• The Journal of Korean Institute of Information Technology
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• v.17 no.12
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• pp.125-134
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• 2019

As the limitations of the passive recognition domain, which is not guaranteed transparency of the operation process, AI technology has a vulnerability that depends on the data. Human error is inherent because raw data for artificial intelligence learning must be processed and inspected manually to secure data quality for the advancement of AI learning. In this study, we examine the necessity of learning data management before machine learning by analyzing inaccurate cases of AI learning data and cyber security attack method through the approach from cyber security perspective. In order to verify the learning data integrity, this paper presents the direction of data-preserving artificial intelligence system, a blockchain-based learning data environment model. The proposed method is expected to prevent the threats such as cyber attack and data corruption in providing and using data in the open network for data processing and raw data collection.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.335-339
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• 2021

기계독해(MRC)는 인공지능 알고리즘이 문서를 이해하고 질문에 대한 정답을 찾는 기술이다. MRC는 사전 학습 모델을 사용하여 높은 성능을 내고 있고, 일반 텍스트문서 뿐만 아니라 문서 내의 테이블(표)에서도 정답을 찾고자 하는 연구에 활발히 적용되고 있다. 본 연구에서는 기존의 사전학습 모델을 테이블 데이터에 활용하여 질의응답을 할 수 있는 방법을 제안한다. 더불어 테이블 데이터를 효율적으로 학습하기 위한 데이터 구성 방법을 소개한다. 사전학습 모델은 BERT[1]를 사용하여 테이블 정보를 인코딩하고 Masked Entity Recovery(MER) 방식을 사용한다. 테이블 질의응답 모델 학습을 위해 한국어 위키 문서에서 표와 연관 텍스트를 추출하여 사전학습을 진행하였고, 미세 조정은 샘플링한 테이블에 대한 질문-답변 데이터 약 7만건을 구성하여 진행하였다. 결과로 KorQuAD2.0 데이터셋의 테이블 관련 질문 데이터에서 EM 69.07, F1 78.34로 기존 연구보다 우수한 성능을 보였다.

• Journal of the Korean Society for Library and Information Science
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• v.54 no.2
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• pp.419-434
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• 2020

The purpose of the study is to design and propose metadata standard for building AI training dataset of landmark images. In order to achieve the purpose, we first examined and analyzed the state of art of the types of image retrieval systems and their indexing methods, comprehensively. We then investigated open training dataset and machine learning tools for image object recognition. Sequentially, we selected metadata elements optimized for the AI training dataset of landmark images and defined the input data for each element. We then concluded the study with implications and suggestions for the development of application services using the results of the study.

• Journal of The Korean Association of Information Education
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• v.26 no.4
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• pp.273-284
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• 2022

The goal of this paper is to propose the use of machine learning platforms in education to train learners to recognize data biases. Learners can cultivate the ability to recognize when learners deal with AI data and systems when they want to prevent damage caused by data bias. Specifically, this paper presents a method of data bias education using MachineLearningforKids, focusing on the case of AI baseball referee. Learners take the steps of selecting a specific topic, reviewing prior research, inputting biased/unbiased data on a machine learning platform, composing test data, comparing the results of machine learning, and present implications. Learners can learn that AI data bias should be minimized and the impact of data collection and selection on society. This learning method has the significance of promoting the ease of problem-based self-directed learning, the possibility of combining with coding education, and the combination of humanities and social topics with artificial intelligence literacy.

• Education of Primary School Mathematics
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• v.26 no.4
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• pp.333-348
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• 2023

Clear analysis and diagnosis of various characteristic factors of individual students is the most important in order to realize individual customized teaching and learning, which is considered the most essential function of math artificial intelligence-based digital textbooks. In this study, analysis factors and tools for individual customized learning diagnosis and construction models for data collection and analysis were derived from mathematical AI digital textbooks. To this end, according to the Ministry of Education's recent plan to apply AI digital textbooks, the demand for AI digital textbooks in mathematics, personalized learning and prior research on data for it, and factors for learner analysis in mathematics digital platforms were reviewed. As a result of the study, the researcher summarized the factors for learning analysis as factors for learning readiness, process and performance, achievement, weakness, and propensity analysis as factors for learning duration, problem solving time, concentration, math learning habits, and emotional analysis as factors for confidence, interest, anxiety, learning motivation, value perception, and attitude analysis as factors for learning analysis. In addition, the researcher proposed noon data on the problem, learning progress rate, screen recording data on student activities, event data, eye tracking device, and self-response questionnaires as data collection tools for these factors. Finally, a data collection model was proposed that time-series these factors before, during, and after learning.

• Convergence Security Journal
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• v.23 no.2
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• pp.55-66
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• 2023

Recently, there is an increasing movement to increase the value of AI learning data and to secure high-quality data based on previous research on AI learning data in all areas of society. Therefore, quality management is very important in construction projects to secure high-quality data. In this paper, quality management to secure high-quality data when building AI learning data and improvement plans for each construction process are presented. In particular, more than 80% of the data quality of unstructured data built for AI learning is determined during the construction process. In this paper, we performed quality inspection of image/video data. In addition, we identified inspection procedures and problem elements that occurred in the construction phases of acquisition, data cleaning, labeling, and models, and suggested ways to secure high-quality data by solving them. Through this, it is expected that it will be an alternative to overcome the quality deviation of data for research groups and operators participating in the construction of AI learning data.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.201-207
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• 2021

As artificial intelligence gradually expands into jobs, iIt is necessary to nurture talents with AI literacy capabilities required for non-majors. Therefore, in this study, based on the necessity and current status of AI education, AI literacy competency improvement education was conducted for non-majors so that AI learning could be sustainable in relation to future majors. For non-majors at University D, problem-solving solutions through project-based data analysis and visualization were applied over 15 weeks, and the AI ability improvement and effectiveness of learners before and after education were analyzed and verified. As a result, it was possible to confirm a statistically significant level of positive change in the learners' data analysis and utilization ability, AI literacy ability, and AI self-efficacy. In particular, it not only improved the learners' ability to directly utilize public data to analyze and visualize it, but also improved their self-efficacy to solve problems by linking this with the use of AI.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.49-56
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• 2023

As AI (Artificial Intelligence)-related technologies are highly developed due to the 4th industrial revolution, cases of applying AI in various fields are increasing. The main reason is that there are practical limits to direct processing and analysis of exponentially increasing data as information and communication technology develops, and the risk of human error can be reduced by applying new technologies. In this study, after collecting the data received from the 'remote potential measurement terminal (T/B, Test Box)' and the output of the 'remote rectifier' at that time, AI was trained. AI learning data was obtained through data augmentation through regression analysis of the initially collected data, and the learning model applied the value-based Q-Learning model among deep reinforcement learning (DRL) algorithms. did The AI that has completed data learning is put into the actual city gas supply area, and based on the received remote T/B data, it is verified that the AI responds appropriately, and through this, AI can be used as a suitable means for electricity management in the future. want to verify.

• Journal of Practical Engineering Education
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• v.16 no.4
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• pp.513-520
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• 2024

This study investigates the changes in teachers' roles as the impact of AI on school education expands. Traditionally, teachers have been responsible for core aspects of classroom instruction, curriculum development, assessment, and feedback. AI can automate these processes, particularly enhancing efficiency through personalized learning. AI also supports complex classroom management tasks such as student tracking, behavior detection, and group activity analysis using integrated camera and microphone systems. However, AI struggles to automate aspects of counseling and interpersonal communication, which are crucial in student life guidance. While direct conversational replacement by AI is challenging, AI can assist teachers by providing data-driven insights and pre-conversation resources. Key competencies required for teachers in the AI era include expertise in advanced instructional methods, dataset analysis, personalized learning facilitation, student and parent counseling, and AI digital literacy. Teachers should collaborate with AI to emphasize creativity, adjust personalized learning paths based on AI-generated datasets, and focus on areas less amenable to AI automation, such as individualized learning and counseling. Essential skills include AI digital literacy and proficiency in understanding and managing student data.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.01a
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• pp.77-82
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• 2022

GUI(Graphical User Interface)를 대신하는 차세대 인터페이스로서 NUI(Natural User Interace)에 기대가 모이는 것은 자연스러운 흐름이다. 본 연구는 NUI의 손가락 관절을 포함한 손동작 전체를 인식시키기 위해 웹캠과 카메라를 활용하여 다양한 배경과 각도의 손동작 데이터를 수집한다. 수집된 데이터는 전처리를 거쳐 데이터셋을 구축하며, ResNet50 모델을 활용하여 전이학습한 합성곱 신경망(Convolutional Neural Network) 알고리즘 분류기를 설계한다. 구축한 데이터셋을 입력시켜 분류학습 및 예측을 진행하며, 실시간 영상에서 인식되는 손동작을 설계한 모델에 입력시켜 나온 결과를 통해 가위바위보 게임을 구현한다.