• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.12
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• pp.4275-4291
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• 2021

With the unprecedented growth of textual information on the Internet, an efficient automatic summarization system has become an urgent need. Recently, the neural network models based on the encoder-decoder with an attention mechanism have demonstrated powerful capabilities in the sentence summarization task. However, for paragraphs or longer document summarization, these models fail to mine the core information in the input text, which leads to information loss and repetitions. In this paper, we propose an abstractive document summarization method by applying guidance signals of key sentences to the encoder based on the hierarchical encoder-decoder architecture, denoted as KI-HABS. Specifically, we first train an extractor to extract key sentences in the input document by the hierarchical bidirectional GRU. Then, we encode the key sentences to the key information representation in the sentence level. Finally, we adopt key information representation guided selective encoding strategies to filter source information, which establishes a connection between the key sentences and the document. We use the CNN/Daily Mail and Gigaword datasets to evaluate our model. The experimental results demonstrate that our method generates more informative and concise summaries, achieving better performance than the competitive models.

• Anxiety and mood
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• v.19 no.1
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• pp.19-26
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• 2023

Objective : This study aimed to investigate the effect of a 5-minute short mindfulness intervention on emotional regulation and cognitive improvement compared to self-awareness intervention. Methods : A total of 40 participants were randomly assigned and divided into a mindfulness group and a self-aware group. Participants responded to Korean Version of Positive Affect and Negative Affect Schedule (K-PANAS), and Korean version of Toronto Mindfulness Scale (K-TMS) to confirm prior homogeneity. Both groups performed processing according to each group after completing sentences related to themselves for self-focusing. Afterwards, the participants performed the Emotional Attentional Blink (EAB) task as a behavioral measure, and responded to K-PANAS and K-TMS post hoc. Results : The mindfulness group showed lower negative emotions in the K-PANAS than the self-awareness group. The mindfulness group showed higher accuracy than the self-awareness group in negative stimuli presented in the 200 ms condition and neutral stimuli presented in the 800 ms condition. However, there was no difference between groups in K-TMS. Conclusion : The study suggests that mindfulness and self-awareness have different emotion regulation strategies in negative stimuli. Additionally, 5-minute mindfulness intervention was relatively beneficial to improve cognitive function.

• Journal of East-West Nursing Research
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• v.15 no.1
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• pp.18-25
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• 2009

Purpose: The study was investigated to identify the relationship between nursing workload and aseptic technique performance by clinical nurses, and to decrease the incidence rate of nosocomial infection. Methods: Participants (N=283) were recruited in B city from April to June 2007. The data were collected by a structured questionnaire and analyzed with descriptive statistics, t-test, ANOVA and Pearson's correlation coefficient. Results: Nursing workload was rated 9.85 out of a total score of 15. The level of aseptic technique performance as the basis for insertion of a Foley catheter was 42.72 out of a total score of 50, and as basis for insertion of intravenous catheter for fluid therapy was 40.11 out of a total score of 55. There was not a significant relationship between aseptic technique performance and nursing workload. There was a significant positive relationship between the aseptic technique performance in insertion of Foley catheter and that of intravenous catheter for fluid therapy (r=.279, p<.001). Conclusions: Attention to asepsis by nurses is crucial in nosocomial infection-related clinical nursing skills.

• Research in Community and Public Health Nursing
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• v.33 no.2
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• pp.139-152
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• 2022

Purpose: This study aims to describe and understand the meaning and nature of community health practitioners' coping with COVID-19. Methods: In-depth interviews were conducted with 12 community health practitioners from August to October 2021, to describe and understand the nature of their coping with COVID-19. The collected data were examined and described based on Colaizzi's phenomenological method. Results: 16 theme clusters and five categories were derived from the community health practitioners' experience of coping with COVID-19. The categories derived were: a war that began without notice, a variety of correspondence tasks assigned, struggling to fulfill given roles, correspondence tasks becoming more systematic, and a fight that has not yet ended. Conclusion: Community health practitioners handled various response tasks related to COVID-19, and faithfully fulfilled their professional roles while performing the primary task of medical care. Hence, attention is required on issues related to the establishment of the national emergency healthcare system and improvement of professional competence of community health practitioners even after the COVID-19 pandemic is over. Furthermore, it is necessary to make ceaseless efforts to address those issues and, in order to do so, social interest and institutional support are needed.

• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.37-48
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• 2022

Metabus, a three-dimensional virtual environment for digital communication, has gained a lot of attention recently. Even so, there are still many unanswered questions about the user's consciousness and behavior. A difference in nature from conventional digital communication is also unclear. This paper aims to study Metabus on a hypothetical premise by organizing the possibility of Metabus for communication activation based on a research project aimed at using Metabus to support the formation of a community of school dormitories and studio rental residents. After reviewing the establishment process and application cases of Metabus, we focus on the character (avatar) used as the user's alter ego within the metabus, which will allow customization of both form and content different from text-based communication. The "physicality" and the "spatiality" of the metaverse and the "immersion" they bring are among the most important, functional innovations. Based on this summary, a case of using metabuses will be reviewed to describe the research plan aimed at supporting community formation.

• Journal of IKEEE
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• v.26 no.1
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• pp.19-26
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• 2022

In this paper, we propose an algorithm that applies a different variable lambda to each loss value to solve the performance degradation caused by domain differences in LwF, and show that the retention of past knowledge is improved. The lambda value could be variably adjusted so that the current task to be learned could be well learned, by the variable lambda method of this paper. As a result of learning by this paper, the data accuracy improved by an average of 5% regardless of the scenario. And in particular, the performance of maintaining past knowledge, the goal of this paper, was improved by up to 70%, and the accuracy of past learning data increased by an average of 22% compared to the existing LwF.

• Smart Media Journal
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• v.12 no.1
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• pp.32-46
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• 2023

Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

• Annual Conference of KIPS
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• 2022.11a
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• pp.559-561
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• 2022

인공지능 시대에 들어서면서 개인 맞춤형 환경을 제공하기 위하여 사람의 감정을 인식하고 교감하는 기술이 많이 발전되고 있다. 사람의 감정을 인식하는 방법으로는 얼굴, 음성, 신체 동작, 생체 신호 등이 있지만 이 중 가장 직관적이면서도 쉽게 접할 수 있는 것은 표정이다. 따라서, 본 논문에서는 정확도 높은 얼굴 감정 식별을 위해서 Convolution Block Attention Module(CBAM)의 각 Gate와 Residual Block, Skip Connection을 이용한 Identity- CBAM Module을 제안한다. CBAM의 각 Gate와 Residual Block을 이용하여 각각의 표정에 대한 핵심 특징 정보들을 강조하여 Context 한 모델로 변화시켜주는 효과를 가지게 하였으며 Skip-Connection을 이용하여 기울기 소실 및 폭발에 강인하게 해주는 모듈을 제안한다. AI-HUB의 한국인 감정 인식을 위한 복합 영상 데이터 세트를 이용하여 총 6개의 클래스로 구분하였으며, F1-Score, Accuracy 기준으로 Identity-CBAM 모듈을 적용하였을 때 Vanilla ResNet50, ResNet101 대비 F1-Score 0.4~2.7%, Accuracy 0.18~2.03%의 성능 향상을 달성하였다. 또한, Guided Backpropagation과 Guided GradCam을 통해 시각화하였을 때 중요 특징점들을 더 세밀하게 표현하는 것을 확인하였다. 결과적으로 이미지 내 표정 분류 Task에서 Vanilla ResNet50, ResNet101을 사용하는 것보다 Identity-CBAM Module을 함께 사용하는 것이 더 적합함을 입증하였다.

• Journal of English Language & Literature
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• v.56 no.3
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• pp.445-459
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• 2010

William Carlos Williams discovers important sources of inspiration in the revolutionary avant-garde movements, in particular, Dada and Surrealism and attempted to embody the innovations in them in his poetic theory and practice. Williams's passion to create an indigenous American poetic work is compatible with his Dadaist experimentation with objets trouvés. Williams pays deep attention to objets trouvés, physical objects and marginalized people he comes across and transcribes his observations with poetic words freed from their instrumental contexts. In his characteristic poems written in the 1920s and 1930s, Williams records the social ruination and his task to give voice to the conflictual and fragmentary character of modernity is pursued through the Surrealist formulation of montage. In the Surrealist formulation of montage, the dialectical image is a central trope for reading the myth of modernity; it is positioned as both subject and object in the historiographic narratives of Walter Benjamin and Williams. As Benjamin tries to obliterate all traces of the author in the Arcades Project, Williams's montage poems like Spring and All only disperse argument into materialistic, dialectical images. The dialectical image in Williams's poetics becomes an organon of historical awakening so that truth can emerge from an unmediated juxtaposition of "things."

• Annual Conference of KIPS
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• 2023.05a
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• pp.647-649
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• 2023

Sentiment analysis as part of natural language processing (NLP) has received much attention following the demand to understand people's opinions. Aspect-based sentiment analysis (ABSA) is a fine-grained subtask from sentiment analysis that aims to classify sentiment at the aspect level. Throughout the years, researchers have formulated ABSA into various tasks for different scenarios. Unlike the early works, the current ABSA utilizes many elements to improve performance and provide more details to produce informative results. These ABSA formulations have provided greater challenges for researchers. However, it is difficult to explore ABSA's works due to the many different formulations, terms, and results. In this paper, we conduct a comparative analysis of recent studies on ABSA. We mention some key elements, problem formulations, and datasets currently utilized by most ABSA communities. Also, we conduct a short review of the latest papers to find the current state-of-the-art model. From our observations, we found that span-level representation is an important feature in solving the ABSA problem, while multi-task learning and generative approach look promising. Finally, we review some open challenges and further directions for ABSA research in the future.