• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.657-667
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• 2020

The Android framework allows apps to take full advantage of personal information through granting single permission, and does not determine whether the data being leaked is actual personal information. To solve these problems, we propose a tool with static/dynamic analysis. The tool analyzes the Source and Sink used by the target app, to provide users with information on what personal information it used. To achieve this, we extracted the Source and Sink through Control Flow Graph and make sure that it leaks the user's privacy when there is a Source-to-Sink flow. We also used the sensitive permission information provided by Google to obtain information from the sensitive API corresponding to Source and Sink. Finally, our dynamic analysis tool runs the app and hooks information from each sensitive API. In the hooked data, we got information about whether user's personal information is leaked through this app, and delivered to user. In this process, an automated Source/Sink classification model was applied to collect latest Source/Sink information, and the we categorized latest release version of Android(9.0) with 88.5% accuracy. We evaluated our tool on 2,802 APKs, and found 850 APKs that leak personal information.

• Steel and Composite Structures
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• v.34 no.5
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• pp.743-767
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• 2020

Due to the impressive flexural performance, enhanced compressive strength and more constrained crack propagation, Fibre-reinforced concrete (FRC) have been widely employed in the construction application. Majority of experimental studies have focused on the seismic behavior of FRC columns. Based on the valid experimental data obtained from the previous studies, the current study has evaluated the seismic response and compressive strength of FRC rectangular columns while following hybrid metaheuristic techniques. Due to the non-linearity of seismic data, Adaptive neuro-fuzzy inference system (ANFIS) has been incorporated with metaheuristic algorithms. 317 different datasets from FRC column tests has been applied as one database in order to determine the most influential factor on the ultimate strengths of FRC rectangular columns subjected to the simulated seismic loading. ANFIS has been used with the incorporation of Particle Swarm Optimization (PSO) and Genetic algorithm (GA). For the analysis of the attained results, Extreme learning machine (ELM) as an authentic prediction method has been concurrently used. The variable selection procedure is to choose the most dominant parameters affecting the ultimate strengths of FRC rectangular columns subjected to simulated seismic loading. Accordingly, the results have shown that ANFIS-PSO has successfully predicted the seismic lateral load with R² = 0.857 and 0.902 for the test and train phase, respectively, nominated as the lateral load prediction estimator. On the other hand, in case of compressive strength prediction, ELM is to predict the compressive strength with R² = 0.657 and 0.862 for test and train phase, respectively. The results have shown that the seismic lateral force trend is more predictable than the compressive strength of FRC rectangular columns, in which the best results belong to the lateral force prediction. Compressive strength prediction has illustrated a significant deviation above 40 Mpa which could be related to the considerable non-linearity and possible empirical shortcomings. Finally, employing ANFIS-GA and ANFIS-PSO techniques to evaluate the seismic response of FRC are a promising reliable approach to be replaced for high cost and time-consuming experimental tests.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.2
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• pp.236-244
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• 2005

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks(FPNN) that is based on fuzzy relation and evolutionally optimized Multi-Layer Perceptron, discuss a comprehensive design methodology and carry out a series of numeric experiments. The construction of the evolutionally optimized FPNN(EFPNN) exploits fundamental technologies of Computational Intelligence. The architecture of the resulting EFPNN results from a synergistic usage of the genetic optimization-driven hybrid system generated by combining rule-based Fuzzy Neural Networks(FNN) with polynomial neural networks(PNN). FNN contributes to the formation of the premise part of the overall rule-based structure of the EFPNN. The consequence part of the EFPNN is designed using PNN. As the consequence part of the EFPNN, the development of the genetically optimized PNN(gPNN) dwells on two general optimization mechanism: the structural optimization is realized via GAs whereas in case of the parametric optimization we proceed with a standard least square method-based learning. To evaluate the performance of the EFPNN, the models are experimented with the use of several representative numerical examples. A comparative analysis shows that the proposed EFPNN are models with higher accuracy as well as more superb predictive capability than other intelligent models presented previously.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.1
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• pp.649-655
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• 2020

This study determined effective educational strategies by investigating and analyzing the related educational demands for SMEs (small and medium-sized enterprises) in the 4th Industrial Revolution based area of smart farms. In order to derive the approprate educational strategies, Importance-Performance Analysis (IPA) and Borich's Needs Assessment Model were conducted based on the smart farm technological field. As a result, the education demand survey showed high demand for production systems and intelligent farm machinery. In detail, Borich's analysis showed the need for pest prevention and diagnosis technology (8.03), network and analysis SW linkage technology (7.83), and intelligent farm worker-agricultural power system-electric energy hybrid technology (7.43). In contrast, smart plant factories (4.09), lighting technology for growth control (4.46) and structure construction technology (4.62) showed low demands. Based on this, the IPA portfolio shows that the network and analysis SW linkage technology and the CAN-based complex center are urgently needed. However, the technology that has already been developed, such as smart factory platform development, growth control lighting technology and structure construction technology, was oversized. Based on these results, it is possible to strategically suggest the customized training programs for industrial sectors of SMEs that reflect the needs for efficiently operating smart farms. This study also provides effective ways to operate the relevant training programs.

• The KIPS Transactions:PartB
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• v.15B no.1
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• pp.45-52
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• 2008

As the recent development of the IT services, there is a urgent need of effective diagnosis system to present appropriate solution for the complicated problems of breakdown control, a cause analysis of breakdown and others. So we propose an intelligent diagnosis system that integrates the case-based reasoning and the artificial neural network to improve the system performance and to achieve optimal diagnosis. The case-based reasoning is a reasoning method that resolves the problems presented in current time through the past cases (experience). And it enables to make efficient reasoning by means of less complicated knowledge acquisition process, especially in the domain where it is difficult to extract formal rules. However, reasoning by using the case-based reasoning alone in diagnosis problem domain causes a problem of suggesting multiple causes on a given symptom. Since the suggested multiple causes of given symptom has the same weight, the unnecessary causes are also examined as well. In order to resolve such problems, the back-propagation learning algorithm of the artificial neural network is used to train the pairs of the causes and associated symptoms and find out the cause with the highest weight for occurrence to make more clarified and reliable diagnosis.

• Journal of Korean Philosophical Society
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• v.148
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• pp.49-72
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• 2018

In this paper, I tackle the question as to why the Confucian tradition in East Asia failed to generate democracy. In the first section, I discuss various forms of Confucianism and come up with a most suitable one before I define democracy. I then consider the view that, even though Confucianism, thus defined, had the democratic ideals, it could not generate democracy because it failed to secure democratic institutional structure. I call this view 'No Institutions' View. However, there are two versions of it. First, a thin version of the view holds that the theoretical resources are clearly found in Confucianism yet they failed to provide the democratic institutions. Second, there is the view (a thick version of 'No Institutions' View), according to which the theoretical resources do exist in the Confucian tradition, though only as potentiality and not as a historical reality, and this is why the tradition failed to produce democracy. Third, some hold the view (which I call 'No Ideals' View) that Confucianism simply lacks not only the practical institutions but also theoretical ideals of democracy. In the conclusion, I discuss the reason why I reject these views and offer my own view. In particular, I offer a hybrid view concerning the relationship between Confucianism and democracy.

• Journal of The Korean Association of Information Education
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• v.25 no.4
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• pp.653-664
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• 2021

This study proposes a STEAM (Science, Technology, Engineering, Arts, and Mathematics) program for career exploration of middle school students. The proposed program utilizes VR (Virtual Reality) for new digital technology and webtoon as a popular cultural element. It enables the students to investigate promising fields and experience them virtually for themselves. We design the program based on the 2015 revised curriculum, which enhances the learning effects with existing subjects. In particular, the program provides a hybrid education to combine contact and untact classes considering the COVID-19 situation. The educational goal of the proposed program is to improve creativity and convergence capability. Specifically, it aims to prepare an educational foundation that integrates new digital technologies into education and applies the programs to school education fields. To prove the effectiveness of the developed program, we applied the program to the second graders of A middle school located in Seoul. We expect that the proposed program helps students learn how to utilize new digital technologies and explore future career paths.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.1
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• pp.27-40
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• 2014

The functions of urban open space, which embraces community revitalization, are diverse. It is the means of public healthcare, learning centers for children, hub of arts and cultural programs, as well as promoter of urban tourism. However, in-depth discourse and research on the topic of urban open spaces has been limited so far. Hence, this study aims to investigate the motivations and satisfaction of visitation based on four representative waterfront open space in Seoul; Cheongyecheon Waterfront, Seoul Forest Park, Seonyudo Park and Banpo Hangang Park. The methods of study are literature review, observation investigation, and questionnaire survey. The findings are analyzed through the Exploratory Factor Analysis, Reliability Analysis, ANOVA Analysis and Regression Analysis by SPSS 18.0. The results of the study are as follows. First, urban waterfront open spaces in Seoul has 5 factors of visitor motivation; community amenity, nature access, cultural and educational assets, aesthetic enjoyment, and lastly means of escape. Second, factors of recognizing urban waterfront open spaces as community amenity and nature access indicate meaningful differences in visitor's perception by spatial characteristics. Third, distances between the destination and the visitor's residence influence significantly their perceived motivation. Close-range visitors perceived nature access as a principal factor, whilst medium to long-range visitors perceived visitation for aesthetic purposes more importantly. Lastly, the will to escape was shown as the influential factor in visitor satisfaction. Visiting open spaces for the enjoyment of nature and aesthetic purposes were factors that also closely relate to visitor satisfaction. In addition, it was found that there are different visitor motivations that influence visitor satisfaction in accordance with the spatial characteristics of each open space. In summary, it can be said that urban waterfront open space is a hybrid space connected to various types of urban contents beyond daily experiences. It was found that several visitor motivations including community development, design aesthetics, education and culture, entertainment, enjoyment of natural landscape, and relaxation, affect the overall satisfaction of the visiting experience. It is anticipated that the results of the study will be used by the local government in setting up strategies for the creation and management of successful urban waterfront open space, and for those involved in planning and design act as a starting point for spatial programming and amenities arrangement in accordance to the city's tourism and urban marketing approach.

• Korean Journal of Food Science and Technology
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• v.49 no.6
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• pp.649-658
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• 2017

The potential of the ethyl acetate fraction from Pteridium aquilinum (EFPA) to improve the cognitive function in high-fat diet (HFD)-induced diabetic mice was investigated. EFPA-treatment resulted in a significant improvement in the spatial, learning, and memory abilities compared to the HFD group in behavioral tests, including the Y-maze, passive avoidance, and Morris water maze. The diabetic symptoms of the EFPA-treated groups, such as fasting glucose and glucose tolerance, were alleviated. The administration of EFPA reduced the acetylcholinesterase (AChE) activity and malondialdehyde (MDA) content in mice brains, but increased the acetylcholine (ACh) and superoxide dismutase (SOD) levels. Finally, kaempferol-3-o-glucoside, a major physiological component of EFPA, was identified by using high-performance liquid chromatography coupled with a hybrid triple quadrupole-linear ion trap mass spectrometer (QTRAP LC-MS/MS).