• Journal of Korea Multimedia Society
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• v.15 no.9
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• pp.1126-1132
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• 2012

To enhance network efficiency, CCN allow intermediate network nodes between a content consumer and a content publisher to temporarily cache transmitted contents. Then the network nodes immediately return back the cached contents to another consumers when the nodes receives relevant contents request messages from the consumers. For that, CCN utilizes hierarchical content names to forward a request message as well as a response message. However, such content names semantically contain much information about domain/user as well as content itself. So it is possible to invade users' privacy. In this paper, we first review both the problem of CCN name in the view point of privacy and proposed schemes. Then we propose an improved name management scheme for users' privacy preservation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.29-30
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• 2022

The unit-water content has a very significant effect on the durability of the construction structure and the quality of concrete. Although there are various methods for measuring the unit-water content, there are problems of time required for measurement, precision, and reproducibility. Recently, there is an FDR sensor capable of measuring moisture content in real time through an apparent dielectric constant change of electromagnetic waves. In addition, various artificial intelligence techniques that can non-linearly supplement the accuracy of FDR sensors are being studied. In this study, the accuracy of unit-water content measurement was compared and evaluated using machine learning and deep learning techniques after normalizing the data secured in concrete using frequency domain reflectometry (FDR) sensors used to measure soil moisture at home and abroad. The result of comparing the accuracy of machine learning and deep learning is judged to be excellent in the accuracy of deep learning, which can well express the nonlinear relationship between FDR sensor data and concrete unit-water content.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.107-113
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• 2016

Following the recent advancement in the use of social networks, a vast amount of different online reviews is created. These variable online reviews which provide feedback data of contents' are being used as sources of valuable information to both contents' users and providers. With the increasing importance of online reviews, studies on opinion mining which analyzes online reviews to extract opinions or evaluations, attitudes and emotions of the writer have been on the increase. However, previous sentiment analysis techniques of opinion-mining focus only on the classification of reviews into positive or negative classes but does not include detailed information analysis of the user's satisfaction or sentiment grounds. Also, previous designs of the sentiment analysis technique only applied to one content domain that is, either product or movie, and could not be applied to other contents from a different domain. This paper suggests a sentiment analysis technique that can analyze detailed satisfaction of online reviews and extract detailed information of the satisfaction level. The proposed technique can analyze not only one domain of contents but also a variety of contents that are not from the same domain. In addition, we design a system based on Hadoop to process vast amounts of data quickly and efficiently. Through our proposed system, both users and contents' providers will be able to receive feedback information more clearly and in detail. Consequently, potential users who will use the content can make effective decisions and contents' providers can quickly apply the users' responses when developing marketing strategy as opposed to the old methods of using surveys. Moreover, the system is expected to be used practically in various fields that require user comments.

• Journal of Korean Elementary Science Education
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• v.17 no.2
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• pp.55-65
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• 1998

This study was intended to suggest the desirable direction in the 7th national curriculum revision through the analysis of the elementary science textbooks in the 6th national curriculum. The analysis system was composed of three categories, (1)knowledge (2)inquiry process and (3)attitude. And knowledge was divided into fact, concept and rule. And inquiry process was divided into thirteen subcategories such as manipulating experimental apparatus, observing, measuring, recording data, classifying, interpreting/ predicting, determining relationship/ causal explanation, extrapolating/ interpolating, drawing conclusions/ formulating a generalization or model, evaluating, formulating a problem, generating a hypothesis and designing an experiment/ controlling variables. Each sentence in the textbooks was considered as an analyzing unit. The frequency and percentage of each category were counted and the ratios were calculated. The findings could be summarized as follows: 1. The content of the elementary science textbooks was composed of knowledge 10.3%, inquiry process 88.8%, attitude 0.8% respectively. 2. As increasing the grades, the ratio of knowledge showed high frequency, but that of attitude showed low frequency. 3. In All the grades, the ratio of observing was the highest in inquiry process. 4. In the domain of physics and chemistry, the manipulating experimental apparatus showed high frequency. In the domain of biology and earth science, the role of observing was emphasized.

• Journal of Internet Computing and Services
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• v.18 no.3
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• pp.49-61
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• 2017

Wikipedia infoboxes have emerged as an important structured information source on the web. To compose infobox for an article, considerable amount of manual effort is required from an author. Due to this manual involvement, infobox suffers from inconsistency, data heterogeneity, incompleteness, schema drift etc. Prior works attempted to solve those problems by generating infobox automatically based on the corresponding article text. However, there are many articles in Wikipedia that do not have enough text content to generate infobox. In this paper, we present an automated approach to generate infobox for movie domain of Wikipedia by extracting information from several sources of the web instead of relying on article text only. The proposed methodology has been developed using semantic relations of article content and available semi-structured information of the web. It processes the article text through some classification processes to identify the template from the large pool of template list. Finally, it extracts the information for the corresponding template attributes from web and thus generates infobox. Through a comprehensive experimental evaluation the proposed scheme was demonstrated as an effective and efficient approach to generate Wikipedia infobox.

• The Journal of the Korea Contents Association
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• v.10 no.6
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• pp.43-48
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• 2010

Useful emails influence on consumers' purchase behavior and activate them to visit retail stores. Regular contact with consumers by e-mail has positive effects on brand loyalty. However, email marketing has a limitation. Spam now accounts for over half of all e-mail traffic. The increase of email users has resulted in the dramatic increase of spam emails during the past few years. In this paper, we proposed an ontology-based system offering personalized email services to overcome such limitation. Our method is not the ontology-driven spam filtering, but a personalized content service considering personal interests and relations among people by using FOAF and domain ontologies. Our system was successfully tested in email marketing domain.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8351-8358
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• 2016

Electronic cleansing is an image post processing technique in which the tagged colonic content is subtracted from colon using CTC images. There are post processing artefacts, like: 1) soft tissue degradation; 2) incomplete cleansing; 3) misclassification of polyp due to pseudo enhanced voxels; and 4) pseudo soft tissue structures. The objective of the study was to subtract the tagged colonic content without losing the soft tissue structures. This paper proposes a novel adaptive method to solve the first three problems using a multi-step algorithm. It uses a new edge model-based method which involves colon segmentation, priori information of Hounsfield units (HU) of different colonic contents at specific tube voltages, subtracting the tagging materials, restoring the soft tissue structures based on selective HU, removing boundary between air-contrast, and applying a filter to clean minute particles due to improperly tagged endoluminal fluids which appear as noise. The main finding of the study was submerged soft tissue structures were absolutely preserved and the pseudo enhanced intensities were corrected without any artifact. The method was implemented with multithreading for parallel processing in a high performance computer. The technique was applied on a fecal tagged dataset (30 patients) where the tagging agent was not completely removed from colon. The results were then qualitatively validated by radiologists for any image processing artifacts.

• Journal of the Korean Ceramic Society
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• v.49 no.2
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• pp.185-190
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• 2012

The purpose of this study was to examine a possibility that fly ash could be used as raw material for carbonation by conducting the experiment on magnetic separation and hydration of fly ash that contained a large amount of CaO composite. Wet magnetic separation experiment was performed to remove the component of magnetic substance that contained fly ash, which aimed at increasing the content of CaO in the non-magnetic domain. The selected fly ash was used for hydration experiment before the TG-DTA, XRF and XRD analyses were made to confirm the Ca component that could be carbonated. Then, the fly ash was turned to a hydrate that was favorable to dissociation of $Ca^{2+}$ ion. As a result, the magnetic separation enabled detecting the content of CaO component by up to 61 wt% in the non-magnetic domain. Since the hydrate was confirmed, it is believed that the fly ash can be used as raw material for carbonation.

• Journal of Information Science Theory and Practice
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• v.3 no.1
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• pp.6-23
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• 2015

This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.

• Journal of Korean Elementary Science Education
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• v.32 no.4
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• pp.423-436
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• 2013

This research purports to analyze released items and G4 students' science achievement from TIMSS 2011 according to their academic achievement levels and gender. By doing so, it aims to draw educational implications for Korea from analyses results. Korea showed a lower rate of students at the advanced international benchmark - the highest achievement level - compared to Singapore. The difference was the smallest in Life Science among three content domains and knowing among three cognitive domains. The results of analysis according to gender showed that male students' achievement was significantly higher in Physical Science and Earth Science, and their achievement was also higher in the cognitive domains of Knowing and Applying. From the analysis of the released items, it was revealed that the students' achievement was low in items related to classification of organisms, functions of heart, matters that combust or emit light, and the concept of rotation. Moreover, students drew some illogical conclusions based on their personal experience. Male students were found to show high achievements in items that were not included in curriculum, constructed-response items in the form of short-answer questions, and multiple-choice items in the Knowing domain. Female students were found to show high achievement in items that were included in curriculum, constructed-response items that require reasons and methods, and items that represent experimental situations. Male students showed high achievement in forces concept and movements concept of bodies in the universe, while female students showed high achievement in solubility concept.