The Scientific World Journal: Computer Science The latest articles from Hindawi Publishing Corporation © 2014 , Hindawi Publishing Corporation . All rights reserved. Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization Tue, 19 Aug 2014 06:50:03 +0000 Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model. Asrul Adam, Mohd Ibrahim Shapiai, Mohd Zaidi Mohd Tumari, Mohd Saberi Mohamad, and Marizan Mubin Copyright © 2014 Asrul Adam et al. All rights reserved. A Novel BA Complex Network Model on Color Template Matching Tue, 19 Aug 2014 06:15:44 +0000 A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template’s color distribution. And then the template’s BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching. Risheng Han, Shigen Shen, Guangxue Yue, and Hui Ding Copyright © 2014 Risheng Han et al. All rights reserved. Towards Enhancement of Performance of K-Means Clustering Using Nature-Inspired Optimization Algorithms Mon, 18 Aug 2014 06:55:25 +0000 Traditional K-means clustering algorithms have the drawback of getting stuck at local optima that depend on the random values of initial centroids. Optimization algorithms have their advantages in guiding iterative computation to search for global optima while avoiding local optima. The algorithms help speed up the clustering process by converging into a global optimum early with multiple search agents in action. Inspired by nature, some contemporary optimization algorithms which include Ant, Bat, Cuckoo, Firefly, and Wolf search algorithms mimic the swarming behavior allowing them to cooperatively steer towards an optimal objective within a reasonable time. It is known that these so-called nature-inspired optimization algorithms have their own characteristics as well as pros and cons in different applications. When these algorithms are combined with K-means clustering mechanism for the sake of enhancing its clustering quality by avoiding local optima and finding global optima, the new hybrids are anticipated to produce unprecedented performance. In this paper, we report the results of our evaluation experiments on the integration of nature-inspired optimization methods into K-means algorithms. In addition to the standard evaluation metrics in evaluating clustering quality, the extended K-means algorithms that are empowered by nature-inspired optimization methods are applied on image segmentation as a case study of application scenario. Simon Fong, Suash Deb, Xin-She Yang, and Yan Zhuang Copyright © 2014 Simon Fong et al. All rights reserved. Further Study of Multigranulation -Fuzzy Rough Sets Sun, 17 Aug 2014 12:48:35 +0000 The optimistic multigranulation -fuzzy rough set model was established based on multiple granulations under -fuzzy approximation space by Xu et al., 2012. From the reference, a natural idea is to consider pessimistic multigranulation model in -fuzzy approximation space. So, in this paper, the main objective is to make further studies according to Xu et al., 2012. The optimistic multigranulation -fuzzy rough set model is improved deeply by investigating some further properties. And a complete multigranulation -fuzzy rough set model is constituted by addressing the pessimistic multigranulation -fuzzy rough set. The full important properties of multigranulation -fuzzy lower and upper approximation operators are also presented. Moreover, relationships between multigranulation and classical -fuzzy rough sets have been studied carefully. From the relationships, we can find that the -fuzzy rough set model is a special instance of the two new types of models. In order to interpret and illustrate optimistic and pessimistic multigranulation -fuzzy rough set models, a case is considered, which is helpful for applying these theories to practical issues. Wentao Li, Xiaoyan Zhang, and Wenxin Sun Copyright © 2014 Wentao Li et al. All rights reserved. A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation Thu, 14 Aug 2014 15:40:24 +0000 The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented. Ramalingam Gomathi and Dhandapani Sharmila Copyright © 2014 Ramalingam Gomathi and Dhandapani Sharmila. All rights reserved. Resource Management Scheme Based on Ubiquitous Data Analysis Wed, 13 Aug 2014 11:55:23 +0000 Resource management of the main memory and process handler is critical to enhancing the system performance of a web server. Owing to the transaction delay time that affects incoming requests from web clients, web server systems utilize several web processes to anticipate future requests. This procedure is able to decrease the web generation time because there are enough processes to handle the incoming requests from web browsers. However, inefficient process management results in low service quality for the web server system. Proper pregenerated process mechanisms are required for dealing with the clients’ requests. Unfortunately, it is difficult to predict how many requests a web server system is going to receive. If a web server system builds too many web processes, it wastes a considerable amount of memory space, and thus performance is reduced. We propose an adaptive web process manager scheme based on the analysis of web log mining. In the proposed scheme, the number of web processes is controlled through prediction of incoming requests, and accordingly, the web process management scheme consumes the least possible web transaction resources. In experiments, real web trace data were used to prove the improved performance of the proposed scheme. Heung Ki Lee, Jaehee Jung, and Gangman Yi Copyright © 2014 Heung Ki Lee et al. All rights reserved. Novel Real-Time Facial Wound Recovery Synthesis Using Subsurface Scattering Tue, 12 Aug 2014 13:17:28 +0000 We propose a wound recovery synthesis model that illustrates the appearance of a wound healing on a 3-dimensional (3D) face. The H3 model is used to determine the size of the recovering wound. Furthermore, we present our subsurface scattering model that is designed to take the multilayered skin structure of the wound into consideration to represent its color transformation. We also propose a novel real-time rendering method based on the results of an analysis of the characteristics of translucent materials. Finally, we validate the proposed methods with 3D wound-simulation experiments using shading models. Taeyoung Choi and Seongah Chin Copyright © 2014 Taeyoung Choi and Seongah Chin. All rights reserved. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism Tue, 12 Aug 2014 10:29:04 +0000 The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system. However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis. The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate. On the other hand, for each satellite fault, there is not enough fault data for training. To most of the classification algorithms, it will degrade the performance of model. In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples. Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved. Hong Yin, Shuqiang Yang, Xiaoqian Zhu, Songchang Jin, and Xiang Wang Copyright © 2014 Hong Yin et al. All rights reserved. Group Search Optimizer for the Mobile Location Management Problem Mon, 11 Aug 2014 12:11:32 +0000 We propose a diversity-guided group search optimizer-based approach for solving the location management problem in mobile computing. The location management problem, which is to find the optimal network configurations of management under the mobile computing environment, is considered here as an optimization problem. The proposed diversity-guided group search optimizer algorithm is realized with the aid of diversity operator, which helps alleviate the premature convergence problem of group search optimizer algorithm, a successful optimization algorithm inspired by the animal behavior. To address the location management problem, diversity-guided group search optimizer algorithm is exploited to optimize network configurations of management by minimizing the sum of location update cost and location paging cost. Experimental results illustrate the effectiveness of the proposed approach. Dan Wang, Congcong Xiong, and Wei Huang Copyright © 2014 Dan Wang et al. All rights reserved. Tracking Pedestrians across Multiple Microcells Based on Successive Bayesian Estimations Mon, 11 Aug 2014 11:44:43 +0000 We propose a method for tracking multiple pedestrians using a binary sensor network. In our proposed method, sensor nodes are composed of pairs of binary sensors and placed at specific points, referred to as gates, where pedestrians temporarily change their movement characteristics, such as doors, stairs, and elevators, to detect pedestrian arrival and departure events. Tracking pedestrians in each subregion divided by gates, referred to as microcells, is conducted by matching the pedestrian gate arrival and gate departure events using a Bayesian estimation-based method. To improve accuracy of pedestrian tracking, estimated pedestrian velocity and its reliability in a microcell are used for trajectory estimation in the succeeding microcell. Through simulation experiments, we show that the accuracy of pedestrian tracking using our proposed method is improved by up to 35% compared to the conventional method. Yoshiaki Taniguchi, Masahiro Sasabe, Takafumi Watanabe, and Hirotaka Nakano Copyright © 2014 Yoshiaki Taniguchi et al. All rights reserved. A Novel Algorithm for Imbalance Data Classification Based on Neighborhood Hypergraph Mon, 11 Aug 2014 08:26:54 +0000 The classification problem for imbalance data is paid more attention to. So far, many significant methods are proposed and applied to many fields. But more efficient methods are needed still. Hypergraph may not be powerful enough to deal with the data in boundary region, although it is an efficient tool to knowledge discovery. In this paper, the neighborhood hypergraph is presented, combining rough set theory and hypergraph. After that, a novel classification algorithm for imbalance data based on neighborhood hypergraph is developed, which is composed of three steps: initialization of hyperedge, classification of training data set, and substitution of hyperedge. After conducting an experiment of 10-fold cross validation on 18 data sets, the proposed algorithm has higher average accuracy than others. Feng Hu, Xiao Liu, Jin Dai, and Hong Yu Copyright © 2014 Feng Hu et al. All rights reserved. Approximation Set of the Interval Set in Pawlak's Space Mon, 11 Aug 2014 06:45:42 +0000 The interval set is a special set, which describes uncertainty of an uncertain concept or set with its two crisp boundaries named upper-bound set and lower-bound set. In this paper, the concept of similarity degree between two interval sets is defined at first, and then the similarity degrees between an interval set and its two approximations (i.e., upper approximation set () and lower approximation set ()) are presented, respectively. The disadvantages of using upper-approximation set () or lower-approximation set () as approximation sets of the uncertain set (uncertain concept) are analyzed, and a new method for looking for a better approximation set of the interval set is proposed. The conclusion that the approximation set () is an optimal approximation set of interval set is drawn and proved successfully. The change rules of () with different binary relations are analyzed in detail. Finally, a kind of crisp approximation set of the interval set is constructed. We hope this research work will promote the development of both the interval set model and granular computing theory. Qinghua Zhang, Jin Wang, Guoyin Wang, and Feng Hu Copyright © 2014 Qinghua Zhang et al. All rights reserved. Aggregated Recommendation through Random Forests Mon, 11 Aug 2014 05:31:32 +0000 Aggregated recommendation refers to the process of suggesting one kind of items to a group of users. Compared to user-oriented or item-oriented approaches, it is more general and, therefore, more appropriate for cold-start recommendation. In this paper, we propose a random forest approach to create aggregated recommender systems. The approach is used to predict the rating of a group of users to a kind of items. In the preprocessing stage, we merge user, item, and rating information to construct an aggregated decision table, where rating information serves as the decision attribute. We also model the data conversion process corresponding to the new user, new item, and both new problems. In the training stage, a forest is built for the aggregated training set, where each leaf is assigned a distribution of discrete rating. In the testing stage, we present four predicting approaches to compute evaluation values based on the distribution of each tree. Experiments results on the well-known MovieLens dataset show that the aggregated approach maintains an acceptable level of accuracy. Heng-Ru Zhang, Fan Min, and Xu He Copyright © 2014 Heng-Ru Zhang et al. All rights reserved. Congestion Control for a Fair Packet Delivery in WSN: From a Complex System Perspective Sun, 10 Aug 2014 08:24:04 +0000 In this work, we propose that packets travelling across a wireless sensor network (WSN) can be seen as the active agents that make up a complex system, just like a bird flock or a fish school, for instance. From this perspective, the tools and models that have been developed to study this kind of systems have been applied. This is in order to create a distributed congestion control based on a set of simple rules programmed at the nodes of the WSN. Our results show that it is possible to adapt the carried traffic to the network capacity, even under stressing conditions. Also, the network performance shows a smooth degradation when the traffic goes beyond a threshold which is settled by the proposed self-organized control. In contrast, without any control, the network collapses before this threshold. The use of the proposed solution provides an effective strategy to address some of the common problems found in WSN deployment by providing a fair packet delivery. In addition, the network congestion is mitigated using adaptive traffic mechanisms based on a satisfaction parameter assessed by each packet which has impact on the global satisfaction of the traffic carried by the WSN. Daniela Aguirre-Guerrero, Ricardo Marcelín-Jiménez, Enrique Rodriguez-Colina, and Michael Pascoe-Chalke Copyright © 2014 Daniela Aguirre-Guerrero et al. All rights reserved. A Variable Neighborhood Walksat-Based Algorithm for MAX-SAT Problems Wed, 06 Aug 2014 10:48:03 +0000 The simplicity of the maximum satisfiability problem (MAX-SAT) combined with its applicability in many areas of artificial intelligence and computing science made it one of the fundamental optimization problems. This NP-complete problem refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weights of satisfied clauses) in a Boolean formula. The Walksat algorithm is considered to be the main skeleton underlying almost all local search algorithms for MAX-SAT. Most local search algorithms including Walksat rely on the 1-flip neighborhood structure. This paper introduces a variable neighborhood walksat-based algorithm. The neighborhood structure can be combined easily using any local search algorithm. Its effectiveness is compared with existing algorithms using 1-flip neighborhood structure and solvers such as CCLS and Optimax from the eighth MAX-SAT evaluation. Noureddine Bouhmala Copyright © 2014 Noureddine Bouhmala. All rights reserved. A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis Wed, 06 Aug 2014 00:00:00 +0000 Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection. Jaison Bennet, Chilambuchelvan Arul Ganaprakasam, and Kannan Arputharaj Copyright © 2014 Jaison Bennet et al. All rights reserved. A Modified Active Appearance Model Based on an Adaptive Artificial Bee Colony Wed, 06 Aug 2014 00:00:00 +0000 Active appearance model (AAM) is one of the most popular model-based approaches that have been extensively used to extract features by highly accurate modeling of human faces under various physical and environmental circumstances. However, in such active appearance model, fitting the model with original image is a challenging task. State of the art shows that optimization method is applicable to resolve this problem. However, another common problem is applying optimization. Hence, in this paper we propose an AAM based face recognition technique, which is capable of resolving the fitting problem of AAM by introducing a new adaptive ABC algorithm. The adaptation increases the efficiency of fitting as against the conventional ABC algorithm. We have used three datasets: CASIA dataset, property 2.5D face dataset, and UBIRIS v1 images dataset in our experiments. The results have revealed that the proposed face recognition technique has performed effectively, in terms of accuracy of face recognition. Mohammed Hasan Abdulameer, Siti Norul Huda Sheikh Abdullah, and Zulaiha Ali Othman Copyright © 2014 Mohammed Hasan Abdulameer et al. All rights reserved. A Comprehensive Availability Modeling and Analysis of a Virtualized Servers System Using Stochastic Reward Nets Tue, 05 Aug 2014 13:13:00 +0000 It is important to assess availability of virtualized systems in IT business infrastructures. Previous work on availability modeling and analysis of the virtualized systems used a simplified configuration and assumption in which only one virtual machine (VM) runs on a virtual machine monitor (VMM) hosted on a physical server. In this paper, we show a comprehensive availability model using stochastic reward nets (SRN). The model takes into account (i) the detailed failures and recovery behaviors of multiple VMs, (ii) various other failure modes and corresponding recovery behaviors (e.g., hardware faults, failure and recovery due to Mandelbugs and aging-related bugs), and (iii) dependency between different subcomponents (e.g., between physical host failure and VMM, etc.) in a virtualized servers system. We also show numerical analysis on steady state availability, downtime in hours per year, transaction loss, and sensitivity analysis. This model provides a new finding on how to increase system availability by combining both software rejuvenations at VM and VMM in a wise manner. Tuan Anh Nguyen, Dong Seong Kim, and Jong Sou Park Copyright © 2014 Tuan Anh Nguyen et al. All rights reserved. A Survey on Personal Data Cloud Tue, 05 Aug 2014 11:48:13 +0000 Personal data represent the e-history of a person and are of great significance to the person, but they are essentially produced and governed by various distributed services and there lacks a global and centralized view. In recent years, researchers pay attention to Personal Data Cloud (PDC) which aggregates the heterogeneous personal data scattered in different clouds into one cloud, so that a person could effectively store, acquire, and share their data. This paper makes a short survey on PDC research by summarizing related papers published in recent years. The concept, classification, and significance of personal data are elaborately introduced and then the semantics correlation and semantics representation of personal data are discussed. A multilayer reference architecture of PDC, including its core components and a real-world operational scenario showing how the reference architecture works, is introduced in detail. Existing commercial PDC products/prototypes are listed and compared from several perspectives. Five open issues to improve the shortcomings of current PDC research are put forward. Jiaqiu Wang and Zhongjie Wang Copyright © 2014 Jiaqiu Wang and Zhongjie Wang. All rights reserved. A New Approach for Resolving Conflicts in Actionable Behavioral Rules Tue, 05 Aug 2014 06:37:57 +0000 Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users’ best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous valid method was proposed. However, inconsistency of the measure for rule evaluating may hinder its performance. To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. We also introduce a tunable weight parameter to allow the flexibility of integration. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results indicate that our approach outperforms those from previous research. Peng Su, Dan Zhu, and Daniel Zeng Copyright © 2014 Peng Su et al. All rights reserved. Tuning of Kalman Filter Parameters via Genetic Algorithm for State-of-Charge Estimation in Battery Management System Tue, 05 Aug 2014 00:00:00 +0000 In this work, a state-space battery model is derived mathematically to estimate the state-of-charge (SoC) of a battery system. Subsequently, Kalman filter (KF) is applied to predict the dynamical behavior of the battery model. Results show an accurate prediction as the accumulated error, in terms of root-mean-square (RMS), is a very small value. From this work, it is found that different sets of and values (KF’s parameters) can be applied for better performance and hence lower RMS error. This is the motivation for the application of a metaheuristic algorithm. Hence, the result is further improved by applying a genetic algorithm (GA) to tune and parameters of the KF. In an online application, a GA can be applied to obtain the optimal parameters of the KF before its application to a real plant (system). This simply means that the instantaneous response of the KF is not affected by the time consuming GA as this approach is applied only once to obtain the optimal parameters. The relevant workable MATLAB source codes are given in the appendix to ease future work and analysis in this area. T. O. Ting, Ka Lok Man, Eng Gee Lim, and Mark Leach Copyright © 2014 T. O. Ting et al. All rights reserved. A Novel Method of the Generalized Interval-Valued Fuzzy Rough Approximation Operators Mon, 04 Aug 2014 09:02:56 +0000 Rough set theory is a suitable tool for dealing with the imprecision, uncertainty, incompleteness, and vagueness of knowledge. In this paper, new lower and upper approximation operators for generalized fuzzy rough sets are constructed, and their definitions are expanded to the interval-valued environment. Furthermore, the properties of this type of rough sets are analyzed. These operators are shown to be equivalent to the generalized interval fuzzy rough approximation operators introduced by Dubois, which are determined by any interval-valued fuzzy binary relation expressed in a generalized approximation space. Main properties of these operators are discussed under different interval-valued fuzzy binary relations, and the illustrative examples are given to demonstrate the main features of the proposed operators. Tianyu Xue, Zhan’ao Xue, Huiru Cheng, Jie Liu, and Tailong Zhu Copyright © 2014 Tianyu Xue et al. All rights reserved. A CBR-Based and MAHP-Based Customer Value Prediction Model for New Product Development Mon, 04 Aug 2014 07:29:15 +0000 In the fierce market environment, the enterprise which wants to meet customer needs and boost its market profit and share must focus on the new product development. To overcome the limitations of previous research, Chan et al. proposed a dynamic decision support system to predict the customer lifetime value (CLV) for new product development. However, to better meet the customer needs, there are still some deficiencies in their model, so this study proposes a CBR-based and MAHP-based customer value prediction model for a new product (C&M-CVPM). CBR (case based reasoning) can reduce experts’ workload and evaluation time, while MAHP (multiplicative analytic hierarchy process) can use actual but average influencing factor’s effectiveness in stimulation, and at same time C&M-CVPM uses dynamic customers’ transition probability which is more close to reality. This study not only introduces the realization of CBR and MAHP, but also elaborates C&M-CVPM’s three main modules. The application of the proposed model is illustrated and confirmed to be sensible and convincing through a stimulation experiment. Yu-Jie Zhao, Xin-xing Luo, and Li Deng Copyright © 2014 Yu-Jie Zhao et al. All rights reserved. An Adaboost-Backpropagation Neural Network for Automated Image Sentiment Classification Mon, 04 Aug 2014 05:06:50 +0000 The development of multimedia technology and the popularisation of image capture devices have resulted in the rapid growth of digital images. The reliance on advanced technology to extract and automatically classify the emotional semantics implicit in images has become a critical problem. We proposed an emotional semantic classification method for images based on the Adaboost-backpropagation (BP) neural network, using natural scenery images as examples. We described image emotions using the Ortony, Clore, and Collins emotion model and constructed a strong classifier by integrating 15 outputs of a BP neural network based on the Adaboost algorithm. The objective of the study was to improve the efficiency of emotional image classification. Using 600 natural scenery images downloaded from the Baidu photo channel to train and test the model, our experiments achieved results superior to the results obtained using the BP neural network method. The accuracy rate increased by approximately 15% compared with the method previously reported in the literature. The proposed method provides a foundation for the development of additional automatic sentiment image classification methods and demonstrates practical value. Jianfang Cao, Junjie Chen, and Haifang Li Copyright © 2014 Jianfang Cao et al. All rights reserved. Crossover versus Mutation: A Comparative Analysis of the Evolutionary Strategy of Genetic Algorithms Applied to Combinatorial Optimization Problems Mon, 04 Aug 2014 00:00:00 +0000 Since their first formulation, genetic algorithms (GAs) have been one of the most widely used techniques to solve combinatorial optimization problems. The basic structure of the GAs is known by the scientific community, and thanks to their easy application and good performance, GAs are the focus of a lot of research works annually. Although throughout history there have been many studies analyzing various concepts of GAs, in the literature there are few studies that analyze objectively the influence of using blind crossover operators for combinatorial optimization problems. For this reason, in this paper a deep study on the influence of using them is conducted. The study is based on a comparison of nine techniques applied to four well-known combinatorial optimization problems. Six of the techniques are GAs with different configurations, and the remaining three are evolutionary algorithms that focus exclusively on the mutation process. Finally, to perform a reliable comparison of these results, a statistical study of them is made, performing the normal distribution -test. E. Osaba, R. Carballedo, F. Diaz, E. Onieva, I. de la Iglesia, and A. Perallos Copyright © 2014 E. Osaba et al. All rights reserved. A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification Sun, 03 Aug 2014 10:36:34 +0000 Feature selection in cancer classification is a central area of research in the field of bioinformatics and used to select the informative genes from thousands of genes of the microarray. The genes are ranked based on T-statistics, signal-to-noise ratio (SNR), and F-test values. The swarm intelligence (SI) technique finds the informative genes from the top-m ranked genes. These selected genes are used for classification. In this paper the shuffled frog leaping with Lévy flight (SFLLF) is proposed for feature selection. In SFLLF, the Lévy flight is included to avoid premature convergence of shuffled frog leaping (SFL) algorithm. The SI techniques such as particle swarm optimization (PSO), cuckoo search (CS), SFL, and SFLLF are used for feature selection which identifies informative genes for classification. The k-nearest neighbour (k-NN) technique is used to classify the samples. The proposed work is applied on 10 different benchmark datasets and examined with SI techniques. The experimental results show that the results obtained from k-NN classifier through SFLLF feature selection method outperform PSO, CS, and SFL. Chellamuthu Gunavathi and Kandasamy Premalatha Copyright © 2014 Chellamuthu Gunavathi and Kandasamy Premalatha. All rights reserved. Improved Bat Algorithm Applied to Multilevel Image Thresholding Sun, 03 Aug 2014 08:21:39 +0000 Multilevel image thresholding is a very important image processing technique that is used as a basis for image segmentation and further higher level processing. However, the required computational time for exhaustive search grows exponentially with the number of desired thresholds. Swarm intelligence metaheuristics are well known as successful and efficient optimization methods for intractable problems. In this paper, we adjusted one of the latest swarm intelligence algorithms, the bat algorithm, for the multilevel image thresholding problem. The results of testing on standard benchmark images show that the bat algorithm is comparable with other state-of-the-art algorithms. We improved standard bat algorithm, where our modifications add some elements from the differential evolution and from the artificial bee colony algorithm. Our new proposed improved bat algorithm proved to be better than five other state-of-the-art algorithms, improving quality of results in all cases and significantly improving convergence speed. Adis Alihodzic and Milan Tuba Copyright © 2014 Adis Alihodzic and Milan Tuba. All rights reserved. Focusing on the Golden Ball Metaheuristic: An Extended Study on a Wider Set of Problems Sun, 03 Aug 2014 07:02:15 +0000 Nowadays, the development of new metaheuristics for solving optimization problems is a topic of interest in the scientific community. In the literature, a large number of techniques of this kind can be found. Anyway, there are many recently proposed techniques, such as the artificial bee colony and imperialist competitive algorithm. This paper is focused on one recently published technique, the one called Golden Ball (GB). The GB is a multiple-population metaheuristic based on soccer concepts. Although it was designed to solve combinatorial optimization problems, until now, it has only been tested with two simple routing problems: the traveling salesman problem and the capacitated vehicle routing problem. In this paper, the GB is applied to four different combinatorial optimization problems. Two of them are routing problems, which are more complex than the previously used ones: the asymmetric traveling salesman problem and the vehicle routing problem with backhauls. Additionally, one constraint satisfaction problem (the n-queen problem) and one combinatorial design problem (the one-dimensional bin packing problem) have also been used. The outcomes obtained by GB are compared with the ones got by two different genetic algorithms and two distributed genetic algorithms. Additionally, two statistical tests are conducted to compare these results. E. Osaba, F. Diaz, R. Carballedo, E. Onieva, and A. Perallos Copyright © 2014 E. Osaba et al. All rights reserved. Models and Frameworks: A Synergistic Association for Developing Component-Based Applications Sun, 03 Aug 2014 00:00:00 +0000 The use of frameworks and components has been shown to be effective in improving software productivity and quality. However, the results in terms of reuse and standardization show a dearth of portability either of designs or of component-based implementations. This paper, which is based on the model driven software development paradigm, presents an approach that separates the description of component-based applications from their possible implementations for different platforms. This separation is supported by automatic integration of the code obtained from the input models into frameworks implemented using object-oriented technology. Thus, the approach combines the benefits of modeling applications from a higher level of abstraction than objects, with the higher levels of code reuse provided by frameworks. In order to illustrate the benefits of the proposed approach, two representative case studies that use both an existing framework and an ad hoc framework, are described. Finally, our approach is compared with other alternatives in terms of the cost of software development. Diego Alonso, Francisco Sánchez-Ledesma, Pedro Sánchez, Juan A. Pastor, and Bárbara Álvarez Copyright © 2014 Diego Alonso et al. All rights reserved. A Partition-Based Active Contour Model Incorporating Local Information for Image Segmentation Thu, 24 Jul 2014 11:19:32 +0000 Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images. Jiao Shi, Jiaji Wu, Anand Paul, Licheng Jiao, and Maoguo Gong Copyright © 2014 Jiao Shi et al. All rights reserved. N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering Thu, 24 Jul 2014 11:15:34 +0000 This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements. Farman Ullah, Ghulam Sarwar, and Sungchang Lee Copyright © 2014 Farman Ullah et al. All rights reserved. A Primal Analysis System of Brain Neurons Data Thu, 24 Jul 2014 10:02:52 +0000 It is a very challenging work to classify the 86 billions of neurons in the human brain. The most important step is to get the features of these neurons. In this paper, we present a primal system to analyze and extract features from brain neurons. First, we make analysis on the original data of neurons in which one neuron contains six parameters: room type, , , coordinate range, total number of leaf nodes, and fuzzy volume of neurons. Then, we extract three important geometry features including rooms type, number of leaf nodes, and fuzzy volume. As application, we employ the feature database to fit the basic procedure of neuron growth. The result shows that the proposed system is effective. Dong-Mei Pu, Da-Qi Gao, and Yu-Bo Yuan Copyright © 2014 Dong-Mei Pu et al. All rights reserved. A User Authentication Scheme Using Physiological and Behavioral Biometrics for Multitouch Devices Thu, 24 Jul 2014 09:27:06 +0000 With the rapid growth of mobile network, tablets and smart phones have become sorts of keys to access personal secured services in our daily life. People use these devices to manage personal finances, shop on the Internet, and even pay at vending machines. Besides, it also helps us get connected with friends and business partners through social network applications, which were widely used as personal identifications in both real and virtual societies. However, these devices use inherently weak authentication mechanism, based upon passwords and PINs that is not changed all the time. Although forcing users to change password periodically can enhance the security level, it may also be considered annoyances for users. Biometric technologies are straightforward because of the simple authentication process. However, most of the traditional biometrics methodologies require diverse equipment to acquire biometric information, which may be expensive and not portable. This paper proposes a multibiometric user authentication scheme with both physiological and behavioral biometrics. Only simple rotations with fingers on multitouch devices are required to enhance the security level without annoyances for users. In addition, the user credential is replaceable to prevent from the privacy leakage. Chorng-Shiuh Koong, Tzu-I Yang, and Chien-Chao Tseng Copyright © 2014 Chorng-Shiuh Koong et al. All rights reserved. Instance Transfer Learning with Multisource Dynamic TrAdaBoost Thu, 24 Jul 2014 08:08:19 +0000 Since the transfer learning can employ knowledge in relative domains to help the learning tasks in current target domain, compared with the traditional learning it shows the advantages of reducing the learning cost and improving the learning efficiency. Focused on the situation that sample data from the transfer source domain and the target domain have similar distribution, an instance transfer learning method based on multisource dynamic TrAdaBoost is proposed in this paper. In this method, knowledge from multiple source domains is used well to avoid negative transfer; furthermore, the information that is conducive to target task learning is obtained to train candidate classifiers. The theoretical analysis suggests that the proposed algorithm improves the capability that weight entropy drifts from source to target instances by means of adding the dynamic factor, and the classification effectiveness is better than single source transfer. Finally, experimental results show that the proposed algorithm has higher classification accuracy. Qian Zhang, Haigang Li, Yong Zhang, and Ming Li Copyright © 2014 Qian Zhang et al. All rights reserved. An Efficient Algorithm for Maximizing Range Sum Queries in a Road Network Thu, 24 Jul 2014 07:32:32 +0000 Given a set of positive-weighted points and a query rectangle r (specified by a client) of given extents, the goal of a maximizing range sum (MaxRS) query is to find the optimal location of r such that the total weights of all the points covered by r are maximized. All existing methods for processing MaxRS queries assume the Euclidean distance metric. In many location-based applications, however, the motion of a client may be constrained by an underlying (spatial) road network; that is, the client cannot move freely in space. This paper addresses the problem of processing MaxRS queries in a road network. We propose the external-memory algorithm that is suited for a large road network database. In addition, in contrast to the existing methods, which retrieve only one optimal location, our proposed algorithm retrieves all the possible optimal locations. Through simulations, we evaluate the performance of the proposed algorithm. Tien-Khoi Phan, HaRim Jung, and Ung-Mo Kim Copyright © 2014 Tien-Khoi Phan et al. All rights reserved. AVQS: Attack Route-Based Vulnerability Quantification Scheme for Smart Grid Thu, 24 Jul 2014 00:00:00 +0000 A smart grid is a large, consolidated electrical grid system that includes heterogeneous networks and systems. Based on the data, a smart grid system has a potential security threat in its network connectivity. To solve this problem, we develop and apply a novel scheme to measure the vulnerability in a smart grid domain. Vulnerability quantification can be the first step in security analysis because it can help prioritize the security problems. However, existing vulnerability quantification schemes are not suitable for smart grid because they do not consider network vulnerabilities. We propose a novel attack route-based vulnerability quantification scheme using a network vulnerability score and an end-to-end security score, depending on the specific smart grid network environment to calculate the vulnerability score for a particular attack route. To evaluate the proposed approach, we derive several attack scenarios from the advanced metering infrastructure domain. The experimental results of the proposed approach and the existing common vulnerability scoring system clearly show that we need to consider network connectivity for more optimized vulnerability quantification. Jongbin Ko, Hyunwoo Lim, Seokjun Lee, and Taeshik Shon Copyright © 2014 Jongbin Ko et al. All rights reserved. A Service Based Adaptive U-Learning System Using UX Wed, 23 Jul 2014 11:37:22 +0000 In recent years, traditional development techniques for e-learning systems have been changing to become more convenient and efficient. One new technology in the development of application systems includes both cloud and ubiquitous computing. Cloud computing can support learning system processes by using services while ubiquitous computing can provide system operation and management via a high performance technical process and network. In the cloud computing environment, a learning service application can provide a business module or process to the user via the internet. This research focuses on providing the learning material and processes of courses by learning units using the services in a ubiquitous computing environment. And we also investigate functions that support users’ tailored materials according to their learning style. That is, we analyzed the user’s data and their characteristics in accordance with their user experience. We subsequently applied the learning process to fit on their learning performance and preferences. Finally, we demonstrate how the proposed system outperforms learning effects to learners better than existing techniques. Hwa-Young Jeong and Gangman Yi Copyright © 2014 Hwa-Young Jeong and Gangman Yi. All rights reserved. Multiobjective Memetic Estimation of Distribution Algorithm Based on an Incremental Tournament Local Searcher Wed, 23 Jul 2014 10:06:00 +0000 A novel hybrid multiobjective algorithm is presented in this paper, which combines a new multiobjective estimation of distribution algorithm, an efficient local searcher and ε-dominance. Besides, two multiobjective problems with variable linkages strictly based on manifold distribution are proposed. The Pareto set to the continuous multiobjective optimization problems, in the decision space, is a piecewise low-dimensional continuous manifold. The regularity by the manifold features just build probability distribution model by globally statistical information from the population, yet, the efficiency of promising individuals is not well exploited, which is not beneficial to search and optimization process. Hereby, an incremental tournament local searcher is designed to exploit local information efficiently and accelerate convergence to the true Pareto-optimal front. Besides, since ε-dominance is a strategy that can make multiobjective algorithm gain well distributed solutions and has low computational complexity, ε-dominance and the incremental tournament local searcher are combined here. The novel memetic multiobjective estimation of distribution algorithm, MMEDA, was proposed accordingly. The algorithm is validated by experiment on twenty-two test problems with and without variable linkages of diverse complexities. Compared with three state-of-the-art multiobjective optimization algorithms, our algorithm achieves comparable results in terms of convergence and diversity metrics. Kaifeng Yang, Li Mu, Dongdong Yang, Feng Zou, Lei Wang, and Qiaoyong Jiang Copyright © 2014 Kaifeng Yang et al. All rights reserved. The Study on Stage Financing Model of IT Project Investment Wed, 23 Jul 2014 00:00:00 +0000 Stage financing is the basic operation of venture capital investment. In investment, usually venture capitalists use different strategies to obtain the maximum returns. Due to its advantages to reduce the information asymmetry and agency cost, stage financing is widely used by venture capitalists. Although considerable attentions are devoted to stage financing, very little is known about the risk aversion strategies of IT projects. This paper mainly addresses the problem of risk aversion of venture capital investment in IT projects. Based on the analysis of characteristics of venture capital investment of IT projects, this paper introduces a real option pricing model to measure the value brought by the stage financing strategy and design a risk aversion model for IT projects. Because real option pricing method regards investment activity as contingent decision, it helps to make judgment on the management flexibility of IT projects and then make a more reasonable evaluation about the IT programs. Lastly by being applied to a real case, it further illustrates the effectiveness and feasibility of the model. Si-hua Chen, Sheng-hua Xu, Changhoon Lee, Neal N. Xiong, and Wei He Copyright © 2014 Si-hua Chen et al. All rights reserved. The Coral Reefs Optimization Algorithm: A Novel Metaheuristic for Efficiently Solving Optimization Problems Tue, 22 Jul 2014 14:09:53 +0000 This paper presents a novel bioinspired algorithm to tackle complex optimization problems: the coral reefs optimization (CRO) algorithm. The CRO algorithm artificially simulates a coral reef, where different corals (namely, solutions to the optimization problem considered) grow and reproduce in coral colonies, fighting by choking out other corals for space in the reef. This fight for space, along with the specific characteristics of the corals' reproduction, produces a robust metaheuristic algorithm shown to be powerful for solving hard optimization problems. In this research the CRO algorithm is tested in several continuous and discrete benchmark problems, as well as in practical application scenarios (i.e., optimum mobile network deployment and off-shore wind farm design). The obtained results confirm the excellent performance of the proposed algorithm and open line of research for further application of the algorithm to real-world problems. S. Salcedo-Sanz, J. Del Ser, I. Landa-Torres, S. Gil-López, and J. A. Portilla-Figueras Copyright © 2014 S. Salcedo-Sanz et al. All rights reserved. Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm Tue, 22 Jul 2014 14:05:07 +0000 Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program. Soodabeh Darzi, Tiong Sieh Kiong, Mohammad Tariqul Islam, Mahamod Ismail, Salehin Kibria, and Balasem Salem Copyright © 2014 Soodabeh Darzi et al. All rights reserved. δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions Tue, 22 Jul 2014 14:02:52 +0000 Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation. Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively. The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities. This study suggests potential application areas and new research trends concerning rough set theory. Hengrong Ju, Huili Dou, Yong Qi, Hualong Yu, Dongjun Yu, and Jingyu Yang Copyright © 2014 Hengrong Ju et al. All rights reserved. A Cuckoo Search Algorithm for Multimodal Optimization Tue, 22 Jul 2014 11:45:51 +0000 Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration. Erik Cuevas and Adolfo Reyna-Orta Copyright © 2014 Erik Cuevas and Adolfo Reyna-Orta. All rights reserved. A Survey of Partition-Based Techniques for Copy-Move Forgery Detection Tue, 22 Jul 2014 11:32:55 +0000 A copy-move forged image results from a specific type of image tampering procedure carried out by copying a part of an image and pasting it on one or more parts of the same image generally to maliciously hide unwanted objects/regions or clone an object. Therefore, detecting such forgeries mainly consists in devising ways of exposing identical or relatively similar areas in images. This survey attempts to cover existing partition-based copy-move forgery detection techniques. Wandji Nanda Nathalie Diane, Sun Xingming, and Fah Kue Moise Copyright © 2014 Wandji Nanda Nathalie Diane et al. All rights reserved. A Community Detection Algorithm Based on Topology Potential and Spectral Clustering Tue, 22 Jul 2014 10:17:43 +0000 Community detection is of great value for complex networks in understanding their inherent law and predicting their behavior. Spectral clustering algorithms have been successfully applied in community detection. This kind of methods has two inadequacies: one is that the input matrixes they used cannot provide sufficient structural information for community detection and the other is that they cannot necessarily derive the proper community number from the ladder distribution of eigenvector elements. In order to solve these problems, this paper puts forward a novel community detection algorithm based on topology potential and spectral clustering. The new algorithm constructs the normalized Laplacian matrix with nodes’ topology potential, which contains rich structural information of the network. In addition, the new algorithm can automatically get the optimal community number from the local maximum potential nodes. Experiments results showed that the new algorithm gave excellent performance on artificial networks and real world networks and outperforms other community detection methods. Zhixiao Wang, Zhaotong Chen, Ya Zhao, and Shaoda Chen Copyright © 2014 Zhixiao Wang et al. All rights reserved. Information Filtering via Biased Random Walk on Coupled Social Network Tue, 22 Jul 2014 09:23:24 +0000 The recommender systems have advanced a great deal in the past two decades. However, most researchers focus their attentions on mining the similarities among users or objects in recommender systems and overlook the social influence which plays an important role in users’ purchase process. In this paper, we design a biased random walk algorithm on coupled social networks which gives recommendation results based on both social interests and users’ preference. Numerical analyses on two real data sets, Epinions and Friendfeed, demonstrate the improvement of recommendation performance by taking social interests into account, and experimental results show that our algorithm can alleviate the user cold-start problem more effectively compared with the mass diffusion and user-based collaborative filtering methods. Da-Cheng Nie, Zi-Ke Zhang, Qiang Dong, Chongjing Sun, and Yan Fu Copyright © 2014 Da-Cheng Nie et al. All rights reserved. Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection Tue, 22 Jul 2014 07:35:31 +0000 We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches. Changyu Liu, Bin Lu, and Huiling Li Copyright © 2014 Changyu Liu et al. All rights reserved. CUDT: A CUDA Based Decision Tree Algorithm Tue, 22 Jul 2014 00:00:00 +0000 Decision tree is one of the famous classification methods in data mining. Many researches have been proposed, which were focusing on improving the performance of decision tree. However, those algorithms are developed and run on traditional distributed systems. Obviously the latency could not be improved while processing huge data generated by ubiquitous sensing node in the era without new technology help. In order to improve data processing latency in huge data mining, in this paper, we design and implement a new parallelized decision tree algorithm on a CUDA (compute unified device architecture), which is a GPGPU solution provided by NVIDIA. In the proposed system, CPU is responsible for flow control while the GPU is responsible for computation. We have conducted many experiments to evaluate system performance of CUDT and made a comparison with traditional CPU version. The results show that CUDT is 5∼55 times faster than Weka-j48 and is 18 times speedup than SPRINT for large data set. Win-Tsung Lo, Yue-Shan Chang, Ruey-Kai Sheu, Chun-Chieh Chiu, and Shyan-Ming Yuan Copyright © 2014 Win-Tsung Lo et al. All rights reserved. MAC Protocol for Ad Hoc Networks Using a Genetic Algorithm Mon, 21 Jul 2014 11:13:42 +0000 The problem of obtaining the transmission rate in an ad hoc network consists in adjusting the power of each node to ensure the signal to interference ratio (SIR) and the energy required to transmit from one node to another is obtained at the same time. Therefore, an optimal transmission rate for each node in a medium access control (MAC) protocol based on CSMA-CDMA (carrier sense multiple access-code division multiple access) for ad hoc networks can be obtained using evolutionary optimization. This work proposes a genetic algorithm for the transmission rate election considering a perfect power control, and our proposition achieves improvement of 10% compared with the scheme that handles the handshaking phase to adjust the transmission rate. Furthermore, this paper proposes a genetic algorithm that solves the problem of power combining, interference, data rate, and energy ensuring the signal to interference ratio in an ad hoc network. The result of the proposed genetic algorithm has a better performance (15%) compared to the CSMA-CDMA protocol without optimizing. Therefore, we show by simulation the effectiveness of the proposed protocol in terms of the throughput. Omar Elizarraras, Marco Panduro, Aldo L. Méndez, and Alberto Reyna Copyright © 2014 Omar Elizarraras et al. All rights reserved. Comprehensive Aspectual UML Approach to Support AspectJ Mon, 21 Jul 2014 10:37:59 +0000 Unified Modeling Language is the most popular and widely used Object-Oriented modelling language in the IT industry. This study focuses on investigating the ability to expand UML to some extent to model crosscutting concerns (Aspects) to support AspectJ. Through a comprehensive literature review, we identify and extensively examine all the available Aspect-Oriented UML modelling approaches and find that the existing Aspect-Oriented Design Modelling approaches using UML cannot be considered to provide a framework for a comprehensive Aspectual UML modelling approach and also that there is a lack of adequate Aspect-Oriented tool support. This study also proposes a set of Aspectual UML semantic rules and attempts to generate AspectJ pseudocode from UML diagrams. The proposed Aspectual UML modelling approach is formally evaluated using a focus group to test six hypotheses regarding performance; a “good design” criteria-based evaluation to assess the quality of the design; and an AspectJ-based evaluation as a reference measurement-based evaluation. The results of the focus group evaluation confirm all the hypotheses put forward regarding the proposed approach. The proposed approach provides a comprehensive set of Aspectual UML structural and behavioral diagrams, which are designed and implemented based on a comprehensive and detailed set of AspectJ programming constructs. Aws Magableh, Zarina Shukur, and Noorazean Mohd. Ali Copyright © 2014 Aws Magableh et al. All rights reserved. A Novel Deployment Method for Communication-Intensive Applications in Service Clouds Mon, 21 Jul 2014 10:26:57 +0000 The service platforms are migrating to clouds for reasonably solving long construction periods, low resource utilizations, and isolated constructions of service platforms. However, when the migration is conducted in service clouds, there is a little focus of deploying communication-intensive applications in previous deployment methods. To address this problem, this paper proposed the combination of the online deployment and the offline deployment for deploying communication-intensive applications in service clouds. Firstly, the system architecture was designed for implementing the communication-aware deployment method for communication-intensive applications in service clouds. Secondly, in the online-deployment algorithm and the offline-deployment algorithm, service instances were deployed in an optimal cloud node based on the communication overhead which is determined by the communication traffic between services, as well as the communication performance between cloud nodes. Finally, the experimental results demonstrated that the proposed methods deployed communication-intensive applications effectively with lower latency and lower load compared with existing algorithms. Chuanchang Liu and Jingqi Yang Copyright © 2014 Chuanchang Liu and Jingqi Yang. All rights reserved. A Review of Subsequence Time Series Clustering Mon, 21 Jul 2014 08:18:11 +0000 Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. Seyedjamal Zolhavarieh, Saeed Aghabozorgi, and Ying Wah Teh Copyright © 2014 Seyedjamal Zolhavarieh et al. All rights reserved. Advanced Approach to Information Security Management System Model for Industrial Control System Mon, 21 Jul 2014 00:00:00 +0000 Organizations make use of important information in day-to-day business. Protecting sensitive information is imperative and must be managed. Companies in many parts of the world protect sensitive information using the international standard known as the information security management system (ISMS). ISO 27000 series is the international standard ISMS used to protect confidentiality, integrity, and availability of sensitive information. While an ISMS based on ISO 27000 series has no particular flaws for general information systems, it is unfit to manage sensitive information for industrial control systems (ICSs) because the first priority of industrial control is safety of the system. Therefore, a new information security management system based on confidentiality, integrity, and availability as well as safety is required for ICSs. This new ISMS must be mutually exclusive of an ICS. This paper provides a new paradigm of ISMS for ICSs, which will be shown to be more suitable than the existing ISMS. Sanghyun Park and Kyungho Lee Copyright © 2014 Sanghyun Park and Kyungho Lee. All rights reserved. Nonlinear Secret Image Sharing Scheme Mon, 21 Jul 2014 00:00:00 +0000 Over the past decade, most of secret image sharing schemes have been proposed by using Shamir's technique. It is based on a linear combination polynomial arithmetic. Although Shamir's technique based secret image sharing schemes are efficient and scalable for various environments, there exists a security threat such as Tompa-Woll attack. Renvall and Ding proposed a new secret sharing technique based on nonlinear combination polynomial arithmetic in order to solve this threat. It is hard to apply to the secret image sharing. In this paper, we propose a -threshold nonlinear secret image sharing scheme with steganography concept. In order to achieve a suitable and secure secret image sharing scheme, we adapt a modified LSB embedding technique with XOR Boolean algebra operation, define a new variable , and change a range of prime in sharing procedure. In order to evaluate efficiency and security of proposed scheme, we use the embedding capacity and PSNR. As a result of it, average value of PSNR and embedding capacity are 44.78 (dB) and bit-per-pixel (bpp), respectively. Sang-Ho Shin, Gil-Je Lee, and Kee-Young Yoo Copyright © 2014 Sang-Ho Shin et al. All rights reserved. Software Authority Transition through Multiple Distributors Sun, 20 Jul 2014 13:21:20 +0000 The rapid growth in the use of smartphones and tablets has changed the software distribution ecosystem. The trend today is to purchase software through application stores rather than from traditional offline markets. Smartphone and tablet users can install applications easily by purchasing from the online store deployed in their device. Several systems, such as Android or PC-based OS units, allow users to install software from multiple sources. Such openness, however, can promote serious threats, including malware and illegal usage. In order to prevent such threats, several stores use online authentication techniques. These methods can, however, also present a problem whereby even licensed users cannot use their purchased application. In this paper, we discuss these issues and provide an authentication method that will make purchased applications available to the registered user at all times. Kyusunk Han and Taeshik Shon Copyright © 2014 Kyusunk Han and Taeshik Shon. All rights reserved. Automatic Foreground Extraction Based on Difference of Gaussian Sun, 20 Jul 2014 00:00:00 +0000 A novel algorithm for automatic foreground extraction based on difference of Gaussian (DoG) is presented. In our algorithm, DoG is employed to find the candidate keypoints of an input image in different color layers. Then, a keypoints filter algorithm is proposed to get the keypoints by removing the pseudo-keypoints and rebuilding the important keypoints. Finally, Normalized cut (Ncut) is used to segment an image into several regions and locate the foreground with the number of keypoints in each region. Experiments on the given image data set demonstrate the effectiveness of our algorithm. Yubo Yuan, Yun Liu, Guanghui Dai, Jing Zhang, and Zhihua Chen Copyright © 2014 Yubo Yuan et al. All rights reserved. Semi-Supervised Learning of Statistical Models for Natural Language Understanding Sun, 20 Jul 2014 00:00:00 +0000 Natural language understanding is to specify a computational model that maps sentences to their semantic mean representation. In this paper, we propose a novel framework to train the statistical models without using expensive fully annotated data. In particular, the input of our framework is a set of sentences labeled with abstract semantic annotations. These annotations encode the underlying embedded semantic structural relations without explicit word/semantic tag alignment. The proposed framework can automatically induce derivation rules that map sentences to their semantic meaning representations. The learning framework is applied on two statistical models, the conditional random fields (CRFs) and the hidden Markov support vector machines (HM-SVMs). Our experimental results on the DARPA communicator data show that both CRFs and HM-SVMs outperform the baseline approach, previously proposed hidden vector state (HVS) model which is also trained on abstract semantic annotations. In addition, the proposed framework shows superior performance than two other baseline approaches, a hybrid framework combining HVS and HM-SVMs and discriminative training of HVS, with a relative error reduction rate of about 25% and 15% being achieved in -measure. Deyu Zhou and Yulan He Copyright © 2014 Deyu Zhou and Yulan He. All rights reserved. An Evaluation and Implementation of Rule-Based Home Energy Management System Using the Rete Algorithm Sun, 20 Jul 2014 00:00:00 +0000 In recent years, sensors become popular and Home Energy Management System (HEMS) takes an important role in saving energy without decrease in QoL (Quality of Life). Currently, many rule-based HEMSs have been proposed and almost all of them assume “IF-THEN” rules. The Rete algorithm is a typical pattern matching algorithm for IF-THEN rules. Currently, we have proposed a rule-based Home Energy Management System (HEMS) using the Rete algorithm. In the proposed system, rules for managing energy are processed by smart taps in network, and the loads for processing rules and collecting data are distributed to smart taps. In addition, the number of processes and collecting data are reduced by processing rules based on the Rete algorithm. In this paper, we evaluated the proposed system by simulation. In the simulation environment, rules are processed by a smart tap that relates to the action part of each rule. In addition, we implemented the proposed system as HEMS using smart taps. Tomoya Kawakami, Naotaka Fujita, Tomoki Yoshihisa, and Masahiko Tsukamoto Copyright © 2014 Tomoya Kawakami et al. All rights reserved. Covert Network Analysis for Key Player Detection and Event Prediction Using a Hybrid Classifier Sun, 20 Jul 2014 00:00:00 +0000 National security has gained vital importance due to increasing number of suspicious and terrorist events across the globe. Use of different subfields of information technology has also gained much attraction of researchers and practitioners to design systems which can detect main members which are actually responsible for such kind of events. In this paper, we present a novel method to predict key players from a covert network by applying a hybrid framework. The proposed system calculates certain centrality measures for each node in the network and then applies novel hybrid classifier for detection of key players. Our system also applies anomaly detection to predict any terrorist activity in order to help law enforcement agencies to destabilize the involved network. As a proof of concept, the proposed framework has been implemented and tested using different case studies including two publicly available datasets and one local network. Wasi Haider Butt, M. Usman Akram, Shoab A. Khan, and Muhammad Younus Javed Copyright © 2014 Wasi Haider Butt et al. All rights reserved. An Ant Colony Optimization Based Feature Selection for Web Page Classification Thu, 17 Jul 2014 13:51:36 +0000 The increased popularity of the web has caused the inclusion of huge amount of information to the web, and as a result of this explosive information growth, automated web page classification systems are needed to improve search engines’ performance. Web pages have a large number of features such as HTML/XML tags, URLs, hyperlinks, and text contents that should be considered during an automated classification process. The aim of this study is to reduce the number of features to be used to improve runtime and accuracy of the classification of web pages. In this study, we used an ant colony optimization (ACO) algorithm to select the best features, and then we applied the well-known C4.5, naive Bayes, and k nearest neighbor classifiers to assign class labels to web pages. We used the WebKB and Conference datasets in our experiments, and we showed that using the ACO for feature selection improves both accuracy and runtime performance of classification. We also showed that the proposed ACO based algorithm can select better features with respect to the well-known information gain and chi square feature selection methods. Esra Saraç and Selma Ayşe Özel Copyright © 2014 Esra Saraç and Selma Ayşe Özel. All rights reserved. Induced Unbalanced Linguistic Ordered Weighted Average and Its Application in Multiperson Decision Making Thu, 17 Jul 2014 12:53:45 +0000 Linguistic variables are very useful to evaluate alternatives in decision making problems because they provide a vocabulary in natural language rather than numbers. Some aggregation operators for linguistic variables force the use of a symmetric and uniformly distributed set of terms. The need to relax these conditions has recently been posited. This paper presents the induced unbalanced linguistic ordered weighted average (IULOWA) operator. This operator can deal with a set of unbalanced linguistic terms that are represented using fuzzy sets. We propose a new order-inducing criterion based on the specificity and fuzziness of the linguistic terms. Different relevancies are given to the fuzzy values according to their uncertainty degree. To illustrate the behaviour of the precision-based IULOWA operator, we present an environmental assessment case study in which a multiperson multicriteria decision making model is applied. Lucas Marin, Aida Valls, David Isern, Antonio Moreno, and José M. Merigó Copyright © 2014 Lucas Marin et al. All rights reserved. A Routing Path Construction Method for Key Dissemination Messages in Sensor Networks Thu, 17 Jul 2014 12:53:20 +0000 Authentication is an important security mechanism for detecting forged messages in a sensor network. Each cluster head (CH) in dynamic key distribution schemes forwards a key dissemination message that contains encrypted authentication keys within its cluster to next-hop nodes for the purpose of authentication. The forwarding path of the key dissemination message strongly affects the number of nodes to which the authentication keys in the message are actually distributed. We propose a routing method for the key dissemination messages to increase the number of nodes that obtain the authentication keys. In the proposed method, each node selects next-hop nodes to which the key dissemination message will be forwarded based on secret key indexes, the distance to the sink node, and the energy consumption of its neighbor nodes. The experimental results show that the proposed method can increase by 50–70% the number of nodes to which authentication keys in each cluster are distributed compared to geographic and energy-aware routing (GEAR). In addition, the proposed method can detect false reports earlier by using the distributed authentication keys, and it consumes less energy than GEAR when the false traffic ratio (FTR) is ≥10%. Soo Young Moon and Tae Ho Cho Copyright © 2014 Soo Young Moon and Tae Ho Cho. All rights reserved. Big Data: Survey, Technologies, Opportunities, and Challenges Thu, 17 Jul 2014 09:58:07 +0000 Big Data has gained much attention from the academia and the IT industry. In the digital and computing world, information is generated and collected at a rate that rapidly exceeds the boundary range. Currently, over 2 billion people worldwide are connected to the Internet, and over 5 billion individuals own mobile phones. By 2020, 50 billion devices are expected to be connected to the Internet. At this point, predicted data production will be 44 times greater than that in 2009. As information is transferred and shared at light speed on optic fiber and wireless networks, the volume of data and the speed of market growth increase. However, the fast growth rate of such large data generates numerous challenges, such as the rapid growth of data, transfer speed, diverse data, and security. Nonetheless, Big Data is still in its infancy stage, and the domain has not been reviewed in general. Hence, this study comprehensively surveys and classifies the various attributes of Big Data, including its nature, definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a data life cycle that uses the technologies and terminologies of Big Data. Future research directions in this field are determined based on opportunities and several open issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal techniques to address Big Data. Nawsher Khan, Ibrar Yaqoob, Ibrahim Abaker Targio Hashem, Zakira Inayat, Waleed Kamaleldin Mahmoud Ali, Muhammad Alam, Muhammad Shiraz, and Abdullah Gani Copyright © 2014 Nawsher Khan et al. All rights reserved. Realistic Facial Expression of Virtual Human Based on Color, Sweat, and Tears Effects Thu, 17 Jul 2014 08:57:23 +0000 Generating extreme appearances such as scared awaiting sweating while happy fit for tears (cry) and blushing (anger and happiness) is the key issue in achieving the high quality facial animation. The effects of sweat, tears, and colors are integrated into a single animation model to create realistic facial expressions of 3D avatar. The physical properties of muscles, emotions, or the fluid properties with sweating and tears initiators are incorporated. The action units (AUs) of facial action coding system are merged with autonomous AUs to create expressions including sadness, anger with blushing, happiness with blushing, and fear. Fluid effects such as sweat and tears are simulated using the particle system and smoothed-particle hydrodynamics (SPH) methods which are combined with facial animation technique to produce complex facial expressions. The effects of oxygenation of the facial skin color appearance are measured using the pulse oximeter system and the 3D skin analyzer. The result shows that virtual human facial expression is enhanced by mimicking actual sweating and tears simulations for all extreme expressions. The proposed method has contribution towards the development of facial animation industry and game as well as computer graphics. Mohammed Hazim Alkawaz, Ahmad Hoirul Basori, Dzulkifli Mohamad, and Farhan Mohamed Copyright © 2014 Mohammed Hazim Alkawaz et al. All rights reserved. Investigation of a Novel Common Subexpression Elimination Method for Low Power and Area Efficient DCT Architecture Wed, 16 Jul 2014 10:27:05 +0000 A wide interest has been observed to find a low power and area efficient hardware design of discrete cosine transform (DCT) algorithm. This research work proposed a novel Common Subexpression Elimination (CSE) based pipelined architecture for DCT, aimed at reproducing the cost metrics of power and area while maintaining high speed and accuracy in DCT applications. The proposed design combines the techniques of Canonical Signed Digit (CSD) representation and CSE to implement the multiplier-less method for fixed constant multiplication of DCT coefficients. Furthermore, symmetry in the DCT coefficient matrix is used with CSE to further decrease the number of arithmetic operations. This architecture needs a single-port memory to feed the inputs instead of multiport memory, which leads to reduction of the hardware cost and area. From the analysis of experimental results and performance comparisons, it is observed that the proposed scheme uses minimum logic utilizing mere 340 slices and 22 adders. Moreover, this design meets the real time constraints of different video/image coders and peak-signal-to-noise-ratio (PSNR) requirements. Furthermore, the proposed technique has significant advantages over recent well-known methods along with accuracy in terms of power reduction, silicon area usage, and maximum operating frequency by 41%, 15%, and 15%, respectively. M. F. Siddiqui, A. W. Reza, J. Kanesan, and H. Ramiah Copyright © 2014 M. F. Siddiqui et al. All rights reserved. Reinforcement Learning for Routing in Cognitive Radio Ad Hoc Networks Wed, 16 Jul 2014 10:06:36 +0000 Cognitive radio (CR) enables unlicensed users (or secondary users, SUs) to sense for and exploit underutilized licensed spectrum owned by the licensed users (or primary users, PUs). Reinforcement learning (RL) is an artificial intelligence approach that enables a node to observe, learn, and make appropriate decisions on action selection in order to maximize network performance. Routing enables a source node to search for a least-cost route to its destination node. While there have been increasing efforts to enhance the traditional RL approach for routing in wireless networks, this research area remains largely unexplored in the domain of routing in CR networks. This paper applies RL in routing and investigates the effects of various features of RL (i.e., reward function, exploitation, and exploration, as well as learning rate) through simulation. New approaches and recommendations are proposed to enhance the features in order to improve the network performance brought about by RL to routing. Simulation results show that the RL parameters of the reward function, exploitation, and exploration, as well as learning rate, must be well regulated, and the new approaches proposed in this paper improves SUs’ network performance without significantly jeopardizing PUs’ network performance, specifically SUs’ interference to PUs. Hasan A. A. Al-Rawi, Kok-Lim Alvin Yau, Hafizal Mohamad, Nordin Ramli, and Wahidah Hashim Copyright © 2014 Hasan A. A. Al-Rawi et al. All rights reserved. Event-Based User Classification in Weibo Media Wed, 16 Jul 2014 08:52:42 +0000 Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately. Liang Guo, Wendong Wang, Shiduan Cheng, and Xirong Que Copyright © 2014 Liang Guo et al. All rights reserved. Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm Wed, 16 Jul 2014 08:51:57 +0000 For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. Jie-sheng Wang, Shuang Han, Na-na Shen, and Shu-xia Li Copyright © 2014 Jie-sheng Wang et al. All rights reserved. A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods Wed, 16 Jul 2014 07:41:01 +0000 Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one. Jaehee Jung, Heung Ki Lee, and Gangman Yi Copyright © 2014 Jaehee Jung et al. All rights reserved. A Novel User Classification Method for Femtocell Network by Using Affinity Propagation Algorithm and Artificial Neural Network Wed, 16 Jul 2014 00:00:00 +0000 An artificial neural network (ANN) and affinity propagation (AP) algorithm based user categorization technique is presented. The proposed algorithm is designed for closed access femtocell network. ANN is used for user classification process and AP algorithm is used to optimize the ANN training process. AP selects the best possible training samples for faster ANN training cycle. The users are distinguished by using the difference of received signal strength in a multielement femtocell device. A previously developed directive microstrip antenna is used to configure the femtocell device. Simulation results show that, for a particular house pattern, the categorization technique without AP algorithm takes 5 indoor users and 10 outdoor users to attain an error-free operation. While integrating AP algorithm with ANN, the system takes 60% less training samples reducing the training time up to 50%. This procedure makes the femtocell more effective for closed access operation. Afaz Uddin Ahmed, Mohammad Tariqul Islam, Mahamod Ismail, Salehin Kibria, and Haslina Arshad Copyright © 2014 Afaz Uddin Ahmed et al. All rights reserved. Part-Based Visual Tracking via Online Weighted P-N Learning Tue, 15 Jul 2014 07:26:00 +0000 We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers. Heng Fan, Jinhai Xiang, Jun Xu, and Honghong Liao Copyright © 2014 Heng Fan et al. All rights reserved. Preserving Differential Privacy for Similarity Measurement in Smart Environments Tue, 15 Jul 2014 00:00:00 +0000 Advances in both sensor technologies and network infrastructures have encouraged the development of smart environments to enhance people’s life and living styles. However, collecting and storing user’s data in the smart environments pose severe privacy concerns because these data may contain sensitive information about the subject. Hence, privacy protection is now an emerging issue that we need to consider especially when data sharing is essential for analysis purpose. In this paper, we consider the case where two agents in the smart environment want to measure the similarity of their collected or stored data. We use similarity coefficient function as the measurement metric for the comparison with differential privacy model. Unlike the existing solutions, our protocol can facilitate more than one request to compute without modifying the protocol. Our solution ensures privacy protection for both the inputs and the computed results. Kok-Seng Wong and Myung Ho Kim Copyright © 2014 Kok-Seng Wong and Myung Ho Kim. All rights reserved. A Simple Quality Assessment Index for Stereoscopic Images Based on 3D Gradient Magnitude Tue, 15 Jul 2014 00:00:00 +0000 We present a simple quality assessment index for stereoscopic images based on 3D gradient magnitude. To be more specific, we construct 3D volume from the stereoscopic images across different disparity spaces and calculate pointwise 3D gradient magnitude similarity (3D-GMS) along three horizontal, vertical, and viewpoint directions. Then, the quality score is obtained by averaging the 3D-GMS scores of all points in the 3D volume. Experimental results on four publicly available 3D image quality assessment databases demonstrate that, in comparison with the most related existing methods, the devised algorithm achieves high consistency alignment with subjective assessment. Shanshan Wang, Feng Shao, Fucui Li, Mei Yu, and Gangyi Jiang Copyright © 2014 Shanshan Wang et al. All rights reserved. Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis Mon, 14 Jul 2014 11:30:35 +0000 One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%. Vahab Iranmanesh, Sharifah Mumtazah Syed Ahmad, Wan Azizun Wan Adnan, Salman Yussof, Olasimbo Ayodeji Arigbabu, and Fahad Layth Malallah Copyright © 2014 Vahab Iranmanesh et al. All rights reserved. Node Deployment Algorithm Based on Viscous Fluid Model for Wireless Sensor Networks Mon, 14 Jul 2014 09:24:29 +0000 With the scale expands, traditional deployment algorithms are becoming increasingly complicated than before, which are no longer fit for sensor networks. In order to reduce the complexity, we propose a node deployment algorithm based on viscous fluid model. In wireless sensor networks, sensor nodes are abstracted as fluid particles. Similar to the diffusion and self-propagation behavior of fluid particles, sensor nodes realize deployment in unknown region following the motion rules of fluid. Simulation results show that our algorithm archives good coverage rate and homogeneity in large-scale sensor networks. Jiguang Chen and Huanyan Qian Copyright © 2014 Jiguang Chen and Huanyan Qian. All rights reserved. Obtaining P3P Privacy Policies for Composite Services Sun, 13 Jul 2014 09:39:27 +0000 With the development of web services technology, web services have changed from single to composite services. Privacy protection in composite services is becoming an important issue. P3P (platform for privacy preferences) is a privacy policy language which was designed for single web services. It enables service providers to express how they will deal with the privacy information of service consumers. In order to solve the problem that P3P cannot be applied to composite services directly, we propose a method to obtain P3P privacy policies for composite services. In this method, we present the definitions of Purpose, Recipient, and Retention elements as well as Optional and Required attributes for P3P policies of composite services. We also provide an instantiation to illustrate the feasibility of the method. Yi Sun, Zhiqiu Huang, and Changbo Ke Copyright © 2014 Yi Sun et al. All rights reserved. The Potential of Using Brain Images for Authentication Thu, 10 Jul 2014 19:41:07 +0000 Biometric recognition (also known as biometrics) refers to the automated recognition of individuals based on their biological or behavioral traits. Examples of biometric traits include fingerprint, palmprint, iris, and face. The brain is the most important and complex organ in the human body. Can it be used as a biometric trait? In this study, we analyze the uniqueness of the brain and try to use the brain for identity authentication. The proposed brain-based verification system operates in two stages: gray matter extraction and gray matter matching. A modified brain segmentation algorithm is implemented for extracting gray matter from an input brain image. Then, an alignment-based matching algorithm is developed for brain matching. Experimental results on two data sets show that the proposed brain recognition system meets the high accuracy requirement of identity authentication. Though currently the acquisition of the brain is still time consuming and expensive, brain images are highly unique and have the potential possibility for authentication in view of pattern recognition. Fanglin Chen, Zongtan Zhou, Hui Shen, and Dewen Hu Copyright © 2014 Fanglin Chen et al. All rights reserved. Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression Thu, 10 Jul 2014 12:29:23 +0000 We introduce a new nonparametric outlier detection method for linear series, which requires no missing or removed data imputation. For an arithmetic progression (a series without outliers) with elements, the ratio () of the sum of the minimum and the maximum elements and the sum of all elements is always . always implies the existence of outliers. Usually, implies that the minimum is an outlier, and implies that the maximum is an outlier. Based upon this, we derived a new method for identifying significant and nonsignificant outliers, separately. Two different techniques were used to manage missing data and removed outliers: (1) recalculate the terms after (or before) the removed or missing element while maintaining the initial angle in relation to a certain point or (2) transform data into a constant value, which is not affected by missing or removed elements. With a reference element, which was not an outlier, the method detected all outliers from data sets with 6 to 1000 elements containing 50% outliers which deviated by a factor of to from the correct value. K. K. L. B. Adikaram, M. A. Hussein, M. Effenberger, and T. Becker Copyright © 2014 K. K. L. B. Adikaram et al. All rights reserved. Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle Thu, 10 Jul 2014 11:27:01 +0000 This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset. Joko Hariyono, Van-Dung Hoang, and Kang-Hyun Jo Copyright © 2014 Joko Hariyono et al. All rights reserved. A Synchronous-Asynchronous Particle Swarm Optimisation Algorithm Thu, 10 Jul 2014 08:36:20 +0000 In the original particle swarm optimisation (PSO) algorithm, the particles’ velocities and positions are updated after the whole swarm performance is evaluated. This algorithm is also known as synchronous PSO (S-PSO). The strength of this update method is in the exploitation of the information. Asynchronous update PSO (A-PSO) has been proposed as an alternative to S-PSO. A particle in A-PSO updates its velocity and position as soon as its own performance has been evaluated. Hence, particles are updated using partial information, leading to stronger exploration. In this paper, we attempt to improve PSO by merging both update methods to utilise the strengths of both methods. The proposed synchronous-asynchronous PSO (SA-PSO) algorithm divides the particles into smaller groups. The best member of a group and the swarm’s best are chosen to lead the search. Members within a group are updated synchronously, while the groups themselves are asynchronously updated. Five well-known unimodal functions, four multimodal functions, and a real world optimisation problem are used to study the performance of SA-PSO, which is compared with the performances of S-PSO and A-PSO. The results are statistically analysed and show that the proposed SA-PSO has performed consistently well. Nor Azlina Ab Aziz, Marizan Mubin, Mohd Saberi Mohamad, and Kamarulzaman Ab Aziz Copyright © 2014 Nor Azlina Ab Aziz et al. All rights reserved. A Review of Norms and Normative Multiagent Systems Wed, 09 Jul 2014 09:04:32 +0000 Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm’s life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work. Moamin A. Mahmoud, Mohd Sharifuddin Ahmad, Mohd Zaliman Mohd Yusoff, and Aida Mustapha Copyright © 2014 Moamin A. Mahmoud et al. All rights reserved. Scene Consistency Verification Based on PatchNet Wed, 09 Jul 2014 08:54:12 +0000 In the real world, the object does not exist in isolation, and it always appears in a certain scene. Usually the object is fixed in a particular scene and even in special spatial location. In this paper, we propose a method for judging scene consistency effectively. Scene semantics and geometry relation play a key role. In this paper, we use PatchNet to deal with these high-level scene structures. We construct a consistent scene database, using semantic information of PatchNet to determine whether the scene is consistent. The effectiveness of the proposed algorithm is verified by a lot of experiments. Jinjiang Li, Xiaoqing Guo, Zhen Hua, and Zhiyong An Copyright © 2014 Jinjiang Li et al. All rights reserved. Security Techniques for Prevention of Rank Manipulation in Social Tagging Services including Robotic Domains Wed, 09 Jul 2014 08:53:00 +0000 With smartphone distribution becoming common and robotic applications on the rise, social tagging services for various applications including robotic domains have advanced significantly. Though social tagging plays an important role when users are finding the exact information through web search, reliability and semantic relation between web contents and tags are not considered. Spams are making ill use of this aspect and put irrelevant tags deliberately on contents and induce users to advertise contents when they click items of search results. Therefore, this study proposes a detection method for tag-ranking manipulation to solve the problem of the existing methods which cannot guarantee the reliability of tagging. Similarity is measured for ranking the grade of registered tag on the contents, and weighted values of each tag are measured by means of synonym relevance, frequency, and semantic distances between tags. Lastly, experimental evaluation results are provided and its efficiency and accuracy are verified through them. Okkyung Choi, Hanyoung Jung, and Seungbin Moon Copyright © 2014 Okkyung Choi et al. All rights reserved. Stochastic Optimized Relevance Feedback Particle Swarm Optimization for Content Based Image Retrieval Wed, 09 Jul 2014 00:00:00 +0000 One of the major challenges for the CBIR is to bridge the gap between low level features and high level semantics according to the need of the user. To overcome this gap, relevance feedback (RF) coupled with support vector machine (SVM) has been applied successfully. However, when the feedback sample is small, the performance of the SVM based RF is often poor. To improve the performance of RF, this paper has proposed a new technique, namely, PSO-SVM-RF, which combines SVM based RF with particle swarm optimization (PSO). The aims of this proposed technique are to enhance the performance of SVM based RF and also to minimize the user interaction with the system by minimizing the RF number. The PSO-SVM-RF was tested on the coral photo gallery containing 10908 images. The results obtained from the experiments showed that the proposed PSO-SVM-RF achieved 100% accuracy in 8 feedback iterations for top 10 retrievals and 80% accuracy in 6 iterations for 100 top retrievals. This implies that with PSO-SVM-RF technique high accuracy rate is achieved at a small number of iterations. Muhammad Imran, Rathiah Hashim, Abd Khalid Noor Elaiza, and Aun Irtaza Copyright © 2014 Muhammad Imran et al. All rights reserved. Estimating Body Related Soft Biometric Traits in Video Frames Wed, 09 Jul 2014 00:00:00 +0000 Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames. Olasimbo Ayodeji Arigbabu, Sharifah Mumtazah Syed Ahmad, Wan Azizun Wan Adnan, Salman Yussof, Vahab Iranmanesh, and Fahad Layth Malallah Copyright © 2014 Olasimbo Ayodeji Arigbabu et al. All rights reserved. Applying Dynamic Priority Scheduling Scheme to Static Systems of Pinwheel Task Model in Power-Aware Scheduling Tue, 08 Jul 2014 09:20:04 +0000 Power-aware scheduling reduces CPU energy consumption in hard real-time systems through dynamic voltage scaling (DVS). In this paper, we deal with pinwheel task model which is known as static and predictable task model and could be applied to various embedded or ubiquitous systems. In pinwheel task model, each task’s priority is static and its execution sequence could be predetermined. There have been many static approaches to power-aware scheduling in pinwheel task model. But, in this paper, we will show that the dynamic priority scheduling results in power-aware scheduling could be applied to pinwheel task model. This method is more effective than adopting the previous static priority scheduling methods in saving energy consumption and, for the system being still static, it is more tractable and applicable to small sized embedded or ubiquitous computing. Also, we introduce a novel power-aware scheduling algorithm which exploits all slacks under preemptive earliest-deadline first scheduling which is optimal in uniprocessor system. The dynamic priority method presented in this paper could be applied directly to static systems of pinwheel task model. The simulation results show that the proposed algorithm with the algorithmic complexity of O(n) reduces the energy consumption by 10–80% over the existing algorithms. Ye-In Seol and Young-Kuk Kim Copyright © 2014 Ye-In Seol and Young-Kuk Kim. All rights reserved. Efficient and Scalable Graph Similarity Joins in MapReduce Tue, 08 Jul 2014 08:19:07 +0000 Along with the emergence of massive graph-modeled data, it is of great importance to investigate graph similarity joins due to their wide applications for multiple purposes, including data cleaning, and near duplicate detection. This paper considers graph similarity joins with edit distance constraints, which return pairs of graphs such that their edit distances are no larger than a given threshold. Leveraging the MapReduce programming model, we propose MGSJoin, a scalable algorithm following the filtering-verification framework for efficient graph similarity joins. It relies on counting overlapping graph signatures for filtering out nonpromising candidates. With the potential issue of too many key-value pairs in the filtering phase, spectral Bloom filters are introduced to reduce the number of key-value pairs. Furthermore, we integrate the multiway join strategy to boost the verification, where a MapReduce-based method is proposed for GED calculation. The superior efficiency and scalability of the proposed algorithms are demonstrated by extensive experimental results. Yifan Chen, Xiang Zhao, Chuan Xiao, Weiming Zhang, and Jiuyang Tang Copyright © 2014 Yifan Chen et al. All rights reserved. Gait Signal Analysis with Similarity Measure Mon, 07 Jul 2014 11:41:56 +0000 Human gait decision was carried out with the help of similarity measure design. Gait signal was selected through hardware implementation including all in one sensor, control unit, and notebook with connector. Each gait signal was considered as high dimensional data. Therefore, high dimensional data analysis was considered via heuristic technique such as the similarity measure. Each human pattern such as walking, sitting, standing, and stepping up was obtained through experiment. By the results of the analysis, we also identified the overlapped and nonoverlapped data relation, and similarity measure analysis was also illustrated, and comparison with conventional similarity measure was also carried out. Hence, nonoverlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considered high dimensional data analysis was designed with consideration of neighborhood information. Proposed similarity measure was applied to identify the behavior patterns of different persons, and different behaviours of the same person. Obtained analysis can be extended to organize health monitoring system for specially elderly persons. Sanghyuk Lee and Seungsoo Shin Copyright © 2014 Sanghyuk Lee and Seungsoo Shin. All rights reserved. A Procedure for Extending Input Selection Algorithms to Low Quality Data in Modelling Problems with Application to the Automatic Grading of Uploaded Assignments Mon, 07 Jul 2014 11:27:13 +0000 When selecting relevant inputs in modeling problems with low quality data, the ranking of the most informative inputs is also uncertain. In this paper, this issue is addressed through a new procedure that allows the extending of different crisp feature selection algorithms to vague data. The partial knowledge about the ordinal of each feature is modelled by means of a possibility distribution, and a ranking is hereby applied to sort these distributions. It will be shown that this technique makes the most use of the available information in some vague datasets. The approach is demonstrated in a real-world application. In the context of massive online computer science courses, methods are sought for automatically providing the student with a qualification through code metrics. Feature selection methods are used to find the metrics involved in the most meaningful predictions. In this study, 800 source code files, collected and revised by the authors in classroom Computer Science lectures taught between 2013 and 2014, are analyzed with the proposed technique, and the most relevant metrics for the automatic grading task are discussed. José Otero, Ana Palacios, Rosario Suárez, Luis Junco, Inés Couso, and Luciano Sánchez Copyright © 2014 José Otero et al. All rights reserved. Detecting Community Structures in Networks by Label Propagation with Prediction of Percolation Transition Mon, 07 Jul 2014 11:16:01 +0000 Though label propagation algorithm (LPA) is one of the fastest algorithms for community detection in complex networks, the problem of trivial solutions frequently occurring in the algorithm affects its performance. We propose a label propagation algorithm with prediction of percolation transition (LPAp). After analyzing the reason for multiple solutions of LPA, by transforming the process of community detection into network construction process, a trivial solution in label propagation is considered as a giant component in the percolation transition. We add a prediction process of percolation transition in label propagation to delay the occurrence of trivial solutions, which makes small communities easier to be found. We also give an incomplete update condition which considers both neighbor purity and the contribution of small degree vertices to community detection to reduce the computation time of LPAp. Numerical tests are conducted. Experimental results on synthetic networks and real-world networks show that the LPAp is more accurate, more sensitive to small community, and has the ability to identify a single community structure. Moreover, LPAp with the incomplete update process can use less computation time than LPA, nearly without modularity loss. Aiping Zhang, Guang Ren, Yejin Lin, Baozhu Jia, Hui Cao, Jundong Zhang, and Shubin Zhang Copyright © 2014 Aiping Zhang et al. All rights reserved. A Rhythm-Based Authentication Scheme for Smart Media Devices Mon, 07 Jul 2014 11:04:01 +0000 In recent years, ubiquitous computing has been rapidly emerged in our lives and extensive studies have been conducted in a variety of areas related to smart devices, such as tablets, smartphones, smart TVs, smart refrigerators, and smart media devices, as a measure for realizing the ubiquitous computing. In particular, smartphones have significantly evolved from the traditional feature phones. Increasingly higher-end smartphone models that can perform a range of functions are now available. Smart devices have become widely popular since they provide high efficiency and great convenience for not only private daily activities but also business endeavors. Rapid advancements have been achieved in smart device technologies to improve the end users’ convenience. Consequently, many people increasingly rely on smart devices to store their valuable and important data. With this increasing dependence, an important aspect that must be addressed is security issues. Leaking of private information or sensitive business data due to loss or theft of smart devices could result in exorbitant damage. To mitigate these security threats, basic embedded locking features are provided in smart devices. However, these locking features are vulnerable. In this paper, an original security-locking scheme using a rhythm-based locking system (RLS) is proposed to overcome the existing security problems of smart devices. RLS is a user-authenticated system that addresses vulnerability issues in the existing locking features and provides secure confidentiality in addition to convenience. Jae Dong Lee, Young-Sik Jeong, and Jong Hyuk Park Copyright © 2014 Jae Dong Lee et al. All rights reserved. Density-Based Penalty Parameter Optimization on C-SVM Mon, 07 Jul 2014 09:32:26 +0000 The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system’s outliers. Traditional C-SVM holds a uniform parameter C for both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall. Yun Liu, Jie Lian, Michael R. Bartolacci, and Qing-An Zeng Copyright © 2014 Yun Liu et al. All rights reserved. An Improved Ant Colony Optimization Approach for Optimization of Process Planning Sun, 06 Jul 2014 09:12:26 +0000 Computer-aided process planning (CAPP) is an important interface between computer-aided design (CAD) and computer-aided manufacturing (CAM) in computer-integrated manufacturing environments (CIMs). In this paper, process planning problem is described based on a weighted graph, and an ant colony optimization (ACO) approach is improved to deal with it effectively. The weighted graph consists of nodes, directed arcs, and undirected arcs, which denote operations, precedence constraints among operation, and the possible visited path among operations, respectively. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPCs). A pheromone updating strategy proposed in this paper is incorporated in the standard ACO, which includes Global Update Rule and Local Update Rule. A simple method by controlling the repeated number of the same process plans is designed to avoid the local convergence. A case has been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been carried out to validate the feasibility and efficiency of the proposed approach. JinFeng Wang, XiaoLiang Fan, and Haimin Ding Copyright © 2014 JinFeng Wang et al. All rights reserved. Protecting Location Privacy for Outsourced Spatial Data in Cloud Storage Sun, 06 Jul 2014 00:00:00 +0000 As cloud computing services and location-aware devices are fully developed, a large amount of spatial data needs to be outsourced to the cloud storage provider, so the research on privacy protection for outsourced spatial data gets increasing attention from academia and industry. As a kind of spatial transformation method, Hilbert curve is widely used to protect the location privacy for spatial data. But sufficient security analysis for standard Hilbert curve (SHC) is seldom proceeded. In this paper, we propose an index modification method for SHC (SHC∗) and a density-based space filling curve (DSC) to improve the security of SHC; they can partially violate the distance-preserving property of SHC, so as to achieve better security. We formally define the indistinguishability and attack model for measuring the privacy disclosure risk of spatial transformation methods. The evaluation results indicate that SHC∗ and DSC are more secure than SHC, and DSC achieves the best index generation performance. Feng Tian, Xiaolin Gui, Jian An, Pan Yang, Jianqiang Zhao, and Xuejun Zhang Copyright © 2014 Feng Tian et al. All rights reserved. Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading Sun, 06 Jul 2014 00:00:00 +0000 Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used to generate the buy and sell signals for the target currency pair based on the relative strength index (RSI), while it is also combined with MKL as a trading signal. The new hybrid implementation is applied to EUR/USD trading, which is the most traded foreign exchange (FX) currency pair. MKL is essential for utilizing information from multiple information sources and DE is essential for formulating a trading rule based on a mixture of discrete structures and continuous parameters. Initially, the prediction model optimized by MKL predicts the returns based on a technical indicator called the moving average convergence and divergence. Next, a combined trading signal is optimized by DE using the inputs from the prediction model and technical indicator RSI obtained from multiple timeframes. The experimental results showed that trading using the prediction learned by MKL yielded consistent profits. Shangkun Deng and Akito Sakurai Copyright © 2014 Shangkun Deng and Akito Sakurai. All rights reserved. A Local Stability Supported Parallel Distributed Constraint Optimization Algorithm Thu, 03 Jul 2014 12:01:12 +0000 This paper presents a new distributed constraint optimization algorithm called LSPA, which can be used to solve large scale distributed constraint optimization problem (DCOP). Different from the access of local information in the existing algorithms, a new criterion called local stability is defined and used to evaluate which is the next agent whose value needs to be changed. The propose of local stability opens a new research direction of refining initial solution by finding key agents which can seriously effect global solution once they modify assignments. In addition, the construction of initial solution could be received more quickly without repeated assignment and conflict. In order to execute parallel search, LSPA finds final solution by constantly computing local stability of compatible agents. Experimental evaluation shows that LSPA outperforms some of the state-of-the-art incomplete distributed constraint optimization algorithms, guaranteeing better solutions received within ideal time. Duan Peibo, Zhang Changsheng, and Zhang Bin Copyright © 2014 Duan Peibo et al. All rights reserved. Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes Thu, 03 Jul 2014 12:00:32 +0000 We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance. Xing Hu, Shiqiang Hu, Xiaoyu Zhang, Huanlong Zhang, and Lingkun Luo Copyright © 2014 Xing Hu et al. All rights reserved. A Distributed Parallel Genetic Algorithm of Placement Strategy for Virtual Machines Deployment on Cloud Platform Thu, 03 Jul 2014 11:39:26 +0000 The cloud platform provides various services to users. More and more cloud centers provide infrastructure as the main way of operating. To improve the utilization rate of the cloud center and to decrease the operating cost, the cloud center provides services according to requirements of users by sharding the resources with virtualization. Considering both QoS for users and cost saving for cloud computing providers, we try to maximize performance and minimize energy cost as well. In this paper, we propose a distributed parallel genetic algorithm (DPGA) of placement strategy for virtual machines deployment on cloud platform. It executes the genetic algorithm parallelly and distributedly on several selected physical hosts in the first stage. Then it continues to execute the genetic algorithm of the second stage with solutions obtained from the first stage as the initial population. The solution calculated by the genetic algorithm of the second stage is the optimal one of the proposed approach. The experimental results show that the proposed placement strategy of VM deployment can ensure QoS for users and it is more effective and more energy efficient than other placement strategies on the cloud platform. Yu-Shuang Dong, Gao-Chao Xu, and Xiao-Dong Fu Copyright © 2014 Yu-Shuang Dong et al. All rights reserved. QoS Measurement of Workflow-Based Web Service Compositions Using Colored Petri Net Thu, 03 Jul 2014 08:36:06 +0000 Workflow-based web service compositions (WB-WSCs) is one of the main composition categories in service oriented architecture (SOA). Eflow, polymorphic process model (PPM), and business process execution language (BPEL) are the main techniques of the category of WB-WSCs. Due to maturity of web services, measuring the quality of composite web services being developed by different techniques becomes one of the most important challenges in today’s web environments. Business should try to provide good quality regarding the customers’ requirements to a composed web service. Thus, quality of service (QoS) which refers to nonfunctional parameters is important to be measured since the quality degree of a certain web service composition could be achieved. This paper tried to find a deterministic analytical method for dependability and performance measurement using Colored Petri net (CPN) with explicit routing constructs and application of theory of probability. A computer tool called WSET was also developed for modeling and supporting QoS measurement through simulation. Hossein Nematzadeh, Homayun Motameni, Radziah Mohamad, and Zahra Nematzadeh Copyright © 2014 Hossein Nematzadeh et al. All rights reserved. An Adaptive Framework for Real-Time ECG Transmission in Mobile Environments Thu, 03 Jul 2014 07:09:04 +0000 Wireless electrocardiogram (ECG) monitoring involves the measurement of ECG signals and their timely transmission over wireless networks to remote healthcare professionals. However, fluctuations in wireless channel conditions pose quality-of-service challenges for real-time ECG monitoring services in a mobile environment. We present an adaptive framework for layered coding and transmission of ECG data that can cope with a time-varying wireless channel. The ECG is segmented into layers with differing importance with respect to the quality of the reconstructed signal. According to this observation, we have devised a simple and efficient real-time scheduling algorithm based on the earliest deadline first (EDF) policy, which decides the order of transmitting or retransmitting packets that contain ECG data at any given time for the delivery of scalable ECG data over a lossy channel. The algorithm takes into account the differing priorities of packets in each layer, which prevents the perceived quality of the reconstructed ECG signal from degrading abruptly as channel conditions worsen, while using the available bandwidth efficiently. Extensive simulations demonstrate this improvement in perceived quality. Kyungtae Kang Copyright © 2014 Kyungtae Kang. All rights reserved. The Laws of Natural Deduction in Inference by DNA Computer Thu, 03 Jul 2014 05:53:22 +0000 We present a DNA-based implementation of reaction system with molecules encoding elements of the propositional logic, that is, propositions and formulas. The protocol can perform inference steps using, for example, modus ponens and modus tollens rules and de Morgan’s laws. The set of the implemented operations allows for inference of formulas using the laws of natural deduction. The system can also detect whether a certain proposition a can be deduced from the basic facts and given rules. The whole protocol is fully autonomous; that is, after introducing the initial set of molecules, no human assistance is needed. Only one restriction enzyme is used throughout the inference process. Unlike some other similar implementations, our improved design allows representing simultaneously a fact a and its negation ~a, including special reactions to detect the inconsistency, that is, a simultaneous occurrence of a fact and its negation. An analysis of correctness, completeness, and complexity is included. Łukasz Rogowski and Petr Sosík Copyright © 2014 Łukasz Rogowski and Petr Sosík. All rights reserved. A Survey of Research Progress and Development Tendency of Attribute-Based Encryption Wed, 02 Jul 2014 12:29:08 +0000 With the development of cryptography, the attribute-based encryption (ABE) draws widespread attention of the researchers in recent years. The ABE scheme, which belongs to the public key encryption mechanism, takes attributes as public key and associates them with the ciphertext or the user’s secret key. It is an efficient way to solve open problems in access control scenarios, for example, how to provide data confidentiality and expressive access control at the same time. In this paper, we survey the basic ABE scheme and its two variants: the key-policy ABE (KP-ABE) scheme and the ciphertext-policy ABE (CP-ABE) scheme. We also pay attention to other researches relating to the ABE schemes, including multiauthority, user/attribute revocation, accountability, and proxy reencryption, with an extensive comparison of their functionality and performance. Finally, possible future works and some conclusions are pointed out. Liaojun Pang, Jie Yang, and Zhengtao Jiang Copyright © 2014 Liaojun Pang et al. All rights reserved. Utility-Oriented Placement of Actuator Nodes with a Collaborative Serving Scheme for Facilitated Business and Working Environments Wed, 02 Jul 2014 11:43:54 +0000 Places to be served by cyber-physical systems (CPS) are usually distributed unevenly over the area. Thus, different areas usually have different importance and values of serving. In other words, serving power can be excessive or insufficient in practice. Therefore, actuator nodes (ANs) in CPS should be focused on serving around points of interest (POIs) with considerations of “service utility.” In this paper, a utility-oriented AN placement framework with a collaborative serving scheme is proposed. Through spreading serving duties among correctly located ANs, deployment cost can be reduced, utility of ANs can be fully utilized, and the system longevity can be improved. The problem has been converted into a binary integer linear programming optimization problem. Service fading, 3D placements, multiscenario placements, and fault-tolerant placements have been modeled in the framework. An imitated example of a CPS deployment in a smart laboratory has been used for evaluations. Chi-Un Lei, Woon Kian Chong, and Ka Lok Man Copyright © 2014 Chi-Un Lei et al. All rights reserved. Real-Time Terrain Storage Generation from Multiple Sensors towards Mobile Robot Operation Interface Wed, 02 Jul 2014 08:06:54 +0000 A mobile robot mounted with multiple sensors is used to rapidly collect 3D point clouds and video images so as to allow accurate terrain modeling. In this study, we develop a real-time terrain storage generation and representation system including a nonground point database (PDB), ground mesh database (MDB), and texture database (TDB). A voxel-based flag map is proposed for incrementally registering large-scale point clouds in a terrain model in real time. We quantize the 3D point clouds into 3D grids of the flag map as a comparative table in order to remove the redundant points. We integrate the large-scale 3D point clouds into a nonground PDB and a node-based terrain mesh using the CPU. Subsequently, we program a graphics processing unit (GPU) to generate the TDB by mapping the triangles in the terrain mesh onto the captured video images. Finally, we produce a nonground voxel map and a ground textured mesh as a terrain reconstruction result. Our proposed methods were tested in an outdoor environment. Our results show that the proposed system was able to rapidly generate terrain storage and provide high resolution terrain representation for mobile mapping services and a graphical user interface between remote operators and mobile robots. Wei Song, Seoungjae Cho, Yulong Xi, Kyungeun Cho, and Kyhyun Um Copyright © 2014 Wei Song et al. All rights reserved. A Socially Aware Routing Based on Local Contact Information in Delay-Tolerant Networks Tue, 01 Jul 2014 07:59:52 +0000 In delay-tolerant networks, network topology changes dynamically and there is no guarantee of continuous connectivity between any two nodes. These features make DTN routing one of important research issues, and the application of social network metrics has led to the design of recent DTN routing schemes. In this paper, we propose an efficient routing scheme by using a node’s local contact history and social network metrics. Each node first chooses a proper relay node based on the closeness to the destination node. A locally computed betweenness centrality is additionally utilized to enhance the routing efficiency. Through intensive simulation, we finally demonstrate that our algorithm performs efficiently compared to the existing epidemic or friendship routing scheme. Chan-Myung Kim, Youn-Hee Han, Joo-Sang Youn, and Young-Sik Jeong Copyright © 2014 Chan-Myung Kim et al. All rights reserved. A Study of Lock-Free Based Concurrent Garbage Collectors for Multicore Platform Mon, 30 Jun 2014 07:24:47 +0000 Concurrent garbage collectors (CGC) have recently obtained extensive concern on multicore platform. Excellent designed CGC can improve the efficiency of runtime systems by exploring the full potential processing resources of multicore computers. Two major performance critical components for designing CGC are studied in this paper, stack scanning and heap compaction. Since the lock-based algorithms do not scale well, we present a lock-free solution for constructing a highly concurrent garbage collector. We adopt CAS/MCAS synchronization primitives to guarantee that the programs will never be blocked by the collector thread while the garbage collection process is ongoing. The evaluation results of this study demonstrate that our approach achieves competitive performance. Hao Wu and Zhen-Zhou Ji Copyright © 2014 Hao Wu and Zhen-Zhou Ji. All rights reserved. LED Context Lighting System in Residential Areas Sun, 29 Jun 2014 09:43:07 +0000 As issues of environment and energy draw keen interest around the globe due to such problems as global warming and the energy crisis, LED with high optical efficiency is brought to the fore as the next generation lighting. In addition, as the national income level gets higher and life expectancy is extended, interest in the enhancement of life quality is increasing. Accordingly, the trend of lightings is changing from mere adjustment of light intensity to system lighting in order to enhance the quality of one’s life as well as reduce energy consumption. Thus, this study aims to design LED context lighting system that automatically recognizes the location and acts of a user in residential areas and creates an appropriate lighting environment. The proposed system designed in this study includes three types of processing: first, the creation of a lighting environment index suitable for the user’s surroundings and lighting control scenarios and second, it measures and analyzes the optical characteristics that change depending on the dimming control of lighting and applies them to the index. Lastly, it adopts PIR, piezoelectric, and power sensor to grasp the location and acts of the user and create a lighting environment suitable for the current context. Sook-Youn Kwon, Kyoung-Mi Im, and Jae-Hyun Lim Copyright © 2014 Sook-Youn Kwon et al. All rights reserved. Energy Saving in Data Processing and Communication Systems Sun, 29 Jun 2014 00:00:00 +0000 The power management of ICT systems, that is, data processing (Dp) and telecommunication (Tlc) systems, is becoming a relevant problem in economical terms. Dp systems totalize millions of servers and associated subsystems (processors, monitors, storage devices, etc.) all over the world that need to be electrically powered. Dp systems are also used in the government of Tlc systems, which, besides requiring Dp electrical power, also require Tlc-specific power, both for mobile networks (with their cell-phone towers and associated subsystems: base stations, subscriber stations, switching nodes, etc.) and for wired networks (with their routers, gateways, switches, etc.). ICT research is thus expected to investigate into methods to reduce Dp- and Tlc-specific power consumption. However, saving power may turn into waste of performance, in other words, into waste of ICT quality of service (QoS). This paper investigates the Dp and Tlc power management policies that look at compromises between power saving and QoS. Giuseppe Iazeolla and Alessandra Pieroni Copyright © 2014 Giuseppe Iazeolla and Alessandra Pieroni. All rights reserved. An Artificial Bee Colony Algorithm for Uncertain Portfolio Selection Thu, 26 Jun 2014 13:35:06 +0000 Portfolio selection is an important issue for researchers and practitioners. In this paper, under the assumption that security returns are given by experts’ evaluations rather than historical data, we discuss the portfolio adjusting problem which takes transaction costs and diversification degree of portfolio into consideration. Uncertain variables are employed to describe the security returns. In the proposed mean-variance-entropy model, the uncertain mean value of the return is used to measure investment return, the uncertain variance of the return is used to measure investment risk, and the entropy is used to measure diversification degree of portfolio. In order to solve the proposed model, a modified artificial bee colony (ABC) algorithm is designed. Finally, a numerical example is given to illustrate the modelling idea and the effectiveness of the proposed algorithm. Wei Chen Copyright © 2014 Wei Chen. All rights reserved. Similarity Measure Learning in Closed-Form Solution for Image Classification Thu, 26 Jun 2014 13:32:49 +0000 Adopting a measure is essential in many multimedia applications. Recently, distance learning is becoming an active research problem. In fact, the distance is the natural measure for dissimilarity. Generally, a pairwise relationship between two objects in learning tasks includes two aspects: similarity and dissimilarity. The similarity measure provides different information for pairwise relationships. However, similarity learning has been paid less attention in learning problems. In this work, firstly, we propose a general framework for similarity measure learning (SML). Additionally, we define a generalized type of correlation as a similarity measure. By a set of parameters, generalized correlation provides flexibility for learning tasks. Based on this similarity measure, we present a specific algorithm under the SML framework, called correlation similarity measure learning (CSML), to learn a parameterized similarity measure over input space. A nonlinear extension version of CSML, kernel CSML, is also proposed. Particularly, we give a closed-form solution avoiding iterative search for a local optimal solution in the high-dimensional space as the previous work did. Finally, classification experiments have been performed on face databases and a handwritten digits database to demonstrate the efficiency and reliability of CSML and KCSML. Jing Chen, Yuan Yan Tang, C. L. Philip Chen, Bin Fang, Zhaowei Shang, and Yuewei Lin Copyright © 2014 Jing Chen et al. All rights reserved. Adaptive Broadcasting Mechanism for Bandwidth Allocation in Mobile Services Thu, 26 Jun 2014 11:33:58 +0000 This paper proposes a tree-based adaptive broadcasting (TAB) algorithm for data dissemination to improve data access efficiency. The proposed TAB algorithm first constructs a broadcast tree to determine the broadcast frequency of each data and splits the broadcast tree into some broadcast wood to generate the broadcast program. In addition, this paper develops an analytical model to derive the mean access latency of the generated broadcast program. In light of the derived results, both the index channel’s bandwidth and the data channel’s bandwidth can be optimally allocated to maximize bandwidth utilization. This paper presents experiments to help evaluate the effectiveness of the proposed strategy. From the experimental results, it can be seen that the proposed mechanism is feasible in practice. Gwo-Jiun Horng, Chi-Hsuan Wang, and Chih-Lun Chou Copyright © 2014 Gwo-Jiun Horng et al. All rights reserved. Hot News Recommendation System from Heterogeneous Websites Based on Bayesian Model Thu, 26 Jun 2014 08:13:17 +0000 The most current news recommendations are suitable for news which comes from a single news website, not for news from different heterogeneous news websites. Previous researches about news recommender systems based on different strategies have been proposed to provide news personalization services for online news readers. However, little research work has been reported on utilizing hundreds of heterogeneous news websites to provide top hot news services for group customers (e.g., government staffs). In this paper, we propose a hot news recommendation model based on Bayesian model, which is from hundreds of different news websites. In the model, we determine whether the news is hot news by calculating the joint probability of the news. We evaluate and compare our proposed recommendation model with the results of human experts on the real data sets. Experimental results demonstrate the reliability and effectiveness of our method. We also implement this model in hot news recommendation system of Hangzhou city government in year 2013, which achieves very good results. Zhengyou Xia, Shengwu Xu, Ningzhong Liu, and Zhengkang Zhao Copyright © 2014 Zhengyou Xia et al. All rights reserved. A Combination of Extended Fuzzy AHP and Fuzzy GRA for Government E-Tendering in Hybrid Fuzzy Environment Thu, 26 Jun 2014 07:48:58 +0000 The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach. Yan Wang, Chengyu Xi, Shuai Zhang, Dejian Yu, Wenyu Zhang, and Yong Li Copyright © 2014 Yan Wang et al. All rights reserved. Fault Detection of Aircraft System with Random Forest Algorithm and Similarity Measure Thu, 26 Jun 2014 07:32:09 +0000 Research on fault detection algorithm was developed with the similarity measure and random forest algorithm. The organized algorithm was applied to unmanned aircraft vehicle (UAV) that was readied by us. Similarity measure was designed by the help of distance information, and its usefulness was also verified by proof. Fault decision was carried out by calculation of weighted similarity measure. Twelve available coefficients among healthy and faulty status data group were used to determine the decision. Similarity measure weighting was done and obtained through random forest algorithm (RFA); RF provides data priority. In order to get a fast response of decision, a limited number of coefficients was also considered. Relation of detection rate and amount of feature data were analyzed and illustrated. By repeated trial of similarity calculation, useful data amount was obtained. Sanghyuk Lee, Wookje Park, and Sikhang Jung Copyright © 2014 Sanghyuk Lee et al. All rights reserved. Enhancing Business Intelligence by Means of Suggestive Reviews Thu, 26 Jun 2014 00:00:00 +0000 Appropriate identification and classification of online reviews to satisfy the needs of current and potential users pose a critical challenge for the business environment. This paper focuses on a specific kind of reviews: the suggestive type. Suggestions have a significant influence on both consumers’ choices and designers’ understanding and, hence, they are key for tasks such as brand positioning and social media marketing. The proposed approach consists of three main steps: (1) classify comparative and suggestive sentences; (2) categorize suggestive sentences into different types, either explicit or implicit locutions; (3) perform sentiment analysis on the classified reviews. A range of supervised machine learning approaches and feature sets are evaluated to tackle the problem of suggestive opinion mining. Experimental results for all three tasks are obtained on a dataset of mobile phone reviews and demonstrate that extending a bag-of-words representation with suggestive and comparative patterns is ideal for distinguishing suggestive sentences. In particular, it is observed that classifying suggestive sentences into implicit and explicit locutions works best when using a mixed sequential rule feature representation. Sentiment analysis achieves maximum performance when employing additional preprocessing in the form of negation handling and target masking, combined with sentiment lexicons. Atika Qazi, Ram Gopal Raj, Muhammad Tahir, Erik Cambria, and Karim Bux Shah Syed Copyright © 2014 Atika Qazi et al. All rights reserved. Real-Time Hand Gesture Recognition Using Finger Segmentation Wed, 25 Jun 2014 12:43:25 +0000 Hand gesture recognition is very significant for human-computer interaction. In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand region is extracted from the background with the background subtraction method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 1300 images show that our method performs well and is highly efficient. Moreover, our method shows better performance than a state-of-art method on another data set of hand gestures. Zhi-hua Chen, Jung-Tae Kim, Jianning Liang, Jing Zhang, and Yu-Bo Yuan Copyright © 2014 Zhi-hua Chen et al. All rights reserved. An Effective Approach to Improving Low-Cost GPS Positioning Accuracy in Real-Time Navigation Wed, 25 Jun 2014 08:19:08 +0000 Positioning accuracy is a challenging issue for location-based applications using a low-cost global positioning system (GPS). This paper presents an effective approach to improving the positioning accuracy of a low-cost GPS receiver for real-time navigation. The proposed method precisely estimates position by combining vehicle movement direction, velocity averaging, and distance between waypoints using coordinate data (latitude, longitude, time, and velocity) of the GPS receiver. The previously estimated precious reference point, coordinate translation, and invalid data check also improve accuracy. In order to evaluate the performance of the proposed method, we conducted an experiment using a GARMIN GPS 19xHVS receiver attached to a car and used Google Maps to plot the processed data. The proposed method achieved improvement of 4–10 meters in several experiments. In addition, we compared the proposed approach with two other state-of-the-art methods: recursive averaging and ARMA interpolation. The experimental results show that the proposed approach outperforms other state-of-the-art methods in terms of positioning accuracy. Md. Rashedul Islam and Jong-Myon Kim Copyright © 2014 Md. Rashedul Islam and Jong-Myon Kim. All rights reserved. First- and Second-Order Full-Differential in Edge Analysis of Images Wed, 25 Jun 2014 05:47:23 +0000 Two concepts of first- and second-order differential of images are presented to deal with the changes of pixels. These are the basic ideas in mathematics. We propose and reformulate them with a uniform definition framework. Based on our observation and analysis with the difference, we propose an algorithm to detect the edge from image. Experiments on Corel5K and PASCAL VOC 2007 are done to show the difference between the first order and the second order. After comparison with Canny operator and the proposed first-order differential, the main result is that the second-order differential has the better performance in analysis of changes of the context of images with good selection of control parameter. Dong-Mei Pu and Yu-Bo Yuan Copyright © 2014 Dong-Mei Pu and Yu-Bo Yuan. All rights reserved. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud Tue, 24 Jun 2014 10:46:48 +0000 A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame. Seoungjae Cho, Jonghyun Kim, Warda Ikram, Kyungeun Cho, Young-Sik Jeong, Kyhyun Um, and Sungdae Sim Copyright © 2014 Seoungjae Cho et al. All rights reserved. A DAG Scheduling Scheme on Heterogeneous Computing Systems Using Tuple-Based Chemical Reaction Optimization Tue, 24 Jun 2014 06:20:10 +0000 A complex computing problem can be solved efficiently on a system with multiple computing nodes by dividing its implementation code into several parallel processing modules or tasks that can be formulated as directed acyclic graph (DAG) problems. The DAG jobs may be mapped to and scheduled on the computing nodes to minimize the total execution time. Searching an optimal DAG scheduling solution is considered to be NP-complete. This paper proposed a tuple molecular structure-based chemical reaction optimization (TMSCRO) method for DAG scheduling on heterogeneous computing systems, based on a very recently proposed metaheuristic method, chemical reaction optimization (CRO). Comparing with other CRO-based algorithms for DAG scheduling, the design of tuple reaction molecular structure and four elementary reaction operators of TMSCRO is more reasonable. TMSCRO also applies the concept of constrained critical paths (CCPs), constrained-critical-path directed acyclic graph (CCPDAG) and super molecule for accelerating convergence. In this paper, we have also conducted simulation experiments to verify the effectiveness and efficiency of TMSCRO upon a large set of randomly generated graphs and the graphs for real world problems. Yuyi Jiang, Zhiqing Shao, and Yi Guo Copyright © 2014 Yuyi Jiang et al. All rights reserved. Swarm Intelligence and Its Applications 2014 Mon, 23 Jun 2014 07:55:45 +0000 Yudong Zhang, Praveen Agarwal, Vishal Bhatnagar, Saeed Balochian, and Xuewu Zhang Copyright © 2014 Yudong Zhang et al. All rights reserved. Path Planning Method for UUV Homing and Docking in Movement Disorders Environment Sun, 22 Jun 2014 08:19:12 +0000 Path planning method for unmanned underwater vehicles (UUV) homing and docking in movement disorders environment is proposed in this paper. Firstly, cost function is proposed for path planning. Then, a novel particle swarm optimization (NPSO) is proposed and applied to find the waypoint with minimum value of cost function. Then, a strategy for UUV enters into the mother vessel with a fixed angle being proposed. Finally, the test function is introduced to analyze the performance of NPSO and compare with basic particle swarm optimization (BPSO), inertia weight particle swarm optimization (LWPSO, EPSO), and time-varying acceleration coefficient (TVAC). It has turned out that, for unimodal functions, NPSO performed better searching accuracy and stability than other algorithms, and, for multimodal functions, the performance of NPSO is similar to TVAC. Then, the simulation of UUV path planning is presented, and it showed that, with the strategy proposed in this paper, UUV can dodge obstacles and threats, and search for the efficiency path. Zheping Yan, Chao Deng, Dongnan Chi, Tao Chen, and Shuping Hou Copyright © 2014 Zheping Yan et al. All rights reserved. A Comparative Study of Routing Protocols of Heterogeneous Wireless Sensor Networks Sun, 22 Jun 2014 05:13:56 +0000 Recently, heterogeneous wireless sensor network (HWSN) routing protocols have drawn more and more attention. Various HWSN routing protocols have been proposed to improve the performance of HWSNs. Among these protocols, hierarchical HWSN routing protocols can improve the performance of the network significantly. In this paper, we will evaluate three hierarchical HWSN protocols proposed recently—EDFCM, MCR, and EEPCA—together with two previous classical routing protocols—LEACH and SEP. We mainly focus on the round of the first node dies (also called the stable period) and the number of packets sent to sink, which is an important aspect to evaluate the monitoring ability of a protocol. We conduct a lot of experiments and simulations on Matlab to analyze the performance of the five routing protocols. Guangjie Han, Xu Jiang, Aihua Qian, Joel J. P. C. Rodrigues, and Long Cheng Copyright © 2014 Guangjie Han et al. All rights reserved. Singularity-Free Neural Control for the Exponential Trajectory Tracking in Multiple-Input Uncertain Systems with Unknown Deadzone Nonlinearities Thu, 19 Jun 2014 13:02:42 +0000 The trajectory tracking for a class of uncertain nonlinear systems in which the number of possible states is equal to the number of inputs and each input is preceded by an unknown symmetric deadzone is considered. The unknown dynamics is identified by means of a continuous time recurrent neural network in which the control singularity is conveniently avoided by guaranteeing the invertibility of the coupling matrix. Given this neural network-based mathematical model of the uncertain system, a singularity-free feedback linearization control law is developed in order to compel the system state to follow a reference trajectory. By means of Lyapunov-like analysis, the exponential convergence of the tracking error to a bounded zone can be proven. Likewise, the boundedness of all closed-loop signals can be guaranteed. J. Humberto Pérez-Cruz, José de Jesús Rubio, Rodrigo Encinas, and Ricardo Balcazar Copyright © 2014 J. Humberto Pérez-Cruz et al. All rights reserved. New Sufficient Conditions for Hamiltonian Paths Thu, 19 Jun 2014 13:00:25 +0000 A Hamiltonian path in a graph is a path involving all the vertices of the graph. In this paper, we revisit the famous Hamiltonian path problem and present new sufficient conditions for the existence of a Hamiltonian path in a graph. M. Sohel Rahman, M. Kaykobad, and Jesun Sahariar Firoz Copyright © 2014 M. Sohel Rahman et al. All rights reserved. Recent Advances in Information Technology Thu, 19 Jun 2014 11:52:27 +0000 Fei Yu, Chin-Chen Chang, Yiqin Lu, Jian Shu, Yan Gao, Guangxue Yue, and Zuo Chen Copyright © 2014 Fei Yu et al. All rights reserved. Intuitionistic Linguistic Weighted Bonferroni Mean Operator and Its Application to Multiple Attribute Decision Making Thu, 19 Jun 2014 11:41:53 +0000 The intuitionistic linguistic variables are easier to describe the fuzzy information which widely exists in the real world, and Bonferroni mean can capture the interrelationship of the individual arguments. However, the traditional Bonferroni mean can only process the crisp number. In this paper, we will extend Bonferroni mean to the intuitionistic linguistic environment and propose a multiple attribute decision making method with intuitionistic linguistic information based on the extended Bonferroni mean which can consider the interrelationship of the attributes. Firstly, score function and accuracy function of intuitionistic linguistic numbers are introduced. Then, an intuitionistic linguistic Bonferroni mean (ILBM) operator and an intuitionistic linguistic weighted Bonferroni mean (ILWBM) operator are developed, and some desirable characteristics of them are studied. At the same time, some special cases with respect to the parameters and in Bonferroni are analyzed. Based on the ILWBM operator, the approach to multiple attribute decision making with intuitionistic linguistic information is proposed. Finally, an illustrative example is given to verify the developed approach and to demonstrate its effectiveness. Peide Liu, Lili Rong, Yanchang Chu, and Yanwei Li Copyright © 2014 Peide Liu et al. All rights reserved. A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream Thu, 19 Jun 2014 00:00:00 +0000 Data streams are continuously generated over time from Internet of Things (IoT) devices. The faster all of this data is analyzed, its hidden trends and patterns discovered, and new strategies created, the faster action can be taken, creating greater value for organizations. Density-based method is a prominent class in clustering data streams. It has the ability to detect arbitrary shape clusters, to handle outlier, and it does not need the number of clusters in advance. Therefore, density-based clustering algorithm is a proper choice for clustering IoT streams. Recently, several density-based algorithms have been proposed for clustering data streams. However, density-based clustering in limited time is still a challenging issue. In this paper, we propose a density-based clustering algorithm for IoT streams. The method has fast processing time to be applicable in real-time application of IoT devices. Experimental results show that the proposed approach obtains high quality results with low computation time on real and synthetic datasets. Amineh Amini, Hadi Saboohi, Teh Ying Wah, and Tutut Herawan Copyright © 2014 Amineh Amini et al. All rights reserved. Smart HVAC Control in IoT: Energy Consumption Minimization with User Comfort Constraints Wed, 18 Jun 2014 12:40:15 +0000 Smart grid is one of the main applications of the Internet of Things (IoT) paradigm. Within this context, this paper addresses the efficient energy consumption management of heating, ventilation, and air conditioning (HVAC) systems in smart grids with variable energy price. To that end, first, we propose an energy scheduling method that minimizes the energy consumption cost for a particular time interval, taking into account the energy price and a set of comfort constraints, that is, a range of temperatures according to user’s preferences for a given room. Then, we propose an energy scheduler where the user may select to relax the temperature constraints to save more energy. Moreover, thanks to the IoT paradigm, the user may interact remotely with the HVAC control system. In particular, the user may decide remotely the temperature of comfort, while the temperature and energy consumption information is sent through Internet and displayed at the end user’s device. The proposed algorithms have been implemented in a real testbed, highlighting the potential gains that can be achieved in terms of both energy and cost. Jordi Serra, David Pubill, Angelos Antonopoulos, and Christos Verikoukis Copyright © 2014 Jordi Serra et al. All rights reserved. A Novel Support Vector Machine with Globality-Locality Preserving Tue, 17 Jun 2014 05:28:46 +0000 Support vector machine (SVM) is regarded as a powerful method for pattern classification. However, the solution of the primal optimal model of SVM is susceptible for class distribution and may result in a nonrobust solution. In order to overcome this shortcoming, an improved model, support vector machine with globality-locality preserving (GLPSVM), is proposed. It introduces globality-locality preserving into the standard SVM, which can preserve the manifold structure of the data space. We complete rich experiments on the UCI machine learning data sets. The results validate the effectiveness of the proposed model, especially on the Wine and Iris databases; the recognition rate is above 97% and outperforms all the algorithms that were developed from SVM. Cheng-Long Ma and Yu-Bo Yuan Copyright © 2014 Cheng-Long Ma and Yu-Bo Yuan. All rights reserved. An Improved Pearson’s Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes Mon, 16 Jun 2014 12:05:16 +0000 Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. P. M. Booma, S. Prabhakaran, and R. Dhanalakshmi Copyright © 2014 P. M. Booma et al. All rights reserved. Signal Waveform Detection with Statistical Automaton for Internet and Web Service Streaming Mon, 16 Jun 2014 08:55:06 +0000 In recent years, many approaches have been suggested for Internet and web streaming detection. In this paper, we propose an approach to signal waveform detection for Internet and web streaming, with novel statistical automatons. The system records network connections over a period of time to form a signal waveform and compute suspicious characteristics of the waveform. Network streaming according to these selected waveform features by our newly designed Aho-Corasick (AC) automatons can be classified. We developed two versions, that is, basic AC and advanced AC-histogram waveform automata, and conducted comprehensive experimentation. The results confirm that our approach is feasible and suitable for deployment. Kuo-Kun Tseng, Yuzhu Ji, Yiming Liu, Nai-Lun Huang, Fufu Zeng, and Fang-Ying Lin Copyright © 2014 Kuo-Kun Tseng et al. All rights reserved. The Need for Specific Penalties for Hacking in Criminal Law Mon, 16 Jun 2014 06:06:33 +0000 In spite of the fact that hacking is a widely used term, it is still not legally established. Moreover, the definition of the concept of hacking has been deployed in a wide variety of ways in national literature. This ambiguity has led to various side effects. Recently in the United States, reforms collectively known as Aaron's Law were proposed as intended amendments to the Computer Fraud and Abuse Act (CFAA). Most experts expect that this change will put the brakes on the CFAA as a severe punishment policy, and result in a drop in controversial court decisions. In this study, we analyze the definitions and the penalties for hacking for each country and compare them with the national law and then make suggestions through more specific legislation. We expect it will reduce legal controversy and prevent excessive punishment. Sangkyo Oh and Kyungho Lee Copyright © 2014 Sangkyo Oh and Kyungho Lee. All rights reserved. Adaptive MANET Multipath Routing Algorithm Based on the Simulated Annealing Approach Mon, 16 Jun 2014 00:00:00 +0000 Mobile ad hoc network represents a system of wireless mobile nodes that can freely and dynamically self-organize network topologies without any preexisting communication infrastructure. Due to characteristics like temporary topology and absence of centralized authority, routing is one of the major issues in ad hoc networks. In this paper, a new multipath routing scheme is proposed by employing simulated annealing approach. The proposed metaheuristic approach can achieve greater and reciprocal advantages in a hostile dynamic real world network situation. Therefore, the proposed routing scheme is a powerful method for finding an effective solution into the conflict mobile ad hoc network routing problem. Simulation results indicate that the proposed paradigm adapts best to the variation of dynamic network situations. The average remaining energy, network throughput, packet loss probability, and traffic load distribution are improved by about 10%, 10%, 5%, and 10%, respectively, more than the existing schemes. Sungwook Kim Copyright © 2014 Sungwook Kim. All rights reserved. Emerging Trends in Soft Computing Models in Bioinformatics and Biomedicine Sun, 15 Jun 2014 07:24:39 +0000 Yudong Zhang, Saeed Balochian, and Vishal Bhatnagar Copyright © 2014 Yudong Zhang et al. All rights reserved. Bioinspired Computation and Its Applications in Operation Management Thu, 12 Jun 2014 11:53:48 +0000 Tinggui Chen, Jianjun Yang, Kai Huang, and Qiang Cheng Copyright © 2014 Tinggui Chen et al. All rights reserved. Hybrid PolyLingual Object Model: An Efficient and Seamless Integration of Java and Native Components on the Dalvik Virtual Machine Thu, 12 Jun 2014 10:52:43 +0000 JNI in the Android platform is often observed with low efficiency and high coding complexity. Although many researchers have investigated the JNI mechanism, few of them solve the efficiency and the complexity problems of JNI in the Android platform simultaneously. In this paper, a hybrid polylingual object (HPO) model is proposed to allow a CAR object being accessed as a Java object and as vice in the Dalvik virtual machine. It is an acceptable substitute for JNI to reuse the CAR-compliant components in Android applications in a seamless and efficient way. The metadata injection mechanism is designed to support the automatic mapping and reflection between CAR objects and Java objects. A prototype virtual machine, called HPO-Dalvik, is implemented by extending the Dalvik virtual machine to support the HPO model. Lifespan management, garbage collection, and data type transformation of HPO objects are also handled in the HPO-Dalvik virtual machine automatically. The experimental result shows that the HPO model outweighs the standard JNI in lower overhead on native side, better executing performance with no JNI bridging code being demanded. Yukun Huang, Rong Chen, Jingbo Wei, Xilong Pei, Jing Cao, Prem Prakash Jayaraman, and Rajiv Ranjan Copyright © 2014 Yukun Huang et al. All rights reserved. Multiple R&D Projects Scheduling Optimization with Improved Particle Swarm Algorithm Thu, 12 Jun 2014 08:27:03 +0000 For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment. Mengqi Liu, Miyuan Shan, and Juan Wu Copyright © 2014 Mengqi Liu et al. All rights reserved. Minimization of Temperature Ranges between the Top and Bottom of an Air Flow Controlling Device through Hybrid Control in a Plant Factory Wed, 11 Jun 2014 12:01:04 +0000 To maintain the production timing, productivity, and product quality of plant factories, it is necessary to keep the growth environment uniform. A vertical multistage type of plant factory involves different levels of growing trays, which results in the problem of difference in temperature among vertically different locations. To address it, it is necessary to install air flow devices such as air flow fan and cooling/heating device at the proper locations in order to facilitate air circulation in the facility as well as develop a controlling technology for efficient operation. Accordingly, this study compares the temperature and air distribution within the space of a vertical multistage closed-type plant factory by controlling cooling/heating devices and air flow fans harmoniously by means of the specially designed testbed. The experiment results indicate that in the hybrid control of cooling and heating devices and air flow fans, the difference in temperature decreased by as much as 78.9% compared to that when only cooling and heating devices were operated; the air distribution was improved by as much as 63.4%. Seung-Mi Moon, Sook-Youn Kwon, and Jae-Hyun Lim Copyright © 2014 Seung-Mi Moon et al. All rights reserved. Power Quality Improvement by Unified Power Quality Conditioner Based on CSC Topology Using Synchronous Reference Frame Theory Wed, 11 Jun 2014 11:49:28 +0000 This paper deals with the performance of unified power quality conditioner (UPQC) based on current source converter (CSC) topology. UPQC is used to mitigate the power quality problems like harmonics and sag. The shunt and series active filter performs the simultaneous elimination of current and voltage problems. The power fed is linked through common DC link and maintains constant real power exchange. The DC link is connected through the reactor. The real power supply is given by the photovoltaic system for the compensation of power quality problems. The reference current and voltage generation for shunt and series converter is based on phase locked loop and synchronous reference frame theory. The proposed UPQC-CSC design has superior performance for mitigating the power quality problems. Rajasekaran Dharmalingam, Subhransu Sekhar Dash, Karthikrajan Senthilnathan, Arun Bhaskar Mayilvaganan, and Subramani Chinnamuthu Copyright © 2014 Rajasekaran Dharmalingam et al. All rights reserved. A Solution Quality Assessment Method for Swarm Intelligence Optimization Algorithms Wed, 11 Jun 2014 10:57:39 +0000 Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of “value performance,” the “ordinal performance” is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and “good enough” set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method. Zhaojun Zhang, Gai-Ge Wang, Kuansheng Zou, and Jianhua Zhang Copyright © 2014 Zhaojun Zhang et al. All rights reserved. Bare-Bones Teaching-Learning-Based Optimization Tue, 10 Jun 2014 06:24:18 +0000 Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms. Feng Zou, Lei Wang, Xinhong Hei, Debao Chen, Qiaoyong Jiang, and Hongye Li Copyright © 2014 Feng Zou et al. All rights reserved. SOA-Based Model for Value-Added ITS Services Delivery Tue, 10 Jun 2014 05:42:18 +0000 Integration is currently a key factor in intelligent transportation systems (ITS), especially because of the ever increasing service demands originating from the ITS industry and ITS users. The current ITS landscape is made up of multiple technologies that are tightly coupled, and its interoperability is extremely low, which limits ITS services generation. Given this fact, novel information technologies (IT) based on the service-oriented architecture (SOA) paradigm have begun to introduce new ways to address this problem. The SOA paradigm allows the construction of loosely coupled distributed systems that can help to integrate the heterogeneous systems that are part of ITS. In this paper, we focus on developing an SOA-based model for integrating information technologies (IT) into ITS to achieve ITS service delivery. To develop our model, the ITS technologies and services involved were identified, catalogued, and decoupled. In doing so, we applied our SOA-based model to integrate all of the ITS technologies and services, ranging from the lowest-level technical components, such as roadside unit as a service (RSS), to the most abstract ITS services that will be offered to ITS users (value-added services). To validate our model, a functionality case study that included all of the components of our model was designed. Luis Felipe Herrera-Quintero, Francisco Maciá-Pérez, Diego Marcos-Jorquera, and Virgilio Gilart-Iglesias Copyright © 2014 Luis Felipe Herrera-Quintero et al. All rights reserved. Domain Adaptation for Pedestrian Detection Based on Prediction Consistency Tue, 10 Jun 2014 05:39:55 +0000 Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain. Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene. Yu Li-ping, Tang Huan-ling, and An Zhi-yong Copyright © 2014 Yu Li-ping et al. All rights reserved. Empirical Analysis of Retirement Pension and IFRS Adoption Effects on Accounting Information: Glance at IT Industry Mon, 09 Jun 2014 15:23:42 +0000 This study reviews new pension accounting with K-IFRS and provides empirical changes in liability for retirement allowances with adoption of K-IFRS. It will help to understand the effect of pension accounting on individual firm’s financial report and the importance of public announcement of actuarial assumptions. Firms that adopted K-IFRS had various changes in retirement liability compared to the previous financial report not based on K-IFRS. Their actuarial assumptions for pension accounting should be announced, but only few of them were published. Data analysis shows that the small differences of the actuarial assumption may result in a big change of retirement related liability. Firms within IT industry also have similar behaviors, which means that additional financial regulations for pension accounting are recommended. JeongYeon Kim Copyright © 2014 JeongYeon Kim. All rights reserved. Recommendation Based on Trust Diffusion Model Mon, 09 Jun 2014 08:17:22 +0000 Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, and Measure. Jinfeng Yuan and Li Li Copyright © 2014 Jinfeng Yuan and Li Li. All rights reserved. Structural Optimization of a Knuckle with Consideration of Stiffness and Durability Requirements Sun, 08 Jun 2014 00:00:00 +0000 The automobile’s knuckle is connected to the parts of the steering system and the suspension system and it is used for adjusting the direction of a rotation through its attachment to the wheel. This study changes the existing material made of GCD45 to Al6082M and recommends the lightweight design of the knuckle as the optimal design technique to be installed in small cars. Six shape design variables were selected for the optimization of the knuckle and the criteria relevant to stiffness and durability were considered as the design requirements during the optimization process. The metamodel-based optimization method that uses the kriging interpolation method as the optimization technique was applied. The result shows that all constraints for stiffness and durability are satisfied using A16082M, while reducing the weight of the knuckle by 60% compared to that of the existing GCD450. Geun-Yeon Kim, Seung-Ho Han, and Kwon-Hee Lee Copyright © 2014 Geun-Yeon Kim et al. All rights reserved. Application of Reinforcement Learning in Cognitive Radio Networks: Models and Algorithms Thu, 05 Jun 2014 11:57:42 +0000 Cognitive radio (CR) enables unlicensed users to exploit the underutilized spectrum in licensed spectrum whilst minimizing interference to licensed users. Reinforcement learning (RL), which is an artificial intelligence approach, has been applied to enable each unlicensed user to observe and carry out optimal actions for performance enhancement in a wide range of schemes in CR, such as dynamic channel selection and channel sensing. This paper presents new discussions of RL in the context of CR networks. It provides an extensive review on how most schemes have been approached using the traditional and enhanced RL algorithms through state, action, and reward representations. Examples of the enhancements on RL, which do not appear in the traditional RL approach, are rules and cooperative learning. This paper also reviews performance enhancements brought about by the RL algorithms and open issues. This paper aims to establish a foundation in order to spark new research interests in this area. Our discussion has been presented in a tutorial manner so that it is comprehensive to readers outside the specialty of RL and CR. Kok-Lim Alvin Yau, Geong-Sen Poh, Su Fong Chien, and Hasan A. A. Al-Rawi Copyright © 2014 Kok-Lim Alvin Yau et al. All rights reserved. Obscenity Detection Using Haar-Like Features and Gentle Adaboost Classifier Thu, 05 Jun 2014 10:53:04 +0000 Large exposure of skin area of an image is considered obscene. This only fact may lead to many false images having skin-like objects and may not detect those images which have partially exposed skin area but have exposed erotogenic human body parts. This paper presents a novel method for detecting nipples from pornographic image contents. Nipple is considered as an erotogenic organ to identify pornographic contents from images. In this research Gentle Adaboost (GAB) haar-cascade classifier and haar-like features used for ensuring detection accuracy. Skin filter prior to detection made the system more robust. The experiment showed that, considering accuracy, haar-cascade classifier performs well, but in order to satisfy detection time, train-cascade classifier is suitable. To validate the results, we used 1198 positive samples containing nipple objects and 1995 negative images. The detection rates for haar-cascade and train-cascade classifiers are 0.9875 and 0.8429, respectively. The detection time for haar-cascade is 0.162 seconds and is 0.127 seconds for train-cascade classifier. Rashed Mustafa, Yang Min, and Dingju Zhu Copyright © 2014 Rashed Mustafa et al. All rights reserved. Minimum Variance Distortionless Response Beamformer with Enhanced Nulling Level Control via Dynamic Mutated Artificial Immune System Thu, 05 Jun 2014 07:40:51 +0000 In smart antenna applications, the adaptive beamforming technique is used to cancel interfering signals (placing nulls) and produce or steer a strong beam toward the target signal according to the calculated weight vectors. Minimum variance distortionless response (MVDR) beamforming is capable of determining the weight vectors for beam steering; however, its nulling level on the interference sources remains unsatisfactory. Beamforming can be considered as an optimization problem, such that optimal weight vector should be obtained through computation. Hence, in this paper, a new dynamic mutated artificial immune system (DM-AIS) is proposed to enhance MVDR beamforming for controlling the null steering of interference and increase the signal to interference noise ratio (SINR) for wanted signals. Tiong Sieh Kiong, S. Balasem Salem, Johnny Koh Siaw Paw, K. Prajindra Sankar, and Soodabeh Darzi Copyright © 2014 Tiong Sieh Kiong et al. All rights reserved. Recent Advancements in Computer & Software Technology Thu, 05 Jun 2014 07:27:59 +0000 K. K. Mishra, A. K. Misra, Peter Mueller, Gregorio Martinez Perez, Sanjiv K. Bhatia, and Yong Wang Copyright © 2014 K. K. Mishra et al. All rights reserved. Cost-Sensitive Learning for Emotion Robust Speaker Recognition Wed, 04 Jun 2014 11:20:38 +0000 In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved. Dongdong Li, Yingchun Yang, and Weihui Dai Copyright © 2014 Dongdong Li et al. All rights reserved. The Design and Implementation of Postprocessing for Depth Map on Real-Time Extraction System Wed, 04 Jun 2014 10:00:41 +0000 Depth estimation becomes the key technology to resolve the communications of the stereo vision. We can get the real-time depth map based on hardware, which cannot implement complicated algorithm as software, because there are some restrictions in the hardware structure. Eventually, some wrong stereo matching will inevitably exist in the process of depth estimation by hardware, such as FPGA. In order to solve the problem a postprocessing function is designed in this paper. After matching cost unique test, the both left-right and right-left consistency check solutions are implemented, respectively; then, the cavities in depth maps can be filled by right depth values on the basis of right-left consistency check solution. The results in the experiments have shown that the depth map extraction and postprocessing function can be implemented in real time in the same system; what is more, the quality of the depth maps is satisfactory. Zhiwei Tang, Bin Li, Huosheng Li, and Zheng Xu Copyright © 2014 Zhiwei Tang et al. All rights reserved. A Node Influence Based Label Propagation Algorithm for Community Detection in Networks Wed, 04 Jun 2014 09:01:37 +0000 Label propagation algorithm (LPA) is an extremely fast community detection method and is widely used in large scale networks. In spite of the advantages of LPA, the issue of its poor stability has not yet been well addressed. We propose a novel node influence based label propagation algorithm for community detection (NIBLPA), which improves the performance of LPA by improving the node orders of label updating and the mechanism of label choosing when more than one label is contained by the maximum number of nodes. NIBLPA can get more stable results than LPA since it avoids the complete randomness of LPA. The experimental results on both synthetic and real networks demonstrate that NIBLPA maintains the efficiency of the traditional LPA algorithm, and, at the same time, it has a superior performance to some representative methods. Yan Xing, Fanrong Meng, Yong Zhou, Mu Zhu, Mengyu Shi, and Guibin Sun Copyright © 2014 Yan Xing et al. All rights reserved. Intelligent Advisory Speed Limit Dedication in Highway Using VANET Wed, 04 Jun 2014 07:38:59 +0000 Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle’s travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle’s characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions. Ali Jalooli, Erfan Shaghaghi, Mohammad Reza Jabbarpour, Rafidah Md Noor, Hwasoo Yeo, and Jason J. Jung Copyright © 2014 Ali Jalooli et al. All rights reserved. Multilabel Image Annotation Based on Double-Layer PLSA Model Wed, 04 Jun 2014 05:58:23 +0000 Due to the semantic gap between visual features and semantic concepts, automatic image annotation has become a difficult issue in computer vision recently. We propose a new image multilabel annotation method based on double-layer probabilistic latent semantic analysis (PLSA) in this paper. The new double-layer PLSA model is constructed to bridge the low-level visual features and high-level semantic concepts of images for effective image understanding. The low-level features of images are represented as visual words by Bag-of-Words model; latent semantic topics are obtained by the first layer PLSA from two aspects of visual and texture, respectively. Furthermore, we adopt the second layer PLSA to fuse the visual and texture latent semantic topics and achieve a top-layer latent semantic topic. By the double-layer PLSA, the relationships between visual features and semantic concepts of images are established, and we can predict the labels of new images by their low-level features. Experimental results demonstrate that our automatic image annotation model based on double-layer PLSA can achieve promising performance for labeling and outperform previous methods on standard Corel dataset. Jing Zhang, Da Li, Weiwei Hu, Zhihua Chen, and Yubo Yuan Copyright © 2014 Jing Zhang et al. All rights reserved. Hybrid Biogeography-Based Optimization for Integer Programming Tue, 03 Jun 2014 07:13:06 +0000 Biogeography-based optimization (BBO) is a relatively new bioinspired heuristic for global optimization based on the mathematical models of biogeography. By investigating the applicability and performance of BBO for integer programming, we find that the original BBO algorithm does not perform well on a set of benchmark integer programming problems. Thus we modify the mutation operator and/or the neighborhood structure of the algorithm, resulting in three new BBO-based methods, named BlendBBO, BBO_DE, and LBBO_LDE, respectively. Computational experiments show that these methods are competitive approaches to solve integer programming problems, and the LBBO_LDE shows the best performance on the benchmark problems. Zhi-Cheng Wang and Xiao-Bei Wu Copyright © 2014 Zhi-Cheng Wang and Xiao-Bei Wu. All rights reserved. A Graph-Based Ant Colony Optimization Approach for Process Planning Tue, 03 Jun 2014 06:25:00 +0000 The complex process planning problem is modeled as a combinatorial optimization problem with constraints in this paper. An ant colony optimization (ACO) approach has been developed to deal with process planning problem by simultaneously considering activities such as sequencing operations, selecting manufacturing resources, and determining setup plans to achieve the optimal process plan. A weighted directed graph is conducted to describe the operations, precedence constraints between operations, and the possible visited path between operation nodes. A representation of process plan is described based on the weighted directed graph. Ant colony goes through the necessary nodes on the graph to achieve the optimal solution with the objective of minimizing total production costs (TPC). Two cases have been carried out to study the influence of various parameters of ACO on the system performance. Extensive comparative experiments have been conducted to demonstrate the feasibility and efficiency of the proposed approach. JinFeng Wang, XiaoLiang Fan, and Shuting Wan Copyright © 2014 JinFeng Wang et al. All rights reserved. Mathematical Modelling of Thermal Process to Aquatic Environment with Different Hydrometeorological Conditions Mon, 02 Jun 2014 11:28:17 +0000 This paper presents the mathematical model of the thermal process from thermal power plant to aquatic environment of the reservoir-cooler, which is located in the Pavlodar region, 17 Km to the north-east of Ekibastuz town. The thermal process in reservoir-cooler with different hydrometeorological conditions is considered, which is solved by three-dimensional Navier-Stokes equations and temperature equation for an incompressible flow in a stratified medium. A numerical method based on the projection method, divides the problem into three stages. At the first stage, it is assumed that the transfer of momentum occurs only by convection and diffusion. Intermediate velocity field is solved by fractional steps method. At the second stage, three-dimensional Poisson equation is solved by the Fourier method in combination with tridiagonal matrix method (Thomas algorithm). Finally, at the third stage, it is expected that the transfer is only due to the pressure gradient. Numerical method determines the basic laws of the hydrothermal processes that qualitatively and quantitatively are approximated depending on different hydrometeorological conditions. Alibek Issakhov Copyright © 2014 Alibek Issakhov. All rights reserved. A Malware Detection Scheme Based on Mining Format Information Mon, 02 Jun 2014 06:11:09 +0000 Malware has become one of the most serious threats to computer information system and the current malware detection technology still has very significant limitations. In this paper, we proposed a malware detection approach by mining format information of PE (portable executable) files. Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. When the selected features were trained using classification algorithms, the results of our experiments indicate that the accuracy of the top classification algorithm is 99.1% and the value of the AUC is 0.998. We designed three experiments to evaluate the performance of our detection scheme and the ability of detecting unknown and new malware. Although the experimental results of identifying new malware are not perfect, our method is still able to identify 97.6% of new malware with 1.3% false positive rates. Jinrong Bai, Junfeng Wang, and Guozhong Zou Copyright © 2014 Jinrong Bai et al. All rights reserved. The Contribution of Particle Swarm Optimization to Three-Dimensional Slope Stability Analysis Sun, 01 Jun 2014 06:42:50 +0000 Over the last few years, particle swarm optimization (PSO) has been extensively applied in various geotechnical engineering including slope stability analysis. However, this contribution was limited to two-dimensional (2D) slope stability analysis. This paper applied PSO in three-dimensional (3D) slope stability problem to determine the critical slip surface (CSS) of soil slopes. A detailed description of adopted PSO was presented to provide a good basis for more contribution of this technique to the field of 3D slope stability problems. A general rotating ellipsoid shape was introduced as the specific particle for 3D slope stability analysis. A detailed sensitivity analysis was designed and performed to find the optimum values of parameters of PSO. Example problems were used to evaluate the applicability of PSO in determining the CSS of 3D slopes. The first example presented a comparison between the results of PSO and PLAXI-3D finite element software and the second example compared the ability of PSO to determine the CSS of 3D slopes with other optimization methods from the literature. The results demonstrated the efficiency and effectiveness of PSO in determining the CSS of 3D soil slopes. Roohollah Kalatehjari, Ahmad Safuan A Rashid, Nazri Ali, and Mohsen Hajihassani Copyright © 2014 Roohollah Kalatehjari et al. All rights reserved. Optimization and Planning of Emergency Evacuation Routes Considering Traffic Control Thu, 29 May 2014 12:23:52 +0000 Emergencies, especially major ones, happen fast, randomly, as well as unpredictably, and generally will bring great harm to people’s life and the economy. Therefore, governments and lots of professionals devote themselves to taking effective measures and providing optimal evacuation plans. This paper establishes two different emergency evacuation models on the basis of the maximum flow model (MFM) and the minimum-cost maximum flow model (MC-MFM), and proposes corresponding algorithms for the evacuation from one source node to one designated destination (one-to-one evacuation). Ulteriorly, we extend our evaluation model from one source node to many designated destinations (one-to-many evacuation). At last, we make case analysis of evacuation optimization and planning in Beijing, and obtain the desired evacuation routes and effective traffic control measures from the perspective of sufficiency and practicability. Both analytical and numerical results support that our models are feasible and practical. Guo Li, Lijun Zhang, and Zhaohua Wang Copyright © 2014 Guo Li et al. All rights reserved. Two-Layer Fragile Watermarking Method Secured with Chaotic Map for Authentication of Digital Holy Quran Thu, 29 May 2014 11:53:41 +0000 This paper presents a novel watermarking method to facilitate the authentication and detection of the image forgery on the Quran images. Two layers of embedding scheme on wavelet and spatial domain are introduced to enhance the sensitivity of fragile watermarking and defend the attacks. Discrete wavelet transforms are applied to decompose the host image into wavelet prior to embedding the watermark in the wavelet domain. The watermarked wavelet coefficient is inverted back to spatial domain then the least significant bits is utilized to hide another watermark. A chaotic map is utilized to blur the watermark to make it secure against the local attack. The proposed method allows high watermark payloads, while preserving good image quality. Experiment results confirm that the proposed methods are fragile and have superior tampering detection even though the tampered area is very small. Mohammed S. Khalil, Fajri Kurniawan, Muhammad Khurram Khan, and Yasser M. Alginahi Copyright © 2014 Mohammed S. Khalil et al. All rights reserved. Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint Thu, 29 May 2014 09:33:09 +0000 Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. Nebojsa Bacanin and Milan Tuba Copyright © 2014 Nebojsa Bacanin and Milan Tuba. All rights reserved. Two-Cloud-Servers-Assisted Secure Outsourcing Multiparty Computation Wed, 28 May 2014 12:56:10 +0000 We focus on how to securely outsource computation task to the cloud and propose a secure outsourcing multiparty computation protocol on lattice-based encrypted data in two-cloud-servers scenario. Our main idea is to transform the outsourced data respectively encrypted by different users’ public keys to the ones that are encrypted by the same two private keys of the two assisted servers so that it is feasible to operate on the transformed ciphertexts to compute an encrypted result following the function to be computed. In order to keep the privacy of the result, the two servers cooperatively produce a custom-made result for each user that is authorized to get the result so that all authorized users can recover the desired result while other unauthorized ones including the two servers cannot. Compared with previous research, our protocol is completely noninteractive between any users, and both of the computation and the communication complexities of each user in our solution are independent of the computing function. Yi Sun, Qiaoyan Wen, Yudong Zhang, Hua Zhang, Zhengping Jin, and Wenmin Li Copyright © 2014 Yi Sun et al. All rights reserved. An Integrative Behavioral Model of Information Security Policy Compliance Wed, 28 May 2014 12:19:05 +0000 The authors found the behavioral factors that influence the organization members’ compliance with the information security policy in organizations on the basis of neutralization theory, Theory of planned behavior, and protection motivation theory. Depending on the theory of planned behavior, members’ attitudes towards compliance, as well as normative belief and self-efficacy, were believed to determine the intention to comply with the information security policy. Neutralization theory, a prominent theory in criminology, could be expected to provide the explanation for information system security policy violations. Based on the protection motivation theory, it was inferred that the expected efficacy could have an impact on intentions of compliance. By the above logical reasoning, the integrative behavioral model and eight hypotheses could be derived. Data were collected by conducting a survey; 194 out of 207 questionnaires were available. The test of the causal model was conducted by PLS. The reliability, validity, and model fit were found to be statistically significant. The results of the hypotheses tests showed that seven of the eight hypotheses were acceptable. The theoretical implications of this study are as follows: (1) the study is expected to play a role of the baseline for future research about organization members’ compliance with the information security policy, (2) the study attempted an interdisciplinary approach by combining psychology and information system security research, and (3) the study suggested concrete operational definitions of influencing factors for information security policy compliance through a comprehensive theoretical review. Also, the study has some practical implications. First, it can provide the guideline to support the successful execution of the strategic establishment for the implement of information system security policies in organizations. Second, it proves that the need of education and training programs suppressing members’ neutralization intention to violate information security policy should be emphasized. Sang Hoon Kim, Kyung Hoon Yang, and Sunyoung Park Copyright © 2014 Sang Hoon Kim et al. All rights reserved. Path Planning Using a Hybrid Evolutionary Algorithm Based on Tree Structure Encoding Wed, 28 May 2014 10:57:54 +0000 A hybrid evolutionary algorithm using scalable encoding method for path planning is proposed in this paper. The scalable representation is based on binary tree structure encoding. To solve the problem of hybrid genetic algorithm and particle swarm optimization, the “dummy node” is added into the binary trees to deal with the different lengths of representations. The experimental results show that the proposed hybrid method demonstrates using fewer turning points than traditional evolutionary algorithms to generate shorter collision-free paths for mobile robot navigation. Ming-Yi Ju, Siao-En Wang, and Jian-Horn Guo Copyright © 2014 Ming-Yi Ju et al. All rights reserved. Cuckoo Search with Lévy Flights for Weighted Bayesian Energy Functional Optimization in Global-Support Curve Data Fitting Wed, 28 May 2014 10:55:47 +0000 The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. Akemi Gálvez, Andrés Iglesias, and Luis Cabellos Copyright © 2014 Akemi Gálvez et al. All rights reserved. Design of Heat Exchanger for Ericsson-Brayton Piston Engine Wed, 28 May 2014 05:42:22 +0000 Combined power generation or cogeneration is a highly effective technology that produces heat and electricity in one device more efficiently than separate production. Overall effectiveness is growing by use of combined technologies of energy extraction, taking heat from flue gases and coolants of machines. Another problem is the dependence of such devices on fossil fuels as fuel. For the combustion turbine is mostly used as fuel natural gas, kerosene and as fuel for heating power plants is mostly used coal. It is therefore necessary to seek for compensation today, which confirms the assumption in the future. At first glance, the obvious efforts are to restrict the use of largely oil and change the type of energy used in transport. Another significant change is the increase in renewable energy—energy that is produced from renewable sources. Among machines gaining energy by unconventional way belong mainly the steam engine, Stirling engine, and Ericsson engine. In these machines, the energy is obtained by external combustion and engine performs work in a medium that receives and transmits energy from combustion or flue gases indirectly. The paper deals with the principle of hot-air engines, and their use in combined heat and electricity production from biomass and with heat exchangers as primary energy transforming element. Peter Durcansky, Stefan Papucik, Jozef Jandacka, Michal Holubcik, and Radovan Nosek Copyright © 2014 Peter Durcansky et al. All rights reserved. An Adaptive Superpixel Based Hand Gesture Tracking and Recognition System Tue, 27 May 2014 12:15:45 +0000 We propose an adaptive and robust superpixel based hand gesture tracking system, in which hand gestures drawn in free air are recognized from their motion trajectories. First we employed the motion detection of superpixels and unsupervised image segmentation to detect the moving target hand using the first few frames of the input video sequence. Then the hand appearance model is constructed from its surrounding superpixels. By incorporating the failure recovery and template matching in the tracking process, the target hand is tracked by an adaptive superpixel based tracking algorithm, where the problem of hand deformation, view-dependent appearance invariance, fast motion, and background confusion can be well handled to extract the correct hand motion trajectory. Finally, the hand gesture is recognized by the extracted motion trajectory with a trained SVM classifier. Experimental results show that our proposed system can achieve better performance compared to the existing state-of-the-art methods with the recognition accuracy 99.17% for easy set and 98.57 for hard set. Hong-Min Zhu and Chi-Man Pun Copyright © 2014 Hong-Min Zhu and Chi-Man Pun. All rights reserved. Preventing Shoulder-Surfing Attack with the Concept of Concealing the Password Objects’ Information Tue, 27 May 2014 11:48:13 +0000 Traditionally, picture-based password systems employ password objects (pictures/icons/symbols) as input during an authentication session, thus making them vulnerable to “shoulder-surfing” attack because the visual interface by function is easily observed by others. Recent software-based approaches attempt to minimize this threat by requiring users to enter their passwords indirectly by performing certain mental tasks to derive the indirect password, thus concealing the user’s actual password. However, weaknesses in the positioning of distracter and password objects introduce usability and security issues. In this paper, a new method, which conceals information about the password objects as much as possible, is proposed. Besides concealing the password objects and the number of password objects, the proposed method allows both password and distracter objects to be used as the challenge set’s input. The correctly entered password appears to be random and can only be derived with the knowledge of the full set of password objects. Therefore, it would be difficult for a shoulder-surfing adversary to identify the user’s actual password. Simulation results indicate that the correct input object and its location are random for each challenge set, thus preventing frequency of occurrence analysis attack. User study results show that the proposed method is able to prevent shoulder-surfing attack. Peng Foong Ho, Yvonne Hwei-Syn Kam, Mee Chin Wee, Yu Nam Chong, and Lip Yee Por Copyright © 2014 Peng Foong Ho et al. All rights reserved. Improved Feature-Selection Method Considering the Imbalance Problem in Text Categorization Mon, 26 May 2014 12:07:18 +0000 The filtering feature-selection algorithm is a kind of important approach to dimensionality reduction in the field of the text categorization. Most of filtering feature-selection algorithms evaluate the significance of a feature for category based on balanced dataset and do not consider the imbalance factor of dataset. In this paper, a new scheme was proposed, which can weaken the adverse effect caused by the imbalance factor in the corpus. We evaluated the improved versions of nine well-known feature-selection methods (Information Gain, Chi statistic, Document Frequency, Orthogonal Centroid Feature Selection, DIA association factor, Comprehensive Measurement Feature Selection, Deviation from Poisson Feature Selection, improved Gini index, and Mutual Information) using naïve Bayes and support vector machines on three benchmark document collections (20-Newsgroups, Reuters-21578, and WebKB). The experimental results show that the improved scheme can significantly enhance the performance of the feature-selection methods. Jieming Yang, Zhaoyang Qu, and Zhiying Liu Copyright © 2014 Jieming Yang et al. All rights reserved. Master-Slave Control Scheme in Electric Vehicle Smart Charging Infrastructure Mon, 26 May 2014 11:12:02 +0000 WINSmartEV is a software based plug-in electric vehicle (PEV) monitoring, control, and management system. It not only incorporates intelligence at every level so that charge scheduling can avoid grid bottlenecks, but it also multiplies the number of PEVs that can be plugged into a single circuit. This paper proposes, designs, and executes many upgrades to WINSmartEV. These upgrades include new hardware that makes the level 1 and level 2 chargers faster, more robust, and more scalable. It includes algorithms that provide a more optimal charge scheduling for the level 2 (EVSE) and an enhanced vehicle monitoring/identification module (VMM) system that can automatically identify PEVs and authorize charging. Ching-Yen Chung, Joshua Chynoweth, Chi-Cheng Chu, and Rajit Gadh Copyright © 2014 Ching-Yen Chung et al. All rights reserved. Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction Mon, 26 May 2014 09:30:03 +0000 Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63). Alberto Gonzalez-Sanchez, Juan Frausto-Solis, and Waldo Ojeda-Bustamante Copyright © 2014 Alberto Gonzalez-Sanchez et al. All rights reserved. Constrained Multiobjective Biogeography Optimization Algorithm Mon, 26 May 2014 07:14:15 +0000 Multiobjective optimization involves minimizing or maximizing multiple objective functions subject to a set of constraints. In this study, a novel constrained multiobjective biogeography optimization algorithm (CMBOA) is proposed. It is the first biogeography optimization algorithm for constrained multiobjective optimization. In CMBOA, a disturbance migration operator is designed to generate diverse feasible individuals in order to promote the diversity of individuals on Pareto front. Infeasible individuals nearby feasible region are evolved to feasibility by recombining with their nearest nondominated feasible individuals. The convergence of CMBOA is proved by using probability theory. The performance of CMBOA is evaluated on a set of 6 benchmark problems and experimental results show that the CMBOA performs better than or similar to the classical NSGA-II and IS-MOEA. Hongwei Mo, Zhidan Xu, Lifang Xu, Zhou Wu, and Haiping Ma Copyright © 2014 Hongwei Mo et al. All rights reserved. PSO-Based Support Vector Machine with Cuckoo Search Technique for Clinical Disease Diagnoses Sun, 25 May 2014 12:39:32 +0000 Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms. Xiaoyong Liu and Hui Fu Copyright © 2014 Xiaoyong Liu and Hui Fu. All rights reserved. An Island Grouping Genetic Algorithm for Fuzzy Partitioning Problems Thu, 22 May 2014 11:19:56 +0000 This paper presents a novel fuzzy clustering technique based on grouping genetic algorithms (GGAs), which are a class of evolutionary algorithms especially modified to tackle grouping problems. Our approach hinges on a GGA devised for fuzzy clustering by means of a novel encoding of individuals (containing elements and clusters sections), a new fitness function (a superior modification of the Davies Bouldin index), specially tailored crossover and mutation operators, and the use of a scheme based on a local search and a parallelization process, inspired from an island-based model of evolution. The overall performance of our approach has been assessed over a number of synthetic and real fuzzy clustering problems with different objective functions and distance measures, from which it is concluded that the proposed approach shows excellent performance in all cases. S. Salcedo-Sanz, J. Del Ser, and Z. W. Geem Copyright © 2014 S. Salcedo-Sanz et al. All rights reserved. Emission Controls Using Different Temperatures of Combustion Air Wed, 21 May 2014 05:45:21 +0000 The effort of many manufacturers of heat sources is to achieve the maximum efficiency of energy transformation chemically bound in the fuel to heat. Therefore, it is necessary to streamline the combustion process and minimize the formation of emission during combustion. The paper presents an analysis of the combustion air temperature to the heat performance and emission parameters of burning biomass. In the second part of the paper the impact of different dendromass on formation of emissions in small heat source is evaluated. The measured results show that the regulation of the temperature of the combustion air has an effect on concentration of emissions from the combustion of biomass. Radovan Nosek, Michal Holubčík, and Štefan Papučík Copyright © 2014 Radovan Nosek et al. All rights reserved. Kruskal-Wallis-Based Computationally Efficient Feature Selection for Face Recognition Wed, 21 May 2014 00:00:00 +0000 Face recognition in today’s technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques. Sajid Ali Khan, Ayyaz Hussain, Abdul Basit, and Sheeraz Akram Copyright © 2014 Sajid Ali Khan et al. All rights reserved. On the Effectiveness of Nature-Inspired Metaheuristic Algorithms for Performing Phase Equilibrium Thermodynamic Calculations Tue, 20 May 2014 11:46:12 +0000 The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we evaluated and compared the reliability and efficiency of eight selected nature-inspired metaheuristic algorithms for solving difficult phase stability and phase equilibrium problems. These algorithms are the cuckoo search (CS), intelligent firefly (IFA), bat (BA), artificial bee colony (ABC), MAKHA, a hybrid between monkey algorithm and krill herd algorithm, covariance matrix adaptation evolution strategy (CMAES), magnetic charged system search (MCSS), and bare bones particle swarm optimization (BBPSO). The results clearly showed that CS is the most reliable of all methods as it successfully solved all thermodynamic problems tested in this study. CS proved to be a promising nature-inspired optimization method to perform applied thermodynamic calculations for process design. Seif-Eddeen K. Fateen and Adrian Bonilla-Petriciolet Copyright © 2014 Seif-Eddeen K. Fateen and Adrian Bonilla-Petriciolet. All rights reserved. Using Heuristic Value Prediction and Dynamic Task Granularity Resizing to Improve Software Speculation Tue, 20 May 2014 11:07:15 +0000 Exploiting potential thread-level parallelism (TLP) is becoming the key factor to improving performance of programs on multicore or many-core systems. Among various kinds of parallel execution models, the software-based speculative parallel model has become a research focus due to its low cost, high efficiency, flexibility, and scalability. The performance of the guest program under the software-based speculative parallel execution model is closely related to the speculation accuracy, the control overhead, and the rollback overhead of the model. In this paper, we first analyzed the conventional speculative parallel model and presented an analytic model of its expectation of the overall overhead, then optimized the conventional model based on the analytic model, and finally proposed a novel speculative parallel model named HEUSPEC. The HEUSPEC model includes three key techniques, namely, the heuristic value prediction, the value based correctness checking, and the dynamic task granularity resizing. We have implemented the runtime system of the model in ANSI C language. The experiment results show that when the speedup of the HEUSPEC model can reach 2.20 on the average (15% higher than conventional model) when depth is equal to 3 and 4.51 on the average (12% higher than conventional model) when speculative depth is equal to 7. Besides, it shows good scalability and lower memory cost. Fan Xu, Li Shen, Zhiying Wang, Bo Su, Hui Guo, and Wei Chen Copyright © 2014 Fan Xu et al. All rights reserved. Medical Image Visual Appearance Improvement Using Bihistogram Bezier Curve Contrast Enhancement: Data from the Osteoarthritis Initiative Tue, 20 May 2014 06:07:48 +0000 Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of “adequate contrast enhancement” to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the image’s maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fisher’s Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection. Hong-Seng Gan, Tan Tian Swee, Ahmad Helmy Abdul Karim, Khairil Amir Sayuti, Mohammed Rafiq Abdul Kadir, Weng-Kit Tham, Liang-Xuan Wong, Kashif T. Chaudhary, Jalil Ali, and Preecha P. Yupapin Copyright © 2014 Hong-Seng Gan et al. All rights reserved. On the Performance of Video Quality Assessment Metrics under Different Compression and Packet Loss Scenarios Tue, 20 May 2014 00:00:00 +0000 When comparing the performance of video coding approaches, evaluating different commercial video encoders, or measuring the perceived video quality in a wireless environment, Rate/distortion analysis is commonly used, where distortion is usually measured in terms of PSNR values. However, PSNR does not always capture the distortion perceived by a human being. As a consequence, significant efforts have focused on defining an objective video quality metric that is able to assess quality in the same way as a human does. We perform a study of some available objective quality assessment metrics in order to evaluate their behavior in two different scenarios. First, we deal with video sequences compressed by different encoders at different bitrates in order to properly measure the video quality degradation associated with the encoding system. In addition, we evaluate the behavior of the quality metrics when measuring video distortions produced by packet losses in mobile ad hoc network scenarios with variable degrees of network congestion and node mobility. Our purpose is to determine if the analyzed metrics can replace the PSNR while comparing, designing, and evaluating video codec proposals, and, in particular, under video delivery scenarios characterized by bursty and frequent packet losses, such as wireless multihop environments. Miguel O. Martínez-Rach, Pablo Piñol, Otoniel M. López, Manuel Perez Malumbres, José Oliver, and Carlos Tavares Calafate Copyright © 2014 Miguel O. Martínez-Rach et al. All rights reserved. Creation of Reliable Relevance Judgments in Information Retrieval Systems Evaluation Experimentation through Crowdsourcing: A Review Mon, 19 May 2014 07:52:01 +0000 Test collection is used to evaluate the information retrieval systems in laboratory-based evaluation experimentation. In a classic setting, generating relevance judgments involves human assessors and is a costly and time consuming task. Researchers and practitioners are still being challenged in performing reliable and low-cost evaluation of retrieval systems. Crowdsourcing as a novel method of data acquisition is broadly used in many research fields. It has been proven that crowdsourcing is an inexpensive and quick solution as well as a reliable alternative for creating relevance judgments. One of the crowdsourcing applications in IR is to judge relevancy of query document pair. In order to have a successful crowdsourcing experiment, the relevance judgment tasks should be designed precisely to emphasize quality control. This paper is intended to explore different factors that have an influence on the accuracy of relevance judgments accomplished by workers and how to intensify the reliability of judgments in crowdsourcing experiment. Parnia Samimi and Sri Devi Ravana Copyright © 2014 Parnia Samimi and Sri Devi Ravana. All rights reserved. Cloud Model Bat Algorithm Mon, 19 May 2014 06:39:16 +0000 Bat algorithm (BA) is a novel stochastic global optimization algorithm. Cloud model is an effective tool in transforming between qualitative concepts and their quantitative representation. Based on the bat echolocation mechanism and excellent characteristics of cloud model on uncertainty knowledge representation, a new cloud model bat algorithm (CBA) is proposed. This paper focuses on remodeling echolocation model based on living and preying characteristics of bats, utilizing the transformation theory of cloud model to depict the qualitative concept: “bats approach their prey.” Furthermore, Lévy flight mode and population information communication mechanism of bats are introduced to balance the advantage between exploration and exploitation. The simulation results show that the cloud model bat algorithm has good performance on functions optimization. Yongquan Zhou, Jian Xie, Liangliang Li, and Mingzhi Ma Copyright © 2014 Yongquan Zhou et al. All rights reserved. A High Accuracy Pedestrian Detection System Combining a Cascade AdaBoost Detector and Random Vector Functional-Link Net Mon, 19 May 2014 00:00:00 +0000 In pedestrian detection methods, their high accuracy detection rates are always obtained at the cost of a large amount of false pedestrians. In order to overcome this problem, the authors propose an accurate pedestrian detection system based on two machine learning methods: cascade AdaBoost detector and random vector functional-link net. During the offline training phase, the parameters of a cascade AdaBoost detector and random vector functional-link net are trained by standard dataset. These candidates, extracted by the strategy of a multiscale sliding window, are normalized to be standard scale and verified by the cascade AdaBoost detector and random vector functional-link net on the online phase. Only those candidates with high confidence can pass the validation. The proposed system is more accurate than other single machine learning algorithms with fewer false pedestrians, which has been confirmed in simulation experiment on four datasets. Zhihui Wang, Sook Yoon, Shan Juan Xie, Yu Lu, and Dong Sun Park Copyright © 2014 Zhihui Wang et al. All rights reserved. Study on Chaotic Fault Tolerant Synchronization Control Based on Adaptive Observer Sun, 18 May 2014 11:42:49 +0000 Aiming at the abrupt faults of the chaotic system, an adaptive observer is proposed to trace the states of the master system. The sufficient conditions for synchronization of such chaotic systems are also derived. Then the feasibility and effectiveness of the proposed method are illustrated via numerical simulations of chaotic Chen system. Finally, the proposed synchronization schemes are applied to secure communication system successfully. The experimental results demonstrate that the employed observer can manage real-time fault diagnosis and parameter identification as well as states tracing of the master system, and so the synchronization of master system and slave system is achieved. Dongming Chen, Xinyu Huang, and Tao Ren Copyright © 2014 Dongming Chen et al. All rights reserved. Negative and Positive Association Rules Mining from Text Using Frequent and Infrequent Itemsets Sun, 18 May 2014 10:27:44 +0000 Association rule mining research typically focuses on positive association rules (PARs), generated from frequently occurring itemsets. However, in recent years, there has been a significant research focused on finding interesting infrequent itemsets leading to the discovery of negative association rules (NARs). The discovery of infrequent itemsets is far more difficult than their counterparts, that is, frequent itemsets. These problems include infrequent itemsets discovery and generation of accurate NARs, and their huge number as compared with positive association rules. In medical science, for example, one is interested in factors which can either adjudicate the presence of a disease or write-off of its possibility. The vivid positive symptoms are often obvious; however, negative symptoms are subtler and more difficult to recognize and diagnose. In this paper, we propose an algorithm for discovering positive and negative association rules among frequent and infrequent itemsets. We identify associations among medications, symptoms, and laboratory results using state-of-the-art data mining technology. Sajid Mahmood, Muhammad Shahbaz, and Aziz Guergachi Copyright © 2014 Sajid Mahmood et al. All rights reserved. Heat Removal from Bipolar Transistor by Loop Heat Pipe with Nickel and Copper Porous Structures Sun, 18 May 2014 07:51:24 +0000 Loop heat pipes (LHPs) are used in many branches of industry, mainly for cooling of electrical elements and systems. The loop heat pipe is a vapour-liquid phase-change device that transfers heat from evaporator to condenser. One of the most important parts of the LHP is the porous wick structure. The wick structure provides capillary force to circulate the working fluid. To achieve good thermal performance of LHP, capillary wicks with high permeability and porosity and fine pore radius are expected. The aim of this work was to develop porous structures from copper and nickel powder with different grain sizes. For experiment copper powder with grain size of 50 and 100 μm and nickel powder with grain size of 10 and 25 μm were used. Analysis of these porous structures and LHP design are described in the paper. And the measurements’ influences of porous structures in LHP on heat removal from the insulated gate bipolar transistor (IGBT) have been made. Patrik Nemec, Martin Smitka, and Milan Malcho Copyright © 2014 Patrik Nemec et al. All rights reserved. A Vehicle Detection Algorithm Based on Deep Belief Network Thu, 15 May 2014 00:00:00 +0000 Vision based vehicle detection is a critical technology that plays an important role in not only vehicle active safety but also road video surveillance application. Traditional shallow model based vehicle detection algorithm still cannot meet the requirement of accurate vehicle detection in these applications. In this work, a novel deep learning based vehicle detection algorithm with 2D deep belief network (2D-DBN) is proposed. In the algorithm, the proposed 2D-DBN architecture uses second-order planes instead of first-order vector as input and uses bilinear projection for retaining discriminative information so as to determine the size of the deep architecture which enhances the success rate of vehicle detection. On-road experimental results demonstrate that the algorithm performs better than state-of-the-art vehicle detection algorithm in testing data sets. Hai Wang, Yingfeng Cai, and Long Chen Copyright © 2014 Hai Wang et al. All rights reserved. A Bio-Inspired Method for the Constrained Shortest Path Problem Wed, 14 May 2014 14:19:52 +0000 The constrained shortest path (CSP) problem has been widely used in transportation optimization, crew scheduling, network routing and so on. It is an open issue since it is a NP-hard problem. In this paper, we propose an innovative method which is based on the internal mechanism of the adaptive amoeba algorithm. The proposed method is divided into two parts. In the first part, we employ the original amoeba algorithm to solve the shortest path problem in directed networks. In the second part, we combine the Physarum algorithm with a bio-inspired rule to deal with the CSP. Finally, by comparing the results with other method using an examples in DCLC problem, we demonstrate the accuracy of the proposed method. Hongping Wang, Xi Lu, Xiaoge Zhang, Qing Wang, and Yong Deng Copyright © 2014 Hongping Wang et al. All rights reserved. Secure and Privacy Enhanced Gait Authentication on Smart Phone Wed, 14 May 2014 13:54:24 +0000 Smart environments established by the development of mobile technology have brought vast benefits to human being. However, authentication mechanisms on portable smart devices, particularly conventional biometric based approaches, still remain security and privacy concerns. These traditional systems are mostly based on pattern recognition and machine learning algorithms, wherein original biometric templates or extracted features are stored under unconcealed form for performing matching with a new biometric sample in the authentication phase. In this paper, we propose a novel gait based authentication using biometric cryptosystem to enhance the system security and user privacy on the smart phone. Extracted gait features are merely used to biometrically encrypt a cryptographic key which is acted as the authentication factor. Gait signals are acquired by using an inertial sensor named accelerometer in the mobile device and error correcting codes are adopted to deal with the natural variation of gait measurements. We evaluate our proposed system on a dataset consisting of gait samples of 34 volunteers. We achieved the lowest false acceptance rate (FAR) and false rejection rate (FRR) of 3.92% and 11.76%, respectively, in terms of key length of 50 bits. Thang Hoang and Deokjai Choi Copyright © 2014 Thang Hoang and Deokjai Choi. All rights reserved. Distributed Query Plan Generation Using Multiobjective Genetic Algorithm Wed, 14 May 2014 13:36:46 +0000 A distributed query processing strategy, which is a key performance determinant in accessing distributed databases, aims to minimize the total query processing cost. One way to achieve this is by generating efficient distributed query plans that involve fewer sites for processing a query. In the case of distributed relational databases, the number of possible query plans increases exponentially with respect to the number of relations accessed by the query and the number of sites where these relations reside. Consequently, computing optimal distributed query plans becomes a complex problem. This distributed query plan generation (DQPG) problem has already been addressed using single objective genetic algorithm, where the objective is to minimize the total query processing cost comprising the local processing cost (LPC) and the site-to-site communication cost (CC). In this paper, this DQPG problem is formulated and solved as a biobjective optimization problem with the two objectives being minimize total LPC and minimize total CC. These objectives are simultaneously optimized using a multiobjective genetic algorithm NSGA-II. Experimental comparison of the proposed NSGA-II based DQPG algorithm with the single objective genetic algorithm shows that the former performs comparatively better and converges quickly towards optimal solutions for an observed crossover and mutation probability. Shina Panicker and T. V. Vijay Kumar Copyright © 2014 Shina Panicker and T. V. Vijay Kumar. All rights reserved. The Research on Web-Based Testing Environment Using Simulated Annealing Algorithm Wed, 14 May 2014 12:03:49 +0000 The computerized evaluation is now one of the most important methods to diagnose learning; with the application of artificial intelligence techniques in the field of evaluation, the computerized adaptive testing gradually becomes one of the most important evaluation methods. In this test, the computer dynamic updates the learner's ability level and selects tailored items from the item pool. In order to meet the needs of the test it requires that the system has a relatively high efficiency of the implementation. To solve this problem, we proposed a novel method of web-based testing environment based on simulated annealing algorithm. In the development of the system, through a series of experiments, we compared the simulated annealing method and other methods of the efficiency and efficacy. The experimental results show that this method ensures choosing nearly optimal items from the item bank for learners, meeting a variety of assessment needs, being reliable, and having valid judgment in the ability of learners. In addition, using simulated annealing algorithm to solve the computing complexity of the system greatly improves the efficiency of select items from system and near-optimal solutions. Peng Lu, Xiao Cong, and Dongdai Zhou Copyright © 2014 Peng Lu et al. All rights reserved. An Improved Cockroach Swarm Optimization Wed, 14 May 2014 09:14:06 +0000 Hunger component is introduced to the existing cockroach swarm optimization (CSO) algorithm to improve its searching ability and population diversity. The original CSO was modelled with three components: chase-swarming, dispersion, and ruthless; additional hunger component which is modelled using partial differential equation (PDE) method is included in this paper. An improved cockroach swarm optimization (ICSO) is proposed in this paper. The performance of the proposed algorithm is tested on well known benchmarks and compared with the existing CSO, modified cockroach swarm optimization (MCSO), roach infestation optimization RIO, and hungry roach infestation optimization (HRIO). The comparison results show clearly that the proposed algorithm outperforms the existing algorithms. I. C. Obagbuwa and A. O. Adewumi Copyright © 2014 I. C. Obagbuwa and A. O. Adewumi. All rights reserved. Episodic Reasoning for Vision-Based Human Action Recognition Wed, 14 May 2014 06:32:17 +0000 Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning. Maria J. Santofimia, Jesus Martinez-del-Rincon, and Jean-Christophe Nebel Copyright © 2014 Maria J. Santofimia et al. All rights reserved. An Efficient Hierarchical Video Coding Scheme Combining Visual Perception Characteristics Tue, 13 May 2014 12:59:53 +0000 Different visual perception characteristic saliencies are the key to constitute the low-complexity video coding framework. A hierarchical video coding scheme based on human visual systems (HVS) is proposed in this paper. The proposed scheme uses a joint video coding framework consisting of visual perception analysis layer (VPAL) and video coding layer (VCL). In VPAL, effective visual perception characteristics detection algorithm is proposed to achieve visual region of interest (VROI) based on the correlation between coding information (such as motion vector, prediction mode, etc.) and visual attention. Then, the interest priority setting for VROI according to visual perception characteristics is completed. In VCL, the optional encoding method is developed utilizing the visual interested priority setting results from VPAL. As a result, the proposed scheme achieves information reuse and complementary between visual perception analysis and video coding. Experimental results show that the proposed hierarchical video coding scheme effectively alleviates the contradiction between complexity and accuracy. Compared with H.264/AVC (JM17.0), the proposed scheme reduces 80% video coding time approximately and maintains a good video image quality as well. It improves video coding performance significantly. Pengyu Liu and Kebin Jia Copyright © 2014 Pengyu Liu and Kebin Jia. All rights reserved. Convergence Results on Iteration Algorithms to Linear Systems Tue, 13 May 2014 06:56:09 +0000 In order to solve the large scale linear systems, backward and Jacobi iteration algorithms are employed. The convergence is the most important issue. In this paper, a unified backward iterative matrix is proposed. It shows that some well-known iterative algorithms can be deduced with it. The most important result is that the convergence results have been proved. Firstly, the spectral radius of the Jacobi iterative matrix is positive and the one of backward iterative matrix is strongly positive (lager than a positive constant). Secondly, the mentioned two iterations have the same convergence results (convergence or divergence simultaneously). Finally, some numerical experiments show that the proposed algorithms are correct and have the merit of backward methods. Zhuande Wang, Chuansheng Yang, and Yubo Yuan Copyright © 2014 Zhuande Wang et al. All rights reserved. Rough Atanassov’s Intuitionistic Fuzzy Sets Model over Two Universes and Its Applications Tue, 13 May 2014 00:00:00 +0000 Recently, much attention has been given to the rough set models based on two universes. And many rough set models based on two universes have been developed from different points of view. In this paper, a novel model, that is, rough Atanassov’s intuitionistic fuzzy sets model over two different universes, is firstly proposed from Atanassov’s intuitionistic point of view. We study some important properties of approximation operators and investigate the rough degree in the novel model. Furthermore, an illustrated example is employed to demonstrate the conceptual arguments of the model. Finally, rough Atanassov’s intuitionistic fuzzy sets approach to decision is presented in the generalized approximation space over two universes by considering the problem about how to arrange patients to see the doctor reasonably, from which it can be found that the method is valuable and useful in real life. Shuqun Luo and Weihua Xu Copyright © 2014 Shuqun Luo and Weihua Xu. All rights reserved. A Secure and Efficient Audit Mechanism for Dynamic Shared Data in Cloud Storage Mon, 12 May 2014 09:34:24 +0000 With popularization of cloud services, multiple users easily share and update their data through cloud storage. For data integrity and consistency in the cloud storage, the audit mechanisms were proposed. However, existing approaches have some security vulnerabilities and require a lot of computational overheads. This paper proposes a secure and efficient audit mechanism for dynamic shared data in cloud storage. The proposed scheme prevents a malicious cloud service provider from deceiving an auditor. Moreover, it devises a new index table management method and reduces the auditing cost by employing less complex operations. We prove the resistance against some attacks and show less computation cost and shorter time for auditing when compared with conventional approaches. The results present that the proposed scheme is secure and efficient for cloud storage services managing dynamic shared data. Ohmin Kwon, Dongyoung Koo, Yongjoo Shin, and Hyunsoo Yoon Copyright © 2014 Ohmin Kwon et al. All rights reserved.