• Journal of Sensor Science and Technology
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• v.30 no.6
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• pp.429-435
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• 2021

In this study, the creation of emotional dialogue was investigated within the process of developing a robot's natural language understanding and emotional dialogue processing. Unlike an English-based dataset, which is the mainstay of natural language processing, the Korean-based dataset has several shortcomings. Therefore, in a situation where the Korean language base is insufficient, the Korean dataset should be dealt with in detail, and in particular, the unique characteristics of the language should be considered. Hence, the first step is to base this study on a specific Korean dataset consisting of conversations on emotional topics. Subsequently, a model was built that learns to extract the continuous dialogue features from a pre-trained language model to generate sentences while maintaining the context of the dialogue. To validate the model, a chatbot system was implemented and meaningful results were obtained by collecting the external subjects and conducting experiments. As a result, the proposed model was influenced by the dataset in which the conversation topic was consultation, to facilitate free and emotional communication with users as if they were consulting with a chatbot. The results were analyzed to identify and explain the advantages and disadvantages of the current model. Finally, as a necessary element to reach the aforementioned ultimate research goal, a discussion is presented on the areas for future studies.

• Journal of Korea Game Society
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• v.21 no.3
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• pp.3-12
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• 2021

This study is a design study to develop the reading application for English learning courses with fun elements of Gamification incorporating user's immersion. The system is focusing on the story progression of Aesop's fable "Rabbit and Tortoise", which is consisted of chapters in digital technology. We intended to apply the four elements of fun factors by grafting Gamification into the game engine system. The purpose and significance of the study is to present the guideline through evaluation of usability from prototypes by surveying the educator group.

• International Journal of Computer Science & Network Security
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• v.21 no.11
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• pp.73-80
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• 2021

Nowadays microblogs have become the most popular platforms to obtain and spread information. Twitter is one of the most used platforms to share everyday life event. However, rumors and misinformation on Arabic social media platforms has become pervasive which can create inestimable harm to society. Therefore, it is imperative to tackle and study this issue to distinguish the verified information from the unverified ones. There is an increasing interest in rumor detection on microblogs recently, however, it is mostly applied on English language while the work on Arabic language is still ongoing research topic and need more efforts. In this paper, we propose a combined Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to detect rumors on Twitter dataset. Various experiments were conducted to choose the best hyper-parameters tuning to achieve the best results. Moreover, different neural network models are used to evaluate performance and compare results. Experiments show that the CNN-LSTM model achieved the best accuracy 0.95 and an F1-score of 0.94 which outperform the state-of-the-art methods.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.644-647
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• 2020

최근 교육분야에서는 IT 기술을 활용하여 교육을 혁신하는 것을 의미하는 에듀테크에 대한 관심이 높아지고 있다. 단순한 지식의 전달이 아닌 사용자의 수준에 맞춰진 학습을 하고 자신의 학습 내용을 스스로 모니터링할 수 있는 새로운 교육시스템이 필요하다. 이에 본 논문에서는 빅데이터를 활용한 영어학습 애플리케이션를 제안한다. 제안하는 애플리케이션은 영어뉴스 기사에서 추출한 빅데이터를 활용하여 사용자 수준에 맞춘 유용한 문장을 분석해 자동으로 문제를 생성하고 사용자의 음성데이터를 강세 분석 알고리즘으로 원어민 발음과 비교분석 하여 발음 및 강세를 교정할 수 있도록 설계 및 구현하였다.

• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.280-283
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• 2019

공손함은 인간 언어의 가장 흥미로운 특징 중 하나이며, 자연어처리 시스템이 인간과 자연스럽게 대화하기 위해 필수적으로 모델링해야 할 요소이다. 본 연구에서는 인간의 발화가 주어졌을 때, 이의 공손함을 판단할 수 있는 시스템을 구현한다. 이를 위해 딥러닝 방법인 양방향 LSTM 모델과, 최근 자연어처리 분야에서 각광받고 있는 BERT 모델에 대해 성능 비교를 수행하였다. 이 두 기술은 모두 문맥 정보를 반영할 수 있는 모델로서, 같은 단어라도 문맥 정보에 따라 의미가 달라질 수 있는 공손함의 미묘한 차이를 반영할 수 있다. 실험 결과, 여러 설정에 거쳐 BERT 모델이 양방향 LSTM 모델보다 더 우수함을 확인하였다. 또한, 발화가 구어체보다 문어체에 가까울 수록 딥러닝 모델의 성능이 더 좋은 것으로 나타났다. 제안된 두 가지 방법의 성능을 인간의 판단 능력과 비교해본 결과, 위키피디아 도메인에서 BERT 모델이 91.71%의 성능을 보여 인간의 정확도인 86.72%를 상회함을 확인하였다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.317-318
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• 2023

본 논문에서는 CNN(Convolutional Neural Network)을 활용하여 자율주행 자동차가 각 국가별 교통 규칙 및 도로 표시를 이해하고 정확한 주행을 할 수 있도록, Deep Neural Network 시스템을 설계하고 구현하는 방법을 제안한다. 연구 방법으로는 한국도로교통공단(koroad)에서 제공하는 교통안전표지 일람표 이미지를 학습하여, 차량이 자율주행을 하기 위해 요구되는 표지판을 인식할 수 있도록 하였다. 본 논문에서 설계한 학습 시스템으로 도로교통표지판의 인식에 성공했으며, 이를 통해 자율주행차량이 표지판을 인식할 수 있으며, 시각장애인 및 고령운전자를 위한 지원 역시 가능하다고 사료된다.

• Korea Trade Review
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• v.45 no.3
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• pp.55-69
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• 2020

This study examines the research trends and knowledge structure of international trade studies using topic modeling method, which is one of the main methodologies of text mining. We collected and analyzed English abstracts of 1,868 papers of three Korean major journals in the area of international trade from 2003 to 2019. We used the Latent Dirichlet Allocation(LDA), an unsupervised machine learning algorithm to extract the latent topics from the large quantity of research abstracts. 20 topics are identified without any prior human judgement. The topics reveal topographical maps of research in international trade and are representative and meaningful in the sense that most of them correspond to previously established sub-topics in trade studies. Then we conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. We discovered 2 hot topics(internationalization capacity and performance of export companies, economic effect of trade) and 2 cold topics(exchange rate and current account, trade finance). Trade studies are characterized as a interdisciplinary study of three agendas(i.e. international economy, International Business, trade practice), and 20 topics identified can be grouped into these 3 agendas. From the estimated results of the study, we find that the Korean government's active pursuit of FTA and consequent necessity of capacity building in Korean export firms lie behind the popularity of topic selection by the Korean researchers in the area of int'l trade.

• Journal of The Korean Association For Science Education
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• v.28 no.7
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• pp.697-712
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• 2008

The purpose of this study was to seek strengths and weaknesses from analyzing Korea and U.S. science textbooks in terms of general structural features, contents, cognitive levels of questions and the purpose of questions used in science textbooks. This provided insight into improvement of textbooks that can effectively assist teaching and learning. To investigate organization of unit in textbooks in-depth, the evolution unit was selected and scrutinized as one example. The results showed that the number of pages, activities, vocabulary words, and vocabulary lists are considerably different between Korean and the U.S. Commonly, U.S. textbooks were more laden with information and lacking in coherence than those of the Korean textbooks. The findings on the cognitive levels of questions showed that the majority of questions in both nations are concerned with knowledge. However, the difference between the two nations is great in the ratios of analysis, synthesis, and evaluation questions. Questions are concentrated in review section (45% of Korean and 60.6% of U.S.) in textbooks. It suggested that well-planned questions in a review section can provide the basic guidance for strength in a science classroom.

• Journal of the Korea Society of Computer and Information
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• v.25 no.1
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• pp.93-99
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• 2020

With the development of digital technology and the increasing demand for learning how to improve one's ability to solve problems through play and participation interactions, a variety of edutainment game contents are being developed. The edutainment game contents developed until recently have received a large number of contents for intelligence development and transfer of knowledge such as Korean and English mathematics for children and children. Recently, there have been various researches on the necessity and effect of STEAM education that foster convergent science and technology talents with comprehensive thinking ability and scientific inquiry spirit through the fusion education method among the subjects including science, technology, engineering, mathematics, And there is a growing need for the development of a parish suitable for STEAM education. However, there is a lack of STEAM educational content development that incorporates the technology of creative convergence talent training to develop talented people who can think and solve problems by crossing various academic boundaries. Therefore, this study develops game contents for early childhood education by combining STEAM education which foster convergent science and technology talents with comprehensive thinking ability and scientific inquiry spirit. And we designed and implemented STEAM game contents for infant learning education which can induce the interest of children and have fun by using gyroscope sensor of smartphone.

• Journal of The Korean Association For Science Education
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• v.34 no.8
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• pp.745-754
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• 2014

Students experience a variety of achievement-related emotions during the process of learning the science curriculum. The purpose of this study is to develop an achievement emotions questionnaire for Korean middle school science curriculum to measure the achievement emotions that middle school students experience during study of this curriculum, and verified its validity. The Achievement Emotions Questionnaire-Korean Middle School Science is based on the English version of the Achievement Emotions Questionnaire, developed with reference to Korean middle school science curriculum and the characteristics of science study, from the perspective of the control-value theory of achievement. It has 232 questions, configured to measure nine achievement emotions across three types of academic settings. The questionnaire results can be treated with a high degree of confidence according to the result of our validation, which also verified that the achievement emotions of these students are configured with four internal criteria (learning strategy, achievement motivation and course grade), as suggested by the control-value theory; this in turn verifies that the nine achievement emotions are sufficiently distinctive across study situations. Last, it was verified that the questionnaire has sufficient external validity based on a comprehensive examination of the relation between science achievement emotions and the four criterion variables for each student. This suggests that through the development and implementation of this quantitative questionnaire, basic ground was provided to understand the achievement emotions experienced by middle school students learning the science curriculum.