• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.305-326
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• 2019

Discriminative correlation filter (DCF) based tracking algorithms have recently shown impressive performance on benchmark datasets. However, amount of recent researches are vulnerable to heavy occlusions, irregular deformations and so on. In this paper, we intend to solve these problems and handle the contradiction between accuracy and real-time in the framework of tracking-by-detection. Firstly, we propose an innovative strategy to combine the template and color-based models instead of a simple linear superposition and rely on the strengths of both to promote the accuracy. Secondly, to enhance the discriminative power of the learned template model, the spatial regularization is introduced in the learning stage to penalize the objective boundary information corresponding to features in the background. Thirdly, we utilize a discriminative multi-scale estimate method to solve the problem of scale variations. Finally, we research strategies to limit the computational complexity of our tracker. Abundant experiments demonstrate that our tracker performs superiorly against several advanced algorithms on both the OTB2013 and OTB2015 datasets while maintaining the high frame rates.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.24 no.4
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• pp.265-276
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• 2017

Purpose: This study was conducted to design and implement a fundamental nursing practice based on flipped learning and to examine the effects. Methods: Participants were 57 students who were taking the fundamental nursing practice course at D university in N city. The study included processes of instructional design, action/effects and reflection. Data were analyzed using paired t-test with the SPSS/WIN 23.0. Results: In the instructional design stage, the class consisted of 3 parts: outside class (pre-learning), inside class (assessment, collaborative practice, peer review, reflection), after-class (self-directed practice, feedback). In the action/effects stage, the flipped learning was applied for 15 weeks according to the instructional design and then the effects of flipped learning were evaluated. Students showed a significant improvement in self-directed learning ability (t=-3.56, p=.001) and critical thinking disposition after the class (t=-3.72, p<.001). Finally, in the reflection stage, the researchers examined whether the four pillars of flipped learning occurred. Conclusion: Findings indicate that flipped learning applied in fundamental nursing practice is effective in improving self-directed learning ability and critical thinking disposition. The action research method was a useful way to foster professor's educational competency as well as to verify effects of a new nursing education method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.12
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• pp.4776-4794
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• 2020

We propose a rapid adaptive learning framework for streaming object detection, called EER-ASSL. The method combines the expected error reduction (EER) dependent rollback learning and the active semi-supervised learning (ASSL) for a rapid adaptive CNN detector. Most CNN object detectors are built on the assumption of static data distribution. However, images are often noisy and biased, and the data distribution is imbalanced in a real world environment. The proposed method consists of collaborative sampling and EER-ASSL. The EER-ASSL utilizes the active learning (AL) and rollback based semi-supervised learning (SSL). The AL allows us to select more informative and representative samples measuring uncertainty and diversity. The SSL divides the selected streaming image samples into the bins and each bin repeatedly transfers the discriminative knowledge of the EER and CNN models to the next bin until convergence and incorporation with the EER rollback learning algorithm is achieved. The EER models provide a rapid short-term myopic adaptation and the CNN models an incremental long-term performance improvement. EER-ASSL can overcome noisy and biased labels in varying data distribution. Extensive experiments shows that EER-ASSL obtained 70.9 mAP compared to state-of-the-art technology such as Faster RCNN, SSD300, and YOLOv2.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.9 no.3
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• pp.447-461
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• 2002

Purpose: The purpose of this study was to identify and understand the self-directed learning patterns of nurses. Q methodology was used to collect the data. Method: For the research method, 43 Q-statements were collected through individual interviews and a review of related literature. The 43 Q-statements were classified by the 34 participants in the study and the data was analyzed by the PC-QUANL program with principal component analysis. Result: There were 4 different patterns of self-directed learning classified as follows : Nurses in Type I the Future Provision Type, studied to promote their own professional development and leadership qualities for the future. Nurses in Type II, the Learning Passion Type, enjoyed learning something new and had a strong learning desire. Nurses in Type III, the Self-reflective Type, continuously evaluated self and their own practice by introspection. Nurses in Type IV, the Accompanying Companion Type, studies with companion support and maintained a collaborative relationship rather than competing with each other. Conclusion: This study explains and allows us to understand self-directed learning in nurses. Thus this study will contribute to building a theoretical base for the development of a self-directed learning model in nursing practice.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.339-345
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• 2022

Outcome-based education (OBE) is a tried-and-true teaching technique based on a set of predetermined goals. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes (COs) are the components of OBE. At the end of each year, the Program Outcomes are evaluated, and faculty members can submit many recommended measures which dependent on the relationship between the program outcomes and its courses outcomes to improve the quality of program and hence the overall educational program. When a vast number of courses are considered, bad actions may be proposed, resulting in unwanted and incorrect decisions. In this paper, a recommender system, using collaborative filtering and association rules algorithms, is proposed for predicting the best relationship between the program outcomes and its courses in order to improve the attributes of the graduates. First, a parallel algorithm is used for Collaborative Filtering on Data Model, which is designed to increase the efficiency of processing big data. Then, a parallel similar learning outcomes discovery method based on matrix correlation is proposed by mining association rules. As a case study, the proposed recommender system is applied to the Computer Information Systems program, College of Computer Sciences and Information Technology, Al-Baha University, Saudi Arabia for helping Program Quality Administration improving the quality of program outcomes. The obtained results revealed that the suggested recommender system provides more actions for boosting Graduate Attributes quality.

• Journal of KIISE:Software and Applications
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• v.31 no.4
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• pp.457-466
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• 2004

Recommendation is to offer information which fits user's interests and tastes to provide better services and to reduce information overload. It recently draws attention upon Internet users and information providers. The collaborative filtering is one of the widely used methods for recommendation. It recommends an item to a user based on the reference users' preferences for the target item or the target user's preferences for the reference items. In this paper, we propose a neural network based collaborative filtering method. Our method builds a model by learning correlation between users or items using a multi-layer perceptron. We also investigate integration of diverse information to solve the sparsity problem and selecting the reference users or items based on similarity to improve performance. We finally demonstrate that our method outperforms the existing methods through experiments using the EachMovie data.

• Journal of KIISE:Software and Applications
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• v.30 no.3_4
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• pp.316-325
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• 2003

A user preference prediction method using an exiting collaborative filtering technique has used the nearest-neighborhood method based on the user preference about items and has sought the user's similarity from the Pearson correlation coefficient. Therefore, it does not reflect any contents about items and also solve the problem of the sparsity. This study suggests the preference prediction system using the similarity weight granted Bayesian estimated value and the associative user clustering to complement problems of an exiting collaborative preference prediction method. This method suggested in this paper groups the user according to the Genre by using Association Rule Hypergraph Partitioning Algorithm and the new user is classified into one of these Genres by Naive Bayes classifier to slove the problem of sparsity in the collaborative filtering system. Besides, for get the similarity between users belonged to the classified genre and new users, this study allows the different estimated value to item which user vote through Naive Bayes learning. If the preference with estimated value is applied to the exiting Pearson correlation coefficient, it is able to promote the precision of the prediction by reducing the error of the prediction because of missing value. To estimate the performance of suggested method, the suggested method is compared with existing collaborative filtering techniques. As a result, the proposed method is efficient for improving the accuracy of prediction through solving problems of existing collaborative filtering techniques.

• Journal of Korea Game Society
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• v.8 no.4
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• pp.3-16
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• 2008

This study tried to look into various linguistic elements of MMORPG and suggested teaching methods to be applied in teaching and learning English. To support this idea, diverse attributes of CALL applications were investigated and suggested suitable position of MMORPG in technological stream of CALL. After that, focusing on 'Goonzu Global', the linguistic environment of MMORPG was considered. Based on aforementioned results, this study proposed an effective method to utilize MMORPG in teaching English adopting three teaching methods; collaborative method, task-based learning and problem-based learning.

• Journal of Platform Technology
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• v.11 no.4
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• pp.80-88
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• 2023

To construct a learning model in the field of artificial intelligence, a dataset should be collected and be delivered to the central server where the learning model is constructed. Federated learning is a machine learning method building a global learning model without transmitting data located in a client side in a collaborative manner. It can be used to protect privacy, and after constructing a local trained model on individual clients, the parameters of the local model are aggregated centrally to update the global model. In this paper, we reuse the existing learning parameter to improve federated learning, describe incremental federated learning. For this work, we do experiments using the federated learning framework named Flower, and evaluate the experiment results with regard to elapsed time and precision when executing optimization algorithms.

• The Mathematical Education
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• v.61 no.4
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• pp.521-537
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• 2022

The unprecedented COVID-19 pandemic has disrupted, interrupted, and changed the way we normally prepare our teacher candidates in teacher preparation programs. In this paper, we, two mathematics teacher educators (MTEs), reflect our own experiences in appropriating, transforming, reconstructing, and modifying our pedagogies of teacher education in making a transition from face-to-face to online environment during the COVID-19 pandemic. Using a collaborative self-study, we discussed issues, challenges, changes, opportunities, and innovations of teaching an elementary mathematics methods course in the online environment. Using a constant comparison method, we explored the following three themes: (1) using virtual manipulatives; (2) creating collaborative, interactive, and shared learning experiences for preservice teachers; and (3) making preservice teachers engaged in student thinking. These findings indicated that online teaching requires transformative knowledge for teacher educators. Transferring face-to-face to online is not a simple matter of putting the existing content to online; it should focus on pedagogical improvement in teaching mathematics rather than technology's sake or how it can be repurposed in a new online environment in a way that students' learning is optimized. The findings of this study provide implications for unpacking MTEs' technological pedagogical content knowledge (TPACK), creating collaborative learning experiences for preservice teachers, and designing a collaborative self-study between MTEs engaged in the community of professional learning.