• Journal of Convergence for Information Technology
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• v.11 no.12
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• pp.70-79
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• 2021

The purpose of this study is as follows: First, this study investigates the effect of the supervisor's coaching leadership on the employee's protean career attitude. Second, we examine the effect of a protean career attitude on career satisfaction. Finally, we examine the mediating effect of protean career attitude on the relationship between supervisor's coaching leadership and career satisfaction. As a result of analyzing the data of 329 employees from pharmaceutical companies, the positive influence of coaching leadership on the protean career attitude was significant. The positive effect of the protean career attitude on career satisfaction was significant. The mediating effect of the protean career attitude on the relationship between coaching leadership and career satisfaction was significant. The findings of this study provide an understanding regarding the concept of career development, which has recently changed from an organization-centered to an individual-centered perspective. Also, this study has theoretical and practical implications for the field of human resource development in that it suggests the type of leadership as an antecedent of protean career attitudes.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.214-220
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• 2021

With the rapid spread of smartphones, it has become common to listen to music everywhere, just like background music in life, so it is necessary to create a music database that can make recommendations according to individual circumstances and conditions. This paper proposes a music recommendation model through social media. Since emotions, situations, time of day, weather, etc. are included in hashtags, it is possible to build a social media-based database that reflects the opinions of various people with collective intelligence. We use web crawling to collect and categorize different hashtags from posts with music title hashtags to use real listeners' opinions about music in a database. Data from social media is used to create a music database, and music is classified in a different way from collaborative filtering, which is mainly used by existing music platforms.

• Journal of Convergence for Information Technology
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• v.11 no.9
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• pp.201-207
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• 2021

This study investigated the effects of self-esteem on the depression of baby boomer and eco-boomer generation (the children of baby boomers) living alone. It was identified from the point of view of comparison between the two groups. Using the 15th data of the 2020 Korea Welfare Panel, an independent sample t-test and hierarchical regression analysis were conducted with the data of baby boomers who live alone (born in 1955-1963) and eco-boomers who live alone (born in 1979-1992). The results of this study show that baby boomers who live alone had lower levels of education, income, and health condition than the eco-boomers who live alone, and had higher level of depression, but relatively lower level of self-esteem. In both groups, self-esteem had an effect on depression, but it was confirmed that the influence was greater in the group of baby boomers (Adjusted R2 .259) than in eco-boomers (Adjusted R2 .083). Based on the results of this study, practical and policy alternatives were suggested to prevent the depression among middle-aged, elderly people, and young adults who live alone.

• Journal of Convergence for Information Technology
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• v.11 no.10
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• pp.45-52
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• 2021

In this paper, we proposed an Atrous Residual U-Net (AR-UNet) to improve the segmentation accuracy of semantic segmentation method based on U-Net. The U-Net is mainly used in fields such as medical image analysis, autonomous vehicles, and remote sensing images. The conventional U-Net lacks extracted features due to the small number of convolution layers in the encoder part. The extracted features are essential for classifying object categories, and if they are insufficient, it causes a problem of lowering the segmentation accuracy. Therefore, to improve this problem, we proposed the AR-UNet using residual learning and ASPP in the encoder. Residual learning improves feature extraction ability and is effective in preventing feature loss and vanishing gradient problems caused by continuous convolutions. In addition, ASPP enables additional feature extraction without reducing the resolution of the feature map. Experiments verified the effectiveness of the AR-UNet with Cityscapes dataset. The experimental results showed that the AR-UNet showed improved segmentation results compared to the conventional U-Net. In this way, AR-UNet can contribute to the advancement of many applications where accuracy is important.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.312-326
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• 2021

Building on the framework of language socialization [10] in language learning and use, the present study examines the environmental factors involved in four college students' English learning in the situated place of the home during the COVID-19 pandemic. Using narrative inquiry, this study implements a time-series analysis to investigate undergraduates' online English learning in a rural area of northwest China. The data were collected via oral and written narration, semi-structured interviews, and class documents. Leveraging the field-habitus theories, the findings reveal that each of the students had a different habitus in the family field that influenced their English learning at home between March to July of 2020. Ultimately, all four students felt that their habitus made their online English learning ineffective and expressed that they did not wish to continue learning at home. The findings imply that it is important for rural parents to pay more attention to building college students' learning environments and helping students cultivate a strong learning habitus in the family field in northwest China.

• Journal of Convergence for Information Technology
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• v.11 no.8
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• pp.100-108
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• 2021

The purpose of this study is to examine the sequential mediating effects of social withdrawal and depression in the relationship between achievement motivation and career decision of multicultural adolescents. To this end, we analyzed adolescents data from the 8th year survey(2018) of the 'Multicultural Adolescent Panel Study(MAPS)' provided by the National Youth Policy Institute(NYPI). As a result of the study, first, achievement motivation was found to have a significant and positive effect on career decision. Second, it was found that social withdrawal did not mediate the relationship between achievement motivation and career decision. Third, it was found that depression mediates the relationship between achievement motivation and career decision. Fourth, it was found that social withdrawal and depression sequentially mediate the relationship between achievement motivation and career decision. Based on the results of this study, we searched for social support measures to improve the career decision of multicultural adolescents and support their career paths, and suggested implications and limitations of the results of this study and suggestions for follow-up studies.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.209-215
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• 2018

In this paper, we propose a method to detect and block Meltdown malicious code which is increasing rapidly using dynamic sandbox tool. Although some patches are available for the vulnerability of Meltdown attack, patches are not applied intentionally due to the performance degradation of the system. Therefore, we propose a method to overcome the limitation of existing signature detection method by using machine learning method for infrastructures without active patches. First, to understand the principle of meltdown, we analyze operating system driving methods such as virtual memory, memory privilege check, pipelining and guessing execution, and CPU cache. And then, we extracted data by using Linux strace tool for detecting Meltdown malware. Finally, we implemented a decision tree based dynamic detection mechanism to identify the meltdown malicious code efficiently.

• Journal of Convergence for Information Technology
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• v.8 no.6
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• pp.231-236
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• 2018

Recently, a wide variety of IoT environment is being created due to the rapid development of information and communication technology. And accordingly in a variety of network structures, a countless number of attack techniques and new types of vulnerabilities are producing a social disturbance. In May of 2018, Talos Intelligence, the Cisco threat intelligence team has newly discovered 'VPN-Filter', which constitutes a large-scale IoT-based botnet, is infecting consumer routers in over 54 countries around the world. In this paper, types of IoT-based botnets and the attack techniques utilizing botnet will be examined and the countermeasure technique through EXIF metadata removal method which is the cause of connection method of C & C Server will be proposed by examining the characteristics of attack vulnerabilities and attack scenarios of VPN-Filter.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.201-209
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• 2018

This study seeks to examine the effect of customer orientation on customer loyalty and employee loyalty mediated by two dimensions of corporate social responsibility (CSR), discretionary and ethical dimensions. This study examined the effects of customer orientation on discretionary responsibility and ethical responsibility. Additionally, it examined the effect of discretionary responsibility and ethical responsibility on customer loyalty and organizational loyalty. In order to verify the hypothesis, we surveyed the employees of large companies and analyzed 239 valid data. First, customer orientation has a significant positive impact on discretionary responsibility. Second, customer orientation has a significant positive impact on ethical responsibility. Third, discretionary responsibility has a significant positive impact on customer loyalty. Fourth, discretionary responsibility has a significant positive impact on organizational loyalty. However, ethical responsibility does not predict customer loyalty and organizational loyalty. Theoretical and practical implications of the results of this study, limitations and directions for future research are discussed.

• The Journal of the Korea Contents Association
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• v.19 no.9
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• pp.242-253
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• 2019

There has been a large literature on bank liquidity risk since the 2008 global financial crisis because liquidity risk was at the heart of the crisis. However, there is no study that investigates whether the level of investor protection influences liquidity risk-taking behavior of banks. Therefore, this study aims to explore the relationship between investor protection and liquidity risk as well as to provide policy implications. Using a panel dataset of commercial banks in 21 OECD countries, we found that strong investor protection encourages banks to take lower liquidity risk. Furthermore, this positive role of shareholder protection is more prominent during a crisis, implying that legal protection of investors plays an essential role in bank stability while market discipline is largely ineffective due to extensive government guarantees in turbulent times.