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There are many accommodation rental services like Hotels.com, Hotels Combined, Trivago, Airbnb, and so on. Airbnb, in particular, uses the service known as P2P (peer to peer) technology. When the guest searches for rooms or a house to rent, he or she will have to consider a lot of information that Airbnb provides to the guest such as photos of the room/house, the host, rating of reviews, number of reviews, number of guests who can stay, number of bedrooms and bathrooms, description of the room, price. When there is a lot of information, it needs to be displayed effectively. Otherwise, it can complicate the guest’s choice. This research aims to make a personalized recommendation model and to analyze the guest preferences for accommodation using Airbnb. For this process, the study constructs criteria from accommodation information in Airbnb and it calculates and analyzes the guest’s preference by AHP (Analytic Hierarchy Process). The result shows the optimal room choices from the Airbnb website according to the guest’s preferences.

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Smart manufacturing system must be designed based on a set of strategic objectives and a variety of surrounding environments; therefore, it includes a series of complex decisions and trade-offs between investment and profit. The MSDD based on the axiomatic design principle has an advantage on breaking down requirements and parameters and considering the relationships of them, but there is a limit to quantify relative weights of each requirement and parameter. To complement the limitation, a quantification method applying the analytic network process is proposed. The proposed method can calculate the relative importance degrees of the requirement and parameter objectively and help us understand and focus on more important factors of the smart manufacturing system design.

Drastical increase of a variety of information devices with networks are based on a rapid development and expansion of network infrastructures and technology. Cloud computing is a main technology which makes the information devices lighter and allows users to access their data and applications through a variety of networks. Under the circumstances that the importance and use of cloud computing system is rapidly increasing the virtualization technology becomes one of the key components consisting the cloud computing. Therefore, a quality of a variety of cloud computing systems is affected by the virtualization quality. Many factors which decide the virtualization quality and characteristics have been studied. However, when we apply the cloud computing system to our organization the priorities of the key factors should be decided and according to the priorities resourves must be alloted. In this paper, we suggested a relative weight evaluation process applying Analytic Network Process to analyze the interrelations between the key factors and calculate the relative weights of the factors. Especially, through the demonstration we showed that the interrelations between the factors affect the relative weights at large. With the proposed method we can find hidden priority and allot our resources and efforts more effectively.

The quality offered by cloud computing services is becoming an issue of the utmost priority and, regardless of the cloud delivery models, the adoption of cloud services will depend on having the capability to ensure quality of service to the users. However, studies to find and select cloud computing services according to their service quality are still in their infancy. Here, we propose a system that calculates the priority weights for each quality attribute according to the quality preference of a user and the interrelation analysis results between the attributes, and reflects the weights in selecting the cloud computing service. To calculate the quality preference of the user, we applied a pairwise comparison matrix and an eigenvector of the matrix. Through the proposed system, users can easily perform the process of calculating the weights and selecting the best services according to their quality preference. The simulation results show that the weights of the quality attributes and the quality score ranking of the sample cloud computing services vary according to users' preferences and interrelations between attributes.

The quality offered by web services is becoming the utmost priority and various studies to search and select web services according to their Quality of Service (QoS) as well as functionality have been proposed. However, most studies have applied each attribute independently as a constraint to select a web service. Each attribute has a different priority weight that represents the degree of influence on the overall quality and the priority weight can be changed depending on the purpose of the quality evaluation or the subjectivity of the user. Here, we propose a system that calculates the priority weights for each quality attribute according to the QoS preference of a user easily and consistently and reflects the weights in selecting the web service. To calculate the weights, we apply a pairwise comparison matrix and an eigenvector of the matrix. Through the proposed algorithms and system, users can easily perform the process calculating the weights and select the web services according to their QoS preference. We implement the broker-based quality evaluation system and the simulation results show that the weights of the quality attributes and the quality score ranking of the web services are changed according to users’ preferences.

Despite the convenience, ubiquitous computing suffers from many threats and security risks. Security considerations in the ubiquitous network are required to create enriched and more secure ubiquitous environments. The address resolution protocol (ARP) is a protocol used to identify the IP address and the physical address of the associated network card. ARP is designed to work without problems in general environments. However, since it does not include security measures against malicious attacks, in its design, an attacker can impersonate another host using ARP spoofing or access important information. In this paper, we propose a new detection scheme for ARP spoofing attacks using a routing trace, which can be used to protect the internal network. Tracing routing can find the change of network movement path. The proposed scheme provides high constancy and compatibility because it does not alter the ARP protocol. In addition, it is simple and stable, as it does not use a complex algorithm or impose extra load on the computer system.

A manufacturing system is composed of and operated by many components; motors, cylinders, and sensors. Its purpose is to increase operational performance without system error or jam. And, recently, the Green IT/S (information technologies and systems) refer to processes that directly or indirectly address environmental sustainability in organizations. Green IT addresses energy consumption, reduces the bad productivity, and increases system operational efficiency using the computer. However, when the system experiences some problem or operational error, users (system operators or managers) often find it difficult to locate the jam position immediately and precisely. It is also difficult to know the cause of the jam and to manage jam information even after they read and find relevant error text in a repair manual for the system. This research proposes a method to efficiently manage jam status for manufacturing systems. For this purpose, a topic map was used. The topic model has topic types, associations, and occurrences, which relate to operational jam, system jam, and system components. All jam status information to support users is managed by XML specification, distinctly and efficiently.

Cloud computing delivers almost all of its services including software, user's data, system resources, processes and their computation over the Internet. Cloud computing consists of three main classes; Software as a Service, Infrastructure as a Service and Platform as a Service. Using Software as a Service (SaaS), users are able to rent application software and databases which they then install onto their computer in the traditional way. In the Enterprise Resource Planning (ERP) system, the system service environment changed so as to allow the application of the SaaS in the cloud computing environment. This change was implemented in order to provide the ERP system service to users in a cheaper, more convenient and efficient form through the Internet as opposed to having to set up their own computer. Recently many SaaS ERP packages are available on the Internet. For this reason, it is very difficult for users to find the SaaS ERP package that would best suit their requirements. The QoS (Quality of Service) model can provide a solution to this problem. However, according to recent research, not only quality attributes' identification for SaaS ERP, but also a process for finding and recommending software in the cloud computing environment, has proved to be lacking. In this paper, we propose a QoS model for SaaS ERP. The proposed QoS model consists of 6 criteria; Functionality, Reliability, Usability, Efficiency, Maintainability and Business. Using this QoS model, we propose a Multi Criteria Decision Making (MCDM) system that finds the best fit for the SaaS ERP in the cloud computing environment and makes recommendations to users in priority order. In order to organize the quality clusters, we organized an expert group and got their opinion to organize the quality clusters using Social Network Group. Social Networks can be used efficiently to get opinion by various types of expert groups. In order to establish the priority, we used pairwise comparisons to calculate the priority weights of each quality attribute while accounting for their interrelation. Finally, using the quality network model and priority weights, this study evaluated three types of SaaS ERPs. Our results show how to find the most suitable SaaS ERPs according to their correlation with the criteria and to recommend a SaaS ERP package which best suits users' needs.

Traditional culture`s researcher has attempting to public use as a various method. Example is design of digital archive and digital contents. However, in spite of this effort, traditional culture`s researcher has difficulty to public use. Because traditional culture is hard to understand, and less interest than the other area. Especially, traditional culture has not environment that user can searching and using the culture`s information due to difficult to search the data and layers. We propose a design to make an ontology using information profile for digital contents of traditional culture. Also, we use topic-map for the factors of ontology`s relation, and specify their relation using topic vector.

This research aimed an application method to connect traditional culture and information technology that is one of the digital convergence area. For this purpose, we proposed cloud digital archive framework for traditional culture. Actually, cloud computing environment is one of new technology trend and would be a important keyword for information technology. This research focus on functional framework that is able to support service in cloud computing environment to connect pansori archive model, one of traditional culture. We also make a data structure and metadata by each characteristics, and insert a service framework to support pansori archive. This structure consists of data evaluation, preservation, and access process. This archive has a relation model with digital archive and metadata.Key words : cloud computing environmen, Traditional Culture's, Digital Archive, database, pansori * Ayz{|}~i[S (Humanitas College, KyungHee University) ‧ 1€r(First Author) : ‚ƒ ‧ {„€r(Corresponding Author) : † ‧ ‡ ˆr: 20121 19ˆ ‧ ‰Š(^ )ˆr: 20121 26ˆ(^ ˆr: 20122 16ˆ) ‧ ‹Œˆr: 20122 28ˆ

Humanities is very difficult area to use the computer. However, recently, convergence trend has increase widely at all of the academic area. Therefore, in this paper, we propose a technical method to use an IT for the popularity of humanities. For this purpose, we implement a path process that use Petri-net to apply digital storytelling to humanities. We also make a structure to connect an examples and questions from sentences or articles as digital storytelling. The digital storytelling consists of six factors; author, synopsis, background, construction, view point, and user`s or reader`s review. Proposed method provides a process to analyze the data path of a literary work using Petri-net.

Highlights? We design Personalized Learning Course Planner (PLCP) applied to E-learning systems. ? PLCP analyzes student's implicit requirements. ? Learning courses are organized according to characteristics and educational levels of students. ? English learning system consisting of PLCP is implemented. ? Learning effectiveness and student satisfaction is improved in experiment results. Various methods of E-learning systems, based on information and communications, and geared towards improving learning effectiveness and students' attention span, have been studied. However, most E-learning systems force students to follow the learning course or content established by a teacher. These methods are convenient, but they limit the effectiveness of E-learning.To overcome this limitation and increase effective learning, new techniques that reflect alternative learning styles, such as adaptive learning and personalized learning, have been studied. In this study, we proposed a Personalized Learning Course Planner (PLCP) that allows students to easily select the learning course they desire. User profile data was collected from the students' initial priorities about learning contents as well as the test scores after their study. E-Learning Decision Support System (EL-DSS) in PLCP suggests an appropriate learning course organization, according to calculated results based on the user profile data.To verify the effectiveness of the proposed system, we implemented an English learning system consisting of PLCP. We conducted an experiment with 30 university students and evaluated students' satisfaction by questionnaire analysis. The results indicate that the proposed system improved learning effectiveness and student satisfaction. Further investigation of the participants indicated that suggesting a learning course suitable for students' previous test scores and priorities encouraged students to concentrate on the lesson.

This study used 3 kinds of experimental toothpaste with different contents(0%, 1.1%,2.2% of SLS. A total of 150 young adults in their 20`s without any systemic disease were recruited for the study. These 150 subjects were subdivided into three groups(each having 50 subjects) according to the results of preliminary oral examination.After 4 weeks application of the three groups of toothpaste, Group C showed the highest halitosis index, followed by Groups A and B which had similar halitosis indices. Both oral sulfur compound and aerobic sulfur compound were measured at high content possibly because higher SLS content which is associated with the lower salivary flow rate is causing the xerostomia. SLS content was correlated with oral sulfur compound (r

Web based learning systems are operating with various and lots of learning contents. But it is hard to construct learning contents to fit learners when they select learning contents for learning. In this paper, we proposed the recommendation method that can support the learning contents as calculate learner`s preference using the learning history information of learner`s profile when learner design and compose learning course. In the applying result of this method, we`ve selected testing learner group and was able to know it can help to learner processing learning by themselves as we`ve got great learning satisfaction after test.

A lot of learning systems are applying and verify evaluating the item difficulty to increase learner`s learning effect. But most of this methods ware calculated the item difficulty when it analyze the learning result before or after learning. So, it is hard to support learning contents with changing item difficulty during learning to learner. In this research, we proposed the method that system can support learning contents to next learning to fit leaner`s level immediately as apply to calculate item difficulty during the proceed learning. Through this method, learner could supported learning contents by calculated difficulty through pre-test and it caused this method was helped learner to increase learning effect.

A lot of learning systems are applying self-directed learning to increase learner`s learning effect. But most of this methods are hardly applied to fit the construction of learning contents considering learner`s characteristics or it was processing the learning course without learner`s choice. In this research, we proposed the recommendation method that can support the learning contents as calculate learner`s preference contents based on learning history information when learner design the learning course. In the result, we chose test learner group and was able to know to generally increase average score of each learner after test between existing method and proposal one.

Green and cloud computing (G-cloud) are new trends in all areas of computing. The G-cloud provides an efficient function, which enables users to access their programs, systems and platforms at anytime and anyplace. Green computing can also yield greener technology by reducing power consumption for sustainable environments. Furthermore, in order to apply user needs to the system development, the user characteristics are regarded as some of the most important factors to be considered in product industries. In this paper, we propose a cloud monitoring system to observe and manage the manufacturing system/factory automation for sustainable green computing. For monitoring systems, we utilized the resources in the G-cloud environments, and hence, it can reduce the amount of system resources and devices, such as system power and processes. In addition, we propose adding a user profile to the monitoring system in order to provide a user-friendly function. That is, this function allows system configurations to be automatically matched to the individual’s requirements, thus increasing efficiency.

1 Humanitas College, Kyung Hee University, 26 Kyungheedae-ro, Dongdaemun-gu, Seoul 130-701, Republic of Korea 2Department of Computer Science, The University of Alabama, P.O. Box 870290, Tuscaloosa, AL 35487-0290, USA 3Department of Computer Science and Information Engineering, National Taipei University, No. 151 University Road, San Shia District, New Taipei City 23741, Taiwan 4 School of Engineering & Applied Science, Aston University, Birmingham B4 7ET, UK

Frequent packet loss of media data is a critical problem that degrades the quality of streaming services over mobile networks. Packet loss invalidates frames containing lost packets and other related frames at the same time. Indirect loss caused by losing packets decreases the quality of streaming. A scalable streaming service can decrease the amount of dropped multimedia resulting from a single packet loss. Content providers typically divide one large media stream into several layers through a scalable streaming service and then provide each scalable layer to the user depending on the mobile network. Also, a scalable streaming service makes it possible to decode partial multimedia data depending on the relationship between frames and layers. Therefore, a scalable streaming service provides a way to decrease the wasted multimedia data when one packet is lost. However, the hierarchical structure between frames and layers of scalable streams determines the service quality of the scalable streaming service. Even if whole packets of layers are transmitted successfully, they cannot be decoded as a result of the absence of reference frames and layers. Therefore, the complicated relationship between frames and layers in a scalable stream increases the volume of abandoned layers. For providing a high-quality scalable streaming service, we choose a proper relationship between scalable layers as well as the amount of transmitted multimedia data depending on the network situation. We prove that a simple scalable scheme outperforms a complicated scheme in an error-prone network. We suggest an adaptive set-top box (AdaptiveSTB) to lower the dependency between scalable layers in a scalable stream. Also, we provide a numerical model to obtain the indirect loss of multimedia data and apply it to various multimedia streams. Our AdaptiveSTB enhances the quality of a scalable streaming service by removing indirect loss.

In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users’ tailored materials according to their learning style. That is, we analyzed the user’s data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques.

Since the fire causes serious disasters, fire detection has been an important study to protect human life. Based on the deficiencies of existing fire detection on real-time and monitoring accuracy, the wireless sensor network technique for fire detection was introduced and needed. In this paper, we proposed the fire detection algorithm in video sequences on wireless sensor network. The proposed fire detection algorithm processes visual information acquired through static camera that lets us incorporate the algorithm to CCTV surveillance system, and therefore does not require an additional expanses on conventional fire sensors. Fire detection method based on motion information, any input image, is compared to background in order to identify foreground. Background is statistically modeled by mixture of Gaussians. To detect the foreground of video sequences, the proposed color detection algorithm was performed in RGB space. The procedure of algorithm eliminates all objects that do not fulfill color requirements without fire-like objects. And the change map and blob's area are computed, change map shows temporal variation of pixel between two consecutive binary frames, and percentage area increase or decrease characterizes a fire property for swinging.

The process of creating secure embedded systems with sensor network is highly challenging and complex, particularly from a technical viewpoint. A secure embedded system is used for a specific purpose and operation, and needs hardware-software codesign methodology. As such, there are many considerations in terms of system design when the system developer creates or constructs a secure embedded system with sensor network. However, there is a lack of research into quality models in the area of secure embedded systems in sensor network. This paper seeks to redress this issue by presenting a quality model for a secure embedded system with sensor network. Each quality for the secure embedded system is constructed according to DeLone and McLean's success information system, and has subcriteria according to their characteristics. The present research also focuses on making a quality model network which considers the correlation between the qualities' attributes.

RFID is a new trend in industrial technology such as manufacturing system and product flow management in the supply chain. A successful RFID application can enhance the industrial technology change in organizations and help to manage growth in an increasingly competitive environment. Recently, the RFID sensor tag has also recently received a great deal of attention. In general, the RFID sensor was designed to collect environmental information such as temperature, pressure, humidity, inclination, and acceleration into ordinary RFID tags for internal information processing. In this environment, to apply the RIFID technique to manufacturing systems successfully and efficiently, identifying accurate quality attributes and making a quality model for a RFID enabled application system are necessary. However the research in this area is lacking. This paper focuses on identification of the quality attributes for the RFID application system and making the corresponding model. Each criterion for the quality model was extracted in accordance with ISO/IEC 9126 and the DeLone and McLean model. The proposed quality model also considers the relationship between their criteria.

The request about green IT technology is increasing recently. M2M is more request to IT Technology for Cloud computing. And effective use request of computer resource is increasing. So, necessity about Cloud computing is increasing rapidly thereby. However, Cloud computing environment have big security problems because embody using virtualization technology. It is Authentication that must solve in Cloud computing environment urgently. Therefore, we wishes to propose protocol that can solve suitable user and services certification in cloud computing environment. An novel authentication protocol that based on Kerberos. But it is faster than Kerberos and implement is easy.