• Journal of Gifted/Talented Education
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• v.11 no.3
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• pp.23-44
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• 2001

In the current classrooms a teacher has been merely able to inculcate the procedural knowledge of how-and-what. In doing so, however, we lose sight of the essence of "doing science."Though desire of the gifted children is qualitatively different from that of normal children, it is an undesirable reality that we have not developed sufficient researches and programs in conformity with the necessary desire and demand of the gifted children. Curriculum for gifted children in the domain of science necessitates markedly the specializations for the specific areas of the contents, the processes, and the products of studies. In an effort to provide the optimum learning experience for the gifted, this paper deals with the development of project-and-discovery-based science program, its method of application to the real field of education, and its effect, however limited and partial that effect may be. What this study has found are the following: on the one hand, the students acquired and developed the higher levels of thinking when they were under the influence of project-and-discovery-based science program that dealt with concrete real-world problems and issues; on the other, the students were capable of solving creatively the complex and real problems through small group activities. This study also suggests the possible implications of project-and-discovery-based science program: the students can not only learn the contents of study but also apply them creatively; the students can cultivate critical thinking skills that can be a fundamental base for a life-time leaner; the students can naturally acquire the abilities of communication and coordination. Project-and-discovery-based program is currently used in the various disciplines. However, the field of gifted education does not yet implement this type of program. So the overall contribution of this study is to show the successful implementation of project-and-discovery-based science program in developing optimal teaming experience for gifted children in the domain of science, since this type of study is most compatible with the characteristic of the gifted children. children.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.29 no.2
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• pp.104-109
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• 2006

We can offer suitable information to users analyzing the pattern of users. An association rule is one of data mining techniques which can discover the pattern. We use an association rule which considers the web page visiting time and we should the pattern analyse of users. The offered method puts the weights in Web page visiting time of the user and produces an association rule. Weight is web page visiting time unit divide to total of web page visiting time. We offer rather meaningful result the association rule by Apriori algorithm. This method that proposes in the paper offers rather meaningful result Apriori algorithm

• Journal of the Korean Society for information Management
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• v.31 no.1
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• pp.231-250
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• 2014

Due to the recent development of Information and Communication Technologies (ICT), the amount of research publications has increased exponentially. In response to this rapid growth, the demand of automated text processing methods has risen to deal with massive amount of text data. Biomedical text mining discovering hidden biological meanings and treatments from biomedical literatures becomes a pivotal methodology and it helps medical disciplines reduce the time and cost. Many researchers have conducted literature-based discovery studies to generate new hypotheses. However, existing approaches either require intensive manual process of during the procedures or a semi-automatic procedure to find and select biomedical entities. In addition, they had limitations of showing one dimension that is, the cause-and-effect relationship between two concepts. Thus;this study proposed a novel approach to discover various relationships among source and target concepts and their intermediate concepts by expanding intermediate concepts to multi-levels. This study provided distinct perspectives for literature-based discovery by not only discovering the meaningful relationship among concepts in biomedical literature through graph-based path interference but also being able to generate feasible new hypotheses.

• IE interfaces
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• v.25 no.1
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• pp.52-63
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• 2012

Up-to-date enlargement of the scale of global outsourcing has brought about the need of systematic and efficient tools for competitive supplier discovery located in various areas. A web-based business supporting system, referred to as Excellent Manufacturer Scouting System(EMSS), is being developed to serve core business functions including supplier discovery, negotiation, and collaboration between overseas buyers and domestic suppliers throughout the process of supply chain formation. In this paper, a supplier assessment model devoted to evaluation of core manufacturing capability is proposed by targeting small and medium sized mold companies. The assessment model will eventually be loaded to EMSS. Even if many well-designed models for supplier assessment have been presented in literature, most of them limit the evaluation criteria to somewhat general information on a given supplier, such as cost, delivery time, quality, rather than core manufacturing capability itself. This research is pioneering work on supplier assessment from the viewpoint of manufacturability. The proposed assessment model classifies assessment indices into six criteria, which have been drawn by intensive survey and analysis of the mold industry. Actual assessment indices for each criterion are also presented along with an exemplary evaluation result.

• Journal of KIISE:Software and Applications
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• v.36 no.5
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• pp.420-429
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• 2009

Patent text is a rich source for discovering technological trends. In order to automate such a discovery process, we attempt to identify phrases corresponding to the problem and its solution method which together form a technology. Problem and solution phrases are identified by a SVM classifier using features based on a combination of a language modeling approach and linguistic clues. Based on the occurrence statistics of the phrases, we identify the time span of each problem and solution and finally generate a trend. Based on our experiment, we show that the proposed semantic phrase identification method is promising with its accuracy being 77% in R-precision. We also show that the unsupervised method for discovering technological trends is meaningful.

• Journal of KIISE:Information Networking
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• v.28 no.4
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• pp.455-462
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• 2001

In this paper, we propose a deterministic algorithm to solve the resource discovery problem, that is, some subset of machines to learn the existence of each other in a large distributed network. Harchol et al. proposed a randomized algorithm solving this problem within O($log^2\;n$) rounds with high probability, which requires O($nlog^2\;n$) connection communication complexity and O($n^2log^2\;n$) pointer communication complexity, where n is the number of machines in the network. His solution is based on randomization method and it is difficult to determine convergence time. We propose an efficient algorithm which improve performance and the non-deterministic characteristics. Our algorithm requires O(log n) rounds which shows O(mlog n) connection communication complexity and O($n^2log\;n$) pointer communication complexity, where m is the number of links in the network.

• The KIPS Transactions:PartD
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• v.10D no.2
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• pp.327-336
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• 2003

Recently the rapid growth of Internet and the construction of high speed networks make many kinds of multimedia services possible. But most of current multimedia services are designed being by client/server model, which incurs high load of central server. In order to solve this problem, we propose a peer-to-peer network-based discovery mechanism for multimedia services. In the proposed scheme, mobile agents that have autonomy and mobility are used to search the location of resources. Use of mobile agents can solve the loss problem of the search result that occurs when the network is unsettled in pure peer-to-peer network. It also supports interoperability in heterogeneous system environments. In the proposed scheme, each host maintains the location information of resources which are locally requested or recently requested by other hosts. So, the proposed scheme has faster response time than the pre-existing mechanisms in pure peer-to-peer network environments.

• The KIPS Transactions:PartD
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• v.9D no.2
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• pp.215-226
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• 2002

Considering the rapid process of media's breakthrough and diverse patterns of consumptions's analysis, a uniform analysis might be much rooms to be desired for interpretation of new phenomena. In special, the products happening intensive sails on around an anniversary or fresh food have the restricted marketing hours. Moreover, traditional association rule discovery algorithms might not be appropriate for analysis of sales pattern given in a specific time because existing approaches require iterative scan operation to find association rule in large scale transaction databases. in this paper, we propose an incremental candidate set management model based on twin-hashing technique to find association rule in special sales pattern using database trigger and stored procedure. We also prove performance of the proposed model through implementation and experiment.

• BMB Reports
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• v.53 no.6
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• pp.299-310
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• 2020

Chronic liver disease progresses through several stages, fatty liver, steatohepatitis, cirrhosis, and eventually, it leads to hepatocellular carcinoma (HCC) over a long period of time. Since a large proportion of patients with HCC are accompanied by cirrhosis, it is considered to be an important factor in the diagnosis of liver cancer. This is because cirrhosis leads to an irreversible harmful effect, but the early stages of chronic liver disease could be reversed to a healthy state. Therefore, the discovery of biomarkers that could identify the early stages of chronic liver disease is important to prevent serious liver damage. Biomarker discovery at liver cancer and cirrhosis has enhanced the development of sequencing technology. Next generation sequencing (NGS) is one of the representative technical innovations in the biological field in the recent decades and it is the most important thing to design for research on what type of sequencing methods are suitable and how to handle the analysis steps for data integration. In this review, we comprehensively summarized NGS techniques for identifying genome, transcriptome, DNA methylome and 3D/4D chromatin structure, and introduced framework of processing data set and integrating multi-omics data for uncovering biomarkers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1396-1412
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• 2023

Conversation modeling is an important and challenging task in the field of natural language processing because it is a key component promoting the development of automated humanmachine conversation. Most recent research concerning conversation modeling focuses only on the current utterance (considered as the current question) to generate a response, and thus fails to capture the conversation's logic from its beginning. Some studies concatenate the current question with previous conversation sentences and use it as input for response generation. Another approach is to use an encoder to store all previous utterances. Each time a new question is encountered, the encoder is updated and used to generate the response. Our approach in this paper differs from previous studies in that we explicitly separate the encoding of the question from the encoding of its context. This results in different encoding models for the question and the context, capturing the specificity of each. In this way, we have access to the entire context when generating the response. To this end, we propose a deep neural network-based model, called the Context Model, to encode previous utterances' information and combine it with the current question. This approach satisfies the need for context information while keeping the different roles of the current question and its context separate while generating a response. We investigate two approaches for representing the context: Long short-term memory and Convolutional neural network. Experiments show that our Context Model outperforms a baseline model on both ConvAI2 Dataset and a collected dataset of conversational English.