• Journal of Korean Elementary Science Education
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• 제27권1호
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• pp.42-48
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• 2008

The purpose of this study was to develop an instrument for assessing teachers' awareness of science teaching through analogy (ASTA) and to establish its validity and reliability. Based on the literatures on science teaching with analogies, we constructed 23 survey items. Face validity of the items was established using three science education experts. Through exploratory factor analysis with responses of 35 pre- and 26 inservice elementary school teachers, the instruments were constructed on four subcategories: awareness of analogies in science, use of analogy in teaching and learning, self-efficacy in science knowledge, and knowledge of analogy. The data collected from pre- and in-service elementary teachers demonstrated that overall the teachers' awareness of analogy in science was neutral, which indicated they did not have clear standpoints of science teaching through analogy. Further examination demonstrated that there was no significant difference between pre- and in-service teachers and between two genders. Moreover, there was no significant difference among teachers who preferred either didactic or discovery teaching approaches. We conclude that ASTA test would contribute assessment of teachers' awareness of analogy in science teaching while further examination of the instrument will warrant for its broader use.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제58권6호
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• pp.1230-1237
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• 2009

In this paper, we develop a design methodology of Granular-based Radial Basis Function Neural Networks(GRBFNN) by context-based clustering. In contrast with the plethora of existing approaches, here we promote a development strategy in which a topology of the network is predominantly based upon a collection of information granules formed on a basis of available experimental data. The output space is granulated making use of the K-Means clustering while the input space is clustered with the aid of a so-called context-based fuzzy clustering. The number of information granules produced for each context is adjusted so that we satisfy a certain reconstructability criterion that helps us minimize an error between the original data and the ones resulting from their reconstruction involving prototypes of the clusters and the corresponding membership values. In contrast to "standard" Radial Basis Function neural networks, the output neuron of the network exhibits a certain functional nature as its connections are realized as local linear whose location is determined by the values of the context and the prototypes in the input space. The other parameters of these local functions are subject to further parametric optimization. Numeric examples involve some low dimensional synthetic data and selected data coming from the Machine Learning repository.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권2호
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• pp.959-981
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• 2017

Existing Android malware detection approaches mostly have concentrated on superficial features such as requested or used permissions, which can't reflect the essential differences between benign apps and malware. In this paper, we propose a quantitative calculation model of application risks based on the key observation that the essential differences between benign apps and malware actually lie in the way how permissions are used, or rather the way how their corresponding permission methods are used. Specifically, we employ a fine-grained analysis on Android application risks. We firstly classify application risks into five specific categories and then introduce comprehensive risk, which is computed based on the former five, to describe the overall risk of an application. Given that users' risk preference and risk-bearing ability are naturally fuzzy, we design and implement a fuzzy logic system to calculate the comprehensive risk. On the basis of the quantitative calculation model, we propose a risk classification based approach for Android malware detection. The experiments show that our approach can achieve high accuracy with a low false positive rate using the RandomForest algorithm.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 한국감성과학회 2008년도 추계학술대회
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• pp.90-93
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• 2008

In natural language, it is common that repetitive constituents in an expression are to be left out and it is necessary to figure out the constituents omitted at analyzing the meaning of the sentence. This paper is on recognition of boundaries of parallel noun phrases by figuring out constituents omitted. Recognition of parallel noun phrases can greatly reduce complexity at the phase of sentence parsing. Moreover, in natural language information retrieval, recognition of noun with modifiers can play an important role in making indexes. We propose an unsupervised probabilistic model that identifies parallel cores as well as boundaries of parallel noun phrases conjoined by a conjunctive particle. It is based on the idea of swapping constituents, utilizing symmetry (two or more identical constituents are repeated) and reversibility (the order of constituents is changeable) in parallel structure. Semantic features of the modifiers around parallel noun phrase, are also used the probabilistic swapping model. The model is language-independent and in this paper presented on parallel noun phrases in Korean language. Experiment shows that our probabilistic model outperforms symmetry-based model and supervised machine learning based approaches.

• Food Science and Industry
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• 제54권3호
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• pp.145-159
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• 2021

Conventional nutrition services involve producer-oriented approaches without considering the differences in the characteristics and circumstances of each individual, whereas personalized nutrition services are consumer-oriented concepts that provide products and services for maintaining optimal health conditions based on the genetic, physiological, and metabolic characteristics of individuals, with these products based on balanced nutrition and healthy living. Currently, methods for evaluating dietary habits, monitoring dietary behaviors, deep phenotyping, and metabotyping via microbiota profiling, as well as methods for predicting big data by using machine learning, have been previously studied in Korea and abroad. With the development of medical technology and the improvement of hygiene, the demand for personalized nutrition and health services for healthier, happier, and more satisfying lives is rapidly increasing. Therefore, based on scientific technologies, attempts are needed to advance these services into global personalized markets and to boost the global competitiveness of countries and companies.

• Journal of Broadcast Engineering
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• 제24권3호
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• pp.420-429
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• 2019

Recently, researches using Convolutional Neural Network (CNN)-based approaches have been actively conducted to improve the reduced quality of compressed video using block-based video coding standards such as H.265/HEVC. This paper aims to summarize and analyze the network models in these quality enhancement studies. At first the detailed components of CNN for quality enhancement are overviewed and then we summarize prior studies in the image domain. Next, related studies are summarized in three aspects of network structure, dataset, and training methods, and present representative models implementation and experimental results for performance comparison.

• Journal of The Korean Association of Information Education
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• 제26권3호
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• pp.187-195
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• 2022

Recently, with interest in metaverse, attempts are being made to utilize the metaverse platform in various forms. In this paper, we focused on the educational application potential of metaverse, and developed and applied a metaverse STEAM program to provide an effective learning experience to learners in non-face-to-face educational situations. The developed program utilizes Minecraft and ZEPETO, familiar to students, as metaverse learning platforms, and consists of a total of 16 lessons of 5 modules in the form of modules so that alternative classes can take place in the educational field. Through this, the learner's change in STEAM attitude and learning satisfaction were measured, and through the developed STEAM program, the learner's interest, consideration, communication, usefulness, self-concept, self-efficacy, and career choice areas significantly increased. In addition, positive results were confirmed in all areas of the learner satisfaction test related to satisfaction, interest, and overall class. In the future, considering the characteristics of the metaverse, it is necessary to break free from the constraints of time and space to communicate anew, and various learner-centered educational approaches based on a high degree of freedom and immersion should be implemented.

• Journal of The Korean Association For Science Education
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• 제43권6호
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• pp.563-572
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• 2023

This study aimed to analyze cases of online class design by pre-service science teachers to identify the teaching strategies employed for online classes. For this purpose, the real-time online class lesson plans of 12 pre-service science teachers, who had experienced education utilizing online teaching tools for a semester, were collected and analyzed. The pre-service science teachers considered all the elements that were essential in traditional face-to-face class designs, including prerequisites, statements of learning objectives, stimulating motivation, teaching and learning methods, wrapping up, teacher-student interaction, and assessment. They devised teaching strategies that could overcome the limitations of online teaching and were not feasible in face-to-face classes for each element. Additionally, they were considering new instructional strategies tailored to the online teaching environment, such as creating a conducive environment for using online teaching tools and strategies related to checking the online teaching environment. However, for statements of learning objectives, stimulating motivation, and wrapping up, most of the pre-service science teachers predominantly utilized teaching strategies from traditional face-to-face classes, especially those involving the presentation of visual materials through online tools. Student-centered approaches were rarely implemented in stimulating motivation or wrapping up. These findings imply that one semester of exposure to the utilization of online teaching tools may be insufficient in teacher education. Thus, there is a need for a continuous and expanded educational program on the utilization of online teaching tools as part of pre-service teacher education.

• Journal of the Korea Convergence Society
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• 제8권12호
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• pp.55-62
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• 2017

The Named Entity Recognition system is a system that recognizes words or phrases with object names such as personal name (PS), place name (LC), and group name (OG) in the document as corresponding object names. Traditional approaches to named entity recognition include statistical-based models that learn models based on hand-crafted features. Recently, it has been proposed to construct the qualities expressing the sentence using models such as deep-learning based Recurrent Neural Networks (RNN) and long-short term memory (LSTM) to solve the problem of sequence labeling. In this research, to improve the performance of the Korean named entity recognition system, we used a hand-crafted feature, part-of-speech tagging information, and pre-built lexicon information to augment features for representing sentence. Experimental results show that the proposed method improves the performance of Korean named entity recognition system. The results of this study are presented through github for future collaborative research with researchers studying Korean Natural Language Processing (NLP) and named entity recognition system.

• Journal of KIISE:Software and Applications
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• 제34권6호
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• pp.539-548
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• 2007

Mobile content adaptation is a technology of effectively representing the contents originally built for the desktop PC on wireless mobile devices. Previous approaches for Web content adaptation are mostly device-dependent. Also, the content transformation to suit to a smaller device is done manually. Furthermore, the same contents are provided to different users regardless of their individual preferences. As a result, the user has difficulty in selecting relevant information from a heavy volume of contents since the context information related to the content is not provided. To resolve these problems, this paper proposes an enhanced method of Web content adaptation for mobile devices. In our system, the process of Web content adaptation consists of 4 stages including block filtering, block title extraction, block content summarization, and personalization through learning. Learning is initiated when the user selects the full content menu from the content summary page. As a result of learning, personalization is realized by showing the information for the relevant block at the top of the content list. A series of experiments are performed to evaluate the content adaptation for a number of Web sites including online newspapers. The results of evaluation are satisfactory, both in block filtering accuracy and in user satisfaction by personalization.