• Journal of Plant Biotechnology
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• v.42 no.3
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• pp.168-179
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• 2015

We previously identified the rice gene, OsSAP, as an encoder of a highly conserved putative senescence-associated protein that was shown to have anti-apoptotic activity. To confirm the role of OsSAP in inducing abiotic stress tolerance in rice, we introduced OsSAP and AtBI-1, a plant homologue of Bax inhibitor-1, under the control of the CaMV 35S promoter into the rice genome through Agrobacterium-mediated transformation. The OsSAP transformants showed a similar chlorophyll index after salinity treatments with AtBI-1. Furthermore, we compared the effects of salinity stress on leaves and roots by examining the hormone levels of abscisic acid (ABA), jasmonic acid (JA), gibberellic acid (GA3), and zeatin in transformants compared to the control. With the exception of phytohormones, stress-induced changes in hormone levels putatively related to stress tolerance have not been investigated previously. Hormonal level analysis confirmed the lower rate of stress in the transformants compared to the control. The levels of ABA and JA in OsSAP and AtBI-1 transformants were similar, where stress rates increased after one week and decreased after a two week period of drought; there was a slightly higher accumulation compared to the control. However, a similar trend was not observed for the level of zeatin, as the decrease in the level of zeatin accumulation differed in both OsSAP and AtBI-1 transformants for all genotypes during the early period of salinity stress. The GA3 level was detected under normal conditions, but not under salinity stress.

• Journal of Korea Multimedia Society
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• v.22 no.11
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• pp.1223-1232
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• 2019

The injection is the input method of the image feature vector from the encoder to the decoder. Since the image feature vector contains object details such as color and texture, it is essential to generate image captions. However, the bidirectional decoder model using the existing injection method only inputs the image feature vector in the first step, so image feature vectors of the backward sequence are vanishing. This problem makes it difficult to describe the context in detail. Therefore, in this paper, we propose the parallel injection method to improve the description performance of image captions. The proposed Injection method fuses all embeddings and image vectors to preserve the context. Also, We optimize our image caption model with Bidirectional Gated Recurrent Unit (Bi-GRU) to reduce the amount of computation of the decoder. To validate the proposed model, experiments were conducted with a certified image caption dataset, demonstrating excellence in comparison with the latest models using BLEU and METEOR scores. The proposed model improved the BLEU score up to 20.2 points and the METEOR score up to 3.65 points compared to the existing caption model.

• Journal of Broadcast Engineering
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• v.24 no.3
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• pp.463-471
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• 2019

This paper proposes a method for reducing the complexity of LIC (Local Illuminance Compensation) for bi-directional inter prediction. The LIC performs local illumination compensation using neighboring reconstruction samples of the current block and the reference block to improve the accuracy of the inter prediction. Since the weight and offset required for local illumination compensation are calculated at both sides of the encoder and decoder using the reconstructed samples, there is an advantage that the coding efficiency is improved without signaling any information. Since the weight and the offset are obtained in the encoding prediction step and the decoding step, encoder and decoder complexity are increased. This paper proposes two methods for low complexity LIC. The first method is a method of applying illumination compensation with offset only in bi-directional prediction, and the second is a method of applying LIC after weighted average step of reference block obtained by bidirectional prediction. To evaluate the performance of the proposed method, BD-rate is compared with BMS-2.0.1 using B, C, and D classes of MPEG standard experimental image under RA (Random Access) condition. Experimental results show that the proposed method reduces the average of 0.29%, 0.23%, 0.04% for Y, U, and V in terms of BD-rate performance compared to BMS-2.0.1 and encoding/decoding time is almost same. Although the BD-rate was lost, the calculation complexity of the LIC was greatly reduced as the multiplication operation was removed and the addition operation was halved in the LIC parameter derivation process.

• KIISE Transactions on Computing Practices
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• v.24 no.3
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• pp.138-144
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• 2018

A video is a vivid medium similar to human's visual-linguistic experiences, since it can inculcate a sequence of situations, actions or dialogues that can be told as a story. In this study, we propose story learning/regeneration frameworks from videos with successive event order supervision for contextual coherence. The supervision induces each episode to have a form of trajectory in the latent space, which constructs a composite representation of ordering and semantics. In this study, we incorporated the use of kids videos as a training data. Some of the advantages associated with the kids videos include omnibus style, simple/explicit storyline in short, chronological narrative order, and relatively limited number of characters and spatial environments. We build the encoder-decoder structure with successive event order embedding, and train bi-directional LSTMs as sequence models considering multi-step sequence prediction. Using a series of approximately 200 episodes of kids videos named 'Pororo the Little Penguin', we give empirical results for story regeneration tasks and SEOE. In addition, each episode shows a trajectory-like shape on the latent space of the model, which gives the geometric information for the sequence models.

• Journal of Broadcast Engineering
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• v.25 no.3
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• pp.346-352
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• 2020

Versatile Video Coding (VVC), a next-generation video coding standard that is in the final stage of standardization, has adopted various techniques to achieve more than twice the compression performance of HEVC (High-Efficiency Video Coding). VVC adopted Bi-prediction with CU-level Weight (BCW), which generates the final prediction signal with the weighted combination of bi-predictions with various weights, to enhance the performance of the bi-predictive inter prediction. The syntax element of the BCW index is adaptively coded according to the value of NoBackwardPredFlag which indicates if there is no future picture in the display order among the reference pictures. Such syntax structure for signaling the BCW index could violate the flexibility of video codec and cause the dependency issue at the stage of bitstream parsing. To address these issues, this paper proposes an efficient BCW weight signaling method which enables all weights and parsing without any condition check. The performance of the proposed method was evaluated with various weight searching methods in the encoder. The experimental results show that the proposed method gives negligible BD-rate losses and minor gains for 3 weights searching and 5 weights searching, respectively, while resolving the issues.

• Smart Media Journal
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• v.11 no.10
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• pp.65-75
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• 2022

Deep learning is used as a creative tool that could overcome the limitations of existing analysis models and generate various types of results such as text, image, and music. In this paper, we propose a method necessary to preprocess audio data using the Niko's MIDI Pack sound source file as a data set and to generate music using Bi-LSTM. Based on the generated root note, the hidden layers are composed of multi-layers to create a new note suitable for the musical composition, and an attention mechanism is applied to the output gate of the decoder to apply the weight of the factors that affect the data input from the encoder. Setting variables such as loss function and optimization method are applied as parameters for improving the LSTM model. The proposed model is a multi-channel Bi-LSTM with attention that applies notes pitch generated from separating treble clef and bass clef, length of notes, rests, length of rests, and chords to improve the efficiency and prediction of MIDI deep learning process. The results of the learning generate a sound that matches the development of music scale distinct from noise, and we are aiming to contribute to generating a harmonistic stable music.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.820-822
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• 2019

개체명이란, 문서에서 특정한 의미를 가지고 있는 단어나 어구를 뜻하는 말로 사람, 기관명, 지역명, 날짜, 시간 등이 있으며 이 개체명을 찾아서 해당하는 의미의 범주를 결정하는 것을 개체명 인식이라고 한다. 본 논문에서는 BERT(Bidirectional Encoder Representations from Transformers) 활용한 한국어 개체명 인식기를 제안한다. 제안하는 모델은 기 학습된 BERT 모델을 활용함으로써 성능을 극대화하여, 최종 F1-Score 는 90.62 를 달성하였고, Bi-LSTM-Attention-CRF 모델에 비해 매우 뛰어난 결과를 보였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.3
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• pp.571-576
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• 2024

Ontologies are created by domain experts, but the same content may be expressed differently by each expert due to different understandings of domain knowledge. Since the ontology standardization is still lacking, multiple ontologies can be exist within the same domain, resulting in a phenomenon called the ontology heterogeneity. Therefore, we propose a novel ontology matching method that combines SCBOW(: Siames Continuois Bag Of Words) and BERT(: Bidirectional Encoder Representations from Transformers) models to solve the ontology heterogeneity issue. Ontologies are expressed as a graph and the SimRank algorithm is used to solve the one-to-many problem that can occur in ontology matching problems. Experimental results showed that our approach improves performance by about 8% over traditional matching algorithm. Proposed method can enhance and refine the alignment technology used in ontology matching.

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.242-248
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• 2022

The article presents the analysis of modern methods of automatic comparison of original and unoriginal text to detect textual plagiarism. The study covers two types of plagiarism - literal, when plagiarists directly make exact copying of the text without changing anything, and intelligent, using more sophisticated techniques, which are harder to detect due to the text manipulation, like words and signs replacement. Standard techniques related to extrinsic detection are string-based, vector space and semantic-based. The first, most common and most successful target models for detecting literal plagiarism - N-gram and Vector Space are analyzed, and their advantages and disadvantages are evaluated. The most effective target models that allow detecting intelligent plagiarism, particularly identifying paraphrases by measuring the semantic similarity of short components of the text, are investigated. Models using neural network architecture and based on natural language sentence matching approaches such as Densely Interactive Inference Network (DIIN), Bilateral Multi-Perspective Matching (BiMPM) and Bidirectional Encoder Representations from Transformers (BERT) and its family of models are considered. The progress in improving plagiarism detection systems, techniques and related models is summarized. Relevant and urgent problems that remain unresolved in detecting intelligent plagiarism - effective recognition of unoriginal ideas and qualitatively paraphrased text - are outlined.