Eigenspectra Versus Eigenfaces: Classification with a Kernel-Based Nonlinear Representor
This short paper proposes a face recognition scheme, wherein features called eigenspectra are extracted successively by the fast Fourier transform (FFT) and the principle component analysis (PCA) and classification results are obtained by a classifier called kernel-based nonlinear representor (KNR). Its effectiveness is shown by experimental results on the Olivetti Research Laboratory (ORL) face database.

title: Adaptive Training of a Kernel-Based Representative and Discriminative Nonlinear Classifier
abstract: Adaptive training of a classifier is necessary when feature selection and sparse representation are considered. Previously, we proposed a kernel-based nonlinear classifier for simultaneous representation and discrimination of pattern features. Its batch training has a closed-form solution. In this paper we implement an adaptive training algorithm using an incremental learning procedure that exactly retains the generalization ability of batch training. It naturally yields a sparse representation. The feasibility of the presented methods is illustrated by experimental results on handwritten digit classification.

title: Image Computing: Fractional Spectra and Circular Moments via FrFT
abstract: In image computing, feature extraction plays a key part for image pattern classification. In this article we adopt discrete Fractional Fourier Transform (FrFT) for fractional feature extraction. Firstly, a criterion is proposed to determine the FrFT order for an image class so that it may be optimally discriminated from other classes in the FrFT domain, and the transformed features are called fractional spectra. Secondly, four types of fractional moments respectively called circular center, circular range, circular skewness, and circular kurtosis are defined and computed from the FrFT results of an image with different FrFT orders. The extracted image features are then classified with a previously proposed nonlinear classifier called Kernel-based Nonlinear Representor (KNR). And face recognition experiments are taken for illustrative examples.

title: Specifying adaptation semantics
abstract: Computer software must dynamically adapt to changing conditions. In order to fully realize the benefit of dynamic adaptation, it must be performed correctly. The correctness of adaptation cannot be properly addressed without precisely specifying the requirements for adaptation. This paper introduces an approach to formally specifying adaptation requirements in temporal logic. We introduce A-LTL, an adaptation-based extension to linear temporal logic, and use this logic to specify three commonly used adaptation semantics. Neighborhood composition and sequential composition techniques are developed and applied to A-LTL to construct the specification of an adaptive system. We introduce adaptation semantics graphs to visually present the adaptation semantics. Specifications for adaptive systems can be automatically generated from adaptation semantics graphs.

title: A Novel Approach Using PCA and SVM for Face Detection
abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

title: Specification and Runtime Enforcement of Security Policies
abstract: The rapid growth in mobile and wireless communications entails serious problem of security. Formal methods can be used to help building secure mobile computing environment. Tabular expressions have proved to be useful and practical in formulating precise and complete documentation for computer systems. In this paper a framework for specification and runtime enforcement of security policies is proposed basing on the use of tabular expressions. A security policy can be specified with a tabular expression, and checking whether an application adheres to a given policy can be achieved by evaluating the tabular expression with respect to information intercepted at runtime. The advantages of our approach includes: (1) providing precise and readable specification of security policies; (2) developing a general policy enforcement engine rather than one policy enforcement engine for each security policy; (3)achieving low overheads by simplifying tabular expressions with static information of mobile code.

title: A Pixel-level Statistical Structural Descriptor for Shape Measure and Recognition
abstract: A novel shape descriptor based on the histogram matrix of pixel-level structural features is presented. First, length ratios and angles between the centroid and contour points of a shape are calculated as two structural attributes. Then, the attributes are combined to construct a new histogram matrix in the feature space statistically. The proposed shape descriptor can measure circularity, smoothness, and symmetry of shapes, and be used to recognize shapes. Experimental results demonstrate the effectiveness of our method.

title: Applying PageRank Algorithm in Requirement Concern Impact Analysis
abstract: As an important part of requirement management, managing requirement change plays a key role in controlling project schedule and costs at early stage. Effective requirement impact analysis would give proper assessment on the effect of certain requirement changes on the whole system, and provide useful information for making trade-off decisions on future system design and implementation. In this paper a quantitative approach to concern impact analysis at requirement level has been proposed with the application of PageRank algorithm, which is a successful link based web page sorting algorithm. At first, separation of concerns is applied during deriving formal requirement specification from textual requirement statements. Next, concerns are specified and concern relationship graph is established. Finally, PageRank algorithm is utilized on concern relationship graph for assessing the impact of concern changes. Our approach has been applied to hallway section in Light Control System and validation of analysis result has been stated.