• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2839-2852
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• 2013

Context-awareness is an essential part of ubiquitous computing, and over the past decade video based activity recognition (VAR) has emerged as an important component to identify user's context for automatic service delivery in context-aware applications. The accuracy of VAR significantly depends on the performance of the employed human body segmentation algorithm. Previous human body segmentation algorithms often engage modeling of the human body that normally requires bulky amount of training data and cannot competently handle changes over time. Recently, active contours have emerged as a successful segmentation technique in still images. In this paper, an active contour model with the integration of Chan Vese (CV) energy and Bhattacharya distance functions are adapted for automatic human body segmentation using depth cameras for VAR. The proposed technique not only outperforms existing segmentation methods in normal scenarios but it is also more robust to noise. Moreover, it is unsupervised, i.e., no prior human body model is needed. The performance of the proposed segmentation technique is compared against conventional CV Active Contour (AC) model using a depth-camera and obtained much better performance over it.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.653-662
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• 2006

In ubiquitous computing that is to build computing environments to provide proper services according to user's context, human being's emotion recognition based on facial expression is used as essential means of HCI in order to make man-machine interaction more efficient and to do user's context-awareness. This paper addresses a problem of rigidly basic emotion recognition in context-sensitive facial expressions through a new Bayesian classifier. The task for emotion recognition of facial expressions consists of two steps, where the extraction step of facial feature is based on a color-histogram method and the classification step employs a new Bayesian teaming algorithm in performing efficient training and test. New context-sensitive Bayesian learning algorithm of EADF(Extended Assumed-Density Filtering) is proposed to recognize more exact emotions as it utilizes different classifier complexities for different contexts. Experimental results show an expression classification accuracy of over 91% on the test database and achieve the error rate of 10.6% by modeling facial expression as hidden context.

• Journal of Information Technology Services
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• v.17 no.1
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• pp.105-119
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• 2018

The healthcare business is growing as a global core business because of the phenomenon of global aging, as well as in South Korea, skyrocketing health care costs accordingly, and changing the paradigm from treatment to the prevention-centered medical service. Especially, as the digital healthcare service stands out as a solution, major countries actively promote and support policies at the government level. Thus, this study will present attributes of a market-oriented service that would vitalize the digital healthcare service industry by investigating major attributes of the digital healthcare service. To analyze the relationships of the influences of attributes, this study used Interpretive Structural Modeling. As a result of literature research and ISM, this study can understand the eight basic attributes of the digital healthcare service (network scalability, context awareness, connection among information platforms, cost, trust, security, ease of use, usefulness) and analyze the relationships of the influences among the attributes. In addition, as this study finds some significant differences in Order Winner and Order Qualifier between the experts' group (security) and the users' group (trust, ease of use, usefulness), It provides meaningful implications for revitalization and promotion of digital healthcare service industry.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.4
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• pp.737-743
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• 2017

The intelligent life-log system proposed in this paper is intended to identify and record a myriad of everyday life information as to the occurrence of various events based on when, where, with whom, what and how, that is, a wide variety of contextual information involving person, scene, ages, emotion, relation, state, location, moving route, etc. with a unique tag on each piece of such information and to allow users to get a quick and easy access to such information. Context awareness generates and classifies information on a tag unit basis using the auto-tagging technology and biometrics recognition technology and builds a situation information database. In this paper, we developed an active modeling method and an application that recognizes expressionless and smile expressions using lip lines to automatically record emotion information.

• Asian Journal of Innovation and Policy
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• v.8 no.1
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• pp.162-179
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• 2019

Purpose: Companies in haste for higher consumers' preference tend to appear as 'green' and mislead about environmental concerns, which are termed as "Greenwashing." The purpose of the study is to investigate the consumer perception on greenwashing activities and analyze its impact on green brand image, green brand loyalty and green brand trust among Indian consumers. Design/methodology: The study makes use of a written questionnaire method to collect survey data from approximately 500 consumers all over India. The study uses Structural Equation Modeling (SEM) to study the hypothesized relationship between constructs affected by greenwashing based on consumer perspective in the Indian context. Findings: The study shows that Indian consumers are becoming aware of greenwashing activities, which have a negative impact on green brand trust and undermines green brand image and green brand loyalty. Implications: The study results are beneficial to policy-makers, researchers, practitioners, and managers to create awareness among Indian consumers on greenwashing activities.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.642-649
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• 2006

In order to recognize a situation from a environment which provides an intelligent service, we propose state-transition based situation modeling which is suitable for a low computing power and restricted resources like embedded systems, and we designed its application to a situation-action converter(SAC)which is consist of two parts; situation detector recognized wanted situations and action generator generated various control actions. Then, we implemented a situation manager for smart scheduler service by using a SAC which is installed to a ARM processor based embedded Linux evaluation board.

• Journal of Digital Convergence
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• v.10 no.10
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• pp.215-220
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• 2012

This paper proposes a ontology of tree structure approach for adaptive object recognition in a situation-variant environment. In this paper, we introduce a new concept, ontology of tree structure ontology, for context sensitivity, as we found that many developed systems work in a context-invariant environment. Due to the effects of illumination on a supreme obstinate designing context-sensitive recognition system, we have focused on designing such a context-variant system using ontology of tree structure. Ontology can be defined as an explicit specification of conceptualization of a domain typically captured in an abstract model of how people think about things in the domain. People produce ontologies to understand and explain underlying principles and environmental factors. In this research, we have proposed context ontology, context modeling, context adaptation, and context categorization to design ontology of tree structure based on illumination criteria. After selecting the proper light-ontology domain, we benefit from selecting a set of actions that produces better performance on that domain. We have carried out extensive experiments on these concepts in the area of object recognition in a dynamic changing environment, and we have achieved enormous success, which will enable us to proceed on our basic concepts.

• Informatization Policy
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• v.23 no.4
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• pp.76-94
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• 2016

Recently in Korea, the importance of information security awareness has been receiving a growing attention. Attacks such as social engineering and ransomware are hard to be prevented because it cannot be solved by information security technology. Also, the profitability of information security industry has been decreasing for years. Therefore, many companies try to find a new growth-engine and an entry to the foreign market. The main purpose of this paper is to draw out some information security issues and to analyze them. Finally, this study identifies issues and suggests how to improve the situation in Korea. For this, topic modeling analysis has been used to find information security issues of each country. Moreover, the score of sentiment analysis has been used to compare them. The study is exploring and explaining what critical issues are and how to improve the situation based on the identified issues of the Korean information security industry. Also, this study is also demonstrating how text mining can be applied to the context of information security awareness. From a pragmatic perspective, the study has the implications for information security enterprises. This study is expected to provide a new and realistic method for analyzing domestic and foreign issues using the analysis of real data of the Twitter API.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.9
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• pp.1785-1790
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• 2016

There is exhibit a degree of pain in the occurrence and course of treatment levels or oral, pain rating scale actions, such as illness, for example, the discomfort scale because of the pain "annoying", "unpleasant" "annoyed am", "painful" represents a pain scale of the order of "painful", "hard to bear", "very difficult to bear". Depression is recognized based on the premise of the situation, because it is difficult to recognize themselves. In this paper we define the components of the depression can be seen lifestyle which can lead to depression or through a biological signal. The depression index was derived from the ontology modeling to understand the state of depression. Depression ontology components and depression index will be aware of the situation based information services for depression. Combined with the situational awareness based devices and can be synchronized to verify the results of the depression index. It will be applied to improve lifestyle factors that of depression.

• Journal of Information Technology Applications and Management
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• v.18 no.4
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• pp.1-20
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• 2011

The recent development of mobile devices, including smartphones has maximized the user's convenience. This phenomenon has triggered the specification and activation of mobile cloud service and transformed the user's information usage and communication means. Despite such development of new technology, there is little understanding of mobile cloud. Therefor, in order to help the understanding of the usage of mobile cloud computing technology, this study developed the research model based on the UTAUT (the Unified Theory of Acceptance and Usage of Technology) model. The proposed research model included five external determinants, such as Interoperability, Information Organization, Adequacy, Information Security and Context Awareness, along with the moderating effects of Job Relevance, and empirically proved their influence on the user's job performance. This study proved the research model by analyzing the data collected from 287 copies of survey with the structural equation modeling approach. The result showed that all determinants, except Information Security have positive impact on the usage of mobile cloud computing technology. Thus, this study can compliment the limitations of previous research, suggest a new direction to analyze the user's perspective in the related research on mobile cloud and build a theoretical foundation for the further development of mobile cloud.