• KIPS Transactions on Computer and Communication Systems
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• v.4 no.4
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• pp.117-124
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• 2015

As recent developing computing environments, service is provided without time or place's limitation, and it is constituted dynamically. Above all, in terms of education supporting system, there is need to conceive user's information and provide optimal service as to right with applying context awareness under Ubiquitous computing environment. It should have groundwork based on user location and time. It is requires service providing based on user location under the condition of class to offer appropriate service with the context. In addition, context information providing technology is needed to offer right education support service in the class, where are different context information. Hence, in this research it is suggested education supporting system to provide context-based for instructor. The framework of this system has a foundation within previous work about Multi-agent based Distributed Framework. To examine implementation of context-based education supporting system in Ubiquitous computing environments suggested by this report, arranging system can fit information relates to instructor's location for service applying in the class.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.463-468
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• 2011

Context prediction methods have been developed in two ways - one is a prediction for discrete context and the other is for continuous context. As most of the prediction methods have been used with prediction algorithms in specific domains suitable to the environment and characteristics of contexts, it is difficult to conduct a prediction for a user's context which is based on various environments and characteristics. This study suggests a context prediction method available for both discrete and continuous contexts without being limited to the characteristics of a specific domain or context. For this, we conducted a context prediction based on sequence matching by generating sequences from contexts in consideration of association rules between context attributes and by applying variable weights according to each context attribute. Simulations for discrete and continuous contexts were conducted to evaluate proposed methods and the results showed that the methods produced a similar performance to existing prediction methods with a prediction accuracy of 80.12% in discrete context and 81.43% in continuous context.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.83-88
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• 1999

In this paper, a novel gray-scale lossless image coder combining context-based minimum mean squared error (MMSE) prediction and entropy coding is proposed. To obtain context of prediction, this paper first defines directional difference according to sharpness of edge and gradients of localities of image data. Classification of 4 directional differences forms“geometry context”model which characterizes two-dimensional general image behaviors such as directional edge region, smooth region or texture. Based on this context model, adaptive DPCM prediction coefficients are calculated in MMSE sense and the prediction is performed. The MMSE method on context-by-context basis is more in accord with minimum entropy condition, which is one of the major objectives of the predictive coding. In entropy coding stage, context modeling method also gives useful performance. To reduce the statistical redundancy of the residual image, many contexts are preset to take full advantage of conditional probability in entropy coding and merged into small number of context in efficient way for complexity reduction. The proposed lossless coding scheme slightly outperforms the CALIC, which is the state-of-the-art, in compression ratio.

• Journal of Intelligence and Information Systems
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• v.11 no.1
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• pp.53-63
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• 2005

Adaptive services need to become as mobile as their users and be extended to take advantage of the constantly changing context in which they are accessed. Context-awareness is a technology to facilitate information acquisition and execution by supporting interoperability between users and devices based on users' context. The objective of this study is to develop a middleware fer dealing with context-awareness in ubiquitous computing. To achieve it, our middleware plays an important role in recognizing a moving node with mobility by using a bluetooth wireless communication technology as well as in executing an appropriate execution module according to the context acquired from a context server. In addition, for verifying the usefulness of the Proposed middleware, we develop an application system which Provides a music playing service based on context information by using our context middleware.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.1
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• pp.24-34
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• 2007

Since Context Driven Component model is based on the idea that a context-aware application consists of the components that are context sensitive and the components that do not depend on the context, it divides the context sensitive part into components according to which context information they are related to. The model supports the scalability of context information by building an application through composing Context Driven Components. Furthermore, it solves the embeddedness of context information inside the application logic. To show the contributions of the model, this paper applies it to Call-forwarding application, and analyses how the model supports the scalability and the embeddedness.

• Journal of Information Processing Systems
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• v.15 no.6
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• pp.1378-1391
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• 2019

In recent years, automatic document summarization has been widely studied in the field of natural language processing thanks to the remarkable developments made using deep learning models. To decode a word, existing models for abstractive summarization usually represent the context of a document using the weighted hidden states of each input word when they decode it. Because the weights change at each decoding step, these weights reflect only the local context of a document. Therefore, it is difficult to generate a summary that reflects the overall context of a document. To solve this problem, we introduce the notion of a general context and propose a model for summarization based on it. The general context reflects overall context of the document that is independent of each decoding step. Experimental results using the CNN/Daily Mail dataset show that the proposed model outperforms existing models.

• ETRI Journal
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• v.29 no.2
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• pp.169-178
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• 2007

With the advent of ubiquitous computing environments, it has become increasingly important for applications to take full advantage of contextual information, such as the user's location, to offer greater services to the user without any explicit requests. In this paper, we propose context-aware active services based on context-aware middleware for URC systems (CAMUS). The CAMUS is a middleware that provides context-aware applications with a development and execution methodology. Accordingly, the applications based on CAMUS respond in a timely fashion to contextual information. This paper presents the system architecture of CAMUS and illustrates the content recommendation and control service agents with the properties, operations, and tasks for context-aware active services. To evaluate CAMUS, we apply the proposed active services to a TV application domain. We implement and experiment with a TV content recommendation service agent, a control service agent, and TV tasks based on CAMUS. The implemented content recommendation service agent divides the user's preferences into common and specific models to apply other recommendations and applications easily, including the TV content recommendations.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2006.06a
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• pp.122-129
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• 2006

The context-awareness has become the one of core technologies and the indispensable function. for application services in ubiquitous computing environment. In this research, we incorporated the capability of context-awareness in a music recommendation system. Our proposed system consists of such components as Intention Module, Mood Module and Recommendation Module. Among these modules, the Intention Module infers whether a user wants to listen to the music or not from the environmental context information. We built the Intention Module using data mining techniques such as decision tree, support vector machine and case-based reasoning. The results showed that the case-based reasoning model outperformed the other models and its accuracy was 84.1%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2007.10a
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• pp.501-505
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• 2007

현재 기술 동향에 따라, 언제 어디서나 통신 및 컴퓨팅이 가능한 유비쿼터스 컴퓨팅 시스템(Ubiquitous Computing System)을 위해 사용자 및 주변 환경의 상황을 인식하는 기술(Context-Aware Computing System)이 필수적인 요소로 부각되고 있다. 하지만, Context-Aware Computing System을 구현하기 위해서 사용자 및 주변 환경으로부터 입력되는 불확실하거나 모호한 상황정보에 대한 표현과 추론에 대한 연구는 부족한 실정이다. 이를 해결하기 위해 Rule-based System을 기반으로 CRS와 DOS의 개념을 도입하여 상황인식 기술을 SoC로 구현할 수 있도록 새로운 Architecture를 제안한다. 마지막으로는 CRS와 DOS가 Network Topolgy에 적용된 RODMRP(Resilient Ontology-based Dynamic Multicast Routing Protocol)의 예를 통해 그 효용성을 입증하고, Ubiquitous Computing System에서 활용 가능한 방법 등을 제시한다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.253-255
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• 2003

We address an integrated multimedia framework based on a software agent platform for context-aware multimedia computing. We adopt the FIPA (Foundation for Intelligent Physical Agents) platform which provides agent communications and management mechanisms. In order to express context information, we use MPEG-21 metadata for describing user characteristics and usage environment. We encapsulate such context information as a FIPA message to be delivered between agents. Based on context information, appropriate multimedia content delivery becomes possible. We present our system implementation with a use case scenario and show that our proposed framework is effective for context-aware multimedia computing so that personalization of multimedia consumption can be possible.