a method for measuring the accuracy of the proposed methodology should be established. That is to say, assuming that global topic is ideal answer, the difference in a local topic on a global topic needs to be measured. By those difficulties, the study in this method is not performed sufficiently, compare with other studies dealing with topic modeling. In this paper, we propose a topic modeling approach to solve the above two problems. First of all, we divide the entire document cluster(Global set) into sub-clusters(Local set), and generate the reduced entire document cluster(RGS, Reduced global set) that consist of delegated documents extracted from each local set. We try to solve the first problem by mapping RGS topics and local topics. Along with this, we verify the accuracy of the proposed methodology by detecting documents, whether to be discerned as the same topic at result of global and local set. Using 24,000 news articles, we conduct experiments to evaluate practical applicability of the proposed methodology. In addition, through additional experiment, we confirmed that the proposed methodology can provide similar results to the entire topic modeling. We also proposed a reasonable method for comparing the result of both methods.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.39 no.3
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• pp.189-196
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• 2003

In this study, the vertical opening of the non-float midwater pair trawl net was maintained by controlling the length of upper warp. This was because the head rope was able to be kept linearly and the working depth was not nearly as changed with the variation of flow speed as former experiments in this series of studies have demonstrated. We confirmed that the opening efficiency of the non-float midwater pair trawl net was able to be developed according to the increase in front weight and wing-end weight. In this study, we described the opening efficiency of the non-float midwater pair trawl net according to the variation of front weight and wing-end weight obtained by model experiment in circulation water channel. We compared the opening efficiency of the proto type with that of the non-float type. The results obtained can be summarized as follows：1. The hydrodynamic resistance was almost increased linearly in proportion to the flow speed and was increased in accordance with the increase in front weight and wing-end weight. The increasing rate of hydrodynamic resistance was displayed as an increasing tendency in accordance with the increase in flow speed. 2. The net height of the non-float type was almost decreased linearly in accordance with the increase in flow speed. As the reduced rate of the net height of the non-float type was smaller than that of the net height of the proto type against increase of flow speed, the net height of the non-float type was bigger than that of the proto type over 4.0 knot. The net width of the non-float type was about 10 m bigger than that of the proto type and the change rate of net width varied by no more than 2 m according to the variation of the front weight and wing-end weight. 3. The mouth area of the non-float type was maximized at 1.75 ton of the front weight and 1.11 ton of the wing-end weight, and was smaller than that of the proto type at 2.0∼3.0 knot, but was bigger than that of the proto type at 4.0∼5.0 knot. 4. The filtering volume was maximized at 3.0 knot in the proto type and at 4.0 knot in the non-float type. The optimal front weight was 1.40 ton.

• Korean Journal of Environment and Ecology
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• v.22 no.4
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• pp.409-419
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• 2008

The purpose of this study is to compare the differences in aboveground flora and underground flora between a forest road and a forest edge and to clarify each characteristic through ecological approach to a forest road. The study site was the forest planted with Pinus koraiensis and Abies holophylla, and located at an altitude of 45m($36^{\circ}36'23''N127^{\circ}21'45''E$). The width of the forest road is 3.2m. This research set the forest edge within the areas 5m away from the forest road and also conducted a survey on vegetation 5 times from september 2006 to August 2007. In addition, it installed thirty six quadrats to make an analysis of reclaimed soil seed bank. Soil amounting to 600$cm^3$ was collected from each quadrat using soil samplers(100$cm^3$),which was preserved in low temperature refrigeration for a month. Soil was thinly strewed evenly on trays and watered every four or five days; then, this research did experiment for six months until no more germination took place. Through this process, this research identified species and counted the number of germinating individuals by using emerging seedlings. The research result showed that on the whole, the similarity index between aboveground flora and underground flora was low. The correlation coefficient between the aboveground flora vegetations both on the forest road and on its edge was found to be 0.36, showing a correlation with each other(p<0.05). On the other hand, the correlation coefficient between underground flora vegetations through the analysis of reclaimed soil seed bank was 0.20, showing no correlation with each other(p>0.05). As the survey result of naturalized plants, there existed 7 species of naturalized plants on the forest road in case woody plants were included, showing 11.11% naturalization rate and 2.61% urbanization index(UI). On the other hand in case woody plants were not included among the naturalized plants, the naturalization rate on the forest road was 12.50% while the naturalization rate on the edge of the forest was 9.09%.