• International Journal of Contents
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• v.13 no.1
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• pp.38-44
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• 2017

Increased use of software and complexity of software functions, as well as shortened software quality evaluation periods, have increased the importance and necessity for automation of software testing. Automating software testing by using machine learning not only minimizes errors in manual testing, but also allows a speedier evaluation. Research on machine learning in automated software testing has so far focused on solving special problems with algorithms, leading to difficulties for the software developers and testers, in applying machine learning to software testing automation. This paper, proposes a new machine learning framework for software testing automation through related studies. To maximize the performance of software testing, we analyzed and categorized the machine learning algorithms applicable to each software test phase, including the diverse data that can be used in the algorithms. We believe that our framework allows software developers or testers to choose a machine learning algorithm suitable for their purpose.

• International Journal of Contents
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• v.12 no.4
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• pp.31-44
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• 2016

Knowledge creation and management are regarded as critical success factors for an organization's survival in the knowledge era. As a process of knowledge acquisition and sharing, organizational learning mechanisms (OLMs) guide the learning function of organizations represented by its different learning activities. We examined a variety of learning processes that constitute OLMs. In this study, we aimed to capture the process and framework of OLMs and knowledge sharing and acquisition. Factors facilitating OLMs were investigated at three levels: individual, group, and organizational. The concept of an OLM has received some attention in the field of organizational learning, however, the relationship among the factors generating OLMs has not been empirically tested. As part of the ongoing discussion, we attempted a systemic approach for OLMs. OLMs can be represented by factors that are inherent to the organization's system; therefore, prior to empirically testing the OLM generating factor(s), evaluation of its organizational integration is required to determine effective treatment of each factor. Thus, we developed a framework to manage knowledge and proposed a method to numerically evaluate factors influencing the OLMs. Specifically, composite importance (CI) of the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was applied to explore the interaction effect of these factors based on systemic approach. The augmented matrix thus generated is expected to serve as a stochastic matrix of an absorbing Markov chain.

• The Mathematical Education
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• v.58 no.4
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• pp.507-518
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• 2019

The OECD launched the Education 2030 project to develop a learning framework and to conduct the international comparative study on curriculum. As a part of the OECD international curriculum analysis, Korea Institute for Curriculum and Evaluation(KICE) conducted a main study of Curriculum Content Mapping (hereafter, CCM) in the 7 learning areas/subject areas such as national languages, mathematics, humanities/social sciences, natural science, physical education/health, arts, and technologies. The CCM study aimed to identify how the competencies on CCM framework were reflected in the Korea curriculum. For this purpose, KICE identified the competencies on CCM framework, revised the coding framework, and undertook the mapping process. In this study, we gathered the CCM data as an evidence of how competencies on CCM framework were embedded in the 2015 revised mathematics curriculum. For this purpose, experts in mathematics education undertook the mapping process, we summarized the results of CCM main study in mathematics. As the results, numeracy, critical thinking, problem solving, anticipation, action, reflection were perfectly embedded in the 2015 revised mathematics curriculum. the competencies on CCM framework were embedded in the 2015 revised mathematics curriculum, and but literacy, physical/health literacy, trust, learning to learn, reconciling tension and dilemmas, literacy for sustainable development, financial literacy, and entrepreneurship/enterprising were not clearly related to mathematics curriculum. The mapping results should help the Korea Ministry of Education and KICE for preparing the future curriculum revision and development.

• Journal of Korea Society of Digital Industry and Information Management
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• v.13 no.4
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• pp.125-139
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• 2017

In this paper, we developed a framework to detect and predict insider information leakage by collecting and restoring network traffic. For automated behavior analysis, many meta information and behavior information obtained using network traffic collection are used as machine learning features. By these features, we created and learned behavior model, network model and protocol-specific models. In addition, the ensemble model was developed by digitizing and summing the results of various models. We developed a function to present information leakage candidates and view meta information and behavior information from various perspectives using the visual analysis. This supports to rule-based threat detection and machine learning based threat detection. In the future, we plan to make an ensemble model that applies a regression model to the results of the models, and plan to develop a model with deep learning technology.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.2
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• pp.81-92
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• 2008

With the increasing prevalence of e-learning courses in human resource development of Enterprise, it is important to investigate which courses are better economic performance. In this study, we proposed the framework for the cost-benefit analysis of e-learning, and attempted to identify cost and benefits factors. In order to achieve the research goal, we firstly tries to analyze the current IT adoption performance framework and e-learning staged performance model. The methodology adopted in the research was mainly that relevant materials, literatures were collected and analyzed to draw a comprehensive picture of the current situation and problems.

• Research in Mathematical Education
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• v.22 no.2
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• pp.135-152
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• 2019

This conceptual paper proposes a conceptualization regarding teacher candidates' experiences as learners during instructional activities implemented by teacher educators in practice-based teacher education programs. We argue that the current learning cycle framework for teacher candidates to engage in core teaching practices does not fully address teacher candidates' own learning experiences as learners. To provide a rationale for our proposal, we examine the current conceptualization of learning to enact core practices and suggest the need for integrating teacher candidates' experiences into the current conceptualization. We also draw on research on figured worlds as an effort to conceptualize teacher candidates' experiences coming from multiple figured world. We present some examples from our own mathematics methods courses to illustrate how this newly proposed framework can be used in practice and share remaining questions for future research.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.97-105
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• 2022

Web services show a rapid evolution and integration to meet the increased users' requirements. Thus, web services undergo updates and may have performance degradation due to undetected faults in the updated versions. Due to these faults, many performances and regression anomalies in web services may occur in real-world scenarios. This paper proposed applying the deep learning model and innovative explainable framework to detect performance and regression anomalies in web services. This study indicated that upper bound and lower bound values in performance metrics provide us with the simple means to detect the performance and regression anomalies in updated versions of web services. The explainable deep learning method enabled us to decide the precise use of deep learning to detect performance and anomalies in web services. The evaluation results of the proposed approach showed us the detection of unusual behavior of web service. The proposed approach is efficient and straightforward in detecting regression anomalies in web services compared with the existing approaches.

• Journal of the Korean Operations Research and Management Science Society
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• v.19 no.2
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• pp.153-176
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• 1994

In this paper we introduce machine learning theory to business domains for business decisions. First, we review machine learning in general. We give a new look on a previous framework, version space approach, and we introduce PAC (probably approximately correct) learning paradigm which has been developed recently. We illustrate major results of PAC learning with business examples. And then, we give a theoretical analysis is decision tree induction algorithms by the frame work of PAC learning. Finally, we will discuss implications of learning theory toi business domains.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.303-308
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• 2018

Demand is increasing rapidly in recent years than supply to machine learning professionals. To alleviate this gap, user-friendly machine learning software that can be used by non-specialists has emerged, which is Machine Learning-as-a-Service(MLaaS). MLaaS provides services that enable businesses to easily leverage ML capabilities without expertise. In this paper, we will compare and analyze features, interfaces, supporting programming language, ML framework, and Machine Learning services of MLaaS, to help companies easily use ML service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4291-4310
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• 2015

This paper investigates the traffic offloading over unlicensed bands for two-tier multi-mode small cell networks. We formulate this problem as a Stackelberg game and apply a hierarchical learning framework to jointly maximize the utilities of both macro base station (MBS) and small base stations (SBSs). During the learning process, the MBS behaves as a leader and the SBSs are followers. A pricing mechanism is adopt by MBS and the price information is broadcasted to all SBSs by MBS firstly, then each SBS competes with other SBSs and takes its best response strategies to appropriately allocate the traffic load in licensed and unlicensed band in the sequel, taking the traffic flow payment charged by MBS into consideration. Then, we present a hierarchical Q-learning algorithm (HQL) to discover the Stackelberg equilibrium. Additionally, if some extra information can be obtained via feedback, we propose an improved hierarchical Q-learning algorithm (IHQL) to speed up the SBSs' learning process. Last but not the least, the convergence performance of the proposed two algorithms is analyzed. Numerical experiments are presented to validate the proposed schemes and show the effectiveness.