• ETRI Journal
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• v.43 no.1
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• pp.53-63
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• 2021

Cloud computing has drastically reduced the price of computing resources through the use of virtualized resources that are shared among users. However, the established large cloud data centers have a large carbon footprint owing to their excessive power consumption. Inefficiency in resource utilization and power consumption results in the low fiscal gain of service providers. Therefore, data centers should adopt an effective resource-management approach. In this paper, we present a novel load-balancing framework with the objective of minimizing the operational cost of data centers through improved resource utilization. The framework utilizes a modified genetic algorithm for realizing the optimal allocation of virtual machines (VMs) over physical machines. The experimental results demonstrate that the proposed framework improves the resource utilization by up to 45.21%, 84.49%, 119.93%, and 113.96% over a recent and three other standard heuristics-based VM placement approaches.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.268-282
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• 2022

One of the most important technologies today is augmented reality technology, it allows users to experience the real world using virtual objects that are combined with the real world. This technology is interesting and has become applied in many sectors such as the shopping and medicine, also it has been included in the sector of education. In the field of education, AR technology has become widely used due to its effectiveness. It has many benefits, such as arousing students' interest in learning imaginative concepts that are difficult to understand. On the other hand, studies have proven that collaborative between students increases learning opportunities by exchanging information, and this is known as Collaborative Learning. The use of multimodal creates a distinctive and interesting experience, especially for students, as it increases the interaction of users with the technologies. The research aims at developing collaborative framework for developing achievement of 6th graders through designing a framework that integrated a collaborative framework with a multimodal input "hand-gesture and touch", considering the development of an effective, fun and easy to use framework with a multimodal interaction in AR technology that was applied to reformulate the genetics and traits lesson from the science textbook for the 6th grade, the first semester, the second lesson, in an interactive manner by creating a video based on the science teachers' consultations and a puzzle game in which the game images were inserted. As well, the framework adopted the cooperative between students to solve the questions. The finding showed a significant difference between post-test and pre-test of the experimental group on the mean scores of the science course at the level of remembering, understanding, and applying. Which indicates the success of the framework, in addition to the fact that 43 students preferred to use the framework over traditional education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2236-2257
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• 2020

Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately.

• Structural Engineering and Mechanics
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• v.35 no.6
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• pp.659-676
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• 2010

An energy-based fatigue life prediction framework was previously developed by the authors for prediction of axial and bending fatigue life at various stress ratios. The framework for the prediction of fatigue life via energy analysis was based on a new constitutive law, which states the following: the amount of energy required to fracture a material is constant. In this study, the energy expressions that construct the new constitutive law are integrated into minimum potential energy formulation to develop new finite elements for uniaxial and bending fatigue life prediction. The comparison of finite element method (FEM) results to existing experimental fatigue data, verifies the new finite elements for fatigue life prediction. The final output of this finite element analysis is in the form of number of cycles to failure for each element in ascending or descending order. Therefore, the new finite element framework can provide the number of cycles to failure for each element in structural components. The performance of the fatigue finite elements is demonstrated by the fatigue life predictions from Al6061-T6 aluminum and Ti-6Al-4V. Results are compared with experimental results and analytical predictions.

• Educational Technology International
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• v.14 no.1
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• pp.75-108
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• 2013

The purpose of this study is to find a way to improve digital textbooks for self-regulated learning by applying cognitive scaffolding designs to an elementary math digital textbook and examining the effectiveness of the system. Hence this study was conducted in two steps. First, a framework for scaffolding design was devised by examining the problems and difficulties students encounter when using a mathematics digital textbook. Second, after the digital textbook was revised by applying the scaffolding design frameworks, the effectiveness of the scaffolding framework was examined by comparing students' achievement levels in an experimental group and that of students in a control group. Seventy fifth-graders participated in this study. Students were divided into two groups: an experimental group and a control group. The students in the experimental group studied with the revised version of the digital textbook and the students in the control group studied with the original version of the digital textbook. The students received a pretest before the experiment. After the experiment, they took an achievement test and completed a usability questionnaire. The data were analyzed by ANCOVA with the SPSS Windows version. The results revealed that the students who used the revised program (to which design strategies for scaffolding were applied) showed higher levels of achievement than those who used the original version. In addition, students in the experimental group generally showed higher scores on the usability survey, which consisted of four sub-categories such as 'effectiveness', 'efficiency', 'satisfaction', and 'learnability'. There was a statistically significant effect on 'efficiency'. These results implied that scaffolding strategies were effective for mathematics learning through the use of an elementary digital textbook.

• Journal of The Korean Association For Science Education
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• v.32 no.1
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• pp.146-159
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• 2012

The purpose of this study was to examine the relation between students' reflective thinking and providing the reading framework in implementation of argument-based inquiry using the Science Writing Heuristic (SWH) approach. Participants of this study were 60 $8^{th}$ grade students (two classes). One class (31 students) was assigned to an experimental group and the other class (29 students) was assigned to a comparative group. For the experimental group, five activities using the reading framework with SWH writing template were implemented, while three activities using the reading framework with the SWH writing template and two SWH activities without the reading framework were implemented for the comparative group. The result of this study showed that there was no significant difference in students' reflective thinking between both groups. However, results indicated that providing the reading framework with SWH approach facilitated students' reflective thinking. Therefore, the findings show that providing the reading framework consistently in the SWH approach was effective when it came to facilitating students' reflective thinking.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.97-100
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• 2002

An e-catalog system provides whole product data and services by publishing newly updated information that connects PDM systems in an enterprise. With its capability to connect customers, suppliers, producers, distributors and downstream manufacturing activities, the e-catalog system can be applied to integrate e-business and provide immense business opportunities. In this paper we propose a framework focusing on constructing an efficient e-catalog system that can be used for online mass customization. The effectiveness of the proposed framework is validated with experimental results.

• ETRI Journal
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• v.29 no.5
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• pp.697-699
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• 2007

Spatiograms are histograms augmented with spatial means and covariances to capture a richer description of the target. We present a particle filtering framework for region-based tracking using spatiograms. Unlike mean shift, the framework allows for non-differentiable similarity measures to compare two spatiograms; we present one such similarity measure, a combination of a recent weighting scheme and histogram intersection. Experimental results show improved performance with the new measure as well as the importance of global spatial information for tracking. The performance of spatiograms is compared with color histograms and several texture histogram methods.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5S
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• pp.1687-1695
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• 2000

Recently, many researchers have tried to establish EC framework EC framework for automated contract and brokerage using agent technologies in electronic commerce. Traditional researches, however, often enforced the user's participations during the automated brokerage process of agents an did not supported the procedures and methodologies for satisfying various user's requirements. In this paper, we propose a brokerge framework based on multi-agent to overcome the defects of the previous researches. For the optimal brokerage, the proposed framework is divided into tow layers, which are called competition layer and constraint satisfaction layer. The Agents process behaviors of brokerage through two layers accroding to th proposed message driven communication protocol. W also apply CSP (Constraint Satisfaction Problems) techniques to brokerage process to satisfy various preferential requirements from the user. That is, e model the relationship between buyer agents and seller agents using CSP and propose efficient CSP models for the optimal brokerage as the types of brokerage. Finally, e implemented prototype systems applying the proposed framework and showed the efficiency of he proposed framwork with the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.1
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• pp.232-248
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• 2014

A series of kernel regression (KR) algorithms, such as the classic kernel regression (CKR), the 2- and 3-D steering kernel regression (SKR), have been proposed for image and video super-resolution. In existing KR frameworks, a single algorithm is usually adopted and applied for a whole image/video, regardless of region characteristics. However, their performances and computational efficiencies can differ in regions of different characteristics. To take full advantage of the KR algorithms and avoid their disadvantage, this paper proposes a kernel regression framework for video super-resolution. In this framework, each video frame is first analyzed and divided into three types of regions: flat, non-flat-stationary, and non-flat-moving regions. Then different KR algorithm is selected according to the region type. The CKR and 2-D SKR algorithms are applied to flat and non-flat-stationary regions, respectively. For non-flat-moving regions, this paper proposes a similarity-assisted steering kernel regression (SASKR) algorithm, which can give better performance and higher computational efficiency than the 3-D SKR algorithm. Experimental results demonstrate that the computational efficiency of the proposed framework is greatly improved without apparent degradation in performance.