Dynamic binary analysis (DBA) programs are used to automatically find vulnerabilities in programs. Among them is Triton, a recently developed open-source DBA program with user-friendly functionalities such as Python interface. In this paper, we improve the performance of Triton by applying a caching technique to the opcode disassembly task in Triton. We designed and implemented two modified versions of Triton and compared their performance with the original version. According to our experimental results, the speed of Triton was accelerated by up to 25% on average. This improvement was due to the high hit ratio of the caches we added in our modification, which was higher than 95%.

Acknowledgements - This work was supported in part by the IITP grant funded by the Korea government (MSIP) [No.R7122-16-0077, Development of the 10G Level RegEx Packet Processing SW for the Security Intelligence] and in part by the MSIP, Korea, under the ITRC support program [IITP-2016-H8501-16-1008] supervised by the IITP.

Format string bugs (FSBs) are caused by careless use of the printf function in the standard C library and can cause abnormal actions in a program such as memory content leak or shell code execution. We modify a shared object in Memcheck which is one of the Valgrind tools for dynamic program analysis, and propose a solution for detecting FSBs. Our solution effectively detects an FSB by verifying whether the first argument of printf is allocated in the read-only data section or not. In other words, we can detect an FSB where the format string is not a read-only string, but a variable.

Acknowledgements - This work was supported in part by the IITP grant funded by the Korea government (MSIP) [No.R7122-16-0077, Development of the 10G Level RegEx Packet Processing SW for the Security Intelligence] and in part by the MSIP, Korea, under the ITRC support program [IITP-2016-H8501-16-1008] supervised by the IITP.

Now mobile Internet outbreak, in this wave, this article designs good dating class software, software is specifically for students in the school dating software, named "Looking for partners ". The main features of the application are a personal hobby to initiate or participate in the activities sponsored by others based on , and has the common interests in the buddies together online or offline activities , so as to make new friends and contacts , to enrich my life . After a careful comparison of various classification algorithms, it is found that the "K-nearest-neighbor algorithm" is the most easy to comprehend and easy to combine with the classification algorithm. Therefore, this paper discusses how to apply the "K-nearest-neighbor algorithm "to" Looking for partners" project to improve the matching accuracy, to solve the problem of friends matching.

This paper presents a reservation-based intersection traffic control infrastructure. The proposed scheme manages the flow of vehicles at intersection in consideration of the turn type and waiting time of the vehicles. The proposed scheme has a queuing zone and then followed by an acceleration zone right before the intersection area. A vehicle can only request reservation when it is in the queuing zone. Then, it can only enter the acceleration area once its reservation request is accepted. The proposed scheme guarantees collision-free travel and reduces queuing delay of vehicles at intersection. The simulation results shows better performance and the vehicles can reach to their own respective destinations without any collision.

Frequency offsets are the most destructive effects to the orthogonality between carriers, also known as Inter-Carrier-Interference (ICI) in OFDM systems. This offset can be corrected by implementing a proper correction scheme which relies on channel estimation techniques such as Maximum Likelihood Channel Estimator (MMLE). Further improvements enhance the reliability, Accuracy and Quality of Service (QoS) of OFDM. This paper studies MMLE behavior to improve the limitation and the overall performance of channel estimation process using Matlab simulation software and propose an improved version of MMLE which shows the noticeable degradation of Bit Error Rate (BER). Simulation results shows that lower Bit Error Rates (BER) can be achieved using this improved method which further achieves more orthogonal OFDM carriers and less ICI.

This paper studies enhancement of Multi-Path TCP(MPTCP), which is one of new transport-layer protocols of TCP/IP protocol suites, by IP routing for improvement of Quality of Experience for a Web service (WebQoE). In order to confirm the effect of IP routing on WebQoE, the author performed experiments with subjects. In the experiment, the subjects assess Quality of Experience of two actual Web services for different paths under 5 experimental environments. The author utilizes Web usability as QoE parameters. The results show that it is desirable to use appropriate routing for MPTCP enhancement in order to improve Web-Quality of Experience.

One of the best techniques to increase data rate and improve spectral efficiency for indoor visible light communication (VLC) is by employing Orthogonal Frequency Division Multiplexing (OFDM). To efficiently exploit the optical bandwidth, we propose a novel transmission approach for OFDM based VLC that increases the bandwidth efficiency by 50% compared to conventional optical OFDM systems. Unlike existing approaches of OFDM-based VLC systems, the real and positive signal is obtained without the Hermitian symmetry and signal clipping. Through simulation results, the proposed scheme shows significant peak-to-average power ratio (PAPR) reduction can be achieved, namely up to 10dB compared to the conventional asymmetrically clipped optical- OFDM (ACO-OFDM) and 5dB compared to DC biased optical -OFDM (DCO-OFDM) at complementary cumulative distribution function (CCDF) of 10-1 with acceptable bit-error-rate (BER) performance.

In this paper, we suggested a system to collect information through smart devices using the apps development based on the color environment. However, the composition of the complete color environment of the existing studies in the visual environment was difficult. So, to supplement this problem, the color environment was configured using a smart device of the HDM. It was configured to collect the vital signs through the BMS (Bio-Medical-System). The collection of vital signs by the proposed system will be based on the physiological quantification analysis and new markers development and analysis patterns in the color environment. Through implementing this, the proposed system expects to help in medical diagnosis and treatment as well as in developing programs for the patient rehabilitation.

This paper describes the skin care management support system for skin diseases which provides patient management and treatments advice from medical staffs. It focuses on the skin disease management for psoriasis and melanoma. The existing systems have been made to create various medical applications for self-monitoring using smartphones. Despite its benefits, the rate of patient participation in the application has been shown to be low after initial use because self-administration is difficult in getting the information for the treatment of the relevant disease than the treatment information in the hospital. For this reason, we suggested a skin care management support system using synchronization method. Our approach is designed as an active interaction method using smartphone application. Also, we used the cloud computing environment, which provided secure communication by using Advanced Encryption Standard (AES) between patients and medical staffs. Finally, we show the skin care management process of skin diseases with management application service based on cloud-computing environments.

Some of the numerous social problems caused by the rapidly changing modern society become social issues. Until now, selecting social issues is conducted by a top-down process in which the opinions of specialists in concerned fields are collected. Without any seed, however, such a process consumes too much time. Moreover, it poses various biases, including the intervention of specialists' subjectivity in the process. To overcome such problems, this study aims to establish seed terms from trustworthy documents that deal with social problems, and to extract expanded keywords of social issues using scientific data. Furthermore, it strives to propose a methodology that seeks a solution based on social issues.

In modern society, a variety of things or systems have been proposed for easy living. However, if you do not produce it based on human characteristics in such a system, users will find it inconvenient to use superior design products. The same is true when designing an in-vehicle system. It is not necessary unless it know the driver's cognitive physiological characteristics. In this research, driving assistant methods considering the cognitive characteristics of elderly drivers were proposed. Furthermore, the driving characteristics of young people and the elderly were examined. Such approaches and ideas will help researchers who design advanced systems was conceivable.

Recently, inspired by Internet of Everything (IoE), the era of connected all the things and people are coming. These advances in IoT and IoE technology have greatly increased the demand to use various things and services in everyday life at anytime and anywhere. Especially, as rapidly changing into an aging society and increasing interest in healthcare, disease prediction and management through various healthcare devices is getting attention. In this paper, we propose a platform structure that can be applied to user-customized health management and real-time disease prediction for elderly people by using IoE technology. The proposed platform structure can be extended for rapidly detecting and predicting new diseases.

We developed a prototype in demonstrating the interoperability of IoT healthcare platform for daily healthcare integrated with emergency medical services. For the processes of developing a prototype, we implement a smart emergency medical system (SEMS) which utilizes the wearable devices called Lifetag providing extracted information from electronic medical records (EMR). Lifetag facilitates the information management of paramedics by referring the current health status of people who are in need through the NFC communication between paramedics' smartphones and Lifetags. In addition, SEMS integrates physiological signals from patient monitoring devices in an ambulance to a pre-hospital care chart which transfers collected information to the national emergency department information system (NEDIS).

For information security management, agencies such as public agencies and national infrastructure are collectively measuring and evaluating information security capabilities. However, these security level evaluation system is not based on the characteristics and information protection activities of the organization, so it is difficult to objectively evaluate the security level. There is a maturity model as a way to measure and improve organizational capabilities within the organization's area. This maturity concept has been started in the field of software engineering and is not a new concept. However, since the effectiveness and efficiency of the maturity concept has already been verified, we intend to introduce it in the field of information security. Although there are studies that have been conducted to introduce a maturity model in the field of information security, only abstract concepts are presented and no practical studies. Cybersecurity standards for protecting the cyber environment of infrastructure, also recommends the adoption of the maturity concept to assess the organization's information security capacity, but there are no specific guidelines. Therefore, this paper proposes a prototype of the information security maturity model that can measure and manage the information security capability of the organization and simulate it in Korea infrastructure.

Non-stop computation can be achieved on cloud computing platform which is capable of migrating a virtual machine from a host to another. The migration may degenerate the performance of a virtual machine due to page swapping either from the source host or remote storages. Thus, bounded downtime is a fundamental requirement for many important applications. In this paper, a method which analyses memory access pattern vertically and horizontally is developed for bounded-downtime applications. The method predicts and resizes memory space such that the computation can proceed in accord with our need. Experiments are also conducted to show that bounded downtime can be achieved with negligible performance variation.

With the rapid development of communication technology, HIS has been widely used in modern hospitals. At present, the research of medical information system has attracted a lot of researchers at home and abroad. A classification of emergency medical information system is designed in literature, in the literature, it is proposed the medical information exchange, sharing and secure access system for the same patient. The literature describes the use of mobile clients in South Korea to collect a large number of clinical information to the hospital. In literature, the ERM system can improve the safety and effectiveness of medical services. And it extends the security planning process. Physician workstation is an important part of HIS. The author combines the research and development of mobile office platform. The system needs analysis, outline design, system implementation and testing applications. Using PB development language, 10g Oracle database and IOS related technologies to achieve the function of the system.

This paper proposes a conceptually simple but practical management of outsourced data for provably secure duplicate elimination with efficient storage utilization. We focus on cutting off dependency of encryption key on underlying plain data, making data owners relieved from information leakage as long as their keys are kept secret. Rather than forcing data owners for the same content sharing the same key, we restrict storage space in which duplicate identification and elimination occur in order to increase duplication. This allows data owners to choose their own keys with no restriction, and eventually the security of the proposed approach would not be affected by other data owners as well as inside and/or outside adversaries. Our approach can be adaptively applied to existing remote storage systems according to storage capacity and security requirements.

In recently, radar technology to support safe driving is one of the sectors receiving a lot of attention for auto-navigation system of a vehicle. In particular, adaptive cruise control (ACC) radar in the K-band and W-band is important. The horn antenna has advantages to achieve high gain and low VSWR in the millimeter-wave band because it is commonly simple structure. In this paper, coaxial-fed waveguide pyramidal horn antennas are designed with an optimum size 20Х20Х30mm3 to operate at the W-band. As a result, this paper presented the possibility of the design of a millimeter-wave pyramidal horn antenna for automotive radar systems.

The implementation of indoor visible light communication (VLC) systems using a direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) scheme is investigated in this paper. The main contribution of this work is to analyze the VLC system using Simulink design. Simulink design uses blocks that are much easy and deliver optimized results for hardware implementation. The system analysis comprises, first the effect of signal scaling and biasing operation on the peak to average power ratio (PAPR) which is a major drawback in OFDM based VLC system. Second the use of the channel estimation for the received signal. Simulation results show that the PAPR is significantly reduced by employing the signal scaling combined with biasing operation. Furthermore, the bit error rate (BER) can be reduced by utilizing channel estimation algorithms such as minimum mean square error (MMSE) and least squares (LS).

Over the past few years, a considerable number of studies have been devoted to the core technologies of Smart Home system, which is usually constructed by integrating various sensors, actuators, and software. Meanwhile, the concept of Internet of Things (IoT) becomes popular so that the concept of Web of Things (WoT) also emerges out of the application layer. In a WoT network, all things can be accessed and operated via HTTP so that the development of smart home applications is simplified. However, as there has been a little study of a common approach for describing the capabilities of a constrained WoT, what seems to be lacking is an effective solution for the "Findability" issues of constrained WoT. In this paper, we propose a solution for dealing the WoT "Findability" issues by combining the Web Things Model (WTM) and Constrained Application Protocol (CoAP). We hope that this research outcome is able to make the applications constrained WoT in Smart Home easier to develop and thus streamlines the research of WoT.

This paper presents a method to solve the problem of speech emotion recognition using deep convolutional neural network (CNN). Spectrograms generated from the speech signals are input to the deep CNN. The proposed model consisting of three convolutional layers and three fully connected layers extract discriminative features from spectrogram images and outputs predictions for the seven emotions. In this study, we trained the proposed model on the spectrograms obtained from Berlin emotions dataset. Furthermore, we also investigated the effectiveness of transfer learning for emotions recognition. Preliminary results indicate that the proposed approach based on freshly trained model is better than the fine-tuned model, and is capable of predicting emotions accurately and efficiently.

Previous studies, which have explained the ways of recommending of movies that are based on movie genre and other users' recommendation. Existing research using movie genres showed improvement in accuracy. These kinds of recommender systems, however, have limits that are large complexity, intense heuristic nature, and artificial recommendations. To overwhelm these limits, this study will suggest improved and efficient genre based movie recommendation algorithm. This study has used 'MovieLens' data set and applied the suggested method to calculate the accuracy. According to the result, when the data set is larger, the accuracy appears greater, that as the greatest recommendation accuracy has resulted in the largest data set, MovieLens10M. Also, overall complexity has been decreased significantly if the suggested method has applied to the larger data set. Thus, it has been shown that the suggested method is more efficient than earlier methods.

The number of websites and applications that provide beauty data to consumers is drastically increasing. Consumers usually select and purchase products based on product information and customer reviews on the Internet, as well as listening to the opinions of friends. Consumers, however, often find it difficult to find the perfect product because there is a vast amount of data available online, which makes the process of selecting products more difficult. To address this problem, we have analyzed vast amounts of big data provided by both consumers and the beauty industry using mind mining, data mining, text mining, and natural language techniques to identify preferences. We suggest a way of implementing a personalized beauty web magazine by identifying preferences from analyzed data.

One of the significant issues in everyday modern life is a healthier environment. For improving the environment, huge amount of data is gathered, manipulated, and utilized with noise, uncertainty, or unexpected mistreatment of data. In some data sets, the class imbalance problem hinders the learning performance of classification algorithms. In this paper, we propose a reasoning method that combines use of crowd knowledge from open data and collective knowledge to resolve the class imbalance issue in a data set related to diagnosing wellness in patients with stress or depression. The results of this research suggest that the proposed hybrid reasoning method using crowd knowledge extracted from open data and collective knowledge performs better than other traditional methods.

As the Data Communication Technology develops, the confidential information of the corporations and institutions have been increasingly leaked out in diverse ways and the hacking technology has developed compared to the past. An information leakage security incident can be divided in two different cases. One, by a malicious hacker from outside intrudes and leaks information and two, by a previous or present staff or affiliated worker who has access to internal information. The leakage of critical information causes enormous damage to the institutions and businesses. Therefore, the organizations and the companies are currently re-examining the data protection system to prevent any internal information from leaking out and strengthening the internal control policies by introducing internal information leakage prevention solution. However, in the current state of lack of dedicated information security personnel, the effective management of various security solutions is impossible. Therefore, this paper proposes a checklist for the operation and management of the solution to prevent the internal information leakage.

Recently, along with the development of ICT technology, there is a significant increase in IoE more advanced than IoT is one of the technologies that not only connects people and things, data and services, but also provides users with more intelligent and smart services However, it is difficult to efficiently process data generated from various kinds of things and services in this IoT environment. Also, it is difficult to provide objective analysis and knowledge-based services in an adaptive manner in response to IoE environment changes. In this paper, we propose a virtuous circle based knowledge convergence and extension model to accommodate the above intrinsic requirements. The proposed model exploits 1) the advanced information during preprocessing phase got by analyzing the high volume data of various IoE devices on real-time and 2) gives machine learning based learning models and their results during learning model phase to support the decision making accurately. These self-learning and learning results can be generated, converged, inferenced, and expanded with new knowledge of processing and learning. Finally, our system has the added advantage of providing knowledge and understanding of physical and virtual things, domains and services, and analysis information so that the user can easily understand them.

Failure prevention and protection are very critical issues nowadays. Although there are many commercial high availability (HA) solutions, they are expensive and do not support intelligent platform management interface (IPMI) that can improves failure detection efficiency. We present NCU-HA, a HA solution based on the open source project KVM(Kernel-based Virtual Machine). NCU-HA can be run on both low-cost personal computers (PCs) and IPMI-based servers. Moreover, to prevent single point of failure, we classify the nodes of the cluster into three roles: Primary node, Backup node, and General nodes. HA agent on those nodes will provide the functions depending on its role. We also define failure models and describe the recovery methods implemented in NCU-HA. Evaluation results show that our system can reach four nines (>99.99 %) availability on low cost PCs.

Due to the increased dependency on fusion of heterogeneous technologies, there is an increased demand for better provisioning of utility services such as electricity, water, and telecommunication, from end-users. Such demand has put more pressure to preserve the over-utilized natural resources such as water, energy, and RF spectrum. Energy and water are the precious key resources provided to us in direct or indirect forms. Particularly, water is a fundamental ingredient for any living organism and the electrical energy is one of the most sought-after resources for various activities that are happening on Earth. Unfortunately, both of these resources are vulnerable to becoming rare entities in the near future due to various factors such as deforestation, global warming, improper distribution, and undetected waste. Hence, there is a strong requirement at different levels to preserve or save these two resources. In this article, we propose low-cost hybrid network architecture, named as Learning Automata-Based Architecture for Utility Management (LAUM) that attempts to satisfy the above issues to large extent. The application of Learning Automata (LA) reduces the overall design complexity by downsizing the hardware and software resource requirements. Moreover, LAUM facilitates hassle-free deployment. This paper also presents a cost-effective implementation of a smart campus network, based on LAUM to manage utility services within the campus.

This study is to explore the effects of integrated theme and English education. To this end, this study collected 30 studies conducted in elementary school setting satisfying the meta-analysis criteria such as studies being quantitative, experimental or having affective statistical results after searching for the key words of "theme-based", "integrated learning" and "bilingual learning" on the accessible databases such as RISS and Google scholar. The results of meta-study are as follows: 1) Theme-based integrated English education is more effective in improving English skills than improving affective factors. 2) There is no big difference in effect sizes among different graders. 3) Reading and writing skills were more improved than that of listening and speaking. 4) Integrating with knowledge-based subjects were more effective than integrating with skill-based subjects. 5) Game was the most effective teaching method.

This current study aims to investigate the effects of L1 and L2 glosses on vocabulary learning and reading comprehension of Korean English learners by a quantitative meta-analysis. The 42 research findings in 15 papers were synthesized by calculating their mean effect sizes. The meta-analysis was conducted in terms of subjects, language used in glosses, types of dependent variables, etc. The results showed that the use of glosses in foreign language teaching had above the medium effect (g=0.680, p=.000). It was also found that the effects on vocabulary learning were stronger than reading comprehension in both L1 glosses (Q=20.737, df=1, p=.000) and L2 glosses (Q=8.472, df=1, p=.004).

The aim of this study is to develop the prototype of learner-initiated instructional syllabus in which the whole curricular sequence is invented as a digital growing-up storytelling process of the students. In the current educational paradigm, the role of students is much more essential than that of the teacher. With the development of ICT, more and more people are interacting each other via social networks. Accordingly, the value of storytelling becomes much more important than ever, so these online platforms can be served as a powerful channel to share stories among people. In this study, the concrete learning and teaching strategies will be suggested with the use of ICT-based storytelling teaching method.

The purpose of this study was to analyze the results of the integrated implementation of Internet-based intercultural exchange into the Nuri curriculum to shed light on the significance of global education for young children. The subjects in this study were teachers and 52 preschoolers in South Korea. They participated in an intercultural exchange with young children from Puerto Rico, Thailand, and Senegal. The findings of the study were as follows: First, among the different areas of the Nuri curriculum, the program was mostly conducted the in social relations area, followed by communication area, artistic experience area, natural inquiry area, and workout/health area. Second, the articulated areas of the Nuri were combined in the activities. As a result, the integration of communication and social relations was most prevailing. This type appeared a lot when the preschoolers had a circle time and introduced their activities to the foreign friends both in oral and written forms.

Recently, concerns have increased over careless accidents and invasion of privacy caused by drones, in proportion to the growth of the consumer drone market. Accordingly, some countries including the US and Japan, and in Europe are establishing regulations and guidelines for drones aviation. However, the rules primarily concern aviation safety, and define broad criteria for invasion of privacy. Lack of specific criteria for invasion of privacy has caused disputes continually. Furthermore, fear of invasion of privacy led some people to take strong measures against drones. In this paper, we conduct a survey on image identifiability determined by resolution, and propose criteria on invasion of privacy induced by aerial drone filming. Based on the criteria, we calculate the range of distance and height where drone-induced invasion of privacy may be prevented. Our research may provide a basis for substantive legal criteria that constitute invasion of privacy, and for preventing disputes related to invasion of privacy.

In this paper, an approach to holistic process of credit card fraud detection based on semi-supervised method is introduced. Semi-supervised method is a combine method of supervised and unsupervised learning method. A proposed approach applied unsupervised learning method to detect anomalies with unlabeled data and supervised learning method to classify fraud data. Removing local outliers for accurate prediction and performing random sampling in various ratios to deal with imbalanced card fraud data has performed. By adopting a feature selection process with various feature selection algorithms, our proposed process improved our research's efficiency and accuracy. The efficiency and accuracy of the proposed method verified with F1 score. As a result, integrating different data mining methods in credit card fraud detection in proposed process shows that our approach has higher performance than the previous ones.

In this study, we derive various characteristics related to excellent research outcomes related to social issues. We performed pilot analysis firstly to generate clusters between terms, and a population to analyze by using vector space model in conjunction with science data was constructed. In addition, we derive the linkage terms of the pilot analysis clusters and analyze the results in connection with news data that reflect social issues over time. As a result, the flow of excellent research issues and information related directly and indirectly in connection with social issues can be derived. It is expected that this will be a milestone whether technologies linked to social issues in the utilizations of sciences and technologies.

With the recent development of personal medical information services, as personal medical information is stored and processed in large quantities, the risk of sensitive privacy information being leaked or used for other purposes is increasing. To resolve this problem, anonymization of personal medical information has been actively researched. However, as the level of anonymization of personal medical information increases, a paradoxical situation may arise in which the level of personalization of the medical information service is lowered. Therefore, this study investigates the characteristics of features used in the anonymization algorithm and investigates the effects of these characteristics on the prediction algorithm.

Recently, as medical information leakage has been rapidly increasing in Korea, personal medical information has a high value and special protection measures are needed. In order to strengthen the security of the medical industry, it is important to identify possible threats before medical information is leaked and to prepare countermeasures according to the threat factors. Therefore, this study aims to design an evaluation model for systematically establishing pre - response to medical information leakage. As a preliminary task for the design of the model in detail, identify the clear definition and scope of medical information and analyze the state of security incidents. Finally, we propose an evaluation model by deriving the determinants for establishing evaluation criteria from related research.

The primary importance of information protection within small and medium-sized hospital is continuously emphasized. Even in situations of increased cyber threats, investment for information protection has not been done properly in especially for those organizations. And actually it becoming a lot of malware infection, data-loss and financial damage caused by various cyber threats as like ransomware. In addition to malware and targeted attacks, medical organizations face other threats and risks targeting people, process and technology - and underestimating these risks can also have serious consequences. In this paper we have checked about characteristics of malicious code detection occurred in small and medium-sized hospital information system and consider protecting method's conversion according to concept of 'Availability, Integrity and Confidentiality'.

Acknowledgement - This work was supported by Institute for Information & communications Technology Promotion(IITP) grant funded by the Korea government(MSIP) (No.B0713-16-0007, Development of International Standards Smart Medical Security Platform focused on the Field Considering Life Cycle of Medical Information).

As health services improve in quality, interest in healthy lives is increasing. Accordingly, the medical industry and the market are increasing day by day, and the ICT convergence environment is spreading on the basis of this. The use of ICT is rapidly increasing in medical institutions evolving into ICT convergence. However, companies in the medical industry composed of a closed ICT environment have different security vulnerabilities than open environments. Generally, it has various environments ranging from small medical institutions equipped with home security systems to large medical institutions equipped with various security solutions. Because the management characteristics of primary, secondary and tertiary medical institutions are different, it is difficult to build all security solutions. In this study, we will design a system for building security solutions considering the characteristics of the size of medical institutions.

• Recently, with the Advanced Persistent Threat(APT) as a social engineering attack using sophisticated program against precise target has been increased, also the commercial detection technology has been rapidly increased. The existing detection techniques can effectively respond to known malicious codes, but it is difficult to detect malicious codes such as zero-day attacks without any information. In particular, existing detection techniques have limits because recent malicious codes are rapidly generating variants. This study aims at designing and implementing document attachment sanitization based on DAST for APT response and applies the document flowing into the inside. DAST can deal with APT fundamentally and actively through secure sanitization of documents.

• Recently, there are many researches which are focused on the secondary utilization of mass medical information in the medical field. In the case of large-scale medical information, it is useful information to contribute to the development of the medical field by being utilized for preventing disease. However, since medical information includes personal information such as patients and medical staff, there are many restrictions on the secondary use such as privacy issues. In this case, non - discrimination is needed to protect the patient's privacy in medical information. To solve this problem, we have previously proposed the approach using differential privacy. In general, a researcher who makes secondary use of medical data can re-request necessary data such as additional research after receiving un-identified data, in which case re-identification is required. In this paper, we try to apply the existing REDIT-C re-identification method to the previously proposed differential privacy method to solve these re-identification requirements. In this paper, we propose a new method to solve the re-identification which could be replacing the existing differential privacy scheme.

Embedded software, such as railway, aviation, and medical devices, must provide users with safety and reliability software called safety-critical software. It is required to thoroughly verify requirements, and to provide users with software with high quality attributes of reliability and safety as a result of clear requirement analysis. To verify the requirements, we mainly use the white box view and the black box view. But, The requirements analysis of the black box view has a disadvantage in that it does not consider the behavior and structure of the system. On the other hand, the analysis method of the white box view has limitations in expressing information of requirements. In this paper, we propose a method for analysis and analysis of requirements with UML diagram using black box view and white box view. Using the proposed method, we can check for discrepancies in requirements and help test engineers understand their requirements as well as express their requirements clearly. Also test engineers to intuitively identify, analyze and validate user requirements.

Software is being operated in a combined form in different fields. Multi-operated software is designed with a configuration in cooperation with others to achieve a goal. Design should be presented to be able to handle different events between systems to configure cooperation. For this type of design, it takes long to generate products regarding the action between systems in design phase. Moreover, it takes longer to verify this. To address this issue, studies on requirements specification technique based on Gray-Box and on automation of state diagram generation have been conducted. A toolchain which automates conversion to system model and verification of it using sequence diagram has been proposed in the paper.

Hadoop Map-Reduce has been attracting attention for large-scale image data parallel processing environment for searching and analyzing large-sized images. Hadoop Map-Reduce is a platform for processing large amounts of data, allowing Map-Reduce operations to be processed in parallel to speed up complex tasks. It takes a lot of development time to integrate Hadoop Map-Reduce with various image analysis technologies, and it is difficult to confirm the results before compiling and building the source code. In order to solve this problem, this paper proposes a method to create a model using a modeling tool for platform independent development and automatically generate source code based on the model.

This presentation is to introduce the theory of Universal Design for Learning (UDL) and explore the effects and implication of UDL on English classes in Korea. In this study, we will focus on several elementary school English classes. The experiment is designed to observe and analyze English classes in elementary schools in terms of Universal Design for Learning methodology. The classes have ever been broadcasted by the Korean education channel - Education Broadcasting System (EBS). The idea for Universal Design (UD) stems from the building and architectural world. There was a need to make all buildings accessible to all people. This concern for change brought about the idea of UDL. The central role of technology in UDL is to provide varied ways of presenting content information to students in their primary learning style in instruction and in the learning process. UDL allows students more access to information, makes getting baseline assessment easier, and helps all types of learners with multiple ways to learn the same concept. The results of this study can give some ideas how we, English teachers can let all the students access our class with fun and efficiency.

The goal of the study is to investigate gifted students' in English language metacognitive reading strategy in English and Korean. Based on the findings, this study suggests interventions and services which English teachers can plan or develop for teaching English reading for gifted students. The participants were 140 gifted students who are enrolled in a special program for gifted students in English language in A city. In order to compare L1 and L2 metacognitive reading strategies, the Metacognitive Awareness of Reading Strategies Inventory (MARSI) and the Survey of Reading Strategies (SORS) were used. The data were analyzed by using the Statistical Packages for the Social Science (SPSS) 21.0. Descriptive statistics, paired sample t-tests, and stepwise multiple regression were conducted. Students can show higher correlations in global and problem-solving strategies as well as the overall strategies of support strategy. They use L1 global strategy more often than in the L2. Also, they tend to use more support strategies when reading in L2 reading than in their L1 reading. Finally, they may use the problem solving strategy equally in L1 and L2 reading. Pedagogical implications for strategy-based reading instruction and students' effective reading of L2 were suggested.

If many are still skeptical about the contribution of virtual environments in the learning process, others are still debating on the significant differences between traditional and online education. In other words, information and communication technologies are yet to be viewed as the indispensable pedagogical and learning tool, enabling meta-cognitive development and autonomous learning. Virtual environments as well as learning strategies are implemented in order to guide online learners in achieving their objectives. However, it was found that there is a lack of cognitive interactions. Therefore, this research's aim is to assess the gaps between the desired learning type of interactions and the actual learning activities of the learners. As methodology, a questionnaire was designed to identify synchronous interactions, done both individually and in teams. The participants in the survey are eighteen online learners, enrolled in a completely online master's course in France. With the help of activity theory the existing system is assessed and most of the learners claim that they go through the prescribed learning activities. The type of interactions obtained after compilation of the questionnaire refers to learner-tools, learner-content, tutor-learner as well as learner-learners interactions. Considering the shortcomings, improved processes are proposed which promote quality interactions among actors.

The purpose of this study is to explore university students' perspectives and attitudes of using mobile applications for studying English. With the development of ICT, various intelligent mobile applications have been used in English language learning and teaching. Smartphone applications have shown great potentials for effective instructional tools due to their easy accessibility, user-friendly interface, and multi-functional facets. From the data of this study, it was confirmed that students' perceptions of engaging in mobile learning were positive. This study revealed that integration of the mobile application could increase student motivation and make their learning more convenient and enjoyable than the traditional instructional methods. Participants of this study reported that important benefits of using mobile application for their English study were accessibility, portability, flexibility, interactivity, and enhanced English learning performance.

In this paper we build an MQTT(Message Queue Telemetry Transportation) broker on Amazon Web Service(AWS). The MQTT broker has been utilized as a platform to provide the Internet of Things(IoT) services which monitor and control room temperatures, and sense, alarm, and suppress fire. Arduino was used as the IoT end device connecting sensors and actuators to the platform via Wi-Fi channel. We created smart home scenario and designed IoT massages satisfying the scenario requirement. We also implemented the smart some system in hardware and software, and verified the system operation. We show that MQTT and AWS are good technical candidates for small IoT business applications.

A mobile augmented reality (AR) game application was developed based on simultaneous localization and mapping (SLAM). The SLAM-based AR game requires to estimate the pose from the camera input image in real time. Before running the game, point cloud data for a real-world game space is built. While the game is running, the camera pose is estimated by matching the prebuilt point cloud data and camera input image. To minimize errors in the matching, we present a hybrid method using an optical flow and ORB descriptor, where the optical flow accurately tracks the displacement of keypoints in consecutive images, and the ORB is a fast keypoint descriptor under a BSD license. The performance of the hybrid method is compared with a method using only the ORB descriptor matching. In addition, a mobile AR game embedding the hybrid method was tested in both indoor and outdoor environments.

This paper proposes an extensible application prototype for enterprises using RESTful web service. The prototype utilizes Groovy/Grails which is an open source web application framework on the Java platform. Database applications in traditional enterprises are based on the two-tier with simple client/server or the three-tier with additional RPC middleware. Because the outdated old configuration is not extensible, an alternative requires to link the database to the Web or mobile platform. This type of modification on the legacy system causes large maintenance cost since the modified architecture can affect other part of legacy system. Motivated by these issues, this research proposes a simple and flexible application prototype utilizing open source frameworks and the RESTful web service. The major benefits of the proposed prototype are simply able to extend to the heterogeneous client platforms such as mobile and .NET platform, and maintain by using high-productive web application framework. As a case study, the proposed prototype is applied to the management task of BOMs for the shipbuilding business.

Recently, outbreaks of medical information on medical institutions are frequently occurring. Since there is no provision in the hospital for measures to respond to fundamental medical information leak accidents, it is not responding properly. Therefore, medical institutions need a rapid evidence collection and management system to prevent accidents of medical information leakage and prompt response. Most of the digital evidence is limited, and it requires high commissioning cost and long analysis time. In this paper, we propose a digital forensic readiness model to cope with security incidents at medical institutions based on analysis of various digital forensic preparation related models.

Recently, as medical information leakage has been rapidly increasing in Korea, personal medical information has a high value and special protection measures are needed. In order to strengthen the security of the medical industry, it is important to identify possible threats before medical information is leaked and to prepare countermeasures according to the threat factors. Therefore, this study aims to design an evaluation model for systematically establishing pre - response to medical information leakage. As a preliminary task for the design of the model in detail, identify the clear definition and scope of medical information and analyze the state of security incidents. Finally, we propose an evaluation model by deriving the determinants for establishing evaluation criteria from related research.

The previous apporaches with medical information privacy was mainly focused on non-discrimination of the identifiers and semi-identifiers of formal data. However, there are various types of sensitive information in the big data environment, and in the case of specific sensitive information leakage, there is a high possibility of infringing on personal privacy. Recently, the damage of medical institutions and individual patients has become a serious social problem due to accidents in which patient sensitive information of Big Data is leaked. In this study, we classified sensitive information by morphological analysis of unstructured data and compared it with medical code data, and confirmed that there are many personal sensitive information in unstructured data. However, it is concluded that excessive non - discrimination of sensitive information has the opposite effect on data availability, and it is necessary to apply appropriate non-discrimination. These are likely to be the same in other industries and need to be further explored.

IoT devices produce large amounts of unsecured data. As a result, security is perhaps the biggest challenge that is faced by healthcare organizations that want to implement an IoT solution. Whether data is transmitted from smartphones, cars, household appliances, or industrial sensors, any end-to-end IoT solution must meet requirements in data privacy, safety, governance, and trust. Sensor devices that are used in healthcare need to be secured by using authentication, authorization, transport level security (encrypted communication), and federal information processing standards. Therefore, we propose OAuth2 based authorization model for privacy protection by providing patient-centric control mechanism in health data sharing with others such as doctors or others.

Recently, information technology trends link information convergence between offline and online and features that are characteristic of each technology. It is being developed in the direction of convergence technology that enables these new services. As a result, the volume of information is becoming larger and larger, and the amount of digital information accumulated offline and online is increasing exponentially. Personal health information of patients and medical personnel among the systems in which such digital information is accumulated is stored in a designated system in a large amount. As a result, digital information including personal information is leaked to a system in which the information is managed and other external systems, or information is occasionally infringed due to infringement of personal privacy. In this background, it is necessary to study the method of building an anonymization system that identifies the digital personal health information stored and stored in the medical information system including personal health information and enables flexible anonymization processing, and to contribute to the activation of safe and protected personal health information

Mobile health care integrated security service configuration and demonstration -Mobile app protection and anti-hacking services Prevents forgery and hacking attempts on healthcare applications that will be installed on smartphones that are the gateway to key data receiving and sending -Mobile Antivirus (Antivirus) Dealing with key data Detecting malicious code on the smartphone as a gateway and detecting / responding to infection to maintain a safe environment -Mobile & server segment encryption Encryption of data communication between smart phone (gateway) and server section to enable safe data transmission and reception -Security service between PHD and gateway Implement security and authentication functions applicable to additional PHD -Monitoring Integrated Security Services Implemented integrated security monitoring system reflecting the reality of medical institutions that do not have many security experts

In the healthcare domain, there is frequent exchange of medical information between hospitals for patients care. All of this information is provided by computer system software. If there is a vulnerability in the software, an evaluation of the vulnerability is required. Therefore, in this paper, we discuss Common Vulnerability Scoring System(CVSS) which is widely used security vulnerability assessment method in the general. We introduce summary of CVSS focused on the three metrics, which represents properties of vulnerability, e.g., impact, accessibility, complexity. After that, we point out the problems that should be considered in the healthcare domain. Finally, we discuss two options that take into account the importance of personal information for using CVSS in healthcare domain.

Users from different cultures might have different expectations. It is assumed that every person is influenced by his or her culture stated that, perceptions, preferences, communications and social acceptance are subject to culture. A slow interaction between users and user interface is one of the problem occur due to lack of limited standard of web object and users' expectation. In addition, limited standard of web object makes the User Interface Design complex and complicated. A guideline has been proposed for user interface design for e-learning website based localization of web objects. The proposed guideline has been used in this research to redesign an existing e-learning website in Univeristi Malaysia Sabah, namely smart2ums and user interaction with the adapted user interface has been evaluated through simulation tool, namely Camtasia Studio. This research proven that adapted interface design with guideline of localization was more efficient compared with interface design without adapted guideline.

Digital technologies enhance our daily life living, even though they alter the way how we operate, organize, communicate, and how we do project development. Recently academia started to explore an efficient ways of utilizing cloud education technologies among teachers and learners. Although the benefits to practice teaching or learning into cloud environment, there is a differ perception of intention to use clouds between individuals and organizations. This paper, investigate success factors on projects development that aims to implement education on the cloud technologies into organizations. Result from confirmatory factor analysis employing structural question modeling confirm that collaboration and task-technology fit had positive impact on project development, technology implementation in organization and perceived net benefits. Based on the analyzed results collaboration as success factor had strong influence on project development in government supported elementary, middle and high schools in Republic of Bulgaria.

Modern cryptosystem uses authentication mechanisms for secure communication and authentication mechanism is essential even at cryptosystems based on QKD (Quantum Key Distribution). However, since any practical authentication mechanisms dedicated to quantum cryptosystem are not available yet, we propose an authentication scheme for cryptosystem based on QKD through comparing the authentication mechanisms have been studied in this paper. Our authentication scheme is designed using FIPS compliant algorithms and complies with FIPS requirements. Key materials generated by QKD help the cryptosystem enhance its security. In addition, we select security parameters so that our scheme can be implemented efficiently for one-to-many communication model.

Intrusion Detection System (IDS) is one of the important issues in network security. IDSs are built to detect both known and unknown malicious attacks. There are several techniques which are applied in IDS such as machine learning so on. In this paper, we propose a new approach to deep learning can apply to this field. Our contribution is to build a new classifier of IDS by finding the most suitable optimizer for LSTM RNN model on Intrusion Detection. By this way, we improve classification performance for IDS. We implement our approach with six optimizers to find the most suitable optimizer. Our results show that the LSTM RNN model with Nadam optimizer outperforms to results previously published results on this field. In other words, we demonstrate our approach is really efficiency to intrusion detection with accuracy is 97.54%, 98.95% detection rate, and the false alarm rate is extremely reasonable with 9.98%.

This paper involves trajectory planning for a low-cost UAV with limited sensing capability. The UAV must be able to safely reach its destination provided that it can only gather limited information about its environment. We apply the velocity obstacle approach since it is applicable even with our model's limitation. When an obstacle is detected, the only information available is the distance to the obstacle and we estimate the needed information of the obstacle to be able to choose a collision-free trajectory towards the destination. We performed simulation on different obstacle movements and the collision-free trajectory of the UAV is shown in the simulation results.

Recently, Internet of Things (IoT)-related studies actively being conducted in various fields. Like conventional network system, IoT can also be a target for security attacks. With these problems for IoT security being magnified, many researchers are studying and developing countermeasures. Although a lot of companies launch products and services, they do not know how to apply countermeasures to products and services without interoperability problems. In IoT environments, integration is a necessary process between heterogeneous products and services from multiple vendors. Therefore, in order to provide an interoperability between diverse products and services, they are able to follow the standard, which is one way of overcoming technical barriers caused by differences among them. However, a study related with the IoT security standard has not been previously reported in the research literature although some international organizations have published the IoT-related standards. In this paper, we analyze international standard organization and their standards for IoT security for business, consumers, and government to support IoT security-related considerations for developing products and services in IoT environments. Furthermore, we indicate the limitations of existing standards for IoT security, and propose improved directions to construct secure IoT environment.

Due to the rapid growth of network infrastructure and sensor, the age of the Internet of Things (IoT) that can be implemented into the smart car, smart home, smart building, and smart city is coming. IoT is a very useful ecosystem that provides various services (e.g., Amazon Echo); however, at the same time, risk can be huge too. Collecting information to help people could lead serious information leakage, and if IoT is combined with critical control system (i.e., train control system), security attack would cause loss of lives. Furthermore, there is no overall research about IoT security requirements. Therefore, this paper focuses on IoT security, and its requirements. First, we propose basic security requirements of IoT by analyzing three basic characteristics (i.e., heterogeneity, resource constraint, dynamic environment) Then, we suggest six key elements of IoT (i.e., IoT network, cloud, user, attacker, service, platform) and analyze their security issues for overall security requirements. In addition, we evaluate several security requirement researches in IoT.

Supervisory control and data acquisition (SCADA) systems, are part of industrial control system (ICS), have been playing crucial roles in real-time industrial automation and controls. Through the evolution of 3rd generation, or networks based system, SCADA systems are connected to almost types of networks such as wired, wireless, and cellular and satellite communication, but security is still a big challenge for SCADA system while communicating within. Internet of things (IoT) is a ubiquitous platform, a new advance enhancement, for efficient SCADA system, where billions of network devices, with smart sensing capabilities, are networked over the Internet access. Deployment of smart IoT platform, SCADA system will significantly increase system efficiency, scalability, and reduce cost. Security is still a major issue for both-, as they were initially designed without any priority and requirements of security. This study modeled IoT-SCADA system and deployed a security mechanism, employing of cryptography based algorithm, which provided a secure transmission channel while each time communication occurred, between the field devices in the SCADA system. Proposed security implementation, and computed measurements analyzed as potential security building block against authentication and confidentiality attacks.

Cloud computing is a potential technique to deal with big data. Apache Hadoop which provides the MapReduce parallel processing framework becomes a popular system for distributed storage and distributed processing of large data sets on computer clusters. The performance of Hadoop in parallel data processing is relied on the efficiency of a MapReduce scheduling algorithm underlying. In this paper, we improve the performance of the well-known fair scheduling algorithm adopted in Hadoop by introducing several mechanisms. The modified scheduling algorithm can properly adapt to the runtime environment's condition with the objective of job fairness and short response time. Performance evaluations verify the superiority of the proposed algorithm over the original fair sharing algorithm.

Power line inspection is one of the most difficult and time consuming steps in power line maintenance. Even for a sizeable group of workers it takes months to inspect all of them, especially when they are not visible from the road and must be inspected on foot or with an aerial vehicle. That problem is even more prominent when the inspection must be done as fast as possible when the power cuts out in certain regions after natural disaster. To save time and reduce expenditure Unmanned Aerial Vehicles (UAV) could be used to film the power lines and automatically find problems (e.g. a broken cable or a tree branch too close to the line). Our research focuses on planning the route for the survey.

We can perceive the advent of Smart Home attributed to the rapid emerging of embedded and tiny intelligent devices and sensors. However, most of current Smart Home systems are still being developed based on the so-called "the system is the application" philosophy, causing the developers to take care of all technical details from ground up. There is relatively little research focuses on the theoretical aspects so that the independent research achievements or results are not interoperable. The concept of the "Ambient", which refers to a bounded place with computing capability, is one of the most important issues when implementing a system in Smart Home. The results of a service composition are not optimized if the concept of Ambient is not taken into account. This paper aims to investigate the spatial issues systematically from theoretical aspects. We propose several new spatial abstractions and a spatial-aware service management scheme on top of a UPnP-based robust service management protocol. Formal validation is also performed to verify the robustness of the proposed approach.

Cable TV Set-top box is one of major home electronics which can receive public broadcast signals in an emergency situation. The set-top boxes stay in standby mode while not being used. In the standby mode, the set-top boxes cannot receive information broadcasted for emergency. To receive the emergency information, the set-top boxes in the standby mode should be transited to operation mode. To transfer the set-top boxes from standby mode to operation mode, it should be possible to wake up the set-top boxes from remote site. In this paper, it is proposed how to wake up the set-top boxes in a cable TV network environment.

This paper proposes context-aware platform that can provide services for disaster training and response. The platform can analyze context of the platform users on configuration data set by the users and measured data from cameras and sensors. The platform provides appropriate instructions for the users to perform disaster training and response. The instructions can be displayed via displays of digital signage and augmented reality technologies. The context-aware platform enables the user to learn methods of disaster response and to respond quickly to disasters. The platform has efficiency to reduce the number of victims in disasters. The platform will be an important platform for reducing magnitude of damages in disasters.

This paper proposes location based disaster information distribution (LDID) platform for South Korea by referring to L-Alert platform. The L-Alert platform is a platform for gathering disaster information (e.g., damage type and size) from local and government organizations (e.g., national weather service and fire department) and distributing disaster information for people. Centric servers with L-Alert platform can collect disaster information with standardized formats and can change disaster information's format to a format that corresponds to transmission media (e.g., television, radio, Internet, and cellphone) for distributing the disaster information. The L-Alert platform is a good platform for distributing disaster information accurately and quickly. South Korea has developed mobile systems for broadcasts and communications. LDID platform in this paper provides people with disaster information corresponding location of the devices for mobile broadcasts and broadcast devices. Therefore, the platform of this paper enables the people to perform appropriate responses corresponding to their locations.

This paper aims to predict the factors and build prediction models for the persuasive message changing student's attitude by applying classification techniques. We used a questionnaire to collect data such as gender, age and their satisfaction with persuasive messages, obtained from students at Khon Kaen University. The classification rule generation process is based on the decision tree as a classification method where the generated rules are studied and evaluated. We compared the results obtained from three algorithms. The results shown that the average classification correct rate for the ID3 was higher than the CART and the C4.5 algorithms. The best efficiency is 98.04%, 97.27%, and 96.73%, respectively.

Advance in ICT and convergence technology, latest trend of e-commercial is O2O. Scale of O2O market is growing and famous platform firms start to invest this industry. However, O2O service's security requirements for safe O2O service has not been researched yet. In O2O Service, Data flows from Online to Offline or from Offline to Online, So, Traditional Online Security countermeasures is not appropriate. For best understand of O2O security threat, prerequisite requirement is figure out what is Traditional IT and Commercial security. In this paper, we research about O2Os' new security risks with scenarios and the way of its countermeasures.

In this study, we employed Collaborative Filtering and Sentiment Analysis. This is to automatically produce a recommendations to consumers. The researcher will be using Social media stream as a test-bed which was currently being utilized by researchers around the globe to produced information which are useful in different fields. For test-bed TwitterAPI will be employed to gather real time tweets and to be stored in a database for analysis. The researchers will proposed a framework to automatically produced a real time recommendation. The primary objective of the study is to test the effectiveness of the framework proposed, through the use of DeLone and McClean IS Success Model standard for evaluation of software quality. This study will also contribute to the growing research projects on recommender system and data mining.

Firefighters are typically exposed to dangerous environments where a sense of vision, auditory, and direction is blocked by smoke, dusts, or flame. Some environments are even "infra-less"; power and communication infra is destroyed. Any commands and signals from outside are unavailable, so recognizing directions and making decisions are usually difficult. To protect and save lives of firefighters, we propose a system, ATHENA that provides salient features including tracking and navigation, emergency monitoring and notification, and information sharing among co-workers. ATHENA is a result of an on-going project supported by Korea Government. This paper provides an overview and demonstration of ATHENA.

The standard routing protocols are used to find an optimal path between two given endpoints. Nowadays, the solution of multipath routing becomes fundamental requirement to improve the overall throughput of networks. On the other hand, due to the dynamic and high volume of traffic, it is also more challenging to control the congestion in an efficient way within the networks. Recently, Software-Defined Networking (SDN) becomes more and more popular to achieve the future network goals. In this paper, we proposed a Congestion Aware Multipath Optimal Routing (CAMOR) solution by using SDN and manage the routing intelligence centrally. We proposed a technique that combines the multipath and congestion aware mechanisms for an optimal route selection by considering the congestion in all available paths. The load of each link participating in routing paths is considered as congestion parameter. For better utilization of bandwidth and resources, we proposed per-flow based differentiation method and evenly distribute the traffic on equal cost multiple paths. The experimental results show that our solution can evenly distribute the traffic on several optimal paths instead of routing traffic on a single best/shortest path. It also can improve the overall network performance by avoiding the congestion.

Nowadays, the large scale data and big data are big issues in information technology. High energy physics experiments also require analysis facilities for analyzing large scale data from large equiment exeriments. So that in large experiment such as LHC has been using linux clusters and grid systems for such analysis. However, there is a quota limitation on the usage of the grid system for the distribution of resources, therefore a cluster farm without such restriction is needed for domestic researchers. Nevertheless, the analysis macro used in the two systems were different from each other, which made it difficult to use cluster farm. We have built a physical analysis farm that enhances user convenience by using a workflow management application for HTCondor, called DAGMan, and confirmed that the number of complicated job processes in the farm has increased compared to the previous farm.

A data center is a physical or virtual central repository for storing, managing, and disseminating data and information related to a particular business, as well as providing computing resources for analyzing stored data. As science and technology of modern society develops, the size of experiment increases, and as the size and amount of data generated through experiments increase exponentially, the computing resources for analyzing this increase are also increasing. As the size of the analytical data increases, the effort and research for efficiently managing the computing resources have been continuously carried out. We also increase the number of experiments we support and the computing resources we need to manage as we grow. Therefore, this paper describes and proposes programs and structures for managing and monitoring increasing computing resources each year.

In this paper, we propose a music management framework to manage the distribution of large volume music contents at home and abroad. In this paper, we define the sound source contents as an expression model that can be distributed internationally, and distribute the sound sources, analyze all the transaction information and related tasks of the distributed sound sources, process them into various types of data. By defining standardized music expression model specific to music contents and managing music by using big data technology using proposed model, it is possible to automate all transaction information and related tasks online in the music market to provide statistical, analysis, and visualization information and proposed a music management framework to provide the sound source.

To create an algorithm with Machine Learning, users should understand all the knowledge such as learning rate, activation, dimension reduction, hyper parameter, neural network, etc. Therefore, in order to construct the machine learning procedure, expert knowledge is required. So, it is difficult for general users to use it. Also, experts are also hard to regenerate well-defined model if it is described only in the paper. In this paper, we propose a knowledge based Continuous Learning System(CLS) which persistently collect and infer new knowledge from information for the existing learning setup and results instantiated based on a hierarchically designed ontology model.

Mortality rate and recurrent rate of Acute Myocardial Infarction is still high even though the procedure is performed in golden hours. A review of past medical history is an important element when the clinician determines appropriate treatment for the patient. However, this information cannot be delivered promptly and effectively once an unknown patient is coming into the new hospital. In this paper, we present the medical image based emergency record which is composed of essential data required from clinician at urgent moment for AMI patients according to 3 cardiologist's interviews. Then image labeling methodology is proposed to automatically select precise medical image among a bunch of medical images for generating emergency record. This method produces accurate labeling in CAG image rather than angiographic angles.

A human can read the other party's emotion through his/her face, and an eye is the most important factor to deliver emotion. By extracting eyes from a person in a picture, and applying them to a character, the character having eyes suitable for a video direction situation can be created, and emotion can be effectively delivered. This study suggested a system that can create fairy tale's characters having eyes similar to the image of a picture by extracting the eyes from a picture inputted by a user, and move the characters, record voices, and produce a fairy tale. The client that can produce an e-book using a smart device and the complex functions extracting eyes from a picture and creating characters are carried out by a server. The suggested system can be applied to useful and diverse learning contents for children.

Recently, a study using virtual reality in order to provide an effective self-directed training experience has been actively pursued. Especially, in case of dangerous safety education, it is difficult to conduct practical training, so education using virtual reality technology is emerging as an alternative. This paper presents to virtual reality simulator model, and in particular, in reality, experience person to be able to experience the virtual reality through the visual and tactile experience customer electrical accidents variety might actually occur. It is what electrical safety accident prevention education that allows the immersive sense of reality and substantial, to enhance the teaching effectiveness while also ensuring the safety of, about the virtual reality simulator system.

A Smart-Eye platform is a technology that detects disasters (e.g., forest fire, flood) and responds them quickly. In the Smart-Eye, an unmanned aerial vehicle (UAV) is used to monitor the disasters in real time, where the UAV is equipped with a camera and multiple sensors, since it can go into hazards (i.e., unsafe areas to humans) cost-effectively. However, since the UAV has flight time of 10~25 minutes in general, it requires to optimize its parameters for image stitching. In this paper, UAV planning is studied to increases the efficiency of image stitching, in terms of an altitude, a camera angle of view, and a velocity.

Customer churn refers to the registered users who are gradually leaving away because of loss of interests. For online service providers, customer churn analysis can be used to make business decision and optimize their products. Juzi Entertainment is one of the most popular Professional Generated Content (PGC) platform, which is loved by most young adults in China. In this paper, we try to predict the churner of Juzi Entertainment. We collect 100 thousand user samples from Juzi Entertainment and build the churn prediction model. Over 80% churners can be recognized by our model. Results show that column gif and beauty of Juzi Entertainment are relatively more popular than other columns, while articles of life are not welcomed by users.

• Compressive Sensing (CS) is a stable and robust technique that allows for the sub-sampling of data at a given data rate: 'compressive sampling' or 'compressive sensing' at rates smaller than the Nyquist sampling rate. It makes it possible to create standalone and net-centric applications with fewer resources required in Internet of Things (IoT). In this paper, we investigate how CS can provide new insights into coexisting heterogeneous IoT environments. First, we briefly introduce the CS theory with respect to the sampling through providing a compressive sampling process with low computation costs. Then, a CS-based framework is proposed for IoT, in which the hub nodes measure, transmit, and store the sampled data into the fusion center. Then, an efficient cluster-sparse reconstruction algorithm is proposed for in-network compression aiming at more accurate data reconstruction and lower energy efficiency. Therefore, compression should be performed locally at each hub node and reconstruction is executed jointly to consider dependencies in the acquired data by the final fusion center.

In order to serve emergency calls, figuring out the types of emergency is one of the most important tasks. The reaction for the call is dependent on the type of the emergency. However, serving emergency calls is based on not only the evaluation but the interaction. The emergency type should be decided with insufficient information and the decided type leads to the next information. In this paper, we propose a model of dialogue classification to determine the objective of the dialogue while the conversation is on its way. Situation of the conversation is used for decision making. Until the call is over, the situation supports the objective of the call and the decided objective leads next action for acquiring more specific situation recursively.

Stroke onset during night-time sleep referred as wake-up stroke, where a patient awakens with stroke symptoms that were not present before falling asleep. The symptoms of wake-up stroke are not clearly known; it is only noticed upon waking. Without knowledge of the stroke onset time, this large group of patients is excluded from treatment with tissue-plasminogen (tPA) activator. This research studies one of the objectives is to predict stroke while at sleep, i.e., wake-up stroke prediction using Internet of Things (IoT). Stroke prediction through intelligence technology and prediction algorithms which controlled by hyper-connected self-machine learning engine. The idea achieved through building a knowledge base including physiological data, motion data, bio signal, risk factors and electronic health record. The physiological, biosignal, and motion data will be measured through wearables and embedded sensors available. This paper focused on briefly explaining the conceptual idea and related information of the elderly stroke prediction while sleeping using IoT.

Many modern scientific discoveries would not be possible without the aid of computers and specialized experimental instruments. A computing farm used as an experimental data-based research platform can accelerate scientific research with integrated computing infra-structure and data management tools. This paradigm shift is driving the demand for computing farms commonly used in scientific experiments such as physics, chemistry, biology, and medicine. Computing farm services are becoming more complex and larger as a complementary effort to meet the increasing demands of these diverse research areas, but maintaining sophisticated infra-structures and services with limited staff and budget squeeze is a major pain point for data centers that provide computing farm. Therefore, we propose architecture for intelligent management and automation system that not only allows scientists to easily manage, share and analyze large data sets, but also reduces the complexity of the system and the burden on the computing system administrator.

GSDC (Global Science experimental Data hub Center) has limited computing resources, and GSDC is supporting several kinds of scientific experiments, such as ALICE (A Large Ion Collider Experiment), Belle II, Genome and so on. We have to share computing resources with community users for data analysis. Therefore, there is an issue of how to allocate resources effectively. Currently GSDC allocates resources based on MoU. For this reason, other experiment resources may not be used even if the resources for the experiment are fully utilized and new tasks must wait for a long time in the queue. In this paper, I analyzed workload of GSDC cluster that are using PBS (Portable Batch System) batch system.

With the ongoing depletion of IPv4 addresses, large-scale data center are experiencing difficulties to acquire the necessary IPv4 addresses. Therefore, several large-scale data center are actively applying the IPv6(Internet Protocol version 6). IPv6 has an unlimited number of address spaces, and there is an important feature, in that the server can generate and configure its own IPv6 address. This feature is called Stateless Address Auto Configuration(SLAAC). However, this feature has drawbacks related to security in the large-scale data center system. Because a malicious server can make paralyzed or confused to the large-scale data center systems. In this paper, we will discuss what IPv6 security issues in large-scale data center.

The significance of higher education in Korea can be found at big and small studies over the past two decades. Indeed, Korean higher education has entered into the universal stage, after passing through the elite stage and mass stage, according to Martin Trow's distinguishes. Nonetheless, a number of university professors are still using teaching methods proper to the elite stage, while a number of professors emphasize the importance of major education focused on employment. This paper introduces the design of experiential education method for Korean higher education system. This paper shows how the designed experience-based education has been applied experimentally to liberal arts curricula, major curricula, and extra-curricula programs, and then it discusses findings on those experimental applications.

Our dynamic integration scheme among heterogeneous middlewares will be a fundamental technique for integrating systems in CPS applications such as automobiles, medical devices, etc. Our dynamic control and fault tolerance scheme can be applied to automatic control systems such as intelligent buildings and smart homes in order to improve their reliability.

In this paper, we introduce a new devastating cryptanalytic tool named "Triangle Cryptanalysis" which can be used to evaluate the security of block ciphers, hash functions, stream ciphers and MAC algorithms. By observing the changes for three plaintexts through rounds this attack retrieves the master key. This new attack is an extended differential attack who observes the changes for two plaintexts through rounds. First this paper shows how to use the changes for three plaintexts through rounds, and second retrieve various master keys of several primitives. We confirm the validness of our attack with experiments: various keys have been found within 10 seconds on PC. We expect that our triangle cryptanalysis will be effectively used as a general tool to analyze the security of various primitives.

Color is a powerful tool to describe and interpret information from an image. Colors can be stored in an array called color palette. In the world of product design, palettes are mostly used to inspire creativeness and Interactive Genetic Algorithm has been widely increased to develop wide creativity solutions and generate new ideas for colors. In this paper, we simulate IGA using simulated binary crossover (SBX) to create color palettes based on user satisfaction as fitness value. When it comes to the choice of colors, the users do not normally have a clear idea about it. What users can do is use the color palette that is available as the standard palette and produce a color combination of their own choice. Thereafter, from the creation of a new palette, impression words also be shown to the user appeared to represent matching ideas or feelings from the user about the new palette. The aim of this research is to achieve user satisfaction of color palette and try to assist in the selection process.

In this technological age, handwriting communication is still an essential aspect in the lives of people and relating to each other. This study was created to identify the most suitable set of algorithms that can be used and determine how effective it would be in recognizing cursive handwritten texts. The proponents created a system that accepts a handwritten text image as input, undergoes processing stages and outputs a text based on the features extracted per character using the Diagonal Feature Extraction, and classification using Euler Number with the use of the Modified One-Pixel Width Character Segmentation Algorithm. A total of 100 handwritten text images are used in evaluating the system. The system achieved a character recognition rate of 88.7838% and word recognition rate of 50.4348%.

Due to the rapid deployment of IT technology, health care service has entered a new era. Some services such as cardiac monitoring are critical for life and have contributed to saving lives. On the other hand, monitoring hair loss is another interesting health care service. Even though it is not critical for life, people tend to pay much attention to their hair condition. Hair loss is one of the major issues related to the hair condition since excessive and uncared hair loss might lead to bald head. Hair care can be done professionally at the hair care shop but it requires much time and cost. Recently, due to inexpensive smart devices, self-diagnosis on the hair condition has become possible. Still, few applications have been developed to evaluate hair condition. In this paper, we propose a new scheme to evaluate the condition of hair and scalp by extracting diverse features from their microscopy image. The features include hair thickness, hair density and scalp blotch. We show the effectiveness of our scheme by extensive experiments on the prototype system.

Indoor systems cannot obtain a precise estimate of the location, due to unstable signals. In this paper, we use realistic wireless data from the IEEE International Conference on Data Mining (ICDM) dataset and Azure Machine Learning Studio to perform Bagging (also called bootstrap aggregating). By using the machine leaning technique in the Azure Machine Learning Studio, we can obtain more than 69 percent precision in identifying the correct area among 247 areas with only 505 training data. This result is equivalent to the second place entry in the IEEE ICDM Data Mining Contest. We show that this can achieve a highly accurate location estimation.

Recently, rise of smartphone use for daily activities is phenomenal and the security of mobile platforms is receiving great attention by the security community. Unlike server or workstations, the mobile system deals with personal information including locations, emails, social activities, and even photos. Therefore, privacy concern is the most important matter to every user. Many adversarial attempts install malicious Apps in the victim's smartphone and steal sensitive information. This work focuses on detecting such stealthy access to the information in the smartphone. In particular, we monitor Apps' activities in a level of system call. We also built a privacy policy engine and policy enforcement mechanism. We propose a novel approach that allows fine-grained privacy level control by modifying the parameters and return values of a system call on-line. We demonstrate the feasibility of the watchdog service in two privacy-sensitive scenarios: location-sharing and photo-sharing

The recent advances in quantum computer technology and the Shor algorithm have increased the importance of data integrity verification and digital signatures on the stability of existing public key cryptography. Keyless digital signatures are capable of high-speed data integrity verification by using only a hash operation, and not a conventional public key method; they are also robust against quantum computers as they use OTS (One Time Signature). However, a keyless digital signature has to generate a very long one-time signing key for OTS, and an equally long digital signature. In this study, we propose a keyless digital signature model with efficient key generation and storage space through hash chains.

The Internet started life as a network for the convenient sharing of data and the Internet has long been used by public and private institutions as a means of sharing information due to its effective and convenient nature of information sharing. Recently, various methods to detect personal information leakage in the stage of information sharing are being discussed in order to prevent the leakage of personal information. In this study, we will discuss the basic characteristics of the Korean resident registration number system and propose a method to effectively detect the leakage of the resident registration number before transferring large files. The results of this study are expected to be utilized as an effective detection method transmitting large files through the Internet.

With the birth of "big data" concept, there are lots of effort to use the data efficiently. The first condition to use those data is gathering of small data in one spot. We call the data spot (or data pool) as data center. Also in the data center, there are a million and one computing facilities in a data center. Those computing devices connected each other through network. In the field of network management, it is interesting question how network traffic is flowed in data center network. In the research, we try to understand data center network traffic flow through GSDC data center case study. For that, we have gathered in/out bound traffic in whole network devices. The GSDC serves computing service and data storage service for various users.

Pilot-based workload management system (WMS) is designed to ensure in advance that the Grid computing resources are suitable to run the payload submitted by users (Grid jobs). Also it is designed to run a series of user jobs in a local resource management system (LRMS) queue. However traditional LRMS is not capable to trace the series of user jobs pulled by the pilot. Therefore the accounting information in the LRMS is inconsistent with VO monitored information, the actual consumed resources by user jobs. In this paper, the traceability for Grid job in LRMS with pilot-based WMS is reviewed and a tag-based logging feature is proposed to address the problem.

Developed countries consider High Performance Computing (HPC) as one of key instruments to boost scientific and industrial competitiveness in the state level. To strengthen HPC technology capability, each country drives various investments and supports such as related R&D, HPC platform services, and professional manpower training. Korea also enacted "National HPC Promotion Act" in 2011. KISTI has endeavored to secure HPC resources and encouraged its utilization as an only provider open HPC platform service since the introduction of first supercomputer in 1988. KISTI's supercomputer is only public HPC resources in Korea therefore its effective uses and efficient resource allocation can have positive impact on national competitiveness. In this context, this study tries to analyze economic benefits of 4th supercomputer empirically based B/C analysis and then explore strategic significance of open HPC platform.

Up until now, the Advanced Metering Infrastructure has only assumed the power customer to purchase and consume electricity. After renewable energy technologies continue to be developed, and finally general consumers will be able to produce and sell energy to the utilities. The current intelligent power grid is called as Smartgrid, but the intelligent electric grid in the era when the consumer produces and sells the electricity will be called as Microgrid. AMI in Microgrid environment may have more vulnerabilities than in the current Smart Grid environment, and security analyses and countermeasures for that circumstance should be conducted thoroughly. This paper introduces and investigates AMI security issues in Microgrid environments.

With recent progress in the field of music information retrieval and recommendation, we face a new era that computers can analyze and understand music automatically to some semantic level. However, due to the diversity and richness of music contents, this goal requires multidisciplinary efforts, ranging from computer science, digital signal processing, mathematics and statistics, to musicology. Most traditional content-based music retrieval (CBMR) techniques have focused on low-level features such as energy, zero crossing rate, audio spectrum, and etc. However, these features failed to provide semantic information of music contents and this is a serious limitation in retrieving and recommending appropriate music. To overcome these limitations, more semantic information such as mood and emotion must be recognized from low-level features such as beat, pitch, rhythm and tempo. Due to the abovementioned limitations of low-level feature-based approaches, some researchers have tried to bridge the semantic difference (known as the semantic gap) between the low-level features and high-level concepts using various machine learning technologies. Therefore, this survey paper describes for machine learning techniques with applications to music retrieval and recommendation and some practical issues, such as robustness to noise and scalability.

Total Variation (TV) using the split-Bregman method is one of the most effective noise reduction approaches. The edge of the image collapses during TV minimization due to lack of edge orientation. We take a quick convergence of the partitioned Bregman method and present a noise reduction method while maintaining clear boundaries. In repetition, Laplacian is replaced by a localized Laplacian with a tensor structure to resolve edge collapse. Secondary variables and updates are affected by locally oriented Laplacian. Experimental results show an average 4% improvement in peak signal-to-noise ratio (PSNR) in the enhanced domain and improved picture quality. We also generated the extensibility of the proposed method using structural tensor alignment and several diffusion tensors.

This study can be used as the possibility of the success analysis indicator which uses input and output performance factors in order to perform quantitative analysis for programs. We can quantitatively define the satisfactory/unsatisfactory level of each program analysis factor by probability values of factor. This possibility of the success analysis framework can infer posteriori probability using the prior probability and the likelihood function of each possibility factor using AHP and Fuzzy. In addition, by inferring the relationships among the possibility of the success factors, it allows performing probability analyses on the satisfactory / unsatisfactory conditions, which can provide further feedback.

Visualizing multiple sensors or parameters from a device is not a trivial task. In this paper, we suggest sensor based data visualization method using raw data extracted temporal sensor or parameters. Senor based data visualization system consists of data acquisition, data preprocessing, data analysis, visualization mapping and data rendering step. To visualize temporal sensor values, we use statistical analysis such as sum, average and deviation calculated from each sensor and use spectrum based color using HSV color model. We implemented box based data visualization and circle based data visualization mapping system from temporal sensor values. Suggested method can be used in various fields such as image generation, data confusion, fault detection and machine learning.

In this paper, we offer an efficient real-time video recoloring technique that enables interaction with the real-world environment for combining augmented reality (AR) and virtual content. The one of AR technology is image synthesis which combines real video and virtual contents. The recoloring technique is proposed in this paper for reducing the difference between real and virtual scene.

The productivity increase by technological advance is the biggest driving force of economic growth. The purpose of this study is to focus on the Korean ICT industry, which is a key part of the economic growth. The contribution of capital was highest in the 1990s, but in the 2000s, gradually decreased. The contribution of technological change is increasing, and the growth drivers of the 2010s have shifted from capital to technology change. And its role is gradually increasing. Given the continuing appearance of technologically innovative industries, The analysis of technological change and contribution to economic growth of ICT industry, which is a key driving force, is very meaningful.

We introduce Docker based datacenter for grid computing, which is used by Global Science Experimental Data Hub Center of Korea Science and Technology Information Institute. Our computing groups fall into three categories depending on their usage characteristics. The categories are physical group, static group, and dynamic group. Physical group are fixed and difficult to change. Nevertheless, some special applications must run on the physical group. Static group and dynamic group are implemented using Docker container virtualization. Static group are beneficial for less changing usage characteristics that are common to most of the resources for grid computing. We maintain simple and flexible management for most applications with a static group. Dynamic group support lively changes while operating with the implementation of additional scheduler and executor.

In this paper, we show a tape storage-based real time mass archiving storage system for WLCG Tier-1. This system stores a raw data generated by LHC. And the system is used instead of disk storage.

In this paper, the measurement of pressure gauge using color segmentation is proposed for the safety management of fire extinguisher. Fire extinguisher pressure gauge, there is a pressure range from 0 to 15MPa, also includes a green color indicating the positive pressure. In order to measure the fire extinguisher pressure based computer vision, proposed algorithm has a two steps. The first step is performed the image cropping to find the region of the pressure gauge using CHT algorithm and green detection. The next step is calculated to find the indicator needle of the pressure gauge. After the end of the two steps, the actual pressure can be calculated via a distance calculation trigonometric. Some experimental results are conducted so as to verify the proposed method, and the proposed method is well performed effectively.

In this paper, we revisit the security analysis of various blockcipher-based hash functions. Especially, we devise new collision attacks on provably secure blockcipher-based hash functions with specific blockciphers whose attack computations are all negligible. Our experimental results show that various collisions have been found within 1 second on PC. We expect that our method will be effectively used as a general tool to analyze the security of blockcipher-based hash functions.

In this paper, we propose a real - time framework for second - level noise removal through feedback on the noise canceling function using the first noise canceling image as information. Using the Bregman method, you can see that noise reduction corrects errors and approaches correct answers. The suggested method starts with this error correction idea. More specifically, in the first noise reduction, all local pixels contribute to noise reduction. In this noise-free image, a local difference in noise is detected relative to the local center point. These detected points have a low correlation with the central point of the area in which they are located. The correlation information is feedback to the noise reduction function, and the noise reduction function is performed again with the compensated function. The proposed method has advantages over the bidirectional filtering method for edge noise. Also, since the convergence is not necessary unlike the split Bregman method, the proposed method can obtain the result within a certain time and the management effort is low. Finally, the proposed framework is applied to noise reduction technique of color image and noise reduction technique of range image.

In this paper, we propose new IoT network structure using SDN and IoT middleware for efficient ransomware mitigation

Over the past decade, there have been many studies of RFID. Most researches and field test were related to how effectively RFID could be introduced to real world. In addition, a number of researches on the security of RFID and RFID networks have been conducted. Main concerns of these security researches are on ensuring the confidentiality and integrity for RFID tags, RFID networks and the entire RFID system. However, in the real world, more important factor is the invasion of privacy of users who buy and hold RFID-attached goods. This paper investigates the RFID privacy protection schemes that have been published so far and compares the pros and cons of these techniques.

No one would argue with the similarity of Software development industry and human society. Thus, studying of Software Engineering is to reach humanities and philosophy. Nowadays, we are talking about fourth industrial revolution, 4IR. In our history, all industrial revolutions were embodied philosophy or vise versa; philosophical theories explain the consequence of those industrial revolutions. In 21 century's software industry, we cannot survive without open source software such as GNU/Linux, Android, OpenSSL and etc. Experimentally, software is getting be complexed; more number of source codes, more number developers and more various sort of files are involved. In Software Engineering, solving those complexities are a big issue. Many methodologies and tools are introducing with very fancy marketing words; Agile, Scrum, Sprint, SAFe, CD, and DevOps. We lost the next step if we add the open source software top of those things. This paper can be an interpreting of the open source software by a philosophy; Taoism. In Asia, we have many different kinds of philosophies, among those philosophies, Confucianism and Taoism are still read by many. It is very interested in to see the similarity between those philosophies and modern Software Engineering movement. And This study can be an idea of how to manage software in such complex world.