• The Transactions of the Korea Information Processing Society
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• v.13 no.2
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• pp.76-90
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• 2024

Although artificial intelligence (AI) can be utilized in various domains such as smart city, healthcare, it is limited due to concerns about the exposure of personal and sensitive information. In response, the concept of distributed machine learning has emerged, wherein learning occurs locally before training a global model, mitigating the concentration of data on a central server. However, overall learning phase in a collaborative way among multiple participants poses threats to data privacy. In this paper, we systematically analyzes recent trends in privacy protection within the realm of distributed machine learning, considering factors such as the presence of a central server, distribution environment of the training datasets, and performance variations among participants. In particular, we focus on key distributed machine learning techniques, including horizontal federated learning, vertical federated learning, and swarm learning. We examine privacy protection mechanisms within these techniques and explores potential directions for future research.

• Management & Information Systems Review
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• v.35 no.4
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• pp.185-211
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• 2016

This study examined the impact of learning organization activities on burnout and the moderating effect of supervisor trust in a learning organization. The results of the study shows that among the activities of a learning organization, independent variables in this study, promoting inquiry and dialogue as well as encouraging collaboration and team learning affect burnout. In other words, the dedication of an organization to creating a culture in which various learning approaches are experimented through questioning and giving feedback as well as collaborative learning that can reinforce the effective use of team resources have an impact on reducing emotional exhaustion, which is considered to be at the core of burnout. Plus, these factors reduce impersonalization, which is activated to prevent further emotional exhaustion by dealing with customers, colleagues and jobs in a cold, negative and perfunctory way. In this study, the dimensions of promoting inquiry and dialogue as well as encouraging collaboration and team learning were found to reduce the decline in personal sense of achievement of an employee with a negative assessment of himself or herself derived from a lack of achievement in his or her job. Supervisor trust (integrity, benevolence and ability) had a moderating effect on the relationship between strategic learning leadership and impersonalization/emotional exhaustion. This suggests that the trust of supervisor helps mediate and moderate the emotional exhaustion and impersonalization of organizational members by encouraging leaders to drive change and take the organization to a new direction. The study has provided implications that communication plays an important role in reducing burnout in the learning context such as positive, appreciative inquiry and feedback analysis to identify strength, and that supervisor trust is critical in order to ensure strategic learning leadership exerts greater influence on the organization.

• 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.

• Information Systems Review
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• v.25 no.1
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• pp.111-128
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• 2023

With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.

• Journal of the Korean Institute of Landscape Architecture
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• v.46 no.5
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• pp.44-58
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• 2018

Cities face new challenges not only in natural disasters by climate change but also in social and economic fluctuations. With the existing simple reconstruction method, it is difficult to solve the overall problems that a city or region may face. As a new approach to cope with various changes, the concept of resilience is emerging. Resilience is also one of the themes of recent major urban design projects. Design with the concept of resilience is a new strategy that can deal with various changes of urban space, rather than a temporary trend. The purpose of this paper is to explore the design method by analyzing cases where the concept of resilience is employed. We aim to examine what kind of design strategies are needed for the resilience design and how this design process differ in character, as compared to general design projects. Cases for this study include the "Rebuild by Design" competition held in 2013 and the "Resilient by Design/Bay Area Challenge" competition held in 2017. This paper consists of literature reviews and case studies. The latter is divided into two aspects: content analysis based on the theory of resilience and characteristics of the design process. Cases are analyzed through literature reviews and process characteristics of resilience design in response to the general design process. The main categories for urban resilience used as the framework for analysis include: Urban Infrastructure, Social Dynamics, Economic Dynamics, Health and Wellbeing, Governance Networks, and Planning and Institutions. As a result, the aspects of resilience concepts considered and design strategies undertaken by each team were identified. Each team tried to connect all 6 categories to their design strategies, placing special value on the role of governance, a system that enables collaborative design and project persistency. In terms of the design process, the following characteristics were found: planning the whole project process in the pre-project phase, analyzing predictable socioeconomic risk factors in addition to physical vulnerabilities, aiming for landscape-oriented integrated design, and sustainable implementation strategies with specific operations and budget plans. This paper is meaningful to connect the concept of resilience, which has been discussed in various articles, to design strategy, and to explore the possibility of constructing a practical methodology by deriving the characteristics of the resilience design process. It remains a future task to research design strategies that apply the concept of resilience to various types of urban spaces, in addition to areas that are vulnerable to disasters.