• Journal of The Korean Association For Science Education
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• v.14 no.3
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• pp.304-311
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• 1994

Four science concepts were selected from high school science textbook to investigate the differences in priorities of students knowledge activated during solving earth science problems in laboratory and earth science environmental contexts. Two items, one for laboratory context and the other for earth environmental context, were developed for earth selected concept The subjects were constituted of 192 students in 11th grade and 196 in 12th grade in one senior high school. Students' responses were categorized using graph models and analyzed in terms of 'Common Activated Knowledge'(CAK). and 'Specific Activated Knowledge'(SAK) across students' cognitive frames, grades, and sex. As contextual differences of the problems increased, context effects in priorities of CAK were reported in favor of laboratory context, on the contrary those of SAK in favor of earth environmental context. Context effects were reported across cognitive frames, especially students with laboratory cognitive frames showed more significant context effects than others. Lower graders and girls showed relatively large context effects. The results of this study showed that science concepts learned in a laboratory context are not easily transferred to earth environmental context. Therefore, special instructional strategies should be developed to overcome the context effect s according to activated knowledges with high priorities in laboratory and earth environmental context.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.4
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• pp.254-259
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• 2008

Uncertainty of result of context awareness always exists in any context-awareness computing. This falling-off in accuracy of context awareness result is mostly caused by the imperfectness and incompleteness of sensed data, because of this reasons, we must improve the accuracy of context awareness. In this article, we propose a novel approach to model the uncertain context by using ontology and context reasoning method based on Bayesian Network. Our context aware processing is divided into two parts; context modeling and context reasoning. The context modeling is based on ontology for facilitating knowledge reuse and sharing. The ontology facilitates the share and reuse of information over similar domains of not only the logical knowledge but also the uncertain knowledge. Also the ontology can be used to structure learning for Bayesian network. The context reasoning is based on Bayesian Networks for probabilistic inference to solve the uncertain reasoning in context-aware processing problem in a flexible and adaptive situation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.05a
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• pp.131-138
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• 2005

Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Most of the existing recommender systems focused on what kind of items to recommend, although when to recommend to the target customer considering their context is an important issue. Even right item might be a spam advertisement or wrong recommendation for the customer if it can not be recommended at the right context. It is particularly important for recommendations where the user's context is changing rapidly, such as in both handheld and ubiquitous computing environment. Therefore, we propose CARS (Context-Aware Recommender System) based on CBR and context-awareness for ubiquitous computing environment. CBR is used to generate a target customer class and proper context. Context-awareness is used to gather suer context information from sensors, networks, device status, user profiles, and other sources. An illustrative case example is suggested to explain the procedure of CARS.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.99-109
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• 2011

Ubiquitous computing technology makes an impact on the appearance of u-learning and presents an advanced direction of futuristic school education. In ubiquitous learning environments, various embedded computational devices will be pervasive and interoperable across the network for supporting the learning, so users may utilize these devices anytime anywhere. An important next step for ubiquitous learning is the introduction of context-aware learning service that employing knowledge and reasoning to understand the local context and share this information in support of intelligent learning services. However, the existing studies on design and application of ontology context model to support context-aware service in actual school environments are incomplete state. This paper, therefore, suggests a scheme of constructing ubiquitous learning space for existing school network by introducing USN to support context-aware ubiquitous learning services. This paper, also, designs an ontology based context model for ubiquitous school environments which describes context information through OWL. To determine the suitability of proposed ubiquitous learning space and ontology context model, we implement some of context-aware learning services in the ubiquitous learning environments.

• Proceedings of the Korea Society of Design Studies Conference
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• 2005.05a
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• pp.262-263
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• 2005

• Journal of Intelligence and Information Systems
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• v.13 no.1
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• pp.29-47
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• 2007

Acquiring context data in a timely and correct way is now regarded as one of the crucial characteristics of the proactive service which runs on ubiquitous computing environment. Moreover, context model should be well designed to provide a solid context-aware system. Since the ubiquitous computing systems aim to provide context-aware services everywhere with any available devices, legacy services which uses context models assuming single or limited domain should be extended enough to be useful even for multi-domain muli-services. This leads us to a motivation to build a generic context model with an appropriate type of model. Hence, the purpose of this paper is to propose a generic context model by assessing a variety of model types with a sort of evaluation measures.

• Journal of IKEEE
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• v.21 no.3
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• pp.280-283
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• 2017

HEVC (high efficiency video coding) exploits CABAC (context-based adaptive binary arithmetic coding) for entropy coding, where a context model estimates the probability for each syntax element. In this paper, a context modeler was designed and implemented for CABAC decoding. lookup table was used to reduce computation and to increase speed. 12 simulations for HEVC standard test sequences and encoder configurations were performed, and the context modeler was verified to perform correction operations. The designed context modeler was synthesized in 0.18um technology. Maximum frequency, maximum throughput, and gate count are 200 MHz, 200 Mbin/s, and 29,268 gates, respectively.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.288-289
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• 2004

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1275-1292
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• 2021

As a fine-grained classification problem, aspect-level sentiment classification predicts the sentiment polarity for different aspects in context. To address this issue, researchers have widely used attention mechanisms to abstract the relationship between context and aspects. Still, it is difficult to effectively obtain a more profound semantic representation, and the strong correlation between local context features and the aspect-based sentiment is rarely considered. In this paper, a hybrid attention capsule network for aspect-level sentiment classification (ABASCap) was proposed. In this model, the multi-head self-attention was improved, and a context mask mechanism based on adjustable context window was proposed, so as to effectively obtain the internal association between aspects and context. Moreover, the dynamic routing algorithm and activation function in capsule network were optimized to meet the task requirements. Finally, sufficient experiments were conducted on three benchmark datasets in different domains. Compared with other baseline models, ABASCap achieved better classification results, and outperformed the state-of-the-art methods in this task after incorporating pre-training BERT.

• Journal of Information Processing Systems
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• v.11 no.2
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• pp.184-204
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• 2015

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.