Support Vector Machine and Artificial Neural Networks for Hydrological Cycles Classifications of a Water Reservoir in the Amazon

This paper presents a methodology based on computational intelligence techniques for classification of hydrological cycles that can infer the change in the physico-chemieal parameters and metals from the water of a reservoir in the Amazon. The methodology initially consists in perform a pre-processing the data to select the most relevant variables of the samples. After that, we compared two different machine learning classifiers, namely SVM （support vector machine） and ANN （artificial neural network）. The automatic model selection is made to choose the parameters of the classifiers. The results indicate that the support vector machine classifier using radial basis function or polynomial kernel exhibited superior results to ANN in terms of overall accuracy and robustness. The SVM classifier accuracy （89.1%） can be considered satisfactory, since there is a great variability of physico-chemical parameters and metals in the hydrological cycles and in the different ecosystems where are the sampling station.     

Facial Beautification Method Based on Age Evolution

This paper proposes a new facial beautification method using facial rejuvenation based on the age evolution. Traditional facial beautification methods only focus on the color of skin and deformation and do the transformation based on an experimental standard of beauty. Our method achieves the beauty effect by making facial image looks younger, which is different from traditional methods and is more reasonable than them. Firstly, we decompose the image into different layers and get a detail layer. Secondly, we get an age-related parameter： the standard deviation of the Gaussian distribution that the detail layer follows, and the support vector machine （SVM） regression is used to fit a function about the age and the standard deviation. Thirdly, we use this function to estimate the age of input image and generate a new detail layer with a new standard deviation which is calculated by decreasing the age. Lastly, we combine the original layers and the new detail layer to get a new face image. Experimental results show that this algo- rithm can make facial image become more beautiful by facial rejuvenation. The proposed method opens up a new way about facial beautification, and there are great potentials for applications.     

Performance Evaluation of Quicksort with GPU Dynamic Parallelism for Gene-Expression Quantile Normalization

High-density oligonucleotide microarrays allow several millions of genetic markers in a single experiment to be observed. Current bioinformatics tools for gene-expression quantile data normalization are unable to process such huge data sets. In parallel with this reality, the huge volume of molecular data produced by current high-throughput technologies in modern molecular biology has increased at a similar pace, challenging our capacity to process and understand data. On the other hand, the arrival of CUDA （compute unified device architecture） has unveiled the extraordinary power of GPUs （graphics processing units） to accelerate data intensive general purpose computing more and more as time goes by. In this work, we have evaluated the use of dynamic parallelism for ordering gene-expression data, where the management of kernels launching can be done not only by the host, but also by the device. Each sample has more than 6.5 million genes. We optimized the Quicksort parallel implementation available in the CUDA-5.5 Toolkit Samples and compared the performance of the sequential Quicksort algorithm from the GNU C Library （glibc） and with the parallel radix sort implementation available in the CUDPP-2.1 library. The Quicksort parallel implementation is designed to run on the GPU Kepler architecture, which supports dynamic parallelism. The results show that in the studied application the GPU parallel version with dynamic parallelism attains speed-ups in the data-sorting step. However, to achieve an effective overall speed-up considering the radix sort algorithm, performance of the whole application needs further optimizations.     

Singular Boolean networks： Semi-tensor product approach

Singular Boolean networks are introduced in this paper. Via semi-tensor product of matrices and the matrix expression of logical functions, two kinds of the condensed algebraic expressions of singular Boolean networks are obtained. The normalization problem of singular Boolean networks is addressed; that is, under what condition singular Boolean networks can be converted into normal Boolean networks with algebraic restric- tions. Then one sufficient condition and one necessary and sufficient condition are derived for the normalization problem. Furthermore, the solvability of singular Boolean networks is discussed and the concept of admissible initial values of singular Boolean networks is presented. Finally, fixed points and cycles of singular Boolean networks are also investigated.     

3D Modelling,Simulation and Prediction of Facial Wrinkles

The FA （facial ageing） process has been of a great interest to many researchers and some finns like airports and police departments, this is due to the fact that the face appearance changes as people age resulting in difficulties identifying certain individuals. In this paper, 2D wrinkle maps will be used in the design of a 3D system for the purpose of facial wrinkles simulation and prediction. Our findings will challenge many commercial soffwares in the innovation of the techniques in setting solid grounds to generate real-time 3D wrinkles that can be used later for various reasons. The 2D binary wrinkles will be mapped on the corresponding 3D face models using the generated outlined images. NURBS curves will then be projected on those wrinkles to form a 3D wrinkle map. The coloured wrinkle map, as well as some parameters, will be combined together in an algorithm to predict the appearance of the individual wrinkles in every age group that are divided into decades, starting from the age of 20. The novelty of the adopted procedure in comparison to the previous works is the new elements that have been integrated and collaborated to boost accuracy and generate a more realistic outcome.     

Detection of semantically similar code

The traditional similar code detection approaches are limited in detecting semantically similar codes, impeding their applications in practice. In this paper, we have improved the traditional metrics-based approach as well as the graph- based approach and presented a metrics-based and graph- based combined approach. First, source codes are represented as augmented system dependence graphs. Then, metrics- based candidate similar code extraction is performed to filter out most of the dissimilar code pairs so as to lower the computational complexity. After that, code normalization is performed on the candidate similar codes to remove code variations so as to detect similar code at the semantic level. Finally, program matching is performed on the normalized control dependence trees to output semantically similar codes. Experiment results show that our approach can detect similar codes with code variations, and it can be applied to large software.     

On the simulation of expressional animation based on facial MoCap

MoCap(motion capture)-based animation is a hot issue in computer animation research currently.Based on the optical MoCap system,this paper proposes a novel cross-mapping based facial expression simulating method.To overcome the problem of the false upper and lower jaw correlation derived from the facial global RBFbased cross-mapping method,we construct a functional partition based RBF cross-mapping method.During model animating,enhanced markers are added and animated by our proposed skin motion mechanism.In addition,based on the enhanced markers,an improved RBF-based animating approach is raised to derive realistic facial animation.Further more,a pre-computing algorithm is presented to reduce computational cost for real-time simulation.The experiments proved that the method can not only map the MoCap data of one subject to diferent personalized faces but generate realistic facial animation.     

A polynomial time algorithm for GapCVPP in l1 norm

This paper concerns the hardness of approximating the closest vector in a lattice with preprocessing in l1norm,and gives a polynomial time algorithm for GapCVPPγin l1norm with gapγ=O(n/log n).The gap is smaller than that obtained by simply generalizing the approach given by Aharonov and Regev.The main technical ingredient used in this paper is the discrete Laplace distribution on lattices which may be of independent interest.     

A new approach of conditions on for s-sparse recovery δ2s（φ）

In this paper, we provide a unified expression to obtain the conditions on the restricted isometry constant δ2s（φ）. These conditions cover the important results proposed by Candes et al. and each of them is a sufficient condition for sparse signal recovery. In the noiseless case, when δ2s（φ） satisfies any one of these conditions, the s-sparse signal can be exactly recovered via （11） constrained minimization.     

Statistical characteristics of the normalized Stokes parameters

The statistical properties of the normalized Stokes parameters in a Gaussian stochastic plane wave field are described in detail. Via the expression of the three normalized Stokes parameters, the mean, variance, and high-order moments are calculated, which simplify C. Brosseau＇s results. The new dispersion, normalized contrast function, skewness, and kurtosis are defined to describe the non-Gaussian distribution characteristics, which can be applied to Gaussian wave fields relating to depolarization of light by a spatially random medium.     

Why Higher Education＇s Pursuit of eLearning Technology Fails Minority Learners

As technology becomes increasingly integrated into the teaching and learning processes at the university level, it is imperative that research be conducted in relation to the impact of technology acquisition on minority learning populations. Research suggests that we need to improve the ways technology is applied, adopted and introduced and that higher levels of support should be provided to minority and nontraditional learning populations as they immerse themselves into higher education environments. Avenues for discussion of cost-effective technology integration and transition are explored; data identifies a need for more effective selection and alignment of learning needs with learning tools earlier on in the process of technology implementation across campuses. Research suggests this supports student presence, persistence, retention and success. Without it, however, we fail to support the very learners we seek to provide higher levels of access and opportunity. This failure will impact learners and institutions alike by placing disadvantage populations in precarious positions and universities having to choose between cultural, economic and human capital. The paper is organized as follows： Section 1： Introduction; Section 2： Need for Understanding Minority Enrollment Patterns; Section 3： Analysis of Report Data; Section 4： Strategy; Section 5： Conclusions.     

Methodological Approaches to Development and Implementation of Multi-agents Investment DSS Using JADE Platform

In these latter days software agents are used for the development and implementation of intellectual decision support systems. In order to implement intelligence in a system some or several dozen of software agents are used and the made system becomes multi-agent. For the development of these systems a set of methodologies, i.e., the sequence of consequent steps of analysis, designing and implementation, is offered. The carried out analysis of the methodologies showed that as a rule they are limited by the spectrum of their pending problem （within the pales of the requirements of specific applied task, within the pales of the possibilities of technical implementation） or within the pales of amount of detail. The variety of methodologies is influenced by the fact that for the development of these systems the requirements and attitudes are offered by the specialists of related spheres such as software, numeral intellect engineers. In the course of the development of hardware and software appeared possibilities to implement mobile multi-agents systems, however, there is no one united mobile multi-agent systems design methodology, whereas existing systems are underdeveloped and their number is small. In this article we introduce the course of the designing of an intellectual real time multi-agent investment management decision support information system adapting and combining some methodologies where the choice to use either communicating or mobile agents is the question of rather technical implementation than methodological. In the article we introduce two ways of system implementation by JADE platform： the first one-using communicating agents, and the second one-using mobile agents.     

Three-dimensional view-invariant face recognition using a hierarchical pose-normalization strategy

Face recognition from three-dimensional (3D) shape data has been proposed as a method of biometric identification as a way of either supplanting or reinforcing a two-dimensional approach. This paper presents a 3D face recognition system capable of recognizing the identity of an individual from a 3D facial scan in any pose across the view-sphere, by suitably comparing it with a set of models (all in frontal pose) stored in a database. The system makes use of only 3D shape data, ignoring textural information completely. Firstly, we propose a generic learning strategy using support vector regression [Burges, Data Mining Knowl Discov 2(2): 121–167, 1998] to estimate the approximate pose of a 3D head. The support vector machine (SVM) is trained on range images in several poses belonging to only a small set of individuals and is able to coarsely estimate the pose of any unseen facial scan. Secondly, we propose a hierarchical two-step strategy to normalize a facial scan to a nearly frontal pose before performing any recognition. The first step consists of either a coarse normalization making use of facial features or the generic learning algorithm using the SVM. This is followed by an iterative technique to refine the alignment to the frontal pose, which is basically an improved form of the Iterated Closest Point Algorithm [Besl and Mckay, IEEE Trans Pattern Anal Mach Intell 14(2):239–256, 1992]. The latter step produces a residual error value, which can be used as a metric to gauge the similarity between two faces. Our two-step approach is experimentally shown to outperform both of the individual normalization methods in terms of recognition rates, over a very wide range of facial poses. Our strategy has been tested on a large database of 3D facial scans in which the training and test images of each individual were acquired at significantly different times, unlike all except two of the existing 3D face recognition methods.     

Research printing fault diagnose system based on RSVM and C4.5

For the characteristics of printing malfunction diagnose system, a model to classify fault printing based on Reduced Support Vector Machines （RSVM） and C4.5 is discussed. The support vector machines have been promising methods for classification because of their solid mathematical foundation. However they are not favored for large-scale because the training complexity of SVM is highly dependent on the size of data set. This paper use RSVM with an improved nonlinear Kernel to reduced the size of the quadratic program to be solved and simplified the classification of the nonlinear separating surface. After classified root-classes by RSVM, it uses C4.5 algorithm for further diagnosis to remedy the defect of RSVM in classing subclasses. The hybrid method makes fully use of the RSVM efficiency in multidimensional character space Simultaneously, it also brings the accuracy of C4.5 algorithm into full play. That is suited to class the complicated print faults. Computational results indicate the hybrid method has a good efficiency for adjustable printing fault, and computational times as well as memory usage are much smaller for RSVM than that of conventional SVM. And it has a good efficiency for adjustable faults.     

改进的脉冲耦合神经网络人脸特征提取方法

针对基于子空间分解的人脸特征提取方法对人脸图像在采集过程中的光照、旋转、位置等变化较为敏感的问题,提出了一种改进的脉冲耦合神经网络人脸特征提取方法。该方法模拟生物视觉的感知过程,将人脸图像分解成由若干二值图像组成的认知序列,计算序列中的每幅二值图像的熵作为人脸特征,基于支持向量机实现分类与识别;同时克服了标准的脉冲耦合神经网络模型参数过多的缺点,识别率也有所改善。理论与实验结果表明,该方法与现有的基于子空间分解的人脸特征提取方法相比,对人脸图像在采集过程中的光照、旋转、位置等变化有较强的鲁棒性,而且具有较低的维数。     

Platoon dispersion prediction under the condition of adjacent cycle traffic flow overlapping based on Support Vector Regression

Coordinated signal control can improve the continuity of vehicular traffic flow movement and reduce delay. Cycle Flow Profile is the base for calculating coordinated signal control parameters. Platoon dispersion characteristic determines the Cycle Flow Profile. So, improving platoon dispersion prediction accuracy can obtain significant benefit for signal coordination. When the velocities of the vehicles vary greatly, faster vehicles of next cycle can catch up the slower vehicles of the current cycle. Traffic flow overlapping of adjacent cycle is an important characteristic. Support Vector Regression is adopted to predict platoon dispersion and compares prediction accuracy with Robertson formula. The results are encouraging; Support Vector Regression has higher prediction accuracy.     

Blind source separation algorithm based on support vector machines

This paper presents a novel blind source separation algorithm integrating the estimation of the probability density function with the fixed-point algorithm. Firstly, the kernel function is constructed by the radial basis function; then the sparse representation of the probability density function of the mixed signals are established, this sparse representation is based on the support vector machines recursion method of neural network theory, thus the closed form expression of the probability density function is obtained; finally, a new estimation method of the activation function is put forward, combining the Fast ICA with the estimation method, we can get a new algorithm of blind source separation. The simulation results have verified that the algorithm can successfully separate the mixed sub-Gaussian and super-Gaussian source signals, and the performance of the algorithm is excellent.     

Application of Support Vector Machine in Weld Defect Detection and Recognition of X-ray Images

Support vector machines（SVM） received wide attention for its excellent ability to learn, it has been applied in many fields. A review of the application of SVM in weld defect detection and recognition of X-ray image is been presented. We will show some commonly used methods of weld defect detection and recognition using SVM, and the advantages and disadvantages of each method will be discussed. SVM appears to be promising in weld defect detection and recognition, but future research is needed before it fully mature in this filed.     

New normalization methods using support vector machine quantile regression approach in microarray analysis

There are many sources of systematic variations in cDNA microarray experiments which affect the measured gene expression levels. Print-tip lowess normalization is widely used in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. However, print-tip lowess normalization performs poorly in situations where error variability for each gene is heterogeneous over intensity ranges. We first develop support vector machine quantile regression (SVMQR) by extending support vector machine regression (SVMR) for the estimation of linear and nonlinear quantile regressions, and then propose some new print-tip normalization methods based on SVMR and SVMQR. We apply our proposed normalization methods to previous cDNA microarray data of apolipoprotein AI-knockout (apoAI-KO) mice, diet-induced obese mice, and genistein-fed obese mice. From our comparative analyses, we find that our proposed methods perform better than the existing print-tip lowess normalization method.     

Features classification using geometrical deformation feature vector of support vector machine and active appearance algorithm for automatic facial expression recognition

This paper proposes a method for facial expression recognition in image sequences. Face is detected from the scene and then facial features are detected using image normalization, and thresholding techniques. Using an optimization algorithm the Candide wire frame model is adapted properly on the first frame of face image sequence. In the subsequent frames of image sequence facial features are tracked using active appearance algorithm. Once the model fits on the first frame, animation parameters of model are set to zero, to obtain the shape of model for the neutral facial expression of the same face. The last frame of the image sequence corresponds to greatest facial expression intensity. The geometrical displacement of the Candide wire frame nodes, between the neutral expression frame and the last frame, is used as an input to the multiclass support vector machine, which classifies facial expression into one of the class such as happy, surprise, sadness, anger, disgust, fear and neutral. This method is applicable for frontal as well as tilted faces with angle $\pm 30\,^{\circ }, \pm 45\,^{\circ }, \pm 60\,^{\circ }$ with respect to y axis.