• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1773-1793
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• 2023

Automated machine learning, often referred to as "AutoML," is the process of automating the time-consuming and iterative procedures that are associated with the building of machine learning models. There have been significant contributions in this area across a number of different stages of accomplishing a data-mining task, including model selection, hyper-parameter optimization, and preprocessing method selection. Among them, preprocessing method selection is a relatively new and fast growing research area. The current work is focused on the recommendation of preprocessing methods, i.e., feature subset selection (FSS) algorithms. One limitation in the existing studies regarding FSS algorithm recommendation is the use of a single learner for meta-modeling, which restricts its capabilities in the metamodeling. Moreover, the meta-modeling in the existing studies is typically based on a single group of data characterization measures (DCMs). Nonetheless, there are a number of complementary DCM groups, and their combination will allow them to leverage their diversity, resulting in improved meta-modeling. This study aims to address these limitations by proposing an architecture for preprocess method selection that uses ensemble learning for meta-modeling, namely AutoFE-Sel. To evaluate the proposed method, we performed an extensive experimental evaluation involving 8 FSS algorithms, 3 groups of DCMs, and 125 datasets. Results show that the proposed method achieves better performance compared to three baseline methods. The proposed architecture can also be easily extended to other preprocessing method selections, e.g., noise-filter selection and imbalance handling method selection.

• Journal of the Korea Society of Computer and Information
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• v.22 no.11
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• pp.125-133
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• 2017

Recently, as the programming education has become essential in school, discussion of how to teach programming has been important. This study performed a meta-analysis of the effect size depending on the teaching and learning method for the programming education. 78 research data selected from 45 papers were analyzed from cognitive and affective aspects according to dependent variables. The analysis from the cognitive aspect showed that there was no statistically significant difference in the effect size depending on whether or not the teaching and learning method was specified in the research paper. Meta-analysis of the research data where the teaching and learning method was designated displayed significances in CPS, PBL and Storytelling. Unlike the cognitive aspect, the analysis from the affective aspect showed that the effect size of the research data without the specified teaching and learning method was larger than those with specified teaching and learning method with a statistical significance. Meta-analysis of the data according to the teaching and learning method displayed no statistical significance. Based upon these research results, this study suggested implications for the effective programming education.

• Proceedings of the Korea Information Processing Society Conference
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• 2014.11a
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• pp.443-446
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• 2014

Code reuse attack (CRA)는 기존의 코드 내에서 필요한 코드 조각들 (gadgets)을 모아 indirect branch 명령어들로 잇는 방식으로 공격자가 원하는 악성 프로그램을 구성할 수 있는 강력한 공격 방법이다. 공격자는 자신의 코드를 대상 시스템에 심는 대신 기존의 코드를 이용하기 때문에, 대부분의 범용 운영체제 (OS)가 강제하는 W^X protection 을 무력화할 수 있다. 이러한 CRA 에 대응하기 위하여 다수의 연구들에서 branch 의 trace 를 분석하여 CRA 고유의 특성을 찾아내는 Signature 기반 탐지 기술을 제안하였다. 본 논문에서는 ARM 프로세서 상에서의 CRA 를 대응하기 위한 Signature 기반 탐지 기술을 효율적으로 도울 수 있는 binary 분석 및 meta-data 생성 기술을 제안한다. 특히, 본 논문은 우리의 이전 논문에서 고려 되지 못했던 ARM 의 두 가지 명령어 세트의 특성을 고려하여, 공격자가 어느 명령어 세트를 이용하여 CRA 를 시도하더라도 막아낼 수 있도록 meta-data 를 두 가지 mode 에 대해서 생성하였다. 실험 결과, meta-data 는 본래 바이너리 코드 대비 20.8% 정도의 크기 증가를 일으키는 것으로 나타났다.

• Journal of Intelligence and Information Systems
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• v.6 no.1
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• pp.35-50
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• 2000

Diverse requirements of users on web-based model management force a system agent to develop user-adaptive building a model in reality and providing an adequate solution method of the model. The relationship between models is important knowledge for the agent to effectively build a new model to adaptively adjust an existing model under a problem and to efficiently connect the new model into an adequate solution method. Since the generating process of the inter-model relationship is more difficult than the building a new model however the process mostly depends on the knowledge of operation research experts. Without the adequate scheme of the inter-model relationship the burden of the management for the agent increases rapidly and the quality of the services may worsen. This study shows that meta-knowledge generated from relationship between models is important for the user to build a model in reality and to acquire the solver appropriate to the model. The relationship that consists of common and exclusive objects between models can be represented by frames. The system under development to implement the idea includes user-adaptive ability which identifies a model through forward chaining method and searches the solver appropriate to the model by using the meta knowledge. We illustrate the meta knowledge with an applied delivery system in supply chain management.

• Journal of Korean Academy of Nursing
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• v.49 no.3
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• pp.225-240
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• 2019

Purpose: The purpose of this study was to evaluate the effects of reminiscence therapy on depressive symptoms in older adults with dementia using a systematic review and meta-analysis. Methods: Randomized controlled trials (RCTs) published from January 2000 to January 2018 were searched through Research Information Sharing Service (RISS), Korean Studies Information Service System (KISS), Korean Medical Database (KMbase), KoreaMed, PubMed, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Ovid MEDLINE. Two researchers independently performed the search, selection, and coding. Comprehensive Meta-Analysis 3.0 was used for meta-analysis, and Review Manager program 5.3 was used for quality assessment. Results: Out of the 1,250 retrieved articles, 22 RCTs were selected for analysis. The overall effect size of reminiscence therapy for mitigating depressive symptoms in older adults with dementia was -0.62 (95% Cl: -0.92 to -0.31). The effect size was greater in older adults under 80, those with less disease severity, and those for whom the therapy session lasted less than 40 minutes. Conclusion: Reminiscence therapy is an effective non-pharmacological therapy to improve depressive symptoms in older adults with dementia. Because its effectiveness is also influenced by age, disease severity, and application method, it is necessary to consider treatment designs based on individual characteristics as well as methodological approaches.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1755-1777
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• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.

• Journal of Korean Academy of Nursing
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• v.43 no.6
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• pp.736-745
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• 2013

Purpose: The purpose of the study was to assess the quality of meta-analyses on nursing published in South Korea. Methods: Relevant meta-analyses were identified through searches of the National Assembly Library, KISS (Korean Studies Information Service System), and the DBpia and RISS4U databases from 1990 to May 2013. Quality assessments were conducted using AMSTAR, a validated tool for assessing the quality of systematic reviews. Results: Forty-two meta-analyses were included in this study. Twenty-nine published between 1990 and 2010, and 13, between 2011 and May 2013. Two high quality studies and 11 moderate quality studies were published in the latter period. The mean score for the reviews was 5.61 (range 3-10); 11 studies were rated as low quality, 29 as moderate quality, and two as high quality. Conclusion: Although an improvement in the quality of meta-analyses conducted by nursing researchers in South Korea was observed across the study period, the study results indicate a need to use of more rigorous research methods when conducting systematic reviews or meta-analyses.

• Journal of Korean Physical Therapy Science
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• v.27 no.2
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• pp.36-47
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• 2020

Background: This study purposed to provide meaningful information for the accumulation of knowledge on coordinative locomotor training in patients with stroke. Design: Meta-analysis. Methods: This study collected articles which the coordinative locomotor training in patients with stroke. For systematic meta-analysis, 6 articles were finally selected after searching based on the PICOSD criteria. This meta-analysis was conducted according to PRISMA guidelines. Randomized controlled trials were included and the risk of bias was evaluated for each study. Pooled standardized mean differences were calculated using a random effects model. To extract the effect size of each study, the R 3.5.3 software was used. Results: The meta-analysis showed that a total effects size was 1.23 indicating that coordinative locomotor training for patients with stroke had a maximum effect size. Conclusion: A meta-analysis is warranted for further research to determine the effects of coordinative locomotor training in patients with stroke on muscle strength, walking and range of motion.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.49-62
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• 2024

In this paper, a sustainable closed-loop supply chain (SCLSC) network model is proposed for domestic mobile phone industry. Economic, environmental and social factors are respectively considered for reinforcing the sustainability of the SCLSC network model. These three factors aim at minimizing total cost, minimizing total amount of CO₂ emission, and maximizing total social influence resulting from the establishment and operation of facilities at each stage of the SCLSC network model. Since they are used as each objective function in modeling, the SCLSC network model can be a multi-objective optimization problem. A mathematical formulation is used for representing the SCLSC network model and a hybrid meta-heuristic approach is proposed for efficiently solving it. In numerical experiment, the performance of the proposed hybrid meta-heuristic approach is compared with those of conventional meta-heuristic approaches using some scales of the SCLSC network model. Experimental results shows that the proposed hybrid meta-heuristic approach outperforms conventional meta-heuristic approaches.

• Journal of Korean Academy of Nursing
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• v.34 no.6
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• pp.1004-1013
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• 2004

Purpose: The purpose of this paper was to describe the characteristics of smoking prevention programs in Korea, to estimate overall effect size of Korean smoking prevention programs, and to investigate effect size variations by program modality and instruction method. Method: Meta-analysis was performed on2l programs in 20 studies. Result: The estimation of overall effect size for knowledge and attitude was not possible because effect sizes were not homogeneous in this meta-analysis. However, effect sizes of studies that were socially influential programs or active/interactive methods were larger than information-oriented programs or passive/non-interactive methods in the pictures. The effects for behavioral outcomes were generally not as positive and not statistically significant. Q statistics showed that variations among effect sizes within program modality and instruction method classifications were heterogeneous. Conclusion: The results from this meta-analysis support the continued use of socially influential programs and active/interactive methods for smoking prevention programs. Because behavioral effect might be the fundamental objective of smoking prevention programs, the present results indicate that smoking prevention programs should consider adopting more effective programs.