• Journal of the Korean Data and Information Science Society
• /
• v.22 no.3
• /
• pp.459-466
• /
• 2011

This article addresses robust Bayesian modeling for meta analysis which derives general conclusion by combining independently performed individual studies. Specifically, we propose hierarchical Bayesian models with unknown variances for meta analysis under priors which are scale mixtures of normal, and thus have tail heavier than that of the normal. For the numerical analysis, we use the Gibbs sampler for calculating Bayesian estimators and illustrate the proposed methods using actual data.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.3
• /
• pp.71-77
• /
• 2018

The purpose of this study was to investigate the effectiveness of elastic band resistance training for muscle strength among community-dwelling older adults. The systematic review and meta-analysis was conducted by following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). Data were pooled using fixed effect models. Sit to stand, arm curl, and grip strength were analyzed for main effects. Heterogeneity between studies was assessed using the I2 statistics and publication bias was evaluated by funnel plots. Twelves studies were included representing 611 participants. Elastic band resistance training was effective for lower (d=3.89, 95% CI: 3.03, 4.75) and upper extremity muscle strength (d=4.08, 95% CI: 2.94, 5.23). Heterogeneity was moderate and no significant publication bias was detected. Based on these findings, there is clear evidence that elastic band resistance training has significant positive effects on muscle strength among community-dwelling older adults. Further study will be needed to perform subgroup analysis using number of sessions and exercise intensity as predictors.

• Journal of Advanced Navigation Technology
• /
• v.13 no.6
• /
• pp.854-860
• /
• 2009

Ubiquitous is a new computing environment with IT technology and information communication, and appling various equipments likes PDA and application parts. Recently, user's using environment is changing to smart phone and is expanded learning tools to learner without educational environment. Thus, in this paper, we designed SCORM based meta-data to use smart phone. For this purpose, we made U-learning server and smart phone process server that is to handling with existence LMS and SCORM. To apply smart phones characteristics that have different ones each other, meta-data was able to have some resource information as like CPU, screen size and memory. The meta-data adapter could be process the characteristics.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.8
• /
• pp.2072-2087
• /
• 1999

As software size ever increases and user's requirements become more and more sophisticated., the importance of software quality is more and more emphasized. However, we are not satisfied for the present techniques on detecting errors and checking consistency in the object-oriented design model. This paper proposes a systematic approach which produces implementable rules to detect errors and check consistency. At first, the meta-models for UML diagrams are constructed, generalized meta-rules are reduced from the meta-models, and then the meta-rules are applied to produce the implementable rules. This approach enables to pursue the completeness of the rules and the automation of rule application. An example of rule application shows the feasibility of the rule application.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.05a
• /
• pp.112-114
• /
• 2016

Big data analysis refers the ability to store, manage and analyze collected data from an existing database management tool. Meta-analysis refers to a statistical literature synthesis method from the quantitative results of many known empirical studies. We conducted a meta-analysis and review of between external factors on perceived value in e-commerce researches. This study focused a total of 11 research papers that established causal relationships between external factors on perceived value in e-commerce published in Korea academic journals during 2000 and 2016. Based on these findings, several theoretical and practical implications were suggested and discussed with the difference from previous researches.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.10
• /
• pp.157-164
• /
• 2018

The purpose of this study was to investigate the effect of empowerment in nurses through a systematic literature review and meta-analysis. 23 studies were collected through a systematic process of using several databases such as NDSL, DBPIA, and KISS. Keywords included 'nurse', 'empowerment', and 'correlation' and the reviewed articles were published from 2002 to 2017. In order to estimate the effect size of correlation between empowerment, 3 variables (job satisfaction, organizational commitment, and nursing performance) were considered. Using the R program, meta-analysis was calculated by using random effects model, and effect sizes on three types were estimated. As the result, it was found, first, the effect size of correlation between job satisfaction and empowerment is .50. Second, the effect size of correlation between organizational commitment and empowerment is .45. Third, for the nursing performance and empowerment relationship, the effect of correlation is 0.50. Also, Egger's regression test, fail-safe N, trim-and-fill test, and funnel plot were showed to evaluate the results. These results highlights the need for appropriate policies of the relationship between empowerment and job satisfaction, organizational commitment and nursing performance in nurses.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2015.04a
• /
• pp.424-427
• /
• 2015

근래 들어 모바일 기기의 시스템을 장악하여 사용자의 기밀 정보를 빼내는 악성 행위의 한 방법으로 Code Reuse Attack (CRA)이 널리 사용되고 있다. 이와 같은 CRA를 막기 위하여 call-return이 일어날 때마다 이들 address를 비교해 보는 shadow stack과 branch에 대한 몇 가지 규칙을 두어 CRA 를 탐지하는 branch regulation과 같은 방식이 연구되었다. 우리는 shadow stack과 branch regulation을 종합하여 여러 종류의 CRA를 적은 성능 오버헤드로 탐지할 수 있는 CRA Detection System을 만들고자 한다. 이를 위하여 반드시 선행 되어야 할 연구인 바이너리 파일 분석과 meta-data 생성 및 압축 기술을 제안한다. 실험 결과 생성된 meta-data는 압축 기술을 적용하기 전보다 1/2에서 1/3 가량으로 그 크기가 줄어들었으며 CRA Detection System의 탐지가 정상적으로 동작하는 것 또한 확인할 수 있었다.

• Journal of Intelligence and Information Systems
• /
• v.26 no.1
• /
• pp.23-45
• /
• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Information Technology Services
• /
• v.13 no.1
• /
• pp.71-86
• /
• 2014

This study investigated knowledge browsing behavior as the factor affecting the increase of knowledge sharing intention. To conduct this study in the specific context of knowledge seeking and sharing behavior of virtual community members, literature on knowledge seeking behavior, meta-knowledge, and knowledge sharing intention was reviewed. Structural Equation Modeling was conducted to analyze survey data to test the research model of this study. The result showed that knowledge browsing have positive effects on creating of virtual community members' subject knowledge and meta-knowledge, which, in turn, affected positively their knowledge sharing intention. One of the main contributions of this study is that knowledge seeking behavior influence one's knowledge sharing intention in a virtual community. Organization managers should consider knowledge seeking behavior as not only a self-interested, consuming activity, but also a productive one through its function of constructing subject knowledge and meta-knowledge.

• MALSORI
• /
• v.55
• /
• pp.77-91
• /
• 2005

In this paper, we propose an XML based meta-data specification for industrial speech databases. Building speech databases is very time-consuming and expensive. Recently, by the government supports, huge amount of speech corpus has been collected as speech databases. However, the formats and meta-data for speech databases are different depending on the constructing institutions. In order to advance the reusability and portability of speech databases, a standard representation scheme should be adopted by all speech database construction institutions. ETRI proposed a XML based annotation scheme [51 for speech databases, but the scheme has too simple and flat modeling structure, and may cause duplicated information. In order to overcome such disadvantages in this previous scheme, we first define the speech database more formally and then identify object appearing in speech databases. We then design the data model for speech databases in an object-oriented way. Based on the designed data model, we develop the meta-data specification for industrial speech databases.