• ETRI Journal
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• v.28 no.4
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• pp.425-434
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• 2006

We propose a fast macroblock mode prediction and decision algorithm based on contextual information for Pslices in the H.264/AVC video standard, in which the mode prediction part is composed of intra and inter modes. There are nine $4{\times}4$ and four $16{\times}16$ modes in the intra mode prediction, and seven block types exist for the best coding gain based on rate-distortion optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a fast inter mode prediction scheme is applied that uses contextual mode information for P-slices. We verify the performance of the proposed scheme through a comparative analysis of experimental results. The suggested mode search procedure increased more than 57% in speed compared to a full mode search and more than 20% compared to the other methods.

• Korean Journal of Cognitive Science
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• v.22 no.2
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• pp.173-192
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• 2011

The visual system applies previously learned contextual knowledge to facilitate the perception and encoding of scenes. When extrapolation following the employment of contextual information occurs, it sometimes leads to scene memory distortion: people report as if they saw more than they actually had seen. This phenomenon is called the "boundary extension" effect (Intraub & Richardson, 1989). The present study aimed to clarify the effects of contextual information on boundary extension in a more systematic way. Based on the assumption that it is harder to extract contextual information from inverted scenes compared to intact scenes, we presented inverted scenes either during encoding or retrieval to manipulate the level of contextual information and compared the magnitude of boundary extension effect for upright versus inverted scenes. In a series of experiments, we found that scene inversion during encoding, but not during retrieval, significantly reduced boundary extension. Showing reduced memory distortion for inverted scenes, the current study directly demonstrated that access to contextual information is a critical component of scene extrapolation process.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.479-489
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• 2020

The purpose of this paper is to uncover whether and how contextual factors (information exchange, participation, trust in management, and training), relate to resistance to change. It also explores the mediating effect of perceived impact of change on the relations between contextual factors and resistance to change. This study is conducted in several manufacturing plants in food processing industry in Vietnam, which is implementing a top-down large-scale change - Lean transformation, adopting Total Productive Maintenance (TPM) program, to be specific. The findings suggest that all four contextual factors are negatively associated with resistance to change, in which training had the strongest impact. Also, the perceived change impact partially mediates the relationships between the four contextual factors and resistance to change. The practical implications of this paper are that employees who receive adequate, timely and useful information relating to change are less likely to show opposing behaviors towards change. Fostering trust in management among employees, and employee involvement in decision-making, also have a significant influence when addressing employee resistance to change. Employees who are well-trained, well-equipped with tools and knowledge about the change, are less likely to resist as they view the benefits of changes more significant than the risks.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.636-645
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• 2006

A tenn with more domain specific information has higher level of term specificity. We propose new specificity calculation methods of terms based on information theoretic measures using compositional and contextual information. Specificity of terms is a kind of necessary conditions in tenn hierarchy construction task. The methods use based on compositional and contextual information of terms. The compositional information includes frequency, $tf{\cdot}idf$, bigram and internal structure of the terms. The contextual information of a tenn includes the probabilistic distribution of modifiers of terms. The proposed methods can be applied to other domains without extra procedures. Experiments showed very promising result with the precision of 82.0% when applied to the terms in MeSH thesaurus.

• The Journal of Information Systems
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• v.17 no.3
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• pp.1-24
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• 2008

This study empirically investigated the relationships among inter-organizational contextual factors(assets specificity, long-term orientation and interdependence), information exchange between trading partners, inter-organizational loaming and trust, and inter-organizational performance. In this study, types of information exchanged between trading firms are classified into two broad kinds: transaction information and management information. from empirical results, we found that inter-organizational contextual factors have a greater positive impact on the exchange of management information. It is also observed that the exchange of information positively influences inter-organizational trust and loaming. finally, the results of this study showed that inter-organizational trust and teaming have positive effects on the improvement of inter-organizational performance. Thus, it is concluded that the amount of information exchanged according to the conditions of inter-organizational contextual factors gives rise to inter-organizational teaming and high levels of trust, and high levels of trust and learning contribute to the increase of inter-organizational performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.587-590
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• 2020

스마트폰, GPS 장비, 위치 기반 소셜네트워크의 발달로 방대한 이동 경로 데이터 수집이 가능하게 됐다. 이를 통해 다양한 분야에서 GPS 데이터를 가지고 사람의 이동성을 분석하고 POI를 예측하는 기회가 많아졌다. 실생활에서 사람의 이동성은 다양한 상황에 영향을 받지만, 실제 GPS 데이터는 위치, 시간 정보의 수준이다. 따라서 다양한 상황을 내재하는 정보가 사람의 이동성 분석과 POI 예측에 필요하다. 본 논문에서는 POI의 순위, 사용자의 POI 활동, 카테고리 선호도 같은 맥락적 특징을 이용하여 이에 관련된 상황에 맞는 POI 시퀀스를 예측하는 Contextual LSTM 기반 딥러닝 기법을 제안한다. Contextual LSTM은 사람의 이동성에 영향을 주는 시퀀스의 맥락적 특징을 모델에 통합하기 위해 LSTM을 확장한다. 제안된 기법은 HITS 알고리즘과 여러 제약조건 기반으로 추출한 맥락적 특징별로 딥 러닝 모델에 통합하여 각각 POI 시퀀스를 검출했으며, 다양한 맥락적 특징에 대해서 공공 데이터와 수집한 데이터로 평가하였다.

• Proceedings of the KOSOMBE Conference
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• v.1995 no.11
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• pp.269-272
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• 1995

• Asia pacific journal of information systems
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• v.11 no.2
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• pp.141-158
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• 2001

This paper examines the effects of environmental uncertainty, structural decentralization, formalization, complexity and task interdependence on the information sharing through information system. 197 firms in Korea are surveyed and analyzed to investigate the relationship between the contextual variables and the information sharing. The result of multiple regression analysis shows that task interdependence, structural decentralization, complexity are significant factors to influence on the Information Sharing. Also, additional analysis shows that task interdependence, structural decentralization are major factors in service industry, and task interdependence, structural complexity are in manufacturing industry.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.633-635
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• 2022

If an error occurs during operation of the video wall controller, the control system creates a log file and records the log. In order to minimize the load on the system due to log recording, the log level is set so that the log is not recorded as much as possible under normal operating conditions. When an error occurs, detailed logs are recorded by changing the log level to analyze and respond to the cause of the error. So work efficiency is reduced, and operator intervention is inevitable to change the log level. In this paper, we propose a model that automatically sets the log level according to the operating situation using Contextual Bandit.

• Investigative Magnetic Resonance Imaging
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• v.3 no.2
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• pp.179-187
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• 1999

Purpose : This paper introduces a computationally inexpensive context-dependent classification of multi-echo MRI with Bayes compound decision model. In order to produce accurate region segmentation especially in homogeneous area and along boundaries of the regions, we propose a classification method that uses contextual information of local enighborhood system in the image. Material and Methods : The performance of the context free classifier over a statistically heterogeneous image can be improved if the local stationary regions in the image are disassociated from each other through the mechanism of the interaction parameters defined at he local neighborhood level. In order to improve the classification accuracy, we use the contextual information which resolves ambiguities in the class assignment of a pattern based on the labels of the neighboring patterns in classifying the image. Since the data immediately surrounding a given pixel is intimately associated with this given pixel., then if the true nature of the surrounding pixel is known this can be used to extract the true nature of the given pixel. The proposed context-dependent compound decision model uses the compound Bayes decision rule with the contextual information. As for the contextual information in the model, the directional transition probabilities estimated from the local neighborhood system are used for the interaction parameters. Results : The context-dependent classification paradigm with compound Bayesian model for multi-echo MR images is developed. Compared to context free classification which does not consider contextual information, context-dependent classifier show improved classification results especially in homogeneous and along boundaries of regions since contextual information is used during the classification. Conclusion : We introduce a new paradigm to classify multi-echo MRI using clustering analysis and Bayesian compound decision model to improve the classification results.