CACS: A Novel Classification Algorithm Based on Concept Similarity
This paper proposes a novel algorithm of classification based on the similarities among data attributes. This method assumes data attributes of dataset as basic vectors of m dimensions, and each tuple of dataset as a sum vector of all the attribute-vectors. Based on transcendental concept similarity information among attributes, this paper suggests a novel distance algorithm to compute the similarity distance of each pairs of attribute-vectors. In the method, the computing of correlation is turned to attribute-vectors and formulas of their projections on each other, and the correlation among any two tuples of dataset can be worked out by computing these vectors and formulas. Based on the correlation computing method, this paper proposes a novel classification algorithm. Extensive experiments prove the efficiency of the algorithm.

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: Similarity computing model of high dimension data for symptom classification of Chinese traditional medicine
abstract: 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: Novel view generation from two reference images based on the same optical axis
abstract: As hopping and walking through different cylindrical panoramic viewpoints, we can feel the view-field jumping between the former and the subsequent view. Based on the same optical axis in the general planar-pinhole camera model, an algorithm is proposed for generating novel views from the two reference images in this paper. On the supposition that the two reference images have the same intrinsic parameter matrix, a warping equation is derived to map points from a reference image to a desired image. The point correspondences can be determined between the two reference images by using the method based on the pairs of feature line segments. The sequence of warping images from each reference image can be computed. Then the corresponding warping images are cross-dissolved to generate the novel views. The problems such as seams, holes, folds and irregular points are analyzed and resolved to generate convincing novel views.

title: Increase of potential intellectual bandwidth in a scientific community through implementation of an end-user information system
abstract: Qureshi, et al. (2002) presented a case study where they used a framework, the intellectual bandwidth model to measure an organization's ability to create value. The model consists of two dimensions, information assimilation and collaboration. The IB model is a useful tool for managers because it enables them to plan solutions to complex business problems and to easily communicate these plans to key individuals. The model can also serve as a guide when making investment decisions. While it is important to increase an organization's intellectual bandwidth, it is equally important to assure that the resulting increase is employed to improve the organization's ability to innovate since, as it has been shown, innovation is the number one creator of organizational wealth (Baum et al., 2000). In this paper we discuss how intellectual bandwidth as proposed by Nunamaker et al. (2001) can be extended beyond the highest level of information assimilation identified in the model, automated sense-making, to ensure implementation of product innovations. To do this we propose adding two new information assimilation features, automated utilization and automated implementation. A real business example from the bioscience industry is presented in order to demonstrate how the IB model with extensions can be used. The study discusses an end-user information system, its current use and its potential to enhance an organization's value-creating capability.

title: Multi-signers Strong Designated Verifier Signature Scheme
abstract: Designated verifier signature (DVS) does not provide non-repudiation property of traditional digital signatures. Multi-signers strong designated verifier signature (MS-SDVS) scheme is required in some cases for the privacy consideration. In this paper the definition and security model of MS-SDVS scheme are presented, and a concrete MS-SDVS scheme is proposed from bilinear pairing. The proposed scheme has the property of non-transferability and unforgeability in the random oracle model under the assumption of the bilinear computational Diffie-Hellman problem (BCDH). This is the first MS-SDVS scheme. The signature length of the proposed scheme is independent of the number of signers, and the proposed scheme can be modified to a multi-signature scheme.

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: Application of Immune Classifier Based on Increment of Diversity in the Model Species Genomes Identification
abstract: The model species genomes project has played a very important role in the research of human genomes. By comparing and identifying the genomes' information in the different biological evolution stages with the model species genomes, it is favorable to understand deeply the genomes' structure and function of the advanced species, especially the human being, and to reveal the life's essential law. In the paper, the DNA sequences of three model species including C. elegans, S. cerevisiae and A.thaliana are divided into three types: intron, exon, intergenic DNA at first. The bases of each sequence are given number (1, 2, hellip, N) base by base from the beginning. According to three phases of the codons, we can extract three subsequences which numbers are respectively 3n+1, 3n+2 and 3n+3 (n=0, 1, 2, ...,N/3-1). Calculating respectively the probability of four bases in three subsequence we can attain 12 parameters which are regarded as the state parameters of the diversity sources. Each sequence of intron, exon and intergenic DNA is expressed by a 12-dimension eigenvector. All of these sequences' 12-dimension samples are divided into the training sample set and the testing sample set. Then, according to the immune network theory, a immune classifier based on the diversity increment is constructed, which considers the reciprocal of the diversity increment as affinity function and makes the immune network evolve in the direction of identifying the antigens by submitting unceasingly the antigens one by one, which is a record of the training sample set. Finally, the classifier has high performance and its prediction accuracy is up to 85% by test.