• Journal of Information Technology and Architecture
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• v.9 no.1
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• pp.43-55
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• 2012

In recent years, outsourcing of information systems, including decision support systems has become a key method for managing the system portfolio of a corporation. Since the outsourced DSSs provide their own models and solvers, which may be created on the basis of different modeling practices and system platforms, the decision maker wishing to solve business problems using the outsourced DSSs frequently faces a difficulty in selecting and/or applying appropriate models and solvers to the problems on hand. This paper proposes a DSS outsourcing architecture that enables a user to discover and execute appropriate models and solvers, even though the user is not knowledgeable enough about all the details of the models and solvers. Specifically, this paper adopts a Web services approach to integrate the heterogeneous models and solvers by encapsulating individual models and solvers as Web services and hiding all system specific implementation details from the users.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.89-94
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• 2021

Recently, the boundaries between people who produce and consume digital contents disappears due to the massive developments in information and communications technology (ICT) and the rapidly increasing spread of smartphones unlike in the traditional mass media (e.g., newspapers, radios, and TVs). Through the open service platform, the problem perception for each individual remains the same, but the problem solving methods varies as the service types have been diversified. The creation of added value through the growth of the new media platform industry is expected to enrich our lives, but it can also cause severe social side effects. For example, communication problems between social classes due to the information gap have led to generational conflict, and if such problems persist, it can cause national and social losses. Therefore, this paper analyzes the policy efforts to resolve the information gap and the necessity of the legalization of multimedia literary education to maximize the synergy effect through psychological model.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.350-360
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• 2021

Due to COVID-19 and minimum wage raise, rate of adoption of zero contact payment methods, such as kiosk, had increased rapidly in 2018. Although there exist users who have trouble utilizing it, kiosk is growing consistently, especially in food service industry. Main goal of this research is to verify antecedent of satisfaction level which affects continuance intention of kiosk in terms of quality and execute through examination of kiosk users on fast-food industry, which employs kiosk most predominantly. The result of this research showed that information accessibility (environment quality), convenience of order process (process quality), and addition of beneficial supplementary service (process quality) of kiosk had influence on customer satisfaction level which in turn, also affected continuance intention, but order payment readiness, which is consequence quality, had no effect on satisfaction level. With pervasion and indispensable increase in zero contact payment market, this research expanded our knowledge on kiosk user and established kiosk quality figure that can improve user satisfaction level and continuance intention, ultimately proposing selection guide of kiosk and securing competitiveness for stores.

• International Area Studies Review
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• v.15 no.1
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• pp.267-288
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• 2011

As world trade becomes more active and expansive, job mobility has progressed correspondingly in growth. In particular, high-skilled scientists (including advanced-degree holders) now possess the option of various occupational and residential mobilities. We explain the "brain drain" by using two empirical examples. One concerns the U.S. experience of foreign-born Ph.D holders living and working in the United States. The other pertains to the Korean experience, where Korean-origin and foreign-born experienced scientists are actively recruited by the government. We also explore the necessary conditions for recruiting and keeping advanced-skilled scientists, the attainment of which will result in strong future economic growth.

• The Transactions of the Korea Information Processing Society
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• v.13 no.9
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• pp.429-436
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• 2024

As Large Language Models (LLMs) continue to advance, effectively harnessing their potential has become increasingly important. LLMs, trained on vast datasets, are capable of generating text across a wide range of topics, making them useful in applications such as content creation, machine translation, and chatbots. However, they often face challenges in generalization due to gaps in specific or specialized knowledge, and updating these models with the latest information post-training remains a significant hurdle. To address these issues, Retrieval-Augmented Generation (RAG) models have been introduced. These models enhance response generation by retrieving information from continuously updated external databases, thereby reducing the hallucination phenomenon often seen in LLMs while improving efficiency and accuracy. This paper presents the foundational architecture of RAG, reviews recent research trends aimed at enhancing the retrieval capabilities of LLMs through RAG, and discusses evaluation techniques. Additionally, it explores performance optimization and real-world applications of RAG in various industries. Through this analysis, the paper aims to propose future research directions for the continued development of RAG models.

• Journal of Elementary Mathematics Education in Korea
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• v.14 no.2
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• pp.241-262
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• 2010

This study was carried out to identify the cognitive obstacles while using addition and subtraction with fractions, and to analyze the sources of cognitive obstacles. For this purpose, the following research questions were established : 1. What errors do elementary students make while performing the operations with fractions, and what cognitive obstacles do they have? 2. What sources cause the cognitive obstacles to occur? The results obtained in this study were as follows : First, the student's cognitive obstacles were classified as those operating with same denominators, different denominators, and both. Some common cognitive obstacles that occurred when operating with same denominators and with different denominators were: the students would use division instead of addition and subtraction to solve their problems, when adding fractions, the students would make a natural number as their answer, the students incorporated different solving methods when working with improper fractions, as well as, making errors when reducing fractions. Cognitive obstacles in operating with same denominators were: adding the natural number to the numerator, subtracting the small number from the big number without carrying over, and making errors when doing so. Cognitive obstacles while operating with different denominators were their understanding of how to work with the denominators and numerators, and they made errors when reducing fractions to common denominators. Second, the factors that affected these cognitive obstacles were classified as epistemological factors, psychological factors, and didactical factors. The epistemological factors that affected the cognitive obstacles when using addition and subtraction with fractions were focused on hasty generalizations, intuition, linguistic representation, portions. The psychological factors that affected the cognitive obstacles were focused on instrumental understanding, notion image, obsession with operation of natural numbers, and constraint satisfaction.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.259-266
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• 2020

Many developments have been steadily carried out by researchers with applying knowledge-based expert system or machine learning algorithms to the financial field. In particular, it is now common to perform knowledge based system trading in using stock prices. Recently, deep learning technologies have been applied to real fields of stock trading marketplace as GPU performance and large scaled data have been supported enough. Especially, LSTM has been tried to apply to stock price prediction because of its compatibility for time series data. In this paper, we implement stock price prediction using LSTM. In modeling of LSTM, we propose a fitness combination of model parameters and activation functions for best performance. Specifically, we propose suitable selection methods of initializers of weights and bias, regularizers to avoid over-fitting, activation functions and optimization methods. We also compare model performances according to the different selections of the above important modeling considering factors on the real-world stock price data of global major companies. Finally, our experimental work brings a fitness method of applying LSTM model to stock price prediction.

• Journal of Educational Research in Mathematics
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• v.20 no.4
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• pp.459-476
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• 2010

The purpose of this study is to suggest a new direction in using LOGO as a gifted education program and to seek an effective approach for LOGO teaching and learning, by analyzing the strategic thinking of mathematically gifted elementary students. This research is exploratory and inquisitive qualitative inquiry, involving observations and analyses of the LOGO Project learning process. Four elementary students were selected and over 12 periods utilizing LOGO programming, data were collected, including screen captures from real learning situations, audio recordings, observation data from lessons involving experiments, and interviews with students. The findings from this research are as follows: First, in LOGO Project Learning, the mathematically gifted elementary students were found to utilize such strategic ways of thinking as inferential thinking in use of prior knowledge and thinking procedures, generalization in use of variables, integrated thinking in use of the integration of various commands, critical thinking involving evaluation of prior commands for problem-solving, progressive thinking involving understanding, and applying the current situation with new viewpoints, and flexible thinking involving the devising of various problem solving skills. Second, the students' debugging in LOGO programming included comparing and constrasting grammatical information of commands, graphic and procedures according to programming types and students' abilities, analytical thinking by breaking down procedures, geometry-analysis reasoning involving analyzing diagrams with errors, visualizing diagrams drawn following procedures, and the empirical reasoning on the relationships between the whole and specifics. In conclusion, the LOGO Project Learning was found to be a program for gifted students set apart from other programs, and an effective way to promote gifted students' higher-level thinking abilities.

• Journal of Convergence for Information Technology
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• v.7 no.5
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• pp.1-13
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• 2017

It is revealed that there are more registered disabled people in our country who have their disability because of acquired factors such as accidents than because of innate deformity. This phenomenon will continue and the incidence of acquired disability will increase more and more. Even though there is noticeable change, the social recognition toward the disabled is still negative. Particularly, university students are in the early adulthood according to the development of life-cycle, and this is the period that people have diverse social relationships, depart from unified frame and work in various fields, and set up their own thoughts and ideology with knowledge and skills acquired from university education. Therefore, in this study, we are going to search the recognition of the university students, who are in the previous period of entering into the society, toward the disabled, and if they have negative prejudice or attitude against the disabled, we are going to find the ways to improve on their awareness positively. The subjects of the survey were randomly selected among 230 out of total 250 students by visiting thirteen universities in Chungcheong Province, and were analyzed by using SPSS (ver. 18.0) program. T-test and One-way ANOVA were used as analytical methods to look into the difference of analysis of frequency, descriptive statistic, reliability analysis and attitudes for comprehending sociodemographic characteristics of the subjects of the survey. In conclusion, it has to be not a temporary or event-like training, but a training that makes people have positive recognition and attitude towards accurate information, knowledge, human rights, disability, and diversity. This thesis has a limitation to be generalized to the university students all over the country since the region is limited to a certain area.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1314-1323
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• 1999

컴퓨터의 사용이 일반화됨에 따라 데이타를 생성하고 수집하는 것이 용이해졌다. 이에 따라 데이타로부터 자동적으로 유용한 지식을 얻는 기술이 필요하게 되었다. 데이타 마이닝에서 얻어진 지식은 정확성과 이해성을 충족해야 한다. 본 논문에서는 데이타 마이닝을 위하여 퍼지 결정트리에 기반한 효율적인 퍼지 규칙을 생성하는 알고리즘을 제안한다. 퍼지 결정트리는 ID3와 C4.5의 이해성과 퍼지이론의 추론과 표현력을 결합한 방법이다. 특히, 퍼지 규칙은 속성 축에 평행하게 판단 경계선을 결정하는 방법으로는 어려운 속성 축에 평행하지 않는 경계선을 갖는 패턴을 효율적으로 분류한다. 제안된 알고리즘은 첫째, 각 속성 데이타의 히스토그램 분석을 통해 적절한 소속함수를 생성한다. 둘째, 주어진 소속함수를 바탕으로 ID3와 C4.5와 유사한 방법으로 퍼지 결정트리를 생성한다. 또한, 유전자 알고리즘을 이용하여 소속함수를 조율한다. IRIS 데이타, Wisconsin breast cancer 데이타, credit screening 데이타 등 벤치마크 데이타들에 대한 실험 결과 제안된 방법이 C4.5 방법을 포함한 다른 방법보다 성능과 규칙의 이해성에서 보다 효율적임을 보인다.Abstract With an extended use of computers, we can easily generate and collect data. There is a need to acquire useful knowledge from data automatically. In data mining the acquired knowledge needs to be both accurate and comprehensible. In this paper, we propose an efficient fuzzy rule generation algorithm based on fuzzy decision tree for data mining. We combine the comprehensibility of rules generated based on decision tree such as ID3 and C4.5 and the expressive power of fuzzy sets. Particularly, fuzzy rules allow us to effectively classify patterns of non-axis-parallel decision boundaries, which are difficult to do using attribute-based classification methods.In our algorithm we first determine an appropriate set of membership functions for each attribute of data using histogram analysis. Given a set of membership functions then we construct a fuzzy decision tree in a similar way to that of ID3 and C4.5. We also apply genetic algorithm to tune the initial set of membership functions. We have experimented our algorithm with several benchmark data sets including the IRIS data, the Wisconsin breast cancer data, and the credit screening data. The experiment results show that our method is more efficient in performance and comprehensibility of rules compared with other methods including C4.5.