• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4625-4647
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• 2020

The Generative Adversarial Networks, as one of the most creative deep learning models in recent years, has achieved great success in computer vision and natural language processing. It uses the game theory to generate the best sample in generator and discriminator. Recently, many deep learning models have been applied to the security field. Along with the idea of "generative" and "adversarial", researchers are trying to apply Generative Adversarial Networks to the security field. This paper presents the development of Generative Adversarial Networks. We review traditional generation models and typical Generative Adversarial Networks models, analyze the application of their models in natural language processing and computer vision. To emphasize that Generative Adversarial Networks models are feasible to be used in security, we separately review the contributions that their defenses in information security, cyber security and artificial intelligence security. Finally, drawing on the reviewed literature, we provide a broader outlook of this research direction.

• Journal of KIBIM
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• v.9 no.1
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• pp.42-51
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• 2019

Maintenance of aging structures has attracted societal attention. Maintenance of the aging structure can be efficiently performed with a digital twin. In order to maintain the structure based on the digital twin, it is required to accurately detect the damage of the structure. Meanwhile, deep learning-based damage detection approaches have shown good performance for detecting damage of structures. However, in order to develop such deep learning-based damage detection approaches, it is necessary to use a large number of data before and after damage, but there is a problem that the amount of data before and after the damage is unbalanced in reality. In order to solve this problem, this study proposed a method based on Generative adversarial network, one of Generative Model, for generating acceleration data usually used for damage detection approaches. As results, it is confirmed that the acceleration data generated by the GAN has a very similar pattern to the acceleration generated by the simulation with structural analysis software. These results show that not only the pattern of the macroscopic data but also the frequency domain of the acceleration data can be reproduced. Therefore, these findings show that the GAN model can analyze complex acceleration data on its own, and it is thought that this data can help training of the deep learning-based damage detection approaches.

• Educational Technology International
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• v.17 no.2
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• pp.253-271
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• 2016

This study investigated the effect of generative concept maps according to learning achievements and cognitive load. A total of 78 students in the first grade of middle school participated in this study. Before the experimental treatment was implemented, students had to fill out a questionnaire assessing prior knowledge. The study was designed where all the students were presented the same learning contents regarding photosynthesis; however, the two experimental groups were provided with different concept map methods: a learner-generative concept map (GCM) and an instructor-provided concept map (PCM). GCM students were asked to make a concept map by themselves in small groups while they are reading material. PCM students were instructed to study in small groups in order to read the material; however, they were provided a concept map developed by their teacher. The control group (CG) had the teacher present the learning contents in traditional lecture format with no accompanying concept map. The results show that there were significant differences in the achievements among the groups. CG showed higher achievement than both the experimental groups. There was also a significant difference in cognitive load. Although the GCM group did not obtain higher achievement than the other groups, the GCM group showed higher mental effort and lower physical fatigue than the other groups. The GCM group might have invested more effort to find and connect ideas when drawing their concept map with peers which is unlike the conditions for the PCM group and CG. In conclusion, we should consider applying GCM in teaching and learning design in order to increase learning achievement and decrease extraneous cognitive load.

• Journal of Information Technology Services
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• v.22 no.6
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• pp.17-35
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• 2023

This study investigated how helpful the use of generative AI such as ChatGPT is in conducting engaged learning at each university. In this study, based on the experiences of users using generative AI technology, we analyzed the relationship between usability and ease in consideration of the characteristics of learners, and examined whether there is an intention to continue using generative AI technology in the future. In this study, in order to verify the factors affecting the intention to use ChatGPT technology in order to solve the problems given in the participating classes, we examined previous papers based on the Technology Acceptance Model (TAM) and the Information System Success Model (IS), extracted the factors affecting the intention of ChatGPT technology, and presented the research model and hypothesis. Empirical research on the continuous use of generative AI in participatory learning using ChatGPT was conducted to determine whether it is suitable for long-term and continuous use in the educational environment, and whether it is sustainable by examining the intention of learners to continue using it. First, user satisfaction was positively related to the intention to continue using generative AI technology. Second, if the user experience has a great influence on the intention to continue using ChatGPT technology, and users gain experiences such as usefulness, interest, and effective response in the process of using the technology, the evaluation of the technology is positively formed and the intention to continue using it is high. Third, the ease of use of the technology also showed that it was intended to be used continuously when an environment was provided in which users could easily and conveniently utilize generative AI technology.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1391-1402
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• 2021

This paper focuses use context-sensitive spelling error correction using generative adversarial network. Generative adversarial network[1] are attracting attention as they solve data generation problems that have been a challenge in the field of deep learning. In this paper, sentences are generated using word embedding information and reflected in word distribution representation. We experiment with DCGAN[2] used for the stability of learning in the existing image processing and D2GAN[3] with double discriminator. In this paper, we experimented with how the composition of generative adversarial networks and the change of learning corpus influence the context-sensitive spelling error correction In the experiment, we correction the generated word embedding information and compare the performance with the actual word embedding information.

• Journal of the Korea Society of Computer and Information
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• v.29 no.4
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• pp.31-38
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• 2024

This study proposes hyper-parameter settings for developing a generative AI-based learning support tool to facilitate programming education in online distance learning. We implemented an experimental tool that can set research hyper-parameters according to three different learning contexts, and evaluated the quality of responses from the generative AI using the tool. The experiment with the default hyper-parameter settings of the generative AI was used as the control group, and the experiment with the research hyper-parameters was used as the experimental group. The experiment results showed no significant difference between the two groups in the "Learning Support" context. However, in other two contexts ("Code Generation" and "Comment Generation"), it showed the average evaluation scores of the experimental group were found to be 11.6% points and 23% points higher than those of the control group respectively. Lastly, this study also observed that when the expected influence of response on learning motivation was presented in the 'system content', responses containing emotional support considering learning emotions were generated.

• Journal of Broadcast Engineering
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• v.23 no.5
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• pp.614-621
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• 2018

This paper proposes a method of restoring corrupted depth image captured by depth camera through unsupervised learning using generative adversarial network (GAN). The proposed method generates restored face depth images using 3D morphable model convolutional neural network (3DMM CNN) with large-scale CelebFaces Attribute (CelebA) and FaceWarehouse dataset for training deep convolutional generative adversarial network (DCGAN). The generator and discriminator equip with Wasserstein distance for loss function by utilizing minimax game. Then the DCGAN restore the loss of captured facial depth images by performing another learning procedure using trained generator and new loss function.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.49-59
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• 2019

Generative adversarial networks(GANs) have received great attention in the machine learning field for their capacity to model high-dimensional and complex data distribution implicitly and generate new data samples from the model distribution. This paper investigates the model training methodology, architecture, and various applications of generative adversarial networks. Experimental evaluation is also conducted for generating synthetic image dataset for defense using two types of GANs. The first one is for military image generation utilizing the deep convolutional generative adversarial networks(DCGAN). The other is for visible-to-infrared image translation utilizing the cycle-consistent generative adversarial networks(CycleGAN). Each model can yield a great diversity of high-fidelity synthetic images compared to training ones. This result opens up the possibility of using inexpensive synthetic images for training neural networks while avoiding the enormous expense of collecting large amounts of hand-annotated real dataset.

• The Journal of Korean Academic Society of Nursing Education
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• v.29 no.3
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• pp.234-246
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• 2023

Purpose: This study aimed to develop a case-based nursing education program using generative artificial intelligence and to assess its usability and applicability in nursing curriculums. Methods: The program was developed by following the five steps of the ADDIE model: analysis, design, development, implementation, and evaluation. A panel of five nursing professors served as experts to implement and evaluate the program. Results: Utilizing ChatGPT, six program modules were designed and developed based on experiential learning theory. The experts' evaluations confirmed that the program was suitable for case-based learning, highly usable, and applicable to nursing education. Conclusion: Generative artificial intelligence was identified as a valuable tool for enhancing the effectiveness of case-based learning. This study provides insights and future directions for integrating generative artificial intelligence into nursing education. Further research should be attempted to implement and evaluate this program with nursing students.

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.327-329
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• 1998

Up-Propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden variables using top-down connections. The inversion process is iterative, utilizing a negative feedback loop that depends on an error signal propagated by bottom-up connections. The error signal is also used to learn the generative model from examples. the algorithm is benchmarked against principal component analysis in experiments on images of handwritten digits.