• Knowledge Management Research
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• v.25 no.3
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• pp.145-171
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• 2024

Literacy initially referred to the ability to read and understand written documents and processed information. With the advancement of digital technology, the scope of literacy expanded to include the access and use of digital information, evolving into the concept of digital literacy. The application and purpose of digital literacy vary across different fields, leading to the use of various terminologies. This study focuses on generative artificial intelligence (AI), which is gaining increasing importance in the AI era, to assess users' literacy levels. The research aimed to extend the concept of literacy proposed in previous studies and develop a tool suitable for Korean users. Through exploratory factor analysis, we identified that generative AI literacy consists of four factors: AI utilization ability, critical evaluation, ethical use, and creative application. Subsequently, confirmatory factor analysis validated the statistical appropriateness of the model structure composed of these four factors. Additionally, correlation analyses between the newly developed literacy tool and existing AI literacy scales and AI service evaluation tools revealed significant relationships, confirming the validity of the tool. Finally, the implications, limitations, and directions for future research are discussed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.223-228
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• 2009

We propose an on-line machine learning approach for object recognition, where new images are continuously added and the recognition decision is made without delay. Random forest (RF) classifier has been extensively used as a generative model for classification and regression applications. We extend this technique for the task of building incremental component-based detector. First we employ object descriptor model based on bag of covariance matrices, to represent an object region then run our on-line RF learner to select object descriptors and to learn an object classifier. Experiments of the object recognition are provided to verify the effectiveness of the proposed approach. Results demonstrate that the propose model yields in object recognition performance comparable to the benchmark standard RF, AdaBoost, and SVM classifiers.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.479-481
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• 2023

Generative Adversarial Networks (GANs) have facilitated substantial improvement in single-image super-resolution (SR) by enabling the generation of photo-realistic images. However, the high memory requirements of GAN-based SRs (mainly generators) lead to reduced performance and increased energy consumption, making it difficult to implement them onto resource-constricted devices. In this study, we propose an efficient and compressed architecture for the SR-GAN (generator) model using the model compression technique Knowledge Distillation. Our approach involves the transmission of knowledge from a heavy network to a lightweight one, which reduces the storage requirement of the model by 58% with also an increase in their performance. Experimental results on various benchmarks indicate that our proposed compressed model enhances performance with an increase in PSNR, SSIM, and image quality respectively for x4 super-resolution tasks.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.1-41.1
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• 2021

We generate solar magnetograms and EUV images from Galileo sunspot drawings using a deep learning model based on conditional generative adversarial networks. We train the model using pairs of sunspot drawing from Mount Wilson Observatory (MWO) and their corresponding magnetogram (or UV/EUV images) from 2011 to 2015 except for every June and December by the SDO (Solar Dynamic Observatory) satellite. We evaluate the model by comparing pairs of actual magnetogram (or UV/EUV images) and the corresponding AI-generated one in June and December. Our results show that bipolar structures of the AI-generated magnetograms are consistent with those of the original ones and their unsigned magnetic fluxes (or intensities) are well consistent with those of the original ones. Applying this model to the Galileo sunspot drawings in 1612, we generate HMI-like magnetograms and AIA-like EUV images of the sunspots. We hope that the EUV intensities can be used for estimating solar EUV irradiance at long-term historical times.

• Journal of KIISE
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• v.41 no.9
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• pp.652-665
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• 2014

Many important terminologies in biomedical text are expressed as abbreviations or acronyms. We newly suggest a semantic link topic model based on the concepts of topic and dependency link to disambiguate biomedical abbreviations and cluster long form variants of abbreviations which refer to the same senses. This model is a generative model inspired by the latent Dirichlet allocation (LDA) topic model, in which each document is viewed as a mixture of topics, with each topic characterized by a distribution over words. Thus, words of a document are generated from a hidden topic structure of a document and the topic structure is inferred from observable word sequences of document collections. In this study, we allow two distinct word generation to incorporate semantic dependencies between words, particularly between expansions (long forms) of abbreviations and their sentential co-occurring words. Besides topic information, the semantic dependency between words is defined as a link and a new random parameter for the link presence is assigned to each word. As a result, the most probable expansions with respect to abbreviations of a given abstract are decided by word-topic distribution, document-topic distribution, and word-link distribution estimated from document collection though the semantic dependency link topic model. The abstracts retrieved from the MEDLINE Entrez interface by the query relating 22 abbreviations and their 186 expansions were used as a data set. The link topic model correctly predicted expansions of abbreviations with the accuracy of 98.30%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.6
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• pp.2627-2647
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• 2016

With the rapid growth of microblog, expert identification on microblog has been playing a crucial role in many applications. While most previous expert identification studies only assess global authoritativeness of a user, there is no way to differentiate the authoritativeness in a particular aspect of topics. In this paper, we propose a novel model, which jointly models text and following relationship in the same generative process. Furthermore, we integrate a similarity-based weight scheme into the model to address the popular bias problem, and use followee topic distribution as prior information to make user's topic distribution more precisely. Our empirical study on two large real-world datasets shows that our proposed model produces significantly higher quality results than the prior arts.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.24-33
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• 1998

Independent Component analysis (ICA) is a new statistical method for extracting statistically independent components from their linear instantaneous mixtures which are generated by an unknown linear generative model. The recognition model is learned in unsupervised manner so that the recovered signals by the recognition model become the possibly scaled estimates of original source signals. This paper addresses the neural learning approach to ICA. As recognition models a linear feedforward network and a linear feedback network are considered. Associated learning algorithms for both networks are derived from maximum likelihood and information-theoretic approaches, using natural Riemannian gradient [1]. Theoretical results are confirmed by extensive computer simulations.

• Proceedings of the Korean Information Science Society Conference
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• 2012.06b
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• pp.480-482
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• 2012

하이퍼네트워크(Hypernetworks)는 하이퍼에지(hyperedge)들로 이루어진 생성 모델(generative model)로서, 주로 이산(binary) 데이터에 적용되어왔다. 본 논문에서는 이산 데이터와 실수 데이터를 모두 다룰 수 있는 새로운 하이퍼네트워크 모델을 에너지 기반 모델(energy-based model)의 형태로 제시하고, 비지도 학습(unsupervised learning) 알고리즘으로 데이터를 성공적으로 학습함을 간단한 실험을 통해 보이겠다.

• Journal of Digital Convergence
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• v.10 no.11
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• pp.383-387
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• 2012

Development of automated document management system specified for scanned invoice images suffers from rigorous accuracy requirements for extraction of monetary data, which necessiate automatic validation on the extracted values for a generative invoice table model. Use of certain internal constraints such as "amount = unit price times quantity" is typical implementation. In this paper, we propose a noble invoice information extraction model with improved auto-validation method by utilizing table header detection and column classification.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06b
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• pp.151-153
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• 2006

we present a method of sequential speech enhancement, where we infer clean speech signal using a Rao-Blackwellized particle filter (RBPF), given a noise-contaminated observed signal. In contrast to Kalman filtering-based methods, we consider a non-Gaussian speech generative model that is based on the generalized auto-regressive (GAR) model. Model parameters are learned by a sequential Newton-Raphson expectation maximization (SNEM), incorporating the RBPF. Empirical comparison to Kalman filter, confirms the high performance of the proposed method.