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The fifth edition of the Reconfigurable Communicationcentric Systems-on-Chip workshop (ReCoSoC’ 2010) was held in Karlsruhe, Germany, from May 17th to May 19th, 2010. ReCoSoC is intended to be a periodic annual meeting to expose and discuss gathered expertise as well as state-ofthe-art research around SoC-related topics through plenary invited papers and posters. ReCoSoC is a 3-day long event which endeavors to encourage scientific exchanges and collaborations. ReCoSoC aims to provide a prospective view of tomorrow’s challenges in the multibillion transistor era, taking into account the emerging techniques and architectures exploring the synergy between flexible on-chip communication and system reconfigurability. This special issue includes the following Topics: embedded reconfigurability in all its forms; on-chip communication architectures; multiprocessor systems-on-chips; system and SoC design methods; asynchronous design techniques; low-power design methods; middleware and OS support for reconfiguration and communication; new paradigms of computation including bioinspired approaches.

Introduction. An electronic Routine Assessment of Patient Index Data 3 (RAPID 3) was incorporated into our electronic health records (EHRs) which did not replicate the visual presentation of the paper version. This study validated the electronic RAPID 3 compared to the paper version.Methods. Rheumatoid arthritis (RA) patients (n=50) completed both the electronic RAPID 3 online in the week prior to and a paper version on the day of their clinic visit.Results. Pairedt-test showed no significant difference (pvalue = 0.46) between versions.Conclusion. The electronic version of RAPID 3 is valid and can be easily integrated in care of RA patients.

There are a number of methods for ranking decision making units (DMUs), among which calculating super efficiency and then ranking the units based on the obtained amount of super efficiency are both valid and efficient. Since most of the proposed models do not provide the projection of Pareto efficiency, a model is developed and presented through this paper based on which in the projection of Pareto-efficient is obtained, in addition to calculating the amount of super efficiency. Moreover, the model is unit invariant, and is always feasible and makes the amount of inefficiency effective in ranking.

This paper invites us to rescue the debates over the use of border check technologies from the twin paradoxes of "excessive visibility" and "low visibility". With this aim, it stresses the importance of considering border check technologies from a consumer perspective. From this point of view, it questions the modes of implementation of some of the most relevant technologies already operating at the borders: body scanners, biometrics, registered travellers' programs, and passenger name records. Finally, the authors propose to combine insights and solutions of both consumer and data protection law, to offer travellers a more "consumer friendly" approach to border check technologies. Such a move would probably pave the way to citizens' debates freed from the "visibility" paradoxes of technology.

In addition to hemorrhagic cystitis, Fanconi Syndrome is a serious clinical side effect during ifosfamide (IFO) therapy. Fanconi syndrome is a generalized dysfunction of the proximal tubule which is characterized by excessive urinary excretion of glucose, phosphate, bicarbonate, amino acids and other solutes excreted by this segment of the nephron including L-carnitine. Carnitine is essential cofactor for β-oxidation of long-chain fatty acids in the myocardium. IFO therapy is associated with increased urinary carnitine excretion with subsequent secondary deficiency of the molecule. Cardiac abnormalities in IFO-treated cancer patients were reported as isolated clinical cases. This study examined whether carnitine deficiency and oxidative stress, secondary to Fanconi Syndrome, provoke IFO-induced cardiomyopathy as well as exploring if carnitine supplementation using Propionyl-L-carnitine (PLC) could offer protection against this toxicity. In the current study, an animal model of carnitine deficiency was developed in rats by D-carnitine-mildronate treatment Adult male Wistar albino rats were assigned to one of six treatment groups: the first three groups were injected intraperitoneally with normal saline, D-carnitine (DC, 250 mg/kg/day) combined with mildronate (MD, 200 mg/kg/day) and PLC (250 mg/kg/day), respectively, for 10 successive days. The 4th, 5thand 6thgroups were injected with the same doses of normal saline, DC-MD and PLC, respectively for 5 successive days before and 5 days concomitant with IFO (50 mg/kg/day). IFO significantly increased serum creatinine, blood urea nitrogen (BUN), urinary carnitine excretion and clearance, creatine phosphokinase isoenzyme (CK-MB), lactate dehydrogenase (LDH), intramitochondrial acetyl-CoA/CoA-SH and thiobarbituric acid reactive substances (TBARS) in cardiac tissues and significantly decreased adenosine triphosphate (ATP) and total carnitine and reduced glutathione (GSH) content in cardiac tissues. In carnitine-depleted rats, IFO induced dramatic increase in serum creatinine, BUN, CK-MB, LDH, carnitine clearance and intramitochondrial acetyl-CoA/CoA-SH, as well as progressive reduction in total carnitine and ATP in cardiac tissues. Interestingly, PLC supplementation completely reversed the biochemical changes-induced by IFO to the control values. In conclusion, data from the present study suggest that: Carnitine deficiency and oxidative stress, secondary to Fanconi Syndrome, constitute risk factors and should be viewed as mechanisms during development of IFO-induced cardiotoxicity. Carnitine supplementation, using PLC, prevents the development of IFO-induced cardiotoxicity through antioxidant signalling and improving mitochondrial function.

Recently, LSI (Latent Semantic Indexing) based on SVD (Singular Value Decomposition) is proposed to overcome the problems of polysemy and homonym in traditional lexical matching. However, it is usually criticized as with low discriminative power for representing documents although it has been validated as with good representative quality. In this paper, SVD on clusters is proposed to improve the discriminative power of LSI. The contribution of this paper is three manifolds. Firstly, we make a survey of existing linear algebra methods for LSI, including both SVD based methods and non-SVD based methods. Secondly, we propose SVD on clusters for LSI and theoretically explain that dimension expansion of document vectors and dimension projection using SVD are the two manipulations involved in SVD on clusters. Moreover, we develop updating processes to fold in new documents and terms in a decomposed matrix by SVD on clusters. Thirdly, two corpora, a Chinese corpus and an English corpus, are used to evaluate the performances of the proposed methods. Experiments demonstrate that, to some extent, SVD on clusters can improve the precision of interdocument similarity measure in comparison with other SVD based LSI methods.

Parkinson disease is a major public health problem all around the world. This paper proposes an expert disease diagnosis system for Parkinson disease based on genetic algorithm- (GA-) wavelet kernel- (WK-) Extreme Learning Machines (ELM). The classifier used in this paper is single layer neural network (SLNN) and it is trained by the ELM learning method. The Parkinson disease datasets are obtained from the UCI machine learning database. In wavelet kernel-Extreme Learning Machine (WK-ELM) structure, there are three adjustable parameters of wavelet kernel. These parameters and the numbers of hidden neurons play a major role in the performance of ELM. In this study, the optimum values of these parameters and the numbers of hidden neurons of ELM were obtained by using a genetic algorithm (GA). The performance of the proposed GA-WK-ELM method is evaluated using statical methods such as classification accuracy, sensitivity and specificity analysis, and ROC curves. The calculated highest classification accuracy of the proposed GA-WK-ELM method is found as 96.81%.

Artificial Neural Network (ANN) is a widely used algorithm in pattern recognition, classification, and prediction fields. Among a number of neural networks, backpropagation neural network (BPNN) has become the most famous one due to its remarkable function approximation ability. However, a standard BPNN frequently employs a large number of sum and sigmoid calculations, which may result in low efficiency in dealing with large volume of data. Therefore to parallelize BPNN using distributed computing technologies is an effective way to improve the algorithm performance in terms of efficiency. However, traditional parallelization may lead to accuracy loss. Although several complements have been done, it is still difficult to find out a compromise between efficiency and precision. This paper presents a parallelized BPNN based on MapReduce computing model which supplies advanced features including fault tolerance, data replication, and load balancing. And also to improve the algorithm performance in terms of precision, this paper creates a cascading model based classification approach, which helps to refine the classification results. The experimental results indicate that the presented parallelized BPNN is able to offer high efficiency whilst maintaining excellent precision in enabling large-scale machine learning.

The mixed research design is a progressive methodological discourse that combines the advantages of quantitative and qualitative methods. Its possibilities of application are, however, dependent on the efficiency with which the particular research techniques are used and combined. The aim of the paper is to introduce the possible combination of Hypothesis with EyeTribe tracker. The Hypothesis is intended for quantitative data acquisition and the EyeTribe is intended for qualitative (eye-tracking) data recording. In the first part of the paper, Hypothesis software is described. The Hypothesis platform provides an environment for web-based computerized experiment design and mass data collection. Then, evaluation of the accuracy of data recorded by EyeTribe tracker was performed with the use of concurrent recording together with the SMI RED 250 eye-tracker. Both qualitative and quantitative results showed that data accuracy is sufficient for cartographic research. In the third part of the paper, a system for connecting EyeTribe tracker and Hypothesis software is presented. The interconnection was performed with the help of developed web application HypOgama. The created system uses open-source software OGAMA for recording the eye-movements of participants together with quantitative data from Hypothesis. The final part of the paper describes the integrated research system combining Hypothesis and EyeTribe.

Security is always a major concern in wireless sensor networks (WSNs). Identity based attacks such as spoofing and sybil not only compromise the network but also slow down its performance. This paper proposes a low complexity sybil attack detection scheme, that is, based on signed response (SRES) authentication mechanism developed for Global System for Mobile (GSM) communications. A probabilistic model is presented which analyzes the proposed authentication mechanism for its probability of sybil attack. The paper also presents a simulation based comparative analysis of the existing sybil attack schemes with respect to the proposed scheme. It is observed that the proposed sybil detection scheme exhibits lesser computational cost and power consumption as compared to the existing schemes for the same sybil detection performance.

In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.

The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

This paper creatively proposes a cluster boundary sampling method based on density clustering to solve the problem of resampling in IDS classification and verify its effectiveness experimentally. We use the clustering density threshold and the boundary density threshold to determine the cluster boundaries, in order to guide the process of resampling more scientifically and accurately. Then, we adopt the penalty factor to regulate the data imbalance effect on SVM classification algorithm. The achievements and scientific significance of this paper do not propose the best classifier or solution of imbalanced data set and just verify the validity and stability of proposed IDS resampling method. Experiments show that our method acquires obvious promotion effect in various imbalanced data sets.

This paper addresses approaches to Human Activity Recognition (HAR) with the aim of monitoring the physical activity of people in the workplace, by means of a smartphone application exploiting the available on-board accelerometer sensor. In fact, HAR via a smartphone or wearable sensor can provide important information regarding the level of daily physical activity, especially in situations where a sedentary behavior usually occurs, like in modern workplace environments. Increased sitting time is significantly associated with severe health diseases, and the workplace is an appropriate intervention setting, due to the sedentary behavior typical of modern jobs. Within this paper, the state-of-the-art components of HAR are analyzed, in order to identify and select the most effective signal filtering and windowing solutions for physical activity monitoring. The classifier development process is based upon three phases; a feature extraction phase, a feature selection phase, and a training phase. In the training phase, a publicly available dataset is used to test among different classifier types and learning methods. A user-friendly Android-based smartphone application with low computational requirements has been developed to run field tests, which allows to easily change the classifier under test, and to collect new datasets ready for use with machine learning APIs. The newly created datasets may include additional information, like the smartphone position, its orientation, and the user’s physical characteristics. Using the mobile tool, a classifier based on a decision tree is finally set up and enriched with the introduction of some robustness improvements. The developed approach is capable of classifying six activities, and to distinguish between not active (sitting) and active states, with an accuracy near to 99%. The mobile tool, which is going to be further extended and enriched, will allow for rapid and easy benchmarking of new algorithms based on previously generated data, and on future collected datasets.

Brain-Computer Interfaces (BCIs) have become a research field with interesting applications, and it can be inferred from published papers that different persons activate different parts of the brain to perform the same action. This paper presents a personalized interface design method, for electroencephalogram- (EEG-) based BCIs, based on channel selection. We describe a novel two-step method in which firstly a computationally inexpensive greedy algorithm finds an adequate search range; and, then, an Estimation of Distribution Algorithm (EDA) is applied in the reduced range to obtain the optimal channel subset. The use of the EDA allows us to select the most interacting channels subset, removing the irrelevant and noisy ones, thus selecting the most discriminative subset of channels for each user improving accuracy. The method is tested on the IIIa dataset from the BCI competition III. Experimental results show that the resulting channel subset is consistent with motor-imaginary-related neurophysiological principles and, on the other hand, optimizes performance reducing the number of channels.

Immersive virtual environments (VEs) have the potential to provide novel cost effective ways for evaluating not only new environments and usability scenarios, but also potential user experiences. To achieve this, VEs must be adequately realistic. The level of perceived authenticity can be ascertained by measuring the levels of immersion people experience in their VE interactions. In this paper the degree of authenticity is measured via anauthenticity indexin relation to three different immersive virtual environment devices. These devices include (1) a headband, (2) 3D glasses, and (3) a head-mounted display (HMD). A quick scale for measuring immersion, feeling of control, and simulator sickness was developed and tested. The HMD proved to be the most immersive device, although the headband was demonstrated as being a more stable environment causing the least simulator sickness. The results have design implication as they provide insight into specific factors which make experience in a VE seem more authentic to users. The paper emphasizes that, in addition to the quality of the VE, focus needs to be placed on ergonomic factors such as the weight of the devices, as these may compromise the quality of results obtained when examining studying human-technology interaction in a VE.

This paper proposes FuMicro, a fused microarchitecture integrating both in-order superscalar and Very Long Instruction Word (VLIW) in a single core. A processor with FuMicro microarchitecture can work under alternative in-order superscalar and VLIW mode, using the same pipeline and the same Instruction Set Architecture (ISA). Small modification to the compiler is made to expand the register file in VLIW mode. The decision of mode switch is made by software, and this does not need extra hardware. VLIW code can be exploited in the form of library function and the users will be exposed under only superscalar mode; by this means, we can provide the users with a convenient development environment. FuMicro could serve as a universal microarchitecture for it can be applied to different ISAs. In this paper, we focus on the implementation of FuMicro with ARM ISA. This architecture is evaluated on gem5, which is a cycle accurate microarchitecture simulation platform. By adopting FuMicro microarchitecture, the performance can be improved on an average of 10%, with the best performance improvement being 47.3%, compared with that under pure in-order superscalar mode. The result shows that FuMicro microarchitecture can improve Instruction Level Parallelism (ILP) significantly, making it promising to expand digital signal processing capability on a General Purpose Processor.

During the past decades, the use of soft computational approaches has been extended to a large variety of software engineering applications. Soft computational methods, such as fuzzy systems, neural networks, evolutionary computation, and probability models including Bayesian network and chaos theory, are currently attractive topics in the extensive software engineering research problems. This special issue provides a platform for the dissemination of knowledge on both the empirical research and applied research in the field of soft computing. The objective of the special issue is to facilitate a forum to a wide spectrum of articles that cover the state-of-the-art techniques and recent results in the research of soft computational approaches. In particular, the special issue focuses on publishing the highly technical articles describing the software development topics: advanced software engineering, computational intelligence, and wireless sensor networks. This special issue has received overwhelming responses from researchers, and it has received a total of 28 high-quality submissions from various countries around the world. All the submitted papers have been evaluated by at least three independent reviewers. However, due to focused scope of the special issue, only a few manuscripts are published. Inevitably, hard editorial decisions had to be made, and some high-quality articles could not even be included. We believe that this special issue presents cohesive information related to the applications of soft computing methods in software development, and it also provides stimulations for future research. In the paper entitled “An Enhanced PSO-Based Clustering Energy Optimization Algorithm for Wireless Sensor Network,” C. Vimalarani et al. propose a novel Enhanced PSO-Based Clustering Energy Optimization (EPSO-CEO) algorithm for the wireless sensor network (WSN), in which the clustering and clustering head selection are done by using the Particle Swarm Optimization (PSO) algorithm with respect to minimizing the power consumption in the WSN. The results obtained have been compared and evaluated on the basis of energy efficiency. The paper of Y. Rastegari and F. Shams entitled “Optimal Decomposition of Service Level Objectives into Policy Assertions” presents a method to decompose service level objectives to web service policy assertions. Transformation of Web Service Choreography Description Language (WS-CDL) to Web Service Business Process Execution Language (WSBPEL) has been addressed in some related works, but neither of them considers quality aspects of transformation or run-time adaptation. In this paper, in conformity with web services standards, the authors proposed an optimal decomposition method to make a set of WS-policy assertions. Assertions applied to WSBPEL elements and affect their run-time behaviors. The decomposition method achieves the best outcome for a performance indicator. It also guarantees the lowest adaptation overhead by reducing the number of service reselections. This research considered securities settlement case study to prototype and evaluated the decomposition method. The results show an acceptable threshold between customer satisfaction, the targeted performance indicator through the case study, and adaptation overhead. G. J. Eanoch and T. S. Sankar present a Neuro-Fuzzy Energy Aware Clustering Scheme (NFEACS) in their paper entitled “Development of Energy Efficient Clustering Protocol in Wireless Sensor Network Using Neuro-Fuzzy Approach” to form the optimum energy aware clusters. The proposed method consists of two part, fuzzy subsystem and neural network system, which can yield energy efficiency in building up clusters and cluster heads in the WSN. More precisely, it uses neural network that provides effective training sets related to energy and received signal strength of all nodes in order to estimate the expected energy for tentative cluster heads. The sensor nodes with higher energy are trained with the center location of base station for selecting the energy aware cluster heads. Fuzzy IF-THEN mapping rules are also used so as to find the clusters and cluster heads. Experiment results show that the NFEACS performs better than the existing solutions. In the paper entitled “Software Design Challenges in Time Series Prediction Systems Using Parallel Implementation of Artificial Neural Networks,” the authors (N. Manikandan and S. Subha) develop a comprehensive approach to architectural design for financial prediction models. During the past two decades, a large number of artificial neural networks-based learning models have been studied to manipulate with financial data, for example, prediction of the future trends and prices. This paper focuses on the architectural design related issues for performance improvement through vectorising the strengths of multivariate econometric time series models and artificial neural networks. It further provides an adaptive and hybrid method for predicting the exchange rates. In the paper entitled “Energy-Aware Multipath Routing Scheme Based on Particle Swarm Optimization in Mobile Ad Hoc Networks” written by Y. H. Robinson and M. Rajaram, a novel energy aware multipath routing scheme is proposed, which is based on the Particle Swarm Optimization (EMPSO) that uses Continuous Time Recurrent Neural Network (CTRNN) to deal with optimization problems. The CTRNN has been applied to find the optimal loop-free paths for solving the link disjoint paths in MANET. In other words, it is used as an optimum path selection technique to produce a set of optimal paths between source and destination. In the CTRNN, the PSO method is particularly utilized for training the RNN. The proposed scheme uses the reliability measures, for example, transmission cost, energy factor, and the optimal traffic ratio between source and destination, to increase the routing performance. Therefore, the optimal loop-free paths can be discovered using the PSO to seek better link quality nodes in the route discovery phase suitable for the dynamic topology changing property of a MANET.

More advanced recognition methods, which may recognize particular copies of radars of the same type, are called identification. The identification process of radar devices is a more specialized task which requires methods based on the analysis of distinctive features. These features are distinguished from the signals coming from the identified devices. Such a process is called Specific Emitter Identification (SEI). The identification of radar emission sources with the use of classic techniques based on the statistical analysis of basic measurable parameters of a signal such as Radio Frequency, Amplitude, Pulse Width, or Pulse Repetition Interval is not sufficient for SEI problems. This paper presents the method of hierarchical data clustering which is used in the process of radar identification. The Hierarchical Agglomerative Clustering Algorithm (HACA) based on Generalized Agglomerative Scheme (GAS) implemented and used in the research method is parameterized; therefore, it is possible to compare the results. The results of clustering are presented in dendrograms in this paper. The received results of grouping and identification based on HACA are compared with other SEI methods in order to assess the degree of their usefulness and effectiveness for systems of ESM/ELINT class.

Due to evolution of digital games towards mobile platforms, a large exposure to mobile games is nowadays recorded. This is because of the availability of the cell phone with individuals anytime anywhere. Hence, a vital role of game quality assessment is raised. This is for the purpose of serving as a guide for developing healthy games. Accordingly, the mobile games would help in boosting the educational entertainment, healthy diets control, and behavioral sciences studies and research. This paper presents quality assurance and quality models for ISO based educational game. In this paper, an ISO quality model is presented and quality application behavioral impact measures are justified to evaluate the development quality of Miftah Alfasaha game. The quality model and proposed quality assessment plan are tested through a CodePro Analytix tool and six sigma evaluations.

Citizen science is increasingly being used in diverse research domains. With the emergence and rapid development of sensor technologies, citizens potentially have more powerful tools to collect data and generate information to understand their living environment. Although sensor technologies are developing fast, citizen sensing has not been widely implemented yet and published studies are only a few. In this paper, we analyse the practical experiences from an implementation of citizen sensing for urban environment monitoring. A bottom-up model in which citizens develop and use sensors for environmental monitoring is described and assessed. The paper focuses on a case study of Amsterdam Smart Citizens Lab using NO2sensors for air quality monitoring. We found that the bottom-up citizen sensing is still challenging but can be successful with open cooperation and effective use of online and offline facilities. Based on the assessment, suggestions are proposed for further implementations and research.

Mobile applications have become amust in every user’s smart device, andmany of these applications make use of the device sensors’ to achieve its goal. Nevertheless, it remains fairly unknown to the user to which extent the data the applications use can be relied upon and, therefore, to which extent the output of a given application is trustworthy or not. To help developers and researchers and to provide a common ground of data validation algorithms and techniques, this paper presents a review of the most commonly used data validation algorithms, along with its usage scenarios, and proposes a classification for these algorithms. This paper also discusses the process of achieving statistical significance and trust for the desired output., Portuguese Foundation for Science and Technology UID/EEA/50008/2013, COST Action Architectures, Algorithms and Protocols for Enhanced Living Environments (AAPELE) IC1303

This paper uses principal-agent theory to study the issue of incentivizing crowdsourcing communities. It proves that enterprises can generate innovation plans of high quality and expected utility using the crowdsourcing community. Outsourcers can encourage high-quality people to join by adopting a linear variable compensation scheme and make the low-quality people quit by requiring them to supply more effort. The paper also shows that enterprises’ participation in crowdsourcing community innovation can effectively improve their innovative ability and that it is necessary for enterprises to construct an effective and cooperative innovative system combining crowdsourcing community innovation and their own internal innovation.

In order to optimize the signal timing for isolated intersection, a new method based on fuzzy programming approach is proposed in this paper. Considering the whole operation efficiency of the intersection comprehensively, traffic capacity, vehicle cycle delay, cycle stops, and exhaust emission are chosen as optimization goals to establish a multiobjective function first. Then fuzzy compromise programming approach is employed to give different weight coefficients to various optimization objectives for different traffic flow ratios states. And then the multiobjective function is converted to a single objective function. By using genetic algorithm, the optimized signal cycle and effective green time can be obtained. Finally, the performance of the traditional method and new method proposed in this paper is compared and analyzed through VISSIM software. It can be concluded that the signal timing optimized in this paper can effectively reduce vehicle delays and stops, which can improve traffic capacity of the intersection as well.

Welcome to this special issue. Advanced computing technologies are one of the hot research fields in artificial intelligence. This special issue aims to promote the research, development, and applications of advanced computing technologies by providing a high-level international forum for researchers and practitioners to exchange research results and share development experiences. The papers in this edition were selected among the highest rated papers in submitted manuscripts. The selection of papers featured here covers the topics of the main advanced computing technologies and experimental studies of some application systems. C.-W. Lin et al. proposed a privacy-preserving data mining method by using a compact prelarge GA-based algorithm to delete transactions for hiding sensitive itemsets. This method solves the limitations of the evolutionary process by adopting both the compact GA-based mechanism and the prelarge concept. A. Adam et al. build a framework for peak detection on EEG signals in time domain analysis using feature selection and classifier parameters estimation based on particle swarm optimization. The proposed framework tries to find the best combination for all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. Y. Pei et al. propose a method to establish a human model in high dimensional search space by kernel classification for enhancing interactive evolutionary computation search. N. Sulaiman et al. propose a modified artificial bee colony algorithm to enhance the convergence speed and improve the ability of the standard artificial bee colony algorithm to reach the global optimum by balancing the exploration and exploitation processes. This method was tested on the reactive power optimization problem and has outperformed other compared algorithms. Aiming at solving the biased feature selection in random forests, T. T. Nguyen et al. propose a modified random forests algorithm to select good features in learning random forests for high dimensional data. L. Li et al. put forward a prediction model based on membrane computing optimization algorithm for chaos time series; the model optimizes simultaneously the parameters of phase space reconstruction and least squares support vector machine by using membrane computing optimization algorithm. M. R. Al-Othman et al. propose a heuristic ranking approach on capacity benefit margin (CBM) determination using Pareto-based evolutionary programming technique. This paper introduces a novel multiobjective approach for CBM assessment taking into account tie-line reliability of interconnected systems and presents a new Pareto-based evolutionary programming (EP) technique used to perform a simultaneous determination of CBM for all areas of the interconnected system. In order to build a QoS-aware bufferless network-on-chip scheme for datacenters, J. Fang et al. propose QBLESS, a QoS-aware bufferless NoC scheme for datacenters. QBLESS consists of two components: a routing mechanism (QBLESS-R) that can substantially reduce flit deflection for high-priority applications and a congestion-control mechanism (QBLESS-CC) that guarantees performance for high-priority applications and improves overall system throughput. S. K. Chandrasekaran et al. propose a framework of primary path reservation admission control protocol, which achieves improved QoS by making use of backup route combined with resource reservation. In this paper, a network topology has been simulated and the present approach proves to be a mechanism that admits the session effectively. V. Uc-Cetina et al. propose a Markov decision process model for solving the web service composition problem. Iterative policy evaluation, value iteration, and policy iteration algorithms are used to experimentally validate the present approach. S. U. Rehman and A. Nadeem propose a novel approach to the testing of multiagent systems based on Prometheus design artifacts. In the proposed approach, different interactions between the agent and actors are considered to test the multiagent system. M. Bal et al. study 11 machine learning methods (SVM, MLP, C4.5, etc.) for the inference mechanism of medical decision support system based on ALARM network. The performances of 11 machine learning algorithms are tested on 44 synthetic data sets (11 different dependent variables and 4 different dataset sizes). The comparison of algorithms applied two different tests (statistically difference and average rank tests). C4.5 decision tree is the best algorithm according to both of the tests for our 44 datasets. The datasets having more samples can be better predicted than having fewer samples. A comparative study on interaction of form and motion processing streams by applying two different classifiers in mechanism for recognition of biological movement is proposed by B. Yousefi and C. K. Loo. The presented approach has addressed a very substantial interrelevant comparison of the interaction of two processing streams of mammalian brain visual system. For mining data streams, A. O. Diaz et al. present a fast adapting ensemble method which adapts very quickly to both abrupt and gradual concept drifts, and the method has been specifically designed to deal with recurring concepts. After initializing color image by utilizing the unsupervised Orchard and Bouman clustering technique, D. Khattab et al. present a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. M.-L. Huang et al. combine feature selection and SVM recursive feature elimination to investigate the classification accuracy of multiclass problems for Dermatology and Zoo databases. Meanwhile, Taguchi's method was jointly combined with SVM classifier in order to optimize parameters to increase classification accuracy for multiclass classification. For image compression, N. A. Abu and F. Ernawan propose a psychovisual threshold on the large discrete cosine transform (DCT) image block which will be used to automatically generate the much needed quantization tables. G. C. Kim proposes a fully autonomous feature selection and camouflaged object detection method based on the online analysis of spectral and spatial features. Z. Wang et al. point out that spectral clustering algorithms applied in community detection have two inadequacies and present a novel community detection algorithm based on topology potential and spectral clustering that contains rich structural information of the network. A. Rodan et al. utilize an ensemble of multilayer perceptrons whose training is obtained using negative correlation learning for predicting customer churn in a telecommunication company. S. Chen et al. describe a hybrid approach for forecasting fraudulent financial statements. The authors firstly screen the important variables using the stepwise regression and then use logistic regression, support vector machine, and decision tree to construct the classification models to make a comparison. J. D. Gazzano et al. propose a complete grid infrastructure for distributed high-performance computing based on dynamically reconfigurable FPGAs. This infrastructure was tested and significant performance gains have been achieved. P systems are a class of distributed parallel computing models, H. Peng et al. present a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. Q. Luo et al. propose a discrete bat algorithm (DBA) for optimal problem of permutation flow shop scheduling. The authors construct a direct relationship between the job sequence and the vector of individuals in DBA.

Every day, huge numbers of instant tweets (messages) are published on Twitter as it is one of the massive social media for e-learners interactions. The options regarding various interesting topics to be studied are discussed among the learners and teachers through the capture of ideal sources in Twitter. The common sentiment behavior towards these topics is received through the massive number of instant messages about them. In this paper, rather than using the opinion polarity of each message relevant to the topic, authors focus on sentence level opinion classification upon using the unsupervised algorithm named bigram item response theory (BIRT). It differs from the traditional classification and document level classification algorithm. The investigation illustrated in this paper is of threefold which are listed as follows:(1)lexicon based sentiment polarity of tweet messages;(2)the bigram cooccurrence relationship using naïve Bayesian;(3)the bigram item response theory (BIRT) on various topics. It has been proposed that a model using item response theory is constructed for topical classification inference. The performance has been improved remarkably using this bigram item response theory when compared with other supervised algorithms. The experiment has been conducted on a real life dataset containing different set of tweets and topics.

With the development of mobile communication networks and intelligent terminals, recent years have witnessed a rapid popularization of location-based service (LBS). While obtaining convenient services, the exploitation of mass location data is inevitably leading to a serious concern about location privacy security. Obviously, high quality of service (QoS) will result in poor location privacy protection, so that a trade-off is needed to fulfill users’ individual demands for both sides. Although existing methods perform well in certain scenarios, few have considered the abovementioned balance problem. Therefore, by combiningk-anonymity-based cloaking technique and obfuscation method, a new distributed user-demand-driven (DUDD) location privacy protection scheme is put forward in this paper. The basic idea is still to select a subcloaking area within the cloaking area generated by Location Anonymization Server. Moreover, by using the improved LBS system model, this paper constructs a distributed framework, in which location privacy protection is wholly occupied in server side and LBS provider is only dedicated to QoS-guarantee. In addition, normalized privacy demand and QoS metrics are given and a user-defined weight parameter is introduced to ensure location privacy security without decreasing QoS. The feasibility of the proposed method is proved through simulation.

Grid computing facilitates the resource sharing through the administrative domains which are geographically distributed. Scheduling in a distributed heterogeneous environment is intrinsically very hard because of the heterogeneous nature of resource collection. Makespan and tardiness are two different measures of scheduling, and many of the previous researches concentrated much on reduction of makespan, which measures the machine utilization. In this paper, we propose a hybrid scheduling algorithm for scheduling independent grid tasks with the objective of reducing total weighted tardiness of grid tasks. Tardiness is to measure the due date performance, which has a direct impact on cost for executing the jobs. In this paper we propose BG_ATC algorithm which is a combination of best gap (BG) search and Apparent Tardiness Cost (ATC) indexing algorithm. Furthermore, we implemented these two algorithms in two different phases of the scheduling process. In addition to that, the comparison was made on results with various benchmark algorithms and the experimental results show that our algorithm outperforms the benchmark algorithms.

Clustering is one of the most commonly used approaches in data mining and data analysis. One clustering technique in clustering that gains big attention in clustering related research isk-means clustering such that the observation is grouped intokcluster. However, some obstacles such as the adherence of results to the initial cluster centers or the risk of getting trapped into local optimality hinder the overall clustering performance. The purpose of this research is to minimize the dissimilarity of all points of a cluster from gravity center of the cluster with respect to capacity constraints in each cluster, such that each element is allocated to only one cluster. This paper proposes an effective combination algorithm to find optimal cluster center for the analysis of data in data mining and a new combination algorithm is proposed to untangle the clustering problem. This paper presents a new hybrid algorithm, which is, based on cluster center initialization algorithm (CCIA), bees algorithm (BA), and differential evolution (DE), known as CCIA-BADE-K, aiming at finding the best cluster center. The proposed algorithm performance is evaluated with standard data set. The evaluation results of the proposed algorithm and its comparison with other alternative algorithms in the literature confirm its superior performance and higher efficiency.

With the avalanche of genomic and proteomic data generated in the postgenomic age, it is highly desirable to develop automated methods for rapidly and effectively analyzing and predicting the structure, function, and other properties of DNA and protein. The machine learning methods have become an important strategy for the discovery of potential knowledge in genomics and proteomics. Researches in recent years have shown tremendous advances in the properties prediction of DNA fragments and protein sequences by various pattern recognition methods. These techniques provide economical and timesaving solutions for identifying the properties of DNA and protein. This special issue was hosted for the recent development of the application of machine learning methods in genomics and proteomics. In this special issue, five works focused on the protein classification. How to extract key features from a protein was a key step in the discrimination of protein class. B. Liu et al. proposed to use Position-Specific Score Matrix (PSSM) and Accessible Surface Area (ASA) to formulate protein samples. The hidden Markov support vector machine (HM-SVM) was employed to predict protein binding site. Simulation in fivefold cross-validation on a benchmark dataset including 1124 protein chains showed that their method is more accurate for protein binding site prediction than some state-of-the art methods. This method can also be applied in DNA binding site, vitamin binding site, and posttranslational modification of proteins. Based on chemical shift (CS) information derived from nuclear magnetic resonance (NMR), F. Yonge proposed a novel feature to predict protein supersecondary structures. The quadratic discriminant (QD) analysis was selected as the prediction algorithm. Overall accuracy in threefold cross-validation is 77.3% for predicting four types of supersecondary structures. According to the concept of pseudo amino acids, G.-L. Fan et al. proposed the average chemical shifts (ACS) composition and established an online webserver called acACS which was calculated from average chemical shift information and protein secondary structure. By using SVM as the classification algorithm, the acACS was used in the discrimination between acidic and alkaline enzymes and between bioluminescent and nonbioluminescent proteins. Encouraging results were achieved. The protein secondary structure, structure class, and disorder region can be predicted using the AC-based method. L. Nanni et al. proposed to combine different features to improve protein prediction. These features include amino acids composition, PSSM, and substitution matrix representation (SMR). Each feature is used to train a separate SVM. Total of 15 benchmark datasets were used to evaluate the performance of their proposed method. Comparative results show that the PSSM always produces good accuracies. However, no single descriptor is superior to all others across all test datasets. The major contribution in this paper is to propose an ensemble of classifiers for sequence-based protein classification. H. Lin et al. briefly reviewed the development of ion channel prediction using machine learning method. They initially introduced how to construct a valid and objective benchmark dataset to train and test the predictor. Subsequently, the mathematical descriptors were presented to formulate the ion channel sequences. Moreover, two feature selection techniques on how to optimize feature set were described. Finally, the support vector machine was suggested performing classification. The methods introduced in that review can be generalized into other protein prediction fields as well. The paper from P. Feng et al. was the unique work focused on DNA prediction using machine learning method. They proposed a novel descriptor called pseudo K-tuple nucleotide composition (PseKNC) to formulate the DNA sequences. The feature is calculated from K-tuple nucleotide composition and the structural correlation of DNA dinucleotides. Subsequently, the SVM was used to predict DNase I hypersensitive sites. The jackknife cross-validated accuracy is 83%, which is competitive with that of the existing method. This new descriptor can also be widely used in DNA regulatory elements prediction. Hao Lin Wei Chen Ramu Anandakrishnan Dariusz Plewczynski

This paper proposes a circuit capable of incorporating buffered delays in the order of picoseconds. To study our proposed circuit in the profound way, we have also explored our proposed circuit using emerging technologies such as FinFET and CNFET. Comparisons between these technologies have been made in terms of different parameters such as duration of incorporated delays (pulse width) and its variability with supply voltages. Further, this paper also proposes a trigger pulse generator by utilizing proposed buffered delay circuit as its basic element. Parametric results obtained for the proposed trigger pulse generator match different application specific requirements. These applications are also mentioned in this paper. The proposed trigger pulse generator requires very low supply voltage (700 mV) and also proves its effectiveness in terms of tunability of pulse width of the generated pulses. The modeling of the circuit has been done using Verilog and the simulation results are extensively verified using SPICE.

With the coming of “Internet of things” (IoT) technology, many studies have sought to apply IoT to mobile platforms, such as smartphones, robots, and moving vehicles. An estimation of ego-motion in a moving platform is an essential and important method to build a map and to understand the surrounding environment. In this paper, we describe an ego-motion estimation method using a vision sensor that is widely used in IoT systems. Then, we propose a new fusion method to improve the accuracy of motion estimation with other sensors in cases where there are limits in using only a vision sensor. Generally, because the dimension numbers of data that can be measured for each sensor are different, by simply adding values or taking averages, there is still a problem in that the answer will be biased to one of the data sources. These problems are the same when using the weighting sum using the covariance of the sensors. To solve this problem, in this paper, using relatively accurate sensor data (unfortunately, low dimension), the proposed method was used to estimate by creating artificial data to improve the accuracy (even of unmeasured dimensions).

This paper offers a hybrid technique combined by artificial neural networks (ANN) and self-organizing map (SOM) as a way to explore factor knowledge. ANN and SOM are two kinds of pattern classification techniques based on supervised and unsupervised mechanisms, respectively. This paper proposes a new aspect to combine ANN and SOM as NNSOM process in order to delve into factor knowledge other than pattern classification. The experimental material is conducted by the investigation of street night market in Taiwan. NNSOM process can yield two results about factor knowledge: first, which factor is the most important factor for the development of street night market; second, what value of this factor is most positive to the development of street night market. NNSOM process can combine the advantages of supervised and unsupervised mechanisms and be applied to different disciplines.

Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

With the spurt of online user-generated contents on web, sentiment analysis has become a very active research issue in data mining and natural language processing. As the most important indicator of sentiment, sentiment words which convey positive and negative polarity are quite instrumental for sentiment analysis. However, most of the existing methods for identifying polarity of sentiment words only consider the positive and negative polarity by the Cantor set, and no attention is paid to the fuzziness of the polarity intensity of sentiment words. In order to improve the performance, we propose a fuzzy computing model to identify the polarity of Chinese sentiment words in this paper. There are three major contributions in this paper. Firstly, we propose a method to compute polarity intensity of sentiment morphemes and sentiment words. Secondly, we construct a fuzzy sentiment classifier and propose two different methods to compute the parameter of the fuzzy classifier. Thirdly, we conduct extensive experiments on four sentiment words datasets and three review datasets, and the experimental results indicate that our model performs better than the state-of-the-art methods.

There are several methods available in measuring food taste. The sensory evaluation, for instance, is a typical method for panels to test of taste and recognize smell with their nose by measuring the degree of taste characteristic, intensity, and pleasure. There are many issues entailed in the traditional sensory evaluation method such as forming a panel and evaluation cost; moreover, it is only localized in particular areas. Accordingly, this paper aimed to select food in one particular area, and compare and review the content between sensory evaluations using a taste biological sensor, as well as presenting an analysis of brainwaves using EEG and finally a proposal of a new method for sensory evaluation. In this paper, the researchers have conducted a sensory evaluation whereas a maximum of nine points were accumulated by purchasing eight types of rice wine. These eight types ofMakgeolliwere generalized by generating multidimensional data with the use of TS-5000z, thus learning mapping points and scaling them. The contribution of this paper, therefore, is to overcome the disadvantages of the sensory evaluation with the usage of the suggested taste biological sensor system.

Data aggregation is an important method of improving transmitting efficiency of WSNs, but existing researches have some disadvantages as follows: several periods delay will be generated when filtering messages; the reduplicated messages filtering ratio is low; complex calculations need to be executed; and extra hardware should be added to obtain high performance. To resolve these problems, this paper proposes a real time and high efficient data aggregation scheme (dynamical message list based data aggregation, DMLDA) based on clustering routing algorithm. DMLDA includes three procedures: activating nodes, clustering nodes, and filtering messages. In filtering procedure, a special data structure named dynamic list will be established in every filtering node; it is designed to store messages transmitted by filtering node, and, comparing current message with list's items, the message's redundancy can be judged without any delay. To improve the filtering efficiency, the message list can be adjusted dynamically. The three procedures of data aggregation are all introduced, and the filtering method is designed in detail in this paper. At last, a series of experiments are simulated to prove our scheme's performance, and the advantages are analyzed in theory.

Consider a web service with different quality of service levels where users may purchase their required web service through a reservation system. The service provider adjusts prices of web service classes over a prespecified time horizon to manage demand and maximize profit. Users may cancel their services as long as they pay a penalty. One of the important challenges for service providers is capacity limitation of the resources employed in offering the web service. Thus, taking this important proposition into account makes pricing strategies considered by the provider has more credit. Another important factor in determining pricing strategies discussed in the present paper is the market influence which can increase or decrease the price that the provider offers. This paper develops a continuous time optimal control model for identifying pricing strategies for the web service classes. We study the optimality condition of the considered model based on maximum principal and propose an algorithm to obtain the optimal pricing policy. Moreover, we perform numerical analyses to evaluate the effect of some parameters on control and state variables and objective function. In addition, we compare the proposed algorithm with genetic algorithm (GA) and simulated annealing (SA) available in Matlab.

With the development of cloud computing and virtualization, a physical router can be multiplexed as a large number of virtual routers. TCP-based interactive applications have an incentive to improve their performance by padding “junk packets” into the network among real communication packets. This padding misbehavior will upgrade short TCP flows from “mice” to “elephants” and consequently lead to network congestion and breakdown. This paper presents a detailed solution and analysis for describing the normal behavior and padding misbehavior of virtual routers. In particular, a system model for analyzing behavior of virtual routers is based on evolutionary game model, and, through analyzing the stability of the equilibrium points, the stable point is the solution to the problem. The clear evolutionary path of network applications with the normal behavior and padding misbehavior is analyzed by the corresponding graph. Then this paper gives the behavior control suggestions to effectively restrain the padding misbehavior and maintain stable high-throughputs of the router. The simulation results demonstrate that our solution can effectively restrain the padding misbehavior and maintain stable high-throughputs of the router simultaneously compared with the classical queue management.

Intrusion detection has become a main part of network security due to the huge number of attacks which affects the computers. This is due to the extensive growth of internet connectivity and accessibility to information systems worldwide. To deal with this problem, in this paper a hybrid algorithm is proposed to integrate Modified Artificial Bee Colony (MABC) with Enhanced Particle Swarm Optimization (EPSO) to predict the intrusion detection problem. The algorithms are combined together to find out better optimization results and the classification accuracies are obtained by 10-fold cross-validation method. The purpose of this paper is to select the most relevant features that can represent the pattern of the network traffic and test its effect on the success of the proposed hybrid classification algorithm. To investigate the performance of the proposed method, intrusion detection KDDCup’99 benchmark dataset from the UCI Machine Learning repository is used. The performance of the proposed method is compared with the other machine learning algorithms and found to be significantly different.

Security management is one of the most significant issues in nowadays data centers. Selection of appropriate security mechanisms and effective energy consumption management together with caring for the environment enforces a profound analysis of the considered system. In this paper, we propose a specialized decision support system with a multilevel, comprehensive analysis scheme. As a result of the extensive use of mathematical methods and statistics, guidelines and indicators returned by the proposed approach facilitate the decision-making process and conserve decision-maker’s time and attention. In the paper we utilized proposed multilevel analysis scheme to manage security-based data flow in the example data center. Determining the most secure, energy-efficient, environmental friendly security mechanisms, we implemented the role-based access control method in Quality of Protection Modeling Language (QoP-ML) and evaluated its performance in terms of mentioned factors.

With the popularity of mobile devices, numerous mobile applications have been and will continue to be developed for various interesting usage scenarios. Riding this trend, recent research community envisions a novel information retrieving and information-sharing platform, which views the users with mobile devices, being willing to accept crowdsourcing tasks ascrowd sensors. With the neat idea, a set of crowd sensors applications have emerged. Among the applications, the geospatial information systems based on crowd sensors show significant potentials beyond traditional ones by providing real-time geospatial information. In the applications, user positioning is of great importance. However, existing positioning techniques have their own disadvantages. In this paper, we study using pervasive Wi-Fi access point as user position indicators. The major challenge for using Wi-Fi access point is that there is no mechanism for mapping observed Wi-Fi signals to human-defined places. To this end, our idea is to employ crowdsourcing model to perform place name annotations by mobile participants to bridge the gap between signals and human-defined places. In this paper, we propose schemes for effectively enabling crowdsourcing-based place name annotation, and conduct real trials with recruited participants to study the effectiveness of the proposed schemes. The experiment results demonstrate the effectiveness of the proposed schemes over existing solutions.

The contribution deals with the fractional heat conduction models and their use for determining thermal diffusivity. A brief historical overview of the authors who have dealt with the heat conduction equation is described in the introduction of the paper. The one-dimensional heat conduction models with using integer- and fractional-order derivatives are listed. Analytical and numerical methods of solution of the heat conduction models with using integer- and fractional-order derivatives are described. Individual methods have been implemented in MATLAB and the examples of simulations are listed. The proposal and experimental verification of the methods for determining thermal diffusivity using half-order derivative of temperature by time are listed at the conclusion of the paper.

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability.

Academically, the research of subject database of tax system aims to set up an efficient, harmonious virtual data application environment. Subject data, in application and management, has been on demand polymerized and autonomously collaborated and has reached a balance between instantaneity and accuracy. This paper defines the connotation and characteristics enterprise informationization, designs a value system of enterprise informationization which is subject database oriented, and builds a model for the import of the subject database of enterprise informationization. Meantime, this paper describes the structure of the subject database based information import model and forges the model’s theoretical basis of subject data import in tax system. Using the model can make an analysis on the information of data warehouse, storage information, and tax information to provide decision support for the tax administrators.

This paper proposes a new technique for hiding secret messages in ordinary English text. The proposed technique exploits the redundancies existing in some English language constructs. Redundancies result from the flexibility in maneuvering certain statement constituents without altering the statement meaning or correctness. For example, one can say “she went to sleep, because she was tired” or “Because she was tired, she went to sleep.” The paper provides a number of such transformations that can be applied concurrently, while keeping the overall meaning and grammar intact. The proposed data hiding technique is blind since the receiver does not keep a copy of the original uncoded text (cover). Moreover, it can hide more than three bits per statement, which is higher than that achieved in the prior work. A secret key that is a function of the various transformations used is proposed to protect the confidentiality of the hidden message. Our security analysis shows that even if the attacker knows how the transforms are employed, the secret key provides enough security to protect the confidentiality of the hidden message. Moreover, we show that the proposed transformations do not affect the inconspicuousness of the transformed statements, and thus unlikely to draw suspicion.

The problems of improving computational efficiency and extending representational capability are the two hottest topics in approaches of global manifold learning. In this paper, a new method called extensive landmark Isomap (EL-Isomap) is presented, addressing both topics simultaneously. On one hand, originated from landmark Isomap (L-Isomap), which is known for its high computational efficiency property, EL-Isomap also possesses high computational efficiency through utilizing a small set of landmarks to embed all data points. On the other hand, EL-Isomap significantly extends the representational capability of L-Isomap and other global manifold learning approaches by utilizing only an available subset from the whole landmark set instead of all to embed each point. Particularly, compared with other manifold learning approaches, the data manifolds with intrinsic low-dimensional concave topologies and essential loops can be unwrapped by the new method more successfully, which are shown by simulation results on a series of synthetic and real-world data sets. Moreover, the accuracy, robustness, and computational complexity of EL-Isomap are analyzed in this paper, and the relation between EL-Isomap and L-Isomap is also discussed theoretically.

The wheeled robots have been successfully applied in many aspects, such as industrial handling vehicles, and wheeled service robots. To improve the safety and reliability of wheeled robots, this paper presents a novel hybrid fault diagnosis framework based on Mittag-Leffler kernel (ML-kernel) support vector machine (SVM) and Dempster-Shafer (D-S) fusion. Using sensor data sampled under different running conditions, the proposed approach initially establishes multiple principal component analysis (PCA) models for fault feature extraction. The fault feature vectors are then applied to train the probabilistic SVM (PSVM) classifiers that arrive at a preliminary fault diagnosis. To improve the accuracy of preliminary results, a novel ML-kernel based PSVM classifier is proposed in this paper, and the positive definiteness of the ML-kernel is proved as well. The basic probability assignments (BPAs) are defined based on the preliminary fault diagnosis results and their confidence values. Eventually, the final fault diagnosis result is archived by the fusion of the BPAs. Experimental results show that the proposed framework not only is capable of detecting and identifying the faults in the robot driving system, but also has better performance in stability and diagnosis accuracy compared with the traditional methods.

Association rules mining is an important topic in the domain of data mining and knowledge discovering. Some papers have presented several interestingness measure methods; the most typical areSupport,Confidence,Lift,Improve, and so forth. But their limitations are obvious, like no objective criterion, lack of statistical base, disability of defining negative relationship, and so forth. This paper proposes three new methods,Bi-lift, Bi-improve, andBi-confidence, forLift, Improve, and Confidence, respectively. Then, on the basis of utility function and the executing cost of rules, we propose interestingness function based on profit (IFBP) considering subjective preferences and characteristics of specific application object. Finally, a novel measure framework is proposed to improve the traditional one through experimental analysis. In conclusion, the new methods and measure framework are prior to the traditional ones in the aspects of objective criterion, comprehensive definition, and practical application.

A current source converter (CSC) based static synchronous compensator (STATCOM) is a shunt flexible AC transmission system (FACTS) device, which has a vital role as a stability support for small and large transient instability in an interconnected power network. This paper investigates the impact of a novel and robust pole-shifting controller for CSC-STATCOM to improve the transient stability of the multimachine power system. The proposed algorithm utilizes CSC based STATCOM to supply reactive power to the test system to maintain the transient stability in the event of severe contingency. Firstly, modeling and pole-shifting controller design for CSC based STATCOM are stated. After that, we show the impact of the proposed method in the multimachine power system with different disturbances. Here, applicability of the proposed scheme is demonstrated through simulation in MATLAB and the simulation results show an improvement in the transient stability of multimachine power system with CSC-STATCOM. Also clearly shown, the robustness and effectiveness of CSC-STATCOM are better rather than other shunt FACTS devices (SVC and VSC-STATCOM) by comparing the results in this paper.