A morphable model for the synthesis of 3D faces

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Probability and Measure

Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm

Nature-Inspired Optimization Algorithms

We develop a novel maximum neighborhood margin discriminant projection (MNMDP) technique for dimensionality reduction of high-dimensional data. It utilizes both the local information and class information to model the intraclass and interclass neighborhood scatters. By maximizing the margin between intraclass and interclass neighborhoods of all points, MNMDP cannot only detect the true intrinsic manifold structure of the data but also strengthen the pattern discrimination among different classes. To verify the classification performance of the proposed MNMDP, it is applied to the PolyU HRF and FKP databases, the AR face database, and the UCI Musk database, in comparison with the competing methods such as PCA and LDA. The experimental results demonstrate the effectiveness of our MNMDP in pattern classification.

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Maximum neighborhood margin discriminant projection for classification

By employing invariant relation between the mean value of the first (n − 1) pixels and the last one pixel (also called the remaining pixel) for every image block containing n pixels, a new reversible watermark scheme capable of mostly carrying 2n−3 bits into one n-sized image block in a single embedding process is presented in this paper. First, the mean value of the first (n − 1) pixel is calculated. Next, the difference value between the last one pixel and this obtained mean value is applied to distinguish which classification (i.e., smooth or complex sub-block) any sub-block belongs to. Consequently, it is determined to embed (n − 2) bits or 2(n − 2) bits into each sub-block according to its final classification results. And meanwhile, the mean value is reapplied to predict this last one pixel. 1-bit watermark is embedded into this last one pixel in accordance with the magnitude of prediction-error value. By multi-employing invariability of the mean value of (n− 1) pixels, the embedding rate can approach to (2− 3 n ) bpp (bit per pixel) for a single embedding process. Meanwhile, the embedding distortion is greatly controlled by embedding more bits into smooth image blocks and fewer bits into the other blocks with complex texture. Experimental results reveal the proposed scheme is effective.

Invariability of Mean Value Based Reversible Watermarking

Clustering in data mining is a discovery process that groups similar objects into the same cluster. Various clustering algorithms have been designed to fit various requirements and constraints of application. In this paper, we study several k-medoids-based algorithms including the PAM, CLARA and CLARANS algorithms. A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search. Experimental results based on elliptic, curve and Gauss-Markov databases demonstrate that the proposed algorithm applied to CLARANS may reduce the number of distance calculations by 67% to 92% while retaining the same average distance per object. In terms of the running time, the proposed algorithm may reduce computation time by 38% to 65% compared with the CLARANS algorithm.

An Efficient K -Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search

In this paper, we propose a novel kernel named Adaptive Data-dependent Matrix Norm Based Gaussian Kernel (ADM-Gaussian kernel) for facial feature extrac- tion. As a popular facial feature extraction method for face recognition, the current kernel method endures some problems. Firstly, the face image must be transformed to the vector, which leads to the large storage requirements and the large computational effort, and sec- ondly since the different geometrical structures lead to the different class discrimination of the data in the feature space, the performance of the kernel method is influenced when kernels are inappropriately selected. In order to solve these problems, firstly we create a novel matrix norm based Gaussian kernel which views images as matrices for facial feature extraction, which is the basic kernel for the data-dependent kernel. Secondly we apply a novel maximum margin criterion to seek the adaptive expansion coefficients of the data-dependent kernel, which leads to the largest class discrimination of the data in the feature space. Experiments on ORL and Yale databases demonstrate the effectiveness of the proposed algorithm. Keywords: Kernel method, Gaussian kernel, Matrix norm based Gaussian kernel, Adaptive matrix norm based Gaussian kernel, Data-dependent kernel

Adaptive data-dependent matrix norm based gaussian kernel for facial feature extraction

Economic Load Dispatch (ELD) is one of the important optimization tasks, which provides an economic condition for the power systems. In this paper, Evolved Bat Algorithm (EBA) as an evolutionary based approach is presented to solve the constraint economic load dispatched problem of thermal plants. The output generating power for all the power-generation units can be determined by the optimal technique for transmission losses, power balance and generation capacity, so that the total constraint cost function is minimized. A piecewise quadratic function is used to show the fuel cost equation of each generation unit, and the B-coefficient matrix is used to represent transmission losses. The systems with six units and fifteen units of thermal plants are used to test the demonstration of the solution quality and computation efficiency of the feasibility of the application of the Evolved Bat Algorithm for ELD. The experimental results compared with the genetic algorithm (GA) method for ELD, and with the particle swarm optimization (PSO) method for ELD, show that the applied EBA method for ELD can provide the higher efficiency and accuracy.

Evolved Bat Algorithm for Solving the Economic Load Dispatch Problem

The wireless sensor network is a network composed of sensor nodes self-organizing through the application of wireless communication technology. The application of wireless sensor networks (WSNs) requires high security, but the transmission of sensitive data may be exposed to the adversary. Therefore, to guarantee the security of information transmission, researchers propose numerous security authentication protocols. Recently, Wu et al. proposed a new three-factor authentication protocol for WSNs. However, we find that their protocol cannot resist key compromise impersonation attacks and known session-specific temporary information attacks. Meanwhile, it also violates perfect forward secrecy and anonymity. To overcome the proposed attacks, this paper proposes an enhanced protocol in which the security is verified by the formal analysis and informal analysis, Burross-Abadii-Needham (BAN) logic, and ProVerif tools. The comparison of security and performance proves that our protocol has higher security and lower computational overhead.

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Shu-Chuan Chu

Papers

Invariability of Mean Value Based Reversible Watermarking

An Efficient K -Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search

Adaptive data-dependent matrix norm based gaussian kernel for facial feature extraction

Evolved Bat Algorithm for Solving the Economic Load Dispatch Problem

A Provably Secure Three-Factor Authentication Protocol for Wireless Sensor Networks