• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.1068-1072
• /
• 2014

We propose a collaborative social tagging for eBook using external DSL approach. The goal of this paper is (1) to provide DSL by which authors can write HTML5 rich contents ebook and tag resources, (2) to make users enhance book by tagging resources easily, (3) to make readers read rich book easily regardless of their devices types, (4) to provide ebook resources of RESTful address style by which other system can identify self-descriptive resources of book. To achieve the goal, we provide Bukle DSL language by which author and users can author and enhance ebook with ease. As a domainspecific language Bukle provides a simple yet expressive language for authoring and tagging books that would otherwise be more difficult to express with a general purpose language. Further work includes visual DSL approach and tools by using that the unskilled users could tag book easily. In order that future work also includes text-to-visual DSL transform engine. UX research is also required to tag and to author book. To tackle the above questions we are looking at using visual notation focusing visual syntax.

• International Journal of Computer Science & Network Security
• /
• v.23 no.11
• /
• pp.93-98
• /
• 2023

Instagram is one of the fastest-growing online photo social web services where users share their life images and videos with other users. Image tagging is an essential step for developing Automatic Image Annotation (AIA) methods that are based on the learning by example paradigm. Hashtags can be used on just about any social media platform, but they're most popular on Twitter and Instagram. Using hashtags is essentially a way to group together conversations or content around a certain topic, making it easy for people to find content that interests them. Practically on average, 20% of the Instagram hashtags are related to the actual visual content of the image they accompany, i.e., they are descriptive hashtags, while there are many irrelevant hashtags, i.e., stophashtags, that are used across totally different images just for gathering clicks and for search ability enhancement. Hence in this work, Sorting instagram hashtags all the way through mass tagging using HITS (Hyperlink-Induced Topic Search) algorithm is presented. The hashtags can sorted to several groups according to Jensen-Shannon divergence between any two hashtags. This approach provides an effective and consistent way for finding pairs of Instagram images and hashtags, which lead to representative and noise-free training sets for content-based image retrieval. The HITS algorithm is first used to rank the annotators in terms of their effectiveness in the crowd tagging task and then to identify the right hashtags per image.

• Journal of KIISE
• /
• v.43 no.3
• /
• pp.362-369
• /
• 2016

This study is directed toward the design of a hybrid algorithm for syllable-based Korean POS tagging. Previous syllable-based works on Korean POS tagging have relied on a sequence labeling method and mostly used only a machine learning method. We present a new algorithm integrating a machine learning method and a pre-analyzed dictionary. We used a Sejong tagged corpus for training and evaluation. While the machine learning engine achieved eojeol precision of 0.964, the proposed hybrid engine achieved eojeol precision of 0.990. In a Quiz domain test, the machine learning engine and the proposed hybrid engine obtained 0.961 and 0.972, respectively. This result indicates our method to be effective for Korean POS tagging.

• Journal of Microbiology and Biotechnology
• /
• v.27 no.2
• /
• pp.357-364
• /
• 2017

In this study, we aimed to generate a series of versatile tagging plasmids that can be used in diverse molecular biological studies of the fungal pathogen Cryptococcus neoformans. We constructed 12 plasmids that can be used to tag a protein of interest with a GFP, mCherry, $4{\times}FLAG$, or $6{\times}HA$, along with nourseothricin-, neomycin-, or hygromycin-resistant selection markers. Using this tagging plasmid set, we explored the adenylyl cyclase complex (ACC), consisting of adenylyl cyclase (Cac1) and its associated protein Aca1, in the cAMP-signaling pathway, which is critical for the pathogenicity of C. neoformans. We found that Cac1-mCherry and Aca1-GFP were mainly colocalized as punctate forms in the cell membrane and non-nuclear cellular organelles. We also demonstrated that Cac1 and Aca1 interacted in vivo by co-immunoprecipitation, using $Cac1-6{\times}HA$ and $Aca1-4{\times}FLAG$ tagging strains. Bimolecular fluorescence complementation further confirmed the in vivo interaction of Cac1 and Aca1 in live cells. Finally, protein pull-down experiments using $aca1{\Delta}$::ACA1-GFP and $aca1{\Delta}$::ACA1-GFP $cac1{\Delta}$ strains and comparative mass spectrometry analysis identified Cac1 and a number of other novel ACC-interacting proteins. Thus, this versatile tagging plasmid system will facilitate diverse mechanistic studies in C. neoformans and further our understanding of its biology.

• Bulletin of the Korean Chemical Society
• /
• v.33 no.11
• /
• pp.3665-3670
• /
• 2012

Metal-p-$NH_2$-Bn-DOTA (paraammionobenzyl-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid: ABDOTA) complex was synthesized and purified for bio-tagging to quantify biological target materials using laser ablation (LA)-ICP-MS. Since the preparation of a pure and stable tagging complex is the key procedure for quantification, magnetic particles were used to purify the synthesized metal-ABDOTA complex. The magnetic particles immobilized with the complex attracted to a permanent magnet, resulting in fast separation from free un-reacted metal ions in solution. Gd ions formed the metal-complex with a higher yield of 64.3% (${\pm}3.9%$ relative standard deviation (RSD)) than Y ions, 52.3% (${\pm}2.5%$ RSD), in the pH range 4-7. The complex bound to the magnetic particles was released by treatment with a strong base, of which the recovery was 81.7%. As a reference, a solid phase extraction (SPE) column packed with Chelex-100 resin was employed for separation under similar conditions and produced comparable results. The tagging technique complemented polydimethylsiloxane (PDMS) microarray chip sampling in LA-ICP-MS, allowing determination of small sample volumes at high throughputs. For application, immunoglobulin G (IgG) was immobilized on the pillars of PDMS microarray chips and then tagged with the prepared Gd complex. IgG could then be determined through measurement of Gd by LA-ICP-MS. A detection limit of 1.61 ng/mL (${\pm}0.75%$ RSD) for Gd was obtained.

• KIPS Transactions on Software and Data Engineering
• /
• v.5 no.9
• /
• pp.419-424
• /
• 2016

Named entity recognition is required to improve the retrieval accuracy of patent documents or similar patents in the claims and patent descriptions. In this paper, we proposed an automatic named entity recognition for patents by using a conditional random field that is one of the best methods in machine learning research. Named entity recognition system has been constructed from the training set of tagged corpus with 660,000 words and 70,000 words are used as a test set for evaluation. The experiment shows that the accuracy is 93.6% and the Kappa coefficient is 0.67 between manual tagging and automatic tagging system. This figure is better than the Kappa coefficient 0.6 for manually tagged results and it shows that automatic named entity tagging system can be used as a practical tagging for patent documents in replacement of a manual tagging.

• Journal of Digital Convergence
• /
• v.17 no.12
• /
• pp.243-248
• /
• 2019

Multi-Task Learning(MTL) is a training method that trains a single neural network with multiple tasks influences each other. In this paper, we compare performance of MTL Named entity recognition(NER) model trained with Korean traditional culture corpus and other NER model. In training process, each Bi-LSTM layer of Part of speech tagging(POS-tagging) and NER are propagated from a Bi-LSTM layer to obtain the joint loss. As a result, the MTL based Bi-LSTM model shows 1.1%~4.6% performance improvement compared to single Bi-LSTM models.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.29 no.3
• /
• pp.297-320
• /
• 2018

This study aims to improve the access and retrieval of images and to find a way to effectively generate tags as a tool for providing explanation of images. To do this, this study investigated the features of human tagging and machine tagging, and compare and analyze them. Machine tags had the highest general attributes, some specific attributes and visual elements, and few abstract attributes. The general attribute of the human tag was the highest, but the specific attribute was high for the object and scene where the human tag constructor can recognize the name. In addition, sentiments and emotions, as well as subjects of abstract concepts, events, places, time, and relationships are represented by various tags. The tag set generated through this study can be used as basic data for constructing training data set to improve the machine learning algorithm.

• Korean Journal of Ecology and Environment
• /
• v.42 no.1
• /
• pp.134-138
• /
• 2009

The survival rate of PIT tagged fish was investigated for five Korean Cyprindae fish species, Acheilognathus lanceolatus (n=18, total length : $92.5{\pm}13.1mm$; body weight : $9.6{\pm}4.3g$), Hemibarbus labeo (n=28, TL : $220{\pm}74.4mm$; BW : $91.8{\pm}76.2g$), Zacco koreanus (n=13, TL : $116.5{\pm}23.8mm$; BW : $13.6{\pm}10.6g$), Zacco platypus (n=108, TL : $100.6{\pm}17.8mm$; BW: $8.7{\pm}4.8g$), Opsariichthys uncirostris amurensis (n=6, TL : $161.8{\pm}26.3mm$; BW : $27.5{\pm}18.3g$) with respect to applicability and effectiveness of PIT tagging. The survival rate were daily checked for 30 days. The survival rate was the highest and lowest for Z. koreanus and Z. platypus, respectively. The survival days were greater as fish total length increased. Based on these results, PIT tagging is not effective for Z. platypus, while PIT tagging for fish>150 mm was effect for field research.

• Mass Spectrometry Letters
• /
• v.6 no.4
• /
• pp.85-90
• /
• 2015

This article reviews recent analytical techniques using inductively coupled plasma-mass spectrometry (ICP-MS) immunoassay for clinical and bio analysis. We classified the techniques into two categories, direct and indirect analysis, which depend upon a guideline of whether tagging materials are used or not. Direct analysis is well known, and generally used in conjunction with various other techniques, such as laser ablation, chromatographic separations, etc. Recently, indirect analysis using tagging elements has intensively been discussed because of its importance in future applications to bio and clinical analysis, including environmental and food industries. The method has shown advantages of multiplex detection, excellent sensitivity, and short analysis time owing to signal amplification and magnetic separation. Now, it expands the application field from small biomolecules to large cells.