• Journal of Information Processing Systems
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• v.18 no.5
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• pp.628-636
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• 2022

Automatic program repair (APR) techniques focus on automatically repairing bugs in programs and providing correct patches for developers, which have been investigated for decades. However, most studies have limitations in repairing complex bugs. To overcome these limitations, we developed an approach that augments datasets by utilizing token tagging and applying machine learning techniques for APR. First, to alleviate the data insufficiency problem, we augmented datasets by extracting all the methods (buggy and non-buggy methods) in the program source code and conducting token tagging on non-buggy methods. Second, we fed the preprocessed code into the model as an input for training. Finally, we evaluated the performance of the proposed approach by comparing it with the baselines. The results show that the proposed approach is efficient for augmenting datasets using token tagging and is promising for APR.

• BMB Reports
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• v.57 no.1
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• pp.66-70
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• 2024

Prime editors (PEs), which are CRISPR-Cas9 nickase (H840A)-reverse transcriptase fusion proteins programmed with prime editing guide RNAs (pegRNAs), can not only edit bases but also install transversions, insertions, or deletions without both donor DNA and double-strand breaks at the target DNA. As the demand for in-locus tagging is increasing, to reflect gene expression dynamics influenced by endogenous genomic contexts, we demonstrated that PEs can be used to introduce the hemagglutinin (HA) epitope tag to a target gene locus, enabling molecular and biochemical studies using in-locus tagged plants. To promote genome-wide in-locus tagging, we also implemented a publicly available database that designs pegRNAs for in-locus tagging of all the Arabidopsis genes.

• Journal of Information Technology Services
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• v.12 no.1
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• pp.271-287
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• 2013

The rapid growth and dissemination of touch-based mobile devices such as smart phones and tablet PCs, gives numerous benefits to people using a variety of multimedia contents. Due to its portability, it enables users to watch a soccer game, search video from YouTube, and sometimes tag on contents on the road. However, the limited screen size of mobile devices and touch-based character input methods based on this, are still major problems of searching and tagging multimedia contents. In this paper, we propose WalkieTagging, which provides a much more intuitive way than that of previous one. Just like any other previous video tagging services, WalkieTagging, as a voice-based annotation service, supports inserting detailed annotation data including start time, duration, tags, with little effort of users. To evaluate our methods, we developed the Android-based WalkieTagging application and performed user study via a two-week. Through our experiments by a total of 46 people, we observed that experiment participator think our system is more convenient and useful than that of touch-based one. Consequently, we found out that voice-based annotation methods can provide users with much convenience and satisfaction than that of touch-based methods in the mobile environments.

• Journal of the Korean Society for information Management
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• v.34 no.3
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• pp.269-290
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• 2017

This study aims to present the framework for promoting active engagement of users in digital archives through social tagging. It analyzed the technological development involved with digital archives, and the user participation and engagement through social media. The analysis explored the aspects of social tagging in terms of communication, sharing and collaboration in digital archives. Based on the analysis and reviews, it developed the model of social tagging for user participation and interaction in digital archives. The study proposed the application of open and game platforms for promoting active engagement of users in digital archives through social tagging.

• Journal of Information Processing Systems
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• v.5 no.3
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• pp.159-166
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• 2009

As shown in Wikipedia, tagging or cross-linking through major keywords in a document collection improves not only the readability of documents but also responsive and adaptive navigation among related documents. In recent years, the Semantic Web has increased the importance of social tagging as a key feature of the Web 2.0 and, as its crucial phenotype, Tag Cloud has emerged to the public. In this paper we provide an efficient method of automated in-text keyword tagging based on large-scale controlled term collection or keyword dictionary, where the computational complexity of O(mN) - if a pattern matching algorithm is used - can be reduced to O(mlogN) - if an Information Retrieval technique is adopted - while m is the length of target document and N is the total number of candidate terms to be tagged. The result shows that automatic in-text tagging with keywords filtered by Information Retrieval speeds up to about 6 $\sim$ 40 times compared with the fastest pattern matching algorithm.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.63-76
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• 2012

We propose a Korean compound noun semantic tagging system using statistical compound noun decomposition and semantic relation information extracted from a lexical semantic network(U-WIN) and dictionary definitions. The system consists of three phases including compound noun decomposition, semantic constraint, and semantic tagging. In compound noun decomposition, best candidates are selected using noun location frequencies extracted from a Sejong corpus, and re-decomposes noun for semantic constraint and restores foreign nouns. The semantic constraints phase finds possible semantic combinations by using origin information in dictionary and Naive Bayes Classifier, in order to decrease the computation time and increase the accuracy of semantic tagging. The semantic tagging phase calculates the semantic similarity between decomposed nouns and decides the semantic tags. We have constructed 40,717 experimental compound nouns data set from Standard Korean Language Dictionary, which consists of more than 3 characters and is semantically tagged. From the experiments, the accuracy of compound noun decomposition is 99.26%, and the accuracy of semantic tagging is 95.38% respectively.

• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.225-239
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• 2010

This study aims to answer whether social tagging through user collaboration could be utilized for the creation of digital knowledge of the web, and whether we could verify the quality and efficacy of social tagging to obtain benefits from it. In particular, this paper examines the inter-indexer consistency of social tagging in comparison to professional indexing. It employs two different similarity measures, both of which are based on the Vector Space Model to deal with numerous indexers. It contributes to the utilization of social tagging in the organization of the web, and encourages to adopt social knowledge in developing suitable vocabularies for resources newly generated in the digital library environment. Furthermore, the comparative analysis with two different measures produced more credible results by illustrating a similar pattern of indexing tendency in both measures.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.1010-1014
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• 2010

Article-related product recommender system is an emerging e-commerce service which recommends items based on association in contexts between items and articles. Current services recommend based on the similarity between tags of articles and items, which is deficient not only due to the high cost in manual tagging but also low accuracies in recommendation. As a component of novel article-related item recommender system, we propose a new method for tagging item images based on pre-defined categories. We suggest a hypernetwork-based algorithm for learning association between images, which is represented by visual words, and categories of products. Learned hypernetwork are used to assign multiple tags to unlabeled item images. We show the ability of our method with a product set of real-world online shopping-mall including 1,251 product images with 10 categories. Experimental results not only show that the proposed method has competitive tagging performance compared with other classifiers but also present that the proposed multi-tagging method based on hypernetworks improves the accuracy of tagging.

• Journal of Korea Multimedia Society
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• v.15 no.2
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• pp.281-289
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• 2012

The goal of our research is to develop more effective bus information system using user generated information and social tagging. In this research, we have developed a smartphone-based bus guide system using social tagging and awareness of location. It will guide users to the nearby bus stops and provides users with information about the bus lines at the bus stops. Information around bus-stops can also be registered as tags into the system by users and can be utilized for bus information service. Simple keyword search utilizing tagging information can provide users with bus information about destinations.

• BMB Reports
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• v.28 no.4
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• pp.353-358
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• 1995

Epitope tagging is the process of fusing a set of amino acid residues that are recognized as an antigenic determinant to a protein of interest. Tagging a protein with an epitope facilitates various immunochemical analyses of the tagged protein with a specific monoclonal antibody. The monoclonal antibody H8 has subtype specificity for an epitope derived from the preS2 region of hepatitis B virus surface antigen. Previous studies on serial deletions of the preS2 region indicated that the preS2 epitope was located in amino acid residues 130~142. To test whether the amino acid sequence in this interval is sufficient to confer on proteins the antigenicity recognizable by the antibody H8, the set of amino acid residues in the interval was tagged to the amino terminal of ${\beta}$-galactosidase and to the carboxyl terminal of the truncated $p56^{lck}$ fragment. The tagged ${\beta}$-galactosidase, expressed in Escherichia coli, maintained the enzymatic activity and was immunoprecipitated efficiently with H8. The tagged $p56^{lck}$ fragment, synthesized in an in vitro translation system, was also immunoprecipitated specifically with H8. These results demonstrate that the amino acid sequence of the preS2 region can be used efficiently for the epitope tagging approach.