• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3055-3073
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• 2019

In this paper, a new user identification method is presented using real environmental human-computer-interaction (HCI) behavior data to improve method usability. User behavior data in this paper are collected continuously without setting experimental scenes such as text length, action number, etc. To illustrate the characteristics of real environmental HCI data, probability density distribution and performance of keyboard and mouse data are analyzed through the random sampling method and Support Vector Machine(SVM) algorithm. Based on the analysis of HCI behavior data in a real environment, the Multiple Kernel Learning (MKL) method is first used for user HCI behavior identification due to the heterogeneity of keyboard and mouse data. All possible kernel methods are compared to determine the MKL algorithm's parameters to ensure the robustness of the algorithm. Data analysis results show that keyboard data have a narrower range of probability density distribution than mouse data. Keyboard data have better performance with a 1-min time window, while that of mouse data is achieved with a 10-min time window. Finally, experiments using the MKL algorithm with three global polynomial kernels and ten local Gaussian kernels achieve a user identification accuracy of 83.03% in a real environmental HCI dataset, which demonstrates that the proposed method achieves an encouraging performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.5
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• pp.938-958
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• 2011

Ubiquitous web services have been expanding in various business areas with the evolution of wireless Internet technologies, accessible and usable with a variety of mobile Internet devices such as smart phones. Ubiquitous mobile-web information services can be evaluated for accessibility and usability with the mobile Internet devices interacting with mobile-web information servers. In human mobile-web activity, a web server could be a unified center for mobile-web interaction services as well as for real-time estimation and analysis of mobile-web interaction sessions. We present a real-time estimation and analysis scheme for time-based accessibility and usability in ubiquitous mobile-web services. With real-time estimation/analysis of sessions in a mobile-web server, we estimated the time-based accessibility and usability for comparison between different web services as well as for applications in mobile cloud computing services. We present empirical results based on the implementation of the real-time estimation/analysis scheme.

• Journal of Multimedia Information System
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• v.3 no.2
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• pp.9-12
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• 2016

This paper is concerned with the effective and efficient identification of the gender and motion of humans. Tracking this nonverbal behavior is useful for providing clues about the interaction of different types of people and their exact motion. This system can also be useful for security in different places or for monitoring patients in hospital and many more applications. Here we describe a novel method of determining identity using machine learning with Microsoft Kinect. This method minimizes the fitting or overlapping error between an ellipsoid based skeleton.

• International Journal of Computer Science & Network Security
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• v.24 no.9
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• pp.202-206
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• 2024

Robust system may not guaranty its applicability and adaptability. That is why research and development go together in the modern research concept. In this paper we are going to examine the applicability and adaptability of gait-based biometric identity verification system especially in the GCC (Gulf Cooperation Council). The system itself closely involved with human interaction where privacy and personality are in concern. As of 1^st phase of our research we will establish gait-based identity verification system and then we will explain them in and out of human interaction with the system. With involved interaction we will conduct an extensive survey to find out both applicability and adoptability of the system. To conduct our experiment, we will use UCMG databased [1] which is readily available for the research community with more than three thousand video sequences in different viewpoint collected in various walking pattern and clothing. For the survey we will prepare questioners which will cover approach of data collection, potential traits to collect and possible consequences. For analyzing gait biometric trait, we will apply multivariate statistical classifier through well-known machine learning algorithms in a ready platform. Similarly, for the survey data analysis we will use similar approach to co-relate the user view point for such system. It will also help us to find the perception of the user for the system.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.616-621
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• 2006

Recently, the researches based on vision about user attention and action awareness are being pushed actively for human computer interaction. Among them, various applications of tabletop display system are developed more in accordance with touch sensing technique, co-located and collaborative work. Formerly, although supported only one user, support multi-user at present. Therefore, collaborative work and interaction of four elements (human, computer, displayed objects, physical objects) that is ultimate goal of tabletop display are realizable. Generally, tabletop display system designs according to four key aspects. 1)multi-touch interaction using bare hands. 2)implementation of collaborative work, simultaneous user interaction. 3)direct touch interaction. 4)use of physical objects as an interaction tool. In this paper, we describe a critical analysis of the state-of-the-art in advanced multi-touch sensing techniques for tabletop display system according to the four methods: vision based method, non-vision based method, top-down projection system and rear projection system. And we also discuss some problems and practical applications in the research field.

• Journal of the Ergonomics Society of Korea
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• v.31 no.1
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• pp.261-269
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• 2012

Objective: The aim of this study is to develop an analysis method to extract plausible types of errors when using a smart mobile in nuclear power plants. Background: Smart mobiles such as a smart-phone and a tablet computer(smart-pad) are to be introduced to the various industries. Nuclear power plant like APR1400 already adopted many up-to-date digital devices within its main control room. With this trend, various types of smart mobiles will be inevitably introduced to the nuclear field in the near future. However nuclear power plants(NPPs) should be managed considering a big risk as a result of the trend not only economically but also socially compared to the other industrial systems. It is formally required to make sure to reasonably prevent the all hazards due to the introduction of new technologies and devices before the application to the specific tasks in nuclear power plants. Method: We define interaction segments(IS) as a main architect of interaction description, and enumerate all plausible error segments(ES) for a part of design evaluation of digital devices. Results: We identify various types of interaction errors which are coped with reasonably by interaction design using smart mobiles. Conclusion: According to the application result of the proposed method, we conclude that the proposed method can be utilized to specify the requirements to the human error hazards in digital devices, and to conduct a human factors review during the design of digital devices. Application: The proposed method can be applied to predict the human errors of the tasks related to the digital devices; therefore we can ensure the safety to apply the digital devices to be introduced to NPPs.

• Journal of Contemporary Eastern Asia
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• v.20 no.1
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• pp.134-150
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• 2021

Dominant approaches to designing morally capable robots have been mainly based on rule-based ethical frameworks such as deontology and consequentialism. These approaches have encountered both philosophical and computational limitations. They often struggle to accommodate remarkably diverse, unstable, and complex contexts of human-robot interaction. Roboticists and philosophers have recently been exploring underrepresented ethical traditions such as virtuous, role-based, and relational ethical frameworks for designing morally capable robots. This paper employs the lens of ethical pluralism to examine the notion of role-based morality in the global context and discuss how such cross-cultural analysis of role ethics can inform the design of morally competent robots. In doing so, it first provides a concise introduction to ethical pluralism and how it has been employed as a method to interpret issues in computer and information ethics. Second, it reviews specific schools of thought in Western ethics that derive morality from role-based obligations. Third, it presents a more recent effort in Confucianism to reconceptualize Confucian ethics as a role-based ethic. This paper then compares the shared norms and irreducible differences between Western and Eastern approaches to role ethics. Finally, it discusses how such examination of pluralist views of role ethics across cultures can be conducive to the design of morally capable robots sensitive to diverse value systems in the global context.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.210-212
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• 2011

Human Computer Interaction is rapidly growing in different aspects and areas. One of the areas that grab many scholars and researchers interest is usability. Usability is a main factor and critical pillar of products success and acceptance. In this paper, we deeply analyze the current usability models that measure the user performance during the interaction with products. Then, we fairly compare between each model to discover and present the strength and weakness of each model for supporting developer and business organization with guidelines during the development process of the products before launching the actual and final one. Decomposing comparison between each model is performed based on specific criteria. Comparison is tabulated, graphically depicted, and analytically decomposed.

• Journal of the Ergonomics Society of Korea
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• v.30 no.6
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• pp.705-713
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• 2011

Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1652-1660
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• 2009

The main objective of this research is to build a behavior prediction model of gameplay for MMO (Massively Multiplayer Online) game using the GOMS analysis method. GOMS analysis is an observational approach to HCI(Human Computer Interaction) to model and predict behaviors of a human operator in a highly interactive task. In this research, a pilot experiment was previously conducted with three skilled gamers. The gamers were provided with the goals and operators through the user's guide book, and they found methods and selection rules while being observed. Based on the results obtained from the pilot study, this research was expanded and the model was further tested with 30 subjects (game experts). The new outcomes revealed that the relevance of GOMS analysis for predicting selection rules is 96.25% according to the degree of abstraction and 77.35% based on the degree of complexity. This research will provide game designers with a new testing mechanism in the early development stages, in order to improve the quality of the game product.