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In recent years, multi-modal optimization has become an important area of active research. Many algorithms have been developed in literature to tackle multi-modal optimization problems. In this work, a dynamic grouping crowding differential evolution (DGCDE) with ensemble of parameter is proposed. In this algorithm, the population is dynamically regrouped into 3 equal subpopulations every few generations. Each of the subpopulations is assigned a set of parameters. The algorithms is tested on 12 classical benchmark multi-modal optimization problems and compared with the crowding differential evolution (Crowding DE) in literature. As shown in the experimental results, the proposed algorithm outperforms the Crowding DE with all three different parameter settings on the benchmark problems. [ABSTRACT FROM AUTHOR]

E-government outsourcing enables government to be relieved from IT system construction and maintenance, and concentrate on their management affairs, obtaining lower costs, more advanced technology, and less personnel demands additionally. This paper analyzes the e-government outsourcing according to types, advantages, risks, process and crucial factors, which gives suggestions on the cooperation between government departments and vendors, and contributes to the further healthy development of e-government outsourcing. [ABSTRACT FROM AUTHOR]

With the explosive growth of multimedia data, video presentation has become an important technology for fast browsing of video content. In this paper, we present a novel video presentation technique called "Free-Shaped Video Collage" (FS-Collage), which is motivated from and built upon our previous work on Video Collage [3]. Video Collage is a kind of static summary which selects the most representative regions-of-interest (ROI) from video and seamlessly arranges them on a synthesized image. Unlike Video Collage in which both the shapes of ROI and final collage are fixed as rectangle, we support arbitrary shapes of ROI and a set of collage templates in FS-Collage. Furthermore, we design three ROI arrangement schemes (i.e., book, diagonal, and spiral) for satisfying different video genres. We formulate the generation of FS-Collage as an energy minimization problem and solve the problem by designing a random sampling process. The experiment results show that our FS-Collage achieves satisfying performance. [ABSTRACT FROM AUTHOR]

Forecasting of Market Clearing Price (MCP) is important to economic benefits of electricity market participants. To accurately forecast MCP, a novel two-stage GA-based neural network model (GA-NN) is proposed. In the first stage, GA chromosome is designed into two parts: boolean coding part for neural network topology and real coding part for connection weights. By hybrid genetic operation of selection, crossover and mutation under the criterion of error minimization between the actual output and the desired output, optimal architecture of neural network is obtained. In the second stage, gradient learning algorithm with momentum rate is imposed on neural network with optimal architecture. After learning process, optimal connection weights are obtained. The proposed model is tested on MCP forecasting in California electricity market. The test results show that GA-NN has self-adaptive ability in its topology and connection weights and can obtain more accurate MCP forecasting values than BP neural network. [ABSTRACT FROM AUTHOR]

AC based nodal pricing method makes market rule less transparent and affects market decisions on risk and opportunity in short-run and long-run periods. Therefore, AC based nodal pricing formulation is simplified into DC based nodal pricing formulation, which maximizes total social welfare with the condition of satisfying Kirchhoff's laws and other operational constraints in modified DC optimal power flow model. Then, a simple but practical linear programming based nodal pricing method, which can be applied to solve market clearing mode with piecewise constant supply and demand curves, is proposed. The proposed method is tested on IEEE 30 bus system. Test results show that it can avoid premature and divergent phenomenon, which always occurs in the solution of AC based nodal pricing formulation, and has potential application in competitive electricity market. [ABSTRACT FROM AUTHOR]

Information isolation has been identified as a big challenge in IT Service Management (ITSM). Existing ITSM practices mostly rely on configuration information and are geared towards individual applications and processes. However, information available in complicated IT infrastructure goes beyond data from the configuration management domain. How to efficiently extract and integrate the hidden knowledge from a wide variety of information sources is a major pain point for ITSM. In this paper, a threading strategy (TS) with KPI mark and knowledge-enhanced information services is proposed to improve ITSM quality. The essential contribution of this work is to organize the highly complex IT service information with KPI mark and to build a knowledge repository for accumulateing and reusing experts' knowledge. In addition, a prototype called BIANCHIN is implemented to explore this knowledge-enhanced information services framework. Finally, a real business application of Cisco VoIP system is used as a case study for evaluating the effectiveness and efficiency of the knowledge-enhanced information services framework. [ABSTRACT FROM AUTHOR]

The purpose of this study is to identify the hierarchical radial basis function neural networks and select important input features for each sub-RBF neural network automatically. Based on the pre-defined instruction/operator sets, a hierarchical RBF neural network is created and evolved by using Extended Compact Genetic Programming (ECGP), and the parameters are optimized by Differential Evolution (DE) algorithm. Empirical results on benchmark system identification problems indicate that the proposed method is efficient. [ABSTRACT FROM AUTHOR]

The data mining techniques used for extracting patterns that represent abnormal network behavior for intrusion detection is an important research area in network security. This paper introduces the concept of gravitation and gravitation field into data classification by utilizing analogical inference, and studied the method to calculate data gravitation. Based on the theoretical model of data gravitation and data gravitation field, the paper presented a new classification model called Data Gravitation based Classifier (DGC). The proposed approach was applied to an Intrusion Detection System (IDS) with 41 inputs (features). Experimental results show that the proposed method was efficient in data classification and suitable for abnormal detection using netowrk processor-based platforms. Keywords: Data Classification, Network Intrusion Detection, Data Gravitation based Classifier, Network Processor. [ABSTRACT FROM AUTHOR]

Hierarchical RBF networks consist of multiple RBF networks assembled in different level or cascade architecture. In this paper, an evolved hierarchical RBF network was employed to detect the breast cancel. For evolving a hierarchical RBF network model, Extended Compact Genetic Programming (ECGP), a tree-structure based evolutionary algorithm and the Differential Evolution (DE) are used to find an optimal detection model. The performance of proposed method was then compared with Flexible Neural Tree (FNT), Neural Network (NN), and RBF Neural Network (RBF-NN) by using the same breast cancer data set. Simulation results show that the obtained hierarchical RBF network model has a fewer number of variables with reduced number of input features and with the high detection accuracy. [ABSTRACT FROM AUTHOR]

Location management is a key function in mobile communication systems to guarantee the mobile terminals to continuously receive services. In this paper, we study an enhanced movement-based location management scheme for 3G cellular networks where the home location registers, gateway location registers and visitor location registers form a three-tier hierarchical mobility database structure. Furthermore, we formulate analytically the cost model of location update and paging for the proposed scheme and compare the proposed scheme with the basic movement-based location management scheme. The outcomes show that the enhanced movement-based location management scheme outperforms the other scheme. [ABSTRACT FROM AUTHOR]

In this paper, a new public-key broadcast encryption scheme, which is asymmetric and without involvement of trusted third parties, is proposed by using the "subset-cover" framework and the identity-based encryption scheme of Boneh and Franklin. This scheme is the first concrete construction of an asymmetric identity-based public-key broadcast encryption that does not rely on trusted agents and the costly Oblivious Polynomial Evaluation mechanism. Moreover, this novel work contains other desirable features, such as efficient encryption and decryption, low memory requirements, traitor tracing, asymmetry and non-repudiation. The tracing algorithm of this system is more efficient than that of the previous ones. [ABSTRACT FROM AUTHOR]

In this paper, we present a method of running-mode to analyze the fairness of two-party optimistic fair exchange protocols. After discussing the premises and assumptions of analysis introduced in this technique, we deduce all the possible running modes that may cause attack on the protocols. Then we illustrate our technique on the Micali's Electronic Contract Signing Protocol (ECS1), and the checking results show that there are three new attacks on the protocol. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how the seemingly chaotic behavior of stock markets could be well represented using neural network, TS fuzzy system and hierarchical TS fuzzy techniques. To demonstrate the different techniques, we considered Nasdaq-100 index of Nasdaq Stock MarketSM and the S&P CNX NIFTY stock index. We analyzed 7 year's Nasdaq 100 main index values and 4 year's NIFTY index values. The parameters of the different techniques are optimized by the particle swarm optimization algorithm. This paper briefly explains how the different learning paradigms could be formulated using various methods and then investigates whether they can provide the required level of performance, which are sufficiently good and robust so as to provide a reliable forecast model for stock market indices. Experiment results reveal that all the models considered could represent the stock indices behavior very accurately. [ABSTRACT FROM AUTHOR]

The J2EE application server provides a primary solution to develop enterprise-wide applications, which uses containers to hold application components. The container framework relieve developers' burden greatly because it encapsulates all the system level services and the developers are able to use these services directly without knowing underlying details. The processing capacity of application servers is becoming more and more important with the requirements of achieving higher performance and higher scalability. This paper uses ECperf, a performance benchmark tool for application servers, to studies the performance issues of the application server OnceAS, which is developed by the Institute of software, Chinese Academy of Sciences, and presents optimization approaches including bean instance pools and high speed naming service. These optimizations are implemented in OnceAS and proved to be effective through ECperf benchmark evaluation. [ABSTRACT FROM AUTHOR]