• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.43-56
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• 2020

Recommender systems help users make the best choice among various options. Especially, recommender systems play important roles in internet sites as digital information is generated innumerable every second. Many studies on recommender systems have focused on an accurate recommendation. However, there are some problems to overcome in order for the recommendation system to be commercially successful. First, there is a lack of transparency in the recommender system. That is, users cannot know why products are recommended. Second, the recommender system cannot immediately reflect changes in user preferences. That is, although the preference of the user's product changes over time, the recommender system must rebuild the model to reflect the user's preference. Therefore, in this study, we proposed a recommendation methodology using topic modeling and sequential association rule mining to solve these problems from review data. Product reviews provide useful information for recommendations because product reviews include not only rating of the product but also various contents such as user experiences and emotional state. So, reviews imply user preference for the product. So, topic modeling is useful for explaining why items are recommended to users. In addition, sequential association rule mining is useful for identifying changes in user preferences. The proposed methodology is largely divided into two phases. The first phase is to create user profile based on topic modeling. After extracting topics from user reviews on products, user profile on topics is created. The second phase is to recommend products using sequential rules that appear in buying behaviors of users as time passes. The buying behaviors are derived from a change in the topic of each user. A collaborative filtering-based recommendation system was developed as a benchmark system, and we compared the performance of the proposed methodology with that of the collaborative filtering-based recommendation system using Amazon's review dataset. As evaluation metrics, accuracy, recall, precision, and F1 were used. For topic modeling, collapsed Gibbs sampling was conducted. And we extracted 15 topics. Looking at the main topics, topic 1, top 3, topic 4, topic 7, topic 9, topic 13, topic 14 are related to "comedy shows", "high-teen drama series", "crime investigation drama", "horror theme", "British drama", "medical drama", "science fiction drama", respectively. As a result of comparative analysis, the proposed methodology outperformed the collaborative filtering-based recommendation system. From the results, we found that the time just prior to the recommendation was very important for inferring changes in user preference. Therefore, the proposed methodology not only can secure the transparency of the recommender system but also can reflect the user's preferences that change over time. However, the proposed methodology has some limitations. The proposed methodology cannot recommend product elaborately if the number of products included in the topic is large. In addition, the number of sequential patterns is small because the number of topics is too small. Therefore, future research needs to consider these limitations.

• Economic and Environmental Geology
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• v.52 no.5
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• pp.499-508
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• 2019

Based on the preliminary surveys for the occurrences of the Muwellut chrome-nickel mineralized zone ($800km^2$) in northwestern Myanmar, Bophivum area was selected as the detailed exploration area after considering data source, geological potential, metallogenic province, necessity of resource development on target mineral, exploration activity, grade, ore deposit type, nearby operating mine, infrastructure and exploration prediction effect. From 2013 to 2016, KIGAM and DGSE carried out geological and geochemical survey with 1:1,000 scale, magnetic survey(areal extent, $1.672km^2$), trench survey(19 trench, total length 392 m), pitting survey(18 pit, total depth 42.6m), exploration drilling(6holes 600m, 2015; 13holes 617.4m). We analyzed Cr and Ni contents of 77 drill cores with specific gravity in Yangon DGSE analytical center. Considering surface geological survey, geochemical exploration, magnetic survey, trench survey and drilling data, we divided Bophivum area into 8 blocks. Resource estimation are divided into measured and indicated resources. Measured resource is about 9,790t and indicated resource is about 12,080t with the average grade of Cr 11.8% and Ni 0.34%. In case of Bophivum area, if we develop by tying up Webula chrome mineralized zone in the south, it will be possible to upgrade the medium-scale mine. Geologically, the ophiolite belt are distributed in the western and eastern part in Myanmar. So, the exploration technology obtained from exploation in Bophivum area will be helpful to discover the hidden chromitite ore body in Myanmar ophiolite belt in the future.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.29-45
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• 2015

Systematic theory, concepts, and methodology for the biological evolution have been developed while patterns and principles of the evolution have been actively studied in the past 200 years. Furthermore, they are applied to various fields such as evolutionary economics, evolutionary psychology, evolutionary linguistics, making significant progress in research. In addition, existing studies have applied main biological evolutionary models to artifacts although such methods do not fit to them. These models are also limited to generalize evolutionary patterns of artifacts because they are designed in terms of a subjective point of view of experts who know well about the artifacts. Unlike biological organisms, because artifacts are likely to reflect the imagination of the human will, it is known that the theory of biological evolution cannot be directly applied to artifacts. In this paper, beyond the individual's subjective, the aim of our research is to present evolutionary patterns of a given artifact based on peeping the idea of the public. For this, we propose a text mining approach that presents a systematic framework that can find out the evolutionary patterns of a given artifact and then visualize effectively. In particular, based on our proposal, we focus mainly on a case study of mobile phone that has emerged as an icon of innovation in recent years. We collect and analyze review posts on mobile phone available in the domestic market over the past decade, and discuss the detailed results about evolutionary patterns of the mobile phone. Moreover, this kind of task is a tedious work over a long period of time because a small number of experts carry out an extensive literature survey and summarize a huge number of materials to finally draw a diagram of evolutionary patterns of the mobile phone. However, in this work, to minimize the human efforts, we present a semi-automatic mining algorithm, and through this research we can understand how human creativity and imagination are implemented. In addition, it is a big help to predict the future trend of mobile phone in business and industries.

• Journal for History of Mathematics
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• v.22 no.2
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• pp.27-52
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• 2009

This article discusses the contributions of the leader Oswald Veblen, who was the president of AMS during 1923-1924. In 2006, Korea ranked 12th in SCIE publications in mathematics, more than doubling its publications in less than 10 years, a successful model for a country with relatively short history of modern mathematical research. Now there are 192 four-year universities in Korea. Some 42 of these universities have Ph.D. granting graduate programs in mathematics and/or mathematical education in Korea. Rapid growth is observed over a broad spectrum including a phenomenal performance surge in International Mathematical Olympiad. Western mathematics was first introduced in Korea in the 17th century, but real significant mathematical contributions by Korean mathematicians in modern mathematics were not much known yet to the world. Surprisingly there is no Korean mathematician who could be found in MaC Tutor History Birthplace Map. We are at the time, to have a clear vision and leadership for the 21st century. Even with the above achievement, Korean mathematical community has had obstacles in funding. Many people thinks that mathematical research can be done without funding rather unlike other science subjects, even though they agree fundamental mathematical research is very important. We found that the experience of early American mathematical community can help us to give a vision and role model for Korean mathematical community. When we read the AMS Notice article 'The Vision, Insight, and Influence of Oswald Veblen' by Steve Batterson, it answers many of our questions on the development of American mathematics in early 20th century. We would like to share the story and analyze its meaning for the development of Korean Mathematics of 21st century.

• Journal of the Korean Child Neurology Society
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• v.26 no.4
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• pp.215-220
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• 2018

Purpose: Few reports have described the prognostic factors affecting the occurrence of subsequent unprovoked seizure in patients who present with febrile seizure (FS) after 6 years of age. We investigated the prognostic factors affecting the development of unprovoked seizures after FS among patients from Jeju Island. Methods: We included patients who developed FS after 6 years of age, who presented to our outpatient clinic between January, 2011 and June, 2017. Clinical data were obtained through chart reviews and phone call interviews. We used logistic regression analysis to analyze the risk factors associated with the occurrence of subsequent unprovoked seizure. Results: Of the 895 patients who presented to our hospital due to their febrile seizure, 83 developed FS after 6 years of age. Among them, 3 patients were prescribed antiepileptic drugs before the onset of the unprovoked seizure, and 4 patients developed an unprovoked seizure before 6 years of age. Thus, overall, 76 patients were included in the study. 51 patients developed first FS before 6 years of age. In the remaining patients, the first FS developed after 6 years of age. The mean observational period since the last outpatient follow-up visit was 3.2 years (median 3.04 years, range: 1.42-4.71 years). Among them, 21% developed an unprovoked seizure. Logistic regression analysis showed that electroencephalographic (EEG) abnormalities served as an independent risk factor for a subsequent unprovoked seizure. Conclusion: EEG is the proper diagnostic tool to predict the risk of a subsequent unprovoked seizure in patients with FS after 6 years of age.

• Journal of Science Education
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• v.42 no.3
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• pp.371-389
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• 2018

This study focuses on the fact that students and teachers commonly have difficulty in 'selecting the topic' in many activities including student-led research that is conducted from topic selection to the drawing of conclusion. The purpose of this study is to develop a manual for science teaching research. The instructional manuals of 4 stages were developed based on practical knowledge that can be implemented in the actual class through previous research and literature. Each stage is composed of , , , and . In the third stage, students are expected to find scientific questions and develop them into research topics through detailed class research on newspaper articles, scientific magazines, traditional knowledge, proverbs, daily life, and textbook experiments. In the experimental group, the final research topic was selected through a variety of sources such as textbook experiments, proverbs, YouTube images, newspaper articles, individual WHY NOTEs, and understood the conditions of the scientific research topic and expressed the variables in the research title. However, in the control group, some students did not consider the research scope of the selected research subjects to be specific or not to be able to study at their level. As a result of giving the students as much autonomy as possible, many groups did not fully understand the previous research and submitted it. Based on the results of this study, it can be concluded that development and use of a 'topic selection stage' centered practice manual for general high school teachers would not only improve the students' abilities to discover solutions to scientific questions, but it will also help shift their attitudes towards science in a positive direction.

• Journal of Life Science
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• v.34 no.5
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• pp.339-355
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• 2024

The pulmonary system is a highly complex system that can only be understood by integrating its functional and structural aspects. Hence, in vivo animal models are generally used for pathological studies of pulmonary diseases and the evaluation of inhalation toxicity. However, to reduce the number of animals used in experimentation and with the consideration of animal welfare, alternative methods have been extensively developed. Notably, the Organization for Economic Co-operation and Development (OECD) and the United States Environmental Protection Agency (USEPA) have agreed to prohibit animal testing after 2030. Therefore, the latest advances in biotechnology are revolutionizing the approach to developing in vitro inhalation models. For example, lung organ-on-a-chip (OoC) and organoid models have been intensively studied alongside advancements in three-dimensional (3D) bioprinting and microfluidic systems. These modeling systems can more precisely imitate the complex biological environment compared to traditional in vivo animal experiments. This review paper addresses multiple aspects of the recent in vitro modeling systems of lung OoC and organoids. It includes discussions on the use of endothelial cells, epithelial cells, and fibroblasts composed of lung alveoli generated from pluripotent stem cells or cancer cells. Moreover, it covers lung air-liquid interface (ALI) systems, transwell membrane materials, and in silico models using artificial intelligence (AI) for the establishment and evaluation of in vitro pulmonary systems.

• Pediatric Infection and Vaccine
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• v.30 no.3
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• pp.129-138
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• 2023

Purpose: Cancer incidence is known to be higher in patients with inborn errors of immunity (IEI) compared to the general population in addition to traditionally well-known infection susceptibility. We aimed to investigate cancer occurrence in patients with IEI in a single center. Methods: Medical records of IEI patients treated at Samsung Medical Center, Seoul, Korea were retrospectively reviewed from November 1994 to September 2023. Patients with IEI and cancer were identified. Results: Among 194 patients with IEI, seven patients (3.6%) were diagnosed with cancer. Five cases were lymphomas, 4 of which were Epstein-Barr virus (EBV)-associated lymphomas. The remaining cases included gastric cancer and multiple myeloma. The median age at cancer diagnosis was 18 years (range, 1-75 years). Among patients with cancer, underlying IEIs included X-linked lymphoproliferative disease-1 (XLP-1, n=3), activated phosphoinositide 3-kinase delta syndrome (APDS, n=2), and cytotoxic T-lymphocyte antigen 4 (CTLA-4) haploinsufficiency (n=2). Seventy-five percent (3/4) of XLP-1 patients, 40.0% (2/5) of APDS patients, and 50.0% (2/4) of CTLA-4 haplo-insufficiency patients developed cancer. Patients with XLP-1 developed cancer at earlier age (median age 5 years) compared to those with APDS and CTLA-4 (P<0.001). One patient with APDS died during hematopoietic cell transplantation. Conclusions: Cancer occurred in 3.6% of IEI patients at a single center in Korea. In addition to infectious complications and inflammation, physicians caring for IEI patients should be aware of the potential risk of cancer, especially in association with EBV infection.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.