• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.160-171
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• 2018

This study aims to find an original form of temple flower decoration patterns, considering floral lattice pattern as a view element composing temple landscape. To that end, we analyzed and interpreted the form and symbol expressed in the floral lattice pattern at Nahanjeon of Seonghyel Temple at Yeongju, Gyeongsangbukdo. The front side of Nahanjeon windows shows a sculpture with 176 pure patterns in a form where two squares are in sequence. The basic concept of main front door (the inner gate of Nahanjeon) frames is considered the design language of lotus pond that symbolizes "square land" in traditional gardens. The four leaf clover and arrowhead are water plants discovered in areas nearby ponds, which are a realistic expression conforming to the water ecology of lotus pond. The lotus, which is the most important plant at the main front door, indicates purity, a non-stained state, and the world of the lotus sanctuary, which is the land of blissful happiness in Buddhism. The lotus expressed in the floral lattice pattern is spread in a diverse form, containing the features of creation and destruction, showing the landscape character of the "One Body of Buddha and Lotus". The expression of flying birds such as kingfishers and egrets is an ecologically aesthetic idea to infuse dynamism and vitality into a seemingly static aquatic ecosystem. The floral lattice pattern contains lotus pond scenery showing symbiosis of animals(i.e., dragons, frogs, crabs, fishes, egrets, wild geese, and kingfishers) and plants(i.e., four leaf clovers and arrowheads), which are symbols of relief faith for longevity, wealth, preciousness, and many sons. The pattern is not just an ecological aesthetic expression but a holistic harmony of ecological components such as growth and disappearance of lotus and its leaves, fitting habitats, symbiosis, and food chain.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.6
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• pp.155-164
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• 2018

Many malicious programs have been compressed or encrypted using various commercial packers to prevent reverse engineering, So malicious code analysts must decompress or decrypt them first. The OEP (Original Entry Point) is the address of the first instruction executed after returning the encrypted or compressed executable file back to the original binary state. Several unpackers, including PinDemonium, execute the packed file and keep tracks of the addresses until the OEP appears and find the OEP among the addresses. However, instead of finding exact one OEP, unpackers provide a relatively large set of OEP candidates and sometimes OEP is missing among candidates. In other words, existing unpackers have difficulty in finding the correct OEP. We have developed new tool which provides fewer OEP candidate sets by adding two methods based on the property of the OEP. In this paper, we propose two methods to provide fewer OEP candidate sets by using the property that the function call sequence and parameters are same between packed program and original program. First way is based on a function call. Programs written in the C/C++ language are compiled to translate languages into binary code. Compiler-specific system functions are added to the compiled program. After examining these functions, we have added a method that we suggest to PinDemonium to detect the unpacking work by matching the patterns of system functions that are called in packed programs and unpacked programs. Second way is based on parameters. The parameters include not only the user-entered inputs, but also the system inputs. We have added a method that we suggest to PinDemonium to find the OEP using the system parameters of a particular function in stack memory. OEP detection experiments were performed on sample programs packed by 16 commercial packers. We can reduce the OEP candidate by more than 40% on average compared to PinDemonium except 2 commercial packers which are can not be executed due to the anti-debugging technique.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.133-148
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• 2014

Recently, with the advent of various information channels, the number of has continued to grow. The main cause of this phenomenon can be found in the significant increase of unstructured data, as the use of smart devices enables users to create data in the form of text, audio, images, and video. In various types of unstructured data, the user's opinion and a variety of information is clearly expressed in text data such as news, reports, papers, and various articles. Thus, active attempts have been made to create new value by analyzing these texts. The representative techniques used in text analysis are text mining and opinion mining. These share certain important characteristics; for example, they not only use text documents as input data, but also use many natural language processing techniques such as filtering and parsing. Therefore, opinion mining is usually recognized as a sub-concept of text mining, or, in many cases, the two terms are used interchangeably in the literature. Suppose that the purpose of a certain classification analysis is to predict a positive or negative opinion contained in some documents. If we focus on the classification process, the analysis can be regarded as a traditional text mining case. However, if we observe that the target of the analysis is a positive or negative opinion, the analysis can be regarded as a typical example of opinion mining. In other words, two methods (i.e., text mining and opinion mining) are available for opinion classification. Thus, in order to distinguish between the two, a precise definition of each method is needed. In this paper, we found that it is very difficult to distinguish between the two methods clearly with respect to the purpose of analysis and the type of results. We conclude that the most definitive criterion to distinguish text mining from opinion mining is whether an analysis utilizes any kind of sentiment lexicon. We first established two prediction models, one based on opinion mining and the other on text mining. Next, we compared the main processes used by the two prediction models. Finally, we compared their prediction accuracy. We then analyzed 2,000 movie reviews. The results revealed that the prediction model based on opinion mining showed higher average prediction accuracy compared to the text mining model. Moreover, in the lift chart generated by the opinion mining based model, the prediction accuracy for the documents with strong certainty was higher than that for the documents with weak certainty. Most of all, opinion mining has a meaningful advantage in that it can reduce learning time dramatically, because a sentiment lexicon generated once can be reused in a similar application domain. Additionally, the classification results can be clearly explained by using a sentiment lexicon. This study has two limitations. First, the results of the experiments cannot be generalized, mainly because the experiment is limited to a small number of movie reviews. Additionally, various parameters in the parsing and filtering steps of the text mining may have affected the accuracy of the prediction models. However, this research contributes a performance and comparison of text mining analysis and opinion mining analysis for opinion classification. In future research, a more precise evaluation of the two methods should be made through intensive experiments.

• Cartoon and Animation Studies
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• s.36
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• pp.375-391
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• 2014

Animation movie is a non-photorealistic animated art that consists of formative language forming a frame based on a story and cuts describing frames that form the cuts. Therefore, in expressing an image, artistic expression methods and devices for a formative space are should be provided in a frame while cuts have the images between frames faithfully. Short animation movie is produced by various image experiments with unique image expressions rather than narration for expressing subjective discourse of a writer. Therefore, image style that forms unique images and various image directions are important factors. This study compared the experimental image directions of and , both of which showed a production method of film manipulation. First, while uses pixilation that produces images obtained from live images through painting and many optical disclosure process on a cell mat, was made with diverse collage techniques such as tearing, cutting, pasting, and folding hundreds of scenes from action movies. Second, expresses non-causal relationship of characters by their repetitive behaviors and circulatory image structure through a fixed camera angle, resisting typical scene transition. On the other hand, has an advancing structure that progresses antagonistic relationship of characters through diverse camera angles and scene transition of unique images. Third, in terms of editing, uses a long-take short cut technique in which the whole image consists of one short cut, though it seems to be many scenes with the appearance of various characters. On the other hand, maximizes visual fun and commitment by image reconstruction with hundreds of various short cuts. That is, both works have common features of an experimental work that shows expansion of animated image expressions through film manipulation that is different form general animation productions. On top of that, delivers routine life of diverse human beings without clear narration through image of conceptualized spaces. expresses it in a new image space through image reconstruction with collage technique and speedy progress, setting a binary opposition structure.

• Journal of Korean society of Dental Hygiene
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• v.5 no.2
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• pp.201-218
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• 2005

The purpose of this study was to examine how dentists perceived dental coordinators including their education, hiring criteria, working condition and job. It's basically attempted to help define the job and role of 5 and to suggest how they should be nurtured. The subjects in this study were dentists at dental hospitals and clinics where dental coordinators were employed among approximately 200 dental institutions in Seoul, Cyeonggi province and Incheon. After a survey was conducted in June 2005, answer sheets from 99 respondents were analyzed. The findings of the study were as follows: 1. Regarding education for dental coordinators, 99.9% of the dentists investigated felt the need for separate education programs for dental coordinators, 42.4% knew what would-be dental coordinators learned about, and 81.8% considered it necessary for them to take intermediate or higher courses. An organization affiliated with the Korea Dental Hygienists Association was viewed as the best institute to educate dental coordinators, and educational institutes that included a department of dental hygiene was looked upon as the second best one. 68.7% believed that dental coordinators should take an official examination to test their qualifications, and concerning educational subsidy, the largest group of the dentists thought that a certain amount of subsidy should be provided. 2. As for coordinator hiring, the top priority was the impression(look) of applicants(55%), followed by adjustability to existing employees(24.5%) and professional competency(17.3%). As to the route of hiring, 41.4 percent, the largest group, reeducated some of existing employees, and dental hygienists were regarded as the best personnels to serve as a coordinator. Concerning job performance, they put the most emphasis on interpersonal relationship, which was followed by executive ability, impression and career, 58.6% the largest group, believed that dental coordinators should have a three-year or higher career to work at a dental institute. 3. As to working conditions, 75.7%, the largest group, paid dental coordinators based on their job performance, and 23.2%, the second largest group, had their pay equal to that of dental hygienists, 88.9% allowed them to determine their own retirement age. 4. In regard to their perception of dental coordinators, the largest number of the dentists considered it necessary for them to keep receiving education(4.29), and the second largest group felt that they served to enhance the image of dental institutes(4.18). The third largest group thought that they contributed to letting patients more satisfied with the quality of dental services. But they tended not to agree that their turnover rate was low(3.04), and they didn't find them to receive appropriate education, either(3.10). 5. The current major job of coordinators associated with customer services was handling appointments with customers(91.9%), treating unsatisfied customers(85.9%), and controling waiting time(84.8%). Regarding self-management, coordinators directed their energy into having good manners(89.9%), acquiring fundamental dental knowledge(84.8%), and learning how to treat customers(83.8%). Concerning hospital affairs handling, they were most responsible for information desk(87.9%), which was followed by receiving(86.9%). As a result of investigating what type of job the dentists hoped dental coordinators to fulfill in consideration of their career, their age and type of investment, the dentists wanted them the most to speak a foreign language, which belonged to the field of self-management.

• 대한공업교육학회지
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• v.33 no.1
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• pp.149-168
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• 2008

The purpose of this study is to verify the effect of PSpice instruction on academic achievement in 'Combination logic circuit' unit of 'Digital Logic Circuit' in industrial high school. Three kinds of null hypotheses were formulated. Two classes of the third grade of C technical high school in Gyeong-buk were divided into experimental group and control group in order to verify null hypotheses. In the experimental design, 'Non-equivalent control group pretest-posttest' model was utilized. This experiment was conducted for six classes, the experimental group was applied to PSpice instruction method before the circuit traning while the control group was applied to traditional lecture oriented method before the circuit traning. Window SPSS 10.0 korean language version program was used for the data analysis and independent sample t-test was used to identify the average of each group. Significance level was set to .05 level. The results obtained in this study were as follows; First, PSpice instruction had not an effect on academic achievement according to a group type. However, these instruction had an effect on the following sub-domains; the psychomotor domain. Second, PSpice instruction had not an effect on academic achievement according to a studies level. However, these instruction for middle and low level students had an effect on the cognitive and psychomotor domain, and for middle level students had an effect on the affective domain. Third, PSpice instruction had not an effect on shortening of a training requirement. However, this instruction for low level students had an effect on shortening of a training requirement. The study results of simulation instruction was chiefly efficient in the psychomotor domain. We could know that simulation instruction is efficient as went to a low level students than an upper level students. Thus, We may make the study effectiveness in various instruction method.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.143-159
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• 2015

Predicting IT trends has been a long and important subject for information systems research. IT trend prediction makes it possible to acknowledge emerging eras of innovation and allocate budgets to prepare against rapidly changing technological trends. Towards the end of each year, various domestic and global organizations predict and announce IT trends for the following year. For example, Gartner Predicts 10 top IT trend during the next year, and these predictions affect IT and industry leaders and organization's basic assumptions about technology and the future of IT, but the accuracy of these reports are difficult to verify. Social media data can be useful tool to verify the accuracy. As social media services have gained in popularity, it is used in a variety of ways, from posting about personal daily life to keeping up to date with news and trends. In the recent years, rates of social media activity in Korea have reached unprecedented levels. Hundreds of millions of users now participate in online social networks and communicate with colleague and friends their opinions and thoughts. In particular, Twitter is currently the major micro blog service, it has an important function named 'tweets' which is to report their current thoughts and actions, comments on news and engage in discussions. For an analysis on IT trends, we chose Tweet data because not only it produces massive unstructured textual data in real time but also it serves as an influential channel for opinion leading on technology. Previous studies found that the tweet data provides useful information and detects the trend of society effectively, these studies also identifies that Twitter can track the issue faster than the other media, newspapers. Therefore, this study investigates how frequently the predicted IT trends for the following year announced by public organizations are mentioned on social network services like Twitter. IT trend predictions for 2013, announced near the end of 2012 from two domestic organizations, the National IT Industry Promotion Agency (NIPA) and the National Information Society Agency (NIA), were used as a basis for this research. The present study analyzes the Twitter data generated from Seoul (Korea) compared with the predictions of the two organizations to analyze the differences. Thus, Twitter data analysis requires various natural language processing techniques, including the removal of stop words, and noun extraction for processing various unrefined forms of unstructured data. To overcome these challenges, we used SAS IRS (Information Retrieval Studio) developed by SAS to capture the trend in real-time processing big stream datasets of Twitter. The system offers a framework for crawling, normalizing, analyzing, indexing and searching tweet data. As a result, we have crawled the entire Twitter sphere in Seoul area and obtained 21,589 tweets in 2013 to review how frequently the IT trend topics announced by the two organizations were mentioned by the people in Seoul. The results shows that most IT trend predicted by NIPA and NIA were all frequently mentioned in Twitter except some topics such as 'new types of security threat', 'green IT', 'next generation semiconductor' since these topics non generalized compound words so they can be mentioned in Twitter with other words. To answer whether the IT trend tweets from Korea is related to the following year's IT trends in real world, we compared Twitter's trending topics with those in Nara Market, Korea's online e-Procurement system which is a nationwide web-based procurement system, dealing with whole procurement process of all public organizations in Korea. The correlation analysis show that Tweet frequencies on IT trending topics predicted by NIPA and NIA are significantly correlated with frequencies on IT topics mentioned in project announcements by Nara market in 2012 and 2013. The main contribution of our research can be found in the following aspects: i) the IT topic predictions announced by NIPA and NIA can provide an effective guideline to IT professionals and researchers in Korea who are looking for verified IT topic trends in the following topic, ii) researchers can use Twitter to get some useful ideas to detect and predict dynamic trends of technological and social issues.

• Journal of Intelligence and Information Systems
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• v.21 no.3
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• pp.53-77
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• 2015

Korea has witnessed an increasing number of domestic patent applications, but a majority of them are not utilized to their maximum potential but end up becoming obsolete. According to the 2012 National Congress' Inspection of Administration, about 73% of patents possessed by universities and public-funded research institutions failed to lead to creating social values, but remain latent. One of the main problem of this issue is that patent creators such as individual researcher, university, or research institution lack abilities to commercialize their patents into viable businesses with those enterprises that are in need of them. Also, for enterprises side, it is hard to find the appropriate patents by searching keywords on all such occasions. This system proposes a patent recommendation system that can identify and recommend intellectual rights appropriate to users' interested fields among a rapidly accumulating number of patent assets in a more easy and efficient manner. The proposed system extracts core contents and technology sectors from the existing pool of patents, and combines it with secondary social knowledge, which derives from tags information created by users, in order to find the best patents recommended for users. That is to say, in an early stage where there is no accumulated tag information, the recommendation is done by utilizing content characteristics, which are identified through an analysis of key words contained in such parameters as 'Title of Invention' and 'Claim' among the various patent attributes. In order to do this, the suggested system extracts only nouns from patents and assigns a weight to each noun according to the importance of it in all patents by performing TF-IDF analysis. After that, it finds patents which have similar weights with preferred patents by a user. In this paper, this similarity is called a "Domain Similarity". Next, the suggested system extract technology sector's characteristics from patent document by analyzing the international technology classification code (International Patent Classification, IPC). Every patents have more than one IPC, and each user can attach more than one tag to the patents they like. Thus, each user has a set of IPC codes included in tagged patents. The suggested system manages this IPC set to analyze technology preference of each user and find the well-fitted patents for them. In order to do this, the suggeted system calcuates a 'Technology_Similarity' between a set of IPC codes and IPC codes contained in all other patents. After that, when the tag information of multiple users are accumulated, the system expands the recommendations in consideration of other users' social tag information relating to the patent that is tagged by a concerned user. The similarity between tag information of perferred 'patents by user and other patents are called a 'Social Simialrity' in this paper. Lastly, a 'Total Similarity' are calculated by adding these three differenent similarites and patents having the highest 'Total Similarity' are recommended to each user. The suggested system are applied to a total of 1,638 korean patents obtained from the Korea Industrial Property Rights Information Service (KIPRIS) run by the Korea Intellectual Property Office. However, since this original dataset does not include tag information, we create virtual tag information and utilized this to construct the semi-virtual dataset. The proposed recommendation algorithm was implemented with JAVA, a computer programming language, and a prototype graphic user interface was also designed for this study. As the proposed system did not have dependent variables and uses virtual data, it is impossible to verify the recommendation system with a statistical method. Therefore, the study uses a scenario test method to verify the operational feasibility and recommendation effectiveness of the system. The results of this study are expected to improve the possibility of matching promising patents with the best suitable businesses. It is assumed that users' experiential knowledge can be accumulated, managed, and utilized in the As-Is patent system, which currently only manages standardized patent information.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2004.05b
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• pp.217-220
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• 2004

According as utilization for web increases rapidly, it is demanded that model about support interaction between web-based applications systematically and solutions can integrate new distributed platforms and existing environment effectively, accordingly, Web Services appeared by solution in reply. These days, a lot of software and hardware companies try to adoption of Web Services to their market, attenpt to construct their applications associationing components from various Web Services providers. However, to execute Web Services completely. it must have interoperability and need the standardization work that avoid thing which is subject to platform, application as well as service and programming language from other companies. WS-I (Web Services Interoperability organization) have established Basic Profile 1.0 based on XML, UDDI, WSDL and SOAP for web services interoperability and developed usage scenario Profile to apply Web Services in practice. In this paper, to verify suitability Web Services interoperability between heterogeneous two applications, have design and implements the Book Information Web Services that based on the Web Services Client of J2SE platform and the Web Services Server of .NET platform, so that analysis and verify the service by adaptation of WS-I Basic Profile.