Network virtualization supports future Internet environments and cloud computing. Virtualization can mitigate many hardware restrictions and provide variable network topologies to support variable cloud services. Owing to several advantages such as low cost, high flexibility, and better manageability, virtualization has been widely adopted for use in network virtualization platforms. Among the many issues related to cloud computing, to achieve a suitable cloud service quality we specifically focus on network and performance isolation schemes, which ensure the integrity and QoS of each virtual cloud network. In this study, we suggest a virtual network platform that uses Xen-based virtualization, and implement multiple virtualized networks to provide variable cloud services on a physical network. In addition, we describe the isolation of virtual networks by assigning a different virtualized network ID (VLAN ID) to each network to ensure the integrity of the service contents. We also provide a method for efficiently isolating the performance of each virtual network in terms of network bandwidth. Our performance isolation method supports multiple virtual networks with different levels of service quality.

Biological data have been increased exponentially in recent years, and analyzing these data using data mining tools has become one of the major issues in the bioinformatics research community. This paper focuses on the protein construction process in higher organisms where the deoxyribonucleic acid, or DNA, sequence is filtered. In the process, "unmeaningful" DNA sub-sequences (called introns) are removed, and their meaningful counterparts (called exons) are retained. Accurate recognition of the boundaries between these two classes of sub-sequences, however, is known to be a difficult problem. Conventional approaches for recognizing these boundaries have sought for solely enhancing machine learning techniques, while inherent nature of the data themselves has been overlooked. In this paper we present an approach which makes use of the data attributes inherent to species in order to increase the accuracy of the boundary recognition. For experimentation, we have taken the data sets for four different species from the University of California Santa Cruz (UCSC) data repository, divided the data sets based on the species types, then trained a preprocessed version of the data sets on neural network(NN)-based and support vector machine(SVM)-based classifiers. As a result, we have observed that each species has its own specific features related to the splice sites, and that it implies there are related distances among species. To conclude, dividing the training data set based on species would increase the accuracy of predicting splicing junction and propose new insight to the biological research.

The purpose of this study was to examine the syllable-word boundary misalignment cost on word segmentation in Korean continuous speech. Previous studies have demonstrated the important role of syllabification in speech segmentation. The current study investigated whether the resyllabification process affects word recognition in Korean continuous speech. In Experiment I, under the misalignment condition, participants were presented with stimuli in which a word-final consonant became the onset of the next syllable. (e.g., /k/ in belsak ingan becomes the onset of the first syllable of ingan 'human'). In the alignment condition, they heard stimuli in which a word-final vowel was also the final segment of the syllable (e.g., /eo/ in heulmeo ingan is the end of both the syllable and word). The results showed that word recognition was faster and more accurate in the alignment condition. Experiment II aimed to confirm that the results of Experiment I were attributable to the resyllabification process, by comparing only the target words from each condition. The results of Experiment II supported the findings of Experiment I. Therefore, based on the current study, we confirmed that Korean, a syllable-timed language, has a misalignment cost of resyllabification.

Various stakeholders are involved in the creation of software projects. In general, the higher the number of stakeholders involved during the requirements elicitation phase, the better are the chances of success for the project. However, it is rather difficult to consider the opinion of all the stakeholders owing to constraints on time and resources. Furthermore, conflicts between stakeholders can become inevitable when the number of stakeholders increases. Thus, the identification of key stakeholders is an important factor in ensuring the success of a project. In this paper, a methodical stakeholder conflict resolution model (s-CRM) is proposed by considering an actual industrial case study. The proposed model uses information gain based on entropy when measuring the impurity of information. We believe that the proposed s-CRM is effective in identifying the key stakeholders and in intuitively indicating those stakeholders whose elicited requirements need to be weighted. In addition, the model provides a solution for conflicts among stakeholders during requirements engineering.

Previously, we described a social media cloud computing service environment (SMCCSE). This SMCCSE supports the development of social networking services (SNSs) that include audio, image, and video formats. A social media cloud computing PaaS platform, a core component in a SMCCSE, processes large amounts of social media in a parallel and distributed manner for supporting a reliable SNS. Here, we propose a Hadoop-based multimedia system for image and video transcoding processing, necessary functions of our PaaS platform. Our system consists of two modules, including an image transcoding module and a video transcoding module. We also design and implement the system by using a MapReduce framework running on a Hadoop Distributed File System (HDFS) and the media processing libraries Xuggler and JAI. In this way, our system exponentially reduces the encoding time for transcoding large amounts of image and video files into specific formats depending on user-requested options (such as resolution, bit rate, and frame rate). In order to evaluate system performance, we measure the total image and video transcoding time for image and video data sets, respectively, under various experimental conditions. In addition, we compare the video transcoding performance of our cloud-based approach with that of the traditional frame-level parallel processing-based approach. Based on experiments performed on a 28-node cluster, the proposed Hadoop-based multimedia transcoding system delivers excellent speed and quality.

In order to block mobile junk messages automatically, many studies on spam filters have applied machine learning algorithms. Most previous research focused only on the accuracy rate of spam filters from the view point of the algorithm used, not on individual user's preferences. In terms of individual taste, the spam filters implemented on a mobile device have the advantage over spam filters on a network node, because it deals with only incoming messages on the users' phone and generates no additional traffic during the filtering process. However, a spam filter on a mobile phone has to consider the consumption of resources, because energy, memory and computing ability are limited. Moreover, as time passes an increasing number of feature words are likely to exhaust mobile resources. In this paper we propose a spam filter model distributed between a users' computer and smart phone. We expect the model to follow personal decision boundaries and use the uniform resources of smart phones. An authorized user's computer takes on the more complex and time consuming jobs, such as feature selection and training, while the smart phone performs only the minimum amount of work for filtering and utilizes the results of the information calculated on the desktop. Our experiments show that the accuracy of our method is more than 95% with Na$\ddot{i}$ve Bayes and Support Vector Machine, and our model that uses uniform memory does not affect other applications that run on the smart phone.

Among the various security threats in online games, the use of game bots is the most serious problem. Previous studies on game bot detection have proposed many methods to find out discriminable behaviors of bots from humans based on the fact that a bot's playing pattern is different from that of a human. In this paper, we look at the chatting data that reflects gamers' communication patterns and propose a communication pattern analysis framework for online game bot detection. In massive multi-user online role playing games (MMORPGs), game bots use chatting message in a different way from normal users. We derive four features; a network feature, a descriptive feature, a diversity feature and a text feature. To measure the diversity of communication patterns, we propose lightly summarized indices, which are computationally inexpensive and intuitive. For text features, we derive lexical, syntactic and semantic features from chatting contents using text mining techniques. To build the learning model for game bot detection, we test and compare three classification models: the random forest, logistic regression and lazy learning. We apply the proposed framework to AION operated by NCsoft, a leading online game company in Korea. As a result of our experiments, we found that the random forest outperforms the logistic regression and lazy learning. The model that employs the entire feature sets gives the highest performance with a precision value of 0.893 and a recall value of 0.965.

Robot programming often sparks students' interest in programming, but it is not easy for them to program both procedure and reactivity of robot movements that are essential requirements. In this study, we reviewed in detail a new programming language, State-Based Squeak. It allows novice students to implement both procedure and reactivity of robots easily. The effect of this new language on robot programming education was also examined using a group of 28 middle school students. According to the results of analyzing the students' understanding of programming, reading and programming abilities the group that used State-Based Squeak (the experimental group) showed a higher completion ratio than the other (control) group. The significance of this study is that a robot programming language has been developed that addresses the concepts of both procedure and reactivity in such a way that middle school students can more easily learn how to program robots, something that is often difficult to attempt even for professional programmers.

Recent research in MEMS (Micro-Electro-Mechanical Systems) and wireless communication has enabled tracking of continuous objects, including fires, nuclear explosions and bio-chemical material diffusions. This paper proposes an energy-efficient scheme that detects and tracks different dynamic shapes of a continuous object (i.e., the inner and outer boundaries of a continuous object). EBCO (Efficient Boundary detection and tracking of Continuous Objects in WSNs) exploits the sensing capabilities of sensor nodes by automatically adjusting the sensing range to be either a boundary sensor node or not, instead of communicating to its neighboring sensor nodes because radio communication consumes more energy than adjusting the sensing range. The proposed scheme not only increases the tracking accuracy by choosing the bordering boundary sensor nodes on the phenomenon edge, but it also minimizes the power consumption by having little communication among sensor nodes. The simulation result shows that our proposed scheme minimizes the energy consumption and achieves more precise tracking results than existing approaches.

Enterprises that develop online games have to deploy supporting infrastructures, including hardware and bandwidth resources, in order to provide a good service to users. First Person Shooter games generate high rates of small UDP packets from the client to the server, so the overhead is significant. This work analyzes a method that saves bandwidth, by the addition of a local agent which queues packets, compresses headers and uses a tunnel to send a number of packets within a multiplexed packet. The behavior of the system has been studied, showing that significant bandwidth savings can be achieved. For certain titles, up to 38% of the bandwidth can be saved for IPv4. This percentage increases to 54% for IPv6, as this protocol has a bigger overhead. The cost of these bandwidth savings is the addition of a new delay, which has an upper bound that can be modified. So there is a tradeoff: the greater the added delays, the greater the bandwidth savings. Significant reductions in the amounts of packets per second generated can also be obtained. Tests have been deployed in an emulated scenario matching an access network, showing that if the number of players is big enough, the added delays can be acceptable in terms of user experience.

In this paper, we propose aggregate signcryption for achieving data security in UWSNs. The main challenge of these networks established in sensitive environments is offline sink visiting. Moreover, the sensors must retain collected data for long enough time to offload them onto the itinerant sink. Thus, the unattended nature of data collection intervals might offer the adversary the opportunity to apply various attacks without detection. In this paper, employing low order operations (in time and space), we propose a new secure scheme in which various security goals such as confidentiality (through encrypting), authentication and integrity (through signing) are achieved. In addition, the aggregation process of our scheme reduces the space and communication overheads both for sensors and sink, i.e. the proposed technique efficiently enables the sensors and sink to protect, verify and recover all the related data. We further compare our scheme with the best alternative work in the literature.

Along with the development of Information Technology, online transactions through Internet have become more popular for the reasons of convenience and efficiency. In order to provide secure and reliable online transactions, an effective electronic payment protocol is crucial. In this paper, we propose a novel electronic payment protocol for digital product transactions with an offline arbiter to achieve fair exchange, automated dispute resolution, customer anonymity, and customer unlinkability. In our protocol a product token is adopted to eliminate the need of key management for digital product decryption in the offline arbiter. In addition, Elliptic Curve Cryptography (ECC)-based self-certified public key is utilized to further reduce computing overheads. According to our analysis, the efficiency of our protocol can be greatly increased in comparison with previous literatures.

Since wireless sensor networks are broadly used in various areas, there have been a number of protocols developed to satisfy specific constraints of each application. The most important and common requirements regardless of application types are to provide a long network lifetime and small end-to-end delay. In this paper, we propose Early Preamble MAC (EP-MAC) with improved energy conservation and low latency. It is based on CMAC but adopts a new preamble type called 'early preamble'. In EP-MAC, a transmitting node can find quickly when a next receiving node wakes up, so EP-MAC enables direct data forwarding in the next phase. From numerical analysis, we show that EP-MAC improves energy consumption and latency greatly compared to CMAC. We also implemented EP-MAC with NS-2, and through extensive simulation, we confirmed that EP-MAC outperforms CMAC.

Recently there has been an increasing interest in exploring the radio irregularity research problem in Wireless Sensor Networks (WSNs). Measurements on real test-beds provide insights and fundamental information for a radio irregularity model. In our previous work "LMAT", we solved the path planning problem of the mobile anchor node without taking into account the radio irregularity model. This paper further studies how the localization performance is affected by radio irregularity. There is high probability that unknown nodes cannot receive sufficient location messages under the radio irregularity model. Therefore, we dynamically adjust the anchor node's radio range to guarantee that all the unknown nodes can receive sufficient localization information. In order to improve localization accuracy, we propose a new 2-hop localization scheme. Furthermore, we point out the relationship between degree of irregularity (DOI) and communication distance, and the impact of radio irregularity on message receiving probability. Finally, simulations show that, compared with 1-hop localization scheme, the 2-hop localization scheme with the radio irregularity model reduces the average localization error by about 20.51%.

In this paper, the downlink resource allocation of OFDMA system with decode-and-forward (DF) relaying is investigated. A non-convex optimization problem maximizing system throughput with users' satisfaction constraints is formulated with joint relay selection, subcarrier assignment and power allocation. We first transform it to a standard convex problem and then solve it by dual decomposition. In particular, an Optimal resource allocation scheme With Time-sharing (OWT) is proposed with combination of relay selection, subcarrier allocation and power control. Due to its poor adaption to the fast-varying environment, an improved version with subcarrier Monopolization (OWM) is put forward, whose performance promotes about 20% compared with that of OWT in the fast-varying vehicular environment. In fact, OWM is the special case of OWT with binary time-sharing factor and OWT can be seen as the tight upper bound of the OWM. To the best of our knowledge, such algorithms and their relation have not been accurately investigated in cooperative OFDMA networks in the literature. Simulation results show that both the system throughput and the users' satisfaction of the proposed algorithms outperform the traditional ones.

In this paper, diversity-multiplexing tradeoff (DMT) of three-user wireless multiple-antenna cooperative systems is investigated in general fading channels when half-duplex and decode-and-forward relay is employed. Three protocols, i.e., adaptive protocol, receive diversity protocol, and dual-hop relaying protocol, are considered. The general fading channels may include transmit and/or receive correlation and nonzero channel means, and are extensions of independent and identically distributed Rayleigh or Rician fading channels. Firstly, simple DMT expressions are derived for general fading channels with zero channel means and no correlation when users employ arbitrary number of antennas. Explicit DMT expressions are also obtained when all users employ the same number of antennas and the channels between any two users are of the same fading statistics. Finally, the impact of nonzero channel means and/or correlation on DMT is evaluated. It is revealed theoretically that the DMTs depend on the number of antennas at each user, channel means (except for Rayleigh and Rician fading statistics), transmit and/or receive correlation, and the polynomial behavior near zero of the channel gain probability density function. Examples are also provided to illustrate the analysis and results.