• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.1
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• pp.39-60
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• 2014

In domestic university libraries, the difference between the knowledge resource collection activities on campus is depending on the size of the university, and their collection is concentrated on some types of digital resources. In recent years, the main universities in developed countries has developed actively in social openness and share activities of their knowledge resources, through the OA-based institutional repository, for the purpose of image improvement and competitiveness as a knowledge production base. This study examined ways to improve the relevant regulations in order to effectively collect and systematically manage the knowledge resources from graduate school, research institutes, center for teaching and learning, e-learning center, museum, press, a variety of campus organizations, so as to enhance the role of the library as the right manager of knowledge resources on campus. To this end, this study, considering the improvement of relevant regulations, investigates the operating situation of the library regulations of 176 universities and suggests necessary improvement methods in order to facilitate the digital legal deposit and expand its scope.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.98-107
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• 2018

In this paper, we propose a method to classify the skeletal maturity with a small amount of hand wrist X-ray image using deep learning-based meta-learning. General deep-learning techniques require large amounts of data, but in many cases, these data sets are not available for practical application. Lack of learning data is usually solved through transfer learning using pre-trained models with large data sets. However, transfer learning performance may be degraded due to over fitting for unknown new task with small data, which results in poor generalization capability. In addition, medical images require high cost resources such as a professional manpower and mcuh time to obtain labeled data. Therefore, in this paper, we use meta-learning that can classify using only a small amount of new data by pre-trained models trained with various learning tasks. First, we train the meta-model by using a separate data set composed of various learning tasks. The network learns to classify the bone maturity using the bone maturity data composed of the radiographs of the wrist. Then, we compare the results of the classification using the conventional learning algorithm with the results of the meta learning by the same number of learning data sets.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.2
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• pp.309-323
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• 2007

Recently, E-Learning based on wide-area infrastructure is being spotlighted as the new means to innovate education at school and develop human resources at society and appeared as the main point of digital content industry. In this paper, we analyze the characteristics of base technology of E-Learning software and developed E-Learning software quality evaluation model by analyzing quality characteristics for quality test and evaluation of E-Learning software. To do so, we established the quality evaluation system and developed the evaluation model to evaluate the quality about E-Learning software by introducing related international standard. We think that this will promote development of competitive E-Learning software products.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.29-29
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• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.7
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• pp.512-518
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• 2013

A resource allocation algorithm has a high impact on user satisfaction as well as the ability to accommodate and process services in a distributed cloud computing. In other words, service rejections, which occur when datacenters have no enough resources, degrade the user satisfaction level. Therefore, in this paper, we propose a resource allocation algorithm considering the cloud domain's remaining resources to minimize the number of service rejections. The resource allocation rate based on Q-Learning increases when the remaining resources are sufficient to allocate the maximum allocation rate otherwise and avoids the service rejection. To demonstrate, We compare the proposed algorithm with two previous works and show that the proposed algorithm has the smaller number of the service rejections.

• Journal of IKEEE
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• v.25 no.1
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• pp.229-233
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• 2021

NPU in an embedded environment performs deep learning algorithms with few hardware resources. By using a technique that reuses data, deep learning algorithms can be efficiently computed with fewer resources. In previous studies, data is reused using a shifter in ScratchPad for data reuse. However, as the ScratchPad's bandwidth increases, the shifter also consumes a lot of resources. Therefore, we present a data reuse technique using the Buffer Round Robin method. By using the Buffer Round Robin method presented in this paper, the chip area could be reduced by about 4.7% compared to the conventional method.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.373-373
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• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.

• Educational Technology International
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• v.13 no.1
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• pp.27-48
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• 2012

In this paper we are comparing how the OER (open educational resources) are developed in Higher Education in Finland and South Korea. We also present a comparison model for further studies. Essential findings based on our comparison are that in both countries there are many best practices of use of the OER and open learning. Open educational resources have great potential and their use can ensure quality teaching and learning. The activity has not inspired the great mass of higher education teachers in Finland and Korea. Traditionally, a teacher's job is working alone, and so a new operational culture is required. Our comparison indicates that numerous questions, fears and problems and cultural differences are also related to the thematic. There is an evident need for a new kind of strategic leadership, a new kind of teaching and learning culture and a doing together and production ideology for the method to spread. Based on our study the following interlinked elements of OER seem to be pivotal: changes to pedagogies, technology and operational culture; educational policy intention; and attitude to culture. Lastly, comparison frame by OER practice model is developed.

• Research in Mathematical Education
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• v.27 no.3
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• pp.335-365
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• 2024

Learning mathematics in a student-centered, problem-based classroom requires students to develop mathematical understanding and reasoning collaboratively with others. Despite its critical role in students' collaborative learning in groups and classrooms, evidence of student thinking has rarely been perceived and utilized as a resource for planning and teaching. This is in part because teachers have limited access to student work in paper-and-pencil classrooms. As an alternative approach to making student thinking visible and accessible, a digital collaborative platform embedded with a problem-based middle school mathematics curriculum is developed through an ongoing design-based research project (Edson & Phillips, 2021). Drawing from a subset of data collected for the larger research project, we investigated how students generated mathematical inscriptions during small group work, and how teachers used evidence of students' solution strategies inscribed on student digital workspaces. Findings show that digital flexibility and mobility allowed students to easily explore different strategies and focus on developing mathematical big ideas, and teachers to foreground student thinking when facilitating whole-class discussions and planning for the next lesson. This study provides insights into understanding mathematics teachers' interactions with digital curriculum resources in the pursuit of students' meaningful engagement in making sense of mathematical ideas.