• BMB Reports
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• v.54 no.3
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• pp.149-156
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• 2021

The well-known second messenger cyclic adenosine monophosphate (cAMP) regulates the morphology and physiology of neurons and thus higher cognitive brain functions. The discovery of exchange protein activated by cAMP (Epac) as a guanine nucleotide exchange factor for Rap GTPases has shed light on protein kinase A (PKA)-independent functions of cAMP signaling in neural tissues. Studies of cAMP-Epac-mediated signaling in neurons under normal and disease conditions also revealed its diverse contributions to neurodevelopment, synaptic remodeling, and neurotransmitter release, as well as learning, memory, and emotion. In this mini-review, the various roles of Epac isoforms, including Epac1 and Epac2, highly expressed in neural tissues are summarized, and controversies or issues are highlighted that need to be resolved to uncover the critical functions of Epac in neural tissues and the potential for a new therapeutic target of mental disorders.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.34-46
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• 2023

A growing number of large scale knowledge graphs raises several issues how knowledge graph data can be organized, discovered, and integrated efficiently. We present a novel semantic-based mashup platform for contents convergence which consists of acquisition, RDF storage, ontology learning, and mashup subsystems. This platform servers a basis for developing other more sophisticated applications required in the area of knowledge big data. Moreover, this paper proposes an entity matching method using graph convolutional network techniques as a preliminary work for automatic classification and discovery on knowledge big data. Using real DBP15K and SRPRS datasets, the performance of our method is compared with some existing entity matching methods. The experimental results show that the proposed method outperforms existing methods due to its ability to increase accuracy and reduce training time.

• CELLMED
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• v.13 no.14
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• pp.20.1-20.6
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• 2023

The purpose of this study is to explore directions for achieving therapeutic and social well-being effects through intercultural citizenship education in language classrooms. To accomplish this, we first clarified the concepts of education as healing, social well-being, and intercultural citizenship education. Subsequently, through the analysis of reflective journals on the writing and peer review processes written by university students, we discovered manifestations of key concepts of intercultural citizenship, such as empathy, recognition, connection, discovery of new knowledge, and attitude change. Based on these insights, we proposed the perspective that addressing the concept of intercultural citizenship in Korean language education can be beneficial for language education as a form of healing and for social well-being. Furthermore, we suggested that future language education should evolve from instruction focused on the interpretation of symbols and functional proficiency to practices that empower learners as members of global society, allowing them to assign value to their lives and build healthy relationships with others.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.926-932
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• 2009

Recently many research reports on effective use of mobile devices for museums to provide information on displayed artifacts providing individualized learning space, collaborative learning, and discovery learning, Such devices have many possibilities to support learning as a participatory media and social network. Mobile devices are used, however, limited for its usability and lack in providing expected learning experiences. It offers one-way interaction and they are often limited in providing customized services for different patrons to experience learning and entertainment. In this research, we have adopted user centered design approach to identify the needs and possible usage of PDA system in the museum. Research methods include contextual observation and inquiry with symbolic interactionism for qualitative research and its epistemology. We have developed conceptual model with scenario and storyboard method, and developed vertical prototype with Flash.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.6
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• pp.684-695
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• 2001

Recent recommender system uses a method of combining collaborative filtering system and content based filtering system in order to solve sparsity and first rater problem in collaborative filtering system. Collaborative filtering systems use a database about user preferences to predict additional topics. Content based filtering systems provide recommendations by matching user interests with topic attributes. In this paper, we describe a method for discovery of user preference through combining two techniques for recommendation that allows the application of machine learning algorithm. The proposed collaborative filtering method clusters user using genetic algorithm based on items categorized by Naive Bayes classifier and the content based filtering method builds user profile through extracting user interest using relevance feedback. We evaluate our method on a large database of user ratings for web document and it significantly outperforms previously proposed methods.

• Journal of the Korea Society of Computer and Information
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• v.14 no.3
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• pp.183-192
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• 2009

ADESS(Adverse drug event surveillance system) is the system which distinguishes adverse drug events using adverse drug signals. This system shows superior effectiveness in adverse drug surveillance than current methods such as volunteer reporting or char review. In this study, we built clinical data mart(CDM) for the development of ADESS. This CDM could obtain data reliability by applying data quality management and the most suitable clustering number(n=4) was gained through the reproducibility assessment in unsupervised learning techniques of knowledge discovery. As the result of analysis, by applying the clustering number(N=4) K-means, Kohonen, and two-step clustering models were produced and we confirmed that the K-means algorithm makes the most closest clustering to the result of adverse drug events.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.427-436
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• 2024

In this paper, we present the results of a study of lessons learned from the life of Caleb (Cal), a significant character in John Steinbeck's "East of Eden." His life serves as a powerful example of the process of self-discovery, overcoming conflicts with self, others, and society. He emphasizes the importance of managing and understanding your emotions, learning to acknowledge and express them throughout your journey. He makes moral judgments while confronting desires and conflicts, and transparently demonstrates the importance of self-determination based on ethical decisions, while his honest expression and acceptance of his own emotions emphasizes the core value of emotion management and understanding. Additionally, his story emphasizes the clear importance of understanding and compromise in human relationships. We present a thorough exploration of these topics and consider how the lessons from Caleb's story can be applied to our everyday lives. As a result of the analysis in this paper, we expect to gain insight into how these lessons can be applied and put into practice.

• Journal of the Korea Convergence Society
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• v.11 no.8
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• pp.33-38
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• 2020

Modern society needs to think in new directions and solve problems by grafting problems from diverse fields with computers. Abstraction and automation of various problems using computing technology with your own ideas is called computational thinking. In this paper, we analyze how to diagnose and improve knowledge information based on computational thinking through the process of presenting a variety of problems in programming education situations and finding several problem-solving methods to solve them. To pretest the learners, they were diagnosed using a measurement sheet and a LightBot. By determining the correlation between the evaluation results and LightBot results, the learners' current knowledge statuses were checked, and the correlation between the evaluation results and the LightBot results, based on what was taught according to the problem-solving learning technique, was analyzed according to the proposed technique. The analysis of the group average score of the learners showed that the learning effect was significant. If the method of deriving and improving knowledge based on computational thinking ability for solving the problem proposed in this paper is applied to software education, it will induce student interest, thereby increasing the learning effect.

• Korean Journal of Cognitive Science
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• v.28 no.4
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• pp.299-314
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• 2017

Predicting compound-protein interactions in-silico is significant for the drug discovery. In this paper, we propose an scalable machine learning model to predict compound-protein interaction. The key idea of this scalable machine learning model is the architecture of pairwise neural network model and feature embedding method from the raw data, especially for protein. This method automatically extracts the features without additional knowledge of compound and protein. Also, the pairwise architecture elevate the expressiveness and compact dimension of feature by preventing biased learning from occurring due to the dimension and type of features. Through the 5-fold cross validation results on large scale database show that pairwise neural network improves the performance of predicting compound-protein interaction compared to previous prediction models.

• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.167-177
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• 2019

The enhancement of national competitiveness in future society is the discovery and training of excellent SW convergence gifted. In order to cultivate these SW convergence gifted, reinforcing competence of teachers in charge should be made first. Therefore, in this paper, artificial intelligence education contents, one of the core technologies of the 4th Industrial Revolution era, were developed to reinforcing competence of SW convergence gifted teachers. After setting the direction of artificial intelligence education content, we constructed educational content suitable for secondary SW convergence gifted education, and designed and developed it in detail. The composition of artificial intelligence education content consists of machine learning and tensor flow understanding, linear regression machine learning implementation for numerical prediction, and multiple linear regression-based price prediction machine learning implementations. The developed educational contents were verified by experts with qualitative aspects. In the future, we expect that the educational content of artificial intelligence proposed in this paper will be useful for strengthening the ability of SW convergence gifted teachers.