• Journal of Korean Library and Information Science Society
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• v.43 no.1
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• pp.221-239
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• 2012

The information that has been created according to the complex environment and usage pattern of library user can provide context-aware information service through knowledge structuralization on whether it is a suitable situation for user. Accordingly, the development of a context model for defining the various contexts of library user and for the structuralization of interrelated context information is an essential requirement. This study examined the context concept and context modeling, and utilizing the concept of Activity Theory by Engestrom, the activity model of library user was designed as 1) subject, 2) object, 3) tools, 4) divison of labor, 5) community, and 6) rules. In addition, for the purpose of analyzing the context of library user, activity information was tracked to utilize the Shadow Tracking for observing and recording their forms, and the methodology of CAbAT (Context Analysis based on Activity Theory) was utilized for the collected activity information to analyze the user context model.

• Journal of the HCI Society of Korea
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• v.8 no.1
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• pp.11-17
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• 2013

Context aware systems are those systems that are aware about the environment and perform productive functions automatically by reducing human computer interactions(HCI). In this paper, we present common architecture principles of context-aware systems to explain the important aspects of context aware systems. Our study focuses on identifying common concepts in pervasive computing approaches, which allows us to devise common architecture principles that may be shared by many systems. The principles consists of context sensing, context modeling, context reasoning, context processing, communication modelling and resource discovery. Such an architecture style can support high degree of reusability among systems and allows for design flexibility, extensibility and adaptability among components that are independent of each other. We also propose a new architecture based on broker-centric middleware and using ontology reasoning mechanism together with an effective behavior based context agent that would be suitable for the design of context-aware architectures in future systems. We have evaluated the proposed architecture based on the design principles and have done an analyses on the different elements in context aware computing based on the presented system.

• Journal of Electrical Engineering and Technology
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• v.7 no.1
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• pp.115-123
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• 2012

A video analysis system used to detect events in video streams generally has several processes, including object detection, object trajectories analysis, and recognition of the trajectories by comparison with an a priori trained model. However, these processes do not work well in a complex environment that has many occlusions, mirror effects, and/or shadow effects. We propose a new approach to a context-aware video surveillance system to detect predefined contexts in video streams. The proposed system consists of two modules: a feature extractor and a context recognizer. The feature extractor calculates the moving energy that represents the amount of moving objects in a video stream and the stationary energy that represents the amount of still objects in a video stream. We represent situations and events as motion changes and stationary energy in video streams. The context recognizer determines whether predefined contexts are included in video streams using the extracted moving and stationary energies from a feature extractor. To train each context model and recognize predefined contexts in video streams, we propose and use a new ensemble classifier based on the AdaBoost algorithm, DAdaBoost, which is one of the most famous ensemble classifier algorithms. Our proposed approach is expected to be a robust method in more complex environments that have a mirror effect and/or a shadow effect.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.73-88
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• 2022

This exploratory study aims to understand the potential of log data analysis and expand its utilization in user research methods. Transaction log data are records of electronic interactions that have occurred between users and web services, reflecting information-seeking behavior in the context of digital libraries where users interact with the service system during the search for information. Two ways were used to analyze South Korea's National Digital Science Library (NDSL) log data for three days, including 150,000 data: a log pattern analysis, and log context analysis using statistics. First, a pattern-based analysis examined the general paths of usage by logged and unlogged users. The correlation between paths was analyzed through a χ² analysis. The subsequent log context analysis assessed 30 identified users' data using basic statistics and visualized the individual user information-seeking behavior while accessing NDSL. The visualization shows included 30 diverse paths for 30 cases. Log analysis provided insight into general and individual user information-seeking behavior. The results of log analysis can enhance the understanding of user actions. Therefore, it can be utilized as the basic data to improve the design of services and systems in the digital library to meet users' needs.

• Journal of Distribution Science
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• v.20 no.8
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• pp.123-133
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• 2022

Purpose: As moment-based marketing messages (i.e., messages related to current moments or event), companies put significant investments to distribute TV advertising related to external moments in a retail environment. While the literature offers strong support for the value of distributions of context-based messaging to advertisers, less attention has been given to how to design those messages to effectively communicate across channels. This research adds a new dimension of analysis to the study of advertising context and its cross-channel effects on online consumer behavior. Research Design, Data and Methodology: A system-of-equations Tobit regression model was adopted using data collected from an advertising agency that consists of 1,223 TV ads aired during the Rio Olympics and NCAA, tagging from consumers, and a text analysis. Results: First, TV ads with high centrality of context lead to lower online search behavior and higher online social actions. Second, how brands can design messages more effectively was explored by using product information as a moderator that could improve the impact of context-based TV advertisements. Conclusions: Given that expenses in traditional channels are still one of the biggest channel management decisions, it is critical to understand how consumer engagement varies by design of context-based TV advertising.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.5
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• pp.655-660
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• 2009

The context prediction algorithms are not suitable to provide real-time personalized service for users in context-awareness environment. The algorithms have problems like time delay in training data processing and the difficulties of implementation in real-time environment. In this paper, we propose a prediction algorithm with user modeling to shorten of processing time and to improve the prediction accuracy in the context prediction algorithm. The algorithm uses moving path of user contexts for context prediction and generates user model by time-series analysis of user's moving path. And that predicts the user context with the user model by sequence matching method. We compared our algorithms with the prediction algorithms by processing time and prediction accuracy. As the result, the prediction accuracy of our algorithm is similar to the prediction algorithms, and processing time is reduced by 40% in real time service environment.

• Proceedings of the Korea Society of Design Studies Conference
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• 2004.05a
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• pp.296-297
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• 2004

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.769-776
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• 1987

A method of on-line recognition of Korean characters (Hangeul) by augmented conterxt free grammar is described in this paper. Syntactic analysis with context free grammar oftern has ambiguity. Insufficient description of relations among Hangrul sub-patterns causes this ambiguity can be determined through repetition of experiments. Flexible syntactic analysis is executed by adapting the condition to the (advice)part of augmented context free grammar. The ratio of correct recognition of this method is more than 99%.

• The Journal of Information Systems
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• v.12 no.1
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• pp.19-45
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• 2003

The ERP system comes to existence in the period of change. In other words, the ERP system is adopted to the company with BPR project. In general, the ERP system is introduced to companies as packages rather than in-housing systems. There are several researches in IS literatures which have a conclusion that organizational context affects the performance of ERP system. This study attempts to review the organizational context from the prospective of organizational structure and change management. Regarding the organizational structure, we choose some widely known variables such as the degree of formalization and centralization. For the analysis of change management, we set the variables, which are the most importantly considered, such as the power of CEO's promotion and user participation. The extent of customizing is introduced as moderating variables between the organizational context and the implementation performance. With collected data, we performed the reliability test, the factor analysis and the regression analysis. In summary, the introduction of an information system such as ERP system has a behavioral and organizational impact. The organizational change may breed resistance and opposition and can lead to the failure of the system. Therefore, implementation of the system requires careful change management, active involvement of users and high level of management support.

• Journal of Information Processing Systems
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• v.18 no.5
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• pp.614-627
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• 2022

Aspect-based sentiment analysis is to discover the sentiment polarity towards an aspect from user-generated natural language. So far, most of the methods only use the implicit position information of the aspect in the context, instead of directly utilizing the position relationship between the aspect and the sentiment terms. In fact, neighboring words of the aspect terms should be given more attention than other words in the context. This paper studies the influence of different position embedding methods on the sentimental polarities of given aspects, and proposes a position embedding interactive attention network based on a long short-term memory network. Firstly, it uses the position information of the context simultaneously in the input layer and the attention layer. Secondly, it mines the importance of different context words for the aspect with the interactive attention mechanism. Finally, it generates a valid representation of the aspect and the context for sentiment classification. The model which has been posed was evaluated on the datasets of the Semantic Evaluation 2014. Compared with other baseline models, the accuracy of our model increases by about 2% on the restaurant dataset and 1% on the laptop dataset.