• KIPS Transactions on Software and Data Engineering
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• v.10 no.1
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• pp.1-8
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• 2021

In the political field of news articles, there are polarized and biased characteristics such as conservative and liberal, which is called political bias. We constructed keyword-based dataset to classify bias of news articles. Most embedding researches represent a sentence with sequence of morphemes. In our work, we expect that the number of unknown tokens will be reduced if the sentences are constituted by subwords that are segmented by the language model. We propose a document embedding model with subword tokenization and apply this model to SVM and feedforward neural network structure to classify the political bias. As a result of comparing the performance of the document embedding model with morphological analysis, the document embedding model with subwords showed the highest accuracy at 78.22%. It was confirmed that the number of unknown tokens was reduced by subword tokenization. Using the best performance embedding model in our bias classification task, we extract the keywords based on politicians. The bias of keywords was verified by the average similarity with the vector of politicians from each political tendency.

• Korean Journal of Cognitive Science
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• v.32 no.2
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• pp.55-73
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• 2021

Psychological essentialism states that people believe some categories to have hidden and defining essential features which cause other features of the category (Gelman, 2003; Hirschfeld, 1996; Medin & Ortony, 1989). Essentialist belief on categories questions the Roschian argument (Rosch, 1973, 1978) that categories merely consist of clusters of correlated features. Unlike family resemblance categories, essentialized categories are likely to have clear between-category boundaries and high within-category coherence (Gelman, 2003; Prentice & Miller, 2007). Two experiments were conducted to test the effects of essentialist belief on category representation (i.e., between-category boundary, within-category coherence). Participants learned family resemblance and essentialized categories in their assigned conditions and then performed categorization task (Expt. 1) and frequency estimation task of category exemplars (Expt. 2). The results showed, in essentialized categories, both boundary intensification and greater category coherence. Theses results are likely to have arisen due to increased cue and category validity in essentialized categories and suggest that essentialist belief influences macroscopic representation of category structure.

• 기호학연구
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• no.54
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• pp.7-36
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• 2018

Michael Polanyi's idea of tacit knowledge came from the realization that scientific objectivity and critical philosophy had become too restrictive for philosophy, especially in the realm of meaning, which is beyond positivistic proof and contains more non-critical elements than critical ones. In social life, people still share certain kinds of knowledge and beliefs which they obtain without making or learning those explicitly. Contemplating the role and significance of tacit knowledge, he called for a post-critical philosophy that integrates the realm of meaning and thereby appreciates the intertwined nature of tacit and explicit knowledge. Polanyi's position towards skepticism and doubt shows similarities with Charles S. Peirce's thinking about the relationship between belief and doubt. Although Peirce's semeiotics stands firmly in the tradition of critical philosophy, he affirms that doubt cannot be a constant state of mind and only belief can form a basis for a specific way of life. Polanyi's approach differs from Peirce's by focusing on the impossibility of scientific knowledge based solely on principles and precision, and his emphasis on the crucial role of the community of scientists. Nevertheless, the deeper implications of Peirce's contemplations on belief and doubt have myriad ramifications on the philosophy of science as well as the sociology of science.

• The Journal of the Korea Contents Association
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• v.21 no.1
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• pp.531-540
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• 2021

As cyber attacks and crimes increase exponentially and hacking attacks become more intelligent and advanced, hacking attack methods and routes are evolving unpredictably and in real time. In order to reinforce the enemy's responsiveness, this study aims to propose a method for developing an artificial intelligence-based security control platform by building a next-generation security system using artificial intelligence to respond by self-learning, monitoring abnormal signs and blocking attacks.The artificial intelligence-based security control platform should be developed as the basis for data collection, data analysis, next-generation security system operation, and security system management. Big data base and control system, data collection step through external threat information, data analysis step of pre-processing and formalizing the collected data to perform positive/false detection and abnormal behavior analysis through deep learning-based algorithm, and analyzed data Through the operation of a security system of prevention, control, response, analysis, and organic circulation structure, the next generation security system to increase the scope and speed of handling new threats and to reinforce the identification of normal and abnormal behaviors, and management of the security threat response system, Harmful IP management, detection policy management, security business legal system management. Through this, we are trying to find a way to comprehensively analyze vast amounts of data and to respond preemptively in a short time.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.117-125
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• 2020

In this paper, we wanted to look at the effects of person-organization fit and person-job fit of social welfare officials on the job enthusiasm, but also examine the mediated effect of organizational committment. The study found that person-job fit has a positive effect on the job enthusiasm of public officials in charge of social welfare, and that the mediating effect of organizational committment is also partially covered. However, person-organization fit does not have a direct impact on job enthusiasm, but has been shown to have a full mediated effect through organizational committment. As a result of these studies, social welfare officials are judged to lack consistency and affinity within the organization, while their individual abilities, purposes, and demands are in line with their duties and job enthusiasm for their duties. Therefore, it is necessary to provide support at the organizational level and to create a sense of unity in order to enhance the job enthusiasm of public officials in charge of social welfare.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.1-12
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• 2021

In this paper, we study empirically examined the adventurous investments in corporate venture capital (CVC) firms' investment in the U.S. based corporate venture capital industry. Unlike existing studies focusing CVC firm's characteristics related to parent corporates and regarding CVC firm as a vehicle of corporate venturing, we identified CVC firm as an independent learning agent to adapt to dynamic environment and investigate their exploration and exploitation in investments based on organizational learning theory. Specifically, we investigate the market-environmental factors affecting CVC's adventurous investment in different sector rather than previously done. First, we examined competition intensity in CVC industry might be related to CVC firm's explorative investments. Second, CVC firm's investment experiences might affect as an inertia to invest on unexperienced sector. Finally, we investigated risk preference effect on CVC firm's venturing investments. The empirical data analyzed in the study contained a total of 85 U.S. based CVC firms and their 2,306 investments from 1996 until 2017. After conducting a GEE regression analysis and a Logit regression analysis, we found the significance and direction of our independent and moderating variables strongly supported all of our four hypotheses in a highly robust manner.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.1
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• pp.13-21
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• 2021

The purpose of this study is to develop the ship valuation model by utilizing the neural network model. The target of the valuation was secondhand VLCC. The variables were set as major factors inducing changes in the value of ship through prior research, and the corresponding data were collected on a monthly basis from January 2000 to August 2020. To determine the stability of subsequent variables, a multi-collinearity test was carried out and finally the research structure was designed by selecting six independent variables and one dependent variable. Based on this structure, a total of nine simulation models were designed using linear regression, neural network regression, and random forest algorithm. In addition, the accuracy of the evaluation results are improved through comparative verification between each model. As a result of the evaluation, it was found that the most accurate when the neural network regression model, which consist of a hidden layer composed of two layers, was simulated through comparison with actual VLCC values. The possible implications of this study first, creative research in terms of applying neural network model to ship valuation; this deviates from the existing formalized evaluation techniques. Second, the objectivity of research results was enhanced from a dynamic perspective by analyzing and predicting the factors of changes in the shipping. market.

• Journal of the Korean Applied Science and Technology
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• v.38 no.1
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• pp.228-240
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• 2021

This study was conducted with the goal of establishing a foothold for lifelong sports as well as establishing golf as a desirable leisure activity through the analysis of the relationship between golf participants' coach support, resilience and self-rated health. To achieve the goal of this study, a total of 300 questionnaires were distributed and 300 copies were collected back. Out of those returned questionnaires, insincerely replied or double-replied questionnaires were excluded and finally 278 questionnaires were analyzed for this study. For analysis of the data, frequency analysis, exploratory factor analysis, reliability analysis, confirmatory factor analysis, correlation analysis, and structural equating modeling were conducted using SPSS 18.0 and AMOS 18.0. Main findings were as follows: First coach support had a positive effect on resilience. Second, resilience had a positive effect on self-rated health. Third, coach support had a positive effect on self-rated health. Fourth, resilience mediated the relationship between golf participant coach support and self-rated health. Therefore, golf instructors should achieve specialization and diversification of educational programs through continuous learning about various teaching methods.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.459-469
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• 2021

In this paper, a case study of basic data science practice education for field teachers and pre-service teachers was studied. In this paper, for basic data science education, spreadsheet software was used as a data collection and analysis tool. After that, we trained on statistics for data processing, predictive hypothesis, and predictive model verification. In addition, an educational case for collecting and processing thousands of public big data and verifying the population prediction hypothesis and prediction model was proposed. A 34-hour, 17-week curriculum using a spreadsheet tool was presented with the contents of such basic education in data science. As a tool for data collection, processing, and analysis, unlike Python, spreadsheets do not have the burden of learning program- ming languages and data structures, and have the advantage of visually learning theories of processing and anal- ysis of qualitative and quantitative data. As a result of this educational case study, three predictive hypothesis test cases were presented and analyzed. First, quantitative public data were collected to verify the hypothesis of predicting the difference in the mean value for each group of the population. Second, by collecting qualitative public data, the hypothesis of predicting the association within the qualitative data of the population was verified. Third, by collecting quantitative public data, the regression prediction model was verified according to the hypothesis of correlation prediction within the quantitative data of the population. And through the satisfaction analysis of pre-service and field teachers, the effectiveness of this education case in data science education was analyzed.

• Journal of the Korea Institute of Building Construction
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• v.21 no.6
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• pp.665-676
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• 2021

As the number of deteriorated buildings increases, the importance of safety diagnosis and maintenance of buildings has been rising. Existing visual investigations and building safety diagnosis objectivity and reliability are poor due to their reliance on the subjective judgment of the examiner. Therefore, this study presented the limitations of the previously conducted appearance investigation and proposed 3D Point Cloud data to increase the accuracy of existing detailed inspection data. In addition, this study conducted a calculation of an objective building safety grade using a Deep-Neural Network(DNN) structure. The DNN structure is generated using the existing detailed inspection data and precise safety diagnosis data, and the safety grade is calculated after applying the state evaluation data obtained using a 3D Point Cloud model. This proposed process was applied to 10 deteriorated buildings through the case study, and achieved a time reduction of about 50% compared to a conventional manual safety diagnosis based on the same building area. Subsequently, in this study, the accuracy of the safety grade calculation process was verified by comparing the safety grade result value with the existing value, and a DNN with a high accuracy of about 90% was constructed. This is expected to improve economic feasibility in the future by increasing the reliability of calculated safety ratings of old buildings, saving money and time compared to existing technologies.