The Scientific World Journal: Computer Science The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Effect Analysis of Design Variables on the Disc in a Double-Eccentric Butterfly Valve Thu, 17 Apr 2014 13:24:01 +0000 We have performed a shape optimization of the disc in an industrial double-eccentric butterfly valve using the effect analysis of design variables to enhance the valve performance. For the optimization, we select three performance quantities such as pressure drop, maximum stress, and mass (weight) as the responses and three dimensions regarding the disc shape as the design variables. Subsequently, we compose a layout of orthogonal array (L16) by performing numerical simulations on the flow and structure using a commercial package, ANSYS v13.0, and then make an effect analysis of the design variables on the responses using the design of experiments. Finally, we formulate a multiobjective function consisting of the three responses and then propose an optimal combination of the design variables to maximize the valve performance. Simulation results show that the disc thickness makes the most significant effect on the performance and the optimal design provides better performance than the initial design. Sangmo Kang, Da-Eun Kim, Kuk-Kyeom Kim, and Jun-Oh Kim Copyright © 2014 Sangmo Kang et al. All rights reserved. Optimized Scheduling Technique of Null Subcarriers for Peak Power Control in 3GPP LTE Downlink Thu, 17 Apr 2014 13:23:23 +0000 Orthogonal frequency division multiple access (OFDMA) is a key multiple access technique for the long term evolution (LTE) downlink. However, high peak-to-average power ratio (PAPR) can cause the degradation of power efficiency. The well-known PAPR reduction technique, dummy sequence insertion (DSI), can be a realistic solution because of its structural simplicity. However, the large usage of subcarriers for the dummy sequences may decrease the transmitted data rate in the DSI scheme. In this paper, a novel DSI scheme is applied to the LTE system. Firstly, we obtain the null subcarriers in single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, respectively; then, optimized dummy sequences are inserted into the obtained null subcarrier. Simulation results show that Walsh-Hadamard transform (WHT) sequence is the best for the dummy sequence and the ratio of 16 to 20 for the WHT and randomly generated sequences has the maximum PAPR reduction performance. The number of near optimal iteration is derived to prevent exhausted iterations. It is also shown that there is no bit error rate (BER) degradation with the proposed technique in LTE downlink system. Soobum Cho and Sang Kyu Park Copyright © 2014 Soobum Cho and Sang Kyu Park. All rights reserved. Balance Maintenance in High-Speed Motion of Humanoid Robot Arm-Based on the 6D Constraints of Momentum Change Rate Thu, 17 Apr 2014 11:37:30 +0000 Based on the 6D constraints of momentum change rate (CMCR), this paper puts forward a real-time and full balance maintenance method for the humanoid robot during high-speed movement of its 7-DOF arm. First, the total momentum formula for the robot’s two arms is given and the momentum change rate is defined by the time derivative of the total momentum. The author also illustrates the idea of full balance maintenance and analyzes the physical meaning of 6D CMCR and its fundamental relation to full balance maintenance. Moreover, discretization and optimization solution of CMCR has been provided with the motion constraint of the auxiliary arm’s joint, and the solving algorithm is optimized. The simulation results have shown the validity and generality of the proposed method on the full balance maintenance in the 6 DOFs of the robot body under 6D CMCR. This method ensures 6D dynamics balance performance and increases abundant ZMP stability margin. The resulting motion of the auxiliary arm has large abundance in joint space, and the angular velocity and the angular acceleration of these joints lie within the predefined limits. The proposed algorithm also has good real-time performance. Da-song Zhang, Rong Xiong, Jun Wu, and Jian Chu Copyright © 2014 Da-song Zhang et al. All rights reserved. Human Behavior-Based Particle Swarm Optimization Thu, 17 Apr 2014 10:14:28 +0000 Particle swarm optimization (PSO) has attracted many researchers interested in dealing with various optimization problems, owing to its easy implementation, few tuned parameters, and acceptable performance. However, the algorithm is easy to trap in the local optima because of rapid losing of the population diversity. Therefore, improving the performance of PSO and decreasing the dependence on parameters are two important research hot points. In this paper, we present a human behavior-based PSO, which is called HPSO. There are two remarkable differences between PSO and HPSO. First, the global worst particle was introduced into the velocity equation of PSO, which is endowed with random weight which obeys the standard normal distribution; this strategy is conducive to trade off exploration and exploitation ability of PSO. Second, we eliminate the two acceleration coefficients and in the standard PSO (SPSO) to reduce the parameters sensitivity of solved problems. Experimental results on 28 benchmark functions, which consist of unimodal, multimodal, rotated, and shifted high-dimensional functions, demonstrate the high performance of the proposed algorithm in terms of convergence accuracy and speed with lower computation cost. Hao Liu, Gang Xu, Gui-yan Ding, and Yu-bo Sun Copyright © 2014 Hao Liu et al. All rights reserved. An Optimization Method for Condition Based Maintenance of Aircraft Fleet Considering Prognostics Uncertainty Thu, 17 Apr 2014 08:53:54 +0000 An optimization method for condition based maintenance (CBM) of aircraft fleet considering prognostics uncertainty is proposed. The CBM and dispatch process of aircraft fleet is analyzed first, and the alternative strategy sets for single aircraft are given. Then, the optimization problem of fleet CBM with lower maintenance cost and dispatch risk is translated to the combinatorial optimization problem of single aircraft strategy. Remain useful life (RUL) distribution of the key line replaceable Module (LRM) has been transformed into the failure probability of the aircraft and the fleet health status matrix is established. And the calculation method of the costs and risks for mission based on health status matrix and maintenance matrix is given. Further, an optimization method for fleet dispatch and CBM under acceptable risk is proposed based on an improved genetic algorithm. Finally, a fleet of 10 aircrafts is studied to verify the proposed method. The results shows that it could realize optimization and control of the aircraft fleet oriented to mission success. Qiang Feng, Yiran Chen, Bo Sun, and Songjie Li Copyright © 2014 Qiang Feng et al. All rights reserved. Modeling and Simulation of Network-on-Chip Systems with DEVS and DEUS Thu, 17 Apr 2014 07:15:22 +0000 Networks on-chip (NoCs) provide enhanced performance, scalability, modularity, and design productivity as compared with previous communication architectures for VLSI systems on-chip (SoCs), such as buses and dedicated signal wires. Since the NoC design space is very large and high dimensional, evaluation methodologies rely heavily on analytical modeling and simulation. Unfortunately, there is no standard modeling framework. In this paper we illustrate how to design and evaluate NoCs by integrating the Discrete Event System Specification (DEVS) modeling framework and the simulation environment called DEUS. The advantage of such an approach is that both DEVS and DEUS support modularity—the former being a sound and complete modeling framework and the latter being an open, general-purpose platform, characterized by a steep learning curve and the possibility to simulate any system at any level of detail. Michele Amoretti Copyright © 2014 Michele Amoretti. All rights reserved. Improved Stabilization Method for Lurie Networked Control Systems Thu, 17 Apr 2014 06:28:48 +0000 The problem of stabilization of Lurie networked control systems (NCSs) is investigated in this paper. The network-induced delays in NCSs are assumed to be time-varying and bounded. By utilizing a reciprocally convex technique to consider the relationship between the network-induced delay and its varying interval, a new absolute stability condition is derived in terms of linear matrix inequalities (LMIs). Based on the obtained condition, an improved cone complementary linearisation (CCL) iteration algorithm is presented to design a state feedback controller. The effectiveness of the proposed method is verified by a numerical example. Hong-Bing Zeng, Lei Ding, Shen-Ping Xiao, and Fei Yu Copyright © 2014 Hong-Bing Zeng et al. All rights reserved. Application of the Artificial Bee Colony Algorithm for Solving the Set Covering Problem Wed, 16 Apr 2014 14:10:39 +0000 The set covering problem is a formal model for many practical optimization problems. In the set covering problem the goal is to choose a subset of the columns of minimal cost that covers every row. Here, we present a novel application of the artificial bee colony algorithm to solve the non-unicost set covering problem. The artificial bee colony algorithm is a recent swarm metaheuristic technique based on the intelligent foraging behavior of honey bees. Experimental results show that our artificial bee colony algorithm is competitive in terms of solution quality with other recent metaheuristic approaches for the set covering problem. Broderick Crawford, Ricardo Soto, Rodrigo Cuesta, and Fernando Paredes Copyright © 2014 Broderick Crawford et al. All rights reserved. Joint Power and Multiple Access Control for Wireless Mesh Network with Rose Projection Method Wed, 16 Apr 2014 13:53:20 +0000 This paper investigates the utility maximization problem for the downlink of the multi-interface multichannel wireless mesh network with orthogonal frequency division multiple access. A cross-layer joint power and multiple access control algorithm are proposed. Rosen projection matrix is combined with Solodov projection techniques to build a three-memory gradient Rosen projection method, which is applied to solve this optimization problem. The convergence analysis is given and simulations show that the proposed solution achieves significant throughput compared with existing approaches. Meiqin Tang, Lili Shang, Yalin Xin, Xiaohua Liu, and Xinjiang Wei Copyright © 2014 Meiqin Tang et al. All rights reserved. A Relationship: Word Alignment, Phrase Table, and Translation Quality Wed, 16 Apr 2014 13:36:59 +0000 In the last years, researchers conducted several studies to evaluate the machine translation quality based on the relationship between word alignments and phrase table. However, existing methods usually employ ad-hoc heuristics without theoretical support. So far, there is no discussion from the aspect of providing a formula to describe the relationship among word alignments, phrase table, and machine translation performance. In this paper, on one hand, we focus on formulating such a relationship for estimating the size of extracted phrase pairs given one or more word alignment points. On the other hand, a corpus-motivated pruning technique is proposed to prune the default large phrase table. Experiment proves that the deduced formula is feasible, which not only can be used to predict the size of the phrase table, but also can be a valuable reference for investigating the relationship between the translation performance and phrase tables based on different links of word alignment. The corpus-motivated pruning results show that nearly 98% of phrases can be reduced without any significant loss in translation quality. Liang Tian, Derek F. Wong, Lidia S. Chao, and Francisco Oliveira Copyright © 2014 Liang Tian et al. All rights reserved. Spatial Analysis on Future Housing Markets: Economic Development and Housing Implications Wed, 16 Apr 2014 09:47:24 +0000 A coupled projection method combining formal modelling and other statistical techniques was developed to delineate the relationship between economic and social drivers for net new housing allocations. Using the example of employment growth in Tyne and Wear, UK, until 2016, the empirical analysis yields housing projections at the macro- and microspatial levels (e.g., region to subregion to elected ward levels). The results have important implications for the strategic planning of locations for housing and employment, demonstrating both intuitively and quantitatively how local economic developments affect housing demand. Xin Liu and Lizhe Wang Copyright © 2014 Xin Liu and Lizhe Wang. All rights reserved. Palmprint Based Multidimensional Fuzzy Vault Scheme Wed, 16 Apr 2014 06:26:14 +0000 Fuzzy vault scheme (FVS) is one of the most popular biometric cryptosystems for biometric template protection. However, error correcting code (ECC) proposed in FVS is not appropriate to deal with real-valued biometric intraclass variances. In this paper, we propose a multidimensional fuzzy vault scheme (MDFVS) in which a general subspace error-tolerant mechanism is designed and embedded into FVS to handle intraclass variances. Palmprint is one of the most important biometrics; to protect palmprint templates; a palmprint based MDFVS implementation is also presented. Experimental results show that the proposed scheme not only can deal with intraclass variances effectively but also could maintain the accuracy and meanwhile enhance security. Hailun Liu, Dongmei Sun, Ke Xiong, and Zhengding Qiu Copyright © 2014 Hailun Liu et al. All rights reserved. iSentenizer-: Multilingual Sentence Boundary Detection Model Tue, 15 Apr 2014 14:00:50 +0000 Sentence boundary detection (SBD) system is normally quite sensitive to genres of data that the system is trained on. The genres of data are often referred to the shifts of text topics and new languages domains. Although new detection models can be retrained for different languages or new text genres, previous model has to be thrown away and the creation process has to be restarted from scratch. In this paper, we present a multilingual sentence boundary detection system (iSentenizer-μ) for Danish, German, English, Spanish, Dutch, French, Italian, Portuguese, Greek, Finnish, and Swedish languages. The proposed system is able to detect the sentence boundaries of a mixture of different text genres and languages with high accuracy. We employ i+Learning algorithm, an incremental tree learning architecture, for constructing the system. iSentenizer-μ, under the incremental learning framework, is adaptable to text of different topics and Roman-alphabet languages, by merging new data into existing model to learn the new knowledge incrementally by revision instead of retraining. The system has been extensively evaluated on different languages and text genres and has been compared against two state-of-the-art SBD systems, Punkt and MaxEnt. The experimental results show that the proposed system outperforms the other systems on all datasets. Derek F. Wong, Lidia S. Chao, and Xiaodong Zeng Copyright © 2014 Derek F. Wong et al. All rights reserved. Novel Web Service Selection Model Based on Discrete Group Search Tue, 15 Apr 2014 09:08:31 +0000 In our earlier work, we present a novel formal method for the semiautomatic verification of specifications and for describing web service composition components by using abstract concepts. After verification, the instantiations of components were selected to satisfy the complex service performance constraints. However, selecting an optimal instantiation, which comprises different candidate services for each generic service, from a large number of instantiations is difficult. Therefore, we present a new evolutionary approach on the basis of the discrete group search service (D-GSS) model. With regard to obtaining the optimal multiconstraint instantiation of the complex component, the D-GSS model has competitive performance compared with other service selection models in terms of accuracy, efficiency, and ability to solve high-dimensional service composition component problems. We propose the cost function and the discrete group search optimizer (D-GSO) algorithm and study the convergence of the D-GSS model through verification and test cases. Jie Zhai, Zhiqing Shao, Yi Guo, and Haiteng Zhang Copyright © 2014 Jie Zhai et al. All rights reserved. PEM-PCA: A Parallel Expectation-Maximization PCA Face Recognition Architecture Tue, 15 Apr 2014 08:16:59 +0000 Principal component analysis or PCA has been traditionally used as one of the feature extraction techniques in face recognition systems yielding high accuracy when requiring a small number of features. However, the covariance matrix and eigenvalue decomposition stages cause high computational complexity, especially for a large database. Thus, this research presents an alternative approach utilizing an Expectation-Maximization algorithm to reduce the determinant matrix manipulation resulting in the reduction of the stages’ complexity. To improve the computational time, a novel parallel architecture was employed to utilize the benefits of parallelization of matrix computation during feature extraction and classification stages including parallel preprocessing, and their combinations, so-called a Parallel Expectation-Maximization PCA architecture. Comparing to a traditional PCA and its derivatives, the results indicate lower complexity with an insignificant difference in recognition precision leading to high speed face recognition systems, that is, the speed-up over nine and three times over PCA and Parallel PCA. Kanokmon Rujirakul, Chakchai So-In, and Banchar Arnonkijpanich Copyright © 2014 Kanokmon Rujirakul et al. All rights reserved. Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks Tue, 15 Apr 2014 08:12:29 +0000 In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immigrants (McBAR) that was used to solve the dynamic problem. However, the solution-searching ability of McBAR was investigated over only a few instances of the dynamic problem. As a consequence, only a small number of characteristics of McBAR, under the dynamics of the RCPS problem, were found. Further, only a few techniques were compared to McBAR with respect to its solution-searching ability for solving the dynamic problem. In this paper, (a) the significance of the subalgorithms of McBAR is investigated by comparing McBAR to several other techniques; and (b) the scope of investigation in the previous work is extended. In particular, McBAR is compared to a technique called, Estimation Distribution Algorithm (EDA). As with McBAR, EDA is applied to solve the dynamic problem, an application that is unique in the literature. Manuel Blanco Abello and Zbigniew Michalewicz Copyright © 2014 Manuel Blanco Abello and Zbigniew Michalewicz. All rights reserved. A Collaborative Scheduling Model for the Supply-Hub with Multiple Suppliers and Multiple Manufacturers Tue, 15 Apr 2014 08:09:50 +0000 This paper investigates a collaborative scheduling model in the assembly system, wherein multiple suppliers have to deliver their components to the multiple manufacturers under the operation of Supply-Hub. We first develop two different scenarios to examine the impact of Supply-Hub. One is that suppliers and manufacturers make their decisions separately, and the other is that the Supply-Hub makes joint decisions with collaborative scheduling. The results show that our scheduling model with the Supply-Hub is a NP-complete problem, therefore, we propose an auto-adapted differential evolution algorithm to solve this problem. Moreover, we illustrate that the performance of collaborative scheduling by the Supply-Hub is superior to separate decision made by each manufacturer and supplier. Furthermore, we also show that the algorithm proposed has good convergence and reliability, which can be applicable to more complicated supply chain environment. Guo Li, Fei Lv, and Xu Guan Copyright © 2014 Guo Li et al. All rights reserved. Cloud Computing Based Systems for Healthcare Tue, 15 Apr 2014 07:09:10 +0000 Vladimir Stantchev, Ricardo Colomo-Palacios, and Michael Niedermayer Copyright © 2014 Vladimir Stantchev et al. All rights reserved. Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources Mon, 14 Apr 2014 12:36:47 +0000 The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. Inspired by the temporal receding horizon control in control engineering, this paper proposes a novel spatial receding horizon control (SRHC) strategy as a network partitioning technology, and then designs an efficient GA to tackle the NCP. Traditional network partitioning methods can be viewed as a special case of the proposed SRHC, that is, one-step-wide SRHC, whilst the method in this paper is a generalized -step-wide SRHC, which can make a better use of global information of network topologies. Besides the SRHC strategy, some useful designs are also reported in this paper. The advantages of the proposed SRHC and GA for the NCP are illustrated by extensive experiments, and they have a good potential of being extended to other large-scale complex problems. Xiao-Bing Hu and Mark S. Leeson Copyright © 2014 Xiao-Bing Hu and Mark S. Leeson. All rights reserved. Trajectory-Based Morphological Operators: A Model for Efficient Image Processing Mon, 14 Apr 2014 09:26:55 +0000 Mathematical morphology has been an area of intensive research over the last few years. Although many remarkable advances have been achieved throughout these years, there is still a great interest in accelerating morphological operations in order for them to be implemented in real-time systems. In this work, we present a new model for computing mathematical morphology operations, the so-called morphological trajectory model (MTM), in which a morphological filter will be divided into a sequence of basic operations. Then, a trajectory-based morphological operation (such as dilation, and erosion) is defined as the set of points resulting from the ordered application of the instant basic operations. The MTM approach allows working with different structuring elements, such as disks, and from the experiments, it can be extracted that our method is independent of the structuring element size and can be easily applied to industrial systems and high-resolution images. Antonio Jimeno-Morenilla, Francisco A. Pujol, Rafael Molina-Carmona, José L. Sánchez-Romero, and Mar Pujol Copyright © 2014 Antonio Jimeno-Morenilla et al. All rights reserved. CGE Simulation Analysis on the Labor Transfer, Agricultural Technical Progress, and Economic Development in Chongqing Mon, 14 Apr 2014 08:14:44 +0000 The basic structure of a CGE model dividing Mainland China into two parts, including Chongqing and rest regions, is described. Based on this CGE model, both the unilateral impact and collaborative impact of two policies, agricultural technical progress and supporting policies for improving rural labor transfer on the economic development in Chongqing, are simulated and analyzed. The results demonstrate that compared with the sum of each unilateral policy effect, the collaboration of two policies has more effective impact on facilitating the labor transfer, promoting regional economic growth, and improving income and welfare of urban and rural residents. Heng Wang and Maosheng Ran Copyright © 2014 Heng Wang and Maosheng Ran. All rights reserved. Cloud Based Metalearning System for Predictive Modeling of Biomedical Data Mon, 14 Apr 2014 06:05:14 +0000 Rapid growth and storage of biomedical data enabled many opportunities for predictive modeling and improvement of healthcare processes. On the other side analysis of such large amounts of data is a difficult and computationally intensive task for most existing data mining algorithms. This problem is addressed by proposing a cloud based system that integrates metalearning framework for ranking and selection of best predictive algorithms for data at hand and open source big data technologies for analysis of biomedical data. Milan Vukićević, Sandro Radovanović, Miloš Milovanović, and Miroslav Minović Copyright © 2014 Milan Vukićević et al. All rights reserved. A High Performance Load Balance Strategy for Real-Time Multicore Systems Mon, 14 Apr 2014 00:00:00 +0000 Finding ways to distribute workloads to each processor core and efficiently reduce power consumption is of vital importance, especially for real-time systems. In this paper, a novel scheduling algorithm is proposed for real-time multicore systems to balance the computation loads and save power. The developed algorithm simultaneously considers multiple criteria, a novel factor, and task deadline, and is called power and deadline-aware multicore scheduling (PDAMS). Experiment results show that the proposed algorithm can greatly reduce energy consumption by up to 54.2% and the deadline times missed, as compared to the other scheduling algorithms outlined in this paper. Keng-Mao Cho, Chun-Wei Tsai, Yi-Shiuan Chiu, and Chu-Sing Yang Copyright © 2014 Keng-Mao Cho et al. All rights reserved. Web Service Reputation Evaluation Based on QoS Measurement Sun, 13 Apr 2014 16:35:10 +0000 In the early service transactions, quality of service (QoS) information was published by service provider which was not always true and credible. For better verification the trust of the QoS information was provided by the Web service. In this paper, the factual QoS running data are collected by our WS-QoS measurement tool; based on these objectivity data, an algorithm compares the difference of the offered and measured quality data of the service and gives the similarity, and then a reputation evaluation method computes the reputation level of the Web service based on the similarity. The initial implementation and experiment with three Web services' example show that this approach is feasible and these values can act as the references for subsequent consumers to select the service. Haiteng Zhang, Zhiqing Shao, Hong Zheng, and Jie Zhai Copyright © 2014 Haiteng Zhang et al. All rights reserved. A Collaborative Recommend Algorithm Based on Bipartite Community Sun, 13 Apr 2014 14:04:28 +0000 The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database. Yuchen Fu, Quan Liu, and Zhiming Cui Copyright © 2014 Yuchen Fu et al. All rights reserved. A Novel Two-Stage Illumination Estimation Framework for Expression Recognition Sun, 13 Apr 2014 13:45:12 +0000 One of the critical issues for facial expression recognition is to eliminate the negative effect caused by variant poses and illuminations. In this paper a two-stage illumination estimation framework is proposed based on three-dimensional representative face and clustering, which can estimate illumination directions under a series of poses. First, 256 training 3D face models are adaptively categorized into a certain amount of facial structure types by -means clustering to group people with similar facial appearance into clusters. Then the representative face of each cluster is generated to represent the facial appearance type of that cluster. Our training set is obtained by rotating all representative faces to a certain pose, illuminating them with a series of different illumination conditions, and then projecting them into two-dimensional images. Finally the saltire-over-cross feature is selected to train a group of SVM classifiers and satisfactory performance is achieved when estimating a number of test sets including images generated from 64 3D face models kept for testing, CAS-PEAL face database, CMU PIE database, and a small test set created by ourselves. Compared with other related works, our method is subject independent and has less computational complexity without 3D facial reconstruction. Zheng Zhang, Guozhi Song, and Jigang Wu Copyright © 2014 Zheng Zhang et al. All rights reserved. Comprehensive Optimization of Emergency Evacuation Route and Departure Time under Traffic Control Sun, 13 Apr 2014 13:39:22 +0000 With the frequent occurrence of major emergencies, emergency management gets high attention from all around the world. This paper investigates the comprehensive optimization of major emergency evacuation route and departure time, in which case the evacuation propagation mechanism is considered under traffic control. Given the practical assumptions, we first establish a comprehensive optimization model based on the simulation of evacuation route and departure time. Furthermore, we explore the reasonable description method of evacuation traffic flow propagation under traffic control, including the establishment of traffic flow propagation model and the design of the simulation mudule that can simulate the evacuation traffic flow. Finally, we propose a heuristic algorithm for the optimization of this comprehensive model. In case analysis, we take some areas in Beijing as the evaluation sources to verify the reliability of our model. A series of constructive suggestions for Beijing's emergency evacuation are proposed, which can be applied to the actual situation under traffic control. Guo Li, Ying Zhou, and Mengqi Liu Copyright © 2014 Guo Li et al. All rights reserved. A Game-Theoretical Approach to Multimedia Social Networks Security Sun, 13 Apr 2014 11:53:38 +0000 The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party’s benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders. Enqiang Liu, Zengliang Liu, Fei Shao, and Zhiyong Zhang Copyright © 2014 Enqiang Liu et al. All rights reserved. Energy Efficiency of Task Allocation for Embedded JPEG Systems Thu, 10 Apr 2014 17:53:48 +0000 Embedded system works everywhere for repeatedly performing a few particular functionalities. Well-known products include consumer electronics, smart home applications, and telematics device, and so forth. Recently, developing methodology of embedded systems is applied to conduct the design of cloud embedded system resulting in the applications of embedded system being more diverse. However, the more energy consumes result from the more embedded system works. This study presents hyperrectangle technology (HT) to embedded system for obtaining energy saving. The HT adopts drift effect to construct embedded systems with more hardware circuits than software components or vice versa. It can fast construct embedded system with a set of hardware circuits and software components. Moreover, it has a great benefit to fast explore energy consumption for various embedded systems. The effects are presented by assessing a JPEG benchmarks. Experimental results demonstrate that the HT, respectively, achieves the energy saving by 29.84%, 2.07%, and 68.80% on average to GA, GHO, and Lin. Yang-Hsin Fan, Jan-Ou Wu, and San-Fu Wang Copyright © 2014 Yang-Hsin Fan et al. All rights reserved. An Approach for Integrating the Prioritization of Functional and Nonfunctional Requirements Thu, 10 Apr 2014 12:38:55 +0000 Due to the budgetary deadlines and time to market constraints, it is essential to prioritize software requirements. The outcome of requirements prioritization is an ordering of requirements which need to be considered first during the software development process. To achieve a high quality software system, both functional and nonfunctional requirements must be taken into consideration during the prioritization process. Although several requirements prioritization methods have been proposed so far, no particular method or approach is presented to consider both functional and nonfunctional requirements during the prioritization stage. In this paper, we propose an approach which aims to integrate the process of prioritizing functional and nonfunctional requirements. The outcome of applying the proposed approach produces two separate prioritized lists of functional and non-functional requirements. The effectiveness of the proposed approach has been evaluated through an empirical experiment aimed at comparing the approach with the two state-of-the-art-based approaches, analytic hierarchy process (AHP) and hybrid assessment method (HAM). Results show that our proposed approach outperforms AHP and HAM in terms of actual time-consumption while preserving the quality of the results obtained by our proposed approach at a high level of agreement in comparison with the results produced by the other two approaches. Mohammad Dabbagh and Sai Peck Lee Copyright © 2014 Mohammad Dabbagh and Sai Peck Lee. All rights reserved. Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion Thu, 10 Apr 2014 12:18:41 +0000 For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. Jie-sheng Wang, Shuang Han, and Na-na Shen Copyright © 2014 Jie-sheng Wang et al. All rights reserved. Research on Dynamic Routing Mechanisms in Wireless Sensor Networks Thu, 10 Apr 2014 12:17:47 +0000 WirelessHART is the most widely applied standard in wireless sensor networks nowadays. However, it does not provide any dynamic routing mechanism, which is important for the reliability and robustness of the wireless network applications. In this paper, a collection tree protocol based, dynamic routing mechanism was proposed for WirelessHART network. The dynamic routing mechanism was evaluated through several simulation experiments in three aspects: time for generating the topology, link quality, and stability of network. Besides, the data transmission efficiency of this routing mechanism was analyzed. The simulation and evaluation results show that this mechanism can act as a dynamic routing mechanism for the TDMA-based wireless sensor network. A. Q. Zhao, Y. N. Weng, Y. Lu, and C. Y. Liu Copyright © 2014 A. Q. Zhao et al. All rights reserved. The Generalization Complexity Measure for Continuous Input Data Thu, 10 Apr 2014 11:14:51 +0000 We introduce in this work an extension for the generalization complexity measure to continuous input data. The measure, originally defined in Boolean space, quantifies the complexity of data in relationship to the prediction accuracy that can be expected when using a supervised classifier like a neural network, SVM, and so forth. We first extend the original measure for its use with continuous functions to later on, using an approach based on the use of the set of Walsh functions, consider the case of having a finite number of data points (inputs/outputs pairs), that is, usually the practical case. Using a set of trigonometric functions a model that gives a relationship between the size of the hidden layer of a neural network and the complexity is constructed. Finally, we demonstrate the application of the introduced complexity measure, by using the generated model, to the problem of estimating an adequate neural network architecture for real-world data sets. Iván Gómez, Sergio A. Cannas, Omar Osenda, José M. Jerez, and Leonardo Franco Copyright © 2014 Iván Gómez et al. All rights reserved. An Algorithm for Critical Nodes Problem in Social Networks Based on Owen Value Thu, 10 Apr 2014 11:07:35 +0000 Discovering critical nodes in social networks has many important applications. For finding out the critical nodes and considering the widespread community structure in social networks, we obtain each node’s marginal contribution by Owen value. And then we can give a method for the solution of the critical node problem. We validate the feasibility and effectiveness of our method on two synthetic datasets and six real datasets. At the same time, the result obtained by using our method to analyze the terrorist network is in line with the actual situation. Xue-Guang Wang Copyright © 2014 Xue-Guang Wang. All rights reserved. A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance Thu, 10 Apr 2014 08:19:27 +0000 Textual stream classification has become a realistic and challenging issue since large-scale, high-dimensional, and non-stationary streams with class imbalance have been widely used in various real-life applications. According to the characters of textual streams, it is technically difficult to deal with the classification of textual stream, especially in imbalanced environment. In this paper, we propose a new ensemble framework, clustering forest, for learning from the textual imbalanced stream with concept drift (CFIM). The CFIM is based on ensemble learning by integrating a set of clustering trees (CTs). An adaptive selection method, which flexibly chooses the useful CTs by the property of the stream, is presented in CFIM. In particular, to deal with the problem of class imbalance, we collect and reuse both rare-class instances and misclassified instances from the historical chunks. Compared to most existing approaches, it is worth pointing out that our approach assumes that both majority class and rareclass may suffer from concept drift. Thus the distribution of resampled instances is similar to the current concept. The effectiveness of CFIM is examined in five real-world textual streams under an imbalanced nonstationary environment. Experimental results demonstrate that CFIM achieves better performance than four state-of-the-art ensemble models. Ge Song and Yunming Ye Copyright © 2014 Ge Song and Yunming Ye. All rights reserved. A Grammar-Based Semantic Similarity Algorithm for Natural Language Sentences Thu, 10 Apr 2014 06:56:42 +0000 This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to “artificial language”, such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure. Ming Che Lee, Jia Wei Chang, and Tung Cheng Hsieh Copyright © 2014 Ming Che Lee et al. All rights reserved. An Improved Mixture-of-Gaussians Background Model with Frame Difference and Blob Tracking in Video Stream Thu, 10 Apr 2014 06:53:09 +0000 Modeling background and segmenting moving objects are significant techniques for computer vision applications. Mixture-of-Gaussians (MoG) background model is commonly used in foreground extraction in video steam. However considering the case that the objects enter the scenery and stay for a while, the foreground extraction would fail as the objects stay still and gradually merge into the background. In this paper, we adopt a blob tracking method to cope with this situation. To construct the MoG model more quickly, we add frame difference method to the foreground extracted from MoG for very crowded situations. What is more, a new shadow removal method based on RGB color space is proposed. Li Yao and Miaogen Ling Copyright © 2014 Li Yao and Miaogen Ling. All rights reserved. A Novel Hybrid Self-Adaptive Bat Algorithm Wed, 09 Apr 2014 11:52:36 +0000 Nature-inspired algorithms attract many researchers worldwide for solving the hardest optimization problems. One of the newest members of this extensive family is the bat algorithm. To date, many variants of this algorithm have emerged for solving continuous as well as combinatorial problems. One of the more promising variants, a self-adaptive bat algorithm, has recently been proposed that enables a self-adaptation of its control parameters. In this paper, we have hybridized this algorithm using different DE strategies and applied these as a local search heuristics for improving the current best solution directing the swarm of a solution towards the better regions within a search space. The results of exhaustive experiments were promising and have encouraged us to invest more efforts into developing in this direction. Iztok Fister Jr., Simon Fong, Janez Brest, and Iztok Fister Copyright © 2014 Iztok Fister Jr. et al. All rights reserved. Chinese Unknown Word Recognition for PCFG-LA Parsing Wed, 09 Apr 2014 08:45:58 +0000 This paper investigates the recognition of unknown words in Chinese parsing. Two methods are proposed to handle this problem. One is the modification of a character-based model. We model the emission probability of an unknown word using the first and last characters in the word. It aims to reduce the POS tag ambiguities of unknown words to improve the parsing performance. In addition, a novel method, using graph-based semisupervised learning (SSL), is proposed to improve the syntax parsing of unknown words. Its goal is to discover additional lexical knowledge from a large amount of unlabeled data to help the syntax parsing. The method is mainly to propagate lexical emission probabilities to unknown words by building the similarity graphs over the words of labeled and unlabeled data. The derived distributions are incorporated into the parsing process. The proposed methods are effective in dealing with the unknown words to improve the parsing. Empirical results for Penn Chinese Treebank and TCT Treebank revealed its effectiveness. Qiuping Huang, Liangye He, Derek F. Wong, and Lidia S. Chao Copyright © 2014 Qiuping Huang et al. All rights reserved. Classifying Normal and Abnormal Status Based on Video Recordings of Epileptic Patients Tue, 08 Apr 2014 07:35:45 +0000 Based on video recordings of the movement of the patients with epilepsy, this paper proposed a human action recognition scheme to detect distinct motion patterns and to distinguish the normal status from the abnormal status of epileptic patients. The scheme first extracts local features and holistic features, which are complementary to each other. Afterwards, a support vector machine is applied to classification. Based on the experimental results, this scheme obtains a satisfactory classification result and provides a fundamental analysis towards the human-robot interaction with socially assistive robots in caring the patients with epilepsy (or other patients with brain disorders) in order to protect them from injury. Jing Li, Xiantong Zhen, Xianzeng Liu, and Gaoxiang Ouyang Copyright © 2014 Jing Li et al. All rights reserved. An Improved Kernel Based Extreme Learning Machine for Robot Execution Failures Tue, 08 Apr 2014 00:00:00 +0000 Robot execution failures prediction (classification) in the robot tasks is a difficult learning problem due to partially corrupted or incomplete measurements of data and unsuitable prediction techniques for this prediction problem with little learning samples. Therefore, how to predict the robot execution failures problem with little (incomplete) or erroneous data deserves more attention in the robot field. For improving the prediction accuracy of robot execution failures, this paper proposes a novel KELM learning algorithm using the particle swarm optimization approach to optimize the parameters of kernel functions of neural networks, which is called the AKELM learning algorithm. The simulation results with the robot execution failures datasets show that, by optimizing the kernel parameters, the proposed algorithm has good generalization performance and outperforms KELM and the other approaches in terms of classification accuracy. Other benchmark problems simulation results also show the efficiency and effectiveness of the proposed algorithm. Bin Li, Xuewen Rong, and Yibin Li Copyright © 2014 Bin Li et al. All rights reserved. Completed Local Ternary Pattern for Rotation Invariant Texture Classification Mon, 07 Apr 2014 09:49:04 +0000 Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors. Taha H. Rassem and Bee Ee Khoo Copyright © 2014 Taha H. Rassem and Bee Ee Khoo. All rights reserved. Gender Recognition from Unconstrained and Articulated Human Body Mon, 07 Apr 2014 07:43:51 +0000 Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. Qin Wu and Guodong Guo Copyright © 2014 Qin Wu and Guodong Guo. All rights reserved. Modeling Markov Switching ARMA-GARCH Neural Networks Models and an Application to Forecasting Stock Returns Sun, 06 Apr 2014 09:58:48 +0000 The study has two aims. The first aim is to propose a family of nonlinear GARCH models that incorporate fractional integration and asymmetric power properties to MS-GARCH processes. The second purpose of the study is to augment the MS-GARCH type models with artificial neural networks to benefit from the universal approximation properties to achieve improved forecasting accuracy. Therefore, the proposed Markov-switching MS-ARMA-FIGARCH, APGARCH, and FIAPGARCH processes are further augmented with MLP, Recurrent NN, and Hybrid NN type neural networks. The MS-ARMA-GARCH family and MS-ARMA-GARCH-NN family are utilized for modeling the daily stock returns in an emerging market, the Istanbul Stock Index (ISE100). Forecast accuracy is evaluated in terms of MAE, MSE, and RMSE error criteria and Diebold-Mariano equal forecast accuracy tests. The results suggest that the fractionally integrated and asymmetric power counterparts of Gray’s MS-GARCH model provided promising results, while the best results are obtained for their neural network based counterparts. Further, among the models analyzed, the models based on the Hybrid-MLP and Recurrent-NN, the MS-ARMA-FIAPGARCH-HybridMLP, and MS-ARMA-FIAPGARCH-RNN provided the best forecast performances over the baseline single regime GARCH models and further, over the Gray’s MS-GARCH model. Therefore, the models are promising for various economic applications. Melike Bildirici and Özgür Ersin Copyright © 2014 Melike Bildirici and Özgür Ersin. All rights reserved. Comparative Study of Popular Objective Functions for Damping Power System Oscillations in Multimachine System Sun, 06 Apr 2014 09:41:58 +0000 Power oscillation damping controller is designed in linearized model with heuristic optimization techniques. Selection of the objective function is very crucial for damping controller design by optimization algorithms. In this research, comparative analysis has been carried out to evaluate the effectiveness of popular objective functions used in power system oscillation damping. Two-stage lead-lag damping controller by means of power system stabilizers is optimized using differential search algorithm for different objective functions. Linearized model simulations are performed to compare the dominant mode’s performance and then the nonlinear model is continued to evaluate the damping performance over power system oscillations. All the simulations are conducted in two-area four-machine power system to bring a detailed analysis. Investigated results proved that multiobjective D-shaped function is an effective objective function in terms of moving unstable and lightly damped electromechanical modes into stable region. Thus, D-shape function ultimately improves overall system damping and concurrently enhances power system reliability. Naz Niamul Islam, M. A. Hannan, Hussain Shareef, Azah Mohamed, and M. A. Salam Copyright © 2014 Naz Niamul Islam et al. All rights reserved. BgCut: Automatic Ship Detection from UAV Images Thu, 03 Apr 2014 11:55:39 +0000 Ship detection in static UAV aerial images is a fundamental challenge in sea target detection and precise positioning. In this paper, an improved universal background model based on Grabcut algorithm is proposed to segment foreground objects from sea automatically. First, a sea template library including images in different natural conditions is built to provide an initial template to the model. Then the background trimap is obtained by combing some templates matching with region growing algorithm. The output trimap initializes Grabcut background instead of manual intervention and the process of segmentation without iteration. The effectiveness of our proposed model is demonstrated by extensive experiments on a certain area of real UAV aerial images by an airborne Canon 5D Mark. The proposed algorithm is not only adaptive but also with good segmentation. Furthermore, the model in this paper can be well applied in the automated processing of industrial images for related researches. Chao Xu, Dongping Zhang, Zhengning Zhang, and Zhiyong Feng Copyright © 2014 Chao Xu et al. All rights reserved. AP-IO: Asynchronous Pipeline I/O for Hiding Periodic Output Cost in CFD Simulation Thu, 03 Apr 2014 09:29:16 +0000 Computational fluid dynamics (CFD) simulation often needs to periodically output intermediate results to files in the form of snapshots for visualization or restart, which seriously impacts the performance. In this paper, we present asynchronous pipeline I/O (AP-IO) optimization scheme for the periodically snapshot output on the basis of asynchronous I/O and CFD application characteristics. In AP-IO, dedicated background I/O processes or threads are in charge of handling the file write in pipeline mode, therefore the write overhead can be hidden with more calculation than classic asynchronous I/O. We design the framework of AP-IO and implement it in OpenFOAM, providing CFD users with a user-friendly interface. Experimental results on the Tianhe-2 supercomputer demonstrate that AP-IO can achieve a good optimization effect for the periodical snapshot output in CFD application, and the effect is especially better for massively parallel CFD simulations, which can reduce the total execution time up to about 40%. Ren Xiaoguang and Xu Xinhai Copyright © 2014 Ren Xiaoguang and Xu Xinhai. All rights reserved. A Topological Framework for Interactive Queries on 3D Models in the Web Thu, 03 Apr 2014 08:04:21 +0000 Several technologies exist to create 3D content for the web. With X3D, WebGL, and X3DOM, it is possible to visualize and interact with 3D models in a web browser. Frequently, three-dimensional objects are stored using the X3D file format for the web. However, there is no explicit topological information, which makes it difficult to design fast algorithms for applications that require adjacency and incidence data. This paper presents a new open source toolkit TopTri (Topological model for Triangle meshes) for Web3D servers that builds the topological model for triangular meshes of manifold or nonmanifold models. Web3D client applications using this toolkit make queries to the web server to get adjacent and incidence information of vertices, edges, and faces. This paper shows the application of the topological information to get minimal local points and iso-lines in a 3D mesh in a web browser. As an application, we present also the interactive identification of stalactites in a cave chamber in a 3D web browser. Several tests show that even for large triangular meshes with millions of triangles, the adjacency and incidence information is returned in real time making the presented toolkit appropriate for interactive Web3D applications. Mauro Figueiredo, José I. Rodrigues, Ivo Silvestre, and Cristina Veiga-Pires Copyright © 2014 Mauro Figueiredo et al. All rights reserved. A Multistrategy Optimization Improved Artificial Bee Colony Algorithm Thu, 03 Apr 2014 07:50:40 +0000 Being prone to the shortcomings of premature and slow convergence rate of artificial bee colony algorithm, an improved algorithm was proposed. Chaotic reverse learning strategies were used to initialize swarm in order to improve the global search ability of the algorithm and keep the diversity of the algorithm; the similarity degree of individuals of the population was used to characterize the diversity of population; population diversity measure was set as an indicator to dynamically and adaptively adjust the nectar position; the premature and local convergence were avoided effectively; dual population search mechanism was introduced to the search stage of algorithm; the parallel search of dual population considerably improved the convergence rate. Through simulation experiments of 10 standard testing functions and compared with other algorithms, the results showed that the improved algorithm had faster convergence rate and the capacity of jumping out of local optimum faster. Wen Liu Copyright © 2014 Wen Liu. All rights reserved. Privacy-Preserving Discovery of Topic-Based Events from Social Sensor Signals: An Experimental Study on Twitter Thu, 03 Apr 2014 06:58:46 +0000 Social network services (e.g., Twitter and Facebook) can be regarded as social sensors which can capture a number of events in the society. Particularly, in terms of time and space, various smart devices have improved the accessibility to the social network services. In this paper, we present a social software platform to detect a number of meaningful events from information diffusion patterns on such social network services. The most important feature is to process the social sensor signal for understanding social events and to support users to share relevant information along the social links. The platform has been applied to fetch and cluster tweets from Twitter into relevant categories to reveal hot topics. Duc T. Nguyen and Jai E. Jung Copyright © 2014 Duc T. Nguyen and Jai E. Jung. All rights reserved. ReHypar: A Recursive Hybrid Chunk Partitioning Method Using NAND-Flash Memory SSD Thu, 03 Apr 2014 06:38:58 +0000 Due to the rapid development of flash memory, SSD is considered to be the replacement of HDD in the storage market. Although SSD retains several promising characteristics, such as high random I/O performance and nonvolatility, its high expense per capacity is the main obstacle in replacing HDD in all storage solutions. An alternative is to provide a hybrid structure where a small portion of SSD address space is combined with the much larger HDD address space. In such a structure, maximizing the space utilization of SSD in a cost-effective way is extremely important to generate high I/O performance. We developed ReHypar (recursive hybrid chunk partitioning) that enables improving the space utilization of SSD in the hybrid structure. The first objective of ReHypar is to mitigate the fragmentation overhead of SSD address space, by reusing the remaining free space of I/O units as much as possible. Furthermore, ReHypar allows defining several, logical data sections in SSD address space, with each of those sections being configured with the different I/O unit. We integrated ReHypar with ext2 and ext4 and evaluated it using two public benchmarks including IOzone and Postmark. Jaechun No, Sung-Soon Park, and Cheol-Su Lim Copyright © 2014 Jaechun No et al. All rights reserved. A Network and Visual Quality Aware N-Screen Content Recommender System Using Joint Matrix Factorization Thu, 03 Apr 2014 06:34:51 +0000 We propose a network and visual quality aware N-Screen content recommender system. N-Screen provides more ways than ever before to access multimedia content through multiple devices and heterogeneous access networks. The heterogeneity of devices and access networks present new questions of QoS (quality of service) in the realm of user experience with content. We propose, a recommender system that ensures a better visual quality on user’s N-screen devices and the efficient utilization of available access network bandwidth with user preferences. The proposed system estimates the available bandwidth and visual quality on users N-Screen devices and integrates it with users preferences and contents genre information to personalize his N-Screen content. The objective is to recommend content that the user’s N-Screen device and access network are capable of displaying and streaming with the user preferences that have not been supported in existing systems. Furthermore, we suggest a joint matrix factorization approach to jointly factorize the users rating matrix with the users N-Screen device similarity and program genres similarity. Finally, the experimental results show that we also enhance the prediction and recommendation accuracy, sparsity, and cold start issues. Farman Ullah, Ghulam Sarwar, and Sungchang Lee Copyright © 2014 Farman Ullah et al. All rights reserved. An Effective News Recommendation Method for Microblog User Wed, 02 Apr 2014 11:39:32 +0000 Recommending news stories to users, based on their preferences, has long been a favourite domain for recommender systems research. Traditional systems strive to satisfy their user by tracing users' reading history and choosing the proper candidate news articles to recommend. However, most of news websites hardly require any user to register before reading news. Besides, the latent relations between news and microblog, the popularity of particular news, and the news organization are not addressed or solved efficiently in previous approaches. In order to solve these issues, we propose an effective personalized news recommendation method based on microblog user profile building and sub class popularity prediction, in which we propose a news organization method using hybrid classification and clustering, implement a sub class popularity prediction method, and construct user profile according to our actual situation. We had designed several experiments compared to the state-of-the-art approaches on a real world dataset, and the experimental results demonstrate that our system significantly improves the accuracy and diversity in mass text data. Wanrong Gu, Shoubin Dong, Zhizhao Zeng, and Jinchao He Copyright © 2014 Wanrong Gu et al. All rights reserved. Iris Recognition Using Image Moments and k-Means Algorithm Tue, 01 Apr 2014 07:12:11 +0000 This paper presents a biometric technique for identification of a person using the iris image. The iris is first segmented from the acquired image of an eye using an edge detection algorithm. The disk shaped area of the iris is transformed into a rectangular form. Described moments are extracted from the grayscale image which yields a feature vector containing scale, rotation, and translation invariant moments. Images are clustered using the k-means algorithm and centroids for each cluster are computed. An arbitrary image is assumed to belong to the cluster whose centroid is the nearest to the feature vector in terms of Euclidean distance computed. The described model exhibits an accuracy of 98.5%. Yaser Daanial Khan, Sher Afzal Khan, Farooq Ahmad, and Saeed Islam Copyright © 2014 Yaser Daanial Khan et al. All rights reserved. OntoTrader: An Ontological Web Trading Agent Approach for Environmental Information Retrieval Tue, 01 Apr 2014 06:55:43 +0000 Modern Web-based Information Systems (WIS) are becoming increasingly necessary to provide support for users who are in different places with different types of information, by facilitating their access to the information, decision making, workgroups, and so forth. Design of these systems requires the use of standardized methods and techniques that enable a common vocabulary to be defined to represent the underlying knowledge. Thus, mediation elements such as traders enrich the interoperability of web components in open distributed systems. These traders must operate with other third-party traders and/or agents in the system, which must also use a common vocabulary for communication between them. This paper presents the OntoTrader architecture, an Ontological Web Trading agent based on the OMG ODP trading standard. It also presents the ontology needed by some system agents to communicate with the trading agent and the behavioral framework for the SOLERES OntoTrader agent, an Environmental Management Information System (EMIS). This framework implements a “Query-Searching/Recovering-Response” information retrieval model using a trading service, SPARQL notation, and the JADE platform. The paper also presents reflection, delegation and, federation mediation models and describes formalization, an experimental testing environment in three scenarios, and a tool which allows our proposal to be evaluated and validated. Luis Iribarne, Nicolás Padilla, Rosa Ayala, José A. Asensio, and Javier Criado Copyright © 2014 Luis Iribarne et al. All rights reserved. Minimizing Thermal Stress for Data Center Servers through Thermal-Aware Relocation Mon, 31 Mar 2014 14:16:02 +0000 A rise in inlet air temperature may lower the rate of heat dissipation from air cooled computing servers. This introduces a thermal stress to these servers. As a result, the poorly cooled active servers will start conducting heat to the neighboring servers and giving rise to hotspot regions of thermal stress, inside the data center. As a result, the physical hardware of these servers may fail, thus causing performance loss, monetary loss, and higher energy consumption for cooling mechanism. In order to minimize these situations, this paper performs the profiling of inlet temperature sensitivity (ITS) and defines the optimum location for each server to minimize the chances of creating a thermal hotspot and thermal stress. Based upon novel ITS analysis, a thermal state monitoring and server relocation algorithm for data centers is being proposed. The contribution of this paper is bringing the peak outlet temperatures of the relocated servers closer to average outlet temperature by over 5 times, lowering the average peak outlet temperature by 3.5% and minimizing the thermal stress. Muhammad Tayyab Chaudhry, T. C. Ling, S. A. Hussain, and Atif Manzoor Copyright © 2014 Muhammad Tayyab Chaudhry et al. All rights reserved. Mining 3D Patterns from Gene Expression Temporal Data: A New Tricluster Evaluation Measure Mon, 31 Mar 2014 14:01:38 +0000 Microarrays have revolutionized biotechnological research. The analysis of new data generated represents a computational challenge due to the characteristics of these data. Clustering techniques are applied to create groups of genes that exhibit a similar behavior. Biclustering emerges as a valuable tool for microarray data analysis since it relaxes the constraints for grouping, allowing genes to be evaluated only under a subset of the conditions. However, if a third dimension appears in the data, triclustering is the appropriate tool for the analysis. This occurs in longitudinal experiments in which the genes are evaluated under conditions at several time points. All clustering, biclustering, and triclustering techniques guide their search for solutions by a measure that evaluates the quality of clusters. We present an evaluation measure for triclusters called Mean Square Residue 3D. This measure is based on the classic biclustering measure Mean Square Residue. Mean Square Residue 3D has been applied to both synthetic and real data and it has proved to be capable of extracting groups of genes with homogeneous patterns in subsets of conditions and times, and these groups have shown a high correlation level and they are also related to their functional annotations extracted from the Gene Ontology project. David Gutiérrez-Avilés and Cristina Rubio-Escudero Copyright © 2014 David Gutiérrez-Avilés and Cristina Rubio-Escudero. All rights reserved. Followee Recommendation in Microblog Using Matrix Factorization Model with Structural Regularization Mon, 31 Mar 2014 12:20:10 +0000 Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising. Yan Yu and Robin G. Qiu Copyright © 2014 Yan Yu and Robin G. Qiu. All rights reserved. User Localization in Complex Environments by Multimodal Combination of GPS, WiFi, RFID, and Pedometer Technologies Mon, 31 Mar 2014 08:52:07 +0000 Many user localization technologies and methods have been proposed for either indoor or outdoor environments. However, each technology has its own drawbacks. Recently, many researches and designs have been proposed to build a combination of multiple localization technologies system which can provide higher precision results and solve the limitation in each localization technology alone. In this paper, a conceptual design of a general localization platform using combination of multiple localization technologies is introduced. The combination is realized by dividing spaces into grid points. To demonstrate this platform, a system with GPS, RFID, WiFi, and pedometer technologies is established. Experiment results show that the accuracy and availability are improved in comparison with each technology individually. Trung-Kien Dao, Hung-Long Nguyen, Thanh-Thuy Pham, Eric Castelli, Viet-Tung Nguyen, and Dinh-Van Nguyen Copyright © 2014 Trung-Kien Dao et al. All rights reserved. Personalized Privacy-Preserving Frequent Itemset Mining Using Randomized Response Sun, 30 Mar 2014 14:45:20 +0000 Frequent itemset mining is the important first step of association rule mining, which discovers interesting patterns from the massive data. There are increasing concerns about the privacy problem in the frequent itemset mining. Some works have been proposed to handle this kind of problem. In this paper, we introduce a personalized privacy problem, in which different attributes may need different privacy levels protection. To solve this problem, we give a personalized privacy-preserving method by using the randomized response technique. By providing different privacy levels for different attributes, this method can get a higher accuracy on frequent itemset mining than the traditional method providing the same privacy level. Finally, our experimental results show that our method can have better results on the frequent itemset mining while preserving personalized privacy. Chongjing Sun, Yan Fu, Junlin Zhou, and Hui Gao Copyright © 2014 Chongjing Sun et al. All rights reserved. New Trends in Robotics for Agriculture: Integration and Assessment of a Real Fleet of Robots Sun, 30 Mar 2014 09:13:04 +0000 Computer-based sensors and actuators such as global positioning systems, machine vision, and laser-based sensors have progressively been incorporated into mobile robots with the aim of configuring autonomous systems capable of shifting operator activities in agricultural tasks. However, the incorporation of many electronic systems into a robot impairs its reliability and increases its cost. Hardware minimization, as well as software minimization and ease of integration, is essential to obtain feasible robotic systems. A step forward in the application of automatic equipment in agriculture is the use of fleets of robots, in which a number of specialized robots collaborate to accomplish one or several agricultural tasks. This paper strives to develop a system architecture for both individual robots and robots working in fleets to improve reliability, decrease complexity and costs, and permit the integration of software from different developers. Several solutions are studied, from a fully distributed to a whole integrated architecture in which a central computer runs all processes. This work also studies diverse topologies for controlling fleets of robots and advances other prospective topologies. The architecture presented in this paper is being successfully applied in the RHEA fleet, which comprises three ground mobile units based on a commercial tractor chassis. Luis Emmi, Mariano Gonzalez-de-Soto, Gonzalo Pajares, and Pablo Gonzalez-de-Santos Copyright © 2014 Luis Emmi et al. All rights reserved. Pain Expression Recognition Based on pLSA Model Thu, 27 Mar 2014 11:55:10 +0000 We present a new approach to automatically recognize the pain expression from video sequences, which categorize pain as 4 levels: “no pain,” “slight pain,” “moderate pain,” and “ severe pain.” First of all, facial velocity information, which is used to characterize pain, is determined using optical flow technique. Then visual words based on facial velocity are used to represent pain expression using bag of words. Final pLSA model is used for pain expression recognition, in order to improve the recognition accuracy, the class label information was used for the learning of the pLSA model. Experiments were performed on a pain expression dataset built by ourselves to test and evaluate the proposed method, the experiment results show that the average recognition accuracy is over 92%, which validates its effectiveness. Shaoping Zhu Copyright © 2014 Shaoping Zhu. All rights reserved. A Hybrid Approach to Protect Palmprint Templates Thu, 27 Mar 2014 10:02:51 +0000 Biometric template protection is indispensable to protect personal privacy in large-scale deployment of biometric systems. Accuracy, changeability, and security are three critical requirements for template protection algorithms. However, existing template protection algorithms cannot satisfy all these requirements well. In this paper, we propose a hybrid approach that combines random projection and fuzzy vault to improve the performances at these three points. Heterogeneous space is designed for combining random projection and fuzzy vault properly in the hybrid scheme. New chaff point generation method is also proposed to enhance the security of the heterogeneous vault. Theoretical analyses of proposed hybrid approach in terms of accuracy, changeability, and security are given in this paper. Palmprint database based experimental results well support the theoretical analyses and demonstrate the effectiveness of proposed hybrid approach. Hailun Liu, Dongmei Sun, Ke Xiong, and Zhengding Qiu Copyright © 2014 Hailun Liu et al. All rights reserved. Towards the Novel Reasoning among Particles in PSO by the Use of RDF and SPARQL Thu, 27 Mar 2014 08:31:42 +0000 The significant development of the Internet has posed some new challenges and many new programming tools have been developed to address such challenges. Today, semantic web is a modern paradigm for representing and accessing knowledge data on the Internet. This paper tries to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose. These tools are combined with particle swarm optimization (PSO) and the selection of the best solutions depends on its fitness. Instead of the local best solution, a neighborhood of solutions for each particle can be defined and used for the calculation of the new position, based on the key ideas from semantic web domain. The preliminary results by optimizing ten benchmark functions showed the promising results and thus this method should be investigated further. Iztok Fister Jr., Xin-She Yang, Karin Ljubič, Dušan Fister, Janez Brest, and Iztok Fister Copyright © 2014 Iztok Fister Jr. et al. All rights reserved. Development of Vision Based Multiview Gait Recognition System with MMUGait Database Thu, 27 Mar 2014 07:56:49 +0000 This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases. Hu Ng, Wooi-Haw Tan, Junaidi Abdullah, and Hau-Lee Tong Copyright © 2014 Hu Ng et al. All rights reserved. A Simulation Approach to Decision Making in IT Service Strategy Wed, 26 Mar 2014 13:03:03 +0000 We propose to use simulation modeling to support decision making in IT service strategy scope. Our main contribution is a simulation model that helps service providers analyze the consequences of changes in both the service capacity assigned to their customers and the tendency of service requests received on the fulfillment of a business rule associated with the strategic goal of customer satisfaction. This business rule is set in the SLAs that service provider and its customers agree to, which determine the maximum percentage of service requests that are permitted to be abandoned because they have exceeded the waiting time allowed. To illustrate the use and applications of the model, we include some of the experiments conducted and describe our conclusions. Elena Orta and Mercedes Ruiz Copyright © 2014 Elena Orta and Mercedes Ruiz. All rights reserved. Network-Based Analysis of Software Change Propagation Wed, 26 Mar 2014 11:43:34 +0000 The object-oriented software systems frequently evolve to meet new change requirements. Understanding the characteristics of changes aids testers and system designers to improve the quality of softwares. Identifying important modules becomes a key issue in the process of evolution. In this context, a novel network-based approach is proposed to comprehensively investigate change distributions and the correlation between centrality measures and the scope of change propagation. First, software dependency networks are constructed at class level. And then, the number of times of cochanges among classes is minded from software repositories. According to the dependency relationships and the number of times of cochanges among classes, the scope of change propagation is calculated. Using Spearman rank correlation analyzes the correlation between centrality measures and the scope of change propagation. Three case studies on java open source software projects Findbugs, Hibernate, and Spring are conducted to research the characteristics of change propagation. Experimental results show that (i) change distribution is very uneven; (ii) PageRank, Degree, and CIRank are significantly correlated to the scope of change propagation. Particularly, CIRank shows higher correlation coefficient, which suggests it can be a more useful indicator for measuring the scope of change propagation of classes in object-oriented software system. Rongcun Wang, Rubing Huang, and Binbin Qu Copyright © 2014 Rongcun Wang et al. All rights reserved. Ontology-Based Multiple Choice Question Generation Wed, 26 Mar 2014 08:31:19 +0000 With recent advancements in Semantic Web technologies, a new trend in MCQ item generation has emerged through the use of ontologies. Ontologies are knowledge representation structures that formally describe entities in a domain and their relationships, thus enabling automated inference and reasoning. Ontology-based MCQ item generation is still in its infancy, but substantial research efforts are being made in the field. However, the applicability of these models for use in an educational setting has not been thoroughly evaluated. In this paper, we present an experimental evaluation of an ontology-based MCQ item generation system known as OntoQue. The evaluation was conducted using two different domain ontologies. The findings of this study show that ontology-based MCQ generation systems produce satisfactory MCQ items to a certain extent. However, the evaluation also revealed a number of shortcomings with current ontology-based MCQ item generation systems with regard to the educational significance of an automatically constructed MCQ item, the knowledge level it addresses, and its language structure. Furthermore, for the task to be successful in producing high-quality MCQ items for learning assessments, this study suggests a novel, holistic view that incorporates learning content, learning objectives, lexical knowledge, and scenarios into a single cohesive framework. Maha Al-Yahya Copyright © 2014 Maha Al-Yahya. All rights reserved. Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization Tue, 25 Mar 2014 12:08:07 +0000 Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. Mohammed Hasan Abdulameer, Siti Norul Huda Sheikh Abdullah, and Zulaiha Ali Othman Copyright © 2014 Mohammed Hasan Abdulameer et al. All rights reserved. A Hybrid Algorithm for Clustering of Time Series Data Based on Affinity Search Technique Tue, 25 Mar 2014 11:28:32 +0000 Time series clustering is an important solution to various problems in numerous fields of research, including business, medical science, and finance. However, conventional clustering algorithms are not practical for time series data because they are essentially designed for static data. This impracticality results in poor clustering accuracy in several systems. In this paper, a new hybrid clustering algorithm is proposed based on the similarity in shape of time series data. Time series data are first grouped as subclusters based on similarity in time. The subclusters are then merged using the k-Medoids algorithm based on similarity in shape. This model has two contributions: (1) it is more accurate than other conventional and hybrid approaches and (2) it determines the similarity in shape among time series data with a low complexity. To evaluate the accuracy of the proposed model, the model is tested extensively using syntactic and real-world time series datasets. Saeed Aghabozorgi, Teh Ying Wah, Tutut Herawan, Hamid A. Jalab, Mohammad Amin Shaygan, and Alireza Jalali Copyright © 2014 Saeed Aghabozorgi et al. All rights reserved. An Improved Physarum polycephalum Algorithm for the Shortest Path Problem Tue, 25 Mar 2014 07:40:00 +0000 Shortest path is among classical problems of computer science. The problems are solved by hundreds of algorithms, silicon computing architectures and novel substrate, unconventional, computing devices. Acellular slime mould P. polycephalum is originally famous as a computing biological substrate due to its alleged ability to approximate shortest path from its inoculation site to a source of nutrients. Several algorithms were designed based on properties of the slime mould. Many of the Physarum-inspired algorithms suffer from a low converge speed. To accelerate the search of a solution and reduce a number of iterations we combined an original model of Physarum-inspired path solver with a new a parameter, called energy. We undertook a series of computational experiments on approximating shortest paths in networks with different topologies, and number of nodes varying from 15 to 2000. We found that the improved Physarum algorithm matches well with existing Physarum-inspired approaches yet outperforms them in number of iterations executed and a total running time. We also compare our algorithm with other existing algorithms, including the ant colony optimization algorithm and Dijkstra algorithm. Xiaoge Zhang, Qing Wang, Andrew Adamatzky, Felix T. S. Chan, Sankaran Mahadevan, and Yong Deng Copyright © 2014 Xiaoge Zhang et al. All rights reserved. Stock Price Change Rate Prediction by Utilizing Social Network Activities Tue, 25 Mar 2014 07:13:19 +0000 Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. Shangkun Deng, Takashi Mitsubuchi, and Akito Sakurai Copyright © 2014 Shangkun Deng et al. All rights reserved. EEG Channel Selection Using Particle Swarm Optimization for the Classification of Auditory Event-Related Potentials Tue, 25 Mar 2014 07:03:41 +0000 Brain-machine interfaces (BMI) rely on the accurate classification of event-related potentials (ERPs) and their performance greatly depends on the appropriate selection of classifier parameters and features from dense-array electroencephalography (EEG) signals. Moreover, in order to achieve a portable and more compact BMI for practical applications, it is also desirable to use a system capable of accurate classification using information from as few EEG channels as possible. In the present work, we propose a method for classifying P300 ERPs using a combination of Fisher Discriminant Analysis (FDA) and a multiobjective hybrid real-binary Particle Swarm Optimization (MHPSO) algorithm. Specifically, the algorithm searches for the set of EEG channels and classifier parameters that simultaneously maximize the classification accuracy and minimize the number of used channels. The performance of the method is assessed through offline analyses on datasets of auditory ERPs from sound discrimination experiments. The proposed method achieved a higher classification accuracy than that achieved by traditional methods while also using fewer channels. It was also found that the number of channels used for classification can be significantly reduced without greatly compromising the classification accuracy. Alejandro Gonzalez, Isao Nambu, Haruhide Hokari, and Yasuhiro Wada Copyright © 2014 Alejandro Gonzalez et al. All rights reserved. Efficient Dynamic Replication Algorithm Using Agent for Data Grid Mon, 24 Mar 2014 13:23:31 +0000 In data grids scientific and business applications produce huge volume of data which needs to be transferred among the distributed and heterogeneous nodes of data grids. Data replication provides a solution for managing data files efficiently in large grids. The data replication helps in enhancing the data availability which reduces the overall access time of the file. In this paper an algorithm, namely, EDRA using agents for data grid, has been proposed and implemented. EDRA consists of dynamic replication of hierarchical structure taken into account for the selection of best replica. Decision for selecting the best replica is based on scheduling parameters. The scheduling parameters are bandwidth, load gauge, and computing capacity of the node. The scheduling in data grid helps in reducing the data access time. The distribution of the load on the nodes of data grid is done evenly by considering scheduling parameters. EDRA is implemented using data grid simulator, namely, OptorSim. European Data Grid CMS test bed topology is used in this experiment. The simulation results are obtained by comparing BHR, LRU, No Replication, and EDRA. The result shows the efficiency of EDRA algorithm in terms of mean job execution time, network usage, and storage usage of node. Priyanka Vashisht, Rajesh Kumar, and Anju Sharma Copyright © 2014 Priyanka Vashisht et al. All rights reserved. Process Correlation Analysis Model for Process Improvement Identification Mon, 24 Mar 2014 07:04:14 +0000 Software process improvement aims at improving the development process of software systems. It is initiated by process assessment identifying strengths and weaknesses and based on the findings, improvement plans are developed. In general, a process reference model (e.g., CMMI) is used throughout the process of software process improvement as the base. CMMI defines a set of process areas involved in software development and what to be carried out in process areas in terms of goals and practices. Process areas and their elements (goals and practices) are often correlated due to the iterative nature of software development process. However, in the current practice, correlations of process elements are often overlooked in the development of an improvement plan, which diminishes the efficiency of the plan. This is mainly attributed to significant efforts and the lack of required expertise. In this paper, we present a process correlation analysis model that helps identify correlations of process elements from the results of process assessment. This model is defined based on CMMI and empirical data of improvement practices. We evaluate the model using industrial data. Su-jin Choi, Dae-Kyoo Kim, and Sooyong Park Copyright © 2014 Su-jin Choi et al. All rights reserved. A Survey on Investigating the Need for Intelligent Power-Aware Load Balanced Routing Protocols for Handling Critical Links in MANETs Sun, 23 Mar 2014 13:20:49 +0000 In mobile ad hoc networks connectivity is always an issue of concern. Due to dynamism in the behavior of mobile nodes, efficiency shall be achieved only with the assumption of good network infrastructure. Presence of critical links results in deterioration which should be detected in advance to retain the prevailing communication setup. This paper discusses a short survey on the specialized algorithms and protocols related to energy efficient load balancing for critical link detection in the recent literature. This paper also suggests a machine learning based hybrid power-aware approach for handling critical nodes via load balancing. B. Sivakumar, N. Bhalaji, and D. Sivakumar Copyright © 2014 B. Sivakumar et al. All rights reserved. A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection Sun, 23 Mar 2014 12:59:22 +0000 This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases. Burhan Ergen Copyright © 2014 Burhan Ergen. All rights reserved. Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain Sun, 23 Mar 2014 09:52:13 +0000 The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. Yonghui Dai, Dongmei Han, and Weihui Dai Copyright © 2014 Yonghui Dai et al. All rights reserved. The Theoretical Limits of Watermark Spread Spectrum Sequence Sun, 23 Mar 2014 09:13:33 +0000 At present, the spread spectrum (SS) sequences used in watermark include i.i.d. random sequences and the sequences used in SS communications. They appear earlier than digital watermark. Almost no researchers pay attention to whether they are really fit for watermark. In this paper, we compare the SS watermark channel and the traditional SS communication channel. We find out that their correlation property is different. Considering cropping and translation attacks, we define watermark auto- and cross-correlation and propose Loose Autocorrelation and Tight Cross-Correlation (LAC&TCC) properties for SS watermark. The LAC&TCC properties are that, whether or not synchronized, the autocorrelation is equal or close to 1 and the cross-correlation is equal or close to 0. Accordingly, the peak correlation is divided into the peak autocorrelation and the peak cross-correlation . We establish the lower bound of and the higher bound of , respectively. The two bounds indicate that, no matter how small the cover is reserved, the extractor can always find a threshold to distinguish auto- and cross-correlation in theory. Nan Jiang and Jian Wang Copyright © 2014 Nan Jiang and Jian Wang. All rights reserved. Evolutionary Approach for Relative Gene Expression Algorithms Sun, 23 Mar 2014 09:10:49 +0000 A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space. Marcin Czajkowski and Marek Kretowski Copyright © 2014 Marcin Czajkowski and Marek Kretowski. All rights reserved. A Survey of Artificial Immune System Based Intrusion Detection Sun, 23 Mar 2014 09:00:36 +0000 In the area of computer security, Intrusion Detection (ID) is a mechanism that attempts to discover abnormal access to computers by analyzing various interactions. There is a lot of literature about ID, but this study only surveys the approaches based on Artificial Immune System (AIS). The use of AIS in ID is an appealing concept in current techniques. This paper summarizes AIS based ID methods from a new view point; moreover, a framework is proposed for the design of AIS based ID Systems (IDSs). This framework is analyzed and discussed based on three core aspects: antibody/antigen encoding, generation algorithm, and evolution mode. Then we collate the commonly used algorithms, their implementation characteristics, and the development of IDSs into this framework. Finally, some of the future challenges in this area are also highlighted. Hua Yang, Tao Li, Xinlei Hu, Feng Wang, and Yang Zou Copyright © 2014 Hua Yang et al. All rights reserved. New Fuzzy Support Vector Machine for the Class Imbalance Problem in Medical Datasets Classification Sun, 23 Mar 2014 08:57:24 +0000 In medical datasets classification, support vector machine (SVM) is considered to be one of the most successful methods. However, most of the real-world medical datasets usually contain some outliers/noise and data often have class imbalance problems. In this paper, a fuzzy support machine (FSVM) for the class imbalance problem (called FSVM-CIP) is presented, which can be seen as a modified class of FSVM by extending manifold regularization and assigning two misclassification costs for two classes. The proposed FSVM-CIP can be used to handle the class imbalance problem in the presence of outliers/noise, and enhance the locality maximum margin. Five real-world medical datasets, breast, heart, hepatitis, BUPA liver, and pima diabetes, from the UCI medical database are employed to illustrate the method presented in this paper. Experimental results on these datasets show the outperformed or comparable effectiveness of FSVM-CIP. Xiaoqing Gu, Tongguang Ni, and Hongyuan Wang Copyright © 2014 Xiaoqing Gu et al. All rights reserved. A Pruning-Based Disk Scheduling Algorithm for Heterogeneous I/O Workloads Sun, 23 Mar 2014 08:52:01 +0000 In heterogeneous I/O workload environments, disk scheduling algorithms should support different QoS (Quality-of-Service) for each I/O request. For example, the algorithm should meet the deadlines of real-time requests and at the same time provide reasonable response time for best-effort requests. This paper presents a novel disk scheduling algorithm called G-SCAN (Grouping-SCAN) for handling heterogeneous I/O workloads. To find a schedule that satisfies the deadline constraints and seek time minimization simultaneously, G-SCAN maintains a series of candidate schedules and expands the schedules whenever a new request arrives. Maintaining these candidate schedules requires excessive spatial and temporal overhead, but G-SCAN reduces the overhead to a manageable level via pruning the state space using two heuristics. One is grouping that clusters adjacent best-effort requests into a single scheduling unit and the other is the branch-and-bound strategy that cuts off inefficient or impractical schedules. Experiments with various synthetic and real-world I/O workloads show that G-SCAN outperforms existing disk scheduling algorithms significantly in terms of the average response time, throughput, and QoS-guarantees for heterogeneous I/O workloads. We also show that the overhead of G-SCAN is reasonable for on-line execution. Taeseok Kim, Hyokyung Bahn, and Youjip Won Copyright © 2014 Taeseok Kim et al. All rights reserved. A Fast and Robust Ellipse-Detection Method Based on Sorted Merging Sun, 23 Mar 2014 00:00:00 +0000 A fast and robust ellipse-detection method based on sorted merging is proposed in this paper. This method first represents the edge bitmap approximately with a set of line segments and then gradually merges the line segments into elliptical arcs and ellipses. To achieve high accuracy, a sorted merging strategy is proposed: the merging degrees of line segments/elliptical arcs are estimated, and line segments/elliptical arcs are merged in descending order of the merging degrees, which significantly improves the merging accuracy. During the merging process, multiple properties of ellipses are utilized to filter line segment/elliptical arc pairs, making the method very efficient. In addition, an ellipse-fitting method is proposed that restricts the maximum ratio of the semimajor axis and the semiminor axis, further improving the merging accuracy. Experimental results indicate that the proposed method is robust to outliers, noise, and partial occlusion and is fast enough for real-time applications. Gangyi Wang, Guanghui Ren, Zhilu Wu, Yaqin Zhao, and Lihui Jiang Copyright © 2014 Gangyi Wang et al. All rights reserved. New Similarity of Triangular Fuzzy Number and Its Application Thu, 20 Mar 2014 13:17:43 +0000 The similarity of triangular fuzzy numbers is an important metric for application of it. There exist several approaches to measure similarity of triangular fuzzy numbers. However, some of them are opt to be large. To make the similarity well distributed, a new method SIAM (Shape’s Indifferent Area and Midpoint) to measure triangular fuzzy number is put forward, which takes the shape’s indifferent area and midpoint of two triangular fuzzy numbers into consideration. Comparison with other similarity measurements shows the effectiveness of the proposed method. Then, it is applied to collaborative filtering recommendation to measure users’ similarity. A collaborative filtering case is used to illustrate users’ similarity based on cloud model and triangular fuzzy number; the result indicates that users’ similarity based on triangular fuzzy number can obtain better discrimination. Finally, a simulated collaborative filtering recommendation system is developed which uses cloud model and triangular fuzzy number to express users’ comprehensive evaluation on items, and result shows that the accuracy of collaborative filtering recommendation based on triangular fuzzy number is higher. Xixiang Zhang, Weimin Ma, and Liping Chen Copyright © 2014 Xixiang Zhang et al. All rights reserved. Usalpharma: A Cloud-Based Architecture to Support Quality Assurance Training Processes in Health Area Using Virtual Worlds Thu, 20 Mar 2014 09:46:04 +0000 This paper discusses how cloud-based architectures can extend and enhance the functionality of the training environments based on virtual worlds and how, from this cloud perspective, we can provide support to analysis of training processes in the area of health, specifically in the field of training processes in quality assurance for pharmaceutical laboratories, presenting a tool for data retrieval and analysis that allows facing the knowledge discovery in the happenings inside the virtual worlds. Francisco J. García-Peñalvo, Juan Cruz-Benito, Cristina Maderuelo, Jonás Samuel Pérez-Blanco, and Ana Martín-Suárez Copyright © 2014 Francisco J. García-Peñalvo et al. All rights reserved. An Adaptive Learning Rate for RBFNN Using Time-Domain Feedback Analysis Thu, 20 Mar 2014 08:50:33 +0000 Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. An intelligent adaptation rule is developed for the learning rate of RBFNN which gives faster convergence via an estimate of error energy while giving guarantee to the stability governed by the upper bounding via small gain theorem. Simulation results are presented to support our theoretical development. Syed Saad Azhar Ali, Muhammad Moinuddin, Kamran Raza, and Syed Hasan Adil Copyright © 2014 Syed Saad Azhar Ali et al. All rights reserved. Accelerating Content-Based Image Retrieval via GPU-Adaptive Index Structure Thu, 20 Mar 2014 08:25:12 +0000 A tremendous amount of work has been conducted in content-based image retrieval (CBIR) on designing effective index structure to accelerate the retrieval process. Most of them improve the retrieval efficiency via complex index structures, and few take into account the parallel implementation of them on underlying hardware, making the existing index structures suffer from low-degree of parallelism. In this paper, a novel graphics processing unit (GPU) adaptive index structure, termed as plane semantic ball (PSB), is proposed to simultaneously reduce the work of retrieval process and exploit the parallel acceleration of underlying hardware. In PSB, semantics are embedded into the generation of representative pivots and multiple balls are selected to cover more informative reference features. With PSB, the online retrieval of CBIR is factorized into independent components that are implemented on GPU efficiently. Comparative experiments with GPU-based brute force approach demonstrate that the proposed approach can achieve high speedup with little information loss. Furthermore, PSB is compared with the state-of-the-art approach, random ball cover (RBC), on two standard image datasets, Corel 10 K and GIST 1 M. Experimental results show that our approach achieves higher speedup than RBC on the same accuracy level. Lei Zhu Copyright © 2014 Lei Zhu. All rights reserved. Novel Back Propagation Optimization by Cuckoo Search Algorithm Thu, 20 Mar 2014 07:34:59 +0000 The traditional Back Propagation (BP) has some significant disadvantages, such as training too slowly, easiness to fall into local minima, and sensitivity of the initial weights and bias. In order to overcome these shortcomings, an improved BP network that is optimized by Cuckoo Search (CS), called CSBP, is proposed in this paper. In CSBP, CS is used to simultaneously optimize the initial weights and bias of BP network. Wine data is adopted to study the prediction performance of CSBP, and the proposed method is compared with the basic BP and the General Regression Neural Network (GRNN). Moreover, the parameter study of CSBP is conducted in order to make the CSBP implement in the best way. Jiao-hong Yi, Wei-hong Xu, and Yuan-tao Chen Copyright © 2014 Jiao-hong Yi et al. All rights reserved. Chaotic Multiquenching Annealing Applied to the Protein Folding Problem Thu, 20 Mar 2014 07:19:42 +0000 The Chaotic Multiquenching Annealing algorithm (CMQA) is proposed. CMQA is a new algorithm, which is applied to protein folding problem (PFP). This algorithm is divided into three phases: (i) multiquenching phase (MQP), (ii) annealing phase (AP), and (iii) dynamical equilibrium phase (DEP). MQP enforces several stages of quick quenching processes that include chaotic functions. The chaotic functions can increase the exploration potential of solutions space of PFP. AP phase implements a simulated annealing algorithm (SA) with an exponential cooling function. MQP and AP are delimited by different ranges of temperatures; MQP is applied for a range of temperatures which goes from extremely high values to very high values; AP searches for solutions in a range of temperatures from high values to extremely low values. DEP phase finds the equilibrium in a dynamic way by applying least squares method. CMQA is tested with several instances of PFP. Juan Frausto-Solis, Ernesto Liñan-García, Mishael Sánchez-Pérez, and Juan Paulo Sánchez-Hernández Copyright © 2014 Juan Frausto-Solis et al. All rights reserved. An Improved Artificial Bee Colony Algorithm Based on Balance-Evolution Strategy for Unmanned Combat Aerial Vehicle Path Planning Thu, 20 Mar 2014 06:57:01 +0000 Unmanned combat aerial vehicles (UCAVs) have been of great interest to military organizations throughout the world due to their outstanding capabilities to operate in dangerous or hazardous environments. UCAV path planning aims to obtain an optimal flight route with the threats and constraints in the combat field well considered. In this work, a novel artificial bee colony (ABC) algorithm improved by a balance-evolution strategy (BES) is applied in this optimization scheme. In this new algorithm, convergence information during the iteration is fully utilized to manipulate the exploration/exploitation accuracy and to pursue a balance between local exploitation and global exploration capabilities. Simulation results confirm that BE-ABC algorithm is more competent for the UCAV path planning scheme than the conventional ABC algorithm and two other state-of-the-art modified ABC algorithms. Bai Li, Li-gang Gong, and Wen-lun Yang Copyright © 2014 Bai Li et al. All rights reserved. Towards Application of One-Class Classification Methods to Medical Data Thu, 20 Mar 2014 06:39:59 +0000 In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present. Itziar Irigoien, Basilio Sierra, and Concepción Arenas Copyright © 2014 Itziar Irigoien et al. All rights reserved. The Expanded Invasive Weed Optimization Metaheuristic for Solving Continuous and Discrete Optimization Problems Wed, 19 Mar 2014 13:36:54 +0000 This paper introduces an expanded version of the Invasive Weed Optimization algorithm (exIWO) distinguished by the hybrid strategy of the search space exploration proposed by the authors. The algorithm is evaluated by solving three well-known optimization problems: minimization of numerical functions, feature selection, and the Mona Lisa TSP Challenge as one of the instances of the traveling salesman problem. The achieved results are compared with analogous outcomes produced by other optimization methods reported in the literature. Henryk Josiński, Daniel Kostrzewa, Agnieszka Michalczuk, and Adam Świtoński Copyright © 2014 Henryk Josiński et al. All rights reserved. Simple-Random-Sampling-Based Multiclass Text Classification Algorithm Wed, 19 Mar 2014 13:20:01 +0000 Multiclass text classification (MTC) is a challenging issue and the corresponding MTC algorithms can be used in many applications. The space-time overhead of the algorithms must be concerned about the era of big data. Through the investigation of the token frequency distribution in a Chinese web document collection, this paper reexamines the power law and proposes a simple-random-sampling-based MTC (SRSMTC) algorithm. Supported by a token level memory to store labeled documents, the SRSMTC algorithm uses a text retrieval approach to solve text classification problems. The experimental results on the TanCorp data set show that SRSMTC algorithm can achieve the state-of-the-art performance at greatly reduced space-time requirements. Wuying Liu, Lin Wang, and Mianzhu Yi Copyright © 2014 Wuying Liu et al. All rights reserved. Adaptive Random Testing with Combinatorial Input Domain Wed, 19 Mar 2014 13:00:54 +0000 Random testing (RT) is a fundamental testing technique to assess software reliability, by simply selecting test cases in a random manner from the whole input domain. As an enhancement of RT, adaptive random testing (ART) has better failure‐detection capability and has been widely applied in different scenarios, such as numerical programs, some object‐oriented programs, and mobile applications. However, not much work has been done on the effectiveness of ART for the programs with combinatorial input domain (i.e., the set of categorical data). To extend the ideas to the testing for combinatorial input domain, we have adopted different similarity measures that are widely used for categorical data in data mining and have proposed two similarity measures based on interaction coverage. Then, we propose a new version named ART‐CID as an extension of ART in combinatorial input domain, which selects an element from categorical data as the next test case such that it has the lowest similarity against already generated test cases. Experimental results show that ART‐CID generally performs better than RT, with respect to different evaluation metrics. Rubing Huang, Jinfu Chen, and Yansheng Lu Copyright © 2014 Rubing Huang et al. All rights reserved. Unsupervised Chunking Based on Graph Propagation from Bilingual Corpus Wed, 19 Mar 2014 09:18:10 +0000 This paper presents a novel approach for unsupervised shallow parsing model trained on the unannotated Chinese text of parallel Chinese-English corpus. In this approach, no information of the Chinese side is applied. The exploitation of graph-based label propagation for bilingual knowledge transfer, along with an application of using the projected labels as features in unsupervised model, contributes to a better performance. The experimental comparisons with the state-of-the-art algorithms show that the proposed approach is able to achieve impressive higher accuracy in terms of F-score. Ling Zhu, Derek F. Wong, and Lidia S. Chao Copyright © 2014 Ling Zhu et al. All rights reserved. Measurement and Analysis of P2P IPTV Program Resource Wed, 19 Mar 2014 08:55:20 +0000 With the rapid development of P2P technology, P2P IPTV applications have received more and more attention. And program resource distribution is very important to P2P IPTV applications. In order to collect IPTV program resources, a distributed multi-protocol crawler is proposed. And the crawler has collected more than 13 million pieces of information of IPTV programs from 2009 to 2012. In addition, the distribution of IPTV programs is independent and incompact, resulting in chaos of program names, which obstructs searching and organizing programs. Thus, we focus on characteristic analysis of program resources, including the distributions of length of program names, the entropy of the character types, and hierarchy depth of programs. These analyses reveal the disorderly naming conventions of P2P IPTV programs. The analysis results can help to purify and extract useful information from chaotic names for better retrieval and accelerate automatic sorting of program and establishment of IPTV repository. In order to represent popularity of programs and to predict user behavior and popularity of hot programs over a period, we also put forward an analytical model of hot programs. Wenxian Wang, Xingshu Chen, Haizhou Wang, Qi Zhang, and Cheng Wang Copyright © 2014 Wenxian Wang et al. All rights reserved. Research on Universal Combinatorial Coding Wed, 19 Mar 2014 08:49:27 +0000 The conception of universal combinatorial coding is proposed. Relations exist more or less in many coding methods. It means that a kind of universal coding method is objectively existent. It can be a bridge connecting many coding methods. Universal combinatorial coding is lossless and it is based on the combinatorics theory. The combinational and exhaustive property make it closely related with the existing code methods. Universal combinatorial coding does not depend on the probability statistic characteristic of information source, and it has the characteristics across three coding branches. It has analyzed the relationship between the universal combinatorial coding and the variety of coding method and has researched many applications technologies of this coding method. In addition, the efficiency of universal combinatorial coding is analyzed theoretically. The multicharacteristic and multiapplication of universal combinatorial coding are unique in the existing coding methods. Universal combinatorial coding has theoretical research and practical application value. Jun Lu, Zhuo Zhang, and Juan Mo Copyright © 2014 Jun Lu et al. All rights reserved. Context-Aware and Locality-Constrained Coding for Image Categorization Tue, 18 Mar 2014 11:09:20 +0000 Improving the coding strategy for BOF (Bag-of-Features) based feature design has drawn increasing attention in recent image categorization works. However, the ambiguity in coding procedure still impedes its further development. In this paper, we introduce a context-aware and locality-constrained Coding (CALC) approach with context information for describing objects in a discriminative way. It is generally achieved by learning a word-to-word cooccurrence prior to imposing context information over locality-constrained coding. Firstly, the local context of each category is evaluated by learning a word-to-word cooccurrence matrix representing the spatial distribution of local features in neighbor region. Then, the learned cooccurrence matrix is used for measuring the context distance between local features and code words. Finally, a coding strategy simultaneously considers locality in feature space and context space, while introducing the weight of feature is proposed. This novel coding strategy not only semantically preserves the information in coding, but also has the ability to alleviate the noise distortion of each class. Extensive experiments on several available datasets (Scene-15, Caltech101, and Caltech256) are conducted to validate the superiority of our algorithm by comparing it with baselines and recent published methods. Experimental results show that our method significantly improves the performance of baselines and achieves comparable and even better performance with the state of the arts. Wenhua Xiao, Bin Wang, Yu Liu, Weidong Bao, and Maojun Zhang Copyright © 2014 Wenhua Xiao et al. All rights reserved. Reflection Reduction on DDR3 High-Speed Bus by Improved PSO Tue, 18 Mar 2014 07:13:39 +0000 The signal integrity of the circuit, as one of the important design issues in high-speed digital system, is usually seriously affected by the signal reflection due to impedance mismatch in the DDR3 bus. In this paper, a novel optimization method is proposed to optimize impedance mismatch and reduce the signal refection. Specifically, by applying the via parasitic, an equivalent model of DDR3 high-speed signal transmission, which bases on the match between the on-die-termination (ODT) value of DDR3 and the characteristic impedance of the transmission line, is established. Additionally, an improved particle swarm optimization algorithm with adaptive perturbation is presented to solve the impedance mismatch problem (IPSO-IMp) based on the above model. The algorithm dynamically judges particles’ state and introduces perturbation strategy for local aggregation, from which the local optimum is avoided and the ability of optimization-searching is activated. IPSO-IMp achieves higher accuracy than the standard algorithm, and the speed increases nearly 33% as well. Finally, the simulation results verify that the solution obviously decreases the signal reflection, with the signal transmission quality increasing by 1.3 dB compared with the existing method. Huiyong Li, Hongxu Jiang, Bo Li, and Miyi Duan Copyright © 2014 Huiyong Li et al. All rights reserved. A Generalized Quantum-Inspired Decision Making Model for Intelligent Agent Mon, 17 Mar 2014 07:02:01 +0000 A novel decision making for intelligent agent using quantum-inspired approach is proposed. A formal, generalized solution to the problem is given. Mathematically, the proposed model is capable of modeling higher dimensional decision problems than previous researches. Four experiments are conducted, and both empirical experiments results and proposed model's experiment results are given for each experiment. Experiments showed that the results of proposed model agree with empirical results perfectly. The proposed model provides a new direction for researcher to resolve cognitive basis in designing intelligent agent. Yuhuang Hu and Chu Kiong Loo Copyright © 2014 Yuhuang Hu and Chu Kiong Loo. All rights reserved. Digital Image Forgery Detection Using JPEG Features and Local Noise Discrepancies Sun, 16 Mar 2014 17:10:22 +0000 Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image. Bo Liu, Chi-Man Pun, and Xiao-Chen Yuan Copyright © 2014 Bo Liu et al. All rights reserved. Multicompare Tests of the Performance of Different Metaheuristics in EEG Dipole Source Localization Sun, 16 Mar 2014 14:13:14 +0000 We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization’s performance in terms of metaheuristics’ operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics’ performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem. Diana Irazú Escalona-Vargas, Ivan Lopez-Arevalo, and David Gutiérrez Copyright © 2014 Diana Irazú Escalona-Vargas et al. All rights reserved. A Novel Key-Frame Extraction Approach for Both Video Summary and Video Index Sun, 16 Mar 2014 14:06:20 +0000 Existing key-frame extraction methods are basically video summary oriented; yet the index task of key-frames is ignored. This paper presents a novel key-frame extraction approach which can be available for both video summary and video index. First a dynamic distance separability algorithm is advanced to divide a shot into subshots based on semantic structure, and then appropriate key-frames are extracted in each subshot by SVD decomposition. Finally, three evaluation indicators are proposed to evaluate the performance of the new approach. Experimental results show that the proposed approach achieves good semantic structure for semantics-based video index and meanwhile produces video summary consistent with human perception. Shaoshuai Lei, Gang Xie, and Gaowei Yan Copyright © 2014 Shaoshuai Lei et al. All rights reserved. Interlayer Simplified Depth Coding for Quality Scalability on 3D High Efficiency Video Coding Sun, 16 Mar 2014 11:34:48 +0000 A quality scalable extension design is proposed for the upcoming 3D video on the emerging standard for High Efficiency Video Coding (HEVC). A novel interlayer simplified depth coding (SDC) prediction tool is added to reduce the amount of bits for depth maps representation by exploiting the correlation between coding layers. To further improve the coding performance, the coded prediction quadtree and texture data from corresponding SDC-coded blocks in the base layer can be used in interlayer simplified depth coding. In the proposed design, the multiloop decoder solution is also extended into the proposed scalable scenario for texture views and depth maps, and will be achieved by the interlayer texture prediction method. The experimental results indicate that the average Bjøntegaard Delta bitrate decrease of 54.4% can be gained in interlayer simplified depth coding prediction tool on multiloop decoder solution compared with simulcast. Consequently, significant rate savings confirm that the proposed method achieves better performance. Mengmeng Zhang, Hongyun Lu, and Huihui Bai Copyright © 2014 Mengmeng Zhang et al. All rights reserved. Fraction Reduction in Membrane Systems Sun, 16 Mar 2014 08:45:28 +0000 Fraction reduction is a basic computation for rational numbers. P system is a new computing model, while the current methods for fraction reductions are not available in these systems. In this paper, we propose a method of fraction reduction and discuss how to carry it out in cell-like P systems with the membrane structure and the rules with priority designed. During the application of fraction reduction rules, synchronization is guaranteed by arranging some special objects in these rules. Our work contributes to performing the rational computation in P systems since the rational operands can be given in the form of fraction. Ping Guo, Hong Zhang, Haizhu Chen, and Ran Liu Copyright © 2014 Ping Guo et al. All rights reserved. Genetic Algorithm and Graph Theory Based Matrix Factorization Method for Online Friend Recommendation Sun, 16 Mar 2014 08:29:45 +0000 Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy. Qu Li, Min Yao, Jianhua Yang, and Ning Xu Copyright © 2014 Qu Li et al. All rights reserved. Dynamic Cooperative Clustering Based Power Assignment: Network Capacity and Lifetime Efficient Topology Control in Cooperative Ad Hoc Networks Sun, 16 Mar 2014 08:27:32 +0000 Cooperative communication (CC) is used in topology control as it can reduce the transmission power and expand the transmission range. However, all previous research on topology control under the CC model focused on maintaining network connectivity and minimizing the total energy consumption, which would lead to low network capacity, transmission interruption, or even network paralysis. Meanwhile, without considering the balance of energy consumption in the network, it would reduce the network lifetime and greatly affect the network performance. This paper tries to solve the above problems existing in the research on topology control under the CC model by proposing a power assignment (DCCPA) algorithm based on dynamic cooperative clustering in cooperative ad hoc networks. The new algorithm clusters the network to maximize network capacity and makes the clusters communicate with each other by CC. To reduce the number of redundant links between clusters, we design a static clustering method by using Kruskal algorithm. To maximize the network lifetime, we also propose a cluster head rotating method which can reach a good tradeoff between residual energy and distance for the cluster head reselection. Experimental results show that DCCPA can improve 80% network capacity with Cooperative Bridges algorithm; meanwhile, it can improve 20% network lifetime. Xiao-Hong Li, Ling Xiao, and Dong Wang Copyright © 2014 Xiao-Hong Li et al. All rights reserved. Efficient Parallel Video Processing Techniques on GPU: From Framework to Implementation Sun, 16 Mar 2014 06:58:41 +0000 Through reorganizing the execution order and optimizing the data structure, we proposed an efficient parallel framework for H.264/AVC encoder based on massively parallel architecture. We implemented the proposed framework by CUDA on NVIDIA’s GPU. Not only the compute intensive components of the H.264 encoder are parallelized but also the control intensive components are realized effectively, such as CAVLC and deblocking filter. In addition, we proposed serial optimization methods, including the multiresolution multiwindow for motion estimation, multilevel parallel strategy to enhance the parallelism of intracoding as much as possible, component-based parallel CAVLC, and direction-priority deblocking filter. More than 96% of workload of H.264 encoder is offloaded to GPU. Experimental results show that the parallel implementation outperforms the serial program by 20 times of speedup ratio and satisfies the requirement of the real-time HD encoding of 30 fps. The loss of PSNR is from 0.14 dB to 0.77 dB, when keeping the same bitrate. Through the analysis to the kernels, we found that speedup ratios of the compute intensive algorithms are proportional with the computation power of the GPU. However, the performance of the control intensive parts (CAVLC) is much related to the memory bandwidth, which gives an insight for new architecture design. Huayou Su, Mei Wen, Nan Wu, Ju Ren, and Chunyuan Zhang Copyright © 2014 Huayou Su et al. All rights reserved. Local Convexity-Preserving Rational Cubic Spline for Convex Data Thu, 13 Mar 2014 14:30:37 +0000 We present the smooth and visually pleasant display of 2D data when it is convex, which is contribution towards the improvements over existing methods. This improvement can be used to get the more accurate results. An attempt has been made in order to develop the local convexity-preserving interpolant for convex data using rational cubic spline. It involves three families of shape parameters in its representation. Data dependent sufficient constraints are imposed on single shape parameter to conserve the inherited shape feature of data. Remaining two of these shape parameters are used for the modification of convex curve to get a visually pleasing curve according to industrial demand. The scheme is tested through several numerical examples, showing that the scheme is local, computationally economical, and visually pleasing. Muhammad Abbas, Ahmad Abd Majid, and Jamaludin Md. Ali Copyright © 2014 Muhammad Abbas et al. All rights reserved. Exposing Image Forgery by Detecting Consistency of Shadow Thu, 13 Mar 2014 12:41:01 +0000 We propose two tampered image detection methods based on consistency of shadow. The first method is based on texture consistency of shadow for the first kind of splicing image, in which the shadow as well as main body is copied and pasted from another image. The suspicious region including shadow and nonshadow is first selected. Then texture features of the shadow region and the nonshadow region are extracted. Last, correlation function is used to measure the similarity of the two texture features. By comparing the similarity, we can judge whether the image is tampered. Due to the failure in detecting the second kind of splicing image, in which main body, its shadow, and surrounding regions are copied and pasted from another image, another method based on strength of light source of shadows is proposed. The two suspicious shadow regions are first selected. Then an efficient method is used to estimate the strength of light source of shadow. Last, the similarity of strength of light source of two shadows is measured by correlation function. By combining the two methods, we can detect forged image with shadows. Experimental results demonstrate that the proposed methods are effective despite using simplified model compared with the existing methods. Yongzhen Ke, Fan Qin, Weidong Min, and Guiling Zhang Copyright © 2014 Yongzhen Ke et al. All rights reserved. Reattack of a Certificateless Aggregate Signature Scheme with Constant Pairing Computations Thu, 13 Mar 2014 11:34:01 +0000 A new attack against a novel certificateless aggregate signature scheme with constant pairing computations is presented. To enhance security, a new certificateless signature scheme is proposed first. Then a new certificateless aggregate signature scheme with constant pairing computations based on the new certificateless signature scheme is presented. Security analysis shows that the proposed certificateless aggregate signature scheme is provably secured in the random oracle. Hang Tu, Debiao He, and Baojun Huang Copyright © 2014 Hang Tu et al. All rights reserved. Self-Supervised Chinese Ontology Learning from Online Encyclopedias Thu, 13 Mar 2014 09:05:49 +0000 Constructing ontology manually is a time-consuming, error-prone, and tedious task. We present SSCO, a self-supervised learning based chinese ontology, which contains about 255 thousand concepts, 5 million entities, and 40 million facts. We explore the three largest online Chinese encyclopedias for ontology learning and describe how to transfer the structured knowledge in encyclopedias, including article titles, category labels, redirection pages, taxonomy systems, and InfoBox modules, into ontological form. In order to avoid the errors in encyclopedias and enrich the learnt ontology, we also apply some machine learning based methods. First, we proof that the self-supervised machine learning method is practicable in Chinese relation extraction (at least for synonymy and hyponymy) statistically and experimentally and train some self-supervised models (SVMs and CRFs) for synonymy extraction, concept-subconcept relation extraction, and concept-instance relation extraction; the advantages of our methods are that all training examples are automatically generated from the structural information of encyclopedias and a few general heuristic rules. Finally, we evaluate SSCO in two aspects, scale and precision; manual evaluation results show that the ontology has excellent precision, and high coverage is concluded by comparing SSCO with other famous ontologies and knowledge bases; the experiment results also indicate that the self-supervised models obviously enrich SSCO. Fanghuai Hu, Zhiqing Shao, and Tong Ruan Copyright © 2014 Fanghuai Hu et al. All rights reserved. Prediction Based Proactive Thermal Virtual Machine Scheduling in Green Clouds Tue, 11 Mar 2014 13:07:33 +0000 Cloud computing has rapidly emerged as a widely accepted computing paradigm, but the research on Cloud computing is still at an early stage. Cloud computing provides many advanced features but it still has some shortcomings such as relatively high operating cost and environmental hazards like increasing carbon footprints. These hazards can be reduced up to some extent by efficient scheduling of Cloud resources. Working temperature on which a machine is currently running can be taken as a criterion for Virtual Machine (VM) scheduling. This paper proposes a new proactive technique that considers current and maximum threshold temperature of Server Machines (SMs) before making scheduling decisions with the help of a temperature predictor, so that maximum temperature is never reached. Different workload scenarios have been taken into consideration. The results obtained show that the proposed system is better than existing systems of VM scheduling, which does not consider current temperature of nodes before making scheduling decisions. Thus, a reduction in need of cooling systems for a Cloud environment has been obtained and validated. Supriya Kinger, Rajesh Kumar, and Anju Sharma Copyright © 2014 Supriya Kinger et al. All rights reserved. A Splay Tree-Based Approach for Efficient Resource Location in P2P Networks Tue, 11 Mar 2014 08:42:45 +0000 Resource location in structured P2P system has a critical influence on the system performance. Existing analytical studies of Chord protocol have shown some potential improvements in performance. In this paper a splay tree-based new Chord structure called SChord is proposed to improve the efficiency of locating resources. We consider a novel implementation of the Chord finger table (routing table) based on the splay tree. This approach extends the Chord finger table with additional routing entries. Adaptive routing algorithm is proposed for implementation, and it can be shown that hop count is significantly minimized without introducing any other protocol overheads. We analyze the hop count of the adaptive routing algorithm, as compared to Chord variants, and demonstrate sharp upper and lower bounds for both worst-case and average case settings. In addition, we theoretically analyze the hop reducing in SChord and derive the fact that SChord can significantly reduce the routing hops as compared to Chord. Several simulations are presented to evaluate the performance of the algorithm and support our analytical findings. The simulation results show the efficiency of SChord. Wei Zhou, Zilong Tan, Shaowen Yao, and Shipu Wang Copyright © 2014 Wei Zhou et al. All rights reserved. Green Channel Guiding Denoising on Bayer Image Tue, 11 Mar 2014 00:00:00 +0000 Denoising is an indispensable function for digital cameras. In respect that noise is diffused during the demosaicking, the denoising ought to work directly on bayer data. The difficulty of denoising on bayer image is the interlaced mosaic pattern of red, green, and blue. Guided filter is a novel time efficient explicit filter kernel which can incorporate additional information from the guidance image, but it is still not applied for bayer image. In this work, we observe that the green channel of bayer mode is higher in both sampling rate and Signal-to-Noise Ratio (SNR) than the red and blue ones. Therefore the green channel can be used to guide denoising. This kind of guidance integrates the different color channels together. Experiments on both actual and simulated bayer images indicate that green channel acts well as the guidance signal, and the proposed method is competitive with other popular filter kernel denoising methods. Xin Tan, Shiming Lai, Yu Liu, and Maojun Zhang Copyright © 2014 Xin Tan et al. All rights reserved. A Cloud-Based X73 Ubiquitous Mobile Healthcare System: Design and Implementation Mon, 10 Mar 2014 13:02:41 +0000 Based on the user-centric paradigm for next generation networks, this paper describes a ubiquitous mobile healthcare (uHealth) system based on the ISO/IEEE 11073 personal health data (PHD) standards (X73) and cloud computing techniques. A number of design issues associated with the system implementation are outlined. The system includes a middleware on the user side, providing a plug-and-play environment for heterogeneous wireless sensors and mobile terminals utilizing different communication protocols and a distributed “big data” processing subsystem in the cloud. The design and implementation of this system are envisaged as an efficient solution for the next generation of uHealth systems. Zhanlin Ji, Ivan Ganchev, Máirtín O’Droma, Xin Zhang, and Xueji Zhang Copyright © 2014 Zhanlin Ji et al. All rights reserved. Processing Uncertain RFID Data in Traceability Supply Chains Mon, 10 Mar 2014 00:00:00 +0000 Radio Frequency Identification (RFID) is widely used to track and trace objects in traceability supply chains. However, massive uncertain data produced by RFID readers are not effective and efficient to be used in RFID application systems. Following the analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. We adjust different smoothing windows according to different rates of uncertain data, employ different strategies to process uncertain readings, and distinguish ghost, missing, and incomplete data according to their apparent positions. We propose a comprehensive data model which is suitable for different application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequence, the position, and the time intervals. The scheme is suitable for cyclic or long paths. Moreover, we further propose a processing algorithm for group and independent objects. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries. Dong Xie, Jie Xiao, Guangjun Guo, and Tong Jiang Copyright © 2014 Dong Xie et al. All rights reserved. Multiview Discriminative Geometry Preserving Projection for Image Classification Sun, 09 Mar 2014 07:38:16 +0000 In many image classification applications, it is common to extract multiple visual features from different views to describe an image. Since different visual features have their own specific statistical properties and discriminative powers for image classification, the conventional solution for multiple view data is to concatenate these feature vectors as a new feature vector. However, this simple concatenation strategy not only ignores the complementary nature of different views, but also ends up with “curse of dimensionality.” To address this problem, we propose a novel multiview subspace learning algorithm in this paper, named multiview discriminative geometry preserving projection (MDGPP) for feature extraction and classification. MDGPP can not only preserve the intraclass geometry and interclass discrimination information under a single view, but also explore the complementary property of different views to obtain a low-dimensional optimal consensus embedding by using an alternating-optimization-based iterative algorithm. Experimental results on face recognition and facial expression recognition demonstrate the effectiveness of the proposed algorithm. Ziqiang Wang, Xia Sun, Lijun Sun, and Yuchun Huang Copyright © 2014 Ziqiang Wang et al. All rights reserved. A Novel Macroblock Level Rate Control Method for Stereo Video Coding Tue, 04 Mar 2014 15:33:57 +0000 To compress stereo video effectively, this paper proposes a novel macroblock (MB) level rate control method based on binocular perception. A binocular just-notification difference (BJND) model based on the parallax matching is first used to describe binocular perception. Then, the proposed rate control method is performed in stereo video coding with four levels, namely, view level, group-of-pictures (GOP) level, frame level, and MB level. In the view level, different proportions of bitrates are allocated for the left and right views of stereo video according to the prestatistical rate allocation proportion. In the GOP level, the total number of bitrates allocated to each GOP is computed and the initial quantization parameter of each GOP is set. In the frame level, the target bits allocated to each frame are computed. In the MB level, visual perception factor, which is measured by the BJND value of MB, is used to adjust the MB level bit allocation, so that the rate control results in line with the human visual characteristics. Experimental results show that the proposed method can control the bitrate more accurately and get better subjective quality of stereo video, compared with other methods. Gaofeng Zhu, Mei Yu, Gangyi Jiang, Zongju Peng, Feng Shao, Fen Chen, and Yo-Sung Ho Copyright © 2014 Gaofeng Zhu et al. All rights reserved. A Hybrid Monkey Search Algorithm for Clustering Analysis Tue, 04 Mar 2014 15:18:40 +0000 Clustering is a popular data analysis and data mining technique. The -means clustering algorithm is one of the most commonly used methods. However, it highly depends on the initial solution and is easy to fall into local optimum solution. In view of the disadvantages of the -means method, this paper proposed a hybrid monkey algorithm based on search operator of artificial bee colony algorithm for clustering analysis and experiment on synthetic and real life datasets to show that the algorithm has a good performance than that of the basic monkey algorithm for clustering analysis. Xin Chen, Yongquan Zhou, and Qifang Luo Copyright © 2014 Xin Chen et al. All rights reserved. SAMuS: Service-Oriented Architecture for Multisensor Surveillance in Smart Homes Tue, 04 Mar 2014 14:08:39 +0000 The design of a service-oriented architecture for multisensor surveillance in smart homes is presented as an integrated solution enabling automatic deployment, dynamic selection, and composition of sensors. Sensors are implemented as Web-connected devices, with a uniform Web API. RESTdesc is used to describe the sensors and a novel solution is presented to automatically compose Web APIs that can be applied with existing Semantic Web reasoners. We evaluated the solution by building a smart Kinect sensor that is able to dynamically switch between IR and RGB and optimizing person detection by incorporating feedback from pressure sensors, as such demonstrating the collaboration among sensors to enhance detection of complex events. The performance results show that the platform scales for many Web APIs as composition time remains limited to a few hundred milliseconds in almost all cases. Sofie Van Hoecke, Ruben Verborgh, Davy Van Deursen, and Rik Van de Walle Copyright © 2014 Sofie Van Hoecke et al. All rights reserved. Cooperative Quantum-Behaved Particle Swarm Optimization with Dynamic Varying Search Areas and Lévy Flight Disturbance Mon, 03 Mar 2014 13:45:24 +0000 This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles’ activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem. Desheng Li Copyright © 2014 Desheng Li. All rights reserved. A GA-Based Approach to Hide Sensitive High Utility Itemsets Mon, 03 Mar 2014 11:14:08 +0000 A GA-based privacy preserving utility mining method is proposed to find appropriate transactions to be inserted into the database for hiding sensitive high utility itemsets. It maintains the low information loss while providing information to the data demanders and protects the high-risk information in the database. A flexible evaluation function with three factors is designed in the proposed approach to evaluate whether the processed transactions are required to be inserted. Three different weights are, respectively, assigned to the three factors according to users. Moreover, the downward closure property and the prelarge concept are adopted in the proposed approach to reduce the cost of rescanning database, thus speeding up the evaluation process of chromosomes. Chun-Wei Lin, Tzung-Pei Hong, Jia-Wei Wong, Guo-Cheng Lan, and Wen-Yang Lin Copyright © 2014 Chun-Wei Lin et al. All rights reserved. A Master-Slave Surveillance System to Acquire Panoramic and Multiscale Videos Mon, 03 Mar 2014 07:45:52 +0000 This paper describes a master-slave visual surveillance system that uses stationary-dynamic camera assemblies to achieve wide field of view and selective focus of interest. In this system, the fish-eye panoramic camera is capable of monitoring a large area, and the PTZ dome camera has high mobility and zoom ability. In order to achieve the precise interaction, preprocessing spatial calibration between these two cameras is required. This paper introduces a novel calibration approach to automatically calculate a transformation matrix model between two coordinate systems by matching feature points. In addition, a distortion correction method based on Midpoint Circle Algorithm is proposed to handle obvious horizontal distortion in the captured panoramic image. Experimental results using realistic scenes have demonstrated the efficiency and applicability of the system with real-time surveillance. Yu Liu, Shiming Lai, Chenglin Zuo, Hao Shi, and Maojun Zhang Copyright © 2014 Yu Liu et al. All rights reserved. Reusable Component Model Development Approach for Parallel and Distributed Simulation Mon, 03 Mar 2014 07:27:09 +0000 Model reuse is a key issue to be resolved in parallel and distributed simulation at present. However, component models built by different domain experts usually have diversiform interfaces, couple tightly, and bind with simulation platforms closely. As a result, they are difficult to be reused across different simulation platforms and applications. To address the problem, this paper first proposed a reusable component model framework. Based on this framework, then our reusable model development approach is elaborated, which contains two phases: (1) domain experts create simulation computational modules observing three principles to achieve their independence; (2) model developer encapsulates these simulation computational modules with six standard service interfaces to improve their reusability. The case study of a radar model indicates that the model developed using our approach has good reusability and it is easy to be used in different simulation platforms and applications. Feng Zhu, Yiping Yao, Huilong Chen, and Feng Yao Copyright © 2014 Feng Zhu et al. All rights reserved. Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU Sun, 02 Mar 2014 13:21:35 +0000 The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures. Jinwei Wang, Xirong Ma, Yuanping Zhu, and Jizhou Sun Copyright © 2014 Jinwei Wang et al. All rights reserved. An Expert Fitness Diagnosis System Based on Elastic Cloud Computing Sun, 02 Mar 2014 06:58:27 +0000 This paper presents an expert diagnosis system based on cloud computing. It classifies a user’s fitness level based on supervised machine learning techniques. This system is able to learn and make customized diagnoses according to the user’s physiological data, such as age, gender, and body mass index (BMI). In addition, an elastic algorithm based on Poisson distribution is presented to allocate computation resources dynamically. It predicts the required resources in the future according to the exponential moving average of past observations. The experimental results show that Naïve Bayes is the best classifier with the highest accuracy (90.8%) and that the elastic algorithm is able to capture tightly the trend of requests generated from the Internet and thus assign corresponding computation resources to ensure the quality of service. Kevin C. Tseng and Chia-Chuan Wu Copyright © 2014 Kevin C. Tseng and Chia-Chuan Wu. All rights reserved. Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks Sun, 02 Mar 2014 00:00:00 +0000 Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. Jingjing Ma, Jie Liu, Wenping Ma, Maoguo Gong, and Licheng Jiao Copyright © 2014 Jingjing Ma et al. All rights reserved. Body Fat Percentage Prediction Using Intelligent Hybrid Approaches Sun, 02 Mar 2014 00:00:00 +0000 Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone’s health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. Yuehjen E. Shao Copyright © 2014 Yuehjen E. Shao. All rights reserved. Multistep-Ahead Air Passengers Traffic Prediction with Hybrid ARIMA-SVMs Models Thu, 27 Feb 2014 12:56:50 +0000 The hybrid ARIMA-SVMs prediction models have been established recently, which take advantage of the unique strength of ARIMA and SVMs models in linear and nonlinear modeling, respectively. Built upon this hybrid ARIMA-SVMs models alike, this study goes further to extend them into the case of multistep-ahead prediction for air passengers traffic with the two most commonly used multistep-ahead prediction strategies, that is, iterated strategy and direct strategy. Additionally, the effectiveness of data preprocessing approaches, such as deseasonalization and detrending, is investigated and proofed along with the two strategies. Real data sets including four selected airlines’ monthly series were collected to justify the effectiveness of the proposed approach. Empirical results demonstrate that the direct strategy performs better than iterative one in long term prediction case while iterative one performs better in the case of short term prediction. Furthermore, both deseasonalization and detrending can significantly improve the prediction accuracy for both strategies, indicating the necessity of data preprocessing. As such, this study contributes as a full reference to the planners from air transportation industries on how to tackle multistep-ahead prediction tasks in the implementation of either prediction strategy. Wei Ming, Yukun Bao, Zhongyi Hu, and Tao Xiong Copyright © 2014 Wei Ming et al. All rights reserved. Quantum Neural Network Based Machine Translator for Hindi to English Thu, 27 Feb 2014 07:53:42 +0000 This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation. Ravi Narayan, V. P. Singh, and S. Chakraverty Copyright © 2014 Ravi Narayan et al. All rights reserved. Stego on FPGA: An IWT Approach Wed, 26 Feb 2014 12:58:14 +0000 A reconfigurable hardware architecture for the implementation of integer wavelet transform (IWT) based adaptive random image steganography algorithm is proposed. The Haar-IWT was used to separate the subbands namely, LL, LH, HL, and HH, from pixel blocks and the encrypted secret data is hidden in the LH, HL, and HH blocks using Moore and Hilbert space filling curve (SFC) scan patterns. Either Moore or Hilbert SFC was chosen for hiding the encrypted data in LH, HL, and HH coefficients, whichever produces the lowest mean square error (MSE) and the highest peak signal-to-noise ratio (PSNR). The fixated random walk’s verdict of all blocks is registered which is nothing but the furtive key. Our system took 1.6 µs for embedding the data in coefficient blocks and consumed 34% of the logic elements, 22% of the dedicated logic register, and 2% of the embedded multiplier on Cyclone II field programmable gate array (FPGA). Balakrishnan Ramalingam, Rengarajan Amirtharajan, and John Bosco Balaguru Rayappan Copyright © 2014 Balakrishnan Ramalingam et al. All rights reserved. A Tangible Programming Tool for Children to Cultivate Computational Thinking Tue, 25 Feb 2014 13:32:56 +0000 Game and creation are activities which have good potential for computational thinking skills. In this paper we present T-Maze, an economical tangible programming tool for children aged 5–9 to build computer programs in maze games by placing wooden blocks. Through the use of computer vision technology, T-Maze provides a live programming interface with real-time graphical and voice feedback. We conducted a user study with 7 children using T-Maze to play two levels of maze-escape games and create their own mazes. The results show that T-Maze is not only easy to use, but also has the potential to help children cultivate computational thinking like abstraction, problem decomposition, and creativity. Danli Wang, Tingting Wang, and Zhen Liu Copyright © 2014 Danli Wang et al. All rights reserved. Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems Tue, 25 Feb 2014 12:27:50 +0000 A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants. Martins Akugbe Arasomwan and Aderemi Oluyinka Adewumi Copyright © 2014 Martins Akugbe Arasomwan and Aderemi Oluyinka Adewumi. All rights reserved. A Layered Searchable Encryption Scheme with Functional Components Independent of Encryption Methods Tue, 25 Feb 2014 09:41:29 +0000 Searchable encryption technique enables the users to securely store and search their documents over the remote semitrusted server, which is especially suitable for protecting sensitive data in the cloud. However, various settings (based on symmetric or asymmetric encryption) and functionalities (ranked keyword query, range query, phrase query, etc.) are often realized by different methods with different searchable structures that are generally not compatible with each other, which limits the scope of application and hinders the functional extensions. We prove that asymmetric searchable structure could be converted to symmetric structure, and functions could be modeled separately apart from the core searchable structure. Based on this observation, we propose a layered searchable encryption (LSE) scheme, which provides compatibility, flexibility, and security for various settings and functionalities. In this scheme, the outputs of the core searchable component based on either symmetric or asymmetric setting are converted to some uniform mappings, which are then transmitted to loosely coupled functional components to further filter the results. In such a way, all functional components could directly support both symmetric and asymmetric settings. Based on LSE, we propose two representative and novel constructions for ranked keyword query (previously only available in symmetric scheme) and range query (previously only available in asymmetric scheme). Guangchun Luo, Ningduo Peng, Ke Qin, and Aiguo Chen Copyright © 2014 Guangchun Luo et al. All rights reserved. -Labeling of the Strong Product of Paths and Cycles Mon, 24 Feb 2014 16:44:15 +0000 An -labeling of a graph is a function from the vertex set to the set of nonnegative integers such that the labels on adjacent vertices differ by at least two and the labels on vertices at distance two differ by at least one. The span of is the difference between the largest and the smallest numbers in . The -number of , denoted by , is the minimum span over all -labelings of . We consider the -number of and for the -number of . We determine -numbers of graphs of interest with the exception of a finite number of graphs and we improve the bounds on the -number of , and . Zehui Shao and Aleksander Vesel Copyright © 2014 Zehui Shao and Aleksander Vesel. All rights reserved. Behavior Identification Based on Geotagged Photo Data Set Mon, 24 Feb 2014 12:00:47 +0000 The popularity of mobile devices has produced a set of image data with geographic information, time information, and text description information, which is called geotagged photo data set. The division of this kind of data by its behavior and the location not only can identify the user’s important location and daily behavior, but also helps users to sort the huge image data. This paper proposes a method to build an index based on multiple classification result, which can divide the data set multiple times and distribute labels to the data to build index according to the estimated probability of classification results in order to accomplish the identification of users’ important location and daily behaviors. This paper collects 1400 discrete sets of data as experimental data to verify the method proposed in this paper. The result of the experiment shows that the index and actual tagging results have a high inosculation. Guo-qi Liu, Yi-jia Zhang, Ying-mao Fu, and Ying Liu Copyright © 2014 Guo-qi Liu et al. All rights reserved. Improved Stability Criteria of Static Recurrent Neural Networks with a Time-Varying Delay Mon, 24 Feb 2014 11:21:45 +0000 This paper investigates the stability of static recurrent neural networks (SRNNs) with a time-varying delay. Based on the complete delay-decomposing approach and quadratic separation framework, a novel Lyapunov-Krasovskii functional is constructed. By employing a reciprocally convex technique to consider the relationship between the time-varying delay and its varying interval, some improved delay-dependent stability conditions are presented in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the merits and the effectiveness of the proposed methods. Lei Ding, Hong-Bing Zeng, Wei Wang, and Fei Yu Copyright © 2014 Lei Ding et al. All rights reserved. Minutiae Matching with Privacy Protection Based on the Combination of Garbled Circuit and Homomorphic Encryption Mon, 24 Feb 2014 00:00:00 +0000 Biometrics plays an important role in authentication applications since they are strongly linked to holders. With an increasing growth of e-commerce and e-government, one can expect that biometric-based authentication systems are possibly deployed over the open networks in the near future. However, due to its openness, the Internet poses a great challenge to the security and privacy of biometric authentication. Biometric data cannot be revoked, so it is of paramount importance that biometric data should be handled in a secure way. In this paper we present a scheme achieving privacy-preserving fingerprint authentication between two parties, in which fingerprint minutiae matching algorithm is completed in the encrypted domain. To improve the efficiency, we exploit homomorphic encryption as well as garbled circuits to design the protocol. Our goal is to provide protection for the security of template in storage and data privacy of two parties in transaction. The experimental results show that the proposed authentication protocol runs efficiently. Therefore, the protocol can run over open networks and help to alleviate the concerns on security and privacy of biometric applications over the open networks. Mengxing Li, Quan Feng, Jian Zhao, Mei Yang, Lijun Kang, and Lili Wu Copyright © 2014 Mengxing Li et al. All rights reserved. A Prefiltered Cuckoo Search Algorithm with Geometric Operators for Solving Sudoku Problems Sun, 23 Feb 2014 17:15:34 +0000 The Sudoku is a famous logic-placement game, originally popularized in Japan and today widely employed as pastime and as testbed for search algorithms. The classic Sudoku consists in filling a grid, divided into nine regions, so that each column, row, and region contains different digits from 1 to 9. This game is known to be NP-complete, with existing various complete and incomplete search algorithms able to solve different instances of it. In this paper, we present a new cuckoo search algorithm for solving Sudoku puzzles combining prefiltering phases and geometric operations. The geometric operators allow one to correctly move toward promising regions of the combinatorial space, while the prefiltering phases are able to previously delete from domains the values that do not conduct to any feasible solution. This integration leads to a more efficient domain filtering and as a consequence to a faster solving process. We illustrate encouraging experimental results where our approach noticeably competes with the best approximate methods reported in the literature. Ricardo Soto, Broderick Crawford, Cristian Galleguillos, Eric Monfroy, and Fernando Paredes Copyright © 2014 Ricardo Soto et al. All rights reserved. Chaotic Image Encryption Based on Running-Key Related to Plaintext Sun, 23 Feb 2014 13:46:37 +0000 In the field of chaotic image encryption, the algorithm based on correlating key with plaintext has become a new developing direction. However, for this kind of algorithm, some shortcomings in resistance to reconstruction attack, efficient utilization of chaotic resource, and reducing dynamical degradation of digital chaos are found. In order to solve these problems and further enhance the security of encryption algorithm, based on disturbance and feedback mechanism, we present a new image encryption scheme. In the running-key generation stage, by successively disturbing chaotic stream with cipher-text, the relation of running-key to plaintext is established, reconstruction attack is avoided, effective use of chaotic resource is guaranteed, and dynamical degradation of digital chaos is minimized. In the image encryption stage, by introducing random-feedback mechanism, the difficulty of breaking this scheme is increased. Comparing with the-state-of-the-art algorithms, our scheme exhibits good properties such as large key space, long key period, and extreme sensitivity to the initial key and plaintext. Therefore, it can resist brute-force, reconstruction attack, and differential attack. Cao Guanghui, Hu Kai, Zhang Yizhi, Zhou Jun, and Zhang Xing Copyright © 2014 Cao Guanghui et al. All rights reserved. Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models Sun, 23 Feb 2014 12:56:06 +0000 The similarity between objects is the core research area of data mining. In order to reduce the interference of the uncertainty of nature language, a similarity measurement between normal cloud models is adopted to text classification research. On this basis, a novel text classifier based on cloud concept jumping up (CCJU-TC) is proposed. It can efficiently accomplish conversion between qualitative concept and quantitative data. Through the conversion from text set to text information table based on VSM model, the text qualitative concept, which is extraction from the same category, is jumping up as a whole category concept. According to the cloud similarity between the test text and each category concept, the test text is assigned to the most similar category. By the comparison among different text classifiers in different feature selection set, it fully proves that not only does CCJU-TC have a strong ability to adapt to the different text features, but also the classification performance is also better than the traditional classifiers. Jin Dai and Xin Liu Copyright © 2014 Jin Dai and Xin Liu. All rights reserved. Evaluation about the Performance of E-Government Based on Interval-Valued Intuitionistic Fuzzy Set Sun, 23 Feb 2014 12:48:40 +0000 The evaluation is an important approach to promote the development of the E-Government. Since the rapid development of E-Government in the world, the E-Government performance evaluation has become a hot issue in the academia. In this paper, we develop a new evaluation method for the development of the E-Government based on the interval-valued intuitionistic fuzzy set which is a powerful technique in expressing the uncertainty of the real situation. First, we extend the geometric Heronian mean (GHM) operator to interval-valued intuitionistic fuzzy environment and proposed the interval-valued intuitionistic fuzzy GHM (IIFGHM) operator. Then, we investigate the relationships between the IIFGHM operator and some existing ones, such as generalized interval-valued intuitionistic fuzzy HM (GIIFHM) and interval-valued intuitionistic fuzzy weighted Bonferoni mean operator. Furthermore, we validate the effectiveness of the proposed method using a real case about the E-Government evaluation in Hangzhou City, China. Shuai Zhang, Dejian Yu, Yan Wang, and Wenyu Zhang Copyright © 2014 Shuai Zhang et al. All rights reserved. Measuring Semantic Relatedness between Flickr Images: From a Social Tag Based View Sun, 23 Feb 2014 10:01:24 +0000 Relatedness measurement between multimedia such as images and videos plays an important role in computer vision, which is a base for many multimedia related applications including clustering, searching, recommendation, and annotation. Recently, with the explosion of social media, users can upload media data and annotate content with descriptive tags. In this paper, we aim at measuring the semantic relatedness of Flickr images. Firstly, four information theory based functions are used to measure the semantic relatedness of tags. Secondly, the integration of tags pair based on bipartite graph is proposed to remove the noise and redundancy. Thirdly, the order information of tags is added to measure the semantic relatedness, which emphasizes the tags with high positions. The data sets including 1000 images from Flickr are used to evaluate the proposed method. Two data mining tasks including clustering and searching are performed by the proposed method, which shows the effectiveness and robustness of the proposed method. Moreover, some applications such as searching and faceted exploration are introduced using the proposed method, which shows that the proposed method has broad prospects on web based tasks. Zheng Xu, Xiangfeng Luo, Yunhuai Liu, Lin Mei, and Chuanping Hu Copyright © 2014 Zheng Xu et al. All rights reserved. Meteorological Data Analysis Using MapReduce Sun, 23 Feb 2014 07:54:15 +0000 In the atmospheric science, the scale of meteorological data is massive and growing rapidly. K-means is a fast and available cluster algorithm which has been used in many fields. However, for the large-scale meteorological data, the traditional K-means algorithm is not capable enough to satisfy the actual application needs efficiently. This paper proposes an improved MK-means algorithm (MK-means) based on MapReduce according to characteristics of large meteorological datasets. The experimental results show that MK-means has more computing ability and scalability. Wei Fang, V. S. Sheng, XueZhi Wen, and Wubin Pan Copyright © 2014 Wei Fang et al. All rights reserved. A Preliminary Investigation of User Perception and Behavioral Intention for Different Review Types: Customers and Designers Perspective Sun, 23 Feb 2014 07:30:06 +0000 Existing opinion mining studies have focused on and explored only two types of reviews, that is, regular and comparative. There is a visible gap in determining the useful review types from customers and designers perspective. Based on Technology Acceptance Model (TAM) and statistical measures we examine users’ perception about different review types and its effects in terms of behavioral intention towards using online review system. By using sample of users and designers , current research work studies three review types, A (regular), B (comparative), and C (suggestive), which are related to perceived usefulness, perceived ease of use, and behavioral intention. The study reveals that positive perception of the use of suggestive reviews improves users’ decision making in business intelligence. The results also depict that type C (suggestive reviews) could be considered a new useful review type in addition to other types, A and B. Atika Qazi, Ram Gopal Raj, Muhammad Tahir, Mahwish Waheed, Saif Ur Rehman Khan, and Ajith Abraham Copyright © 2014 Atika Qazi et al. All rights reserved. Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data Sun, 23 Feb 2014 06:11:04 +0000 Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. A. Gaspar-Cunha, G. Recio, L. Costa, and C. Estébanez Copyright © 2014 A. Gaspar-Cunha et al. All rights reserved. Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm Sun, 23 Feb 2014 00:00:00 +0000 We first present two GPU implementations of the standard Inverse Distance Weighting (IDW) interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP). Then we evaluate the power of GPU acceleration for IDW interpolation algorithm by comparing the performance of CPU implementation with three GPU implementations, that is, the naive version, the tiled version, and the CDP version. Experimental results show that the tilted version has the speedups of 120x and 670x over the CPU version when the power parameter is set to 2 and 3.0, respectively. In addition, compared to the naive GPU implementation, the tiled version is about two times faster. However, the CDP version is 4.8x6.0x slower than the naive GPU version, and therefore does not have any potential advantages in practical applications. Gang Mei Copyright © 2014 Gang Mei. All rights reserved. Maximum Neighborhood Margin Discriminant Projection for Classification Thu, 20 Feb 2014 13:35:18 +0000 We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification. Jianping Gou, Yongzhao Zhan, Min Wan, Xiangjun Shen, Jinfu Chen, and Lan Du Copyright © 2014 Jianping Gou et al. All rights reserved. Emergence of a Snake-Like Structure in Mobile Distributed Agents: An Exploratory Agent-Based Modeling Approach Thu, 20 Feb 2014 11:21:18 +0000 The body structure of snakes is composed of numerous natural components thereby making it resilient, flexible, adaptive, and dynamic. In contrast, current computer animations as well as physical implementations of snake-like autonomous structures are typically designed to use either a single or a relatively smaller number of components. As a result, not only these artificial structures are constrained by the dimensions of the constituent components but often also require relatively more computationally intensive algorithms to model and animate. Still, these animations often lack life-like resilience and adaptation. This paper presents a solution to the problem of modeling snake-like structures by proposing an agent-based, self-organizing algorithm resulting in an emergent and surprisingly resilient dynamic structure involving a minimal of interagent communication. Extensive simulation experiments demonstrate the effectiveness as well as resilience of the proposed approach. The ideas originating from the proposed algorithm can not only be used for developing self-organizing animations but can also have practical applications such as in the form of complex, autonomous, evolvable robots with self-organizing, mobile components with minimal individual computational capabilities. The work also demonstrates the utility of exploratory agent-based modeling (EABM) in the engineering of artificial life-like complex adaptive systems. Muaz A. Niazi Copyright © 2014 Muaz A. Niazi. All rights reserved. Efficient Resources Provisioning Based on Load Forecasting in Cloud Thu, 20 Feb 2014 09:40:00 +0000 Cloud providers should ensure QoS while maximizing resources utilization. One optimal strategy is to timely allocate resources in a fine-grained mode according to application’s actual resources demand. The necessary precondition of this strategy is obtaining future load information in advance. We propose a multi-step-ahead load forecasting method, KSwSVR, based on statistical learning theory which is suitable for the complex and dynamic characteristics of the cloud computing environment. It integrates an improved support vector regression algorithm and Kalman smoother. Public trace data taken from multitypes of resources were used to verify its prediction accuracy, stability, and adaptability, comparing with AR, BPNN, and standard SVR. Subsequently, based on the predicted results, a simple and efficient strategy is proposed for resource provisioning. CPU allocation experiment indicated it can effectively reduce resources consumption while meeting service level agreements requirements. Rongdong Hu, Jingfei Jiang, Guangming Liu, and Lixin Wang Copyright © 2014 Rongdong Hu et al. All rights reserved. Risks and Crises for Healthcare Providers: The Impact of Cloud Computing Thu, 20 Feb 2014 09:21:00 +0000 We analyze risks and crises for healthcare providers and discuss the impact of cloud computing in such scenarios. The analysis is conducted in a holistic way, taking into account organizational and human aspects, clinical, IT-related, and utilities-related risks as well as incorporating the view of the overall risk management. Ronald Glasberg, Michael Hartmann, Michael Draheim, Gerrit Tamm, and Franz Hessel Copyright © 2014 Ronald Glasberg et al. All rights reserved. A Hybrid Evolutionary Algorithm for Wheat Blending Problem Thu, 20 Feb 2014 08:30:55 +0000 This paper presents a hybrid evolutionary algorithm to deal with the wheat blending problem. The unique constraints of this problem make many existing algorithms fail: either they do not generate acceptable results or they are not able to complete optimization within the required time. The proposed algorithm starts with a filtering process that follows predefined rules to reduce the search space. Then the linear-relaxed version of the problem is solved using a standard linear programming algorithm. The result is used in conjunction with a solution generated by a heuristic method to generate an initial solution. After that, a hybrid of an evolutionary algorithm, a heuristic method, and a linear programming solver is used to improve the quality of the solution. A local search based posttuning method is also incorporated into the algorithm. The proposed algorithm has been tested on artificial test cases and also real data from past years. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in terms of both quality and speed. Xiang Li, Mohammad Reza Bonyadi, Zbigniew Michalewicz, and Luigi Barone Copyright © 2014 Xiang Li et al. All rights reserved. Unregistered Biological Words Recognition by Q-Learning with Transfer Learning Wed, 19 Feb 2014 09:50:04 +0000 Unregistered biological words recognition is the process of identification of terms that is out of vocabulary. Although many approaches have been developed, the performance approaches are not satisfactory. As the identification process can be viewed as a Markov process, we put forward a Q-learning with transfer learning algorithm to detect unregistered biological words from texts. With the Q-learning, the recognizer can attain the optimal solution of identification during the interaction with the texts and contexts. During the processing, a transfer learning approach is utilized to fully take advantage of the knowledge gained in a source task to speed up learning in a different but related target task. A mapping, required by many transfer learning, which relates features from the source task to the target task, is carried on automatically under the reinforcement learning framework. We examined the performance of three approaches with GENIA corpus and JNLPBA04 data. The proposed approach improved performance in both experiments. The precision, recall rate, and score results of our approach surpassed those of conventional unregistered word recognizer as well as those of Q-learning approach without transfer learning. Fei Zhu, Quan Liu, Hui Wang, Xiaoke Zhou, and Yuchen Fu Copyright © 2014 Fei Zhu et al. All rights reserved. Proposal for a Security Management in Cloud Computing for Health Care Wed, 19 Feb 2014 09:13:22 +0000 Cloud computing is actually one of the most popular themes of information systems research. Considering the nature of the processed information especially health care organizations need to assess and treat specific risks according to cloud computing in their information security management system. Therefore, in this paper we propose a framework that includes the most important security processes regarding cloud computing in the health care sector. Starting with a framework of general information security management processes derived from standards of the ISO 27000 family the most important information security processes for health care organizations using cloud computing will be identified considering the main risks regarding cloud computing and the type of information processed. The identified processes will help a health care organization using cloud computing to focus on the most important ISMS processes and establish and operate them at an appropriate level of maturity considering limited resources. Knut Haufe, Srdan Dzombeta, and Knud Brandis Copyright © 2014 Knut Haufe et al. All rights reserved. Designing a Multistage Supply Chain in Cross-Stage Reverse Logistics Environments: Application of Particle Swarm Optimization Algorithms Tue, 18 Feb 2014 14:35:07 +0000 This study designed a cross-stage reverse logistics course for defective products so that damaged products generated in downstream partners can be directly returned to upstream partners throughout the stages of a supply chain for rework and maintenance. To solve this reverse supply chain design problem, an optimal cross-stage reverse logistics mathematical model was developed. In addition, we developed a genetic algorithm (GA) and three particle swarm optimization (PSO) algorithms: the inertia weight method (PSOA_IWM), method (PSOA_VMM), and constriction factor method (PSOA_CFM), which we employed to find solutions to support this mathematical model. Finally, a real case and five simulative cases with different scopes were used to compare the execution times, convergence times, and objective function values of the four algorithms used to validate the model proposed in this study. Regarding system execution time, the GA consumed more time than the other three PSOs did. Regarding objective function value, the GA, PSOA_IWM, and PSOA_CFM could obtain a lower convergence value than PSOA_VMM could. Finally, PSOA_IWM demonstrated a faster convergence speed than PSOA_VMM, PSOA_CFM, and the GA did. Tzu-An Chiang, Z. H. Che, and Zhihua Cui Copyright © 2014 Tzu-An Chiang et al. All rights reserved. Enhancing Artificial Bee Colony Algorithm with Self-Adaptive Searching Strategy and Artificial Immune Network Operators for Global Optimization Tue, 18 Feb 2014 13:04:45 +0000 Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. Tinggui Chen and Renbin Xiao Copyright © 2014 Tinggui Chen and Renbin Xiao. All rights reserved. Link-Based Similarity Measures Using Reachability Vectors Tue, 18 Feb 2014 11:50:10 +0000 We present a novel approach for computing link-based similarities among objects accurately by utilizing the link information pertaining to the objects involved. We discuss the problems with previous link-based similarity measures and propose a novel approach for computing link based similarities that does not suffer from these problems. In the proposed approach each target object is represented by a vector. Each element of the vector corresponds to all the objects in the given data, and the value of each element denotes the weight for the corresponding object. As for this weight value, we propose to utilize the probability of reaching from the target object to the specific object, computed using the “Random Walk with Restart” strategy. Then, we define the similarity between two objects as the cosine similarity of the two vectors. In this paper, we provide examples to show that our approach does not suffer from the aforementioned problems. We also evaluate the performance of the proposed methods in comparison with existing link-based measures, qualitatively and quantitatively, with respect to two kinds of data sets, scientific papers and Web documents. Our experimental results indicate that the proposed methods significantly outperform the existing measures. Seok-Ho Yoon, Ji-Soo Kim, Jiwoon Ha, Sang-Wook Kim, Minsoo Ryu, and Ho-Jin Choi Copyright © 2014 Seok-Ho Yoon et al. All rights reserved. Trusted Computing Strengthens Cloud Authentication Tue, 18 Feb 2014 11:01:10 +0000 Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model. Eghbal Ghazizadeh, Mazdak Zamani, Jamalul-lail Ab Manan, and Mojtaba Alizadeh Copyright © 2014 Eghbal Ghazizadeh et al. All rights reserved. An Efficient Fitness Function in Genetic Algorithm Classifier for Landuse Recognition on Satellite Images Tue, 18 Feb 2014 10:25:24 +0000 Genetic algorithm (GA) is designed to search the optimal solution via weeding out the worse gene strings based on a fitness function. GA had demonstrated effectiveness in solving the problems of unsupervised image classification, one of the optimization problems in a large domain. Many indices or hybrid algorithms as a fitness function in a GA classifier are built to improve the classification accuracy. This paper proposes a new index, DBFCMI, by integrating two common indices, DBI and FCMI, in a GA classifier to improve the accuracy and robustness of classification. For the purpose of testing and verifying DBFCMI, well-known indices such as DBI, FCMI, and PASI are employed as well for comparison. A SPOT-5 satellite image in a partial watershed of Shihmen reservoir is adopted as the examined material for landuse classification. As a result, DBFCMI acquires higher overall accuracy and robustness than the rest indices in unsupervised classification. Ming-Der Yang, Yeh-Fen Yang, Tung-Ching Su, and Kai-Siang Huang Copyright © 2014 Ming-Der Yang et al. All rights reserved. A Novel Algorithm Combining Finite State Method and Genetic Algorithm for Solving Crude Oil Scheduling Problem Tue, 18 Feb 2014 08:37:52 +0000 A hybrid optimization algorithm combining finite state method (FSM) and genetic algorithm (GA) is proposed to solve the crude oil scheduling problem. The FSM and GA are combined to take the advantage of each method and compensate deficiencies of individual methods. In the proposed algorithm, the finite state method makes up for the weakness of GA which is poor at local searching ability. The heuristic returned by the FSM can guide the GA algorithm towards good solutions. The idea behind this is that we can generate promising substructure or partial solution by using FSM. Furthermore, the FSM can guarantee that the entire solution space is uniformly covered. Therefore, the combination of the two algorithms has better global performance than the existing GA or FSM which is operated individually. Finally, a real-life crude oil scheduling problem from the literature is used for conducting simulation. The experimental results validate that the proposed method outperforms the state-of-art GA method. Qian-Qian Duan, Gen-Ke Yang, and Chang-Chun Pan Copyright © 2014 Qian-Qian Duan et al. All rights reserved. Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm Mon, 17 Feb 2014 16:18:45 +0000 A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. Jian Wang, Xiaolong Wang, Aipeng Jiang, Shu Jiangzhou, and Ping Li Copyright © 2014 Jian Wang et al. All rights reserved. Palm Vein Verification Using Multiple Features and Locality Preserving Projections Mon, 17 Feb 2014 14:28:44 +0000 Biometrics is defined as identifying people by their physiological characteristic, such as iris pattern, fingerprint, and face, or by some aspects of their behavior, such as voice, signature, and gesture. Considerable attention has been drawn on these issues during the last several decades. And many biometric systems for commercial applications have been successfully developed. Recently, the vein pattern biometric becomes increasingly attractive for its uniqueness, stability, and noninvasiveness. A vein pattern is the physical distribution structure of the blood vessels underneath a person’s skin. The palm vein pattern is very ganglion and it shows a huge number of vessels. The attitude of the palm vein vessels stays in the same location for the whole life and its pattern is definitely unique. In our work, the matching filter method is proposed for the palm vein image enhancement. New palm vein features extraction methods, global feature extracted based on wavelet coefficients and locality preserving projections (WLPP), and local feature based on local binary pattern variance and locality preserving projections (LBPV_LPP) have been proposed. Finally, the nearest neighbour matching method has been proposed that verified the test palm vein images. The experimental result shows that the EER to the proposed method is 0.1378%. Ali Mohsin Al-juboori, Wei Bu, Xiangqian Wu, and Qiushi Zhao Copyright © 2014 Ali Mohsin Al-juboori et al. All rights reserved. An Adaptive Evolutionary Algorithm for Traveling Salesman Problem with Precedence Constraints Mon, 17 Feb 2014 13:38:20 +0000 Traveling sales man problem with precedence constraints is one of the most notorious problems in terms of the efficiency of its solution approach, even though it has very wide range of industrial applications. We propose a new evolutionary algorithm to efficiently obtain good solutions by improving the search process. Our genetic operators guarantee the feasibility of solutions over the generations of population, which significantly improves the computational efficiency even when it is combined with our flexible adaptive searching strategy. The efficiency of the algorithm is investigated by computational experiments. Jinmo Sung and Bongju Jeong Copyright © 2014 Jinmo Sung and Bongju Jeong. All rights reserved. Monitoring Moving Queries inside a Safe Region Sun, 16 Feb 2014 14:37:03 +0000 With mobile moving range queries, there is a need to recalculate the relevant surrounding objects of interest whenever the query moves. Therefore, monitoring the moving query is very costly. The safe region is one method that has been proposed to minimise the communication and computation cost of continuously monitoring a moving range query. Inside the safe region the set of objects of interest to the query do not change; thus there is no need to update the query while it is inside its safe region. However, when the query leaves its safe region the mobile device has to reevaluate the query, necessitating communication with the server. Knowing when and where the mobile device will leave a safe region is widely known as a difficult problem. To solve this problem, we propose a novel method to monitor the position of the query over time using a linear function based on the direction of the query obtained by periodic monitoring of its position. Periodic monitoring ensures that the query is aware of its location all the time. This method reduces the costs associated with communications in client-server architecture. Computational results show that our method is successful in handling moving query patterns. Haidar Al-Khalidi, David Taniar, John Betts, and Sultan Alamri Copyright © 2014 Haidar Al-Khalidi et al. All rights reserved. Research and Application for Grey Relational Analysis in Multigranularity Based on Normality Grey Number Sun, 16 Feb 2014 14:17:26 +0000 Grey theory is an essential uncertain knowledge acquisition method for small sample, poor information. The classic grey theory does not adequately take into account the distribution of data set and lacks the effective methods to analyze and mine big sample in multigranularity. In view of the universality of the normal distribution, the normality grey number is proposed. Then, the corresponding definition and calculation method of the relational degree between the normality grey numbers are constructed. On this basis, the grey relational analytical method in multigranularity is put forward to realize the automatic clustering in the specified granularity without any experience knowledge. Finally, experiments fully prove that it is an effective knowledge acquisition method for big data or multigranularity sample. Jin Dai, Xin Liu, and Feng Hu Copyright © 2014 Jin Dai et al. All rights reserved. Fault Location Based on Synchronized Measurements: A Comprehensive Survey Sun, 16 Feb 2014 13:18:20 +0000 This paper presents a comprehensive survey on transmission and distribution fault location algorithms that utilize synchronized measurements. Algorithms based on two-end synchronized measurements and fault location algorithms on three-terminal and multiterminal lines are reviewed. Series capacitors equipped with metal oxide varistors (MOVs), when set on a transmission line, create certain problems for line fault locators and, therefore, fault location on series-compensated lines is discussed. The paper reports the work carried out on adaptive fault location algorithms aiming at achieving better fault location accuracy. Work associated with fault location on power system networks, although limited, is also summarized. Additionally, the nonstandard high-frequency-related fault location techniques based on wavelet transform are discussed. Finally, the paper highlights the area for future research. A. H. Al-Mohammed and M. A. Abido Copyright © 2014 A. H. Al-Mohammed and M. A. Abido. All rights reserved. Summary on Several Key Techniques in 3D Geological Modeling Sun, 16 Feb 2014 12:19:34 +0000 Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized. Gang Mei Copyright © 2014 Gang Mei. All rights reserved. Genetic Algorithm Application in Optimization of Wireless Sensor Networks Sun, 16 Feb 2014 08:27:09 +0000 There are several applications known for wireless sensor networks (WSN), and such variety demands improvement of the currently available protocols and the specific parameters. Some notable parameters are lifetime of network and energy consumption for routing which play key role in every application. Genetic algorithm is one of the nonlinear optimization methods and relatively better option thanks to its efficiency for large scale applications and that the final formula can be modified by operators. The present survey tries to exert a comprehensive improvement in all operational stages of a WSN including node placement, network coverage, clustering, and data aggregation and achieve an ideal set of parameters of routing and application based WSN. Using genetic algorithm and based on the results of simulations in NS, a specific fitness function was achieved, optimized, and customized for all the operational stages of WSNs. Ali Norouzi and A. Halim Zaim Copyright © 2014 Ali Norouzi and A. Halim Zaim. All rights reserved. Toward Sci-: A Lightweight Cloud PaaS for Developing Embarrassingly Parallel Applications Based on Jini Thu, 13 Feb 2014 16:04:53 +0000 Embarrassingly parallel problems are characterised by a very small amount of information to be exchanged among the parts they are split in, during their parallel execution. As a consequence they do not require sophisticated, low-latency, high-bandwidth interconnection networks but can be efficiently computed in parallel by exploiting commodity hardware. Basically, this means cheap clusters, networks of workstations and desktops, and Computational Clouds. This computational model can be exploited to compute a quite large range of problems. This paper describes Sci-, an almost complete redesign of a previous tool of ours aimed at developing task parallel applications based on Java and Jini that were shown to be an effective and efficient solution in environments like clusters and networks of workstations and desktops. Patrizio Dazzi Copyright © 2014 Patrizio Dazzi. All rights reserved. Bridge Condition Assessment Using Numbers Thu, 13 Feb 2014 15:45:34 +0000 Bridge condition assessment is a complex problem influenced by many factors. The uncertain environment increases more its complexity. Due to the uncertainty in the process of assessment, one of the key problems is the representation of assessment results. Though there exists many methods that can deal with uncertain information, however, they have more or less deficiencies. In this paper, a new representation of uncertain information, called numbers, is presented. It extends the Dempster-Shafer theory. By using numbers, a new method is developed for the bridge condition assessment. Compared to these existing methods, the proposed method is simpler and more effective. An illustrative case is given to show the effectiveness of the new method. Xinyang Deng, Yong Hu, and Yong Deng Copyright © 2014 Xinyang Deng et al. All rights reserved. Research on the Compression Algorithm of the Infrared Thermal Image Sequence Based on Differential Evolution and Double Exponential Decay Model Thu, 13 Feb 2014 13:12:12 +0000 This paper has proposed a new thermal wave image sequence compression algorithm by combining double exponential decay fitting model and differential evolution algorithm. This study benchmarked fitting compression results and precision of the proposed method was benchmarked to that of the traditional methods via experiment; it investigated the fitting compression performance under the long time series and improved model and validated the algorithm by practical thermal image sequence compression and reconstruction. The results show that the proposed algorithm is a fast and highly precise infrared image data processing method. Jin-Yu Zhang, Xiang-Bing Meng, Wei Xu, Wei Zhang, and Yong Zhang Copyright © 2014 Jin-Yu Zhang et al. All rights reserved. Improving Predictions of Multiple Binary Models in ILP Thu, 13 Feb 2014 13:12:04 +0000 Despite the success of ILP systems in learning first-order rules from small number of examples and complexly structured data in various domains, they struggle in dealing with multiclass problems. In most cases they boil down a multiclass problem into multiple black-box binary problems following the one-versus-one or one-versus-rest binarisation techniques and learn a theory for each one. When evaluating the learned theories of multiple class problems in one-versus-rest paradigm particularly, there is a bias caused by the default rule toward the negative classes leading to an unrealistic high performance beside the lack of prediction integrity between the theories. Here we discuss the problem of using one-versus-rest binarisation technique when it comes to evaluating multiclass data and propose several methods to remedy this problem. We also illustrate the methods and highlight their link to binary tree and Formal Concept Analysis (FCA). Our methods allow learning of a simple, consistent, and reliable multiclass theory by combining the rules of the multiple one-versus-rest theories into one rule list or rule set theory. Empirical evaluation over a number of data sets shows that our proposed methods produce coherent and accurate rule models from the rules learned by the ILP system of Aleph. Tarek Abudawood Copyright © 2014 Tarek Abudawood. All rights reserved. Adaptive Iterated Extended Kalman Filter and Its Application to Autonomous Integrated Navigation for Indoor Robot Thu, 13 Feb 2014 11:02:48 +0000 As the core of the integrated navigation system, the data fusion algorithm should be designed seriously. In order to improve the accuracy of data fusion, this work proposed an adaptive iterated extended Kalman (AIEKF) which used the noise statistics estimator in the iterated extended Kalman (IEKF), and then AIEKF is used to deal with the nonlinear problem in the inertial navigation systems (INS)/wireless sensors networks (WSNs)-integrated navigation system. Practical test has been done to evaluate the performance of the proposed method. The results show that the proposed method is effective to reduce the mean root-mean-square error (RMSE) of position by about 92.53%, 67.93%, 55.97%, and 30.09% compared with the INS only, WSN, EKF, and IEKF. Yuan Xu, Xiyuan Chen, and Qinghua Li Copyright © 2014 Yuan Xu et al. All rights reserved. An Exponentiation Method for XML Element Retrieval Thu, 13 Feb 2014 07:55:58 +0000 XML document is now widely used for modelling and storing structured documents. The structure is very rich and carries important information about contents and their relationships, for example, e-Commerce. XML data-centric collections require query terms allowing users to specify constraints on the document structure; mapping structure queries and assigning the weight are significant for the set of possibly relevant documents with respect to structural conditions. In this paper, we present an extension to the MEXIR search system that supports the combination of structural and content queries in the form of content-and-structure queries, which we call the Exponentiation function. It has been shown the structural information improve the effectiveness of the search system up to 52.60% over the baseline BM25 at MAP. Tanakorn Wichaiwong Copyright © 2014 Tanakorn Wichaiwong. All rights reserved. Bioinspired Evolutionary Algorithm Based for Improving Network Coverage in Wireless Sensor Networks Wed, 12 Feb 2014 13:41:49 +0000 Wireless sensor networks (WSNs) include sensor nodes in which each node is able to monitor the physical area and send collected information to the base station for further analysis. The important key of WSNs is detection and coverage of target area which is provided by random deployment. This paper reviews and addresses various area detection and coverage problems in sensor network. This paper organizes many scenarios for applying sensor node movement for improving network coverage based on bioinspired evolutionary algorithm and explains the concern and objective of controlling sensor node coverage. We discuss area coverage and target detection model by evolutionary algorithm. Mohammadjavad Abbasi, Muhammad Shafie Bin Abd Latiff, and Hassan Chizari Copyright © 2014 Mohammadjavad Abbasi et al. All rights reserved. A Study of Practical Proxy Reencryption with a Keyword Search Scheme considering Cloud Storage Structure Wed, 12 Feb 2014 13:33:47 +0000 Data outsourcing services have emerged with the increasing use of digital information. They can be used to store data from various devices via networks that are easy to access. Unlike existing removable storage systems, storage outsourcing is available to many users because it has no storage limit and does not require a local storage medium. However, the reliability of storage outsourcing has become an important topic because many users employ it to store large volumes of data. To protect against unethical administrators and attackers, a variety of cryptography systems are used, such as searchable encryption and proxy reencryption. However, existing searchable encryption technology is inconvenient for use in storage outsourcing environments where users upload their data to be shared with others as necessary. In addition, some existing schemes are vulnerable to collusion attacks and have computing cost inefficiencies. In this paper, we analyze existing proxy re-encryption with keyword search. Sun-Ho Lee and Im-Yeong Lee Copyright © 2014 Sun-Ho Lee and Im-Yeong Lee. All rights reserved. GPU Acceleration of Melody Accurate Matching in Query-by-Humming Wed, 12 Feb 2014 11:37:11 +0000 With the increasing scale of the melody database, the query-by-humming system faces the trade-offs between response speed and retrieval accuracy. Melody accurate matching is the key factor to restrict the response speed. In this paper, we present a GPU acceleration method for melody accurate matching, in order to improve the response speed without reducing retrieval accuracy. The method develops two parallel strategies (intra-task parallelism and inter-task parallelism) to obtain accelerated effects. The efficiency of our method is validated through extensive experiments. Evaluation results show that our single GPU implementation achieves 20x to 40x speedup ratio, when compared to a typical general purpose CPU’s execution time. Limin Xiao, Yao Zheng, Wenqi Tang, Guangchao Yao, and Li Ruan Copyright © 2014 Limin Xiao et al. All rights reserved. Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion Wed, 12 Feb 2014 06:59:42 +0000 For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks’ information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples’ own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. Ying Chen, Yuanning Liu, Xiaodong Zhu, Huiling Chen, Fei He, and Yutong Pang Copyright © 2014 Ying Chen et al. All rights reserved. A Multipopulation Coevolutionary Strategy for Multiobjective Immune Algorithm Wed, 12 Feb 2014 00:00:00 +0000 How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems. Jiao Shi, Maoguo Gong, Wenping Ma, and Licheng Jiao Copyright © 2014 Jiao Shi et al. All rights reserved. A Supervised Approach to Predict the Hierarchical Structure of Conversation Threads for Comments Tue, 11 Feb 2014 13:15:28 +0000 User-generated texts such as comments in social media are rich sources of information. In general, the reply structure of comments is not publicly accessible on the web. Websites present comments as a list in chronological order. This way, some information is lost. A solution for this problem is to reconstruct the thread structure (RTS) automatically. RTS predicts a semantic tree for the reply structure, useful for understanding users’ behaviours and facilitating follow of the actual conversation streams. This paper works on RTS task in blogs, online news agencies, and news websites. These types of websites cover various types of articles reflecting the real-world events. People with different views participate in arguments by writing comments. Comments express opinions, sentiments, or ideas about articles. The reply structure of threads in these types of websites is basically different from threads in the forums, chats, and emails. To perform RTS, we define a set of textual and nontextual features. Then, we use supervised learning to combine these features. The proposed method is evaluated on five different datasets. The accuracy of the proposed method is compared with baselines. The results reveal higher accuracy for our method in comparison with baselines in all datasets. A. Balali, H. Faili, and M. Asadpour Copyright © 2014 A. Balali et al. All rights reserved. Multiobjective Robust Design of the Double Wishbone Suspension System Based on Particle Swarm Optimization Tue, 11 Feb 2014 00:00:00 +0000 The performance of the suspension system is one of the most important factors in the vehicle design. For the double wishbone suspension system, the conventional deterministic optimization does not consider any deviations of design parameters, so design sensitivity analysis and robust optimization design are proposed. In this study, the design parameters of the robust optimization are the positions of the key points, and the random factors are the uncertainties in manufacturing. A simplified model of the double wishbone suspension is established by software ADAMS. The sensitivity analysis is utilized to determine main design variables. Then, the simulation experiment is arranged and the Latin hypercube design is adopted to find the initial points. The Kriging model is employed for fitting the mean and variance of the quality characteristics according to the simulation results. Further, a particle swarm optimization method based on simple PSO is applied and the tradeoff between the mean and deviation of performance is made to solve the robust optimization problem of the double wishbone suspension system. Xianfu Cheng and Yuqun Lin Copyright © 2014 Xianfu Cheng and Yuqun Lin. All rights reserved. Scheduling Projects with Multiskill Learning Effect Mon, 10 Feb 2014 13:08:39 +0000 We investigate the project scheduling problem with multiskill learning effect. A new model is proposed to deal with the problem, where both autonomous and induced learning are considered. In order to obtain the optimal solution, a genetic algorithm with specific encoding and decoding schemes is introduced. A numerical example is used to illustrate the proposed model. The computational results show that the learning effect cannot be neglected in project scheduling. By means of determining the level of induced learning, the project manager can balance the project makespan with total cost. Hong Zha and Lianying Zhang Copyright © 2014 Hong Zha and Lianying Zhang. All rights reserved. A Systematic Comparison of Data Selection Criteria for SMT Domain Adaptation Mon, 10 Feb 2014 12:34:18 +0000 Data selection has shown significant improvements in effective use of training data by extracting sentences from large general-domain corpora to adapt statistical machine translation (SMT) systems to in-domain data. This paper performs an in-depth analysis of three different sentence selection techniques. The first one is cosine tf-idf, which comes from the realm of information retrieval (IR). The second is perplexity-based approach, which can be found in the field of language modeling. These two data selection techniques applied to SMT have been already presented in the literature. However, edit distance for this task is proposed in this paper for the first time. After investigating the individual model, a combination of all three techniques is proposed at both corpus level and model level. Comparative experiments are conducted on Hong Kong law Chinese-English corpus and the results indicate the following: (i) the constraint degree of similarity measuring is not monotonically related to domain-specific translation quality; (ii) the individual selection models fail to perform effectively and robustly; but (iii) bilingual resources and combination methods are helpful to balance out-of-vocabulary (OOV) and irrelevant data; (iv) finally, our method achieves the goal to consistently boost the overall translation performance that can ensure optimal quality of a real-life SMT system. Longyue Wang, Derek F. Wong, Lidia S. Chao, Yi Lu, and Junwen Xing Copyright © 2014 Longyue Wang et al. All rights reserved. Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion Mon, 10 Feb 2014 09:24:53 +0000 In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region’s weights and then weighted different subregions’ matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. Ying Chen, Yuanning Liu, Xiaodong Zhu, Fei He, Hongye Wang, and Ning Deng Copyright © 2014 Ying Chen et al. All rights reserved. The Study of Cooperative Obstacle Avoidance Method for MWSN Based on Flocking Control Mon, 10 Feb 2014 08:13:49 +0000 Compared with the space fixed feature of traditional wireless sensor network (WSN), mobile WSN has better robustness and adaptability in unknown environment, so that it is always applied in the research of target tracking. In order to reach the target, the nodes group should find a self-adaptive method to avoid the obstacles together in their moving directions. Previous methods, which were based on flocking control model, realized the strategy of obstacle avoidance by means of potential field. However, these may sometimes lead the nodes group to fall into a restricted area like a trap and never get out of it. Based on traditional flocking control model, this paper introduced a new cooperative obstacle avoidance model combined with improved SA obstacle avoidance algorithm. It defined the tangent line of the intersection of node’s velocity line and the edge of obstacle as the steering direction. Furthermore, the cooperative obstacle avoidance model was also improved in avoiding complex obstacles. When nodes group encounters mobile obstacles, nodes will predict movement path based on the spatial location and velocity of obstacle. And when nodes group enters concave obstacles, nodes will temporarily ignore the gravity of the target and search path along the edge of the concave obstacles. Simulation results showed that cooperative obstacle avoidance model has significant improvement on average speed and time efficiency in avoiding obstacle compared with the traditional flocking control model. It is more suitable for obstacle avoidance in complex environment. Zuo Chen, Lei Ding, Kai Chen, and Renfa Li Copyright © 2014 Zuo Chen et al. All rights reserved. Simulation and Modeling Efforts to Support Decision Making in Healthcare Supply Chain Management Mon, 10 Feb 2014 07:55:47 +0000 Recently, most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes’ efficiencies. The availability of products through healthcare SCM is often a matter of life or death to the patient; therefore, trial and error approaches are not an option in this environment. Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems. This paper presents and analyzes past SM efforts to support decision making in healthcare SCM and identifies the key challenges associated with healthcare SCM modeling. We also present and discuss emerging technologies to meet these challenges. Eman AbuKhousa, Jameela Al-Jaroodi, Sanja Lazarova-Molnar, and Nader Mohamed Copyright © 2014 Eman AbuKhousa et al. All rights reserved. An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization Sun, 09 Feb 2014 16:18:39 +0000 Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail. Xiaobing Yu, Jie Cao, Haiyan Shan, Li Zhu, and Jun Guo Copyright © 2014 Xiaobing Yu et al. All rights reserved. A Modified Decision Tree Algorithm Based on Genetic Algorithm for Mobile User Classification Problem Sun, 09 Feb 2014 13:29:22 +0000 In order to offer mobile customers better service, we should classify the mobile user firstly. Aimed at the limitations of previous classification methods, this paper puts forward a modified decision tree algorithm for mobile user classification, which introduced genetic algorithm to optimize the results of the decision tree algorithm. We also take the context information as a classification attributes for the mobile user and we classify the context into public context and private context classes. Then we analyze the processes and operators of the algorithm. At last, we make an experiment on the mobile user with the algorithm, we can classify the mobile user into Basic service user, E-service user, Plus service user, and Total service user classes and we can also get some rules about the mobile user. Compared to C4.5 decision tree algorithm and SVM algorithm, the algorithm we proposed in this paper has higher accuracy and more simplicity. Dong-sheng Liu and Shu-jiang Fan Copyright © 2014 Dong-sheng Liu and Shu-jiang Fan. All rights reserved. A TOTP-Based Enhanced Route Optimization Procedure for Mobile IPv6 to Reduce Handover Delay and Signalling Overhead Sun, 09 Feb 2014 12:34:56 +0000 Due to the proliferation of handheld mobile devices, multimedia applications like Voice over IP (VoIP), video conferencing, network music, and online gaming are gaining popularity in recent years. These applications are well known to be delay sensitive and resource demanding. The mobility of mobile devices, running these applications, across different networks causes delay and service disruption. Mobile IPv6 was proposed to provide mobility support to IPv6-based mobile nodes for continuous communication when they roam across different networks. However, the Route Optimization procedure in Mobile IPv6 involves the verification of mobile node’s reachability at the home address and at the care-of address (home test and care-of test) that results in higher handover delays and signalling overhead. This paper presents an enhanced procedure, time-based one-time password Route Optimization (TOTP-RO), for Mobile IPv6 Route Optimization that uses the concepts of shared secret Token, time based one-time password (TOTP) along with verification of the mobile node via direct communication and maintaining the status of correspondent node’s compatibility. The TOTP-RO was implemented in network simulator (NS-2) and an analytical analysis was also made. Analysis showed that TOTP-RO has lower handover delays, packet loss, and signalling overhead with an increased level of security as compared to the standard Mobile IPv6’s Return-Routability-based Route Optimization (RR-RO). Peer Azmat Shah, Halabi B. Hasbullah, Ibrahim A. Lawal, Abubakar Aminu Mu’azu, and Low Tang Jung Copyright © 2014 Peer Azmat Shah et al. All rights reserved. A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM Sun, 09 Feb 2014 08:00:10 +0000 In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs), which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the -regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs). To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs) with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods. Xin-Sheng Zhang Copyright © 2014 Xin-Sheng Zhang. All rights reserved. View-Dependent Tessellation and Simulation of Ocean Surfaces Thu, 06 Feb 2014 16:34:45 +0000 Modeling and rendering realistic ocean scenes have been thoroughly investigated for many years. Its appearance has been studied and it is possible to find very detailed simulations where a high degree of realism is achieved. Nevertheless, among the solutions to ocean rendering, real-time management of the huge heightmaps that are necessary for rendering an ocean scene is still not solved. We propose a new technique for simulating the ocean surface on GPU. This technique is capable of offering view-dependent approximations of the mesh while maintaining coherence among the extracted approximations. This feature is very important as most solutions previously presented must retessellate from the initial mesh. Our solution is able to use the latest extracted approximation when refining or coarsening the mesh. Anna Puig-Centelles, Francisco Ramos, Oscar Ripolles, Miguel Chover, and Mateu Sbert Copyright © 2014 Anna Puig-Centelles et al. All rights reserved. Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models Thu, 06 Feb 2014 14:11:27 +0000 OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy. Jia Li, Yunni Xia, and Xin Luo Copyright © 2014 Jia Li et al. All rights reserved. New Improved Fractional Order Differentiator Models Based on Optimized Digital Differentiators Thu, 06 Feb 2014 11:59:21 +0000 Different evolutionary algorithms (EAs), namely, particle swarm optimization (PSO), genetic algorithm (GA), and PSO-GA hybrid optimization, have been used to optimize digital differential operators so that these can be better fitted to exemplify their new improved fractional order differentiator counterparts. First, the paper aims to provide efficient 2nd and 3rd order operators in connection with process of minimization of error fitness function by registering mean, median, and standard deviation values in different random iterations to ascertain the best results among them, using all the abovementioned EAs. Later, these optimized operators are discretized for half differentiator models for utilizing their restored qualities inhibited from their optimization. Simulation results present the comparisons of the proposed half differentiators with the existing and amongst different models based on 2nd and 3rd order optimized operators. Proposed half differentiators have been observed to approximate the ideal half differentiator and also outperform the existing ones reasonably well in complete range of Nyquist frequency. Maneesha Gupta and Richa Yadav Copyright © 2014 Maneesha Gupta and Richa Yadav. All rights reserved. Facilitating Preemptive Hardware System Design Using Partial Reconfiguration Techniques Thu, 06 Feb 2014 06:25:40 +0000 In FPGA-based control system design, partial reconfiguration is especially well suited to implement preemptive systems. In real-time systems, the deadline for critical task can compel the preemption of noncritical one. Besides, an asynchronous event can demand immediate attention and, then, force launching a reconfiguration process for high-priority task implementation. If the asynchronous event is previously scheduled, an explicit activation of the reconfiguration process is performed. If the event cannot be previously programmed, such as in dynamically scheduled systems, an implicit activation to the reconfiguration process is demanded. This paper provides a hardware-based approach to explicit and implicit activation of the partial reconfiguration process in dynamically reconfigurable SoCs and includes all the necessary tasks to cope with this issue. Furthermore, the reconfiguration service introduced in this work allows remote invocation of the reconfiguration process and then the remote integration of off-chip components. A model that offers component location transparency is also presented to enhance and facilitate system integration. Julio Dondo Gazzano, Fernando Rincon, Carlos Vaderrama, Felix Villanueva, Julian Caba, and Juan Carlos Lopez Copyright © 2014 Julio Dondo Gazzano et al. All rights reserved. A Security-Awareness Virtual Machine Management Scheme Based on Chinese Wall Policy in Cloud Computing Wed, 05 Feb 2014 11:38:55 +0000 Cloud computing gets increasing attention for its capacity to leverage developers from infrastructure management tasks. However, recent works reveal that side channel attacks can lead to privacy leakage in the cloud. Enhancing isolation between users is an effective solution to eliminate the attack. In this paper, to eliminate side channel attacks, we investigate the isolation enhancement scheme from the aspect of virtual machine (VM) management. The security-awareness VMs management scheme (SVMS), a VMs isolation enhancement scheme to defend against side channel attacks, is proposed. First, we use the aggressive conflict of interest relation (ACIR) and aggressive in ally with relation (AIAR) to describe user constraint relations. Second, based on the Chinese wall policy, we put forward four isolation rules. Third, the VMs placement and migration algorithms are designed to enforce VMs isolation between the conflict users. Finally, based on the normal distribution, we conduct a series of experiments to evaluate SVMS. The experimental results show that SVMS is efficient in guaranteeing isolation between VMs owned by conflict users, while the resource utilization rate decreases but not by much. Si Yu, Xiaolin Gui, Jiancai Lin, Feng Tian, Jianqiang Zhao, and Min Dai Copyright © 2014 Si Yu et al. All rights reserved. P-bRS: A Physarum-Based Routing Scheme for Wireless Sensor Networks Wed, 05 Feb 2014 10:02:17 +0000 Routing in wireless sensor networks (WSNs) is an extremely challenging issue due to the features of WSNs. Inspired by the large and single-celled amoeboid organism, slime mold Physarum polycephalum, we establish a novel selecting next hop model (SNH). Based on this model, we present a novel Physarum-based routing scheme (P-bRS) for WSNs to balance routing efficiency and energy equilibrium. In P-bRS, a sensor node can choose the proper next hop by using SNH which comprehensively considers the distance, energy residue, and location of the next hop. The simulation results show how P-bRS can achieve the effective trade-off between routing efficiency and energy equilibrium compared to two famous algorithms. Mingchuan Zhang, Wangyang Wei, Ruijuan Zheng, and Qingtao Wu Copyright © 2014 Mingchuan Zhang et al. All rights reserved. Heterogeneous Differential Evolution for Numerical Optimization Wed, 05 Feb 2014 09:02:36 +0000 Differential evolution (DE) is a population-based stochastic search algorithm which has shown a good performance in solving many benchmarks and real-world optimization problems. Individuals in the standard DE, and most of its modifications, exhibit the same search characteristics because of the use of the same DE scheme. This paper proposes a simple and effective heterogeneous DE (HDE) to balance exploration and exploitation. In HDE, individuals are allowed to follow different search behaviors randomly selected from a DE scheme pool. Experiments are conducted on a comprehensive set of benchmark functions, including classical problems and shifted large-scale problems. The results show that heterogeneous DE achieves promising performance on a majority of the test problems. Hui Wang, Wenjun Wang, Zhihua Cui, Hui Sun, and Shahryar Rahnamayan Copyright © 2014 Hui Wang et al. All rights reserved. Modeling of Task Planning for Multirobot System Using Reputation Mechanism Tue, 04 Feb 2014 13:08:01 +0000 Modeling of task planning for multirobot system is developed from two parts: task decomposition and task allocation. In the part of task decomposition, the conditions and processes of decomposition are elaborated. In the part of task allocation, the collaboration strategy, the framework of reputation mechanism, and three types of reputations are defined in detail, which include robot individual reputation, robot group reputation, and robot direct reputation. A time calibration function and a group calibration function are designed to improve the effectiveness of the proposed method and proved that they have the characteristics of time attenuation, historical experience related, and newly joined robot reward. Tasks attempt to be assigned to the robot with higher overall reputation, which can help to increase the success rate of the mandate implementation, thereby reducing the time of task recovery and redistribution. Player/Stage is used as the simulation platform, and three biped-robots are established as the experimental apparatus. The experimental results of task planning are compared with the other allocation methods. Simulation and experiment results illustrate the effectiveness of the proposed method for multi-robot collaboration system. Zhiguo Shi, Jun Tu, Yuankai Li, and Junming Wei Copyright © 2014 Zhiguo Shi et al. All rights reserved.