• The KIPS Transactions:PartA
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• v.12A no.6 s.96
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• pp.547-556
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• 2005

Online collaborative teaming, which has emerged as a new type of education in knowledge-based society, is being discussed actively in the areas of action learning at companies and project-based learning and inquiry-based learning at schools. It regards as an effective method for improving learners practical and highly advanced problem solving abilities, and for stimulating their absorption into learning through pursuing common goals of learning together. Different from individual learning, however, collaborative learning involves complicated processes such as organizing teams, setting common goals, performing tasks and evaluating the outcome of team activities .Thus, it is difficult for a teacher to promote and evaluate the whole process of collaborative learning, and it is necessary to develop systems to support collaborative learning. Therefore, in order to monitor and promote interaction among learners in the process of collaborative learning, the present study developed an extensible collaborative teaming supporting agent (ECOLA) in online learning environments.

• Journal of Korea Multimedia Society
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• v.21 no.2
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• pp.232-241
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• 2018

Operational Transformation (OT) algorithms for real-time collaborative editing systems are becoming increasingly important due to the increased demand for collaborative data processing. The operational transformation algorithm is a technique for real-time concurrency control and consistency maintenance with non-locking technique, and many studies have been conducted to overcome three issues of convergence, causality-prevention, and intention-prevention. However, previous work has the disadvantage of wasting memory by storing all operations that occurred during an edit operation in the history buffer to solve this problem. Therefore, we propose a memory-saving real-time collaborative editing system that maintains a constant memory space and concurrency control through a method of applying the valid-time to each user-generated operation in order to reduce memory waste. This system prevents long-term memory occupation of client-generated operations, thus it reduces the space and time complexity even with low-rate of collaboration work, so that the performance degradation avoids.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.331-333
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• 2015

Data is increasing explosively with the spread of networks and mobile devices and there are problems in effectively processing the rapidly increasing data using existing recommendation techniques. Therefore, researches are being conducted on how to solve the scalability problem of the collaborative filtering technique. In this paper applies MapReduce, which is a distributed parallel process framework, to the collaborative filtering technique to reduce the scalability problem and heighten accuracy. The proposed technique applies MapReduce and the index technique to a user-based collaborative filtering technique and as a method which improves neighbor numbers which are used in similarity calculations and neighbor suitability, scalability and accuracy improvement effects can be expected.

• Journal of Information Technology Applications and Management
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• v.29 no.6
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• pp.81-93
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• 2022

As deep learning technology in natural language and visual processing has rapidly developed, collaborative filtering-based recommendation systems using deep learning technology are being actively introduced in the recommendation field. In this study, OCF-GAN, a hybrid collaborative filtering model using GAN, was proposed to solve the one-class and cold-start problems, and its usefulness was verified through performance evaluation. OCF-GAN based on conditional GAN consists of a generator that generates a pattern similar to the actual user preference pattern and a discriminator that tries to distinguish the actual preference pattern from the generated preference pattern. When the training is completed, user preference vectors are generated based on the actual distribution of preferred items. In addition, the cold-start problem was solved by using a hybrid collaborative filtering recommendation method that additionally utilizes user and item profiles. As a result of the performance evaluation, it was found that the performance of the OCF-GAN with additional information was superior in all indicators of the Top 5 and Top 20 recommendations compared to the existing GAN-based recommender. This phenomenon was more clearly revealed in experiments with cold-start users and items.

• Journal of Information Processing Systems
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• v.13 no.3
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• pp.435-447
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• 2017

Associative and bidirectional associative memories are examples of associative structures studied intensively in the literature. The underlying idea is to realize associative mapping so that the recall processes (one-directional and bidirectional ones) are realized with minimal recall errors. Associative and fuzzy associative memories have been studied in numerous areas yielding efficient applications for image recall and enhancements and fuzzy controllers, which can be regarded as one-directional associative memories. In this study, we revisit and augment the concept of associative memories by offering some new design insights where the corresponding mappings are realized on the basis of a related collection of landmarks (prototypes) over which an associative mapping becomes spanned. In light of the bidirectional character of mappings, we have developed an augmentation of the existing fuzzy clustering (fuzzy c-means, FCM) in the form of a so-called collaborative fuzzy clustering. Here, an interaction in the formation of prototypes is optimized so that the bidirectional recall errors can be minimized. Furthermore, we generalized the mapping into its granular version in which numeric prototypes that are formed through the clustering process are made granular so that the quality of the recall can be quantified. We propose several scenarios in which the allocation of information granularity is aimed at the optimization of the characteristics of recalled results (information granules) that are quantified in terms of coverage and specificity. We also introduce various architectural augmentations of the associative structures.

• Journal of Information Processing Systems
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• v.9 no.3
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• pp.435-460
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• 2013

Recommender systems are popular applications that help users to identify items that they could be interested in. A recent research area on recommender systems focuses on detecting several kinds of inconsistencies associated with the user preferences. However, the majority of previous works in this direction just process anomalies that are intentionally introduced by users. In contrast, this paper is centered on finding the way to remove non-malicious anomalies, specifically in collaborative filtering systems. A review of the state-of-the-art in this field shows that no previous work has been carried out for recommendation systems and general data mining scenarios, to exactly perform this preprocessing task. More specifically, in this paper we propose a method that is based on the extraction of knowledge from the dataset in the form of rating regularities (similar to frequent patterns), and their use in order to remove anomalous preferences provided by users. Experiments show that the application of the procedure as a preprocessing step improves the performance of a data-mining task associated with the recommendation and also effectively detects the anomalous preferences.

• Journal of Information Processing Systems
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• v.14 no.1
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• pp.56-78
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• 2018

Security issue in mobile ad hoc network (MANET) is a promising research. In 2011, we had accomplished a survey of black hole attacks in MANETs. However network technology is changing with each passing day, a vast number of novel schemes and papers have been proposed and published in recent years. In this paper, we survey the literature on malicious attacks in MANETs published during past 5 years, especially the black hole attack. Black hole attacks are classified into non-cooperative and collaborative black hole attacks. Except black hole attacks, other attacks in MANET are also studied, e.g., wormhole and flooding attacks. In addition, we conceive the open issues and future trends of black hole detection and prevention in MANETs based on the survey results of this paper. We summarize these detection schemes with three systematic comparison tables of non-cooperative black hole, collaborative black hole and other attacks, respectively, for a comprehensive survey of attacks in MANETs.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.594-597
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• 2022

Recommendation systems are a prominent approach for users to make informed automated judgments. In terms of movie recommendation systems, there are two methods used; Collaborative filtering, which is based on user similarities; and Content-based filtering which takes into account specific user's activity. However, there are still issues with these two existing methods, and to address those, a combination of collaborative and content-based filtering is employed to produce a more effective system. In addition, various similarity methodologies are used to identify parallels among users. This paper focuses on a survey of the various tactics and methods to find solutions based on the problems of the current recommendation system.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.173-183
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• 2018

It is a very difficult task to establish a collaborative procedure in a new business that requires multilateral collaboration or to revise the regulation by analyzing and proving objectively the problems in the collaborative process conducted already by multilateral collaboration. In this paper, we proposed an optimization model for collaborative process to establish the operation procedure between collaborative parties using PERT/CPM network diagram which allows us to calculate the processing time. In order to verify the effectiveness and usefulness of our model for the collaboration process optimization developed in this study, we applied the developed collaborative procedure to student selection of the work-and-study-in-parallel course associated with a degree executed by Ministry of Employment and Labor. This study can be useful not only for newly establishing or reconfiguring collaborative procedures but also for standardizing the business procedures for building information systems between collaborative organizations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.2
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• pp.460-470
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• 2017

We address how to handle big data mining effectively using the intelligent SSD (iSSD). ISSD is a storage device equipped with computing power inside SSD for reducing the transferring cost and for processing data nearby SSD where the data is stored. We first introduce the structural characteristics of iSSD for efficient data processing. Then, we present how to process data mining algorithms by using iSSD. Finally, we discuss how to improve the performance of data mining algorithms significantly by exploiting heterogeneous computing environment where host CPUs and GPU coexist for maximizing the performance.