• 한국수학교육학회지시리즈D:수학교육연구
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• 제24권3호
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• pp.137-173
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• 2021

Research and national standards, such as the Next Generation Science Standards (NGSS) in the United States, promote the development and implementation of K-12 interdisciplinary curricula integrating the disciplines of science, technology, engineering, mathematics, and computer science (STEM+CS). However, little research has explored how teachers provide epistemic support in interdisciplinary contexts or the factors that inform teachers' epistemic support in STEM+CS activities. The goal of this paper is to articulate how interdisciplinary instruction complicates epistemic knowledge and resources needed for teachers' instructional decision-making. Toward these ends, this paper builds upon existing models of teachers' instructional decision-making in individual STEM+CS disciplines to highlight specific challenges and opportunities of interdisciplinary approaches on classroom epistemic supports. First, we offer considerations as to how teachers can provide epistemic support for students to engage in disciplinary practices across mathematics, science, engineering, and computer science. We then support these considerations using examples from our studies in elementary classrooms using integrated STEM+CS curriculum materials. We focus on an elementary school context, as elementary teachers necessarily integrate disciplines as part of their teaching practice when enacting NGSS-aligned curricula. Further, we argue that as STEM+CS interdisciplinary curricula in the form of NGSS-aligned, project-based units become more prevalent in elementary settings, careful attention and support needs to be given to help teachers not only engage their students in disciplinary practices across STEM+CS disciplines, but also to understand why and how these disciplinary practices should be used. Implications include recommendations for the design of professional learning experiences and curriculum materials.

• Journal of Radiation Protection and Research
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• 제44권3호
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• pp.90-96
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• 2019

The contributions of nuclear science and technology in enhancing prosperity and quality of life all over the world and its potential to achieve many important Sustainable Developments Goals (SDGs) of the United Nations are well recognized. It also is now recognized that with fewer students getting attracted to Science, Technology, Engineering and Mathematics (STEM) in general and nuclear science and technology (NST) in particular; hence, there is a vital need to reach out to young students to provide the crucial human resources needed for these endeavours to continue in this highly specialized area. The success of a recently completed IAEA project related to introducing NST during 2012-2016 in secondary schools in the Asia-Pacific region countries encouraged the formulation of a new IAEA TC project RAS0079 entitled "Educating Secondary Students and Science Teachers on Nuclear Science and Technology" for 2018-2021, focusing on enhancing existing educational approaches through training and development opportunities both for teachers and students. The project aims at reaching a million students during the project duration while keeping the depth of learning between teacher and student. The strategy of executing the project, implementation status and its impact so far is presented in this paper.

• The Journal of Asian Finance, Economics and Business
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• 제8권1호
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• pp.199-206
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• 2021

This study investigated the main concerns and strategies in Indonesian large family businesses to undertake intergenerational succession effectively. The research data was obtained to shed light on the incumbents' mindsets, key preferences, and experiences during the succession process. Access to incumbents of large family businesses that are conglomerates is scant. The preceding survey research was conducted to sensitize with the intricacy of the intergenerational succession process in large family businesses before entailing interpretative phenomenology analysis of qualitative data from interviews, observations, and field notes by approaching family members in five conglomerate groups that have major impacts on the economy. The findings explicate the incumbents' preferred criteria in choosing their successors as well as their perceived concerns revolving around the appointment. Additionally, the incumbents' succession approaches such as apprentice learning by successors, adaptability to external forces by successors, nurturing the entrepreneurial spirit in successors, governance establishment in the firms, business interest stimulation in successors, role modeling by incumbents, and collaboration between family and key non-family members are elicited during the intergenerational succession process. This study concluded with noteworthy implications for incumbents and successors in large family businesses, especially providing explicit criteria and strategies to appoint suitable successors, and suggesting potential avenues for future research.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1141-1155
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• 2022

Multicultural education is not about learning about a specific ethnic group, but rather developing the ability to cross the border of one's own culture and have conversations with people of other cultures. I think the purpose is to promote empathy and consideration. This study emphasizes the importance of developing multi-dimensional educational programs for all members of society for multicultural social integration, and it is necessary to lead personal, social and civic action movements to create a fair society through media-based multicultural literacy education. said that In order to achieve harmony and integration in a multicultural society, it is the most important to acknowledge cultural diversity and to discard cultural prejudices and inequalities for symbiosis between the mainstream culture and the minority culture. In particular, the United States and Germany, which have successfully led multicultural social integration, are comprehensive in all areas, including interculturalism based on peaceful coexistence and respect, labor market issues, vocational education issues, housing and health issues, and communication issues through media literacy. He led a multicultural national integration system with approaches and methods. Therefore, our multicultural education policy should also pursue a new paradigm that presupposes a change in the public's awareness of a multicultural society.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.59-66
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• 2024

This paper presents the concept and characteristics of cloud computing, and it addresses how cloud computing delivers quality of service (QoS) to the end-user. Next, it discusses how to schedule one's workload in the infrastructure using technologies that have recently emerged such as Machine Learning (ML). That is followed by an overview of how ML can be used for resource management. This paper then looks at the primary goal of this project, which is to outline the benefits of using ML to schedule upcoming demands to achieve QoS and conserve energy. In this survey, we reviewed the research related to ML methods for predicting workloads in cloud computing. It also provides information on the approaches to elasticity, while another section discusses the methods of prediction used in previous studies and those that used in this field. The paper concludes with a summary of the literature on predicting workloads and optimising QoS in the cloud computing.

• 대한기계학회논문집A
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• 제32권4호
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• pp.347-353
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• 2008

Reflected light can be decomposed into specular and diffuse components according to the light reflectance theory and experiments. The specular component appears in smooth surfaces mainly, while the diffuse one is visible in rough surfaces mostly. Therefore, each component can be used in forming their correlations to a surface roughness. However, they cannot represent the whole surface roughness seamlessly, because each formulation is merely validated in their available surface roughness regions. To solve this problem, new approaches to properly blend two light components in all regions are proposed in this paper. First is the weighting function method that a blending zone and rate can be flexibly adjusted, and second is the neural network method based on the learning from the measurement data. Simulations based on the light reflectance theory were conducted to examine its performance, and then experiments conducted to prove the enhancement of the measurement accuracy and reliability through the whole surface roughness regions.

• 소성∙가공
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• 제29권5호
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• pp.282-289
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• 2020

Flexible roll forming is an advanced sheet-metal-forming process that allows the production of parts with various cross-sections. During the flexible process, material is subjected to three-dimensional deformation such as transverse bending, inhomogeneous elongations, or contraction. Because of the effects of process variables on the quality of the roll-formed products, the approaches used to investigate the roll-forming process have been largely dependent on experience and trial- and-error methods. Web-warping is one of the major shape defects encountered in flexible roll forming. In this study, an SVR model was developed to predict the web-warping during the flexible roll forming process. In the development of the SVR model, three process parameters, namely the forming-roll speed condition, leveling-roll height, and bend angle were considered as the model inputs, and the web-warping height was used as the response variable for three blank shapes; rectangular, concave, and convex shape. MATLAB software was used to train the SVR model and optimize three hyperparameters (λ, ε, and γ). To evaluate the SVR model performance, the statistical analysis was carried out based on the three indicators: the root-mean-square error, mean absolute error, and relative root-mean-square error.

• Journal of Information Science Theory and Practice
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• 제3권1호
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• pp.6-23
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• 2015

This study develops an effective method for sentiment analysis of user-generated content on drug review websites, which has not been investigated extensively compared to other general domains, such as product reviews. A clause-level sentiment analysis algorithm is developed since each sentence can contain multiple clauses discussing multiple aspects of a drug. The method adopts a pure linguistic approach of computing the sentiment orientation (positive, negative, or neutral) of a clause from the prior sentiment scores assigned to words, taking into consideration the grammatical relations and semantic annotation (such as disorder terms) of words in the clause. Experiment results with 2,700 clauses show the effectiveness of the proposed approach, and it performed significantly better than the baseline approaches using a machine learning approach. Various challenging issues were identified and discussed through error analysis. The application of the proposed sentiment analysis approach will be useful not only for patients, but also for drug makers and clinicians to obtain valuable summaries of public opinion. Since sentiment analysis is domain specific, domain knowledge in drug reviews is incorporated into the sentiment analysis algorithm to provide more accurate analysis. In particular, MetaMap is used to map various health and medical terms (such as disease and drug names) to semantic types in the Unified Medical Language System (UMLS) Semantic Network.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4204-4222
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• 2015

Cloud is the latest buzz word in the internet community among developers, consumers and security researchers. There have been many attacks on the cloud in the recent past where the services got interrupted and consumer privacy has been compromised. Denial of Service (DoS) attacks effect the service availability to the genuine user. Customers are paying to use the cloud, so enhancing the availability of services is a paramount task for the service provider. In the presence of DoS attacks, the availability is reduced drastically. Such attacks must be detected and prevented as early as possible and the power of computational approaches can be used to do so. In the literature, machine learning techniques have been used to detect the presence of attacks. In this paper, a novel approach is proposed, where intelligent rule based feature selection and classification are performed for DoS attack detection in the cloud. The performance of the proposed system has been evaluated on an experimental cloud set up with real time DoS tools. It was observed that the proposed system achieved an accuracy of 98.46% on the experimental data for 10,000 instances with 10 fold cross-validation. By using this methodology, the service providers will be able to provide a more secure cloud environment to the customers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.