• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.19 no.3
• /
• pp.127-133
• /
• 2019

Most studies of software fault prediction have been about supervised learning models that use only labeled training data. Although supervised learning usually shows high prediction performance, most development groups do not have sufficient labeled data. Unsupervised learning models that use only unlabeled data for training are difficult to build and show poor performance. Semi-supervised learning models that use both labeled data and unlabeled data can solve these problems. Self-training technique requires the fewest assumptions and constraints among semi-supervised techniques. In this paper, we implemented several models using self-training algorithms and evaluated them using Accuracy and AUC. As a result, YATSI showed the best performance.

• Human Ecology Research
• /
• v.53 no.2
• /
• pp.219-228
• /
• 2015

This study evaluated the content of Korean vocabulary learning applications with a focus on tablet PC applications. We analyzed 51 Korean vocabulary learning applications. The instruments in this study were developed based on Yoo et al. (2012)' Vocabulary Learning Game Application Evaluation Criteria and Hyun et al. (2013)' Educational Application Evaluation Criteria. Data were analyzed using a t-test and one-way analysis of variance. The main results are as follows. First, each criteria's score was fairly good; the ease of use had the highest scores and the amusement had the lowest scores. Second, there was a significant difference in the interaction by vocabulary teaching approach. Applications based on a whole language-teaching method had higher scores than applications based on a phonics instructional teaching method inducing more operation and with immediate feedback. Third, there was significant difference in the sum of score and each criteria of developmental appropriateness, educational values, amusement, function and interaction by type of learning. Applications of combining type had higher scores in every criteria except for ease of use than applications of description type. These findings provide a preliminary evidence that the systematic Korean vocabulary learning application facilitates preschoolers' vocabulary learning.

• Korean Medical Education Review
• /
• v.16 no.1
• /
• pp.7-10
• /
• 2014

Portfolios have gained attention in medical education as a tool for promoting student learning and assessment since Miller's call for better tools for assessing students' clinical competencies. This paper reviews the development and use of e-portfolios for promoting learning and assessment in medical schools, both domestically in Korea and internationally. This review finds that some specific features need to be incorporated into e-portfolio systems for medical education and that these systems can be used to manage student learning in clinical clerkships and to support competency-based assessment. The author asserts that the e-portfolio is key to promoting competency-based education and suggests practical tips for effective development and use of e-portfolios in Korean medical schools.

• Journal of the Korea Convergence Society
• /
• v.9 no.11
• /
• pp.15-21
• /
• 2018

In this study, we try to detect anomalies on the network intrusion detection system by learning only one class. We use KDD CUP 1999 dataset, an intrusion detection dataset, which is used to evaluate classification performance. One class classification is one of unsupervised learning methods that classifies attack class by learning only normal class. When using unsupervised learning, it difficult to achieve relatively high classification efficiency because it does not use negative instances for learning. However, unsupervised learning has the advantage for classifying unlabeled data. In this study, we use one class classifiers based on support vector machines and density estimation to detect new unknown attacks. The test using the classifier based on density estimation has shown relatively better performance and has a detection rate of about 96% while maintaining a low FPR for the new attacks.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.2
• /
• pp.61-68
• /
• 2021

Recently, various technologies that use machine learning to classify malicious code have been studied. In order to enhance the effectiveness of machine learning, it is most important to extract properties to identify malicious codes and normal binaries. In this paper, we propose a feature extraction method for use in machine learning using recursive methods. The proposed method selects the final feature using recursive methods for individual features to maximize the performance of machine learning. In detail, we use the method of extracting the best performing features among individual feature at each stage, and then combining the extracted features. We extract features with the proposed method and apply them to machine learning algorithms such as Decision Tree, SVM, Random Forest, and KNN, to validate that machine learning performance improves as the steps continue.

• English Language & Literature Teaching
• /
• v.11 no.3
• /
• pp.89-112
• /
• 2005

This study investigated the effects of collocation-based vocabulary instruction for the experimental group (G2). It was compared to the traditional wordlist-based vocabulary instruction for the control group (G1). This results reflect the development of low level high school EFL learners' vocabulary learning strategy use and the positive change in the affective domain. In the analysis of the survey responses, G1 and G2 did not differ significantly on the first questionnaire. They did, however, differ significantly on the second questionnaire. G2 used more strategies to discover and to consolidate the meaning of the words by means of combining words. In terms of the affective domain, G2 participated more actively in the learning activities, which had a significant effect on vocabulary growth, memory, self-confidence, motivation, and cooperative learning. This is attributable to the fact that G2 was more inquisitive, interested, challenged, participatory, cooperative, and attentive than G1 in performing the vocabulary task activities. Moreover, the data collected from the questionnaire showed that G2 performed more interactive and dynamic activities in solving the given tasks.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.3
• /
• pp.1015-1029
• /
• 2021

Modern technology offers many ways to enhance teaching and learning that in turn promote the development of tools for educational activities both inside and outside the classroom. Many educational programs using the augmented reality (AR) technology are being widely used to provide supplementary learning materials for students. This paper describes the potential and challenges of using GeoGebra AR in mathematical studies, whereby students can view 3D geometric objects for a better understanding of their structure, and verifies the feasibility of its use based on experimental results. The GeoGebra software can be used to draw geometric objects, and 3D geometric objects can be viewed using AR software or AR applications on mobile phones or computer tablets. These could provide some of the required materials for mathematical education at high schools or universities. The use of the GeoGebra application for education in Laos will be particularly discussed in this paper.

• Journal of Engineering Education Research
• /
• v.20 no.4
• /
• pp.28-37
• /
• 2017

The purpose of this study was to analyze the current status of team learning in engineering education. For this, literature review and survey were used. The survey was conducted with 16 professors and 627 students in engineering college. Based on the results, team should be organized in consideration of various characteristics and competencies for effective team learning activities in engineering education. And in the team learning operations, it is necessary to make the conditions for students to immerse in team learning through the activation of communication of team members, tightening management of free riding in team learning, and optimizing team learning period. It is necessary to use the team learning evaluation method in harmony with the team, individual and peer evaluation.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_2
• /
• pp.1053-1065
• /
• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.

• The Journal of Korea Robotics Society
• /
• v.14 no.2
• /
• pp.114-121
• /
• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.