• Korea Information Processing Society Review
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• v.11 no.6
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• pp.56-75
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• 2004

In this article we address some system characteristics and challenging issues in developing Context-aware Middleware for Ubiquitous Computing. The functionalities of a Context-aware Middleware includes gathering context data from hardware/software sensors, reasoning and inferring high-level context data, and disseminating/delivering appropriate context data to interested applications/services. The Middleware should facilitate the query, aggregation, and discovery for the contexts, as well as facilities to specify their privacy policy. Following a formal context model using ontology would enable syntactic and semantic interoperability, and knowledge sharing between different domains. Moddleware should also provide different kinds of context classification mechanical as pluggable modules, including rules written in different types of logic (first order logic, description logic, temporal/spatial logic, fuzzy logic, etc.) as well as machine-learning mechanical (supervised and unsupervised classifiers). Different mechanisms have different power, expressiveness and decidability properties, and system developers can choose the appropriate mechanism that best meets the reasoning requirements of each context. And finally, to promote the context-trigger actions in application level, it is important to provide a uniform and platform-independent interface for applications to express their need for different context data without knowing how that data is acquired. The action could involve adapting to the new environment, notifying the user, communicating with another device to exchange information, or performing any other task.

• Asia pacific journal of information systems
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• v.29 no.4
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• pp.838-855
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• 2019

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.239-245
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• 2020

Recently, BIM (Building Information Modeling) are widely being utilized in Construction industry. However, most structures that have been constructed in the past do not have BIM. For structures without BIM, the use of SfM (Structure from Motion) techniques in the 2D image obtained from the camera allows the generation of 3D model point cloud data and BIM to be established. However, since these generated point cloud data do not contain semantic information, it is necessary to manually classify what elements of the structure. Therefore, in this study, deep learning was applied to automate the process of classifying structural components. In the establishment of deep learning network, Inception-ResNet-v2 of CNN (Convolutional Neural Network) structure was used, and the components of bridge structure were learned through transfer learning. As a result of classifying components using the data collected to verify the developed system, the components of the bridge were classified with an accuracy of 96.13 %.

• Progress in Medical Physics
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• v.31 no.3
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• pp.111-123
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• 2020

Deep learning (DL) is a subset of machine learning and artificial intelligence that has a deep neural network with a structure similar to the human neural system and has been trained using big data. DL narrows the gap between data acquisition and meaningful interpretation without explicit programming. It has so far outperformed most classification and regression methods and can automatically learn data representations for specific tasks. The application areas of DL in radiation oncology include classification, semantic segmentation, object detection, image translation and generation, and image captioning. This article tries to understand what is the potential role of DL and what can be more achieved by utilizing it in radiation oncology. With the advances in DL, various studies contributing to the development of radiation oncology were investigated comprehensively. In this article, the radiation treatment process was divided into six consecutive stages as follows: patient assessment, simulation, target and organs-at-risk segmentation, treatment planning, quality assurance, and beam delivery in terms of workflow. Studies using DL were classified and organized according to each radiation treatment process. State-of-the-art studies were identified, and the clinical utilities of those researches were examined. The DL model could provide faster and more accurate solutions to problems faced by oncologists. While the effect of a data-driven approach on improving the quality of care for cancer patients is evidently clear, implementing these methods will require cultural changes at both the professional and institutional levels. We believe this paper will serve as a guide for both clinicians and medical physicists on issues that need to be addressed in time.

• The Journal of Information Systems
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• v.16 no.1
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• pp.201-230
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• 2007

Recently, the growth of e-Learning systems and its related information technology has presented a unique challenge for both schools and industry. It would make an extremely phenomenal paradigm shift in the educational method and practice. Methods of assessing the quality of e-teaming services and contents are critical issue in both practice and research. Moreover, many researchers are interested in what qualify factors more affect to the Performance of e-Learning service. Nevertheless, service quality is a construct that is difficult to define and measure. e-Learning services are composed of many factors, and they are more complicated than the traditional education services because they we performed on the distance basis and the many platforms of IT infrastructure. The purposes of our research are to classify the e-Learning service dimension and identify their factors, to develop the measurement of the factors, and finally to test empirically their relationship between the service factors and e-Learning service performance. For the development of the service factors we considered SERVQUAL model and SERVPERF model which were developed in the service marketing area. The SERVQUAL model was more fitted to the e-Learning services than the latter. From that we derived several factors that fit to our research domain, ie, tangibles, access, reliability, credibility, security, responsiveness, assurance, empathy. We combined three factors of them(reliability, credibility, security) into a factor, system stability for the semantic simplicity, and divided responsiveness factor into system operator responsiveness and teacher responsiveness as the entity based dimension classification. In the e-Learning services research, Most researcher are mentioned the quality factors of contents, so we added to two contents quality factors, ie, contents production method and richness of contents itself. We examined the relationship between the service quality factors and e-Learning performance(student satisfaction and service reuse intention). As result three quality factors(contents production method, teacher responsiveness, empathy) significantly affected student satisfaction. To the other performance variable, ie, service reuse intention, the teacher related quality factors(such as teacher responsiveness, assurance, empathy) affected only. In conclusion, even in the on-line distance teaming, the teacher's role md earnestness is as important as ever.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.600-608
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• 2017

Recently data growth rates are growing exponentially according to the rapid expansion of internet. Since users need some of all the information, they carry a heavy workload for examination and discovery of the necessary contents. Therefore information retrieval must provide hierarchical class information and the priority of examination through the evaluation of similarity on query and documents. In this paper we propose an Multi-class support vector machines model based clustering for hierarchical document categorization that make semantic search possible considering the word co-occurrence measures. A combination of hierarchical document categorization and SVM classifier gives high performance for analytical classification of web documents that increase exponentially according to extension of document hierarchy. More information retrieval systems are expected to use our proposed model in their developments and can perform a accurate and rapid information retrieval service.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.6
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• pp.411-417
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• 2020

To utilize building information modeling (BIM) technology at the bridge maintenance stage, it is necessary to integrate large quantities of bridge inspection and model data for object-oriented information management. This research aims to establish the benefits of utilizing the extended industry foundation class (IFC)-BIM and ontology for bridge inspection information management. The IFC entities were extended to represent the bridge objects, and a method of generating the extended IFC-based information model was proposed. The bridge inspection ontology was also developed by extraction and classification of inspection concepts from the AASHTO standard. The classified concepts and their relationships were mapped to the ontology based on the semantic triples approach. Finally, the extended IFC-based BIM model was integrated with the ontology for bridge inspection data management. The effectiveness of the proposed framework for bridge inspection information management by integration of the extended IFC-BIM and ontology was tested and verified by extracting bridge inspection data via the SPARQL query.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.339-346
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• 2018

An Industry Foundation Classes(IFC), which is a data schema developed focusing on architecture, is being expanded to civil engineering structures. However, it is difficult to create an information model based on extended IFC since the BIM software cannot provide support functions. To manage a railway track based on the extended IFC, this paper proposed a method to create an alignment-centered separated railway track model and convert it to an extended IFC-based information model. First, railway track elements have been classified into continuous and discontinuous structures. The continuous structures were created by an alignment-based software, and discontinuous structures were created as independent objects through linkage of the discretized alignment. Second, a classification system and extended IFC schema for railway track have been proposed. Finally, the semantic information was identified by using the property of classification code and user interface. The availability of the methods was verified by developing an extended IFC-based information model of the Osong railway site.

• The Journal of the Korea Contents Association
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• v.14 no.1
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• pp.364-373
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• 2014

The relationship between a service provided via the information system and user satisfaction has been thought of as an important factor for the development of a new service for the information system. In this study, the twelve new key services that are applicable to national R&D report system were derived by web environment changes in step with IT technology developments in order to support the new service for the user. The twelve new key services are as follows; semantic search service for national R&D report, associated report service, RSS service, mesh-up service, topic-map service, open API service, personalized service, collective intelligence service, SNS service, unstructured data service, detailed search service, mailing service. To assess the quality attribute of the twelve new key services in the national R&D report system, a survey was performed. In conclusion, a stepwise service plan for the national R&D report system was proposed which would use the satisfaction coefficient and the results of the service classification. The following step-by-step service should be developed by in this way. The unstructured data service, personalized service, associated report service, topic-map service, open API service, and the collective intelligence service are needed to develop the first step and RSS service, mesh-up service, semantic search service for the national R&D report, mailing service, detailed search service, and SNS service are needed to develop the second step.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.2866-2879
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• 2012

Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.