• Korea Journal of Hospital Management
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• v.11 no.4
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• pp.37-62
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• 2006

This study investigates the antecedents of knowledge sharing behavior focusing on the individual level factors in an assumption that the behavior is initiated from the individual level decisions. A hypothesis that the relation between personality trait and behavior is mediated by the social-cognitive constructs contained in the Theory of Planned Behavior(TPB) is tested. For the study, we suggest a TPB extended model that extends original TPB model by including conscientiousness facet of FFM(Five Factor Model). This study uses a cross-sectional design. Data were collected from a self-reported survey on 197 nurses in a tertiary hospital. The results showes a significant positive relationship between the conscientiousness facet of FFM and knowledge sharing behavior. In the TPB extended model, the conscientiousness facet has significant direct effects on all the constructs of original TPB model. Of the TPB exogenous constructs, the social norm construct alone has a significant effect on intention and the perceived behavioral has a direct significant effect on the knowledge sharing behavior. These results confirm the importance of conscientiousness in predicting knowledge sharing behavior and clarify the characteristics of knowledge sharing behavior as a contextual, job oriented behavior in a workplace. We conclude that personality trait as conceptualized in the FFM needs to be integrated into TPB model in explaining the knowledge sharing behavior. Based on these results theoretical and practical implications are discussed.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.842-857
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• 2023

Currently, most sentiment classification models on microblogging platforms analyze sentence parts of speech and emoticons without comprehending users' emotional inclinations and grasping moral nuances. This study proposes a hybrid sentiment analysis model. Given the distinct nature of microblog comments, the model employs a combined stop-word list and word2vec for word vectorization. To mitigate local information loss, the TextCNN model, devoid of pooling layers, is employed for local feature extraction, while BiLSTM is utilized for contextual feature extraction in deep learning. Subsequently, microblog comment sentiments are categorized using a classification layer. Given the binary classification task at the output layer and the numerous hidden layers within BiLSTM, the Tanh activation function is adopted in this model. Experimental findings demonstrate that the enhanced TextCNN-BiLSTM model attains a precision of 94.75%. This represents a 1.21%, 1.25%, and 1.25% enhancement in precision, recall, and F1 values, respectively, in comparison to the individual deep learning models TextCNN. Furthermore, it outperforms BiLSTM by 0.78%, 0.9%, and 0.9% in precision, recall, and F1 values.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.3
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• pp.632-638
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• 2009

Probabilistic Latent Semantic Analysis has many applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. In this paper, we propose an algorithm using weighted Probabilistic Latent Semantic Analysis Model to find the contextual phrases and opinions from documents. The traditional keyword search is unable to find the semantic relations of phrases, Overcoming these obstacles requires the development of techniques for automatically classifying semantic relations of phrases. Through experiments, we show that the proposed algorithm works well to discover semantic relations of phrases and presents the semantic relations of phrases to the vector-space model. The proposed algorithm is able to perform a variety of analyses, including such as document classification, online reputation, and collaborative recommendation.

• Asian review of World Histories
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• v.3 no.1
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• pp.113-135
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• 2015

The word 'urbanity' literally means 'quality or state of being urban' where the criterion of urban economic and civic culture is assumed despite the general celebration of cultural uniqueness of urban centers. The narratives celebrating the uniqueness of urban centers since the ancient past till recent times could not get rid of the broad categorization of the urban models depending on their contextual networks of trade, mobility and culture. This paper attempts to explore whether the urban cultures in South Asia even preceding a global phenomenon like colonialism were actually reflecting an idea of urbanity where the urban culture, including planning and architecture reflected a trans-national model. This paper particularly concentrates on the medieval period when a pattern of urbanity took shape in this subcontinent under the influence of Islam, which could be explained by its particular idea of urban model, cultural exchange and vibrant trade networks.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.305-319
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• 2003

This paper provides a non-Bayesian method based on the expanded EM algorithm for segmenting the magnetic resonance images degraded by bias field. For the images with the intensity as a pixel value, many segmentation methods often fail to segment it because of the bias field(with low frequency) as well as noise(with high frequency). Our contextual approach is appropriately designed by using normal mixture model incorporated with Markov random field for noise-corrective segmentation and by using the penalized likelihood to estimate bias field for efficient bias filed-correction.

• International Journal of Human Ecology
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• v.15 no.2
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• pp.69-80
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• 2014

Guided by the exchange model, altruistic model, intergenerational solidarity theory, and cultural contexts, this study explored the determinants of financial intergenerational transfers between older parents and adult children in South Korea. We examined 18,820 parent-child dyads by using random-effects models on the first wave of the Korean Longitudinal Study of Ageing (KLoSA) data. Findings showed that downward financial intergenerational transfers were consistent with the self-interest exchange model but upward transfers did not support microeconomic theories. Family solidarity theory was generally supported by downward transfers but geographical proximity was not positively associated with upward transfers. Lastly, cultural contextual variables such as marital status, birth order, and sex of a child were found to be significant. Parents tended to both provide and receive more financial support from unmarried children than from married children. Within the same marital status, the hierarchy existed in order of the first-born son, the second or later sons, and daughters when it came to downward financial transfers. Regarding upward financial transfers, the preference in order was more complicated. The findings of this study help in understanding the intergenerational financial transfers in the Korean context.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1345-1360
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• 2016

In this paper, we propose a new real-time human detection under omni-directional cameras for visual surveillance purpose, based on CNN with unified detection and AGMM. Compared to CNN-based state-of-the-art object detection methods. YOLO model-based object detection method boasts of very fast object detection, but with less accuracy. The proposed method adapts the unified detecting CNN of YOLO model so as to be intensified by the additional foreground contextual information obtained from pre-stage AGMM. Increased computational time incurred by additional AGMM processing is compensated by speed-up gain obtained from utilizing 2-D input data consisting of grey-level image data and foreground context information instead of 3-D color input data. Through various experiments, it is shown that the proposed method performs better with respect to accuracy and more robust to environment changes than YOLO model-based human detection method, but with the similar processing speeds to that of YOLO model-based one. Thus, it can be successfully employed for embedded surveillance application.

• ETRI Journal
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• v.43 no.2
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• pp.299-312
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• 2021

It is necessary to achieve high performance in the task of zero anaphora resolution (ZAR) for completely understanding the texts in Korean, Japanese, Chinese, and various other languages. Deep-learning-based models are being employed for building ZAR systems, owing to the success of deep learning in the recent years. However, the objective of building a high-quality ZAR system is far from being achieved even using these models. To enhance the current ZAR techniques, we fine-tuned a pretrained bidirectional encoder representations from transformers (BERT). Notably, BERT is a general language representation model that enables systems to utilize deep bidirectional contextual information in a natural language text. It extensively exploits the attention mechanism based upon the sequence-transduction model Transformer. In our model, classification is simultaneously performed for all the words in the input word sequence to decide whether each word can be an antecedent. We seek end-to-end learning by disallowing any use of hand-crafted or dependency-parsing features. Experimental results show that compared with other models, our approach can significantly improve the performance of ZAR.

• Journal of information and communication convergence engineering
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• v.22 no.1
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• pp.44-55
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• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• Spatial Information Research
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• v.22 no.3
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• pp.79-87
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• 2014

In order to foster rapid disaster response and public life protection, National Emergency Management Agency has been trying to spread 'Emergency Rescue Standard System' on a national scale since 2006. The agency has also intensified management of firefighter's safety on disaster site by implementing danger predication training, specialized training and education and safety procedure check as a part of safety management officer duties. Nevertheless, there are limitations for effective fire fighting steps, such as damage spreading and life damage due to unawareness of illegal converted structure, structure transformation by high temperature and nearby hazardous material storage as well as extemporary situation handling endangered firefighter's life. In order to eliminate these limitations there is a need for an effort and technology application to minimize human errors such as inaccurate situational awareness, wrong decision built on experience and judgment of field commander and firefighters. The purpose of this study is to propose a new disaster response model which is applied with geospatial information. we executed spatial contextual awareness map analysis using fire-fighting vulnerable zone model to propose the new disaster response model and also examined a case study for Dalseo-gu in Daegu Metropolitan City. Finally, we also suggested operational concept of new proposed model on a national scale.