Computer Science and its Applications

Aiming at the irrationality of the given value of observation noise variance in maneuvering target tracking, and based on the idea of the right selection filtering, the author proposes a new observed abnormal quantity of using interest-identify. The author then takes the idea of right selection filtering into consideration, timely adjusting the observation noise variance of filtering, changing the trust level from system to corresponding observation information in rejector so as to adjust the filter’s gains and improve the matching degree between motor model and system mode. The experimental results illustrate that the improvement on filtering algorithm to some extent improves the tracking performance from the system to target as non-maneuverable or weak-maneuverable, as well as better control the error effect of outliers.

Digital watermarking techniques have been developed to protect the copyright of multimedia objects such as text, audio, video. In this paper, we propose a digital watermarking algorithm with a gray image based on two dimensions discrete cosine transform (DCT) and singular value decomposition (SVD) for robust watermarking of digital images in order to protect digital media copyright. The performance of hybrid watermarking technique has presented, including imperceptibility (such as PSNR, NC) and robustness (such as Filtering, Gaussian noise, Rotation and Salt and Pepper). Experimental results demonstrate that the proposed scheme shows superior performance in the presence of signal processing attracks.

In this paper, the hybrid navigation system using Kalman filter is suggested. The hybrid navigation system consists of GPS to measure the position and the velocity of the mobile robot, and IMU to measure the posture and acceleration. The location of the mobile robot is calculated by applying Kalman filter, using the received data of the position and the velocity of the mobile robot from GPS when GPS signal is received, and the data of the acceleration which is received from IMU. The Hybrid navigation system calculates the position of a mobile robot by using acceleration and posture of IMU, and former velocity data of GPS until next the GPS signal is received. The performance of the hybrid navigation system was confirmed through the experiment comparing the actual route and the calculated route, where the calculated route of the simplified hybrid navigation system is the same with the actual route.

This paper proposes an easily attachable/detachable proxy that helps ordinary computer users detect their personal information leakage caused by malicious software. This proxy can behave as a stealth proxy or as a transparent proxy, meaning that the proxy can be placed at any point in a TCP/IP network. Users of this proxy can detect and stop any suspicious TCP/IP connections by placing the proxy at any appropriate point in their network and by using the bundled controller Web application that controls behavior of the proxy. We have used a Raspberry Pi and HTML5 for the proxy implementation, thus making this method affordable and portable. We evaluated the proxy by placing it at several places in the network, and confirmed that the proxy properly works and helps users detect suspicious TCP/IP connections by visualizing them between their computers and the Internet.

Various types of smart devices including smartphones, smart pads, and smart TVs are being introduced to the market but have different size and capabilities. Thus, it is crucial to consider device capabilities to provide seamless multi-screen services in conjunction with these devices. In this paper, we present an adaptive transformation engine to efficiently support smooth multi-screen services using smart devices. The proposed engine dynamically adapts user interfaces to make it suitable for smart devices. In addition, it supports real-time media transcoding and streaming so as to provide these devices with suitable media contents.

We study the improved 16th-order differential attack on 10-round variant of PRESENT that is a 64-bit block cipher supporting 80-bit and 128-bit secret keys proposed by Bogdanov et al. in 2007. Higher-order differential attack is one of the cryptanalytic techniques exploiting a low algebraic degree of an encryption/decryption function. In 2013 Wu et al. reported the 16th-order differential attack on 10-round variant of PRESENT, which requires 2

22.4

pairs of plaintext and ciphertext, and 2

99.3

times of encryption operation. In this article we adopt partial sum technique to solve an attack equation, which leads to low computational complexity. As a result we show that 10-round variant of PRESENT can be attacked with 2

22.3

pairs of plaintext and ciphertext, and 2

81.3

times of encryption operation.

This paper presents the hardware and software co-design and implementation of stereo camera module consisting of two image sensor modules and DSP(Digital Signal Processor) for recognizing hand gestures. The DSP based stereo camera module with high resolution and wide angle lens was developed for the hand gesture recognition. In addition, a hand gesture recognition system was developed with the camera module and PC based application. It recognizes the movement and click gesture by analyzing the location and shape change of fingertips in real time

Comparing to the ordinary text analysis and recommendation, the contents on Social Network Services (SNS) are observably more distinct and less redundant. Content-based recommendation has become the main method on SNSs. Because the limited contents are occurred in SNSs, a considerable effect can’t be reached by using ordinary cluster algorithms. In this paper, we propose a two-phase hybrid clustering algorithm based on Suffix Tree Clustering (STC), which not only uses the words themselves, but relations between them as well. Evaluation experiment and analysis confirm that our techniques have better recommendation results and effects on cold-start scenarios.

Advances in vehicular networks, embedded devices and cloud computing will enable the formation of vehicular clouds of computing, communication, sensing, power and physical resources. Owing to the dynamic nature of the vehicular cloud, continuous monitoring on trust attributes in necessary to enforce service-level agreement, This paper proposes RA-VTrust, Reputation-based Adaptive Vehicular Trust model for efficiently evaluating the competence of a vehicular cloud service based on its multiple trust attributes. In RA-VTrust, an adaptive trust and a reputation managements based on variable precision rough sets are suggested to trust data mining and reputation knowledge discovery. The adaptive trust management provides cloud consumers with the most suitable trust for vehicular services because it is performed by considering vehicular service level agreement (VSLA) documents and reputation services. The performance of RA-VTrust is evaluated through a simulation.

A named entity (NE) dictionary is an important resource to affect the performance of NE recognition, but it is not easy to construct the NE dictionary manually, because human annotation is time-consuming and labor-intensive. We propose a semi-automatic model to construct an NE dictionary from the free online resource DBpedia. The proposed model expands and purifies an NE dictionary based on an active learning technique. In the experiments, the proposed model classified 99.99% (180,008 out of 180,020 entries) of DBpedia entries into 18 NE categories with macro-averaging F1-measures of 0.6980 for 18 NE categories (0.7519 for 17 NE categories).

WHOIS service allows people to ask the current registrant some Internet resources. WHOIS service has become an indispensable information service in the DNS. Unfortunately, WHOIS information is often available by the malicious third party. In this paper, we first analyzed the whole process of the WHOIS protocol. And then the process of the WHOIS privacy collection is analyzed through the clustering method. Several methods of protection of the WHOIS privacy are proposed in this paper. Through the experimental results it can be seen that the protection method can better protect the privacy of WHOIS information in DNS.

Distributed environment has been utilizing to handle a massive data. And a major concern of a distributed system is a management method for resource allocation and task. In order to improve a performance, the scheduling may consider a resource evaluation. However, a resources state shows an uncertainty to evaluate the subject. In this paper, we propose of fuzzy logic-drive resource evaluation to solve this uncertainty for automated negotiation with resource allocation. The FLRE evaluates and manages the resource with fuzzy logic. The proposed method shows the high throughput and performance.

Wireless Mesh Networks (WMNs) are the robust and flexible networks having redundant links. These networks have some fixed nodes (gateways) providing the internet connectivity and having some static or mobile clients. However, WMNs has some security issues that have not been addressed by the existing standards. We proposed an enhanced authentication mechanism for the security of group heads in group communication. We propose and analyze an enhanced reliable authenticated group head communication (ERAGHC) architecture for secure reliable group head communication and to increase the overall efficiency and performance of architecture. The scenario has been developed and implemented in OMnet++ simulator. The simulation results reveal that the ERAGHC is better than the previous schemes in term of packet delivery ratio, packet delay and overall efficiency.

Conducting financial transactions over the Internet has been widely adopted due to the convenience and usability. However, conducting financial transactions via the Internet may be subjected to many types of attacks including password attacks, malware, phishing, and other unauthorized activities. Many banks have enhanced their security by using One-Time Password (OTP) as another authentication method in addition to traditional username and password. The OTP may be sent to the mobile phone number of the account owner via SMS. Even with the enhanced security measure, internet banking is still vulnerable to different types of attacks such as online phishing. We propose, design, and implement a transaction authentication scheme using HMAC-based mobile OTP and QR Code. Our scheme is resilient to known attacks including, but not limited to, eavesdropping, replay, message modification, and phishing.

Sensor nodes used for constructing a wireless sensor network consume a small amount of electrical power and resources, and should be used efficiently in terms of energy consumption. LEACH (low energy adaptive clustering hierarchy), a protocol that periodically changes the cluster head, averages the power consumption of all nodes. Some researchers have suggested the use of a security system for LEACH, but such systems create a possibility in which certain nodes no longer connect with other nodes in the network. In this paper, we propose a new LEACH protocol that improves on these problems using a hierarchical-structured key-sharing scheme. Our method offers greater security than other related methods.

In Shamir’s (k, n)-threshold secret-sharing scheme, a secret is divided into

n

shares, and the secret is recovered from

k

shares. When this scheme is applied to a server system, the

n

shares are distributed to

n

servers. Therefore, the secret can be restored by collecting the shares from

k

servers. In the case of two secrets, the latter are distributed over

n

servers such that each server consists of one share of each secret. Secrecy addition is performed by the addition of the two shares on each server. The combined secret can be restored through the added shares from

k

servers. Therefore, secrecy addition is realized by using

k

servers. However, secrecy multiplication requires a multiplication result from 2k-1 servers. In this paper, we propose a secrecy multiplication based on Shamir’s (k, n)-threshold secret-sharing scheme that uses only

k

servers. Through this scheme, the system can realize secrecy calculation without altering the threshold level.

The data that flows through wireless sensor networks is often private. Although data encryption is required, the calculation ability of each sensor node is weak. Therefore, common key encryption systems are suitable for sensor networks. To encrypt data using a common key, a key exchange scheme is required. For large-scale mesh-type wireless sensor networks, Key Pre-distribution Schemes have been used. However, this scheme involves some nodes that cannot perform key exchange. Moreover, as the number of stolen nodes increases, the analysis of the common key becomes easier. In this paper, we propose a novel key exchange scheme that does not include nodes that cannot perform key exchange. Even if many nodes are stolen, the proposed scheme ensures that the analysis of keys in other nodes is not possible, in the case where the attacker cannot intercept communication between any of them.

SCADA systems have been upgraded from the standard serial bus systems to modern TCP/IP based systems. The Modbus protocol is one of the most widely used protocols in SCADA networks. However, it provides no inherent security mechanisms. Therefore, the Modbus protocol is susceptible to the type of attack that injects false Modbus commands by fabrication or modification. In this paper, we propose an abnormal behavior detection method by using normal traffic pattern learning on Modbus/TCP transactions. Our approach is based on the characteristics of SCADA networks that are likely to have a regular traffic pattern. Most of all, the proposed method is performed according to the analysis of only Modbus/TCP request messages. Therefore, it has the benefit of detecting abnormal behavior on even with the simple traffic pattern learning.

Among the many health care issues for elder people, fall detection has shown up as primary focus. We propose a sensor fusion concept for elder fall monitoring and analysis. The idea is to integrate depth sensor and motion sensor. We illustrate the sensor fusion concept and implement the wireless fall detection sensor sub-system. We experiment with angular acceleration data in three limbs parts of human test target. Different life patterns of elderly are selected to test the motion monitoring system: walking and falling. Two methods are employed as diagnosis tool. The first is the energy expenditure method, while the second is the neural network method. The first tests the sensitivity of motion sensor, while the second filters out the noisy motion data for correct classification purpose. We discovered that the trained weights and bios from the neural network based on three limbs parts measurement data has allowed us to differentiate fall from regular walk motion with high accuracy.

Recent paradime for convergrnce technology between traditional maritime industry and ICT has spotlighted, especially applied for the prevention of accidents and for remedies. With the introduction of samrt ship and recent addvance in the communication technology, the accidents in coastal areas has not been reduced. The reason is that those accidents were caused by small vessels which have not fully eqipped with navigational and communication system. In this paper, we introduce a electronics chart system for mobile devices based on international standard, S-57, in small vessels. For this system, we propose a data transformation scheme applicable to mobile device. Finally, we illustrate some experiments and results of the system which is implemented with Android platform.

Team sports tactics analysis systems often focus on game event detection, which are unable to analyze the tactics in detail and suggest better tactics for training. We introduce a novel tactics training system that is able to assess the state of the basketball game in detail, and find better defense tactics. Instead of tracking only player/ball positions, our system studies players’ attributes together with their court positions to accurately estimate each player’s offense threat or defense ability. We then formulate an optimization problem to find the defense tactics that could minimize the offense team’s threat. The optimal defense tactics found by our system have received favorable evaluations by three professional basketball coaches in Beijing Sports University from a professional point of view, and as opposed to some well known defense conventions, our system opens up new promising defense tactics and possibilities.

To support a directional antenna, IEEE 802.15.3c specifies a high data transmission rate and short frequency range communication based on the characteristics of 60 GHz band. However, the contention-based protocol of IEEE 802.15.3c may cause channel collisions and throughput degradation as the number of stations in the network increases. In order to resolve this problem and reduce channel access latency, we propose an adaptive contention access period (CAP) assignment approach in which the contention window size for optimal transmission probability is derived after the directional information has been obtained by means of access point (AP) control procedures. Simulations demonstrate that the proposed algorithm outperforms the existing channel access scheme in IEEE 802.15.3c networks.

Motion estimation part in video compression has high computational complexity. Still a lot of researches for fast scheme are in progress. In this paper, we propose an efficient motion search method based on content-awareness. The proposed method consists of three steps. The first thing is the motion classification for using the center position distribution. The second step is the calculation of predictors according to motion classification. The last is to set the search pattern size by using between predictor and center position distance, adaptively. Due to not utilization of temporal-spatial information, the proposed algorithm can reduce the required memory requirement. Experimental results show that the proposed algorithm can achieve speed-up factor of up to 30.66% on average with quite good bit rate performance, comparing to UMHexagonS and full search method and based on the H.264/AVC reference software (JM).

This paper describes our efforts in developing real-time projection mapping system, effective technique available in the performance by combining costume and digital technology. Our system can project and align images on the surface of moving actor’s costume by applying augmented reality based on projection mapping, and the images can change frequently during the performance. Therefore, this result can not only describe a particular character effectively, but also offer visual experience to audiences. We explain our result obtained by rehearsing the scenes applying our techniques in the real stage set installed in a wide theater.

With the advent and growth of online bookstores, the importance of book reviews has been increased due to the difficulty to browse actual contents of books in advance. This has led to the necessity of adequate systems for retrieving useful book reviews. Recently, ranking algorithms have been proposed for this purpose. However, most of them cannot consider discussions among users through replies. User discussion is an essential factor in ranking book reviews since it provides additional information. Although a state-of-theart algorithm with consideration of the factor has been proposed, it deals with the discussions only in view of reviewers’ participation. In this paper, we propose a ranking algorithm of estimating discussion quality on the basis of all the users engaging in discussions. Experimental results show that our algorithm outperforms the state-of-the-art one in terms of precision, recall, and NDCG.

Since frequent graph pattern mining was proposed, various approaches have been suggested by devising efficient techniques or integrating graph mining with other mining areas. However, previous methods have limitations that cannot reflect the following important characteristics in the real world to their mining processes. First, elements in the real world have their own importance as well as frequency, but traditional graph mining methods do not consider such features. Second, various elements composing graph databases may need thresholds different from one another according to their characteristics. However, since traditional approaches mine graph patterns on the basis of only a single threshold, losses of important pattern information can be caused. Motivated by these problems, we propose a new graph mining algorithm that can consider both different importance and multiple thresholds for each element of graphs. We also demonstrate outstanding performance of the proposed algorithm by comparing ours with previous state-of-the-art approaches.

Vehicle-related engineers develop techniques for improving vehicle functions using vehicle data and test the performance of the devised techniques, where they need to secure vehicle data with various characteristics for more exact tests. However, it is actually hard to directly collect such vehicle data from the real world because of time and cost constraints. For this reason, we propose a method for generating vehicle data with various characteristics. Our approach can create vehicle data considering changes of vehicle attribute values such as increase or decrease in a linear form. Numerous types of vehicle data generated by the proposed method can be employed in a variety of data mining areas utilizing vehicle data such as clustering, monitoring, diagnosis, classification, and prognosis methods.

A majority of researches for blog retrieval have only focused on a keyword-based search manner under consideration of fundamental blog characteristics. However, these keyword-based methods have limitations such as the limitation of keyword usage. Especially, use of irrelevant or ambiguous keywords leads to low quality of blog information retrieval. In this paper, we propose a blog ranking method considering blog pages instead of keywords as a search query. Consequently, the proposed method can solve the problem caused from the use of irrelevant keywords by efficiently selecting important keywords in accordance with the characteristics of blog pages. In addition, we present that our algorithm outperforms the previous blog retrieval algorithm in terms of Precision and Recall.

Weblogs are used to express knowledge and thoughts of many internet users. With the explosion of information in the weblog space, problems of information retrieval efficiency on weblog posts have been addressed. To provide users with their desired information more precisely, we propose a weblog retrieval algorithm based on the analysis of weblog topics. The proposed algorithm focuses on the

informativeness

, which is one of the important characteristics in weblogs. Our method ranks weblog posts by using both interconnections of weblogs and relevance between weblog topics as well as a given user query in order to offer high-quality weblog information to users. In addition, we demonstrate that our method is more effective than the previous one for retrieving weblogs in terms of the real weblog dataset.

HEVC (high efficiency video coding) achieves much higher coding efficiency compared with previous video coding standards at cost of significant computational complexity. This paper proposes a fast intra mode decision scheme, where a Bayesian classification framework using relative sum of absolute Hadamard transformed difference (SATD) is introduced and combined with conventional fast encoding methods. Experimental results show that this scheme reduces encoding run time by about 30% with a negligible coding loss of 0.9% BD-rate for the all intra coding scenario.

Lately, Cloud Computing environment has been rising as a new IT paradigm beyond technology. While many issues are raised, however, different risks exist to convert actual IT service to SaaS-Cloud computing environment. To reduce such risks, a large party of the software development process must be remodeled. This study analyzes previous cases of converting toward SaaSCloud computing environment, and proposes four new best practices in Software Development in which the cases’ success factors are reflected. The effectiveness of the practices will be verified in the future by being applied to software development project based on various SaaS-Cloud that is operated by Korean Software Engineering Center.

A comprehensive information systems (IS) literature found that there has been little empirical evidence on application users’ mental model that affects their behavioral intention in a smartphone context. The goals of this study are; 1) to suggest a theoretical research model to explain application users’ behavioral intention in a smartphone setting using mental model, and 2) to empirically validate the model and examine the relationships among the variables in the model. A survey of students and practitioners was conducted to collect data. Structural Equation Modeling (SEM) was used to analyze the data. The results showed that there is a direct effect of mental model on intention to use smartphone applications. In addition, there is also an indirect effect of mental model on intention to use applications through a mediating variable, perceived usability.

Software Defined Networking (SDN), as an emerging Internet architecture, improves the scalability and programmability of the network by decoupling the control plane and the forwarding plane effectively. In the data plane, OpenFlow switch, that consists of a flow table and a set of actions associated with flow entries, works as the infrastructure. To deploy SDN for large-scale networks, improving the performance of flow recognition in OpenFlow switch is a challenge. This paper presents a trie-based approach to achieve efficient wildcard-aware search for flow recognition with reasonable cost of both memory and update. Depending on the experimental evaluation, the throughput of our approach is almost 48 times higher than the conventional approach. Meanwhile, our memory cost has been reduced by 500 MB, and the update speed is also improved by 7.5 KPPS.

Packet classification, as one of the key functions of modern routers and firewalls, has confronted with serious challenge in performance due to the increasing link rate and traffic size. Recently, GPU’s massive parallelism has been proved valuable in supporting high-speed packet classification. However, the GPU-based approach that works on the whole router platform with real packet I/O is less clear. In this paper, we firstly study how to accelerate HiCuts on GPU for packet classification. Then, we deploy a simple router platform with an optimized packet I/O framework to evaluate the end-to-end performance. On this basis, we conduct a series of experiments to demonstrate how GPU-based approach works on the router platform.

In this paper, we propose the time division broadcast scheme for congestion control in vehicle-to-vehicle communication. The proposed scheme is designed to distribute the beacon transmissions using time division scheduling. It aims to prevent collisions due to simultaneous channel access. In addition, the proposed scheme reconfigures proper communication parameters based on the channel state measurement and was evaluated using different traffic scenarios with simulations based on the IEEE 802.11p MAC/PHY. The results have shown that the proposed scheme performs better than the legacy scheme in terms of beacon reception ratio and the channel access time.

Multi-label feature selection is an important task that can be done before applying multi-label classification algorithms because the multi-label classification performance is naturally influenced by input features. To solve this problem, feature selection algorithm considers the dependency of each feature to labels as well as the dependency among features simultaneously. However, feature selection methods suffer from additional computational burden for calculating the dependency among features. In this paper, we propose an efficient feature selection algorithm extending quadratic programming feature selection for multi-label datasets and use the Nyström approximation. Experimental results demonstrated the proposed method reduces the computational cost for performing multi-label feature selection.

Music emotion recognition is one of the best attractive researches in music information retrieval. To develop an efficient music information retrieval system, a music data set related to music emotion is required. In this paper, we released two music emotion data sets, and proposed a feature selection algorithm that can be useful to investigate the relation between musical property and multiple emotions.

Avionics software is safety-critical embedded software, so it has higher security and reliability requirements. How to effectively develop safe and reliable avionics software and control the development time and cost becomes a significant problem. An approach to develop the ARINC653-based software architecture is proposed. It converted the high-level requirements into the software architecture model, and then generated the source code of architecture from the software architecture model. The process helps to keep the consistency and traceability between requirements and architecture.

NFC integrated payment service generates information such as user consumption-migration information by processing them through big-data technology after collecting personal information including credit card payment information, location information, electronic coupon usage history, etc. and also provides such service as advertising tailored to a specific user based on the pattern information. Since such sensitive personal information as consumption pattern in addition to simple personal information, only those authorized companies should be able to process the information thereof. Nonetheless, the cooperative relationship for sharing personal information among the companies based on TSM just like a mesh has been formed; thus, it is difficult to control access to personal information. Moreover, as for Hadoop that is the most prominent big-data technology, it has an access control function; however, it lacks a method to grant an authority to process personal information. As a result, it is not able to grant an authority to process information in accordance with the companies to provide simple services using the pattern information and the companies to process personal information for generating pattern information. Thus, this paper proposed a technique for key generation and distribution and a secure communication protocol in order to ensure that access control of personal information and personal information processing for each authority will be securely conducted in NFC integrated payment environment.

Wireless Sensor is used for military purpose, machine management, cultural properties management, etc. However, sensor being transmitted in wireless network channel, relay attack, eavesdropping, and replay attack have occurred. Also, because the sensor’s hardware arithmetic operation capacity is very limited (low cost), it has various security vulnerabilities for which a solution has not yet been developed. Thus, this paper proposed the light-weight distance-bounding mutual authentication security scheme based on the limited hardware computing capability. The proposed scheme is safe against all security vulnerabilities that could occur in sensor network and can be efficiently used for group sensor management because of its ability to authenticate multiple sensors simultaneously and to show efficient performance.

By analyzing brain waves, we can do scientific and objective judgment of human’s attention concentration, perception, stress level and active brain status. Our research results show the specific signal patterns in pupil movements in time domain and frequency domain. The entire system for detecting brain wave is implemented with an head set device and programming using Testbench software by Emotive Corp. This paper provides a possible application of direction control through brain wave using movement of pupil.

The direct mode coding is widely used in the bi-predictive pictures (B-pictures). Because it has small overhead and it generates a predictive image from two reference pictures, it can efficiently improve the performance of bi-predictive coding. The traditional temporal direct mode coding usually derives the motion vector of the current block by scaling the motion vector of the co-located block in the subsequent reference picture. However, when the motion is irregular, the prediction efficiency of traditional direct mode is not very well. In this paper, we present a motion vector continuous tracking technique. With the technique, the motion trajectories of moving objects in temporally adjacent pictures are exploited to get better motion vectors for direct mode macroblocks. The proposed method can accurately calculate the motion vectors for the direct mode macroblocks and enhance coding efficiency.

In dual frame motion compensation (DFMC), one short-term reference frame and one long-term reference frame are utilized for motion compensation. In this paper, an error resilient jump update dual frame motion compensation (JU-DFMC) is proposed. In the error resilient JU-DFMC, a new error resilient prediction structure of DFMC is firstly presented. Then an end-to-end distortion model is applied for Macroblock level mode decision. Finally a frame level rate distortion cost scheme is proposed to determine how many times of the header information will be transmitted in a high quality frame. The experimental results show that the proposed method can achieve better performance than the previous DFMC schemes.

In dual frame motion compensation (DFMC), one short-term reference frame and one long-term reference frame are utilized for motion compensation. In this paper, firstly an extended DFMC Structure is proposed, then HQF jump period in extended DFMC is presented. Considering temporal-view and interview prediction structure, HQF location is determined. Finally bit allocation in the proposed multi-view is proposed. Experimental results show that the proposed method can achieve better performance than the previous schemes.

In paper the method of a estimating of frequency of a harmonic trend in a measurement signal are presented. The method is based on calculation of “quasispectrum” by zero-crossing number and estimation of maxima quasispectrum’s component. Results of research of the proposed method on models of measuring signals with harmonic trends are submitted. It is shown, that the proposed method allows estimating frequency of a harmonic trend with a small error. The practical realization of the method is simple and does not require any appreciable computational costs, since only arithmetic operations of summation and subtraction are used.

Identifies and analyzes the problems arising from the use of software and software - hardware experiment automation. One of the important directions of digital signal processing is the recognition of waveforms at primary classification. Application of wavelet transform as a tool for assessing the waveforms in real time greatly simplify the classification algorithm. The paper proposes a method for implementing the wavelet filter based on a discrete analog components, with the decomposition signal in real time. The modularity and partial implementation of mathematical operations in analog form allows more efficient use of hardware solutions for the problems of varying complexity, and increases the visibility of the result.

This paper proposes novel hardware architecture for real-time face detection, which is efficient and suitable for embedded system. The proposed architecture is based on AdaBoost learning algorithm with Haar-like features and it aims to apply to a low-cost FPGA that can be applied to legacy analog cameras as a target platform. We propose the using of cumulative line sum to calculate integral image and an alternative method to avoid costly division for the computing of a standard deviation. The experimental results show that the processing time for a 320×240 pixel image is 42 frames per second with the 100MHz, which is about 3 times faster than previous works.

This paper describes the issue of data transfer for the embedded SE(Secure Element) on mobile devices. To enhance their security, current mobile devices provide the separate physical secure storage that contains the important and sensitive data for various security services. On the other hand, there is the problem of data migration for above secure storages such as the embedded SE. It is critical and essential problem in the embedded SEs because they cannot be removed from mobile devices. Thus, we explain the above problem in detail and would like to share the problem with service provider and mobile device manufacturers. To this end, we describe the security requirement for the secure data transfer of all data stored in embedded SEs. We also propose the secure data transfer schemes, which meet the proposed security requirements.

The major problems of the nondestructive inspection acoustic emission method are analyzed in the article. The comparative evaluation of the acoustic emission signals effectiveness is conducted. The inappropriateness of the analysis method in the narrow band of frequencies is shown in the article. The comparative results of the acoustic emission signals spectral analysis for different types of ceramic materials are given as an example. The necessity of using spectral analysis methods to increase the accuracy and reliability of results is demonstrated.

The experimental research results of the acoustic emission properties of articles of simple geometrical shapes are analyzed in the article. The research was conducted using samples made of different ceramic materials. The research results allowed making a conclusion on the necessity to analyze acoustic emission signals of a wider band of frequencies. The recommendations on increasing the validity of the nondestructive inspection acoustic emission method were given.

In vehicular ad hoc networks (VANETs), vehicles should exchange safety-related messages with each other while processing them within short time in their embedded on-board units. Most previous authentication schemes unfortunately are not ready to be deployed for vehicle-to-vehicle communications owing to serious practical shortcomings, such as the enormous expense of building additional facilities (e.g., roadside units), computation and communication bottlenecks, a huge certificate revocation list, and low processing capacity. To alleviate these problems, we propose an efficient privacy-preserving scheme with which each vehicle (specifically, its embedded on-board unit) can produce valid one-time certificates without help from any third party. The scheme is cost-effective because additional facilities are not needed and it can also prevent bottlenecks on a certain trusted entity because certificates are self-produced.

Side channel analysis is a powerful cryptanalytic method to reveal secret information of cryptographic devices such as smart cards, USB tokens, OTP devices and secure embedded devices. To prevent side channel attacks we must implement countermeasures against it and evaluate security of these implementations. However, secure embedded devices except smart cards are not easy to evaluate security of them against SCA owing to their complicated components and circumstances. In this paper we designed boards for SCA evaluation of software implementations and tested them with block cipher algorithms.

The main objective of this paper is to demonstrate how simple fluid-flow model of TCP (Transport Control Protocol) behavior allows to exploit robust control methods earlier developed for nonlinear time delayed feedback systems. Test setup consists of “far” and “near”, in terms of transport delay, groups of TCP flows and AQM (Active Queue Management) router. It was proposed to control “near” flows for “far” long-lived flows stabilization in uncertain network parameters environment. The comparison of RED (Random Early Detect) queue controller and robust controller simulation and experimental results are presented.

We have investigated the effects of multiple scattering on electron velocity, current and energy in the drain regions of the Strained-Si diodes. The covered cases in this study are ballistic channel Si-diodes with strained channel or drain, and with strained channel and drain, respectively. For a selected Ge content

,

the simulation results show that the velocity of electrons in the drain regions of strained channel and drain is lower than that of strained drain at the lower bias voltages (V

d

 <0.5V). At the higher bias voltages (V

d

 >0.5V), the velocity in the drain regions of strained channel and drain regions is lower than that of strained drain regions. Meanwhile, the velocity of electrons in the drain regions of ballistic channel diodes with strained channel and drain reduces due to optical phonon scattering, when bias voltage increases.

Augmented reality is a technology which superimposes virtual objects on real world to provide users with useful information and user interface. AR coloring book is interesting application. In this paper, we propose a texture extraction method from video and image warping method for augmented reality coloring book with vuforia SDK. To realize this idea, we made algorithm to extract specific rectangular area on a paper from video in real-time, and then transforms the image of area into rectangular shape for texture mapping. We expect that our approach will be useful when developers implement new function to AR coloring book with vuforia SDK.

The Republic of Korea’s Ministry of National Defense has developed various defense information systems (IS). Measuring IS utilization is important because it is a precondition to get their benefit and it can make it possible to find areas of an IS that needs to be improved. We proposed the framework that can measure the degree of IS utilization with three perspectives such as task support, relevant information provision, and use convenience from the viewpoint of users, and conducted an evaluation of defense ISs with it. Based on the results, we classified the defense ISs into four groups according to the utilization status and the frequency of maintenance demand, and then we made suggestions for better management of the systems in each group.

The rectification of stereoscopic image is indispensable process to implement stereo vision system since the accuracy of estimated disparity strongly depends on the alignment of epipolar line. A new and simple rectification method is proposed in this paper especially when there are vanishing points and lines in two images from separate cameras. In order to compensate the mismatch of viewing directions of separate cameras, we estimate the vanishing points of two images and make common reference point. After that, the mismatch of rotation is compensated by calculating the relative distance between vanishing lines in two images. The proposed method is very efficient for rectification of separate stereo cameras and successfully applicable to the scene of streets or hallway where the vanishing points and lines can be easily obtained.

In this paper, the inter-device authentication and session key distribution system for the device with the encryption module only. In the proposed system, unlike the existing sensor network environment where the key distribution center distributes the key, each sensor node agrees with generation of session keys. In the proposed scheme, the performance is improved so that the authenticated device can calculate the session key in advance. The proposed mutual authentication and session key distribution system can respond against replay attacks, man-in-the-middle attacks and wiretapped secret key attacks.

Storage Class Memory (SCM) such as STT-RAM, PCRAM, and ReRAM can be used as a DRAM or a NAND Flash memory. Owing to the non-volatile characteristic of SCM, its use as main memory can reduce the overhead during boot code loading and decompression whenever the system is switched on. Thus, a system with non-volatile memory suffers from less overhead during booting than a system with DRAM. The purpose of this study is to design a new booting model by analyzing the kernel ELF file and disassembling the current booting sequence for a more compatible startup model. To achieve this goal, we disassembled the ELF file format including the characteristics of the kernel binary. To verify the effectiveness of our theoretical model, the time cost in each step was measured using several different methods in our experimental setup. The results show that our approach reduces the time cost by approximately 45.25%.

This paper presents an

intelligent

multisensor framework based on the BeableBone Black platform, a complete open hardware and software embedded computer; the challenge is to create a multi-purpose hierarchical network composed by

smart

nodes able to gain and manage heterogeneous data and spaces according to the Internet of Things paradigm. Thanks to several expansion modules, the designed nodes can become an instrument for monitoring, preservation and protection of several environments. As a proof of the proposed framework we conducted a first prototypal experimentation within the Cultural Heritage domain; in detail we deployed the system in an art exhibition within the Maschio Angioino Castle, Naples, Italy.

This study attempts to calculate CO2 exhausting volumes as green-house gas with the help of EMME/3 program which is one of the most famous simulation tools in the field of transportation planning. Simulation areas are residential blocks of Curitiba (Brazil) worldly famous for carrying out Transit Oriented Development and Changwon (Korea) where Korea Department of Land & Transportation has executed CO2 reduction project. In this study, social & economic factors, transportation facilities and land-use styles in the pilot sites are under monitoring for calculating CO2 exhausting volumes. As a result, this study shows that Curitiba kind of Transit Oriented Development (TOD) is less than one tenth of general development concept city. Therefore TOD is strongly recommended as an urban development policy for reducing CO2 exhausting volumes and sustainable development for future generations.

This paper introduces an approach to revise a standard projector system into a touch screen system that is not dependent on sensor position. While interactive whiteboards (IWBs) are commonly used for meetings and lectures, new equipment is often required to provide touch screen functionality. Some researchers have attempted to apply the touch function to existing projectors by using cameras. However, a sensor must be mounted to a beam projector as the integrated type, owing to the limits of the existing coordinate correction method. In this study, a touch function has been implemented using a standard projector, and without the need for beam projectors, by utilizing a different correction approach. As a result, this study demonstrated the feasibility of applying advanced touch screen functionality to existing projection equipment.

Recently, the development of smart phones has been reported the number of security vulnerabilities. In spite of these smart phones have a concept of Sandbox for the security, sensitive personal information has been still exposed by internal data exchange or root privilege acquisition. In this paper, we propose a domain group which is separated into general domain and trusted virtual domain (TVD) by virtualization techniques. TVD provides a secure execution environment in mobile device and makes it possible to protect sensitive personal information in the domain. Thus, we suggest together the secure filesystem in TVD to securely store and manage significant data of user. This results presented by secure filesystem based on TVD would raise expectation about the following security virtualization techniques.

Context-aware systems constitute one of the key research areas in Human-Centric Computing. This paper presents the MHNCS, a middleware for developing context-aware mobile applications in low-cost acquisition devices. The middleware provides resources for acquisition, validation and distribution of context data. The paper describes the motivations for the development of MHNCS, its architecture and functionalities, as well as the evaluation results.

We propose a personalized information gathering system PerIGS that exploits implicit user feedback in a multiple personal device environment. PerIGS extracts imposing words that induce user’s choice from recommended news articles, and constructs user preference profile based on observed user behaviors. A user profile is shared by multiple personal devices, and PerIGS gathers, filters, and recommends news articles according to this shared profile. We organize the user profile in a hierarchical category structure to deal with the uncertainty of user interests. This structure also enables the system to track and recognize the changes of user interests for each domain more effectively. The results of a series of experiments measuring user satisfaction for recommended news articles indicated that PerIGS can effectively utilize user profiles for news filtering and recommendation.

Rich-media describe a broad range of digital interactive media that is increasingly used in the Internet and also for the support of synchronous and asynchronous communication. Communication has become an increasingly critical role in the crisis management. EduArt programming system is the new revolutionary product determined for realization of recordings and asynchronous communications based on the rich-media technologies. That software can be operated at any workstation, notebook, netbook, mobile device, etc. with the particular supported operating system and can be used at all the phases of the crisis management for providing timely and accurate information to the public and the support for asynchronous communication. The system surpasses significantly similar commercial solutions mainly in terms of its mobility, simple operation and no requirements for a specialized and costly hardware. EduArt system was developed in the frame of the MERLINGO (MEdia-rich Repository of LearnING Objects) project activities.

This paper proposes a new recommending method using effective purchase pattern mining with weight based on FRAT (Frequency, Regency, Amount and Type of merchandise or service) analysis in e-commerce. In this paper, using an implicit method without onerous question and answer to the users, it is necessary for us to make the task of mining frequent pattern in purchase data extracted the most frequently from whole data, to join customer’s data, to keep the analysis of FRAT to calculate the weigh and to make the task of clustering of item category in order to recommend item with an immediate effect by frequently changing trends of purchase pattern. We consider the importance of type of merchandise or service and then, suggest recommending method using mining frequent pattern with weight based on FRAT analysis to forecast frequently changing trends by emphasizing the important items with efficiency and to reflect different merchandises on e-commerce being extremely diverse for customers’ need. To verify improved better performance of proposing system than the previous systems, we carry out the experiments in the same dataset collected in a cosmetic internet shopping mall.

This paper is focused on design and implementation of a comfortable voice control of operational technical functions in a smart building using KNX technology. The paper is divided into three parts. Development of voice recognition application, programming and bringing to life of an already finished model and implementing of communication interface between these components. The very recognition of voice commands is implemented on .NET Framework 4.0 platform using C# programming language. After recognition of the voice commands, the commands in machine code are sent through UDP (User Datagram Protocol) server using KNX/IP router from the control computer to KNX fieldbus for control of the Smart Home model. In the end, testing of functionality and quality of voice command recognition was carried out.

Recently, a variety of external accessory devices is applied to the smart phone. Among them, external devices which communicate through earphone jack are outstanding. The reason is that communication via earphone jack can be fixed on the smartphone easily. In this paper, The IR MEMS sensor, still under development, is presented. This IR MEMS sensor for smartphone can evaluate current temperature of the target and can be applied in many ways.

Proxy re-encryption with keyword search enables users to search on encrypted data without allowing the storage server or proxy to learn any information from the searching. In this work, we improve the efficiency of a proxy re-encryption scheme which supports conjunctive keyword search by removing a redundant trapdoor algorithm. Subsequently, we implement the improved scheme using J2SE in a data storage environment.

We propose a formal modeling method of malicious software that support its detection and countermeasure. In order to detect malware there is a need to posses either digital signatures or behavioral models. As the obfuscation techniques makes the malware almost undetectable the classic signature-based anti-virus tools must be supported by behavioral analysis. A malware hunting tool we developed bases on the formal models in the form of Colored Petri nets and the attitude to modeling is presented in this article.

This article describes proposal of the semantic model for context aware service provision in disadvantaged environment that is used to dynamically select adaptation actions performed on SOAP messages flowing from the service to the client entity.

Spatio-temporal congestion evolution pattern can be reproduced using the VDS(Vehicle Detection System) historic speed dataset in the TMC(Traffic Management Center)s in that the historical dataset provides a pool of spatio-temporally experienced traffic conditions. It is known that traffic flow pattern spatio-temporally recurs and even non-recurrent congestion caused by incidents has patterns according to the incident conditions. This implies that the information should be useful for traffic prediction and traffic management. Traffic flow predictions are generally performed using black-box approaches such as neural network, genetic algorithm, and etc. Black-box approaches are not designed to provide an explanation of their modeling and reasoning process and not to estimate the benefits and the risks of the implementation of such a solution. That is why the TMCs are reluctant to employ the black-box approaches even though there are numerous valuable research papers published. In this context, this research proposed a more readily understandable and intuitively appealing data-driven approach and developed an algorithm for identifying congestion patterns for recurrent and non-recurrent congestion management and information provision.

Honeypots play an important role in network security, since they obtain information about attackers, their targets, methods, and tools. This paper offers a discussion about the definition of attack. The main matter of discuss is when an activity is considered to be an attack. Paper only focuses on low-level interaction server honeypots and outlines the definition of attack from the perspective of windows service emulation and Linux SSH services emulation.

Advances in both smart card and biometric technologies have encouraged the development of multi-factor remote authentication schemes. For example, the usage of smart cards with biometrics allows the verification of users becomes more secure and reliable. Most of the existing schemes in the literature maintain the assumption that the biometric matching is an exact matching. However, this assumption is not realistic because the biometric features extracted from the same user at different time will be slightly different. In this paper, we concentrate on the security flaws on the matching procedure and propose a practical solution for biometrics match-on-card. Also, our scheme supports password and biometric change without require re-registration.

Current embedded system, such as cell phones and smart-cards, in corporate security devices or cryptographic processor. Therefore hardware attacks targets this security devices. In this paper we discuss brifely Accessibility/Resources/Time

(ART) schema

that quantifies hardware attacks. Hardware attacks could be covert or overt based on awareness of the targeted system. In this paper, we provide an overview of

overt attack

and quantify the attack using the ART schema.

The Data Encryption Standard (DES) has been the most known block cipher since its release in 1976. It has been crypt-analyzed for a very long time and its design principles have been known to the cryptographic community. However, its Substitution Boxes (SBoxes) have inputs of length 6 bits and outputs of length 4 bits. In this paper, we analyze the Linear and Differential properties of DES-like SBoxes which are randomly generated. This shows that the DES SBoxes were not randomly picked up during their design process. Most importantly, it shows what possibly can be improved Linearly and Differentially in designing such SBoxes if we have the choice of re-designing the block cipher again or re-using some of its design components.

Property-preserving Petri net process algebras (PPPA) were designed for the specification and verification of the Petri net processes. PPPA does not need to verify the composition of the Petri net processes because all their algebraic operators preserve the specified set of the properties. These original PPPA are generalized for the newly introduced class of the P/T Petri process nets (PPPN) in this article. The only one PLACE-SUBST algebraic operator is defined for the class of PPPN and its chosen properties are presented. The PPPN can be with the support of the PLACE-SUBST operator easily applied into the area of design, simulation and verification of multithreading programming systems executed in parallel or distributed environment. This fact is then demonstrated on the simple example based on the Asynchronous Partitioned Global Address Space (APGAS) programming model used in the High Performance (Cloud) Computing environment and implemented in the X10 programming language.

The advent of Web 2.0 has provided users with the ability to actively participate in the building, management and sharing of contents. This new modality to experience the Web has led to the development of new ways of collaborative knowledge creation, which have promoted the spread of the earliest user-generated contents, not only textual ones but also multimedia ones. The most important example of collaborative knowledge base on the Web is definitely Wikipedia. The increasing involvement of users in the management and organization of Web content has encouraged the development of other forms related to the knowledge organization and dissemination, including social networks and social bookmarking systems. The proposed work deals with the development of an innovative system of social bookmarking based on a semantic wiki-like approach, characterized by an ex-ante classification conducted on two axes: one vertical (hierarchical and taxonomic axis) and one horizontal (folksonomic axis through tags).

Document management is not often handled appropriately by organisations, if at all. Despite that, and despite the lack of structure in documents, organisations must face regulations that require owning a document collection with semantic content. The technique based on taxonomies and folksonomies can easily produce an adequate semantic classification for documents. It requires an adequate setup among the domain experts that apply it. The approach we propose uses Lean Kanban to coordinate the phases of definition, validation and implementation of taxonomies and folksonomies. It helps organisations to create a semantic classification of existing document resources, making them ready to be used in ways that were not possible before. At the same time, it helps to improve the quality of work of the organisation itself, adding speed to document search.

Never before has data received so much attention from every corner of the world. In order to further explore the Big Data industry, we study business models of Big Data in this paper. The study identifies 15 different cases that are spread across three development stages: introduction, growth, and mature stages. In the introduction stage, companies that hold data engage in the data providing business. Public agencies and large companies gain access to new data resources. In the growth stage, companies deliver solutions based on data analysis. In the mature stage, companies that hold data develop direct services and deliver the unique services to consumers with different needs. We identified the direction and development of the Big Data industry through an understanding of these models. This paper contributes to the revitalization of the business industry by informing government policy.

In this paper Robust link-oriented Routing (ROR) protocol is proposed taking consideration of fast developing vehicular ad hoc network technologies in the urban area. Using the geographical information and speed of vehicles, a future vehicular network topology is anticipated with our prediction algorithm. Based on this prediction, routing overhead such as route reconfiguration due to broken links is minimized by choosing the most robust route. Also, transmission failure can be minimized by establishing a new route before the existing route is completely disconnected. By way of traffic and network simulation, the proposed approach shows improvement in packet transmission rate and low latency, with a slight hop count increase. Moreover, the simulation result shows that the proposed route maintenance scheme reduces the transmission failure dramatically.

Over the last years, there has been an increasing research interest in the application of medical biometrics, which may be applied and implemented in new automated recognition systems. The need for more security and health monitoring is increasing with new functionalities and features made available. To improve different device/application security and health monitoring we present a wireless biometric electrocardiography (ECG) system using both machine learning approaches and stable template creation/comparison algorithms. Unlike other previous work on wearable ECG recognition, which were based on full wired non-wireless systems, this paper reports a new wireless technology and techniques which can improve the performance, by using simple and low-cost components. Several techniques were applied to our 30 volunteer chest-based ECG dataset that resulted in up to 97.5% identification accuracy rate and an Equal Error Rate (EER) of 0.98%.

For the control of operational and technical functions in Smart Home or in Smart Home Care was used the xComfort wireless technology, which was implemented with the developed visualization application software “Smart HomeApp”. Visualization application software “SmartHomeApp” was designed with regard to web interface requirements, ability to control the software via the mobile phone and also with regard to easy expandability, scalability and modularity. For testing the reliability of radio frequency (RF) control system was chosen signal quality measuring method between actuator´s and the USB interface in environment with obstacles.

In order to improve the performance of text classification and information retrieval in big data of electric power domain, we propose a novel Chinese language classification algorithm - De-word classification algorithm. Focusing on the key role played by the De-word in modern Chinese language, this algorithm examines Chinese text classification method from a unique angle. Besides, on the basis of traditional weighted algorithm, it designs a novel relevance weighting model - De-TFIDF, and achieves a higher correlation in text information retrieval. Experiments show that, De-word classification algorithm significantly improves the efficiency of text classification, significantly improved information retrieval performance.

Internet of Things (IoT) refers to the hyper connection revolution, which allows the formulation of intellectual relationships including sensing, networking, and data processing, between human beings, things and services, which are three separated environments, via internet without the physical intervention of human beings, leading changes in a modern society and industries. In this study, users aim to design the IoT-AGS(Application-Gateway-Sensor) HMI system, which is based on the virtual rapid prototyping with rapid expandability and reliability, based on virtual SCC (Sensor-CPU-Communication) and wireless network, through an application and then, evaluate its performance. In the information-oriented age, the virtual prototyping considers of securing of reliability and expandability of IoT beyond its functional and physical boundaries, by reflecting users’ demands and needs in a prompt manner. The suggested system managed to conduct the virtual prototyping of the wireless network HMI platform, which consists of a virtual sensor, components, related devices and applications. As for the components required for communication between IoT factors, 3 managers including Mobile Network Manager, Server Manager and M2M Manager are suggested, and processes of development, modification and evaluation are realized with the virtual prototype, testing an IoT environment and evaluating its performance. With the proper development of the virtual prototype-based IoT system, it would be possible to expect to secure the dominant position in the IoT market in terms of technologies and standardization of technologies, where there is no absolute dominator.

The fusion of images which have different focal lengths can play important role for visual enhancement. It can also be used as a pre-processing step for various processing modules such as edge detection and feature extraction in order to improve the performances. In this paper, we propose a pixel-based algorithm which uses SML(Sum of Modified-Laplacian) as a focus measure for multi-focus image fusion. After the centers of focused areas in images are calculated, the 2-level SML comparison map is made by measuring the distance from the centers. And we adopt the median filter on the comparison map to reduce the effect of noise. Simulation results showed the proposed algorithm can successfully improve the visual quality of combined image subjectively as well as objectively.

Stereo Imaging is a powerful technique for determining the distance to objects using a pairs of camera spaced apart. The extremely high computational requirements of stereo vision limit application to non realtime applications where high computational calculation is available. To overcome the limitation, we reported the general strategy for parallelization of dense matching methods with CUDA (Compute Unified Device Architecture) programming.

There had accumulated a large amount data in business system of university, providing favorable condition for building decision support system based on data warehouse. This paper has introduced the architecture, data warehouse subject and construction process of decision support system in detail. Then the paper introduces a application example to show the value of the decision support system. This system can not only support daily management, but also provide data evidence for leader to draw up policies and regulations of university, promoting the development of the university.

The development framework is the basic and core software resource which can provide a general and reusable structure among all the software applications. This paper proposes a rapid development framework (RDF) of enterprise information system based on software components and JavaEE, the paper not only describes the framework’s structure and the detailed functions of each parts in RDF, but also gives an application example. The development practice proves that this framework has good maintainability, extendibility and stability, it can meet the development needs of most software applications.

This paper designs a malware detection system under the high-speed network environment. In the system, all the traffic of the targeted network is captured by zero copy technology, then they are parsed and filtered by the protocol analysis engine, which restoring the original contents on the network. Finally, we take advantage of the Dynamic Binary Platform DynamoRIO with the characteristics of the buffer overflow attack analysis to detect the malware program of the network. Experimental results show that this system can effectively detect the malware program and outperform the popular antivirus software in the high-speed network environment.

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. In ubiquitous computing environments, personalized agents have to learn user’s preferences in order to assist them. If users search using their profile data, they are able to find relevant documents. So the proposed intelligent search system is collecting information of a user activity and automatically creates the user profile. In addition, this system provides a variety of recommendation data for users. In other words, topics cloud, similar to the user, author navigation and frequently seen keyword/documentation systems are automatically recommended. The feature of the proposed system is re-cycling system using the user’s search results and used recommend systems. In this paper, we had implemented and evaluated the category-based personalized search system.

This paper presents a method to evaluate join aggregates efficiently when overlapping windows are used for data stream processing. To avoid duplicate processing, the proposed method divides overlapping windows into disjoint segments, which is called window panes in literature. For each pane, join results are produced. The pane-level join results are then merged to compute window-level aggregate results. Our experimental results show that the proposed method has significant performance benefits.

Sliding window equijoins can be implemented in two ways. One is to allocate a hash table for each data stream, while the other is to allocate a table for each set of tuples arriving for the slide interval. In the latter, multiple hash tables can be allocated for each stream. In this paper, we analyze costs of the two approaches, and show that using multiple hash tables for each stream always provides better performance than using a single table per stream. We also provide experimental results that coincide with the analysis result.

In LTE-based mobile communication systems, a Tracking Area Code (TAC) is defined by a group of cells for paging operation. The existing TAC configuration has been made manually by considering only the geographical location of cells, and thus it tends to induce an imbalanced TAC configuration and a low paging success rate. In this paper, we propose a reconfiguration of the existing TAC configurations to get a more balanced TAC configuration and to increase the paging success rate. The proposed TAC reconfiguration scheme is designed by considering the mobility (handover) ratio of mobile users between cells as well as the number of users in cells. From the performance analysis with real traffic data of SK Telecom in Korea, we see that the proposed scheme can provide more balanced TAC configurations and also improve the paging success rates by 8%, compared to the existing TAC configurations.

Thanks to the development of the IT convergence technology, the healthcare and wellness industry have been being developed to provide effective healthcare services. In particular, smart devices such as tablet PC or smart phone may be used for rehabilitation of the unilateral neglect patient after stroke. However, existing methods such as Albert Test, Line-bisection Test, and Star-Cancellation Test have been used in healthcare industry even though they have some disadvantages. Thus, we propose a testing and treatment system model for unilateral neglect patient based on smart devices that are portable and are easy to use anytime, anywhere. Particularly, we try to improve the existing evaluation models by developing digital testing methods to mobile applications. Based on visual feedback training methods, we try to develop three treatment models such as card flipping, obstacle avoidance, and human balance.

Smarthome as one of IoT(Internet of Things) services is growing more and more interested. Due to the development of mobile network, proliferation of smartphones and increasing of interest for personal safety, many enterprises enter the smartphone market. However, incidents could happen because they provide their services without considering security. This paper analyzes security vulnerabilities of smarthome and proposes countermeasures.

This paper presents a method to extract the position and size of objects in movie or drama scenes by creating natural image marker from user images that were fed into and recognizing the created natural image marker. First, the range of natural image marker candidate that the user will utilize in the image is defined. Then, the defined natural image marker candidate is created and analyzed to recommend final natural image marker. If the user image and natural image marker were different, the initial value was set from the user, allowing the user to edit the position or size of object. The position and size information of character are extracted by recognizing the natural image marker. The character image is matched with the image by using the extracted information. Therefore, the position of object can be estimated by frame, on which basis services applying various augmented reality techniques can be developed.

Recently, big-data processing applications require a fast and energy-aware cloud storage system. Erasure coding in redundant array of inexpensive disks (RAID) consumes a lot of energy and reduces the performance of cloud storage systems. This is because traditional RAID erasure coding performance is limited to sequential nature of CPU. This paper proposes an energy-aware GPU based flash array cloud storage system with higher input/output performance and less energy consumption. The proposed cloud storage system provides GPU–redundant array of inexpensive disks (GPU-RAID) at a target server based on all–flash array storage. GPU-RAID differs from existing RAID in that it reduces the number of CPU cycles and coding time to generate parity data. Experimental results show that the write performance and read performance GPU-RAID is 33% and 41% higher than that of Linux-RAID, respectively. The results also show that the energy consumption of GPU-RAID is 43% less than that of Linux-RAID.

We propose a meanshift segmentation guided local histogram equalization method which enhances the visual quality of a single low contrast image. The meanshift segmentation partitions the image into local regions according to the spatial and intensity distances between pixels. After the image is partitioned into local regions, the local range of intensity and the number of pixels in the local region are taken into account to compute the new local range of intensity. The local contrast is well enhanced with the proposed method even in regions which have small histogram components, for which the global histogram equalization fails to obtain an enhanced contrast.

In this paper, we present 3D target recognition under photon-starved conditions using axially distributed sensing and nonlinear correlation. 3D object in low light level environment may not be recognized due to lack of the number of photons. To detect active photons for 3D objects, photon counting imaging system can be used. In addition, axially distributed sensing can be introduced to visualize 3D images. However, it may be difficult to recognize 3D object because the visual quality of the 3D image may be remedied under photon-starved condition. To solve this problem, nonlinear correlation technique can be applied for 3D target recognition. We implement optical experiments to prove our 3D target recognition system.

With the development of cloud computing technology, database outsourcing has recently attracted much interest. However, because the users’ data may contain sensitive personal information, it is essential to encrypt the database to be outsourced for protecting users’ privacy. Meanwhile, cache-based Top-k query processing schemes were proposed to support efficient analysis of a large amount of data. However, the existing works have a problem that they cannot process a Top-k query on the encrypted data. To solve this problem, we propose a cache view-based Top-k query processing algorithm by using an order-preserving encrypted index. To improve the performance of the top-k query processing, we newly design a score function for calculating the similarity between a given query and the cached query. Finally, we show from the performance analysis that our scheme outperforms the existing work in terms of the query processing time and the query result accuracy.

Influence of IT revolution represented by Internet in late 20th century keep growing with glaring development of computer and network performance, constructing cyber infrastructure. The paradigm has already changed education and life style rapidly. Especially, It affects behaviors of growing generation and learning style. Method of motivating interest of engineering and science is needed, reflecting growing generation’s learning style. Web contents also get a lot of changes with rapid propagation of web accessible devices like smartphone and tablet pc. Existing web contents were implemented only for PC and supplied same contents regardless of devices. For servicing contents fit for different mobile environment, same contents are developed for every devices. As a result, development duration is enlarged and maintenance expenditure is increased. To resolve these problem, we will develop interactive mobile learning environment for computational science simulation task processing in this paper. It is better than existing education based on experiment in that it enables user to access the service everywhere with mobile device and its maintenance expenditure is cheaper than existing laboratory maintenance. we need not to consider each OS platform because it uses web page platform.

In this paper, we introduce a task scheduling methodology to help systems to resiliently maintain their availability and reliability. Particularly, this method can quickly improve system recovery from failures as well as achieve an optimized performance while considering the customers’ monetary cost and network condition. As comparison is made between our work and some similar existing approaches, apparently, it shows that ours has higher effectiveness and efficiency than the other ones.

This paper designs an interaction mechanism for the service architecture of electric vehicle information systems, focusing on a tour scheduler capable of reducing waiting time involved in battery charging. A common database table coordinates the interaction between the cost estimator which preprocesses traffic information and the tour scheduler which executes a variant of traveling salesman problem solver. According to the criticality of the link cost change, the cost update propagates to the tour scheduler and thus mobile applications either in periodic or an event-driven manner. Each entity, implemented in its own language for better development and run-time efficiency, exchanges message with specific database connectivity and object access protocols, for the sake of implementing a specific information service. The interaction design opens a way for diverse entities to collaborate to implement a new information service.

This paper proposes a novel method that enables location sensing for a mobile object by utilizing deployed passive UHF Radio Frequency Identification (RFID) tags and a stationary RFID reader. In order to estimate the mobile object location, the proposed method utilizes the second order under damped system based tag to tag interference model. The empirical study using RFID systems and a mobile robot verifies the effectiveness and performance of the proposed method.

This paper addresses a high capacity and quality reversible watermarking algorithm for medical imagery which is directly applicable in medical scanners or PACS systems. After the characteristics of medical imagery are analyzed, a reversible watermarking algorithm using difference histogram shifting is modified. The watermark that is private information is inserted into the original image by modifying the difference histogram of adjacent pixels. After the watermark is extracted from the watermarked image, the original image is perfectly restored. The inherent overflow and underflow issues are solved by applying an error compensation technique. In experiments using various medical imagery such as MRI, CT and X-Ray, the performance is analyzed in aspect of capacity, perceptual quality and the reconstruction rates. Results show that the algorithm performs well for medical imagery.

Previous page storage models do not show good performance in mixed OLAP and OLTP workloads because they are originally designed for only one of these workload types. Therefore, we propose a new page storage model for mixed OLAP and OLTP workloads which is based on column groups. We also present a dynamic page layout method that generates correlated column groups by analyzing the current workload and then dynamically reorganizes the data pages. The proposed page model shows a considerable performance improvement in the mixed workload environment.

The IEEE 802.11 standard provides multiple transmission rates. When using multiple transmission rates, the capacity of wireless LAN improves, but the performance anomaly problem may occur. Cooperative communications were introduced to alleviate the performance anomaly problem with the help of relay nodes with higher transmission rates. Previous cooperative communications protocols are based on the IEEE 802.11 DCF MAC protocol. In this paper, we apply the IEEE 802.11e EDCA features such as TXOP and block ACK for cooperative communications. Simulation results show that the proposed protocol works well and improves network performance.

This paper constructs a data processing framework for massive data streams created from electric vehicle-related business models, such as vehicle tracking systems, charger status monitor systems, and vehicle sharing services. Hadoop is employed to cope with the size and heterogeneity of spatio-temporal streams, while PIG provides a data flow style script interface. Focused on the currently available state-of-charge streams and charger operation status records, our framework converts the stream into Hadoop-readable format and makes a PIG script filter a core subset essential for further analysis required by battery consumption modeling and power demand tracing. By this framework, we can explore the massive amount of data which will be collected from our electric vehicle service system, possibly making it possible to design a new sophisticated consumer-friendly information services.

As in the conventional databases, an index can be used to improve performance in MapReduce when processing OLAP queries with it. Regarding this, Hadoop++ suggested Trojan index to reduce network I/O by storing a partitioned data and its index together into a same

data block

, which is a data storage unit in MapReduce. However, this approach requires complex computation to put the data and index into the same block, from which index generation time can significantly increase. In this paper, we propose a new indexing method to resolve this issue. The basic idea of the proposed method is to insert the data and index into separate blocks, and force them to be co-located in the same node. Our experimental results show that the proposed method provides better performance than the existing indexing scheme, including the Trojan index.

The recent trends on the circumstances of the social donation is evolving and the ways of social donation activity or campaign are diversified depending on rapid shifts of state-of-the-art ICT technology, perceptual changes of the individual donor about donation, and the cultural diffusion of sharing economy services. The purpose of this study is empirically focused on that explores and defines the cognitive and behavioral features of millennial (the generation born between 1980 and 1993) generation people in the basis of three theoretical factors, ‘reputation’, ‘emotional satisfaction’, and ‘individual characteristics’ concerning the degree of involvement in gamification contents, so-called mobile game application of social donation program converging with ICT and sharing economy. For this, respondents surveyed are targeted on young people in their twenties who are experienced in mobile game and familiar with the smart devices and wireless network. To conclude, this study proposes the theoretical definition of three differentiated millennial consumer groups through hierarchical clustering analysis and a research framework segmenting market or consumer in a marketing perspective as a preceding research for future studies.

Modern embedded systems are constrained by several factors such as performance, power consumption and size. Since those constraints are affected by memory subsystems, efficient use of a fast data transfer technique in memory subsystems is ever becoming more important. A burst transfer is well known technique to improve the performance of memory subsystems. The burst transfer capability offers an average access time reduction of more than 65 percent for an eight-word sequential transfer. However, the problem of utilizing burst transfer to improve memory performance has not been generally addressed. Thus, we propose an approach that provides the maximum utilization of burst transfer for memory accesses with local variables in code by reorganizing variables placement.

In this paper, we propose a method to detect and mitigate the selfish behavior in 802.11 Wi-Fi networks. The proposed approach promptly detects the presence of selfish misbehavior in a Wi-Fi network based on the distinct characteristics of selfish carrier sensing: the selfish node will (i) cause a severe asymmetry between uplink and downlink collision probabilities, and (ii) have shorter inter-packet backoff intervals than legitimate nodes. To properly mitigate the impact of selfish behavior on system performance, we present a simple 802.11-compliant mitigation mechanism to be used by the AP. The effectiveness of the proposed scheme is evaluated via extensive simulation with ns-2. Our evaluation results show that the proposed approach accurately detects the misbehavior and mitigates the selfish carrier sense behavior effectively.

In recent years, social media has become ubiquitous and important for social networking and content sharing. Especially, in the disaster area, social media supports backchannel communications, allowing for wide-scale interaction that can be collectively resourceful, self-policing, and generative of information that is otherwise hard to obtain. At the time of great Japan and Haiti earthquake, social media channels actively were utilizing to grasp the damage, to warn, and to exchange information. In this paper, we introduce the real-time monitoring system of social big data, named Social Big Board, for disaster management. This system crawls social big data, especially Twitter, analyses the disaster-related tweets in real time, and displays disaster issues and trends in a map. We substantiated that there is the potential for utilization on our system to monitor disasters situations and trends in real time and grasp the point from big tweet data.

NAND flash memories have been widely adopted as storage on computing devices, since it has strong effectiveness on performance. However, asymmetric operation performance of NAND flash memories still impedes high performance. To mitigate this performance hurdle, most storage devices of NAND flash memories have used Flash Translation Layer (FTL). In this paper, we propose a novel workload-aware FTL mapping policy, called Multi-level Associated Sector Translation (MAST). MAST divides log block depending on characteristics of data, and dynamically detects hot pages to avoid unnecessary merge operations of FTL. In experimental results, we show that our approach outperforms FAST method by 10% and LAST by 3%, respectively.

Commodity operating systems have become extremely large to provide a lot of services, and then their vulnerability has been targeted by malicious attack. In order to increase security in operating system, there have been many attempts to reduce the size of Trust Computing Base (TCB). However, most of the approaches have applicability limitations due to hypervisor vulnerability and additional hardware requirements. To address these limitations, instead of reducing TCB size, we propose a novel approach to enhance the security of the system. We hide secure TCB for sensitive applications, and thus build an isolated secure environment using a well-equipped infrastructure. For evaluation, we implement a prototype, called Remote-Launch, which runs a security-sensitive process on the borrowed secure TCB.

The shape space that is composed of the configurations matrices of landmarks extracted from a given video is represented by a product manifold. Therefore, we proposed new method that can recognize a human action using mathematical properties of product manifold and Procrustes shape analysis. In the first step, we factorize each volume images belonging to training dataset as a time ordering sequence of images, and we extract pre-shape configuration vector of landmarks from each frames consisting an image sequence. Then, we have obtained a random sample of pre-shape configuration vectors from all videos stored in tanning database by using similar procedure, and we compute mean shape vectors for random sample of extracted shape vectors. In the second step, in order to recognize the query human action video, we derive a sequence of the pre-shape configuration vectors from given query video, and we project each shape vector on the tangent space with respect to the pole taking on a sequence of the mean shape vectors corresponding with a target video. We recognize a query video as target video that can minimize the distance between two sequences of the pre-shape vectors and the mean shape vectors. We assess the performance of our method using Weizmann human action dataset. Experimental results reveal that the proposed method performs generally very well on this dataset.

The DDoS attack is one of the biggest threats in traditional network infrastructures which increases every year. This threat of making the target unserviceable still exists in the newly arising network architecture called SDN. In this paper, we propose a model for detecting zombie PCs, which are systems infected by malware used for launching the DDoS attack, on the SDN. Using this model, we can block malicious packets from the local network which generates them. It can also detect and treat zombie PCs, vulnerable hosts, to improve the security of both end host and local network.

Although it is important to analyze and predict corporate situation accurately in the rapidly changing environment, there are few systems to support that. Therefore, in this paper, we propose the integrated system which can monitor, predict and evaluate corporate current situation using AHP technique. As AHP, one of the decision making techniques, is based on hierarchical analysis and pairwise comparison method, it can provide fraud classification, assessment list and risk detection scenario through industrial sector analysis. The proposed system is linked to the existing legacy systems and it performs analyzing and verifying items of assets and insolvency evaluation which a corporation needs. Therefore the proposed system can manage the corporate assets and risk reliably and it can increase the company’s competitiveness and profit through non-performing assets recovery. In addition, the proposed system prevents complaints, corporate incidents and legal cost by adopting forensic readiness and forensic monitoring system which collects digital evidences about criminal and civil dispute in advance.

With rise in energy costs, operational costs for managing cloud infrastructures are also increasing. This is an opportunity to present an energy-aware recommendation system for cloud platforms. This paper presents one such system that implements a pure software approach for generating energy efficient recommendations for cloud infrastructures. This system performs offline profiling of cloud nodes to generate energy-aware profiles which are later matched with runtime usage feed. According to the real-time data, energy efficient profile is matched and provided to provisioning for implementation; consequently, achieving an energy efficient cloud platform.

In this paper we propose new authentication scheme that is suitable for Software-Defined Networks (SDN). Basically our approach is based on One Time Password (OTP). To check legitimacy of OTP, our model uses an additional parameter, which is related with the path information where packet passed through. This is possible because SDN controller can monitor the entire network status. Proposed scheme can be briefly described as follows. First, a specific path is assigned to the network manager and his OTP packet should pass through this path. The controller modifies corresponding flow rules to forward OTP packet along correct direction. Consequently, OTP packet of legal user will be forwarded on pre-assigned path. Using this model, SDN controller can be protected from attack even when attacker knows the OTP because SDN controller accepts OTP packet only if it is forwarded along specific path. Finally we analyzed overhead caused from our authentication mechanism.

Recently, there is lots of interesting about Bigdata in Korea. Social Bigdata which is created by socialmedia has people’s opinion and idea, so government and other organizations want to use social Bigdata to find out public opinion and information. Utilization of this social Bigdata has been studied in disaster management. In our institute, we studied a way to quickly detecting disaster occurrence through the analysis of social Bigdata. In this paper, we describe the detecting method using the lexical patterns, and we present the results of applying this method on tweets related to three types of disaster. There are other results depending on the types of disaster. In this experience, tweets of collapse accident are the best result to apply the lexical patterns(94% accuracy). So the possibility of detection disaster using the lexical patterns was confirmed.

A small terminal node with compuation and communication capability is a basic building block for IoT. It is deployed on every objects with sensors and low power wireless communication features and interacts with human as well as other physical or virtual objects. As such, it transmits collected information for IoT to provide intellectual services. Although the progress has been made consistently in sensor technology, human’s intermediate intervention is still required in the industries or the facilities critical for vigilance, security and inspection. Thus, a GUI support in terminal node is needed to make a correct and instant interaction with human. Because of the lightweight computing environment where the terminal node has very small size of storage as well as low performance in computation and communication, the trite GUI programming scheme that makes high computation and memory requirement is inapplicable to the terminal node. In this paper, two image data structures are presented to minimize space requirement of storage for character fonts. They do not reduce the storage requirement for fonts but also decrease communication traffic over the low power and lossy network.

A General Data Protection Regulation that has been proposed in European Commission in January 2012 brings about active debate over the rights to be forgotten. If

the right to be forgotten

is approved, people can protect their personal information through strengthened personal information protect. However, this can lead to restraining the freedom of expression on internet. Thus, this article will present a standard that can protect freedom of expression and right to be forgotten by grading the exposed information on internet. Also, this article will reinterpret

the right to be forgotten

cases by applying new standard.

In recent decades, advances in research and hardware have significantly improved the quality of augmented reality applications. However, efforts of combining real illumination and virtual illumination have made little progress thus far [1, 2, 3]. In this paper, we propose the position estimation of multiple light sources using a specular reflective sphere placed in front of a camera for relighting in augmented reality applications. For position estimation, we take images to estimate light directions, and then, we estimate the positions of light sources in real time using a sequence of images taken by moving the camera. Our method improves illuminant integration, as more accurate shadows of virtual objects are obtained in comparison with the traditional methods of light source estimation [4, 5, 6, 7]. Further, we obtain not only the direction but also the position of light sources.

As more and more electronic devices and their software are applied to vehicles nowadays, the standard on development of vehicular software is getting important to get reusability and structured architecture. AUTOSAR is a leading standard and the majority of companies are using it around the world. However it is hard for software developers to test their own AUTOSAR software components because the testing of the software components needs its physical hardware and high setup cost. To overcome the difficulty, some researchers are working on virtual prototyping as a solution. We propose a method to apply virtual prototyping in AUTOSAR software components by modification of the existing open-source OSEK operating system.

In a saturated IEEE 802.11-based wireless network, stations tend to face back-off delay and retransmissions because of the basic characteristics of DCF based on CSMA/CA. Several relay-based cooperative mechanisms have been proposed to improve the performance of the IEEE 802.11 WLAN. However, they require complicate procedures for the optimal cooperation among stations. In this paper, we propose a compensation-based cooperative mechanism that can improve the throughput of the IEE 802.11 WLAN and give fair chances to stations. The main idea of our proposed mechanism is that when the transmission of a station fails, one of its neighbor nodes with better channel quality transmits the failed frame as well as its own data frame using the MSDU aggregation scheme. After that, when the failed station grabs the channel, it gives its right to transmit to the proxy node as compensation. Simulation results show that the proposed mechanism significantly improves throughput and throughput gain.

TCP suffers from high packet loss rate and tends to overreact to the ever-changing network dynamism of wireless network that experiences considerable delay variation. And also, it undergoes backoff state repeatedly and enters into a slow starting mode, which in turn leading to the low throughput and excessive end-to-end latency. On the other hand, UDP does not consider recovery of lost packets and the problem of out-of-order delivery resulting in much more data loss. For these reasons, RUDP was proposed at the IETF Internet Draft[1]. In this paper, we focused on the minimization of data packet delay time and aimed to reduce packet loss and deliver the data in order with the proposed protocol. Hence, using OPNET Modeler 14.5 PL8 program under the identical environment, we conducted the comparative analysis simulations between TCP, UDP and RUDP considering "Retransmission function" and sequence acknowledge numbers that are used to monitor packet loss and the order of the received packets, and various timers as major testing factors. The purposes of the simulations conducted for RUDP in this paper are to supplement "unreliable" characteristics of UDP, which is considered as a major problem, and thereby produce reliable data like TCP produces by making use of better characteristic of UDP not just imitating TCP’s characteristics.

From cloud computing environment, cloud service consumers may request virtual infrastructure resources and cloud service providers may provision the virtual resources e.g. virtual machines based on the diverse provisioning options. Through the cloud service broker, the cloud service consumers acquire their on-demand services instead of contacting to each cloud service provider. In order to provide VM services in on-demand infrastructure clouds, we deal with the requirements of each cloud stakeholders and metadata from the view of cloud service broker.

One of the most popular service types that Bus Information Systems (BIS) provide is bus line recommedation. Given a start bus stop and a destination bus stop, a bus line recommendation system returns a list of bus lines that run through those bus stops. That is, the user can reach the destination bus stop by taking the buses running the bus lines returned by the bus line recommendation system. We can find recommended bus lines by analysing a directed graph that represents bus stops and bus lines. Bus stops are represented as nodes in the graph and bus lines are represented as arcs. This paper applies node fusion algorithm in order to improve the efficiency of bus line recommendation.

The Closed Circuit Television (CCTV) systems have been used at large scale for tracking and getting popularity with every passing day. The most common goal of CCTV system is prevention of crime and disorder by tracking the objects. In the future, the efficiently usage of CCTV is the protection of crime based the tracking system for predication about abnormal situations. In this paper, we propose a tracking model for prevention of crime by using Kalman Filter. The tracking model uses some extracted characteristic of objects and relationships among objects from CCTVs on the street. So, this paper studies the tracking model that consists of three steps as follows; object assessment, situation assessment, and risk assessment.

In mobile environments, application authentication is necessarily requested for providing secure service and managing transparently the source of application. In this paper, we propose an application authentication and key management for application using a Mobile Trusted Module (MTM). After application is normally authenticated using the expected value stored in MTM, we generate a Storage Root Key (SRK) to independently operate key management system for each application. SRK for each application is created as similar as take ownership process of a Trusted Platform Module (TPM) by using two hash values. Proposed scheme will solve the performance problem of a conventional MTM key management scheme and provide more secure environment as a consequence of key isolation between applications.

In recent, energy consumption in data center is becoming a hot issue as its service scale up. We focus on energy consumption of Web service in data center. To reflect directly real data center, we evaluate proposed mechanism in the structure of realistic Web service. Our main controllers for energy saving are power capping and CPU governors. Also, we apply skyline query mechanism to select an optimized result. The evaluation of proposed mechanism shows that a significant amount of energy is saved without sacrificing the limited performance.

This paper proposed awareness home automation system (HAS) based on mobile sensing. Some of the ideas have been proposed in HAS but most of them still requires human intervention such as click the button, voice commands, etc. We want to design and develop HAS which can understand and comprehend the user desires without having to wait for commands from the user (awareness HAS). In this research we exploit two of android sensors. First, accelerometer sensor for identification and activity recognition, second, magnetic field for user indoor positioning system and defined the context related to physical environment. This paper presenting the result of used both of the sensors for developing awareness HAS.

This paper presents an interaction mechanism between EVs (Electric Vehicles) and a microgrid to broker electricity trades according to V2G (Vehicle-to-Grid) scenarios, aiming at improving energy efficiency by balancing supply and demand. Each EV submits a sales request specifying the earliest and the latest arrival times, plug-in length estimation, and the amount to sell. The brokering agent is activated to assign those requests not yet accepted by submitters to appropriate time slots until either the predefined deadline expires or its electricity demand is met. The scheduler, possibly running on the aggregator of the microgrid, traverses the search space to find the best schedule capable of minimizing the lacking amount of energy for the given time interval. The proposed scheme can enhance the profit for EV owners, cut down energy cost for microgrids, and shift the peak load for the power system.

In this paper, a novel fractional frequency reuse (FFR) based overlap resource power control (FFR.OVER) is proposed. In the FFR.OVER scheme, the FFR technique is implemented in combination with the power control mechanism. For macrocell users (mUEs), the power is uniform in the central and edge regions. The D2D receiver (D2DR) uses uniform power to transmit data in all regions except for the areas where more than one D2DRs use the same resource block (RB). In such situations, the power for those D2DRs which compete for the resource with mUE is reduced to low power in order to avoid further generation of interference. The simulation results show that the proposed scheme brings a significant increase in overall system capacity as well as the user outage probability in the system.

In cellular networks, the base station (BSs) is obligated to more than 50% energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to enhance the energy efficiency of cellular networks. Cognitive radio, an enabling technology, is a new system design paradigm for next cellular networks. In this paper, we proposed an optimal bandwidth sharing based on the selection scheme which exploited the cooperation between multiple primary base stations (PBSs) and several secondary base stations (SBSs) to enhance the spectrum as well as energy efficiency of cellular networks.

One of the major challenges in the design of mobile systems will be provisioning of Quality of Service (QoS) that application’s demands guarantee under diverse networking infrastructure. We believe that it is necessary to use resource reservation and adaptation techniques to deliver these QoS guarantee to applications. However, the reservation and pre-configuration in the entire service region is overly aggressive, and results in schemes that are extremely inefficient and unreliable. To overcome those, the mobility pattern of a user can be exploited. If the movement of a user is known, the reservation and configuration procedure can be limited to the regions of the network a user is likely to visit. Our proposed scheme is not sensitive to increasing of the search cost than other schemes, and it shows that the total cost is lower than others as the Session-to-Mobility Ratio (SMR) increases.

Devices for smart media services have been widely distributed. As everybody caries a smart device all the time, smart media based tourism is extremely demanded. This paper introduces our design of an Android app for Gyeongju smart tourism.

Google’s MapReduce has emerged as a popular framework for data-intensive computing. It is well-known by its elastic scalability and fine-grained fault tolerance. On the other hand, there are some debates in its efficiency. Especially, local and network I/Os can be a primary factor that degrades the performance of MapReduce, because it follows a data shipping paradigm where many partitioned data blocks move along distributed nodes. In this paper, we conduct a performance study to examine the I/O cost of MapReduce. Our results show that the I/O cost accounts for about 80% of the total processing cost when processing OLAP queries in the MapReduce platform.

Energy-awareness is a critical issue in wireless sensor networks (WSNs). Clustering protocol is a efficient way to prolong the network lifetime. To balancing cluster, it’s important to select cluster head(CH). Previous researches have utilized information for selecting CH, probability, residual energy, density of neighbor nodes, neighbor energy and so on. However, some are not sufficient to uniformly distribute CH, others spend numerous energy to collect necessary information. We propose a two-layer cluster head selection based on distance. Sensor nodes are divided into two layer, first layer and second layer that is sub layer of first layer, and sequence per each layer according to distance between nodes and base station(BS). CH is selected in same sequence of second layer without communication with other nodes. Simulations show that this proposed protocol could enhance the network lifetime.

In a typical server-client business model, a server should be in charge of both the authentication of the paid customer and the distribution of the contents. The drawback of it is that if the customers’ requests go on growing rapidly for the specific contents, the quality of service is degraded due to the server overload or bandwidth restriction. On the contrary, in a P2P network, if more and more users participate in the service, the quality of service is improved, because a peer (a customer) that has same contents can act as a seeder (a server). But, the drawback of the P2P is that there are too many servers to manage, thus it’s more prone to occur illegal distribution of contents. In this paper, we propose the dynamic group authentication scheme suited to P2P based contents streaming. The proposed scheme consists of group key generation, key update, signature generation, and verification protocols. It can control the seeder’s state whether the seeder is joining or leaving the network. The proposed scheme can be applied to hybrid P2P based business model where the index server covers sales transactions and the contents are distributed by the P2P protocol.

HPC is representative tools for performing large-scale scientific calculation both academia and industry. Tachyon is a high-performance parallel computing system which constructed based on SUN Blade X6275. It composed of 3,200 computing nodes and infra-facilities. Also, this machine works with various software stacks such as file system, archive manager, compiler, debugger, parallel tools, etc. In this paper, we handle the requirements and requisites to build and manage an HPC cluster environment. In addition, we analyzed the history of batch job which include information performed by scheduler. By doing so, we are able to gauge the needs and performance of the next system to be introduced.

Virtualization is to provide virtually all types of technologies used on the physical PC. However, in a virtualization technique, services can be modified and adjusted to satisfy user requirements, the cost of hardware purchases and hardware constructions is not occurred. Beside, this virtualization supports multiple guest operating systems on a host OS. It is helpful for virtualization to build cost-effective IT infrastructure. In general, virtualization can be classified into two categories: full virtualization and para-virtualization. Full virtualization does not require a direct interaction with host resources, but in para-virtualization a direct interaction with frequently used resources can substantially improve performance of computation. In this paper, we study the effects on the performance when the VCPUs map to the physical CPUs in para-virtualization. And we study the effects of the mapped VCPU in an overcommit environment that the number of virtual cores is more than the number of physical cores. The purpose of these experiments is to find out the best CPU allocation method in various environments of para-virtualization.

Following the recent technological trend, it would be possible to create high quality teaching-learning contents more easily utilizing functions supported by various application programs using accessible, convenient smart devices. Also, not only the teachers but the students who use a smart pad to study will be able to create learning contents autonomously, sharing contents with other students and forming u-Learning with collective intelligence. This study proposes a plan for designing learning contents for teachers and students using the learning contents authoring tool in smart devices in a bid to apply this to the project-based learning model. Apple’s iPads were used as the smart device, and the application used was ‘Explain Everything’. The learning contents created through the project-based learning were in different formats such as video clip, image, and PDF file. And in applying this in pilot test, over 90% said they were satisfied with the learning in the survey on the overall satisfaction of the learning.

The existing digital forensic research on Windows 8 Style UI applications is conducted on native applications and there is a lack of study on other 3

rd

party applications. Therefore, we analyze 3

rd

party instant messengers of Style UI and we propose a digital forensic methodology. In this paper, we identify artifacts of Viber and Line, which are popular instant messengers, and analyze them in various ways. The proposed approach is expected to help criminal investigation by efficiently and rapidly providing information about an ongoing case in court.

As a social infrastructure, emergency rescue system aims increased survival rate. It consists of several steps: spot emergency, report emergency, give first aid and send to hospital. In order to efficiently use the golden time, the survival time of emergency patient, rapid action and proper first aid are necessary. Since emergency situation is reported verbally nowadays, it’s very difficult to identify the patient and the emergency location and hence, the golden time is wasted. Even though there were some attempts to use smartphone GPS sensor to figure out the emergency location, those were abortive since people felt far less responsibility of reporting emergency through smartphone application than through verbal explanation. Smartphone is a complex device that has various sensors of various functions. Recently, with the advent of the finger scan sensor, users are able to not only capture facial images and record voices but also scan fingerprint that can be used to identify a person. In this study, we propose a biometric based emergency rescue system, which utilizes various smartphone sensors. This system minimizes the time waste by automatically locating emergency spot and identifying both the reporter and the patient. Biometric data is used to identify the reporter and the patient. Since reporter is identified with biometric data, reporter holds responsibility of the report. Furthermore, trained technician gives proper first aid instruction to the reporter via visual telecommunication and this process is encrypted to ensure privacy.

In this study, we apply system dynamics approach to simulate the diffusion of intelligence-driven cloud solutions in law enforcement authorities. As such solutions are developed and marketed by commercial enterprises, their diffusion will primarily depend on vendors’ spending on advertisement and on positive word-of-mouth within law enforcement authorities. We propose multi-stage system dynamics model of technology diffusion based on Bass model. We perform base experiment and sensitivity analyses to test potential adoption rates and diffusion patterns of intelligence-driven solutions.

This paper provides a new approach for solving the problems of modeling and structural syntheses of information networks of automated control systems by applying fuzzy sets theory, fuzzy logic and genetic algorithms. Modified genetic operators of crossovering, mutation, and algorithms of evolutionary syntheses of information network systems are developed. The results of computational experiments on the influence of using the crossovering and mutation operators are demonstrated.

Emotion recognition field can be useful for music discovery and recommendation, because emotions can precisely describe the actual habits of a listener. In this paper, we propose a new concept called Ranked Attributes that are useful to make reasonable music recommendations. More precisely, we propose to consider additional attributes to emotion, such as weather and time, and build a Ranked Attributes Tree (RAT) that enables to recommend a music piece based on a combination of all ranked attributes. In this paper, we describe the following parts of the proposed method: database design, voice and emotion recognition, and music recommendation.

Object recognition and tracking are very important task in several computer vision applications in our life. Most of feature matching approaches have problems which are high computational complexity and weak robustness in various environments. In this paper, we proposed a low complexity and robust object recognition and tracking using advanced feature matching for real time environment. Our algorithm recognizes object using invariant features and reduces dimension of feature descriptor to deal with the problems. Our experiments demonstrate that our work is more fast and robust than the traditional methods and can track object accurately in various environments.

In this paper, a genetic algorithm considering the item exposures is applied to improve the efficiency of composing near optimal test sheet from item banks to guarantee item selection uniformly. We compare the results of general genetic algorithm (GA) and Genetic Algorithm with item exposure (GAIE). From the experimental results, the GAIE approach is desirable in composing near optimal test-sheet from large item banks with item exposure evenly.

A defect detection vision system for the automatic optical inspection of wafer has been developed. For the successful defect detection, several thresholding methods are compared and the improved Otsu method is adopted so that it can handle both unimodal and bimodal distributions equally well. An automatic defect detection software was developed with the function of detection of ROI, fast thresholding. Finally each defect pattern in the wafer is classified and grouped into one of user-defined defect categories and more than 14 test wafers are tested for evaluation of detection and classification accuracy.

We present the evaluation of our synchronous mobile learning system that supports tablet clients for instructor and student. It allows students to watch an instructor and slide with annotation as well as to send feedback to instructor and interact through text chat in real time with their tablets. For evaluation, we first identified the categories that affect the effectiveness of learning: learning: content delivery; feedback from the student and interaction; and student’s concentration on learning. Then, we had a student group use the prototype and surveyed them about each category. Then, we further looked into the result in each category, had discussion with students and identified aspects that make the system less effective than the conventional classroom learning environment. Then, we surveyed them about these aspects.

While training set design has received less attention from academia compared to its significance, it becomes crucial in big data environments. We propose a novel way to construct a training set for information extraction. An effective data collection considering the trade-off between system quality and annotation difficulty is the core of the proposed approach. Instead of a random collection of data like usual systems, well-defined key expressions are used as sampling queries. This work is a part of an on-going R&D project and now in process of manual annotation that would be evaluated via final system quality.

Data from Social Networking Services (SNS) are extensively used to convey the users’ sentiments (positive or negative) and detailed opinions on issues promptly. In this paper, we use SNS data to conduct the satisfaction analysis of a school meal. We analyze approximately 15 million tweets in order to investigate students’ satisfaction level with the meals. Moreover, we divide the school meals into three categories – vegetables, meats, and seafood – and determine the satisfaction level of each category along with reasons for it by using 14 criteria.

In recent years, image processing has been used usefully in many fields such as traffic information systems, illegal intrusion detection system. Now image processing techniques are required in position of moving observers rather than fixed observers because it helps the control of the camera-equipped device. In order to achieve the goal, the algorithm should derive the desired results quickly and accurately. So this paper considers two characteristics; Functional performance and temporal performance. The former is about how well the desired results can be derived, and the latter is about how fast the result can be calculated. This paper suggests real-time image algorithm based on optical flow. The algorithm determines the horizontal or vertical movement, etc. and extracts the displacement. And the results will be presented in the functional performance and temporal performance. The proposed algorithm can be used to control the UAV (quad-rotor) in situation of Flows in parallel by the inertia or external forces by wind. The proposed algorithm is efficient for achieving “Drift stabilization” or Detect movement quickly.

Researchers establish their strategies by continually analyzing research performance and estimating future research directions to enhance their capability. Prescriptive analytics can be a solution to an advanced-analytics method that provides the optimal strategy from among the various actions and methods available for achieving an established goal. This paper introduces a journal recommendation model which provides prescribing a strategy to enhance research performance using the 5W1H methodology from the InSciTe system. This model suggests a journal-submission strategy for achieving the goal of strengthening the research capabilities of researchers in terms of their performance by considering their research subjects and capabilities.

This paper proposes an extended association rule mining algorithm to improve the accuracy of the Onto-Apriori algorithm which is one of the association rule mining algorithm. The Onto-Apriori algorithm considered the usage of ontology in the association rule mining algorithms. However it does not reflect relationships between items which might have similarity between them. Therefore generated rules from the algorithm are not sufficient to find association between items. To solve the problem, we propose an algorithm, XOnto-Apriori, that can generates association rules and can suggest more valuable candidate item sets applying ontology reasoning function. Experimental results show our proposed approach. The XOnto-Apriori algorithm shows more effective results.

Due to the development of IT and scientific technology, huge amounts of data are continuously being created and the big data era can be said to have arrived. Therefore, triple store inserting and inquiring into knowledge bases has to be scaled up in order to deal with such large sources of data. To this end, we propose a triple store system based on a distributed database that uses bulk-loading for billions of triples to store data and to respond to user queries quickly. In order to achieve this purpose, we introduce a bulk-loading algorithm using the MapReduce framework and the SPARQL query processing engine to connect to a large distributed database. Experimental results show that the proposed bulk-loading algorithm can use 101K triples per second to load approximately 33 billion triples. This implies that we will be able to deal with billions of triples.

We introduce a revised way of inspecting retrieval engines based on the term’s weight which we call it as discrimination power of a term. The aim of this methodology is to make engine developers find the indexing or query processing components that are problematic in ranking retrieved documents. The internal processes of indexing and query processing are modeled in a Bayesian inference network. The nodes in this model can be terms, phrases, sentences, documents, etc. The layers represent internal step-by-step processing components like stemming, tokenizing, POS Tagging, parsing, etc. By computing and showing the extent to which each node plays a role in causing a relevant (or irrelevant) document to be ranked high (or low), the proposed method can signal the nodes to be examined and hence the components to be modified. We insist that the proposed method can be used for automatic failure analysis of a system with test runs and that its effectiveness can be improved even automatically.

The existing IP mobility management protocols such as mobile IPv6 (MIPv6) and proxy mobile IPv6 (PMIPv6) are based on a centralized mobility entity, which processes all control and data traffic together. However, these centralized mobility management protocols have some limitations such as traffic concentration into the core network and serious service degradation by a single point of failure, etc. Therefore, in this paper, in order to overcome these limitations, we propose an enhanced signal-driven PMIPv6 for distributed mobility control. Numerical results indicate that the proposed scheme has apparent potentials to alleviate serious limitations of a related distributed mobility management protocol (i.e., signal-driven PMIPv6 (S-PMIP)) as well as PMIPv6, while reducing the total mobility management cost.

The purpose of this research is to develop an unconstrained infant monitoring system, including respiratory activity and environment monitoring. For this, we developed the mattress-type sensing platform based on the flexible pressure sensor as well as the respiration detection algorithm. A pilot study was carried out to evaluate the feasibility of the developed system. Two infants were participated and pressure signals were recorded from each subject using developed mattress-type sensing platform. The result shows that the respiratory components are successfully extracted from the measured pressure signal in every subject.

This research aims the development of the preliminary system for driver’s posture correction and the feasibility validation of developed system. For this, we developed the seat-embedded force-sensing platform based on piezo-resistive film as well as a posture classification algorithm. Feasibility test was carried out with five subjects for four types of posture such as sit straight, tilt left, tilt right and lean forward. As a result of the experiment, developed system shows higher than 90% of classification accuracy in right and left tilting posture, however, it shows the limited performance in distinguishing the normal and leaning forward condition (< 50%).

In the end to end data transfer protocols, it is a big issue to maximize the transmission rate in order to occupy available bandwidth without packet loss. UDT(UDP based Data Transfer) estimates the available bandwidth based on the pair packet algorithm on the receiver side, and controls the next sending rate with considering the estimated value. However, for a congested network, the estimated value has a tendency to be much greater than the actual bandwidth, and fluctuated in time, so it results in in high loss probability. In this paper, we propose a congestion degree index based bandwidth estimation method using pair packet delay. Experimental results show that the proposed method reliably occupies the available bandwidth with stable bandwidth estimation. The packet loss probability is greatly reduced compared with that of UDT.

Recently global collaboration research related to High Energy Physics, Astronomy, Climate Change, etc. has been gradually increased based on international research networking with hybrid networking service. For more than 10 years, GLIF Open Lightpath Exchanges (GOLE) in Global LambdaGrid Integrated Facility (GLIF) with hybrid networking technology like lightpath is critical stack to transfer big e-Science data that support global collaborative research. KRLight has become a key GOLE in Asia-pacific area bridging research network between Asian research network and North American and European research network with dynamic circuit networking facility. Today emerging technologies such as Science DMZ, Software-Defined Networking, perfSONAR and GLORIAD Insight system could be leverage networking technology to support data-intensive science with more effective and flexible. In this paper we proposed new GOLE architecture to apply these network technologies into current GOLE architecture as a key international research networking infrastructure and validated it through adaptation into KRLight.

Recently, a huge network backbone with 100Gbps or higher as well as 10Gbps has been developed and used in various R&D sectors. Based on this kind of a large-scale backbone network, international collaboration and sharing of a lot of research resources have become more common in R&D activities. It is very important to design and support an appropriate national R&D network.

Throughout the world, the demand for employing the wireless Internet using a simple authenticaton method is rapidly increasing due to the development of wireless network and the functional advancement of mobile terminals. In this paper, we review the network roaming technologies which support the seamless extension of wireless Internet access and presents the current deployment status of the global WiFi roaming technology in Korea. Besides, we present the development status of WiFi roaming service among domestic universities in Korea. Last, we present some strategies to activate the global WiFi roaming service throughout the country in the future.

Software Defined Network (SDN) emerged to fundamentally solve high cost and complexity in construction/operation/management of existing networks. Current SDN is in the initial stage of research /development and has been studied focusing on small and medium scale networks such as data center network, campus network and enterprise network. In addition, various approaches have been attempted to apply SDN to big scale networks such as the network of internet service provider(ISP). In this regard, this study will analyze the technical trend of SDN and possible problems, and present the future direction for development.

This study attempted to design and construct a scheme to get information on the type and method of recent attacks. When performing specific attacks, the way attackers mostly use IP spoofing, they use IP address barely used to make it their own IP address. This kind of IP spoofing-based attack packet collection & analysis system is called ‘Sinknet’ in this study. This study aims to analyze attacks on the Internet by type after investigating current attack type, method and victim through the design and construction the Sinknet.

Human eyes are important in our lifetime. We first brief how eyes, visual acuity, visual field and phenomenon react when human eye processing over their limit. Next, we introduce display size and optimal viewing distance. With this information, we propose our concept of how technology could assist human protecting their eyes. We implement a prototypical distance monitoring system. This system provides suitable information to notify human keeping their watch distance. Finally we demonstrate system.

In the field of document editing, a real-time collaborative editing that multiple users edit a same document simultaneously has been actively studied. The co-editing session should be kept for the synchronization about the changes during the real-time collaborative editing of documents among the participants. If the session ends during collaborative editing, they wouldn’t be able to continue co-editing anymore. The purpose of this study is to develop a framework for Continuous Real-time Collaborative Editing that disconnects the session in unintended situations. Participants should be able to terminate editing at any time in opened co-editing session, and a certain participant is able to disconnect because of unstable networks. In this case, the editing session should be maintained without termination.

Recently as society enters the aging era and the ageing population as well as senile patients increase, health and safety issues are seriously emerging. The society requires the system that correctly detects and analyzes the faint, one of senile symptoms; and promptly responds the emergency situation by activating an alarm and reporting it to the situational center at the same time. Therefore, this paper proposes the faint situation recognition and response system that continuously tracks certain seniors living alone with images from thermal camera in combination with the Kalman filter and Camshift’s tracking algorithms, and analyzes change in size and time of traced trajectories. The sucess rate of faint situation recognition was 86%. system proved continuous tracking and correct faint situation recognition performance.

This paper discusses the possibility of usage of dynamic biometric signatures (DBS) as an alternative of “advanced” electronic signatures. The authors refer to the issues of uniqueness of DBS in terms of individual’s characteristic motion, and hence the validity of parameters required for methods of identification of users and their authentication in the “electronic” world. Furthermore the authors focused on the issue of the use of DBS in connection with legal acts of people and the integration of authentication tools, based on DBS in the legal systems of the EU countries. The conclusions set out in the paper are supported by the results of experiments, when panelists created their biometric samples under different conditions that may have a significant impact, especially for set-up setting of FNMR parameter (False Non-Match Rate).

While developing high quality software, it is relatively important to perform reliable software testing. However, it may also raise the development cost, manpower and time-consuming. Limitations of different resources such as testing software and computers may also be postponed the development cycle. Cloud computing shares different resources over a network seems an alternative solution to software testing. But how to evaluate and assign these powerful resources to specific testing programs are still needed further investigation. Therefore, this study proposed an automatic software testing service with the benefits of real-time software testing and automatically computation scaling. It can perform software testing in parallel, and can automatically pare the source code to perform statistics analysis, generate test drivers and cases, and profile the consumed resources. Experiments were carried out to help users figure out the bottleneck of existed testing program and cloud systems.

We describe a method for generating H-Anim character animation using general graphics tools and motion capture data. An H-Anim character model can be designed with a general graphics tool using a procedure that defines the skeletal hierarchy of a human character. The character model is then provided with motion capture data. H Anim character animation is generated and displayed using a general X3D browser. The H-Anim character animation is obtained by converting captured motion data into X3D interpolator nodes. We have defined a conversion algorithm that provides H-Anim characters with motion by applying any motion capture data to the H-Anim character model. As well as an H Anim viewer, we have developed an H-Anim editor that is used to display and edit the hierarchy of an H-Anim character model and also generate its animation interactively using motion capture data.

In recent years, wearable computing was more popular. Wearable computing is a device which user can use their own equipment and computer more efficiently and assist them to acquire information from database or sensors. Currently, wearable computing focus on health, such as body sensors which can monitor accurate heartbeat, pulse, and even can count calculate you walking how distance. But not all of them are integrated and connected with computer. This paper will propose wearable computing such smart phone or tablets via wire or wireless connection to connect buildings with Arduino and air sensors. Such sensors are harmful air detecting devices which used statistic to judge and warn human to avoid form danger when they walk into unknown areas and without sufficient information. We describe some background we need. We finished and demonstrate our system.

This study utilizes a panel threshold regression model to investigate the non-linear relationship between economic and life insurance development in 10 Asian countries from 1979 to 2008. The empirical evidence shows that one optimal threshold level exists. Also, when the GDP is higher than the threshold value, the positive impact of GDP growth on life insurance development is clearly more substantial than that when it is lower than the threshold value. Furthermore, this study verifies that financial development, interest rates and savings positively influence the development of the life insurance market.

This paper studies the detection of singing segments using various features, such as MFCC (Mel Frequency Cepstral Coefficients) and LPCC (Linear Predictive Cepstral Coefficients), with the HMM (Hidden Markov Model) models. The audio clips under test in this paper include isolated segments from different sound tracks and all segments entirely from a sound track. In the literature, these two cases are usually individually investigated. However, we have a unified treatment to both types of segments using the same features and classifiers. In the experiments, we design five experiments to fully examine the performance limitation of the approaches studied in this paper.

In the paper, we have developed a music display system in which a music performer can easily turn the page of music by using simple hand gestures. A three-dimensional data representation called motion history volume has been adopted for the feature extraction module of the hand gesture recognition system. In order to reduce the computation of the learning process and to improve the effectiveness of the learning, we propose a feature selection technique based on a modified fuzzy min-max neural network. We have defined a relevance factor which can measure the relevance of a feature to classify the specific pattern classes. The feature selection method can remove ineffective features and erroneous features in the learning data set by using the relevance factor data. Through the experiments using 120 test data for 6 pattern classes, the effectiveness of the proposed method is discussed.

Due to the rapid development of the Internet and need for recommendation systems, there have been several recommendation systems using the various information on the Internet and more and more systems are using the SNS information. However, most of them only consider the simple direct friend relationships. In this paper we use the intimacy and similarity between users on the SNS to compute the weight of an evaluating person for recommendation purpose. The intimacy between users considers the direct and distant friend relationships on an SNS which contains direction and importance information among friends. The similarity between users is computed by using the mutual friends as well as the relationship between the user’s preference and the given item. In order to enhance the objectivity among user’s evaluations, the evaluation was performed on several item attributes. We have used real SNS data to carry out experiments and show how well the intimacy and similarity can predict the target user’s evaluation ratings.

This paper presents preference measurement of user using user response electroencephalogram (EEG). Today users are faced with overflowing image data and images are sorted with time, location, and event. In this paper we introduce a noble way to pick out the preferred image using user response EEG wave. The EEG wave data are gathered through experiment using consumer grade EEG device sensors. Images were classified into preferred image and unnoticed images. The accuracy of image classification using gathered EEG wave was 88.54%. Also we found significant difference of alpha waves on position F8 and T8 between EEG wave recorded during preferred images and not preferred images. Using this analysis, we are able to service preference based personalized service.

An Information Security Management System (ISMS) has become more important for any organization to manage its information assets securely. However, it is not easy for the organization to establish own ISMS, and for employees to work according to the ISMS. Therefore, ISMEE, an Information Security Management Engineering Environment, was proposed as the first engineering environment for supporting organizations with ISMSs. ISMEE integrates various tools to support all participants in an organization to perform all tasks in from preparation to abolishment of an ISMS based on ISO/IEC 27000 series, which are the international standards for ISMSs. This paper presents our implementation of ISMEE. At first, the paper investigates technical issues to realize ISMEE. Then, the paper shows how the issues are addressed and presents our current implementation of ISMEE.

An Information Security Management Engineering Environment ISMEE has been proposed to support organizations with Information Security Management Systems (ISMSs) based on ISMS international standards, ISO/IEC 27000 series. However, there is no evaluation that shows that ISMEE is really useful to support organizations with ISMSs. This paper presents an evaluation of ISMEE to show its usefulness. At first, the paper explains ISMEE briefly. Then, the paper proposes an evaluation method to evaluate the usefulness of ISMEE, shows how the environment is evaluated, and discusses how ISMEE is capable and useful to provide organizations with comprehensive facilities to perform all tasks in ISMS consistently and continuously based on ISO/IEC 27000 series.

The threats to privacy and security have received increasing attention as ubiquitous healthcare applications over the internet become more prevalent, mobile and universal. In particular, we address the communication security issues of access sharing of health information resources for threats of APT attack decrease in the ubiquitous healthcare environment. The proposed scheme resolves the sender and data authentication problem in information systems and group communications. We propose a novel key management scheme for generating and distributing cryptographic keys to constituent users to provide form of data encryption method for certain types of data concerning resource constraints for preparatory secure communications to APT attack in the ubiquitous healthcare domains.

This paper exhibits a Model of integration of the metacognition in the curriculum, taking into account a competency-based formation model, using the computer engineer career as case of study. The model allows to an extreme feedback on physical and virtual environments. Thus, it allows detecting and diagnosing the student weaknesses through three schemes taking into account the following aspects: analysis, application, understanding and knowledge. The model uses an approach based on thermal bath by means of metacognition operators such as: learning guide, integration workshop, competency tracking and academic staff. The introduction of the ICTs allows to the interaction among different peers, therefore, it offers different way to obtain the particular information through the informatics forum and social networks where all answers are published and shared with all students in the group.

Our knowledge base of attacks is always not enough for developing and maintaining information systems. An information system, which is developed and maintained according to an insufficient knowledge base of attacks, has no capability of defending against new attacks from the complicated environment. To mitigate this circumstance, we should continuously predict new attacks and add them into the knowledge base of attacks such that by enriching the knowledge base of attacks, information systems can be developed and maintained more countermeasures to confront those new attacks. However, there is no methodology proposed for predicting new attacks. This paper presents an analysis and discussion for predicting new attacks and shows the challenges issues in predicting new attacks. The paper also proposes a reasoning method for predicting new threats.

ISO/IEC 15408 and ISO/IEC 18045 are a pair of international standards for security evaluation and certification of IT systems. However, security evaluation based on this pair of standards is not an easy work. There are many activities and documents in the whole evaluation process. Complicated tasks in evaluation process may cause people making mistakes in intermediate products and evaluation results. It is also difficult to ensure that evaluation is fair and transparent, although each evaluator tries to evaluate a target system earnestly, evaluation results may be different because of evaluators’ biases. Moreover, to manage a lot of intermediate products in evaluation process is not easy task even for experienced evaluators. This paper presents a supporting environment for IT system security evaluation based on ISO/IEC 15408 and ISO/IEC 18045 which can support all tasks related to security evaluation by guiding and helping evaluators to perform these tasks regularly, and also can support the management of all documents and intermediate products in the whole evaluation process.

Recently, wireless sensor networks can provide a practicable real-time monitoring system because wireless sensors can be easily deployed in various environments. To provide secure and efficient communication, various user authentication schemes have been proposed. In 2012, He et al. proposed a robust biometric-based user authentication scheme but Yoon and Kim demonstrated that He et al.’s scheme has various security problems. And they proposed an advanced biometric-based user authentication scheme. In this paper, we analyze Yoon and Kim’s scheme and perform the cryptanalysis. We find out that it has still various security weakness such as no perfect forward secrecy, session key exposure by GW node, a DoS attack, no revocation phase, and biometric recognition error.

Today, many location-based applications call for both spatial and non-spatial predicates (e.g., keywords). For example, a traveler asks for nearby restaurants (i.e., spatial predicates) with “night view, beach and Korean food” (i.e., non-spatial predicates). In this paper, we focus on the problem of finding nearest neighbors that match user-specified non-spatial keywords. We utilize the IR-tree due to its efficiency for processing such queries, and propose a new search algorithm on the IR-tree. The proposed algorithm uses the MapReduce framework for parallel processing because traditional single machine-based query processing methods might suffer from drastic performance degradation when the size of the dataset becomes extremely large. Our experiments demonstrate the efficiency of the proposed algorithm.

The accomplishments of our study are summarized as follows: the “planned buying” and “impulse buying” process extended models are combined quantitatively after the field research of several different shoppers’ behavior, transformed to be applicable to automatic computer processes, substantiated in our field experiments, and their significance is confirmed in actual shops. Our analysis method focuses on the characteristic of the shoppers’ human motions and specifically human bio-information to indicate the shoppers’ intentions to purchase. The characteristic feature quantity in this work should be effective to distinguish between these two purchasing models. This method should prove helpful to shop owners’ management in order to increase sales efficiency, service quality, merchandising, displays and to produce improved service innovation.

With the development of multiple-input multiple-output (MIMO) techniques, distributed antenna systems gain more attention because of better signal coverage than centralized antenna systems. However, hardware costs and computational complexity appear to be a big problem with the growth in the number of antennas in distributed antenna systems as well. Research on antenna selection performance is conducted on indoor distributed antenna systems environment in order to solve the above problem. An antenna selection algorithm based on eigenvalues is proposed. Simulation results show that the algorithm cannot only achieve good antenna selection performance, but also has a much reduced computational cost.

An emotion assessment method based on supported vector data Description was proposed to get objective assessment of emotional states. Three different physiological signals of our participants were obtained including galvanic skin response, skin temperature, and ECG. Physiological indicators were considered model inputs, to guarantee the method real-time assessment; Supported Vector Data Description (SVDD) was used to infer affective states. The experimental results show that proposed method was useful.

In this paper, two new classifiers based on class mean (CM) and mean representation based classification (MRC), called improved class mean (ICM) and improved mean representation based classification (IMRC), are proposed for face recognition. ICM classifier uses the novel method to gain new mean vector of each class subspace for classification. IMRC classifier utilizes the novel class mean vector and decision rule for classification. A large number of experiments on AR face database and YaleB face database are used to assess the novel classifier. The experimental results show that the proposed classifier gains better recognition rate than MRC classifier, LRC classifier, CM classifier and nearest feature centre (NFC) classifier.

Smart devices with easy Internet accessibility, mobility and usability are changing various service environment such as mobile office, smart work, mobile finance, e-government and so on. Meanwhile, these smart device has widened into military area in an effort to enhance mission command and situational awareness. This paper describes functional considerations in military-grade mobile security platform based on a mobile hypervisor. The military-grade mobile platform will be provided more security enhancement than current mobile services. We suggest four features for enhancing a mobile platform including domain separation, secure file management, secure middleware and access control.