• Journal of Information Processing Systems
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• v.19 no.3
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• pp.355-369
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• 2023

Since the widespread adoption of deep-learning and related distributed representation, there have been substantial advancements in part-of-speech (POS) tagging for many languages. When training word representations, morphology and shape are typically ignored, as these representations rely primarily on collecting syntactic and semantic aspects of words. However, for tasks like POS tagging, notably in morphologically rich and resource-limited language environments, the intra-word information is essential. In this study, we introduce a deep neural network (DNN) for POS tagging that learns character-level word representations and combines them with general word representations. Using the proposed approach and omitting hand-crafted features, we achieve 90.47%, 80.16%, and 79.32% accuracy on our own dataset for three morphologically rich languages: Uyghur, Uzbek, and Kyrgyz. The experimental results reveal that the presented character-based strategy greatly improves POS tagging performance for several morphologically rich languages (MRL) where character information is significant. Furthermore, when compared to the previously reported state-of-the-art POS tagging results for Turkish on the METU Turkish Treebank dataset, the proposed approach improved on the prior work slightly. As a result, the experimental results indicate that character-based representations outperform word-level representations for MRL performance. Our technique is also robust towards the-out-of-vocabulary issues and performs better on manually edited text.

• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.659-666
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• 2023

Among the tools based on generative artificial intelligence, the possibility of using ChatGPT is being explored. However, studies that have confirmed what factors should be considered when using it educationally based on learners' actual perceptions are insufficient. Through qualitative research method, this study was to derive consideration factors when using ChatGPT in the education. The results showed that there were five key factors as follows: critical thinking on generated information, recognizing it as a tool to support learning and avoiding dependent use, conducting prior training on ethical usage, generating clear and appropriate questions, and reviewing and synthesizing answers. It is necessary to develop an instructional design model that comprehensively composes the above elements.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.156-156
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• 2022

Explicitly spatially distributed and reliable data on industrial water demand is very much important for both policy makers and researchers in order to carry a region-specific analysis of water resources management. However, such type of data remains scarce particularly in underdeveloped and developing countries. Current research is limited in using different spatially available socio-economic, climate data and geographical data from different sources in accordance to predict industrial water demand at finer resolution. This study proposes a random forest regression (RFR) model to predict the industrial water demand at 0.50× 0.50 spatial resolution by combining various features extracted from multiple data sources. The dataset used here include National Polar-orbiting Partnership (NPP)/Visible Infrared Imaging Radiometer Suite (VIIRS) night-time light (NTL), Global Power Plant database, AQUASTAT country-wise industrial water use data, Elevation data, Gross Domestic Product (GDP), Road density, Crop land, Population, Precipitation, Temperature, and Aridity. Compared with traditional regression algorithms, RF shows the advantages of high prediction accuracy, not requiring assumptions of a prior probability distribution, and the capacity to analyses variable importance. The final RF model was fitted using the parameter settings of ntree = 300 and mtry = 2. As a result, determinate coefficients value of 0.547 is achieved. The variable importance of the independent variables e.g. night light data, elevation data, GDP and population data used in the training purpose of RF model plays the major role in predicting the industrial water demand.

• Korea Trade Review
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• v.45 no.3
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• pp.55-69
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• 2020

This study examines the research trends and knowledge structure of international trade studies using topic modeling method, which is one of the main methodologies of text mining. We collected and analyzed English abstracts of 1,868 papers of three Korean major journals in the area of international trade from 2003 to 2019. We used the Latent Dirichlet Allocation(LDA), an unsupervised machine learning algorithm to extract the latent topics from the large quantity of research abstracts. 20 topics are identified without any prior human judgement. The topics reveal topographical maps of research in international trade and are representative and meaningful in the sense that most of them correspond to previously established sub-topics in trade studies. Then we conducted a regression analysis on the document-topic distributions generated by LDA to identify hot and cold topics. We discovered 2 hot topics(internationalization capacity and performance of export companies, economic effect of trade) and 2 cold topics(exchange rate and current account, trade finance). Trade studies are characterized as a interdisciplinary study of three agendas(i.e. international economy, International Business, trade practice), and 20 topics identified can be grouped into these 3 agendas. From the estimated results of the study, we find that the Korean government's active pursuit of FTA and consequent necessity of capacity building in Korean export firms lie behind the popularity of topic selection by the Korean researchers in the area of int'l trade.

• Journal of Research and Publication Ethics
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• v.5 no.1
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• pp.1-6
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• 2024

Purpose: Compared to the research results that have achieved remarkable growth, research ethics problems that threaten the quality of research. This issue appears not only in Korea but also in research societies worldwide where research competition has risen This study attempted to prepare improvements and institutional implications to establish research ethics in the research field. Research Design, data and methodology: This study examined total 26 prior studies to examine the current status of aviation tourism research ethics in the literature reviews for the finding section. The procedure of data obtaining included the elimination process to screen dissertation papers, conference papers, and internet sources. Results: Researchers must have an institutional mechanism to publish papers after completing education. Research ethics education programs suitable for aviation tourism research should be developed and detailed and clear guidelines for research ethics should be provided. This can prevent research irregularities. Conclusions: It is necessary to create a clear research ethics education for the spread of positive research ethics on aviation tourism researchers. Develop research ethics education and complete long-term compulsory education. Establish a research culture that requires compulsory completion of research education. It is necessary to support continuous education and learning through various research ethics methods.

• Journal of Ginseng Research
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• v.24 no.1
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• pp.8-17
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• 2000

Estrogen can influence on the expression of behaviors not associated directly with reproduction, including learning and memory. Recently estrogen has received considerable attention for its effects on neuroprotection and neural circuits in brain areas associated with cognition. Although estrogen replacement therapy may be helpful to postmenopausal women, it also results in a number of harmful side effects. Ginseng also has steroidal qualities and contains several ginsenoside components which have similar backbone structure to estrogen. The objectives of this experiment were 1) to examine the effects of estrogen and 2) to investigate the effects of ginsenosides as estrogenic agent on learning and memory using the Morris water maze, a traditional experimental task for spatial memory. In the experiments designed here, ovariectomized mice were implanted subcutaneously with Sila, itic capsules containing 17${\beta}$-estradiol (100∼250 $\mu\textrm{g}$/$m\ell$), panaxadiol (PD) and panaxatriol (PT) saponins (15∼100 $\mu\textrm{g}$/$m\ell$) diluted with sesame oil. In the first set of experiment, the effects of estradiol on learning and memory during the Morris water maze was examined. When estradiol was delivered via Silastic capsules following training improved spatial memory performance in ovariectomized female mice. In the second set of experiment, three different PD and PT saponin concentrations were delivered via Silastic implants to ovariectomized female mice and their effects were compared with estrogenic effects. Results of three separate experiments demonstrated that estradiol, PD and PT administrated by Silastic implants for 2 weeks prior to water maze training significantly improved spatial memory performance compared to ovariectomized (OVX) mice, as indicated by lower escape latency over trial. The positive effect of estradiol suggests that estrogen can affect performance on learning and memory. In addition, the positive effect of PD and PT saponins suggest that ginsenosides have an estrogen-like effects in mediating learning and memory related behavior action.

• Journal of The Korean Association For Science Education
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• v.38 no.4
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• pp.467-480
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• 2018

This study aims to investigate how flipped learning-based socioscientific issue instruction (FL-SSI instruction) affected middle school students' key competencies and character development. Traditional classrooms are constrained in terms of time and resources for exploring the issues and making decision on SSI. To address these concerns, we designed and implemented an SSI instruction adopting flipped learning. Seventy-three 8th graders participated in an SSI program on four topics for over 12 class periods. Two questionnaires were used as a main data source to measure students' key competencies and character development before and after the SSI instruction. In addition, student responses and shared experience from focus group interviews after the instruction were collected and analyzed. The results indicate that the students significantly improved their key competencies and experienced character development after the SSI instruction. The students presented statistically significant improvement in the key competencies (i.e., collaboration, information and technology, critical thinking and problem-solving, and communication skills) and in two out of three factors in character and values as global citizens (social and moral compassion, and socio-scientific accountability). Interview data supports the quantitative results indicating that SSI instruction with a flipped learning strategy provided students in-depth and rich learning opportunities. The students responded that watching web-based videos prior to class enabled them to deeply understand the issue and actively engage in discussion and debate once class began. Furthermore, the resulting gains in available class time deriving from a flipped learning approach allowed the students to examine the issue from diverse perspectives.

• The Journal of Society for e-Business Studies
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• v.22 no.3
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• pp.57-74
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• 2017

The Electronic Shelf Label (ESL) is an alternative to the paper price label attached to merchandise shelves and is attracting attention as a retail IoT infrastructure that will lead the innovation of offline retail outlets. In general, when introducing a substitute product, the company tends to consider the financial factors such as the efficiency of the investment cost compared to the existing product or the reduction of the operating cost. However, considering only financial factors in the decision-making process, it may not properly reflect the various values associated with corporate strategy and the requirements of stakeholders. In this study, 8 evaluation items (Investment Cost, Operating Cost, Quality Level, Customer Management, Job Efficiency, Maintenance, Functional Expandability, and Store Image) based on BSC's 4 perspectives (Financial, Customer, Internal Business Process, Learning & Growth), and using AHP (Analytic Hierarchy Process) to measure the priorities of evaluation items for domestic small supermarket employees. As a result of the research, priority was given in order of Customer, Learning & Growth, Internal Business Process, and Financial aspects among the evaluation items for adopting the price label, and the electronic price label was supported with higher importance than the paper price label. In contrast to the priorities of the financial aspects of most prior studies, the items of Learning & growth and customer perspectives have relatively high priorities. In particular, respondents classified by job group, The priorities of the 8 evaluation items were different among the groups. These results are expected to provide implications for both companies (retail outlets) and ESL providers (manufacturers and service providers) who are considering the introduction of ESL.

• Journal of the Korean earth science society
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• v.33 no.2
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• pp.170-182
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• 2012

The purpose of this study was to develop learning materials based on the Kepler's abductive reasoning and to identify high school students' rule-inferring strategies on the law of planetary motion. The learning materials including the concepts of solar magnetic field, conservation of figure skater's angular momentum and Kepler's polyhedral theory were developed and the questions about Kepler's 2nd and 3rd law of planetary motion were also created. The participants were 79science high school students and 83general high school students. The patterns and properties of their abductive inference were analyzed. The findings revealed that the students showed 'incomplete analogy abduction', 'analogy abduction' and 'reconstruction' to generate the hypotheses concerning the Mars' motion related to the solar magnetic field. There were more general high school students who showed the incomplete analogy abduction than science high school students. On the other hand, there were more science high school students who showed the analogy abduction and reconstruction strategy than general high school students. Also, they showed 'incomplete analogy abduction', 'analogy abduction' and 'model construction and manipulation' to generate the hypotheses concerning Kepler's second law. A number of general high school students showed the incomplete analogy. It is suggested that because the analogy of figure skater cause the students' alternative framework to use, more detailed demonstration is necessary in class. In addition, students combined Kepler's polyhedral theory with their prior knowledge to infer Kepler's third law.

• Korean Security Journal
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• no.62
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• pp.185-203
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• 2020

While overall crime has been on the decline since 2009, voice phishing has rather been on the rise. The government and academia have presented various measures and conducted research to eradicate it, but it is not enough to catch up with evolving voice phishing. In the study, researchers focused on catching criminals and preventing damage from voice phishing, which is difficult to recover from. In particular, a voice phishing prediction method using the Fraud Detection System (FDS), which is being used to detect financial fraud, was studied based on the fact that the victim engaged in financial transaction activities (such as account transfers). As a result, it was conceptually derived to combine big data such as call details, messenger details, abnormal accounts, voice phishing type and 112 report related to voice phishing in machine learning-based Fraud Detection System(FDS). In this study, the research focused mainly on government measures and literature research on the use of big data. However, limitations in data collection and security concerns in FDS have not provided a specific model. However, it is meaningful that the concept of voice phishing responses that converge FDS with the types of data needed for machine learning was presented for the first time in the absence of prior research. Based on this research, it is hoped that 'Voice Phishing Damage Prediction System' will be developed to prevent damage from voice phishing.