• Journal of Biomedical Engineering Research
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• v.43 no.5
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• pp.341-352
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• 2022

Medical image segmentation is the most important task in radiation therapy. Especially, when segmenting medical images, the liver is one of the most difficult organs to segment because it has various shapes and is close to other organs. Therefore, automatic segmentation of the liver in computed tomography (CT) images is a difficult task. Since tumors also have low contrast in surrounding tissues, and the shape, location, size, and number of tumors vary from patient to patient, accurate tumor segmentation takes a long time. In this study, we propose a method algorithm for automatically segmenting the liver and tumor for this purpose. As an advantage of setting the boundaries of the tumor, the liver and tumor were automatically segmented from the CT image using the 2D CoordConv DeepLab V3+ model using the CoordConv layer. For tumors, only cropped liver images were used to improve accuracy. Additionally, to increase the segmentation accuracy, augmentation, preprocess, loss function, and hyperparameter were used to find optimal values. We compared the CoordConv DeepLab v3+ model using the CoordConv layer and the DeepLab V3+ model without the CoordConv layer to determine whether they affected the segmentation accuracy. The data sets used included 131 hepatic tumor segmentation (LiTS) challenge data sets (100 train sets, 16 validation sets, and 15 test sets). Additional learned data were tested using 15 clinical data from Seoul St. Mary's Hospital. The evaluation was compared with the study results learned with a two-dimensional deep learning-based model. Dice values without the CoordConv layer achieved 0.965 ± 0.01 for liver segmentation and 0.925 ± 0.04 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.927 ± 0.02 for liver division and 0.903 ± 0.05 for tumor division. The dice values using the CoordConv layer achieved 0.989 ± 0.02 for liver segmentation and 0.937 ± 0.07 for tumor segmentation using the LiTS data set. Results from the clinical data set achieved 0.944 ± 0.02 for liver division and 0.916 ± 0.18 for tumor division. The use of CoordConv layers improves the segmentation accuracy. The highest of the most recently published values were 0.960 and 0.749 for liver and tumor division, respectively. However, better performance was achieved with 0.989 and 0.937 results for liver and tumor, which would have been used with the algorithm proposed in this study. The algorithm proposed in this study can play a useful role in treatment planning by improving contouring accuracy and reducing time when segmentation evaluation of liver and tumor is performed. And accurate identification of liver anatomy in medical imaging applications, such as surgical planning, as well as radiotherapy, which can leverage the findings of this study, can help clinical evaluation of the risks and benefits of liver intervention.

• Korean Association of Arts Management
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• no.53
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• pp.153-177
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• 2020

Arts organizations commonly face a range of operational challenges, from a lack of skilled workers to limited financial resources and thus are dependent on subsidies from the government. Yet, to fully realize their mission arts organizations must both develop strategies to effectively utilize government support and seek a way forward that does not depend on public subsidies. Business diversification, a strategy from corporate management, entails the expansion of products and services, and entry into new industries, enabling companies to disperse risks and increase profits. We propose that business diversification can be effectively applied to arts organization to address the myriad operational difficulties they face. To understand how an arts organization might deploy business diversification we conducted a case study of an organization that is actively pursuing the strategy: Sanwoollim Theater. We interviewed staff members of Sanwoollim including the executive director, as well as selected audiences, to understand how the business diversification model was being applied at Sanwoollim. Our findings indicate that, in a complex arts and cultural space, business diversification is a fresh and flexible new strategy that can enable private cultural arts organizations to thrive sustainably. It is also evident that government support in the initial stages of the process encourages diversification and that successful private arts organizations will leverage government subsidies into a sustainable business plan.

• Informatization Policy
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• v.29 no.3
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• pp.82-104
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• 2022

One of the interesting social phenomena in short-form video platforms is the hashtag challenge wherein ordinary users are encouraged to create by imitating short viral videos on a particular theme. Despite the increasing popularity of hashtag challenges, theoretical discussion on related user behavior is still very insufficient. In this study, we attempted to examine the impact of micro-influencers in order to understand users' willingness to participate in hashtag challenges. For this purpose, the para-social interaction theory and imitation behavior literature were adopted as key theoretical basis. In an empirical investigation using 243 survey data from TikTok users, our study found that a user's illusion of intimacy with a micro-influencer (i.e., para-social interaction) had significant positive impact on the intention to participate in a hashtag challenge. This study also showed that the degree of para-social interaction in a short-form video platform was determined by both media content-related factors and media character-related factors (i.e., content attractiveness, physical attractiveness, and attitude homophily). Our work in this study provided significant theoretical and practical implications on how to leverage micro-influencers for the success of hashtag challenges in a short-form video platform.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.25-30
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• 2022

This study is a study on the filter bubble reinforcement phenomenon of SNS recommendation algorithm such as YouTube, which is a characteristic of the Russian-Ukraine war (2022), and the victory or defeat factors of the hybrid war. This war is identified as a hybrid war, and the use of New Media based on the SNS recommendation algorithm is emerging as a factor that determines the outcome of the war beyond political leverage. For this reason, the filter bubble phenomenon goes beyond the dictionary meaning of confirmation bias that limits information exposed to viewers. A YouTube video of Ukrainian President Zelensky encouraging protests in Kyiv garnered 7.02 million views, but Putin's speech only 800,000, which is a evidence that his speech was not exposed to the recommendation algorithm. The war of these SNS recommendation algorithms tends to develop into an algorithm war between the US (YouTube, Twitter, Facebook) and China (TikTok) big tech companies. Influenced by US companies, Ukraine is now able to receive international support, and in Russia, under the influence of Chinese companies, Putin's approval rating is over 80%, resulting in conflicting results. Since this algorithmic empowerment is based on the confirmation bias of public opinion by 'filter bubble', the justification that a new guideline setting for this distortion phenomenon should be presented shortly is drawing attention through this Russia-Ukraine war.

• The Mathematical Education
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• v.62 no.2
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• pp.289-302
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• 2023

In today's digital information society, student knowledge and skills to analyze big data and make informed decisions have become an important goal of school mathematics. Integrating big data statistical projects with digital technologies in high school <Artificial Intelligence> mathematics courses has the potential to provide students with a learning experience of high impact that can develop these essential skills. This paper proposes a set of guidelines for designing effective big data statistical project-based tasks and evaluates the tasks in the artificial intelligence mathematics textbook against these criteria. The proposed guidelines recommend that projects should: (1) align knowledge and skills with the national school mathematics curriculum; (2) use preprocessed massive datasets; (3) employ data scientists' problem-solving methods; (4) encourage decision-making; (5) leverage technological tools; and (6) promote collaborative learning. The findings indicate that few textbooks fully align with these guidelines, with most failing to incorporate elements corresponding to Guideline 2 in their project tasks. In addition, most tasks in the textbooks overlook or omit data preprocessing, either by using smaller datasets or by using big data without any form of preprocessing. This can potentially result in misconceptions among students regarding the nature of big data. Furthermore, this paper discusses the relevant mathematical knowledge and skills necessary for artificial intelligence, as well as the potential benefits and pedagogical considerations associated with integrating technology into big data tasks. This research sheds light on teaching mathematical concepts with machine learning algorithms and the effective use of technology tools in big data education.

• Analyses & Alternatives
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• v.7 no.2
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• pp.179-207
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• 2023

Carbon neutrality, the international community's practical challenge in response to climate change, is becoming a key industrial strategy for the future development of nations. Despite concerns that China, as an economic powerhouse in the G2, may face challenges leading global climate change efforts due to its high-carbon-emitting industrial structure, it is leveraging carbon neutrality to enhance its industrial competitiveness. The Chinese government has formulated national policies for achieving carbon neutrality and detailed sector-specific plans to implement them. In particular, it aims to leverage carbon neutrality industrial strategies as a lever for adjusting the domestic industrial structure and fostering new industries, at the same time responding to international climate norms and external pressures. However, the effectiveness of carbon-neutral industrial strategies is expected to vary based on regional conditions such as economic and industrial levels. This article analyzes the regional conditions for implementing carbon neutrality in China, as well as the contents and characteristics of major industrial policies. Due to differing levels of economic development and industrial structures, significant variations in carbon emissions, size, emission sources, and efficiency are inevitable across regions. These disparities introduce diverse initial conditions and endogenous factors in pursuing carbon-neutral goals, limiting the direction and implementation of carbon-neutral industrial strategies favoring certain regions. In particular, the extent of policy autonomy granted to local governments regarding carbon neutrality implementation will influence the regional dynamics of central-local environmental governance. Consequently, it is crucial to emphasize regional monitoring alongside comprehensive national research to accurately navigate the path towards carbon neutrality in China. In summary, the article underscores the importance of understanding regional variations in economic development, industrial structure, and policy autonomy for successful carbon neutrality implementation in China. It highlights the need for regional monitoring and comprehensive national research to determine a more precise direction for achieving carbon neutrality.

• Korean Journal of Legislative Studies
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• v.26 no.3
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• pp.5-35
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• 2020

This article purposes to investigate opening process of the 21st National Assembly in the middle of severe conflicts between two major-parties, and predict the changes it will bring to the operation of the National Assembly. With incumbent party's taking all leadership positions of standing committees, it broke the practice since 13th National Assembly, that is, distribution of the standing committees based on the seat-ratio. It means that our National Assembly has entered a new phase in the decision-making process. While the incumbent party, with overwhelming victory in general election, emphasizes that it should dominate legislative process to support the government, the out-party claims that they should take leverage to check over government. Two opposing trends are characteristically observed in the operation of the Korean National Assembly. First of all, due to the experience under authoritarian regimes, the National Assembly has been institutionalizing decision-making processes in the direction of enforcing cooperation between parties. On the other hand, the polarization in political parties has been stronger, making it difficult to reach consensus between parties. This article claims strongly that the 21st National Assembly need to find a balance amid such two-conflicting trends. To do so, three necessary conditions are proposed: observing decision-making procedures, securing diversity within party and National Assembly, and deliberative legislative activities.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.5
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• pp.105-115
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• 2022

This study aims to investigate the Effect of Money Rush on Entrepreneurial Opportunity Recognition for college students in Busan and Gyeongnam area. We also examine whether Entrepreneurship has a mediating effect between Money Rush and Entrepreneurial Opportunity Recognition. Since the outbreak of COVID-19, digital transformation of the industry have greatly changed the world of work, and job insecurity is becoming more prevalent. As income inequality expands due to the disparity in asset income, the Money Rush phenomenon, which prefers to increase asset income through investment rather than earned income, is becoming common. Money Rush secures an income pipeline and is divided into side hustles and investments that actively utilize Leverage to maximize profits. The findings of this study confirm that Money Rush has a positive effect on Entrepreneurial Opportunity Recognition and a partially positive effect on Entrepreneurship. Entrepreneurship has a partial mediating effect between Money Rush and Entrepreneurial Opportunity Recognition. The study analysis is expected to contribute to strengthening college students' competencies in Entrepreneurial Opportunity Recognition and presenting the policy and practical directions necessary to promote Start-up.

• Journal of Korean Library and Information Science Society
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• v.54 no.4
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• pp.359-392
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• 2023

Digital citizenship includes an evolving set of knowledge and skills related to effectively and ethically using technology, especially when interacting with other people, information, and media in the online context. As public libraries have long provided access to and training with a variety of technologies, this study explores how digital citizenship has been covered in public library programming to identify potential trends and best practices. A purposive sampling of public library recipients of the American Library Association (ALA) and Information Today Inc.'s Library of the Future Award over the past 11 years (2013-2023) identified 7 case libraries to review. The titles and descriptions of 337 relevant library programs for audiences of school-aged children (5 years old and up) to seniors were collected for a 2-month period from each library's website and analyzed using Ribble & Parks (2019) 9 elements of digital citizenship. The findings suggest that programming related to digital citizenship most often addresses themes connected to digital access and digital fluency through coverage of topics related to computer and technology use. Based on themes and examples from the findings, public libraries are encouraged to expand upon existing programs to integrate all elements of digital citizenship, strive for inclusive and accessible digital citizenship education for all ages, and leverage resources and expertise from relevant stakeholders and community partnerships.

• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.41-47
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• 2024

When there were disparities in performance between models trained in the time and frequency domains, even after conducting an ensemble, we observed that the performance of the ensemble was compromised due to imbalances in the individual model performances. Therefore, this paper proposes a leakage detection technique to enhance the accuracy of pipeline leakage detection through a step-wise learning approach that extracts features from both the time and frequency domains and integrates them. This method involves a two-step learning process. In the Stage 1, independent model training is conducted in the time and frequency domains to effectively extract crucial features from the provided data in each domain. In Stage 2, the pre-trained models were utilized by removing their respective classifiers. Subsequently, the features from both domains were fused, and a new classifier was added for retraining. The proposed transfer learning-based feature fusion technique in this paper performs model training by integrating features extracted from the time and frequency domains. This integration exploits the complementary nature of features from both domains, allowing the model to leverage diverse information. As a result, it achieved a high accuracy of 99.88%, demonstrating outstanding performance in pipeline leakage detection.