• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.119-142
• /
• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.65-82
• /
• 2015

Recently, there has been a surge of interest in finding core issues and analyzing emerging trends for the future. This represents efforts to devise national strategies and policies based on the selection of promising areas that can create economic and social added value. The existing studies, including those dedicated to the discovery of future promising fields, have mostly been dependent on qualitative research methods such as literature review and expert judgement. Deriving results from large amounts of information under this approach is both costly and time consuming. Efforts have been made to make up for the weaknesses of the conventional qualitative analysis approach designed to select key promising areas through discovery of future core issues and emerging trend analysis in various areas of academic research. There needs to be a paradigm shift in toward implementing qualitative research methods along with quantitative research methods like text mining in a mutually complementary manner. The change is to ensure objective and practical emerging trend analysis results based on large amounts of data. However, even such studies have had shortcoming related to their dependence on simple keywords for analysis, which makes it difficult to derive meaning from data. Besides, no study has been carried out so far to develop core issues and analyze emerging trends in special domains like the aviation industry. The change used to implement recent studies is being witnessed in various areas such as the steel industry, the information and communications technology industry, the construction industry in architectural engineering and so on. This study focused on retrieving aviation-related core issues and emerging trends from overall research papers pertaining to aviation through text mining, which is one of the big data analysis techniques. In this manner, the promising future areas for the air transport industry are selected based on objective data from aviation-related research papers. In order to compensate for the difficulties in grasping the meaning of single words in emerging trend analysis at keyword levels, this study will adopt topic analysis, which is a technique used to find out general themes latent in text document sets. The analysis will lead to the extraction of topics, which represent keyword sets, thereby discovering core issues and conducting emerging trend analysis. Based on the issues, it identified aviation-related research trends and selected the promising areas for the future. Research on core issue retrieval and emerging trend analysis for the aviation industry based on big data analysis is still in its incipient stages. So, the analysis targets for this study are restricted to data from aviation-related research papers. However, it has significance in that it prepared a quantitative analysis model for continuously monitoring the derived core issues and presenting directions regarding the areas with good prospects for the future. In the future, the scope is slated to expand to cover relevant domestic or international news articles and bidding information as well, thus increasing the reliability of analysis results. On the basis of the topic analysis results, core issues for the aviation industry will be determined. Then, emerging trend analysis for the issues will be implemented by year in order to identify the changes they undergo in time series. Through these procedures, this study aims to prepare a system for developing key promising areas for the future aviation industry as well as for ensuring rapid response. Additionally, the promising areas selected based on the aforementioned results and the analysis of pertinent policy research reports will be compared with the areas in which the actual government investments are made. The results from this comparative analysis are expected to make useful reference materials for future policy development and budget establishment.

• Journal of Intelligence and Information Systems
• /
• v.21 no.3
• /
• pp.19-36
• /
• 2015

Social media is an emerging issue in content services and in current business environment. YouTube is the most representative social media service in the world. YouTube is different from other conventional content services in its open user participation and contents creation methods. To promote a content in YouTube, it is important to understand the diffusion phenomena of contents and the network structural characteristics. Most previous studies analyzed impact factors of contents diffusion from the view point of general behavioral factors. Currently some researchers use network structure factors. However, these two approaches have been used separately. However this study tries to analyze the general impact factors on the view count and content based network structures all together. In addition, when building a content based network, this study forms the network structure by analyzing user comments on 22,370 contents of YouTube not based on the individual user based network. From this study, we re-proved statistically the causal relations between view count and not only general factors but also network factors. Moreover by analyzing this integrated research model, we found that these factors affect the view count of YouTube according to the following order; Uploader Followers, Video Age, Betweenness Centrality, Comments, Closeness Centrality, Clustering Coefficient and Rating. However Degree Centrality and Eigenvector Centrality affect the view count negatively. From this research some strategic points for the utilizing of contents diffusion are as followings. First, it is needed to manage general factors such as the number of uploader followers or subscribers, the video age, the number of comments, average rating points, and etc. The impact of average rating points is not so much important as we thought before. However, it is needed to increase the number of uploader followers strategically and sustain the contents in the service as long as possible. Second, we need to pay attention to the impacts of betweenness centrality and closeness centrality among other network factors. Users seems to search the related subject or similar contents after watching a content. It is needed to shorten the distance between other popular contents in the service. Namely, this study showed that it is beneficial for increasing view counts by decreasing the number of search attempts and increasing similarity with many other contents. This is consistent with the result of the clustering coefficient impact analysis. Third, it is important to notice the negative impact of degree centrality and eigenvector centrality on the view count. If the number of connections with other contents is too much increased it means there are many similar contents and eventually it might distribute the view counts. Moreover, too high eigenvector centrality means that there are connections with popular contents around the content, and it might lose the view count because of the impact of the popular contents. It would be better to avoid connections with too powerful popular contents. From this study we analyzed the phenomenon and verified diffusion factors of Youtube contents by using an integrated model consisting of general factors and network structure factors. From the viewpoints of social contribution, this study might provide useful information to music or movie industry or other contents vendors for their effective contents services. This research provides basic schemes that can be applied strategically in online contents marketing. One of the limitations of this study is that this study formed a contents based network for the network structure analysis. It might be an indirect method to see the content network structure. We can use more various methods to establish direct content network. Further researches include more detailed researches like an analysis according to the types of contents or domains or characteristics of the contents or users, and etc.

• Korean Journal of Soil Science and Fertilizer
• /
• v.36 no.3
• /
• pp.134-144
• /
• 2003

A rhizobacterium Pseudomonas cholororaphis O6 produced several secondary metabolites, such as phenazines, protease, and HCN that may be involved in inhibition of the growth of phytopathogenic fungi. In field study, P. chlororaphis O6 treatment on wheat seed suppressed root rot disease caused by Fusarium culmorum. The major organic acids of cucumber root exudates were fumaric acid, malic acid, benzoic acid, and succinic acid. Glucose and fructose were major monosaccharides in cucumber root exudates. The total amount of organic acids was ten times higher than that of the sugars. P. chlororaphis O6 grew well on cucumber root exudates. The dctA gene of P. chlororaphis O6 consisted of a 1,335 bp open reading frame with a deduced amino acid sequence of 444 residues, corresponding to a molecular size of about 47 kD and pI 8.2. The deduced dctA sequence has ten putative transmembrane domains, as expected of a membrane-embedded protein. Our results indicated that organic acids in cucumber root exudates may play an important role in providing nutrient source for root colonization of biological control bacteria, and the dctA gene of P. chlororaphis O6 may be an important bacterial trait that is involved in utilization of root exudates.

• Journal of Life Science
• /
• v.31 no.10
• /
• pp.913-921
• /
• 2021

Limb-girdle muscular dystrophy (LGMD) which is characterized by progressive muscle weakening of the hip and shoulder shows both dominant and recessive inheritances with many pathogenic genes including TTN. This study performed to identify genetic causes of a male patient with late onset (45 years old) autosomal recessive LGMD and atrial flutter. By application of the whole exome sequencing, we identified bi-allelic variants of TTN gene in the patient. One allele had a single missense variant of [c.24124G>T (p.V8042F)], while the other allele consisted of three missense variants of [c.29222G>C (p.R9741P) + c.67490A>G (p.H22497R) + c.75376C>T (p.R25126C)]. The p.V8042F allele was transmitted from his mother, while the other haplotype allele was putatively transmitted from his father. His two unaffected sons had only the p.R9741P. These variants have been not reported or rarely reported in the public human genome databases (1,000 Genome, gnomAD, and KRGDB). Most variants were located in the highly conserved immunoglobulin or fibronectin domains and were predicted to be pathogenic by the in silico analyses. The TTN giant protein plays a key role in muscle assembly, force transmission at the Z-line, and maintenance of resting tension in the I-band. In conclusion, we think that these bi-allelic compound heterozygous mutations may play a role as the genetic causes of the LGMD phenotype.

• Journal of Christian Education in Korea
• /
• v.66
• /
• pp.413-437
• /
• 2021

Humans are created as spiritual beings that can relate to God. However, when a human spirit refuses to transform through confronting God, it experiences a crisis. A spiritual crisis results from disconnecting with God, who is the ultimate foundation, but we humans try to overcome such absence through accomplishments and efforts. In this technological age, the ethics issues of AI (Artificial Intelligence), robots, and cloning are related to anthropology. The development of the mind, heart, and logic cannot suggest a basis for destruction and confusion as much as the development of the world. In fact, education focused on the human mind cannot be considered holistic. Mind, together with thought, will, and belief, plays a crucial role in making choices and leading a human life. So it is actively studied in other domains other than Christian education. However, although the human spirit takes care of some territory of humanity, unlike the mind, it can neither be partial nor fragmentary. Instead, it manages the transformation that influences the core of human life. Therefore, Christian education must clearly concentrate on the spirit rather than on other human elements, intentionally concerning spiritual transformation through encounters with God. In other words, Christian education is the passage connecting a human spirit to God's presence at work, which enables us to understand the human being as a whole. For this, we must put our efforts to increase the chances of encountering God through Christian education. While "Encounter" requires both parties' interaction, "Transformation" stresses God as the main agent and His proactive nature. I also want to emphasize "worship" as the opportunity to communicate and experience God in our daily lives. By examining the preparation and the process of the spiritual transformation of humans, this paper would offer a theological foundation for continued transformation of the human spirit in the faith community, rather than personal experience or conviction.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.275-290
• /
• 2021

Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.

• Journal of Korean Theatre Studies Association
• /
• no.49
• /
• pp.5-47
• /
• 2013

Among the styles of performing arts, perhaps the genre that has attracted the largest audience would be musical. Popularity of musical has brought diverse changes in our performing arts market, and, upon emerging another musical genre, called 'Korean Traditional Musical Comedy,' it has been well-received by the audiences. 'Korean Traditional Musical Comedy' is a word that are formed by merging two other terms such as 'Korean Traditional Music' and 'Musical (Comedy).' In the meantime, however, it has yet some problems in order to be defined as the genre that has concrete concepts. It is because the term such as Korean Traditional Musical Comedy was created being closely associated with a marketing purpose rather than a term that defines the characteristics of a genre of performing arts. Although this new musical genre has drawn attentions of many audiences by adding 'Musical Comedy' to 'Korean Traditional Music' that was not quite popular to the public, it still does not have any established forms so that there is a fine line between "Korean Traditional Musical Comedy" and another genre like traditional style folk opera ("Changgeuk"). Looking at the characteristics of the musical work called 'Korean Traditional Musical Comedy, in general, first of all, it is a performance where music and drama are played. Here, the distinctive characteristic of this musical is that 'Korean Traditional Music' is sung. And the kinds of Korean traditional musics being sung are mainly Pansori (dramatic story-singing) and folk-songs, and, in most cases, Korean traditional musical instruments are being used as accompanying music. In this paper, the researcher investigated the aspects of experiment centering around Korean Musical Group, Taroo. These days, various experiments has been repeated not only for the works of Taroo but other musical work presently called 'Korean Traditional Musical Comedy' also. Having encompassed overall performance factors including use of musical instruments, dance, acting, materials for drama as well as music in drama, the researcher has gone through experiments repeatedly. Meanwhile, however, the subject matters that make 'Korean Traditional Musical Comedy' mostly attractive to the audiences are music and songs or sounds. ["Sori" also called "Chang" (唱)] Particularly, under the current situation of our musicals, the role of "Sori" is extremely important. The factor that plays absolutely most important role in acceptance and transformation of "Sori" is the created Pansori. Since the created Pansori is composed with new rhythmic patterns and new narrative poems, it tells the present story. Also it draws good responses from the audiences owing to easy conveyance of dialogues. And, its new style brings diversification to organization of musical instruments, so then this leads to the arrangements of music for Korean traditional music instruments, as well as instrumental music ensemble, orchestra, and jazz band, etc. Likewise, upon appearing creative musics in 'Korean Traditional Musical Comedy,' professional music and vocal compositions have begun to emerge naturally. And, the song specialist and writer, of course, staffs including direction, lighting, and sounds, etc are required. That is, professional composition method are forced to be introduced to all areas. Other than this, there are many music pieces which are based on our unique songs and sounds ("Sori") and such traditional factors as use of lead singer for ceremony or chorus, and the method that puts weight on Pansori. Accordingly many things accomplished. However, it is required that 'Korean Traditional Musical Comedy' go through numerous discussions and more experiments. Above all, the most important things are the role of actor and actress, and their changes, and training of actor and actress further. Good news is there are good audience responses. 'Korean Traditional Musical Comedy' is an open genre. As musicals are divided into several domains according to the characteristics thereof, 'Korean Traditional Musical Comedy' will be able to show its distinctive features in various styles according to embodiment.

• Korean Journal of Breeding Science
• /
• v.42 no.5
• /
• pp.547-554
• /
• 2010

Low-molecular-weight glutenin subunit (LMW-GS) in common wheat (Triticum aestivum L.) is important for quality processing of bread and noodles. The objectives of this study were to clarify the composition of LMW-GSs and to identify their corresponding proteins. Using LMW-GS specific primers we cloned and characterized 43 LMW-GS genes in the wheat cultivar 'Jokyoung'. Some of these genes contain polypeptides different in size due to the presence of various deletions or insertions within repetitive and glutamine-rich domains. The comparison of deduced amino acid sequence of the LMW-GS genes in Jokyoung with that of 12 groups LMW-GSs of wheat cultivar Norin 61 showed that the deduced amino acid sequences were nearly the same to LMW-GS groups of 1, 2, 3/4, 5, 7, 10 and 11. All LMW-GS genes contain eight cysteine residues, which are conserved among all of the typical LMW-GS sequences. The relative positions of cysteine residues are also conserved, except those of the first and seventh. Based on phylogenetic analysis, the 43 sequences with the same N-terminal and C-terminal amino acid sequences were clustered in the same group. To identify the proteins containing the corresponding amino acid sequences, we determined the N-terminal amino acid sequence of 7 spots of LMW-GSs of Jokyoung separated by two-dimensional gel electrophoresis (2DE). Of them, Glu-B3 (LMW-m and LMW-s) and Glu-D3 (LMW-m) were detected in two and three spots, respectively and the others were not clear. Collectively, we classified diverse LMW-GSs and identified their corresponding protein products. These results will be helpful in breeding programs for improvement of wheat flour quality.

• Journal of Life Science
• /
• v.29 no.3
• /
• pp.369-375
• /
• 2019

Microtubules form through the polymerization of ${\alpha}-$ and ${\beta}-tubulin$, and tubulin transport plays an important role in defining the rate of microtubule growth inside cellular appendages, such as the cilia and flagella. Heterotrimeric kinesin 2 is a molecular motor member of the kinesin superfamily (KIF) that moves along the microtubules to transport multiple cargoes. It consists of two motor subunits (KIF3A and KIF3B) and a kinesin-associated protein 3 (KAP3), forming a heterotrimeric complex. Heterotrimeric kinesin 2 interacts with many different binding proteins through the cargo-binding domains of the KIF3s, but these binding proteins have not yet been specified. To identify these proteins for KIF3A, we performed yeast two-hybrid (Y2H) screening and found a specific interaction with ${\beta}2-tubulin$ (Tubb2), a microtubule component. Tubb2 was found to bind to the cargo-binding domain of KIF3A but did not interact with KIF3B, KIF5B, or kinesin light chain 1 in the Y2H assay. The carboxyl-terminal region of Tubb2 is essential for interaction with KIF3A. Other Tubb isoforms, including Tubb1, Tubb3, Tubb4, and Tubb5, also interacted with KIF3A in the Y2H screening. However, ${\alpha}1-tubulin$ (Tuba1) did not interact with KIF3A. In addition, an antibody to KIF3A specifically co-immunoprecipitated the KIF3B and KAP3 associated with Tubb2 from mouse brain extracts. In combination, these results suggest that a heterotrimeric kinesin 2 motor protein is capable of binding to tubulin and may transport it in cells.