• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.228-230
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• 2020

The emergence of collaborative learning to the public is to tackle the user's privacy issue in centralized learning by bringing the AI models to the data source or client device for training Collaborative learning employs computing and storage resources on the client's device. Thus, it is privacy preserved by design. In harmony, blockchain is also prominent since it does not require an intermediary to process a transaction. However, these approaches are not yet fully ripe to be implemented in the real world, especially for the complex system (several challenges need to be addressed). In this work, we present the performance of collaborative learning and potential use case of blockchain. Further, we discuss privacy issues in the system.

• Journal of Information Science Theory and Practice
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• v.10 no.spc
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• pp.12-24
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• 2022

This study attempted to investigate changes in collaboration structure for each stage of national Research and Development (R&D) in the artificial intelligence (AI) field through analysis of a co-author network for papers written under national R&D projects. For this, author information was extracted from national R&D outcomes in AI from 2014 to 2019. For such R&D outcomes, NTIS (National Science & Technology Information Service) information from the KISTI (Korea Institute of Science and Technology Information) was utilized. In research collaboration in AI, power function structure, in which research efforts are led by some influential researchers, is found. In other words, less than 30 percent is linked to the largest cluster, and a segmented network pattern in which small groups are primarily developed is observed. This means a large research group with high connectivity and a small group are connected with each other, and a sporadic link is found. However, the largest cluster grew larger and denser over time, which means that as research became more intensified, new researchers joined a mainstream network, expanding a scope of collaboration. Such research intensification has expanded the scale of a collaborative researcher group and increased the number of large studies. Instead of maintaining conventional collaborative relationships, in addition, the number of new researchers has risen, forming new relationships over time.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.437-439
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• 2023

본 연구는 프로그래밍 협업 환경에서 생성형 AI인 ChatGPT-4를 활용한 코딩 프로세스를 제안한다. 일관성 있는 결과를 얻기 위해 프롬프트 생성, GPT 실행, 의사코드 변환, 코드 비교, 동일 코드 생성 여부 판단, 테스트 실행, 동일 결과 생성 여부 판단, 코드 검사 및 수정의 8단계를 거친다. 팀 프로젝트와 페어 프로그래밍 등의 다양한 협업 환경에 적용 가능한 이 프로세스를 통해 생성형 AI를 효과적으로 활용할 수 있음을 보여주었다는 점에서 그 의미가 있다. 본 연구는 생성형 AI를 활용한 협업 환경에서의 코딩이 본격적으로 이루어질 것으로 예상되는 이 시점에서, 인간-AI 협업 환경에서의 코딩 효율성 및 일관성을 높일 수 있을 것으로 기대한다. 이러한 연구는 인간과 AI가 함께 작업하는 미래를 위한 기초를 마련하는 데 중요한 역할을 할 것이다.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.263-270
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• 2020

The growth of the mobile app markets has made it popular among people who recommend relevant information about restaurants. The recommendation service app based on AI Chatbot is that it can efficiently manage time and finances by making it easy for restaurant consumers to easily access the information they want anytime, anywhere. Eating out consumers use smartphone applications for finding restaurants, making reservations, and getting reviews and how to use them. In addition, social attention has recently been focused on the research of AI chatbot. The Chatbot is combined with the mobile messenger platform and enabling various services due to the text-type interactive service. It also helps users to find the services and data that they need information tersely. Applying this to restaurant recommendation services will increase the reliability of the information in providing personal information. In this paper, an artificial intelligence chatbot-based smartphone restaurant recommendation app using personalization information is proposed. The recommendation service app utilizes personalization information such as gender, age, interests, occupation, search records, visit records, wish lists, reviews, and real-time location information. Users can get recommendations for restaurants that fir their purpose through chatting using AI chatbot. Furthermore, it is possible to check real-time information about restaurants, make reservations, and write reviews. The proposed app uses a collaborative filtering recommendation system, and users receive information on dining out using artificial intelligence chatbots. Through chatbots, users can receive customized services using personal information while minimizing time and space limitations.

• Journal of The Korean Association of Information Education
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• v.26 no.1
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• pp.11-22
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• 2022

The educational environment, where the change to blended learning and AI convergence education through non-face-to-face is accelerating, is based on the cultivation of digital literacy. This study attempted to verify the effectiveness of future competencies by creating a collaborative SW education program on the metaverse platform that emerged by supplementing the problems through non-face-to-face. Twenty programs on how to design and create software were organized for small-scale elementary classes in the metaverse. In order to verify the effectiveness 4C competency tool presented as future educational competency was selected, and homogeneity test for the experimental group and t-test were conducted. The results showed the SW education programs based on metaverse was effective in improving collaborative communication skills, confirming the possibility of SW education through blended learning.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.595-596
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• 2023

스마트팩토리의 핵심 설비 기기인 협동로봇의 유지보수를 위해 다양한 센서 데이터를 활용한 딥러닝 기반 결함 진단 연구가 확대되고 있다. 하지만 협동로봇은 기계적 특성과 수행하는 작업의 다양성으로 인해 내부 센서 데이터의 복잡도가 매우 높아 고정적인 결함 진단 기법을 적용하기 어렵다. 따라서 본 논문은 협동로봇의 동작 패턴을 직관적이고 신속하게 인지할 수 있는 비전 기술을 활용하여, 동작 오류 진단을 기반으로 원인이 되는 조인트 결함 위치를 추적하는 딥러닝 기법을 제안한다.

• International journal of advanced smart convergence
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• v.8 no.4
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• pp.113-120
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• 2019

Recently, the artificial intelligence(AI) is used in various environments in life, and research on this is being actively conducted in education. In this paper, we designed a Design-Based Research(DBR)-based AI programming education program and analyzed the application of the program for the improvement of understanding of AI in elementary school. In the artificial intelligence education program in elementary school, we should considerthat itshould be used in conjunction with software education through programming activities, rather than creating interest through simple AI experiences. The designed education program reflects the collaborative problem-solving procedures following the DBR process of analysis - design - execution - redesign, allowing the real-world problem-solving activities using AI experiences and block-type programming language. This paper also examined the examples of education programs to improve understanding of AI by using Machine Learning for Kids and to draw implications for developing and operating such a program.

• Journal of The Korean Association of Information Education
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• v.24 no.6
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• pp.573-584
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• 2020

This study is about the development of AI algorithm education program using cognition modeling to positively improve students' image on AI. First, we analyzed the concept of user-based collaborative filtering and developed the education program using the cognition modeling method. We checked the adequacy of program through the expert validity test. Both CVR values for the content development method of cognitive modeling and the developed program showed validity above .80. We applied the developed program to elementary school students in class. The test was conducted using a semantic discrimination to examine changes in students' perception of artificial intelligence before and after. We were able to confirm that the students' AI images were significant positive change in 12 of the 23 words in the adjective pair.

• Journal of the Korean Society of Clothing and Textiles
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• v.47 no.4
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• pp.666-683
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• 2023

This research aims to develop based clothing design process tailored to the industry-university collaborative setting and apply it in a fashion design class. into three distinct phases: designing and organizing our fashion design class, conducting our class at a university, and gathering student feedback. First, we conducted a literature review on employing new technologies in traditional clothing design processes. We consulted with industry professionals from the Samsung C&T Fashion Group to develop an AI-based clothing design process. We then developed in-class learning activities that leveraged fashion brand product databases, a supervised learning AI model, and operating an AI-based Creativity Support Tool (CST). Next, we setup an industry-university fashion design class at a university in South Korea. Finally, we obtained feedback from undergraduate students who participated in the class. The survey results showed a satisfaction level of 4.7 out of 5. The evaluations confirmed that the instructional methods, communication, faculty, and student interactions within the class were both adequate and appropriate. These research findings highlighted that our AI-based clothing design process applied within the fashion design class led to valuable data-driven convergent thinking and technical experience beyond that of traditional clothing design processes.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.686-693
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• 2022

Construction is a collaborative endeavor. The complexity in delivering construction projects successfully is impacted by the effective collaboration needs of a multitude of stakeholders throughout the project life-cycle. Technologies such as Building Information Modelling and relational project delivery approaches such as Alliancing and Integrated Project Delivery have developed to address this conundrum. However, with the onset of the pandemic, the digital economy has surged world-wide and advances in technology such as in the areas of machine learning (ML) and Artificial Intelligence (AI) have grown deep roots across specializations and domains to the point of matching its capabilities to the human mind. Several recent studies have both explored the role of AI in the construction process and highlighted its benefits. In contrast, literature in the organization studies field has highlighted the fear that tasks currently done by humans will be done by AI in future. Motivated by these insights and with the understanding that construction is a labour intensive sector where knowledge is both fragmented and predominantly tacit in nature, this paper explores the integration of AI in construction processes across project phases from planning, scheduling, execution and maintenance operations using literary evidence and experiential insights. The findings show that AI can complement human skills rather than provide a substitute for them. This preliminary study is expected to be a stepping stone for further research and implementation in practice.