• Journal of The Korean Association For Science Education
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• v.38 no.5
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• pp.667-680
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• 2018

The purpose of this study is to investigate 1) the types of knowledge students ask for in their reading comprehension of science textbooks using an online annotation system, 2) the accuracy of the knowledge provided by the students to their peers, 3) the frequency of knowledge sharing behaviors, 4) the evaluation of the effect of collaborative reading, and 5) the trust among peers as knowledge sharers. Questions made by 241 students in the second grade of middle school using an online annotation system in two chapters of the science textbook were analyzed using Bloom's revised taxonomy and their answers were grouped according to five accuracy categories. Also, questionnaires for the evaluation of the effectiveness of collaborative reading comprehension and of trust among the students were used. The students asked their peers 'understanding questions' which comprised almost 80% of the total questions they made and were similar with individual metacognitive strategies for reading comprehension. Of the total threads, 71% has scientifically correct threads shared by the students. The frequency of the knowledge sharing behaviors was high but this was affected by the rewards (point system). Students evaluated that collaborative reading comprehension conducted through an online annotation system were helpful in their learning. In addition, the ratio of students trusting their peers who did the knowledge sharing is over 80%. This study shows that when students use an online annotation system, they can fill one another's cognitive gaps in the reading process by sharing knowledge. Also, collaborative reading using an online annotation system has proved that cognitive individualization is possible through sharing knowledge interactively and dynamically, unlike reading hard copies of textbooks which are a one way information transfer.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.8
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• pp.601-605
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• 2009

Recently, photo sharing and publishing based Social Network Sites(SNSs) are increasingly attracting the attention of academic and industry researches. Millions of users have integrated these sites into their daily practices to communicate with online people. In this paper, we propose an efficient face annotation and retrieval system under SNS. Since the system needs to deal with a huge database which consists of an increasing users and images, both effectiveness and efficiency are required, In order to deal with this problem, we propose a face annotation classifier which adopts an online learning and social decomposition approach. The proposed method is shown to have comparable accuracy and better efficiency than that of the widely used Support Vector Machine. Consequently, the proposed framework can reduce the user's tedious efforts to annotate face images and provides a fast response to millions of users.

• Journal of Korea Multimedia Society
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• v.12 no.10
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• pp.1486-1498
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• 2009

Modem efforts on digitizing electronic books focus on preserving authentic image representation of the original sources. Unlike the text-based format, it is difficult to recognize the information in the image, so the new format requires new tools to help users to access, process, and make sense of digital information. This paper presents an approach which assists users of these image sources by giving them a combination of annotation and social navigation support. Especially in the education domain, the proposed technique improves the usability of online education system. This approach is currently fully implemented and under evaluation in a classroom study.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.225-230
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• 2018

Recently, there are various employment-related homepages such as Jobkorea and World Job. However, since these websites have the one-to-one communication with corporate users and users, the amount of information that can be obtained from the viewpoint of users is limited. The proposed system improves this limitation, where a user creates a blog through member registration, uploads and creates a document to a blog, and a company official can perform an annotation function. This enables users to easily manage the desired documents, share information with other users, and affix them to the users who want to add to the company, so that the corporate users and users can communicate with each other. Thus, we believe that the proposed model can solve problems in the current job market and provide a new level of portfolio system for new media environment.

• ALGAE
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• v.35 no.3
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• pp.293-301
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• 2020

The applications of DNA barcoding have a wide range of uses, such as in taxonomic studies to help elucidate cryptic species and phylogenetic relationships and analyzing environmental samples for biodiversity monitoring and conservation assessments of species. After obtaining the DNA barcode sequences, sequence similarity-based homology analysis is commonly used. This means that the obtained barcode sequences are compared to the DNA barcode reference databases. This bioinformatic analysis necessarily implies that the overall quantity and quality of the reference databases must be stringently monitored to not have an adverse impact on the accuracy of species identification. With the development of next-generation sequencing techniques, a noticeably large number of DNA barcode sequences have been produced and are stored in online databases, but their degree of validity, accuracy, and reliability have not been extensively investigated. In this study, we investigated the extent to which the amount and types of erroneous barcode sequences were deposited in publicly accessible databases. Over 4.1 million sequences were investigated in three largescale DNA barcode databases (NCBI GenBank, Barcode of Life Data System [BOLD], and Protist Ribosomal Reference database [PR2]) for four major DNA barcodes (cytochrome c oxidase subunit 1 [COI], internal transcribed spacer [ITS], ribulose bisphosphate carboxylase large chain [rbcL], and 18S ribosomal RNA [18S rRNA]); approximately 2% of erroneous barcode sequences were found and their taxonomic distributions were uneven. Consequently, our present findings provide compelling evidence of data quality problems along with insufficient and unreliable annotation of taxonomic data in DNA barcode databases. Therefore, we suggest that if ambiguous taxa are presented during barcoding analysis, further validation with other DNA barcode loci or morphological characters should be mandated.