• Journal of Information Processing Systems
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• 제15권5호
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• pp.1119-1130
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• 2019

With the wide spread of Social Network Services (SNS), fake news-which is a way of disguising false information as legitimate media-has become a big social issue. This paper proposes a deep learning architecture for detecting fake news that is written in Korean. Previous works proposed appropriate fake news detection models for English, but Korean has two issues that cannot apply existing models: Korean can be expressed in shorter sentences than English even with the same meaning; therefore, it is difficult to operate a deep neural network because of the feature scarcity for deep learning. Difficulty in semantic analysis due to morpheme ambiguity. We worked to resolve these issues by implementing a system using various convolutional neural network-based deep learning architectures and "Fasttext" which is a word-embedding model learned by syllable unit. After training and testing its implementation, we could achieve meaningful accuracy for classification of the body and context discrepancies, but the accuracy was low for classification of the headline and body discrepancies.

• International Journal of Computer Science & Network Security
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• 제21권4호
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• pp.59-64
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• 2021

The article proposes the process of formation of the teacher's innovative competence in the space of lifelong education the foundations of the formation of the teacher's innovative competence in the space of continuous education. The concept of the formation of the teacher's innovative competence in the space of lifelong education is proposed; it includes initial ideas, goals, objectives, patterns, principles, stages, content and technologies implementation of this process.

• Journal of Information Processing Systems
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• 제16권6호
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• pp.1437-1446
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• 2020

For the last few years, smart devices have begun to occupy an essential place in the life of children, by allowing them to access a variety of language activities and books. Various studies are being conducted on using smart devices for education. Our study extracts images and texts from kids' book with smart devices and matches the extracted images and texts to create new images that are not represented in these books. The proposed system will enable the use of smart devices as educational media for children. A deep convolutional generative adversarial network (DCGAN) is used for generating a new image. Three steps are involved in training DCGAN. Firstly, images with 11 titles and 1,164 images on ImageNet are learned. Secondly, Tesseract, an optical character recognition engine, is used to extract images and text from kids' book and classify the text using a morpheme analyzer. Thirdly, the classified word class is matched with the latent vector of the image. The learned DCGAN creates an image associated with the text.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.229-236
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• 2022

This study investigates the impact of flipped classroom strategy in developing students' achievement and acquisition of life skills. The study employed a quasi-experimental design where students were divided into two groups: an experimental (N=22) and a control (N=22). The randomly selected and assigned sample consisted of sixth-year elementary school students studying English as a basic course. The findings revealed statistically significant differences between the two group's means in both achievement and life skills tests in favor of the experimental group. Students of the experimental group who studied using the flipped classroom strategy outperformed the control group who studied in the standard way in achieving the English language and in the life situations test, where the effect size of the use of the strategy was large in both dependent variables. The study is concluded with some recommendations to facilitate the use of flipped classroom strategy for EFL teachers. This can be achieved by training teachers on using the strategy and providing technological resources at schools to implement the strategy efficiently.

• Journal of Information Processing Systems
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• 제18권3호
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• pp.344-358
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• 2022

Text summarization is the task of producing a shorter version of a long document while accurately preserving the main contents of the original text. Abstractive summarization generates novel words and phrases using a language generation method through text transformation and prior-embedded word information. However, newly coined words or out-of-vocabulary words decrease the performance of automatic summarization because they are not pre-trained in the machine learning process. In this study, we demonstrated an improvement in summarization quality through the contextualized embedding of BERT with out-of-vocabulary masking. In addition, explicitly providing precise pointing and an optional copy instruction along with BERT embedding, we achieved an increased accuracy than the baseline model. The recall-based word-generation metric ROUGE-1 score was 55.11 and the word-order-based ROUGE-L score was 39.65.

• International Journal of Computer Science & Network Security
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• 제21권7호
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• pp.82-86
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• 2021

The article analyzes the psychological and pedagogical scientific literature on the problem of motivation of students' educational activity, compiled and implemented a diagnostic research program, studied the system of conditions for the development of motivation for educational activity of students, compiled and implemented a program for the development of motivation for educational activity of students, highlighted the features of motivation for educational activity of students and conducted a comparative study analysis.

• Communications for Statistical Applications and Methods
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• 제30권3호
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• pp.343-354
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• 2023

Recently, there have been many improvements in general language models using architectures such as GPT-3 proposed by Brown et al. (2020). Nevertheless, training complex models can hardly be done if the number of data is very small. Data augmentation that addressed this problem was more than normal success in image data. Image augmentation technology significantly improves model performance without any additional data or architectural changes (Perez and Wang, 2017). However, applying this technique to textual data has many challenges because the noise to be added is veiled. Thus, we have developed a novel method for performing data augmentation on text data. We divide the data into signals with positive or negative meaning and noise without them, and then perform data augmentation using k-doc augmentation to randomly combine signals and noises from all data to generate new data.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2020년도 추계학술발표대회
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• pp.189-192
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• 2020

가상현실 기술과 IoT 센서와 연결하여 스마트 항만 원격조정하는 프로그램을 설계하였다. 스마트 항만은 항만 자동화 추세에 맞춰진 현대의 항만 시스템이며 그에 따른 기술력 확보가 국가의 경쟁력을 높이는 핵심이 된다. 아두이노로 하드웨어를, 유니티로 소프트웨어 부분을 설계하여 진행했다.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.764-767
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• 2021

While Generative Adversarial Networks (GANs) have seen huge success in image synthesis tasks, synthesizing high-quality images from text descriptions is a challenging problem in computer vision. This paper proposes a method named Text-to-Face Generation Using Multi-Scale Gradients for Conditional Generative Adversarial Networks (T2F-MSGGANs) that combines GANs and a natural language processing model to create human faces has features found in the input text. The proposed method addresses two problems of GANs: model collapse and training instability by investigating how gradients at multiple scales can be used to generate high-resolution images. We show that T2F-MSGGANs converge stably and generate good-quality images.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2021년도 추계학술발표대회
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• pp.297-299
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• 2021

Text classification is one of the popular tasks in Natural Language Processing (NLP) used to classify text or document applications such as sentiment analysis and email filtering. Nowadays, state-of-the-art (SOTA) Machine Learning (ML) and Deep Learning (DL) algorithms are the core engine used to perform these classification tasks with high accuracy, and they show satisfying results. This paper conducts a benchmarking performance's analysis of multiple SOTA algorithms on the first known labeled Korean voice phishing dataset called KorCCVi. Experimental results reveal performed on a test set of 366 samples reveal which algorithm performs the best considering the training time and metrics such as accuracy and F1 score.