• Journal of The Korean Association of Information Education
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• v.23 no.1
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• pp.1-8
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• 2019

In order to provide basic data necessary for developing a curriculum to enhance SW education for elementary school teacher students, a questionnaire survey was conducted on 1,260 students at G National University of Education. The results are summarized as follows. First, it is necessary to improve class time of SW education for the college students and revise the SW curriculum to improve SW education capacity for teacher students. Second, in the liberal arts course, it is necessary to develop the teaching and learning materials and the textbooks using various software. Third, in the major course, the Subject Pedagogy programs should be expanded rather than the subject content programs. Fourth, in the specialization course, the programming language education focusing on the Entry and EPL should be strengthened so that it can be linked with the elementary school curriculum. In addition, it is necessary to expand the choice of subjects for the students by reducing the number of required courses and increasing the number of elective courses.

• Translational and Clinical Pharmacology
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• v.26 no.4
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• pp.166-171
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• 2018

Although there are many commercially available training software programs for pharmacokinetics, they lack flexibility and convenience. In this study, we develop simulation software to facilitate pharmacokinetics education. General formulas for time courses of drug concentrations after single and multiple dosing were used to build source code that allows users to simulate situations tailored to their learning objectives. A mathematical relationship for a 1-compartment model was implemented in the form of differential equations. The concept of population pharmacokinetics was also taken into consideration for further applications. The source code was written using R. For the convenience of users, two types of software were developed: a web-based simulator and a standalone-type application. The application was built in the JAVA language. We used the JAVA/R Interface library and the 'eval()' method from JAVA for the R/JAVA interface. The final product has an input window that includes fields for parameter values, dosing regimen, and population pharmacokinetics options. When a simulation is performed, the resulting drug concentration time course is shown in the output window. The simulation results are obtained within 1 minute even if the population pharmacokinetics option is selected and many parameters are considered, and the user can therefore quickly learn a variety of situations. Such software is an excellent candidate for development as an open tool intended for wide use in Korea. Pharmacokinetics experts will be able to use this tool to teach various audiences, including undergraduates.

• The Mathematical Education
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• v.58 no.2
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• pp.319-335
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• 2019

In this paper, we examine the effectiveness of calculation according to automation, which is one of Computational Thinking, by coding the conceptual process into Python language, focusing on the concept of divisibility in elementary school textbooks. The educational implications of these considerations are as follows. First, it is possible to make a field of learning that can revise the new mathematical concept through the opportunity to reinterpret the Conceptual Thinking learned in school mathematics from the perspective of Computational Thinking. Second, from the analysis of college students, it can be seen that many students do not have mathematical concepts in terms of efficiency of computation related to the divisibility. This phenomenon is a characteristic of the mathematics curriculum that emphasizes concepts. Therefore, it is necessary to study new mathematical concepts when considering the aspect of utilization. Third, all algorithms related to the concept of divisibility covered in elementary mathematics textbooks can be found to contain the notion of iteration in terms of automation, but little recursive activity can be found. Considering that recursive thinking is frequently used with repetitive thinking in terms of automation (in Computational Thinking), it is necessary to consider low level recursive activities at elementary school. Finally, it is necessary to think about mathematical Conceptual Thinking from the point of view of Computational Thinking, and conversely, to extract mathematical concepts from computer science's Computational Thinking.

• Journal of the Korean Society for information Management
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• v.36 no.1
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• pp.169-190
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• 2019

This study aims to identify the effects of teachers' recognition and application of 2015 revised national curriculum on their educational information needs in high schools. Several in-depth interviews and a questionnaire survey with the teachers, who were in charge of teaching common courses, such as Korean language, mathematics, English, social studies, and science, in general public high schools in Incheon, were executed for the purpose. As a result, the teachers' recognition and application affected their educational information needs in part. Especially, new demands on small sized copies and learning information sources were identified which were related to the application of 2015 revised national curriculum. Based on the results, we proposed several improvements of school library operations, such as small sized local consortium for sharing resources and providing referral services, in order to strengthen the gateway role of school libraries.

• Phonetics and Speech Sciences
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• v.13 no.3
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• pp.71-78
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• 2021

Most neural-network-based speech synthesis models utilize neural vocoders to convert mel-scaled spectrograms into high-quality, human-like voices. However, neural vocoders combined with mel-scaled spectrogram prediction models demand considerable computer memory and time during the training phase and are subject to slow inference speeds in an environment where GPU is not used. This problem does not arise in linear spectrogram prediction models, as they do not use neural vocoders, but these models suffer from low voice quality. As a solution, this paper proposes a Tacotron 2 and Transformer-based linear spectrogram prediction model that produces high-quality speech and does not use neural vocoders. Experiments suggest that this model can serve as the foundation of a high-quality text-to-speech model with fast inference speed.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.279-286
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• 2020

This study was a phenomenological qualitative research that analyzed the experiences of Korean students studying in Korea after the COVID-19 incident. Participants in this study consisted of 22 international students aged 20 to 40 attending the International Exchange Center at C University. The interview period was from September 10, 2020 to October 10, 2020. Giogi qualitative research method was used to analyze vivid experiences of international students. As a result of the analysis, 26 semantic units, 7 subcomponents were derived. The description of the general structure sentence of phenomenology was a description of the meaning of experience from the perspective of participants, and the context and structure descriptions were integrated. The results of this study showed that: The students who came to Korea to study were concerned about Korea in various ways, but they had to adjust to unexpected changes in education methods, anxious about the unexpected COVID-19 disaster. Participants chose to study in Korea based on existing information, so they felt anxiety, regret, fear, and frustration over sudden changes, but taking online classes helped them learn repeatedly and voluntarily became an experience that suited their learning speed. As commuting time has decreased, they were more opportunities to make money in Korea also. Based on the results of this study, the following is suggested: First, the government should establish systematic online infection prevention measures for international students who have poor Korean language skills in preparation for unexpected disasters. Second, non-face-to-face teaching methods should be prepared with the same weight in the face-to-face teaching methods that have been carried out so far in preparation for unexpected disasters.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.167-176
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• 2020

Recommendation Systems is the top requirements for many people and researchers for the need required by them with the proper suggestion with their personal indeed, sorting and suggesting doctor to the patient. Most of the rating prediction in recommendation systems are based on patient's feedback with their information regarding their treatment. Patient's preferences will be based on the historical behaviour of similar patients. The similarity between the patients is generally measured by the patient's feedback with the information about the doctor with the treatment methods with their success rate. This paper presents a new method of predicting Top Ranked Doctor's in recommendation systems. The proposed Recommendation system starts by identifying the similar doctor based on the patients' health requirements and cluster them using K-Means Efficient Clustering. Our proposed K-Means Clustering with Content Based Doctor Recommendation for Cancer (KMC-CBD) helps users to find an optimal solution. The core component of KMC-CBD Recommended system suggests patients with top recommended doctors similar to the other patients who already treated with that doctor and supports the choice of the doctor and the hospital for the patient requirements and their health condition. The recommendation System first computes K-Means Clustering is an unsupervised learning among Doctors according to their profile and list the Doctors according to their Medical profile. Then the Content based doctor recommendation System generates a Top rated list of doctors for the given patient profile by exploiting health data shared by the crowd internet community. Patients can find the most similar patients, so that they can analyze how they are treated for the similar diseases, and they can send and receive suggestions to solve their health issues. In order to the improve Recommendation system efficiency, the patient can express their health information by a natural-language sentence. The Recommendation system analyze and identifies the most relevant medical area for that specific case and uses this information for the recommendation task. Provided by users as well as the recommended system to suggest the right doctors for a specific health problem. Our proposed system is implemented in Python with necessary functions and dataset.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.139-150
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• 2021

Object orientation has become the predominant paradigm for conceptual modeling (e.g., UML), where the notions of class and object form the primitive building blocks of thought. Classes act as templates for objects that have attributes and methods (actions). The modeled systems are not even necessarily software systems: They can be human and artificial systems of many different kinds (e.g., teaching and learning systems). The UML class diagram is described as a central component of model-driven software development. It is the most common diagram in object-oriented models and used to model the static design view of a system. Objects both carry data and execute actions. According to some authorities in modeling, a certain degree of difficulty exists in understanding the semantics of these notions in UML class diagrams. Some researchers claim class diagrams have limited use for conceptual analysis and that they are best used for logical design. Performing conceptual analysis should not concern the ways facts are grouped into structures. Whether a fact will end up in the design as an attribute is not a conceptual issue. UML leads to drilling down into physical design details (e.g., private/public attributes, encapsulated operations, and navigating direction of an association). This paper is a venture to further the understanding of object-orientated concepts as exemplified in UML with the aim of developing a broad comprehension of conceptual modeling fundamentals. Thinging machine (TM) modeling is a new modeling language employed in such an undertaking. TM modeling interlaces structure (components) and actionality where actions infiltrate the attributes as much as the classes. Although space limitations affect some aspects of the class diagram, the concluding assessment of this study reveals the class description is a kind of shorthand for a richer sematic TM construct.

• The Journal of Korean Institute of Information Technology
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• v.17 no.7
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• pp.31-45
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• 2019

The core technologies for intelligent services today are deep learning, that is neural networks, and parallel and distributed processing technologies such as GPU parallel computing and big data. However, for intelligent services and knowledge sharing services through globally shared ontologies in the future, there is a technology that is better than the neural networks for representing and reasoning knowledge. It is a knowledge representation of IF-THEN in RIF or SWRL, which is the standard rule language of the Semantic Web, and can be inferred efficiently using the rete algorithm. However, when the number of rules processed by the rete algorithm running on a single computer is 100,000, its performance becomes very poor with several tens of minutes, and there is an obvious limitation. Therefore, in this paper, we analyze the past and current studies on parallel and distributed processing of rete algorithm, and examine what aspects should be considered to implement an efficient rete algorithm.

• Journal of the Korea Society of Computer and Information
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• v.26 no.3
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• pp.29-34
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• 2021

With the advent of the 4th industrial revolution era, IT technology is creating new services that have not existed by converging with various existing industries and fields. In particular, in the field of artificial intelligence, chatbots and the latest technologies have developed dramatically with the development of natural language processing technology, and various business processes are processed through chatbots. This study is a study on a system that provides a close answer to the question the user wants to find by creating a structural form for legal inquiries through Slot Filling-based chatbot technology, and inputting a predetermined type of question. Using the proposal system, it is possible to construct question-and-answer data in a more structured form of legal information, which is unstructured data in text form. In addition, by managing the accumulated Q&A data through a big data storage system such as Apache Hive and recycling the data for learning, the reliability of the response can be expected to continuously improve.