• Annual Conference on Human and Language Technology
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• 2022.10a
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• pp.135-140
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• 2022

최근 Document Retrieval을 비롯한 대부분의 자연어처리 분야에서는 BERT와 같이 self-attention을 기반으로 한 사전훈련 모델을 활용하여 SOTA(state-of-the-art)를 이루고 있다. 그러나 self-attention 메커니즘은 입력 텍스트 길이의 제곱에 비례하여 계산 복잡도가 증가하기 때문에, 해당 모델들은 선천적으로 입력 텍스트의 길이가 제한되는 한계점을 지닌다. Document Retrieval 분야에서는, 문서를 특정 토큰 길이 단위의 문단으로 나누어 각 문단의 유사 점수 또는 표현 벡터를 추출한 후 집계함으로서 길이 제한 문제를 해결하는 방법론이 하나의 주류를 이루고 있다. 그러나 논문, 특허와 같이 섹션 형식(초록, 결론 등)을 갖는 문서의 경우, 섹션 유형에 따라 고유한 정보 특성을 지닌다. 따라서 문서를 단순히 특정 길이의 문단으로 나누어 학습하는 PARADE와 같은 기존 방법론은 각 섹션이 지닌 특성을 반영하지 못한다는 한계점을 지닌다. 본 논문에서는 섹션 유형에 대한 정보를 포함하는 문단 표현을 학습한 후, 트랜스포머 인코더를 사용하여 집계함으로서, 결과적으로 섹션의 특징과 상호 정보를 학습할 수 있도록 하는 SERADE 모델을 제안하고자 한다. 실험 결과, PARADE-Transformer 모델과 비교하여 평균 3.8%의 성능 향상을 기록하였다.

• Asia pacific journal of information systems
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• v.30 no.4
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• pp.719-740
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• 2020

The stock market changes continuously as new information emerges, affecting the judgments of investors. Online news articles are valued as a traditional window to inform investors about various information that affects the stock market. This paper proposed new ways to utilize online news articles with technical indicators. The suggested hybrid model consists of three models. First, a self-attention-based convolutional neural network (CNN) model, considered to be better in interpreting the semantics of long texts, uses news content as inputs. Second, a self-attention-based, bi-long short-term memory (bi-LSTM) neural network model for short texts utilizes news titles as inputs. Third, a bi-LSTM model, considered to be better in analyzing context information and time-series models, uses 19 technical indicators as inputs. We used news articles from the previous day and technical indicators from the past seven days to predict the share price of the next day. An experiment was performed with Korean stock market data and news articles from 33 top companies over three years. Through this experiment, our proposed model showed better performance than previous approaches, which have mainly focused on news titles. This paper demonstrated that news titles and content should be treated in different ways for superior stock price prediction.

• Journal of Digital Convergence
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• v.15 no.3
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• pp.107-117
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• 2017

This study examined the conditional indirect effect of EEG (electroencephalogram) arousal on the relationship among affective valence, visual attention, perceived self-relevance, and attitudes toward campaign issues in the context of public service announcements (PSAs). Using SPSS macro (No. 14) of conditional process model, the findings in this current study indicated that the perceived self-relevance mediates the relationship between affective valence of PSA and attitudes toward issues and, in turn, is moderated by EEG arousal, indicating goodness-of-fit of the moderated mediation of psychophysiological arousal on PSAs. The results suggested that management of PSAs should be considered the strategic combination between affective valence and perceived self-relevance in advertising appeals.

• Korean Journal of Culture and Social Issue
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• v.30 no.1
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• pp.35-53
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• 2024

As dating violence is recently rising as one of the most serious social issues, the study examined the effect of internalized shame on controlling behavior manifested in dating relationship. We explored the mediation effect of self-absorption, indicating maladaptive self-focused attention, between each of the four sub-factors of internalized shame(inadequacy, emptiness, self punishment, and fear of mistake) and controlling behavior. Based on the data obtained from 200 single people in their 20-30s, it was revealed that the internalized shame, the self-absorption, and the controlling behavior in dating relationships were all positively correlated. The mediation effect of self-absorption was significant between the sub-factors of internalized shame (inadequacy, emptiness, self punishment, and fear of mistake) and controlling behavior. In other words, the higher the inadequacy, emptiness, self punishment, and fear of mistake, the bigger the self-absorption, and the more frequent the controlling behavior in dating relationship.

• The Journal of Asian Finance, Economics and Business
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• v.6 no.2
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• pp.147-160
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• 2019

Compared with the research on consumer engagement in brand community, the research on consumer engagement in brand public has been relatively less. This research aimed at exploring how brand public characteristics such as information variety, various communications and no limitation in expressing self affect the brand public engagement. 274 questionnaires answered by Chinese consumers are used to conduct analysis. Principal component analysis is used to test the reliability and validity of each construct, and structural equation model is used to test hypotheses. The study finds the positive effects of information variety on information benefits, those of various communications on social benefits, and also positive roles of no limitation in expressing self to brand-related self-expression motivation. And each of the information benefits, social benefits and brand-related self-expression motivation is proved to positively affect brand public engagement. The study implies that marketers should give attention to characteristics of brand public, and provide the ways by which members of brand public engage the brand. Additionally, marketers should pay more attention to both direct and indirect engagement activities of consumers toward brand public in social media to better understand their target consumers.

• Korean Journal of Child Studies
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• v.9 no.2
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• pp.95-117
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• 1988

The present research was designed to study the effect of gender differences and focus of attention (on sadness and on joy) in altruistic behavior. The subjects were 74 boys and 76 girls from a junior high school in Seoul. Emotion arousing and focus of attention identifying methods were used. Statistical analyses were with two-way ANOVA and $Scheff\acute{e}$ test. There were significant differences in altruisic behavior between the sadness self-oriented group and the sadness other-oriented group. The results were explained in terms of the accessibility of cognitive contents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4665-4683
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• 2019

Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.300-306
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• 2012

An image attention model and its application to image quality assessment are discussed in this paper. The attention model is based on rarity quantification, which is related to self-information to attract the attention in an image. It is relatively simpler than the others but results in taking more consideration of global contrasts between a pixel and the whole image. The visual attention model is used to develop a local distortion predictor, named color visual differences predictor (CVDP), in color images in order to effectively detect luminance and color distortions.

• Proceedings of the Polymer Society of Korea Conference
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• 2006.10a
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• pp.293-293
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• 2006

Self-assembled nanostructures of block copolymer thin films have gathered significant attention due to their potential applications as templates for nanofabrication. However the lack of a robust strategy to control the structure formation in thin film geometries has been considered a major obstacle for the practical application. In this presentation 'epitaxial self-assembly' will be introduced as a successful strategy to control the self-assembled nanostructure of block copolymer. Chemically patterned surfaces prepared by advanced lithographic techniques successfully registered nanodomains in block copolymer thin film without any single defect over an arbitrarily large area.

• The Journal of the Acoustical Society of Korea
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• v.39 no.5
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• pp.406-413
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• 2020

In this paper, to improve the classification accuracy of bird and amphibian acoustic sound, we utilize GLU (Gated Linear Unit) and Self-attention that encourages the network to extract important features from data and discriminate relevant important frames from all the input sequences for further performance improvement. To utilize acoustic data, we convert 1-D acoustic data to a log-Mel spectrogram. Subsequently, undesirable component such as background noise in the log-Mel spectrogram is reduced by GLU. Then, we employ the proposed temporal self-attention to improve classification accuracy. The data consist of 6-species of birds, 8-species of amphibians including endangered species in the natural environment. As a result, our proposed method is shown to achieve an accuracy of 91 % with bird data and 93 % with amphibian data. Overall, an improvement of about 6 % ~ 7 % accuracy in performance is achieved compared to the existing algorithms.