• Korean Journal of Culture and Social Issue
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• v.29 no.2
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• pp.199-221
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• 2023

This study examined the moderating effects of age and gender on the relationship between values and communication styles of Korean adults. Five hundred adult men and women across the country responded the questionnaires regarding cultural universal values (openness to change, self-enhancement, conservatism, and self-transcendence), cultural-specific values (collectivism, conformity to norms, emotional self-control, family recognition through achievement, and humility), high-context communication style, and low-context communication style. The results of this study are as follows. First, as a result of exploring the factors influencing the communication style, self-enhancement, emotional self-control, and self-transcendence significantly predicted the high-context communication style. Whereas openness to change, self-enhancement, conformity to norms, emotional self-control, and gender significantly predicted the low-context communication style. Second, age moderated the relationship between self-enhancement and high-context communication style. The high-context communication style significantly increased when the level of self-enhancement was high and the age was younger. Third, age and gender moderated the relationship between conformity to norms and high-context communication style. In the case of males with high conformity to norms and younger age, the high-context communication style significantly increased. Fourth, gender moderated the relationship between collectivism and low-context communication. As collectivism increased, men tended to increase low-context communication styles, while women tended to decrease it. Fifth, gender moderated the relationship between humility and low-context communication. In the case of women with high humility, their low-context communication style was significantly lowered. The implications and limitations of the results of this study were discussed.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1793-1801
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• 2016

Speech enhancement is a challenging problem due to the diversity of noise sources and their effects in different applications. The goal of speech enhancement is to improve the quality and intelligibility of speech by reducing noise. Many research works in speech enhancement have been accomplished in English and other European Languages. There has been limited or no such works or efforts in the past in the context of Tamil speech enhancement in the literature. The aim of the proposed method is to reduce the background noise present in the Tamil speech signal by using wavelets. New modified thresholding function is introduced. The proposed method is evaluated on several speakers and under various noise conditions including White Gaussian noise, Babble noise and Car noise. The Signal to Noise Ratio (SNR), Mean Square Error (MSE) and Mean Opinion Score (MOS) results show that the proposed thresholding function improves the speech enhancement compared to the conventional hard and soft thresholding methods.

• Journal of Internet Computing and Services
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• v.15 no.6
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• pp.117-124
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• 2014

Cluttered background is a major obstacle in developing salient object detection and tracking system for mobile device captured natural scene video frames. In this paper we propose a context aware feature vector selection model to provide an efficient noise filtering by machine learning based classifiers. Since the context awareness for feature selection is achieved by searching nearest neighborhoods, known as NP hard problem, we apply a fast approximation method with complexity analysis in details. Separability enhancement in feature vector space by adding the context aware feature subsets is studied rigorously using principal component analysis (PCA). Overall performance enhancement is quantified by the statistical measures in terms of the various machine learning models including MLP, SVM, Naïve Bayesian, CART. Summary of computational costs and performance enhancement is also presented.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.140-146
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• 2019

Andrew Niccols's movie Gattaca (1997) inspired the formulation of the "Gattaca Argument" concerning the negative outcome of biotechnology, which has since been critiqued especially in the context of transhumanism and posthumanism. According this argument the development of genetic enhancement will produce a genetic discrimination and lead us to serious form of inequality. However, in particular transhumanists deny that here are reasons to worry and advocating instead the transformation of human condition in terms of genetic enhancement. Moreover, they question that genetic enhancement will necessarily lead to social inequality. In what follows, we will reexamine the Gattaca Argument and its critiques based on the movie in order to reassess the role the movie plays in the subsequent scholarly discussion. We will argue that existing critiques fall short of capturing the problem posed in the movie - the problem of the inhumane. Based on a hermeneutic approach to the movie we will both reconstruct the arguments and evaluate the transhuman counterarguments in terms of modern history of philosophical ideas.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.4
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• pp.254-263
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• 2006

Ontology-based context models are widely used in ubiquitous computing environment because they have advantages in the acquisition of conceptual context through inferencing, context sharing, and context reusing. Among the benefits, inferencing enables context-aware applications to use conceptual contexts which cannot be acquired by sensors. However, inferencing causes processing delay and thus becomes the major obstacle to the implementation of context-aware applications. The delay becomes longer as the amount of contexts increases. In this paper, we propose a context pre-fetching method to reduce the size of contexts to be processed in a working memory in attempt to speed up inferencing. For this, we extend the query-tree method to identify contexts relevant to the queries of a context-aware application. Maintaining the pre-fetched contexts optimal in a working memory, the processing delay of inference reduces without the loss of the benefits of ontology-based context model. We apply the proposed scheme to our ubiquitous computing middleware, Active Surroundings, and demonstrate the performance enhancement by experiments.

• Journal of Digital Convergence
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• v.10 no.4
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• pp.129-136
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• 2012

This study is presented on the corporate advertising strategy utilizing internet on corporate social responsibility in the context of the articles. To this end, Internet articles divided into positive and negative contexts, and has even attracted the attention of Internet articles and high /low and then separated into groups on ad attitudes and corporate image, the interaction effect was examined. Firstly, the Internet, a low level of condition of the article noted, in the context of a positive than a negative context, recall rates were higher. Second, the context of Internet articles and attention on the interaction effect between attitude toward advertising appeared. Third, the context of Internet articles and attention on the interaction effects between the corporate image appeared. Finally, the context of Internet articles and attention on competitive interactions between the corporate management was effective. Thus, the context of Internet articles based on the level of attention and context to determine the effect of advertising by consumer advertising awareness and favorable attitude toward corporate advertising and corporate image enhancement and competitiveness of business management can be an effective strategic plan suggests that.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.109-114
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• 2008

In this paper, we propose CAMAR Core Platform as a framework for context-aware mobile augmented reality system development after brief introduction of limitations in the previous mobile augmented reality (AR). There have been many research activities on mobile AR due to recent enhancement of functionalities of mobile devices and development of relevant technologies to mobile augmented reality. In order to be used in ubiquitous computing environment, however, mobile AR systems should be able to utilize available contextual information, augment personalized contents, and share the augmented contents with other users. Thus, the proposed CAMAR Core Platform can provide such functionalities to those who want to develop CAMAR systems. To show the feasibility of the proposed framework, we show several applications based on the framework.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.108-113
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• 2009

Significant attention has recently been drawn to digital home photo albums that use face detection technology. The tendency can be found in home photo albums that people prefer to allocate concerned objects in the center of the image rather than the boundary when they take a picture. To improve detection performance and speed that are important factors of face detection task, this paper proposes a face detection method that takes spatial context information into consideration. Experiments were performed to verify the usefulness of the proposed method and results indicate that the proposed face detection method can efficiently reduce the false positive rate as well as the runtime of face detection.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.855-870
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• 2020

Windows are key components of building facades. Detecting windows, crucial to 3D semantic reconstruction and scene parsing, is a challenging task in computer vision. Early methods try to solve window detection by using hand-crafted features and traditional classifiers. However, these methods are unable to handle the diversity of window instances in real scenes and suffer from heavy computational costs. Recently, convolutional neural networks based object detection algorithms attract much attention due to their good performances. Unfortunately, directly training them for challenging window detection cannot achieve satisfying results. In this paper, we propose an approach for window detection. It involves an improved Faster R-CNN architecture for window detection, featuring in a window region proposal network, an RoI feature fusion and a context enhancement module. Besides, a post optimization process is designed by the regular distribution of windows to refine detection results obtained by the improved deep architecture. Furthermore, we present a newly collected dataset which is the largest one for window detection in real street scenes to date. Experimental results on both existing datasets and the new dataset show that the proposed method has outstanding performance.

• Journal of Broadcast Engineering
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• v.23 no.4
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• pp.495-502
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• 2018

JVET (Joint Video Exploration Team) which explored evolving technologies of video coding with capabilities beyond HEVC (High Efficiency Video Coding), released a references software codec named the Joint Exploration Model (JEM) for performance verification of coding technologies. JEM has 67 intra prediction modes that extend the 35 modes of HEVC for intra prediction. Therefore, the enhancement of the coding performance is limited due to the overhead of prediction mode coding. In this paper, we analyze the probabilities of prediction modes selections, and then we propose a more efficient intra prediction mode coding based on the results of analyzed mode occurrence. In addition, we propose a context modeling for CABAC (Context-Adaptive Binary Arithmetic Coding) of the proposed mode coding. Experimental results show that the BD-rate gain is 0.02% on the AI (All Intra) coding structure compared to JEM 7.0. We need to optimize context modeling for additional coding performance enhancement.