• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.169-182
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• 2010

The purpose of the study is to prospect the application of knowledge awareness system in the use of the digital textbook which is one of the main educational political projects based on the exploration of the awareness theory for the computer-supported collaborative learning. To do this, first, knowledge awareness theory for computer-supported collaborative learning (CSCL) as a rationale for digital textbook which supports the collaborative learning was introduced. Second, three functionalities of knowledge awareness systems were extracted by analyzing the representative knowledge awareness tools of CSCL environment. Third, application prospects of knowledge awareness system toward the development and utilization of digital textbook were presented. The paper suggested the need of more researches such as the prototype development of digital textbook which applies the knowledge awareness system's functionalities and empirical researches which examine their effectiveness and efficiency.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.273-280
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• 2003

Recommender System can help users to find products to Purchase. A representative method for recommender systems is collaborative filtering (CF). It predict products that user may like based on a group of similar users. User information is based on user's ratings for products and similarities of users are measured by ratings. As user is increasing tremendously, the performance of the pure collaborative filtering is lowed because of high dimensionality and scarcity of data. We consider the effect of dimension deduction in collaborative filtering to cope with scarcity of data experimentally. We suggest that SVD improves the performance of collaborative filtering in comparison with pure collaborative filtering.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10C
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• pp.1015-1026
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• 2002

As per increasing research interest in the field of collaborative computing in recent year, the importance of security issues on that area is also incrementally growing. Generally, the persistency of collaborative system is facilitated with conventional authentication and cryptography schemes. It is however, hard to meet the access control requirements of distributed collaborative computing environments by means of merely apply the existing access control mechanisms. The distributed collaborative system must consider the network openness, and various type of subjects and objects while, the existing access control schemes consider only some of the access control elements such as identity, rule, and role. However, this may cause the state of security level alteration phenomenon. In order to handle proper access control in collaborative system, various types of access control elements such as identity, role, group, degree of security, degree of integrity, and permission should be taken into account. Futhermore, if we simply define all the necessary access control elements to implement access control algorithm, then collaborative system consequently should consider too many available objects which in consequence, may lead drastic degradation of system performance. In order to improve the state problems, we propose a novel access control framework that is suitable for the distributed collaborative computing environments. The proposed scheme defines several different types of object elements for the accessed objects and subjects, and use them to implement access control which allows us to guarantee more solid access control. Futhermore, the objects are distinguished by three categories based on the characteristics of the object elements, and the proposed algorithm is implemented by the classified objects which lead to improve the systems' performance. Also, the proposed method can support scalability compared to the conventional one. Our simulation study shows that the performance results are almost similar to the two cases; one for the collaborative system has the proposed access control scheme, and the other for it has not.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.242-247
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• 2009

Recently an intelligent system is developed for the service what users want not a passive system which just answered user's request. This intelligent system is used for personalized recommendation system and representative techniques are content-based and collaborative filtering. In this study, we propose a prediction system which is based on the techniques of recommendation system using a collaborative filtering and a fuzzy system to solve the collaborative filtering problems. In order to verify the prediction system, we used the data that is user's rating about movies. We predicted the user's rating using this data. The accuracy of this prediction system is determined by computing the RMSE(root mean square error) of the system's prediction against the actual rating about the each movie and is compared with the existing system. Thus, this prediction system can be applied to base technology of recommendation system and also recommendation of multimedia such as music and books.

• Journal of The Korean Association of Information Education
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• v.22 no.1
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• pp.151-158
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• 2018

The purpose of this study is to develop a system for predicting students' collaborative problem solving ability based on the ICT factors of PISA 2015 that affect collaborative problem solving ability. The PISA 2015 computer-based collaborative problem-solving capability evaluation included 5,581 students in Korea. As a research method, correlation analysis was used to select meaningful variables. And the collaborative problem solving ability prediction model was created by using the deep learning method. As a result of the model generation, we were able to predict collaborative problem solving ability with about 95% accuracy for the test data set. Based on this model, a collaborative problem solving ability prediction system was designed and implemented. This research is expected to provide a new perspective on applying big data and artificial intelligence in decision making for ICT input and use in education.

• Korean Journal of Computational Design and Engineering
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• v.20 no.3
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• pp.254-262
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• 2015

This study proposes an approach to Product Data Management (PDM) education for collaborative product data management, which can support collaborative product development process. This approach introduces social media and a PDM system into a framework for PDM education supported by consistent product development process and product data model. It has been applied to two PDM classes and the result shows that the social media in PDM education can support not only experiences of the collaborative product data management but also interactive and informal communications among instructors and participants using integrated social media with product data during courses.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.351-356
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• 2001

Recently, advanced information technologies including Internet-related technology and distributed object technology have opened new possibilities for collaborative designs. In this paper, we discuss computer supports for collaborative design in a distributed environment. The proposed system is the Internet-centric system composed of an engineering framework, collaborative virtual workspace and engineering service. It allows the distributed designers to more efficiently and collaboratively work their engineering tasks throughout the design process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2310-2332
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• 2020

In recent years, deep learning techniques have achieved tremendous successes in natural language processing, speech recognition and image processing. Collaborative filtering(CF) recommendation is one of widely used methods and has significant effects in implementing the new recommendation function, but it also has limitations in dealing with the problem of poor scalability, cold start and data sparsity, etc. Combining the traditional recommendation algorithm with the deep learning model has brought great opportunity for the construction of a new recommender system. In this paper, we propose a novel collaborative recommendation model based on auxiliary stacked denoising autoencoder(ASDAE), the model learns effective the preferences of users from auxiliary information. Firstly, we integrate auxiliary information with rating information. Then, we design a stacked denoising autoencoder based collaborative recommendation model to learn the preferences of users from auxiliary information and rating information. Finally, we conduct comprehensive experiments on three real datasets to compare our proposed model with state-of-the-art methods. Experimental results demonstrate that our proposed model is superior to other recommendation methods.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.87-92
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• 2010

In this paper, we suggest a PLCS-based architecture and operation method for providing interoperability in SBA integrated collaborative environment. Specifically, the suggested architecture is based on the distributed collaborative environment which employes the PLCS application protocol and integrated repository for representing and sharing product data information between collaborators remotely located. As an example of data representation, a military vehicle system is considered and two kinds of information, including state and activity/process, are explained. We expect that the suggested architecture can be used as a reference model to develop an efficient SBA integrated collaborative environment.

• Journal of information and communication convergence engineering
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• v.17 no.2
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• pp.135-141
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• 2019

Collaborative filtering algorithms often encounter data sparsity issues. To overcome this issue, auxiliary information of relevant items is analyzed and an item attribute matrix is derived. In this study, we combine the user-item attribute preference with the traditional similarity calculation method to develop an improved similarity calculation approach and use weights to control the importance of these two elements. A collaborative filtering algorithm based on user-item attribute preference is proposed. The experimental results show that the performance of the recommender system is the most optimal when the weight of traditional similarity is equal to that of user-item attribute preference similarity. Although the rating-matrix is sparse, better recommendation results can be obtained by adding a suitable proportion of user-item attribute preference similarity. Moreover, the mean absolute error of the proposed approach is less than that of two traditional collaborative filtering algorithms.