PAD Model Based Facial Expression Analysis
The validity of PAD (Pleasure-Arousal-Dominance) theory in vision area and the feasibilityT on PAD based models for facial expression analysis are discussed in this paper. Three new models based on PAD theory are proposed and their feasibility is verified by experiments on Cohn-Kanade dataset and PAD dataset which is collected from well-designed psychological experiments. After combining Gabor feature and SVM (Support Vector Machine), the result can be further improved. Compared with the basic expression models, our experiments show that the predominance of PAD based model is that it can represent almost any states of expression. Finally, our preliminary experiments show that distinguishing different grades of the same expression is promising by our models.

title: A Composite Method to Extract Eye Contour
abstract: An eye contour extraction method which combines a simplied version of Active Shape Model(ASM) with a gradient method is proposed. Considering the large amount of calculations required by ASM, it is only used to extract eyelids. As iris is considered to have some more regular shape, the detection of iris is done by the simple but fast gradient method, which is improved by introducing gradient value to the weight matrix. Our detection method has been implemented in the C programming language and experimental results shows good accuracy and efficiency.

title: An XML-Based Implementation of Multimodal Affective Annotation
abstract: In simple cases, affective computing is a computational device recognizing and acting upon the emotions of its user or having (or simulating having) emotions of its own in complex cases. Multimodal technology is currently one of the hottest focuses in affective computing research. However, the lack of a large-scale multimodal database limits the research to some respective and scattered fields, such as affective recognition by video or by audio. This paper describes the development and implementation of an XML-based multimodal affective annotation system which is called MAAS (Multimodal Affective Annotation System). MAAS contains a hierarchical affective annotation model based on the 3-dimensional affect space derived from Mehrabian’s PAD temperament scale. The final annotation file is formed in XML format in order to interchange the resources with other research groups conveniently.

title: Real-time pose estimation for outdoor mobile robots using range data
abstract: Pose estimation is a key issue in mobile robots and important to moving object tracking. In this paper, we present a tangent based hybrid pose estimation algorithm with real-time performance using range data from laser radar, which consists of Iterative Tangent weighted Closest Point (ITCP) and Hough transform based Tangent Angle Histogram (HTAH) algorithms to overcome problems with past methods, such as local minimum, aperture-like, and high computation problems. The algorithm has been tested on both synthetic and real range data in outdoor environments. Experimental results demonstrate its high accuracy, low computation, wide applicability and high robustness to aperture-like problems, occlusion and noise.

title: Deception Detection Via Blob Motion Pattern Analysis
abstract: Deception dectection is one of the most difficult problems in affect recognition and expression research area. Recently, non-verbal methods of detecting deception have appeared to be promising.  presented a proof-of-concept study based on the blob analysis of some suspects' interviews and mock experiments video clips. In this paper, we present our recent research work in the direction of developing an automated deception detection system. We propose a blob motion pattern analysis approach to solve this problem.

title: Color-based road detection in urban traffic scenes
abstract: Road detection is a key issue for autonomous driving in urban traffic. In this paper, after a brief overview of existing methods, we present a road-area detection algorithm based on color images. This algorithm is composed of two modules: boundaries are first estimated based on the intensity image and road areas are subsequently detected based on the full color image. In the first module, an edge image of the scene is analyzed to obtain the candidates for the left and right road borders and to delimit the area that will subsequently be used to compute the mean and variance of the Gaussian distribution, assumed to be obeyed by the color components of road surfaces. The second module effectively extracts the road area and reinforces boundaries that most appropriately fit the road-extraction result. The combination of these modules can overcome basic problems due to inaccuracies in edge detection based on the intensity image alone and due to the computational complexity of segmentation algorithms based on color images. Experimental results on real road scenes have substantiated the effectiveness of the proposed method.

title: P2p-factorization of a complete bipartite graph

title: Existence of two disjoint long cycles in graphs
abstract: Let n,h be integers with n>=6 and h>=7. We prove that if G is a graph of order n with @s"2(G)>=h, then G contains two disjoint cycles C"1 and C"2 such that |V(C"1)|+|V(C"2)|>=min{h,n}.

title: MIMO Probability Density Function Control Using Simple LOG-MLP Neural Networks
abstract: This paper presents a new model for the control of multivariable output probability density function (PDF). Firstly, a Multi-Layer Perceptron (MLP) neural network is adopted to approximate the static output PDF of the MIMO systems. Nonlinear principal component analysis (NLPCA) is then used to reduce the order of the obtained static neural network model and the dynamics of the system is considered based on the lower-order model. After this, an integrated solution is provided to set up the system with lower-order dynamics for the purpose of stochastic distribution control. The controller design is then presented in detail. Finally, a simulation example is given to demonstrate the effectiveness of the method and encouraging results have been obtained.

title: A Reliability Model for Result Checking in Volunteer Computing
abstract: Large scale volunteer computing has been proved to be an efficient mechanism to solve the complicated problems. Specifically, in such a system, thousands of idle computers on the network are organized to finish a series of tasks dispatched by a central server. However, this computing model is vulnerable to attack from malicious peers returning incorrect results, since it is impossible to check each of thousands of the results. To address this problem, we propose a scheme to evaluate the reliability of each peer, and hence to distinguish whether the peer is honest or malicious. Specifically, the task dispatching pattern is changed so that the results returned from a peer can reflect the type of this peer. We focus on a typical dispatching pattern, and analyze its performance both theoretically and experimentally. The results indicate that the proposed scheme can effectively differentiate the malicious peers for most of the reasonable situations.

title: Robust fault diagnosis for non-Gaussian stochastic systems based on the rational square-root approximation model
abstract: The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Convergency analysis is performed for the error dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demonstrate the effectiveness of the proposed algorithm.