• Proceedings of the KSRS Conference
• /
• 2002.10a
• /
• pp.145-150
• /
• 2002

Web GIS (Geographic Information Systems) service systems provide the various GIS services of analyzing and displaying the spatial data with friendly user-interface. These services are expanding the business domain and many users want to access the distributed various spatial data. But, it is difficult to access diverse data sources because of different spatial data format and data access methods. In this paper, we design and implement web GIS services based on the inter-operability and GML (Geography Markup Language) of OGC(Open GIS Consortium) in web distributed environment. Inter-operability provides unique accessing method to distributed data sources based on OLE DB technology of Microsoft. In addition, GML support web GIS services based on XML. We design these GIS services as components using UML (Unified Modeling Language) of an object-oriented modeling language for specifying, visualizing, constructing, and documenting the artifacts of software system. In addition, they also were developed in object-oriented computing environment, and it provides the interoperability, language-independent, easy developing environment as well as re-usability.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.10
• /
• pp.157-165
• /
• 2021

Recently, studies using deep learning to analyze a large amount of text are being actively conducted. In particular, a pre-trained language model that applies the learning results of a large amount of text to the analysis of a specific domain text is attracting attention. Among various pre-trained language models, BERT(Bidirectional Encoder Representations from Transformers)-based model is the most widely used. Recently, research to improve the performance of analysis is being conducted through further pre-training using BERT's MLM(Masked Language Model). However, the traditional MLM has difficulties in clearly understands the meaning of sentences containing new words such as newly coined words. Therefore, in this study, we newly propose NTM(Newly coined words Target Masking), which performs masking only on new words. As a result of analyzing about 700,000 movie reviews of portal 'N' by applying the proposed methodology, it was confirmed that the proposed NTM showed superior performance in terms of accuracy of sensitivity analysis compared to the existing random masking.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.3
• /
• pp.43-54
• /
• 2024

Recently, there has been a lot of research on utilizing Language Models, and Large Language Models have achieved innovative results in various tasks. However, the practical application faces limitations due to the constrained resources and costs required to utilize Large Language Models. Consequently, there has been recent attention towards methods to effectively utilize models within given resources. Curriculum Learning, a methodology that categorizes training data according to difficulty and learns sequentially, has been attracting attention, but it has the limitation that the method of measuring difficulty is complex or not universal. Therefore, in this study, we propose a methodology based on data heterogeneity-based Curriculum Learning that measures the difficulty of data using reliable prior information and facilitates easy utilization across various tasks. To evaluate the performance of the proposed methodology, experiments were conducted using 5,000 specialized documents in the field of information communication technology and 4,917 documents in the field of healthcare. The results confirm that the proposed methodology outperforms traditional fine-tuning in terms of classification accuracy in both LoRA fine-tuning and full fine-tuning.

• The Journal of Information Technology
• /
• v.6 no.1
• /
• pp.117-128
• /
• 2003

The purpose of this article is to study on the strategy for Success in the Electronic Trade Transaction. The term "electronic trade" means all or part of any such trade as implemented by means of apparatus having the information processing capability, such as a computer, and networks of information and communications. The Internet brings people together from every country in the world. It reduces the distances between people in many ways. The predominant language on the Web is English, although sites in other language barrier is overcome, the technology exists for any business to conduct electronic commerce with any other business or consumer, anywhere in the world.

• Journal of Intelligence and Information Systems
• /
• v.29 no.4
• /
• pp.165-184
• /
• 2023

Recently, Deep learning analysis of unstructured text data using language models, such as Google's BERT and OpenAI's GPT has shown remarkable results in various applications. Most language models are used to learn generalized linguistic information from pre-training data and then update their weights for downstream tasks through a fine-tuning process. However, some concerns have been raised that privacy may be violated in the process of using these language models, i.e., data privacy may be violated when data owner provides large amounts of data to the model owner to perform fine-tuning of the language model. Conversely, when the model owner discloses the entire model to the data owner, the structure and weights of the model are disclosed, which may violate the privacy of the model. The concept of offsite tuning has been recently proposed to perform fine-tuning of language models while protecting privacy in such situations. But the study has a limitation that it does not provide a concrete way to apply the proposed methodology to text classification models. In this study, we propose a concrete method to apply offsite tuning with an additional classifier to protect the privacy of the model and data when performing multi-classification fine-tuning on Korean documents. To evaluate the performance of the proposed methodology, we conducted experiments on about 200,000 Korean documents from five major fields, ICT, electrical, electronic, mechanical, and medical, provided by AIHub, and found that the proposed plug-in model outperforms the zero-shot model and the offsite model in terms of classification accuracy.

• Journal of Digital Contents Society
• /
• v.18 no.6
• /
• pp.1067-1078
• /
• 2017

Previous research on university life of Chinese students studying in Korea has been conducted on students in Korea, but Chinese students have a sense of belonging and university prejudice against Korean universities, so the objectivity of research is not clear. This paper conducted a survey of the Chinese people in China and conducted a media search for attracting foreign students. Not all foreign students are proficient in Korean language. Even students who have completed more than one year of education in the Korean language institutes or passed advanced level of Test of Proficiency in Korean(TOPIK) have difficulties in everyday life and acquiring a degree. According to the results of this study, until foreign students who lack sufficient Korean language skills are able to adapt to culture and acquire information in Korea, it is desirable to prepare multilingual content services so that they can obtain academic information or daily information necessary for studying in their native language.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.9
• /
• pp.119-125
• /
• 2019

The purpose of this study is to find out the relevant factors of the choice tendency of tourism users to Intelligent Tourism platform through big data analysis, which will help enterprises to make accurate positioning and improvement according to user information feedback in the tourism market in the future, so as to gain the favor of users' choice and achieve long-term market competitiveness. This study takes the Intelligent Tourism platform as the independent variable and the user choice tendency as the dependent variable, and explores the related factors between the Intelligent Tourism platform and the user choice tendency. This study make use of text mining and R language text analysis, and uses SPSS and AMOS statistical analysis tools to carry out empirical analysis. According to the analysis results, the conclusions are as follows: service quality has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with tourism trust; Tourism Trust has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with user experience; user experience has a significant positive correlation with user choice tendency Positive correlation effect.

• The Journal of Information Systems
• /
• v.26 no.4
• /
• pp.17-37
• /
• 2017

Purpose Customer Loyalty is the most important factor of customer relationship management (CRM). Especially in retailing industry, where customers have many options of where to spend their money. Classifying loyal customers through customers' data can help retailing companies build more efficient marketing strategies and gain competitive advantages. This study aims to construct classification models of distinguishing the loyal customers within a Korean retailing company using data mining techniques with R language. Design/methodology/approach In order to classify retailing customers, we used combination of support vector machines (SVMs) and other classification algorithms of machine learning (ML) with the support of recursive feature elimination (RFE). In particular, we first clean the dataset to remove outlier and impute the missing value. Then we used a RFE framework for electing most significant predictors. Finally, we construct models with classification algorithms, tune the best parameters and compare the performances among them. Findings The results reveal that ML classification techniques can work well with CRM data in Korean retailing industry. Moreover, customer loyalty is impacted by not only unique factor such as net promoter score but also other purchase habits such as expensive goods preferring or multi-branch visiting and so on. We also prove that with retailing customer's dataset the model constructed by SVMs algorithm has given better performance than others. We expect that the models in this study can be used by other retailing companies to classify their customers, then they can focus on giving services to these potential vip group. We also hope that the results of this ML algorithm using R language could be useful to other researchers for selecting appropriate ML algorithms.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2013.07a
• /
• pp.229-230
• /
• 2013

본 논문에서는 기존의 앱 중에서 사회에서 대두되고 있는 기부와 관련된 앱을 조사 비교하며, 이와 관련해서 기부의 확산을 위해 필요한 기능을 알아보고 이를 설계하도록 한다. 기부문화에 대한 인식이 점차 변화하고, 기부문화를 좀 더 확산시키고자 게임을 통해 쉽게 기부에 다가갈 수 있는 앱이 필요하다. 기부와 관련된 앱으로써 기부를 할 수 있게 한다. 더 나아가 기업과 SNS 매체에 연계하여 자연스레 기부에 대한 문화를 일상생활의 일부분으로 만든다.

• Korean Journal of English Language and Linguistics
• /
• v.1 no.1
• /
• pp.187-209
• /
• 2001

This study attempts to supplement what is known about the influence of prior knowledge on second language listening comprehension. To do so, the study examines the effect of prior knowledge and language proficiency on the ability of L2 listeners to understand texts. The purpose of an experiment was to determine the effect of topic familiarity on the L2 listening comprehension ability of subjects who varied in L2 listening proficiency level. The subjects (N=117) were selected from a population of college students enrolled in the Departments of English and Business in Korea. English listening proficiency levels were designated on the basis of TOEFL listening scores. Subjects listened twice each to texts (more familiar and less familiar). After listening to each text, a ten-item objective test was administered to test the subjects' comprehension of the information presented in the text. Objective tests were analyzed. using repeated measures analysis. A post hoc test was conducted to identify the means that were significantly different. This study yielded the following results: (1) subjects with high prior knowledge comprehended texts significantly better than did subjects with low prior knowledge; (2) the level of L2 listening proficiency had a significant effect on the L2 listening comprehension of texts, but there was no interaction between prior knowledge and the level of L2 listening proficiency.