• English Language & Literature Teaching
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• v.11 no.4
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• pp.1-20
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• 2005

Despite the noteworthy increase in the number of FL anxiety studies, inconsistencies associated with the effects of FL anxiety on language learner performance have been reported in literature. Such conflicting results seem to be attributable in part to unstable conceptualization of the FL anxiety construct and its measure. This paper purported to address the emerging call for a theoretical clarification of the construct at hand as a preface to a clear picture of language anxiety on a conceptual ground. This paper not only covers aspects of general anxiety from psychological perspectives, but examines how FL anxiety and its associated concepts have been conceptualized in the literature. Inconsistent results that pertain to FL learning were also delineated. Given the drawbacks found in the exiting theories of FL anxiety, several points were taken into account for a refinement of the conceptual framework. This attempt will hopefully shed new light on the construct per se and prove conducive to the development of the field of English education.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.264-267
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• 2022

Recently, federated learning (FL) has increased prominence as a viable approach for enhancing user privacy and data security by allowing collaborative multi-party model learning without exchanging sensitive data. Despite this, most present FL systems still depend on a centralized aggregator to generate a global model by gathering all submitted models from users, which could expose user privacy and the risk of various threats from malicious users. To solve these issues, we suggested a safe FL framework that employs differential privacy to counter membership inference attacks during the collaborative FL model training process and empowers blockchain to replace the centralized aggregator server.

• Korean Journal of Community Nutrition
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• v.26 no.2
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• pp.77-92
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• 2021

Objectives: Food literacy (FL) can be an important concept that embodies the nutritional capabilities of individuals. The purpose of this study was to introduce the definition and core elements of FL from previous literature, to summarize measurement tools and intervention programs with FL, and to suggest the direction of future research and programs to integrate the concept of FL. Methods: The literature review was conducted through PubMed and Google Scholar databases by combining the search term 'food literacy' with 'definition', 'measurement', 'questionnaire', 'intervention', and 'program'. Among the 94 papers primarily reviewed 31 manuscripts that suited the purpose of the study were used for analyses. Results: There is no consensus on the definition of FL that encompasses the multidimensional aspects of the concept. The definitions of FL were slightly different depending on the authors, and the interpretation of the core elements also varied. Based on the review, we propose a framework of FL that is in line with the current discussion among international researchers. This focuses on the core elements adapted from health literacy, namely functional, interactive, and critical FL. Specifically, we suggest some detailed elements for interactive and critical FL, which were often the subject of divergent views among researchers in previous literature. We found that most of the tools in the reviewed literature provided information on validity and reliability and were developed for a specific target population. Also, most of the tools were focused on functional FL. Similarly, most of the interventions targeted functional FL. Conclusions: This study reviewed the definition and core elements of FL, available measurement tools, and intervention programs using validated tools. We propose the development of tools with sound reliability and validity that encompass the three core elements of FL for different age groups. This will help to understand whether improving food literacy can translate into better nutritional intake and health status among individuals and communities.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1462-1477
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• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.

• Journal of the Korean Geographical Society
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• v.44 no.3
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• pp.282-300
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• 2009

This paper attempts to focus the ways in which Seoul as an urban space can be read and interpreted from gender perspective, assuming Seoul as a cultural text which represents modernity and post-modernity. Drawing on discussions of urban sociology and human geography which have analyzed the relationship between material spaces and social subjects, this paper explores the gendered segregation and representations of space in Seoul which has been constructed through the process of modernization. The framework of spatial interpretation of Seoul, concentrating on imageablity and legibility, consists of three dimensions; gendered division of labour and sphere, dichotomy of representations along with femininity and masculinity, and the ways of interlocking between modernity and post-modernity. In this paper, 'fl$\check{a}$nerie', Benjamin's method of interpretation of urban culture and the way of seeing with 'speculum' of Irigaray are adopted as metaphoric methodologies. It is an attempt to develop a new methodology to analyze and interpret urban space from gender-cultural perspective.

• Nutrition Research and Practice
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• v.17 no.6
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• pp.1155-1169
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• 2023

BACKGROUND/OBJECTIVES: Unhealthy food choices among young adults are common globally, and the incidence of chronic diseases, such as obesity, is rising. Food literacy (FL) is important for improving and maintaining individual health in a rapidly changing food environment and can form the basis for following a sustainable diet. Therefore, it is essential to improve FL among young adults, particularly college students, who are in the formative years of their lifelong food habits. This study examined the facilitators and barriers of FL and related dietary behavior among college students in South Korea. SUBJECTS/METHODS: This study recruited 25 college students with different residence types using convenience and snowball sampling. In-person, telephone, and video interviews were conducted from March to November 2021. The interview data were analyzed using framework analysis based on the socio-ecological model. RESULTS: At the individual level, prior good experiences with food were the most frequently mentioned facilitator. In contrast, the major barriers were a lack of knowledge, financial hardship, irregular schedules, and academic stress. At the interpersonal level, the influences of family and peers, such as early exposure to healthy eating habits and opportunities to have easy accessibility to farms and farming, are major facilitators, but the lack of a sense of community was the major barrier. At the environmental level, the major barriers were unfavorable food environments at home and in neighborhoods, such as the absence of kitchens in housing and large packaging of produce at markets. CONCLUSIONS: Many factors affected the students' FL and related healthy eating practices. These findings suggest that a campus-based FL program should be developed by reflecting on these facilitators and barriers.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.711-714
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• 2024

In the dynamic landscape of modern machine learning, Federated Learning (FL) has emerged as a compelling paradigm designed to enhance privacy by enabling participants to collaboratively train models without sharing their private data. Specifically, Distillation-based Federated Learning, like Federated Learning with Model Distillation (FedMD), Federated Gradient Encryption and Model Sharing (FedGEMS), and Differentially Secure Federated Learning (DS-FL), has arisen as a novel approach aimed at addressing Non-IID data challenges by leveraging Federated Learning. These methods refine the standard FL framework by distilling insights from public dataset predictions, securing data transmissions through gradient encryption, and applying differential privacy to mask individual contributions. Despite these innovations, our survey identifies persistent vulnerabilities, particularly concerning the susceptibility to logit inversion attacks where malicious actors could reconstruct private data from shared public predictions. This exploration reveals that even advanced Distillation-based Federated Learning systems harbor significant privacy risks, challenging the prevailing assumptions about their security and underscoring the need for continued advancements in secure Federated Learning methodologies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.1-13
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• 2023

As federated learning brings a large paradigm to modern artificial intelligence research, efforts are being made to incorporate federated learning into research in various fields. However, researchers who apply federated learning face the problem of choosing a federated learning framework and benchmark tool suitable for their situation and purpose. This study aims to present guidelines for selection of federated learning frameworks and benchmark tools considering the circumstances of researchers who apply federated learning in practice. In particular, there are three main contributions in this study. First, it generalizes the situation of the researcher applying federated learning by combining it with the goal of federated learning and proposes guidelines for selecting a federated learning framework suitable for each situation. Second, it shows the suitability of selection by comparing the characteristics and performance of each federated learning framework to the researcher. Finally, the limitations of the existing federated learning framework and a plan for real-world federated learning operation are proposed.

• Journal of computational fluids engineering
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• v.10 no.4 s.31
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• pp.39-50
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• 2005

An application of the KFLOW3D code which has been developed at KAIST is presented. This paper briefly describes the underlying methodology and summarizes the results for the DLR-F6 transport configuration recently presented in the second AIAA CFD Drag Prediction Workshop held in Orlando, FL, June 2003. KFLOW3D is a parallelized Reynolds averaged Navier-Stokes solver for multi-block structured grids. For the present computations, 2-equation k-$\omega$ WD+ nonlinear eddy viscosity model is used. The emphasis of the paper is placed on the implementation of the k-$\omega$ WD+ model in the multigrid framework and practicality of KFLOW3D for accurately predicting not only the integrated aerodynamic property such as the drag coefficient but pressure distributions.