In this paper, we propose a performance isolation of shared space for virtualized SSD based storage systems, to solve the weakness in a VSSD framework. The proposed scheme adopts a CFQ scheduler and a shared space-based FTL for the fairness and the performance isolation for multiple users on virtualized SSD based storage systems. Using the CFQ scheduler, we ensure SLOs for the storage systems such as a service time, a allocated space, and a IO latency for users on the virtualized storage systems. In addition, to improve a throughput and reduce a computational latency for garbage collection, a shared space-based FTL is adopted to maintain the information of SLOs for users and it manages shared spaces among the users. In our experiments, the proposal improved the throughput of garbage collection by 7.11%, on average, and reduced the computational latency for garbage collection by 9.63% on average, compared to the previous work.

In this paper, we study the university timetabling problem, which consists of two subproblems, the university course timetabling problem and the examination timetabling problem. Given a set of classrooms, students, teachers, and lectures, the problem is to assign a number of courses (and examinations) to suitable timeslots and classrooms while satisfying the given set of constraints. We discuss the modeling and solution approaches to construct course and examination timetables for one of the largest Korean university. By using binary integer programming formulations, we describe these two complex real-world problems. Then, we propose a solution method, called NOGOOD, to solve the examination timetabling model. The computation results show that NOGOOD finds the optimal examination schedule for the given instance. Although we consider a specific instance of the university timetabling problem, the methods we use can be applicable to modeling and solving other timetabling problems.

In this paper, we propose a deep auto-encoder-based pipe leak detection (PLD) technique from time-series acoustic data collected by microphone sensor nodes. The key idea of the proposed technique is to learn representative features of the leak-free state using leak-free time-series acoustic data and the deep auto-encoder. The proposed technique can be used to create a PLD model that detects leaks in the pipeline in an unsupervised learning manner. This means that we only use leak-free data without labeling while training the deep auto-encoder. In addition, when compared to the previous supervised learning-based PLD method that uses image features, this technique does not require complex preprocessing of time-series acoustic data owing to the unsupervised feature extraction scheme. The experimental results show that the proposed PLD method using the deep auto-encoder can provide reliable PLD accuracy even considering unsupervised learning-based feature extraction.

In Recently, as the interest in health has increased and the spread of smart phones has become common, the development of smart health care related contents has been active. In this paper, we introduce the design, implementation and effects of game-based content that can make walking exercise fun in the Internet of Things environment. This content calculates the consumed calories by walking the stairs with the Beacon installed, and incorporates games to encourage continuous fun activities. It also provides event functions that enable on-off-line coordination. The goal of this content is to enable busy modern people to exercise lightly, funly, and constantly in the surrounding activity space. The effect of this content has been confirmed through the review of many users participating in offline events. This content is expected to be able to converge with various types of healthcare systems with the expansion of the application space.

In this paper, we propose an approach to apply network-based moving target defense into Internet of Things (IoT) networks. The IoT is a technology that provides the high interconnectivity of things like electronic devices. However, cyber security risks are expected to increase as the interconnectivity of such devices increases. One recent study demonstrated a man-in-the-middle attack in the statically configured IoT network. In recent years, a new approach to cyber security, called the moving target defense, has emerged as a potential solution to the challenge of static systems. The approach continuously changes system's attack surface to prevent attacks. After analyzing IPv4 / IPv6-based moving target defense schemes and IoT network-related technologies, we present our approach in terms of addressing systems, address mutation techniques, communication models, network configuration, and node mobility. In addition, we summarize the direction of future research in relation to the proposed approach.

Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

Temporal analysis is very useful and important for digital forensics for reconstructing the timeline of digital events. Forgery of a file's timestamp can lead to inconsistencies in the overall temporal relationship, making it difficult to analyze the timeline in reconstructing actions or events and the results of the analysis might not be reliable. The purpose of the timestamp change is to hide the data in a steganographic way, and the other purpose is for anti-forensics. In both cases, the time stamp change tools are requested to use. In this paper, we propose a classification method based on the behavior of the timestamp change tools. The timestamp change tools are categorized three types according to patterns of the changed timestamps after using the tools. By analyzing the changed timestamps, it can be decided what kind of tool is used. And we show that the three types of the patterns are closely related to API functions which are used to develop the tools.

The service composition based on Service-Oriented Architecture(SOA) can make us view various machines or its functionalities in the Web or Internet-of-Things environment as 'service', and efficiently create new value-added services that users want by compositing different services if there is no service to satisfy the client. The service composition problem with respect to behavioral descriptions deals with the automatic synthesis of a coordinator service that controls a set of services to reach a goal state. Despite its importance, however, solving the service composition problem with only partial observations remains to be doubly exponential in the number of variables in service descriptions, rendering any attempts to compute an exact solution for modest size impractical. Toward this challenge, in this paper, we propose novel approximation-based approaches using abstraction methods. We empirically validate that our proposals can solve realistic problems efficiently.

We propose a method to analyze user requirements to design a virtual reality-based risk management system. This paper presents surveys, interviews, prototype evaluation methods, and implementation process. Architectural heritage is easily exposed to natural and artificial dangers caused by various material combinations and structural features. So, risk management of cultural heritage plays a key role in preserving and managing cultural heritage. However, risk management has been carried out through empirical methods using distributed data. This study analyzes user requirements for designing functions and interfaces of VR-based risk management system and evaluates prototypes to overcome the above problems. As a result, most heritage managers wanted a system function to support risk analysis and response. They also found that they prefer 2D information such as existing drawings and photos rather than 3D information. The results of the user requirements analysis derived from this study will be used to create risk management applications.

Collaborative filtering is a popular technique for recommender systems and used in many practical commercial systems. Its basic principle is select similar neighbors of a current user and from their past preference information on items the system makes recommendations for the current user. One of the major problems inherent in this type of system is data sparsity of ratings. This is mainly caused from the underlying similarity measures which produce neighbors based on the ratings records. This paper handles this problem and suggests a new similarity measure. The proposed method takes users rating patterns into account for computing similarity, without just relying on the commonly rated items as in previous measures. Performance experiments of various existing measures are conducted and their performance is compared in terms of major performance metrics. As a result, the proposed measure reveals better or comparable achievements in all the metrics considered.

Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

Several studies have recently demonstrated that skeletal muscle is an endocrine organ releasing and expressing myokines acting in an endocrine or paracrine manner. Irisin is a hormene-like myokine induced after physical exercise by muscle fibers. It was primarily recognized as a molecule able to advance the "browning response" in white adipose tissue, however, it has been recetly identified that irisin also has a fundamental role in the control of bone mass. We study evidence for its possible skeletal effects, including the fundamental role that irisin is involved in the control of bone mass, with beneficial effects on geometry and cortical mineral density. As loss of muscle mass and bone density occurs with immobility, metabolic disease and aging, future studies researching the efficacy of irisin in reversing muscle wasting and restoring bone would be important to proving irisin as a molecule that combines helpful effects for treating muscular atrophy and osteoporosis in elderly people.

The purpose of this study is to identify the factors that may affect the 5-year survival of colon cancer through network model and to use it as a clinical decision supporting system for colorectal cancer patients. This study was conducted using data from 2,540 patients who underwent colorectal cancer surgery from 1996 to 2018. Eleven factors related to survival of colorectal cancer were selected by consulting medical experts and previous studies. Analysis was proceeded from the data sorted out into 1,839 patients excluding missing values and outliers. Logistic regression analysis showed that age, BMI, and heart disease were statistically significant in order to identify factors affecting 5-year survival of colorectal cancer. Additionally, a correlation analysis was carried out age, BMI, heart disease, diabetes, and other diseases were correlated with 5-year survival of colorectal cancer. Sex was related with BMI, lung disease, and liver disease. Age was associated with heart disease, heart disease, hypertension, diabetes, and other diseases, and BMI with hypertension, diabetes, and other diseases. Heart disease was associated with hypertension, diabetes, hypertension, diabetes, and other diseases. In addition, diabetes and kidney disease were associated. In the correlation analysis, the network model was constructed with the Network Correlation Coefficient less than p <0.001 as the weight. The network model showed that factors directly affecting survival were age, BMI levels, heart disease, and indirectly influencing factors were diabetes, high blood pressure, liver disease and other diseases. If the network model is used as an assistant indicator for the treatment of colorectal cancer, it could contribute to increasing the survival rate of patients.

This study was attempted to provide fundamental data in a disaster response education program by comparing the differences between students of the paramedic and nursing in ego-resilience, disaster-experience and core competencies of disaster response. The data is collected from March 2 to April 2, 2019, on a total of 358 students (196 paramedic students and 162 nursing students) based in Jeolla Province. The structured questionnaire were used as research tools and the collected data were analyzed by using the SPSS program as frequency, percentage, t-test, ANOVA and Pearson's correlation coefficient. The average score of total ego-resilience among the subjects was 86.97 out of 125 points. The number of paramedic students who experienced or witnessed the disaster situation in person was 11.2%, and the number of nursing students was 11.7%. The core competence of disaster response was 3.21% in paramedic students and 3.16% in nursing students. The ego-resilience of the paramedic and nursing students according to their general characteristics is statistically significant differences (t=2.797, p<.005) and the paramedic students has an average score of 3.52 points, which is higher than the nursing students (3.42 points). General characteristics and experience in disasters are statistically significant differences (t=2.797, p<.005), paramedic students had more disaster experiences (3.11 points) than nursing students (2.67 points). It indicated the static correlation relationship between ego-resilience, disaster experience and core competences of disaster response (p<.000). Through this study, the paramedic students were found to be more ego-resilience, more disaster experience and more critical capacity for disaster treatment than nursing students.

The purpose of this study is to find out the relevant factors of the choice tendency of tourism users to Intelligent Tourism platform through big data analysis, which will help enterprises to make accurate positioning and improvement according to user information feedback in the tourism market in the future, so as to gain the favor of users' choice and achieve long-term market competitiveness. This study takes the Intelligent Tourism platform as the independent variable and the user choice tendency as the dependent variable, and explores the related factors between the Intelligent Tourism platform and the user choice tendency. This study make use of text mining and R language text analysis, and uses SPSS and AMOS statistical analysis tools to carry out empirical analysis. According to the analysis results, the conclusions are as follows: service quality has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with tourism trust; Tourism Trust has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with user experience; user experience has a significant positive correlation with user choice tendency Positive correlation effect.

The purpose of this study is to understand the purchasing intentions of Chinese consumers to Huawei and other domestic brands in the context of the current Sino-US trade war. Taking the mass consumers as the research object, this paper designs Likert five-level scale to investigate consumers' purchase intention of domestic products in the future, and uses SPSS 23.0 and AMOS 23.0 statistical software to analyze and process statistical data. Using questionnaire survey and exploratory factor analysis, this paper constructs a model to analyze the impact of consumer ethnocentrism on consumers' purchase intention. By summarizing the overall purchasing intention of consumers, it is concluded that the development of domestic brands in the context of trade war is facing difficulties and challenges in the future, but at the same time, we must seize the opportunity of consumers' ethnocentrism under this background to positively influence their purchasing intention, make up for shortcomings, eliminate overcapacity, and seek greater development through technological innovation.

Given the ability and skills required by modern people, marketing can be divided into knowledge-related skill such as marketing plans, market segmentation, and marketing mix management and supportive skill such as communication, inter-organizational management, creativity, and decision making. Knowledge related skills can be nurtured in existing marketing classes, but it is recognized that special educational programs such as self marketing are needed to develop and train supportive skills regardless of education levels or major education. This paper is aimed to design for marketing educational program for the self marketing. In this study, a DACUM method job analysis to extract contents by specialists such as model setting of task and job, job statement, job analysis, education course development, and so on. In the first place, this report presents job analysis model by procedures for developing selection criteria of examination questions of the self marketing qualification. The first step is preparation for job analysis, the second step: the establishment of job models, the third step : the job specification and task analysis, the fourth step: the review of job model, the fifth step: the establishment of subjects for examination matrix table for making questions.

In recent years, interest in internet primary bank has increased in South Korea, and marketing strategies for the success of the bank are needed. The purpose of this study is to analyze the effect of bank choice attributes on satisfaction and behavioral intention of Internet primary bank users. A convenience sampling was used and a structural equation of AMOS 20.0 was used for hypothesis testing. The results of the study are as follows. First, information, transaction, and communication of the choice attributes have positive effects on satisfaction. Second, information, transaction, and communication of the choice attributes have positive effects on behavioral intention. Third, satisfaction has a positive effect on behavioral intention. The theoretical and practical implications and limitations of the research are presented in the conclusion.

With the advent of the Fourth Industrial Revolution, software(SW) education has become a necessity, not a choice of those who live in the digital age. Recently, many countries around the world have been actively promoting software education based on Computational Thinking(CT) for K-12 students, so software education in schools has bigger meaning as basic literacy education of future digital generation rather than coding skills. However, the integration of software education as a formal curriculum in schools is still ongoing in even other countries. Korea is also pursuing software education, but it is in the beginning stage. Therefore, we need to study the cases of other countries that have already started software education at the national level. In this paper, we first investigate the software education cases of three countries, e.g., UK, France and China with a respect of background & educational objective, development stage, and curriculum and we suggest education policies that software education can settle in Korea schools to foster a creative talented people.

In this paper, we presented directions of future research based on the result of the systematic review about the effect of maker education. Selected paper were 32, and we analyzed papers in terms of research status, research design, participants, areas, learning style, and educational effect. As a result, the study on the effectiveness of the maker education started from 2016 and has been continuously increasing. Concerning participants, many studies focused on middle school students, upper grade elementary school students, and high school students. In the field of research area, maker education was applied most frequently to convergence education. Maker education has been the most studied in formal education, and problem solving ability and attitude were most verified as educational effects.

Computational Thinking is one of the biggest issues in the era of the Fourth Industrial Revolution. It is a core literacy required not only for SW major but also for all students including them. It is not a simple computer software education, but a coding education based on Computational Thinking, and it should be able to solve the problems in everyday life and to express the process and solutions. However, in the case of students who lack background knowledge on SW and programming languages for development, it is hard to know how to algorithmize problems and express them using computer devices. In this study, we proposed a education course model to improve the students' thinking skills and to express them effectively. In addition, we confirmed whether the non-major students who learned through this education course model can express various problems related to the major field by integrating them with computing accidents and improve the problem solving ability.