• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.177-189
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• 2013

In Smart Education, the roles of digital textbook is very important as face-to-face media to learners. The standardization of digital textbook will promote the industrialization of digital textbook for contents providers and distributers as well as learner and instructors. In this study, the following three objectives-oriented digital textbooks are looking for ways to standardize. (1) digital textbooks should undertake the role of the media for blended learning which supports on-off classes, should be operating on common EPUB viewer without special dedicated viewer, should utilize the existing framework of the e-learning learning contents and learning management. The reason to consider the EPUB as the standard for digital textbooks is that digital textbooks don't need to specify antoher standard for the form of books, and can take advantage od industrial base with EPUB standards-rich content and distribution structure (2) digital textbooks should provide a low-cost open market service that are currently available as the standard open software (3) To provide appropriate learning feedback information to students, digital textbooks should provide a foundation which accumulates and manages all the learning activity information according to standard infrastructure for educational Big Data processing. In this study, the digital textbook in a smart education environment was referred to open digital textbook. The components of open digital textbooks service framework are (1) digital textbook terminals such as smart pad, smart TVs, smart phones, PC, etc., (2) digital textbooks platform to show and perform digital contents on digital textbook terminals, (3) learning contents repository, which exist on the cloud, maintains accredited learning, (4) App Store providing and distributing secondary learning contents and learning tools by learning contents developing companies, and (5) LMS as a learning support/management tool which on-site class teacher use for creating classroom instruction materials. In addition, locating all of the hardware and software implement a smart education service within the cloud must have take advantage of the cloud computing for efficient management and reducing expense. The open digital textbooks of smart education is consdered as providing e-book style interface of LMS to learners. In open digital textbooks, the representation of text, image, audio, video, equations, etc. is basic function. But painting, writing, problem solving, etc are beyond the capabilities of a simple e-book. The Communication of teacher-to-student, learner-to-learnert, tems-to-team is required by using the open digital textbook. To represent student demographics, portfolio information, and class information, the standard used in e-learning is desirable. To process learner tracking information about the activities of the learner for LMS(Learning Management System), open digital textbook must have the recording function and the commnincating function with LMS. DRM is a function for protecting various copyright. Currently DRMs of e-boook are controlled by the corresponding book viewer. If open digital textbook admitt DRM that is used in a variety of different DRM standards of various e-book viewer, the implementation of redundant features can be avoided. Security/privacy functions are required to protect information about the study or instruction from a third party UDL (Universal Design for Learning) is learning support function for those with disabilities have difficulty in learning courses. The open digital textbook, which is based on E-book standard EPUB 3.0, must (1) record the learning activity log information, and (2) communicate with the server to support the learning activity. While the recording function and the communication function, which is not determined on current standards, is implemented as a JavaScript and is utilized in the current EPUB 3.0 viewer, ths strategy of proposing such recording and communication functions as the next generation of e-book standard, or special standard (EPUB 3.0 for education) is needed. Future research in this study will implement open source program with the proposed open digital textbook standard and present a new educational services including Big Data analysis.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.3
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• pp.106-115
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• 2011

This study, based on (edited in 18th century), analysed the landscape system and cultural landscape elements of Yuhoidang(Hageowon 何去園) Garden in Musu-dong, Daejeon, and the findings are as in the following. YuHoidang(Gwon Yijin 權以鎭) managed Hageowon Garden in Musu-dong, located on the southern branch of Mt. Bomun, to realize his utopia. The completion of Hageowon Garden was only possible due to his installation of a variety of facilities in family gravesite on the hill behind his house: Shimyoso(Samgeunjeongsa 三近精舍, in 1707), Naboji(納汚池, in 1713), Banhwanwon(in 1714) and expended exterior space(in 1727). With regard to the landscape system of the village, the main range of mountains consists of Mt. Daedun, Mt. Odae and Mt. Bomun. The main high mountain of the three is Mt. Bomun, where 'Blue Dragon' hill branches off on the east side(Eungbong), 'White Tiger' in the west(Cheongeun and Sajeong) and Ansan(inner mountain) in the south. The landscape system is featured by 'mountains in back and rivers in front'. The river in the south-west, with its source in Mt. Juryun is called as the 'Stream of outer perfect spot', while the 'Stream of inner perfect spot' rises from Eungbong, passing through the east part of the village into the south-western direction. Banhwanwon Garden(盤桓園) was created with the stream in the east and natural bedrocks, and its landscape elements includes Naboji, Hwalsudam, Gosudae, Sumi Waterfall, Dogyeong(path of peach trees), Odeeokdae(platform with persimmon trees), Maeryong(Japanese apricot tree), springs and observatories. An expanded version of Banhwanwon was Hageowon garden, where a series of 'water-trees-stone' including streams, four ponds, five observation platforms, three bamboo forests and Chukgyeongwon(縮景園) of an artificial hill gives the origin forest a scenic atmosphere. When it comes to semantics landscape elements, there are (1) Yuhoidang to cherish the memory of a deceased parents, (2) Naboji for family unification, (3) Gosudae to keep fidelity, (4) Odeokdae to collect virtue and wisdom, (5) Sumi Waterfall to aspire to be a man of noble character, (6) Yocheondae for auspicious life, (7) Sumanheon and Gigungjae to be in pursuit of hermitic life, (8) Hwalsudam for development of family and study, (9) Mongjeong to repay favor of ancestors, (10) Seokgasan, a symbol of secluded life, (11) Hageowon to enjoy guarding graves in retired life. The spatial composition of Hageowon was realized through (1) Yuhoidang's inside gardens(Naboji, Jucheondang, Odeokdae, Dogyeong, Back yard garden and others) (2) Sumanheon(收漫軒) Byeolup or Yuhoidang's back yard gardens (Seokyeonji, Yocheondae, Sumanheon, Baegyeongdae, Amseokwon and others) (3) Chukgyeongwon of the artificial hill(which is also the east garden of Sumanheon, being composed of Hwalsudam, Sumi Waterfall and Gasan or 12 mountaintops) (4) the scenic spots for unifying Confucianism, Buddhism and Taoism are Cemetry garden in the back hill of the village, the temple of Yeogyeongam, Sansinkak(ancestral ritual place of folk religion) and Geoeopjae(family school). On top of that, Chagyeongwon Garden(借景園) commands a panoramic distant view of nature's changing beauty through the seasons.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.