• Journal of Korean Library and Information Science Society
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• v.45 no.3
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• pp.37-61
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• 2014

The goal of this study is to suggest the models for establishing and operating the collaborative repository for academic libraries by region in Korea. For this purpose, author analyzed the status of academic libraries by provinces and types, the collection space shortage of academic libraries, location and evaluation criteria of collaborative repository. And based on these analysis results, author proposed the basic principles, key functions and practices to accomplish, architectural scales of the collaborative repositories, transfer criteria and ownership model of library collection, desirable management and operation unit of the collaborative repository.

• World Technopolis Review
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• v.5 no.1
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• pp.71-82
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• 2016

This paper explores, explains and describes a framework for analyzing collaborative governance, decent work and innovation as fundamental elements of sustainable workplaces through case study of Philippine science and technology (S & T) parks. Rules, or the legal infrastructure, are particularly significant considerations that facilitate or hinder collaboration. Industrial relations/human resource (IR/HR) practices are essential to collaboration and decent work. Employee consultation and labor-management council or committee are examples of IR/HR practices that might contribute to collaboration and decent work in firms and workplaces in S & T parks as they are team approaches to production, too. Collaboration and decent work enhance the capacity to innovate. In the long run, collaborative governance, decent work and innovation tend to converge in the concept of sustainable development. The interdependencies and interactions among collaborative governance, decent work and capacity to innovate in firms operating in S & T parks make possible new solutions to new problems (i.e., innovation) and, thus, sustainable workplaces.

• Asian Journal of Innovation and Policy
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• v.5 no.3
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• pp.251-274
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• 2016

Practical methodology for categorizing collaborative disciplines or research in a quantitative manner is presented by developing a Correlation Matrix of Major Disciplines (CMMD) using bibliometric data collected between 2009 and 2014. First, 21 major disciplines in science and engineering are defined based on journal publication frequency. Second, major disciplines using a comparing discipline correlation matrix is created and correlation score using CMMD is calculated based on an analyzer function that is given to the matrix elements. Third, a correlation between the major disciplines and 14 research fields using CMMD is calculated for validation. Collaborative researches are classified into three groups by partially accepting the definition of pluri-discipline from peer review manual, European Science Foundation, inner-discipline, inter-discipline and cross-discipline. Applying simple categorization criteria identifies three groups of collaborative research and also those results can be visualized. Overall, the proposed methodology supports the categorization for each research field.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.19-45
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• 2005

Collaborative filtering is one of the most common methods that e-commerce sites and Internet information services use to personalize recommendations. Collaborative filtering has the advantage of being able to use even sparse evaluation data to predict preference scores for new products. To date, however, no in-depth investigation has been conducted on how the data sparsity effect in customers' evaluation data affects collaborative filtering-based recommendation performance. In this study, we analyzed the sparsity effect and used a hybrid method based on customers' evaluations and purchases collected from an online bookstore. Results indicated that recommendation performance decreased monotonically as sparsity increased, and that performance was more sensitive to sparsity in evaluation data rather than in purchase data. Results also indicated that the hybrid use of two different types of data (customers' evaluations and purchases) helped to improve the recommendation performance when evaluation data were highly sparse.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.10a
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• pp.72-72
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• 2001

• Journal of Korean Library and Information Science Society
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• v.48 no.1
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• pp.191-206
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• 2017

The purpose of this study is to compare the characteristics of research collaborations in the field of LIS. While there are several studies under the unit of analysis of country, there are only a few studies under the unit of analysis of institution in LIS field. For this analysis, we selected eight journals in the KCI (Korea Citation Index) web site, which correspond to the field of LIS through subject classification. The collaborative indices, Collaborative Coefficient, Co-Authorship Index, Local Collaborative Index (LCI), Domestic Collaborative Index (DCI) allowed us to comparatively analyze institutional collaboration patterns in LIS field. In the case of Chung-Ang University, Yonsei University, and Ewha Womans University, collaborative research among professors, graduate students, and professors reflected the fact that collaborations among universities are often performed with professors. In the case of KISTI, which showed a very high index value, the characteristics of project-based research are reflected in the research collaboration pattern.

• Journal of Computational Design and Engineering
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• v.1 no.2
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• pp.103-115
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• 2014

The engineering design process is a creative process, and the designers must repeatedly apply Undo/Redo operations to modify CAD models to explore new solutions. Undo/Redo has become one of most important functions in interactive graphics and CAD systems. Undo/Redo in a collaborative CAD system is also very helpful for collaborative awareness among a group of cooperative designers to eliminate misunderstanding and to recover from design error. However, Undo/Redo in a collaborative CAD system is much more complicated. This is because a single erroneous operation is propagated to other remote sites, and operations are interleaved at different sites. This paper presents a multi-user selective Undo/Redo approach in full distributed collaborative CAD systems. We use site ID and State Vectors to locate the Undo/Redo target at each site. By analyzing the composition of the complex CAD model, a tree-like structure called Feature Combination Hierarchy is presented to describe the decomposition of a CAD model. Based on this structure, the dependency relationship among features is clarified. B-Rep re-evaluation is simplified with the assistance of the Feature Combination Hierarchy. It can be proven that the proposed Undo/Redo approach satisfies the intention preservation and consistency maintenance correctness criteria for collaborative systems.

• Animal Bioscience
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• v.35 no.8
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• pp.1235-1249
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• 2022

Objective: The purpose of this study was to evaluate the protection of glutamate (GLU) against the impairment in intestinal barrier function induced by lipopolysaccharide (LPS) stress in weaned pigs. Methods: Twenty-four weaned pigs were divided into four treatments containing: i) non-challenged control, ii) LPS-challenged control, iii) LPS+1.0% GLU, and iv) LPS+2.0% GLU. On day 28, pigs were treated with LPS or saline. Blood samples were collected at 0, 2, and 4 h post-injection. After blood samples collection at 4 h, all pigs were slaughtered, and spleen, mesenteric lymph nodes, liver and intestinal samples were obtained. Results: Dietary GLU supplementation inhibited the LPS-induced oxidative stress in pigs, as demonstrated by reduced malondialdehyde level and increased glutathione level in jejunum. Diets supplemented with GLU enhanced villus height, villus height/crypt depth and claudin-1 expression, attenuated intestinal histology and ultrastructure impairment induced by LPS. Moreover, GLU supplementation reversed intestinal intraepithelial lymphocyte number decrease and mast cell number increase induced by LPS stress. GLU reduced serum cortisol concentration at 4 h after LPS stress and downregulated the mRNA expression of intestinal corticotropin-releasing factor signal (corticotrophin-releasing factor [CRF], CRF receptor 1 [CRFR1], glucocorticoid receptor, tryptase, nerve growth factor, tyrosine kinase receptor A), and prevented mast cell activation. GLU upregulated the mRNA expression of intestinal transforming growth factor β. Conclusion: These findings indicate that GLU attenuates LPS-induced intestinal mucosal barrier injury, which is associated with modulating CRF signaling pathway.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10a
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• pp.10-12
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• 2003

Recommender systems help consumers to find the useful products from the overloaded information. Researchers have developed content-based recommenders, collaborative recommenders, and a few hybrid systems. In this research, we extend the classic collaborative recommenders by clustering method to form a hybrid recommender system. Using the clustering method, we can recommend the products based on not only the user ratings but also other useful information from user profiles or attributes of items. Through our experiments on well-known MovieLens data set, we found that the information provided by the attributes of item on the item-based collaborative filter shows advantage over the information provided by user profiles on the user-based collaborative filter.