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In this paper, we propose a method of in-depth analysis of internal operation process by recording library calls and related information that occur in the operation process of NoSQL database. It observes and records the specified library calls, compares the internal behavior differences between the NoSQL databases through recorded library call information, and evaluates the characteristics and scalability of each database by observing changes in the number of input data. The development of computing performance and the activation of big data have led to the emergence of different types of NoSQL databases for recording and analyzing various and large amounts of data, and it is necessary to evaluate the scalability of each database in order to select a database suitable for each environment. However, it is difficult to analyze or predict how a database operates in traditional ways, such as benchmarking, observing external behavior through performance models, or analyzing structural features based on design. Therefore, it is necessary to utilize the techniques proposed in this paper to understand the scalability of NoSQL databases with high accuracy.

In this paper, we propose the possibility of using containers in the HPC ecosystem and the criteria for selecting a proper PMI library. Although demand for container has been growing rapidly in the HPC ecosystem, Docker container which is the most widely used has a potential security problem and is not suitable for the HPC. Therefore, several HPC containers have appeared to solve this problem and the chance of performance differences also emerged. For this reason, we measured the performance difference between each HPC container and Docker container through NAS Parallel Benchmark experiment and checked the effect of the type of PMI library. As a result, the HPC container and the Docker container showed almost the same performance as native, or in some cases, rather better performance was observed. In the result of comparison between PMI libraries showed that PMIx was not superior to PMI-2 in all conditions.

In this paper, we propose quantitative evaluation method that enable security comparison between Security Container Runtimes. security container runtime technologies have been developed to address security issues such as Container escape caused by containers sharing the host kernel. However, most literature provides only a analysis of the security of container technologies using rough metrics such as the number of available system calls, making it difficult to compare the secureness of container runtimes quantitatively. While the proposed model uses a new method of combining the degree of exposure of host system calls with various external vulnerability metrics. With the proposed technique, we measure and compare the security of runC (Docker default Runtime) and two representative Security Container Runtimes, gVisor, and Kata container.

This research study designed a location image collecting technology. It provides the exact location information of an image which is not given in the photo to the user. Deep learning technology analysis and collects the images. The purpose of this service system is to provide the exact place name, location and the various information of the place such as nearby recommended attractions when the user upload the image photo to the service system. Suggested system has a deep learning model that has a size of 25.3MB, and the model repeats the learning process 50 times with a total of 15,266 data, performing 93.75% of the final accuracy. This system can also be linked with various services potentially for further development.

AI technology has developed in the form of decision support technology in law, patent, finance and national defense and is applied to disease diagnosis and legal judgment. To search real-time information with Deep Learning, Big data Analysis and Deep Learning Algorithm are required. In this paper, we try to predict the entrance rate to high-ranking universities using a Deep Learning model, RNN(Recurrent Neural Network). First, we analyzed the current status of private academies in administrative districts and the number of students by age in administrative districts, and established a socially accepted hypothesis that students residing in areas with a high educational fever have a high rate of enrollment in high-ranking universities. This is to verify based on the data analyzed using the predicted hypothesis and the government's public data. The predictive model uses data from 2015 to 2017 to learn to predict the top enrollment rate, and the trained model predicts the top enrollment rate in 2018. A prediction experiment was performed using RNN, a Deep Learning model, for the high-ranking enrollment rate in the special education zone. In this paper, we define the correlation between the high-ranking enrollment rate by analyzing the household income and the participation rate of private education about the current status of private institutes in regions with high education fever and the effect on the number of students by age

In this paper, we propose an intention to study the purchase of smart music by Chinese consumers. To study the influence of the originality and usefulness of intelligent music products on the purchase intention of Chinese consumers, and to explore how the originality and usefulness of intelligent music products affect the purchase intention. To achieve this goal ,372 questionnaires were collected through the Internet for frequency analysis, factor analysis, confidence analysis and structural equation analysis of data collection, and were carried out by SPSSV22.0 and AMOSV22.0 methods. Research the validation of assumptions in the model to reveal the psychological and behavioral responses of consumers to smart music products. The results show that the originality and usefulness of new products not only directly affect the purchase intention of Chinese consumers, but also indirectly affect their purchase intention by enhancing their attractiveness. The conclusion of this study is of guiding significance for the development of intelligent music product development and marketing strategy

Product recommendation services that have been researched recently are only recommended through the customer's product purchase history. In this paper, we propose the digital signage service through customers’ behavior pattern analysis that is recommending through not only purchase history, but also behavior pattern that customers take when choosing products. This service analyzes customer behavior patterns and extracts interests about products that are of practical interest. The service is learning extracted interest rate and customers’ purchase history through the Wide & Deep model. Based on this learning method, the sparse vector of other products is predicted through the MF(Matrix Factorization). After derive the ranking of predicted product interest rate, this service uses the indoor signage that can interact with customers to expose the suitable advertisements. Through this proposed service, not only online, but also in an offline environment, it would be possible to grasp customers’ interest information. Also, it will create a satisfactory purchasing environment by providing suitable advertisements to customers, not advertisements that advertisers randomly expose.

Image-image translation is a technology that creates a target image through input images, and has recently shown high performance in creating a more realistic image by utilizing GAN, which is a non-map learning structure. Therefore, there are various studies on image-to-image translation using GAN. At this point, most image-to-image translations basically target one attribute translation. But the data used and obtainable in real life consist of a variety of features that are hard to explain with one feature. Therefore, if you aim to change multiple attributes that can divide the image creation process by attributes to take advantage of the various attributes, you will be able to play a better role in image-to-image translation. In this paper, we propose Multi CycleGAN, a dual attribute transformation structure, by utilizing CycleGAN, which showed high performance among image-image translation structures using GAN. This structure implements a dual transformation structure in which three domains conduct two-way learning to learn about the two properties of an input domain. Experiments have shown that images through the new structure maintain the properties of the input area and show high performance with the target properties applied. Using this structure, it is possible to create more diverse images in the future, so we can expect to utilize image generation in more diverse areas.

As societies become hyperconnected, we need more cyber security experts. To this end, in this paper, based on the analysis results of the real world cyber attacks and the MITRE ATT&CK framework, we developed CyTEA that can model cyber threats and generate simulated cyber threats in a cyber security training system. In order to confirm whether the simulated cyber threat has the effectiveness of the actual cyber threat level, the simulation level was examined based on procedural, environmental, and consequential similarities. in addition, it was confirmed that the actual defense training using cyber simulation threats is the same as the expected defense training when using real cyber threats in the cyber security training system.

In order to operate multi drone efficiently, existing control methods must be improved, and drones must be able to construct communication networks autonomously. FANET(Flying Ad-Hoc Network), which is being considered as an alternative to solving these problems, is based on ad hoc network technology and can be exposed to a variety of security vulnerabilities. However, due to the limited computational power and memory of FANET nodes, and rapid and frequent changes in network topology, it is not easy to apply the existing security measures to FANET without modification. Thus, this paper proposes lightweight security measures applicable to FANET, which have distinct characteristics from existing ad hoc networks by utilizing PUF technology. The proposed security measures utilize unique values generated by non-replicable PUFs to increase the safety of AODV, FANET’s reactive routing protocol, and are resistant to various attacks.

This paper presents an evaluation framework for prototyping multimedia streaming services including audio and video in a distributed and/or decentralized storage that can evaluate service quality and performance under various network conditions. The evaluation framework focuses on important indicators which measure and improve service quality by applying decentralized storage to multimedia streaming services that can mimic the scalability of the existing server-client software architecture and the issue of a single point of failure. The integrated framework not only measures performance indicators for evaluating the quality and performance of multimedia streaming on open source based multimedia content streaming services, but also adjusts network quality using network virtualization technology for comprehensive evaluations. The experimental results show that the integrated framework has low overhead in building and operating a decentralized storage with multimedia streaming services on a single host computer which validates the scalability of the developed framework.

Recommender system is a must-have feature of e-commerce, since it provides customers with convenience in selecting products. Collaborative filtering is a widely-used and representative technique, where it gives recommendation lists of products preferred by other users or preferred by the current user in the past. Recently, researches on the recommendation system using deep learning artificial intelligence technologies are actively being conducted to achieve performance improvement. This study develops a collaborative filtering based recommender system using restricted Boltzmann machines of the deep learning technology by utilizing user ratings. Moreover, a learning parameter update algorithm is proposed for learning efficiency and performance. Performance evaluation of the proposed system is made through experimental analysis and comparison with conventional collaborative filtering methods. It is found that the proposed algorithm yields superior performance than the basic restricted Boltzmann machines.

In this study, we tried to identify the relationship between subjective health Status and job stress and andropause symptoms of middle-aged male who work.. The data was based on a survey of male aged 40 to 59 in four cities and collected by surveying 154 people with jobs. The general characteristics of the difference in andropause symptoms were marital status and job satisfaction. 68.83 percent of the subjects experienced andropause symptoms. There was a negative correlation between subjective health status and job stress(r=-.204, p=.011). The correlated factors of andropause symptoms included subjective health status(r=-.248, p=.002) and job stress(r=.341, p=<.001). Factors influencing andropause symptoms are job stress, subjective health status. There is a need to develop and apply nursing intervention programs that can reduce job stress and improve subjective health status in order to effectively manage menopause symptoms of middle-aged male who work.

This study intended to develop the Performance Evaluation system for the modern pentathlon such as an Olympic sporting event. The performance evaluation index system for the modern pentathlon was developed by Microsoft Excel 2016 with Visual Basic for Application that it is able to be understandable for the practical field in sport. Consequently, the system for the performance evaluation index is able to be developed within the concept of performance analysis of sport such as a notational analysis of sport. And the performance indicators for the performance evaluation index were selected by the skills to make successful outcomes. Finally, the simulation with big data gathering by the developed system would be requested that systematic reviews on successful outcomes in other sporting events would be necessary.

This study aimed to explore the factors having effects on the students’ adaptation to college life at C college, and also to establish the measures for helping their successful adaptation to college life. Using the structured questionnaire, students’ major satisfaction, learning motivation, time management behavior, core self-evaluation, and ADHD for adults were researched. Using the SPSS 21.0 Program for data analysis, this study conducted the descriptive statistics, t-test, ANOVA for analyzing the factors having effects on the adaptation to college life, and Scheffe for post-test. In the results of study, the major satisfaction, learning motivation, time management behavior, and core self-evaluation were significant on the adaptation to college life. Thus, in order to prevent the drop-out of college students by increasing their adaptation to college life, it is necessary to develop and implement college coping program which are increased the major satisfaction and core self-evaluation

This study was conducted to investigate the relationship between sexual consciousness, marriage consciousness, birth consciousness and pre-parent role of college students.,The subjects of this study were college students attending B,K University located in B city and the significance level(α) 0.05, power(1-) 0.8, effect size(fz) was verified in ANOVA using G-POWER 3.1.9.2 program.,The results of the study showed that the average score of sexual consciousness was 2.91±0.43, the marriage consciousness was 3.27±0.40, the birth consciousness was 3.32±0.43, and the pre-parent role was 3.35±0.35. When examining the relationship between sexual consciousness, marriage consciousness, birth consciousness, and pre-parent role according to the general characteristics of the subjects, sexual consciousness showed significant differences according to gender (t=6.910, p<.001), age (F=3.032 p=.030), major (F=5.402, p=.005), and parental orientation (F=2.801, p=.027).,The marriage consciousness of college students showed significant difference according to family members, and the birth consciousness showed significant difference according to grade and family members, and the role of pre-parents was significant according to grade and family members.,The role of pre-parents of college students showed significant correlations in sexual consciousness, marriage consciousness, and birth consciousness.,Repeated studies on the role of pre-parents based on parent-child relationship are needed, and studies are required to identify the relationship between sexual consciousness, marriage consciousness, birth consciousness, and pre-parent role.

The purpose of this study is to investigate the human error of bodyguards caused in the process of performing access control activities between security missions, focusing on multiple risk cases, and to suggest countermeasures accordingly. To verify this, after arranging the sequence of events in a time series, the VTA technique and Why-Why analysis technique that can easily identify the problem centered on the variable node were used. In addition, environmental factors and personal factors that cause human errors were extracted through M-SHEL Metrix. As a result of analyzing multiple risk cases through such a method, the security environment factors that cause access control accidents include lack of time (impatience), prejudice against visitors, intensive work methods, lack of security management, unattended travel, and familiar atmosphere. (Relaxation), formal work activities, convenience provision, and underestimation were surveyed. In addition, human errors caused by personal security guards were investigated as low alertness , formal work, negligence of inspection, and comfortable coping.

The purpose of this study was to analyze the characteristics of each type after categorizing the clothing lifestyle of 394 male office workers in their 30s and 50s. The data were analyzed with PASW 18.0 using frequency analysis, k-means cluster analysis, one-way ANOVA and crosstabs analysis. According to findings, first of all, types of clothing lifestyle are divided into 4 groups: a type of fashion leader(22.3%), a type of price sensitive(12.2%), a type of fashion indifference(27.9%), a type of normcore fashion(37.6%). Secondly, the types of clothing lifestyle showed statistically significant difference age, marital status, job and monthly average household income of socio-economic variables. Thirdly, the types of clothing lifestyle showed statistically significant difference monthly average appearance care cost, suit count, monthly average clothing purchase cost, average purchase cost of one suit.

The purpose of this research is to suggest some practical and policy implications for improving adolescent’s self-esteem through analyzing longitudinally the effects of parental attitude, peer relationship, student-teacher relationship, school learning activity, life-goal on adolescent’s self-esteem by using latent growth curve model. Analytical data was obtained from the KYCPS. The analytical results showed that the change model of adolescent’s self_esteem turned out to be a leaner model in which the change rate and intercept were significant. Second, time dependent variables influence on adolescent’s self_ esteem meaningfully. Third, time dependent variables influence on adolescent’s self_ esteem changes significantly. This study proposed some policy implications basing on these findings.

The purpose of this study was to suggest implications that the level of sound financial management ability of university students could be raised, starting with the awareness that not only college students but also all financial consumers need high levels of financial management ability, such as knowledge and attitudes toward financial management, as economic conditions are in trouble in the aftermath of the ongoing Corona19. A survey was conducted on college students to achieve the purpose of the study. In relation to the research model set in this study based on the survey, financial management ability and financial self-efficacy as the independent variable, and financial management behavior as the dependent variable were selected to analyze the structural relationship according to the study model. The analysis shows that financial management ability had an effect on financial self-efficacy and financial management behavior. Based on these results, theoretical and policy implications were presented. Finally, the limitations of this study and future research directions were suggested.

The purpose of this paper is to study the influence of new service methods of "artificial intelligence (AI) + medical cosmetology" ，a new service means, on customers' purchase intentions. To AI medical beauty APP sales as an empirical study. This paper designed Likert seven scale to investigate, using SPSS 24.0 statistical analysis software and AMOS24.0 structural equation software to analyze the survey data. The analysis method uses reliability analysis, validity analysis, and construct equation model analysis. Through empirical research, the following results can be found ,1. The system quality of AI medical beauty app will have a positive impact on perceived usefulness and perceived ease of use. 2. The information quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness. 3. The service quality of AI medical beauty app will have a positive impact on perceived ease of use and perceived usefulness 4. Consumers' perceived ease of use has a positive impact on perceived usefulness and purchase intention. 5. The usefulness of consumers' notification has a positive effect on purchase intention.

Many learners are encountering e-Learning contents with smart devices and contents provides are carefully observing learners’ reuse intention and behavior. Therefore, this study investigated the effect of e-Learning content platform characteristics on reuse intention for 200 users with smartphone-based e-Learning experience. The results show that the characteristics affecting reuse intention are content quality, interactivity, and ubiquity. Moreover, for men, only interactivity affects reuse intention, and for women, ubiquity and content quality affect reuse intention. When using smartphone-based e-Learning for less than an hour a day, only content quality affects reuse intention. On the contrary, ubiquity, convenience, and interactivity influence reuse intention when learning for more than one hour. Our results suggest meaningful implications that how e-Learning companies change their smartphone-based platform business strategy and how they utilize its key factors.

In this study, we developed a simulation app that performs the Test-and-Set algorithm. The test-and-set algorithm is a highly difficult algorithm, so this simulation app was created for learners who have difficulty understanding it. Learners who want to understand the Test-and-Set algorithm gather to form a team, and use this simulation app to discuss and practice, and these teams can practice at the same time. The test-and-set, which is assumed to be a machine language, is not interrupted by using a queue, and it can be seen that the configured simulation app performs well in all three conditions of 'mutual exclusion', 'progress', and 'bounded waiting' that must be solved in the critical area problem.

The purpose of this study is to analyze the perception of non-face-to-face online undergraduate lectures experienced by foreign learners, to identify problems of online lectures, and to suggest improvements. For this study, the perception of online lectures was investigated and analyzed by foreign undergraduate students who took online lectures at A and B universities. Through this, I explored the design direction, complementary measures, and direction of online lectures to be held at Korean universities in the future. As a result of this study, non-real-time lectures through E campus were recognized as advantages in that they could learn repeatedly and listen to lectures at home.,Real-time lectures using Zoom were recognized as an advantage of being able to communicate between professors and learners. Both types of online lectures had many tasks and had difficulty in focusing on the lecture until the end. In the future, it was found that the amount of lecture contents and the amount of tasks should be reduced and the condition and sound quality of the lecture image should be improved. As for the evaluation method, they preferred online evaluation rather than offline evaluation, and they preferred relative evaluation rather than absolute evaluation. The results of this study were able to closely understand how learners perceive online lectures. Also, when conducting online lectures, I was able to know the points that need to be improved in the future. The results of this study are expected to contribute to the design direction of online lectures and the development of online contents at each university.

In this paper, we propose a study to develop and apply competency-based elementary school teacher training programs for maker education according to the steps of the DACUM-based Systematic Curriculum and Instructional Development (SCID) procedure model. According to the stages of the DACUM-based SCID (Systematic Curriculum and Instructional Development) procedure model, the educational goals, contents, objects, time, methods, and prerequisite subjects of the maker education training program were derived, and based on this, a competency-based training program was developed. In addition, the effectiveness of the program was verified by applying the developed program. As a result of applying the developed training program and evaluating its effectiveness, it was confirmed that overall positive results were found. We expect this training program to be of help in strengthening the competency of elementary school teachers in maker education.