• The Journal of Korean Association of Computer Education
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• v.8 no.3
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• pp.29-42
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• 2005

In this knowledge information society, it cannot be too much to accentuate the necessity and importance of computer education. However, the current computer education in elementary school only focuses on computer application and utilization. It is not satisfactory to the needs of knowledge information society to improve mechanic office operation and/or to provide simple utilization of educational software through learning the application software functions. Therefore, based on creative and logical thought, it is necessary at this era to prepare computer education as the essence of science to create new values and to embody knowledge by understanding social and cultural environment of knowledge information society that is continuously in change and development. Accordingly, this study presents the necessity of change based on problems of computer education, and investigates field teacher's opinion on the direction of change in elementary computer curriculum in this knowledge information society. Also, this study presents a computer education curriculum for elementary school students by focusing on a comparison between our current elementary computer curriculum and that of North Carolina and of California in the United States.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.77-92
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• 2020

When automatic speech recognition (ASR) is provided as a cloud service, it is easy to collect voice and application domain data from users. Harnessing these data will facilitate the provision of more personalized services. In this paper, we demonstrate our transfer learning-based knowledge service that built with the user-generated data collected through our novel system that deliveries personalized ASR service. First, we discuss the motivation, challenges, and prospects of building up such a knowledge-based service-oriented system. Second, we present a Quadruple Transfer Learning (QTL) method that can learn a classification model from a source domain and transfer it to a target domain. Third, we provide an overview architecture of our novel system that collects voice data from mobile users, labels the data via crowdsourcing, utilises these collected user-generated data to train different machine learning models, and delivers the personalised real-time cloud services. Finally, we use the E-Book data collected from our system to train classification models and apply them in the smart TV domain, and the experimental results show that our QTL method is effective in two classification tasks, which confirms that the knowledge transfer provides a value-added service for the upper-layer mobile applications in different domains.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.759-760
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• 2020

최근 사용자, 상품, 그리고 상품의 메타 정보 사이의 관계를 표현한 지식 그래프 (knowledge graph) 가 추천 시스템 분야에서 많은 관심을 받고 있으며 활발히 이용되고 있다. 하지만 기존의 지식 그래프는 각 노드 (사용자, 상품, 메타 정보 등) 사이의 단순한 사실 관계만을 표현하고 있으며, 이는 사용자의 선호도를 정확히 파악하는 데 한계가 있다. 본 논문에서는 지식 그래프의 정보 부족 문제를 보완하기 위해 각 상품에 남겨진 텍스트 리뷰를 감정 분석 (sentiment analysis) 하고, 이를 각 노드 간의 선호도 정보로 활용하여 지식 그래프를 구축하는 방법을 제안한다.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.394-398
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• 2022

The article determines that the study of the development of scientific and didactic knowledge about the educational process in higher education should be built mainly on the basis of qualitative research methods that ensure the identification and understanding of the changes taking place in didactic knowledge, in the unity of their internal and external manifestations. On the basis of the epistemological model of the study of science, a generalized model of didactic knowledge about the educational process in higher education, including didactic relations as a theoretical core, subject of research, research methods and positions of researchers, ways of interaction between science and educational practice, and thematic structures of didactic knowledge; scientific and methodological problems of didactic knowledge about the educational process in higher education at the present stage of its developments due to the post-nonclassical transformation and orientation of research towards the humanitarian ideal of scientific character.

• Journal of Information Processing Systems
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• v.5 no.1
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• pp.11-18
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• 2009

As well as providing various APIs for the development of inference engines and storage models, Jena is widely used in the development of systems or tools related with Web ontology management. However, Jena still has several problems with regard to the development of real applications, one of the most important being that its query processing performance is unacceptable. This paper proposes a storage model to improve the query processing performance of the original Jena storage. The proposed storage model semantically classifies OWL elements, and stores an ontology in separately classified tables according to the classification. In particular, the hierarchical knowledge is managed, which can make the processing performance of inferable queries enhanced and stores information. It enhances the query processing performance by using hierarchical knowledge. For this paper an experimental evaluation was conducted, the results of which showed that the proposed storage model provides a improved performance compared with Jena.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1968-1971
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• 2003

We propose a knowledge-based algorithm for extracting an object boundary from low-quality image like the forward looking infrared image. With the multi-classes training data set, the global shape is modeled by multispace KL(MKL)[1] and curvature model. And the objective function for fitting the deformable boundary template represented by the shape model to true boundary in an input image is formulated by Bales rule. Simulation results show that our method has more accurateness in case of multi-classes training set and performs better in the sense of computation cost than point distribution model(PDM)[2]. It works well in distortion under the noise, pose variation and some kinds of occlusions.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.134-138
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• 2023

The field of digital forensics requires good theoretical and practical knowledge, so practitioners should have an in-depth understanding and knowledge of both theory and practical as they need to take decisions which impacts human lives. With the demand and advancements in the realm of digital forensics, many universities around the globe are offering digital forensics programs, but there is a huge gap between the skills acquired by the student's and the market needs. This research work explores the problems faced by digital forensics programs, and provides solution to overcome the gap between the skills acquired by the student's and the market needs using Activity led learning pedagogy for digital forensics programs.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.1082-1087
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• 1994

A set of text database is indispensable to the probabilistic models for speech recognition, linguistic model, and machine translation. We introduce an environment to canstruct text databases : an automatic tagging system and a set of tools for lexical knowledge acquisition, which provides the facilities of automatic part of speech recognition and guessing.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.39-48
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• 2023

Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.57-64
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• 2003

Achieving the beneficiary goal of recent discovery in human genome project still needs a way to retrieve and analyze the exponentially expanding bio-related information. Research on bio-related fields naturally applies knowledge discovered to the current problem and make inferences to extract new information where shared concepts and data containing information need to be defined and used in a coherent way. In such a professional domain, while the need to help users reduce their work and to improve search results has been emerged. methods for systematic retrieval and adequate exchange of relevant information are still in their infancy. The design of our system aims at improving the quality of information retrieval in a professional domain by utilizing both corpus-based and concept-based ontology. Meta-rules of helping users to make an adequate query are formed into an ontology in the domain. The integration of those knowledge permits the system to retrieve relevant information in a more semantic and systematic fashion. This work mainly describes the query models with details of GUI and a secondary query generation of the system.