• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.141-150
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• 2001

Within a context of rapid technological change and shifting market conditions, the education environment requires new paradigm of education delivery. In accordance of such a technological progress, distance education system, which makes the learning take place at anytime anywhere, overcoming barriers of time, or distance, is emerging as a mainstream of education delivery replacing the convectional one way delivery system from instructor to learners. This paper aims to introduce the development principle and algorithm about Instructional System Desgin(ISD) module, a part of a total solution for distance education services. It was developed on Linux, a free Unix-type operating system. Linux supports so various network protocols, sharing the network resources in a smooth way, that it is able to integrate with other operating system very easily, especially with Windows NT or Windows 2000 servers. In terms of quality, it never falls behind the windows products which are commercially available only. It is a right operating system for the such a school environment that is usually limited in budgets. The development environment of the distance education solution to which ISD module belongs is composing of Apache server for web server, lava bean based on components for ISD module, PHP, server-side scripting language, for HTML documents and MySQL for DBMS.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.435-439
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• 2007

Document categorization system is important in the internet age in which huge number of documents are created and need to be dealt with. By this reason a lot of research has been done in this field. For the development of the system, a supervised learning method is widely used. This approach needs a test collection as a prerequisite. For the case of English, several test collections are available which provide a lot of help for developing systems and doing research. But no public test collections have been reported and are not available in the case of Korean. To improve the situation for Korean we are undergoing the construction of a Korean test collection. In this paper the approaches being used and current stage of the collection will be described.

• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.1-18
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• 2011

With rapid spread of handheld devices like cellular or smart phones, a short message service (SMS) comes on the public as a communication means. SMS is very cheap and can be easily written down on the storage in order not to forget it, hence it is widely used to inform schedules (time and place). In this paper, we develop a system for automatically registering schedules extracted from SMS text messages. SMS text messages are very short and concise, but include a lot of Internet words like slangs and abbreviations. These have made it difficult to extract information on schedules from them. Also handheld devices have some limitations on computing power and storage and then applying general natural language processing modules like morphological analysis to the devices are somewhat hard. To relax these burdens, we extract schedule informations from SMS messages using machine learning methods like condition random field (CRF) without using any language processing modules and register the informations on the schedule management system of handheld devices. Our proposed automatic schedule registration system has implemented on Samsung Omnia phone for experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.129-130
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• 2018

Big data analysis is a technique for effectively analyzing unstructured data such as the Internet, social network services, web documents generated in the mobile environment, e-mail, and social data, as well as well formed structured data in a database. The most big data analysis techniques are data mining, machine learning, natural language processing, and pattern recognition, which were used in existing statistics and computer science. Global research institutes have identified analysis of big data as the most noteworthy new technology since 2011. Therefore, companies in most industries are making efforts to create new value through the application of big data. In this study, we analyzed using the Social Matrics which a big data analysis tool of Daum communications. We analyzed public perceptions of "Artificial Intelligence" keyword, one month as of May 19, 2018. The results of the big data analysis are as follows. First, the 1st related search keyword of the keyword of the "Artificial Intelligence" has been found to be technology (4,122). This study suggests theoretical implications based on the results.

• The KIPS Transactions:PartB
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• v.14B no.1 s.111
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• pp.59-64
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• 2007

In these days, malicious codes have become reality and evolved significantly to become one of the greatest threats to the modern society where important information is stored, processed, and accessed through the internet and the computers. Computer virus is a common type of malicious codes. The standard techniques in anti-virus industry is still based on signatures matching. The detection mechanism searches for a signature pattern that identifies a particular virus or stain of viruses. Though more accurate in detecting known viruses, the technique falls short for detecting new or unknown viruses for which no identifying patterns present. To cope with this problem, anti-virus software has to incorporate the learning mechanism and heuristic. In this paper, we propose a fuzzy diagnosis system(FDS) using fuzzy c-means algorithm(FCM) for the cluster analysis and a decision status measure for giving a diagnosis. We compare proposed system FDS to three well known classifiers-KNN, RF, SVM. Experimental results show that the proposed approach can detect unknown viruses effectively.

• Journal of Convergence for Information Technology
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• v.10 no.10
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• pp.40-46
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• 2020

With the development of the Internet, users share their experiences and opinions. Since related keywords are used witho0ut considering information such as the general emotion or genre of an unstructured document such as a movie review, the sensitivity accuracy according to the appropriate emotional situation is impaired. Therefore, we propose a system that predicts emotions based on information such as the genre to which the unstructured document created by users belongs or overall emotions. First, representative keyword related to emotion sets such as Joy, Anger, Fear, and Sadness are extracted from the unstructured document, and the normalized weights of the emotional feature words and information of the unstructured document are trained in a system that combines CNN and LSTM as a training set. Finally, by testing the refined words extracted through movie information, morpheme analyzer and n-gram, emoticons, and emojis, it was shown that the accuracy of emotion prediction using emotions and F-measure were improved. The proposed prediction system can predict sentiment appropriately according to the situation by avoiding the error of judging negative due to the use of sad words in sad movies and scary words in horror movies.

• Informatization Policy
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• v.20 no.4
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• pp.51-70
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• 2013

The flaming which is defined as posting deliberately hostile messages online has been perceived as one of the most dangerous behaviors on the Internet. Although the previous research on flaming focused on finding the effects of anonymity and individual differences, more comprehensive explanation is still needed. This research attempted to find social variables which affect the attitude toward and the act of flaming based on the differences between high school and college students. Findings show that both high school and college students are strongly influenced by their affiliated groups in the intention to commit online flaming and past experiences of the number of the acts of flaming. At the same time, vicarious learning was positively associated with high school students, but direct utterance was positively associated with college students. The implications of the study was also discussed.

• Journal of Korean Academy of Nursing Administration
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• v.19 no.1
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• pp.95-107
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• 2013

Purpose: The purpose of this study was to examine the effectiveness of Action Research (AR) approach in nursing. Methods: Participants were 64 perioperative nurses recruited from C hospital in Gwangju, Korea. The nurses were engaged in the project through 2 cycles of planning, acting, observing, and reflecting. A mixed-methods design was used to examine changes in participants and their knowledge management practice. Quantitative data were analyzed using SPSS 20.0 program and qualitative reflection data underwent content analysis. Results: During the project, participants developed standardized pre-operative checklists and opened an Internet Cafe to better manage their perioperative nursing information. At the end of the project, there was a significant increase in nurses' knowledge management (p=.015) and the rate of surgical material prescription errors decreased from 8.0% to 2.9%. Core AR project team members' teamwork skills and organizational commitment increased significantly (p=.040, p=.301, respectively). The main themes that emerged from the qualitative data were learning how to solve problems in practice, facilitating team activities through motivation, barriers of large participation, and rewarded efforts and inflated expectations. Conclusion: The AR project contributed to empowering participants to solve local problems. AR is a useful methodology to promote changes in practices and research participants.

• The Journal of Korean Association of Computer Education
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• v.13 no.1
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• pp.9-18
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• 2010

The purpose of this study is to determine the differences in menu-layout preference for learners based on their demographics. The experiment was conducted in this manner: three class second grade and three class sixth grade elementary school students were separated by sex, grade, age and cognitive style such as field-dependent and field-independent. They were asked to choose which menu layouts they preferred or did not prefer out of 6 different types. The results of the research are as follows: First, there were no differences in sex, grade and cognitive style for preferences in the menu layout but there were meaningful differences with regard to age. Second grade students preferred map-type layout, but sixth grade students preferred the type having a main menu across the top with a sub menu in roll over. Second, there was a difference with regard to age as to what they do not prefer. Overall, the results of this study suggest that the menu layout for a learning web site on the internet needs to be different according to their age.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.513-519
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• 2018

The objective of this study was to examine the trends on social network services. The abstracts of 308 articles were extracted from web of science database published between 1994 and 2016. Time series analysis and topic modeling of text mining were implemented. The topic modeling results showed that the research topics were mainly 20 topics: trust, support, satisfaction model, organization governance, mobile system, internet marketing, college student effect, opinion diffusion, customer, information privacy, health care, web collaboration, method, learning effectiveness, knowledge, individual theory, child support, algorithm, media participation, and context system. The time series regression results indicated that trust, support satisfaction model, and remains of the topics were hot topics. This study also provided suggestions for future research.