Similarity computing model of high dimension data for symptom classification of Chinese traditional medicine
In recent years, researchers have paid more and more attention on data mining of practical applications. Aimed to the problem of symptom classification of Chinese traditional medicine, this paper proposes a novel computing model based on the similarities among attributes of high dimension data to compute the similarity between any tuples. This model assumes data attributes as basic vectors of m dimensions and each tuple as a sum vector of all the attribute-vectors. Based on the transcendental concept similarity information among attributes, it suggests a novel distance algorithm to compute the similarity distance of any pair of attribute-vectors. In this method, the computing of similarity between any tuples are turned to the formulas of attribute-vectors and their projections of each other, and the similarity between any pair of tuples can be worked out by computing these vectors and formulas. This paper also presents a novel classification algorithm based on the similarity computing model and successfully applies the algorithm into the symptom classification of Chinese traditional medicine. The efficiency of the algorithm is proved by extensive experiments.

title: Evolutionary Algorithm Based on Overlapped Gene Expression
abstract: Inspired by the overlap gene expression in biological study, this paper proposes a novel evolutionary algorithm-EAOGE i.e. Evolutionary Algorithm based on Overlapped Gene Expression. Different from existing works, EAOGE suggests a new expression structure of genes with probabilities of overlapped expression for some segments. The main contributions are: (1) Proposing a novel model and an algorithm of gene expression while borrowing some ideas from artificial immunity algorithm; (2) Analyzing the expressing space and encode characteristic of the new model; (3) The extensive experiments in function finding shows that new model is 2.8~9.7 times faster than usual GEP method, and in higher-degree polynomial function finding, the success rate of EAOGE is over 10 times than usual GEP.

title: Function Finding and Constants Creation Method in Evolutionary Algorithm Based on Overlapped Gene Expression
abstract: Evolutionary algorithm based on overlapped gene expression (EAOGE) is a new technology of evolutionary algorithm which is inspired by the overlap gene expression in biological research. Different from existing works, EAOGE suggests a new expression structure of genes, and these genes have a probability to overlapped express in some segments. It uses chromosomes of fixed length to represent expression trees of different shapes and sizes. It does unconstrained search in the genome space and still ensures validity of the expression. This paper implements EAOGE algorithm and proposes a new constants creation method. Extensive experiments show that the method significantly improves the precision in the problems of function finding, and the precision of the new method is about 12.8 times to traditional algorithm at least.

title: A Hybrid Restoration Scheme Based on Threshold Reaction Time in Optical Burst-Switched Networks
abstract: Optical Burst-Switched (OBS) networks usually employ one-way reservation by sending a burst control packet (BCP) with a specific offset time, before transmitting each data burst frame (BDF). Therefore, a fiber link failure may lead to loss of several BDFs as the ingress nodes sending these BDFs remain unaware of the failed link till they receive the failure indication signal (FIS). In this paper, we propose a hybrid restoration scheme, wherein we employ a novel combination of sub-path as well as path restoration schemes. In particular, the upstream node preceding the failed link employs sub-path restoration as soon as it detects the failure. Thereafter, when the source ingress node receives the FIS message, it (source ingress node) takes over the responsibility and employs path restoration. However, sub-path restoration is exercised by the upstream node only if the remaining time intervals left for the yet-to-arrive BDFs (for which BCPs have already left the upstream node) are less than a minimum time interval (called as threshold reaction time). Performance of the proposed scheme is evaluated through extensive computer simulation using OPNET. Our results indicate that the proposed restoration scheme significantly reduces burst losses due to link failure and, thus, outperforms the existing restoration schemes.

title: Factor analysis based anomaly detection
abstract: We propose a novel anomaly detection algorithm based on factor analysis and Mahalanobis distance. Factor analysis is used to uncover the latent structure (dimensions) of a set of variables. It reduces attribute space from a larger number of variables to a smaller number of factors. The Mahalanobis distance is used to determine the "similarity" of a set of values from an "unknown" sample to a set of values measured from a collection of "known" samples. Combined with factor analysis, Mahalanobis distance is extended to examine whether a given vector is an outlier from a model identified by "factors" based on factor analysis. We present a factor analysis-based network anomaly detection algorithm and apply it to DARPA intrusion detection evaluation data. The experimental results show that the proposed algorithm is able to detect network intrusions with relatively low false alarms.

title: Landmine Feature Extraction and Classification of GPR Data Based on SVM Method
abstract: In this paper, the problem of detecting buried landmine is tackled in the feature extraction and classification. Determining the likelihood set of an unknown pattern (feature vector), extracted from ground penetrating radar data by using SVM method. The advantage of SVM method in feature extraction and classification of image processing is: A classifier works well both on the training samples and on previously unseen samples; In addition, the SVM provides, enable a classification performance improvement based on from high feature dimensions to two or three feature dimensions. Finally, SVM method has a standard theory and a good implementation algorithm.

title: Simplifying the Reconstruction of 3D Models using Parameter Elimination
abstract: Reconstructing large models from images is a significant challenge for computer vision, computer graphics, and related fields. In this paper, we present an approach for simplifying the reconstruction process by mathematically eliminating external camera parameters. This results in less parameters to estimate and in an overall significantly more robust and accurate reconstruction. We reformulate the problem in such a manner as to be able to identify invariants, eliminate superfluous parameters, and measure the performance of our formulation under various conditions. We compare a two-step camera orientation-free method, where the majority of the points are reconstructed using a linear equation set, and a camera position-and- orientation free method, using a degree-two equation set. Both approaches use a full perspective camera and are applied to synthetic and real-world datasets.

title: A Pi-calculus-Based Business Process Formal Design Method
abstract: The pi-calculus is a model of concurrent computation, which can express communication between correlation processes. It provides a way of constructing systems in a bottom-up manner by composing subsystems, and it also has strict formal semantics. Therefore, pi-calculus is very suitable for designing business processes which are presently becoming more and more complicated. The paper proposes a composition-oriented approach to business process design based on pi-calculus theory and gives the formal definition of business process. It also illustrates the design method by simulating an example of order processing. By this method, a complicated business process can be divided into several independent simple sub-modules, which can greatly reduce the complexity of business process design.