Chinese-based research on subject-covered information retrieval supervised by textual semantic domain

By means of analysis of artificial intervention in ready-retrieved text, training set used to compare with new texts from large-scale real texts corpus is provided. It is based on the data-originated presentation of training set that a special formula to calculate semantic cohesion between new texts and training set is devised. The semantic cohesion of new text is the average value of semantic evaluation of all elements involved, and semantic evaluation of an element depends on its semantic relevance with the training set and on the semantic ratio of its domain to synonymous domain. In terms of empirical verification a conclusion is drawn that semantic cohesion is the key measurement standard of textual retrieval. Despite the advantages of textual retrieval, limitations of formula-raised condition and analyst＇s accomplishments make the analysis involved in this paper imperfect.     

基于投影匹配的钢坯端面字符快速识别方法

由于高温及复杂光照的干扰等成像环境的影响,钢坯端面字符自动识别存在很多困难,其实时自动识别难度大。现存的字符识别方法难以适应实际生产现场的需求。文章提出了一种基于投影匹配的快速识别方法,通过将图象二值化后进行垂直投影,直接将一维投影结果进行模板匹配,降低运算维数。大量的现场实验证明,该方法可有效的减少识别时间,提高生产效率,字符的拒识率和误识率较低,可满足生产线的实时要求。     

Cryo-EM image alignment based on nonuniform fast Fourier transform

In single particle analysis, two-dimensional (2-D) alignment is a fundamental step intended to put into register various particle projections of biological macromolecules collected at the electron microscope. The efficiency and quality of three-dimensional (3-D) structure reconstruction largely depends on the computational speed and alignment accuracy of this crucial step. In order to improve the performance of alignment, we introduce a new method that takes advantage of the highly accurate interpolation scheme based on the gridding method, a version of the nonuniform fast Fourier transform, and utilizes a multi-dimensional optimization algorithm for the refinement of the orientation parameters. Using simulated data, we demonstrate that by using less than half of the sample points and taking twice the runtime, our new 2-D alignment method achieves dramatically better alignment accuracy than that based on quadratic interpolation. We also apply our method to image to volume registration, the key step in the single particle EM structure refinement protocol. We find that in this case the accuracy of the method not only surpasses the accuracy of the commonly used real-space implementation, but results are achieved in much shorter time, making gridding-based alignment a perfect candidate for efficient structure determination in single particle analysis.     

Horizontal Voting Ensemble Based Predictive Modeling System for ColonCancer

Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance according to the combined models from the last epochs of the proposed architecture. Ten thousand (10000) microscopic images are taken to test the classification performance of the proposed PMS with the HVE method. The microscopic images obtained from the colon tissues are classified into ACA or benign by the proposed PMS. Results prove that the proposed PMS has ~8% performance improvement over the architecture without using the HVE method. The proposed PMS for colon cancer reduces the misclassification rate and attains 99.2% of sensitivity and 99.4% of specificity. The overall accuracy of the proposed PMS is 99.3%, and without using the HVE method, it is only 91.3%.     

创新实践教学模式,培养复合型人才

针对目前高校现行实践教学体制存在的弊端,在分析产生原因的基础上,提出一种科学的实践教学模式,该模式与信息与计算科学专业培养的目标以及社会发展的内在需求相结合,在信息处理和软件开发能力培养方面取得一定效果,增强了学生就业竞争力。     

基于CBS模式的档案管理数字化系统的研究

随着我国经济和社会的飞速发展.人民对信息的要求日益提高.档案管理在这种新的形势下.面临巨大的挑战.传统的档案管理已经不能适应新形势的发展,档案管理数字化系统进行研究势在必行.本文主要对档案管理的涵义、数字化系统方案设计.文档图像处理和系统软件部分等进行了相应的阐述.本文的研究对以后指导档案管理工作具有一定的理论意义和现实意义.     

Embedding multi-dimensional meshes into twisted cubes

The twisted cube is an important variant of the most popular hypercube network for parallel processing. In this paper, we consider the problem of embedding multi-dimensional meshes into twisted cubes in a systematic way. We present a recursive method for embedding a family of disjoint multi-dimensional meshes into a twisted cube with dilation 1 and expansion 1. We also prove that a single multi-dimensional mesh can be embedded into a twisted cube with dilation 2 and expansion 1. Our work extends some previously known results.     

Video analysis based on volumetric event detection

During the past decade, feature extraction and knowledge acquisition based on video analysis have been extensively researched and tested on many applications such as closed-circuit television(CCTV)data analysis, large-scale public event control, and other daily security monitoring and surveillance operations with various degrees of success. However, since the actual video process is a multi-phased one and encompasses extensive theories and techniques ranging from fundamental image processing, computational geometry and graphics, and machine vision, to advanced artificial intelligence, pattern analysis, and even cognitive science, there are still many important problems to resolve before it can be widely applied. Among them, video event identification and detection are two prominent ones. Comparing with the most popular frame-to-frame processing mode of most of today's approaches and systems, this project reorganizes video data as a 3D volume structure that provides the hybrid spatial and temporal information in a unified space. This paper reports an innovative technique to transform original video frames to 3D volume structures denoted by spatial and temporal features. It then highlights the volume array structure in a so-called "pre-suspicion" mechanism for a later process. The focus of this report is the development of an effective and efficient voxel-based segmentation technique suitable to the volumetric nature of video events and ready for deployment in 3D clustering operations. The paper is concluded with a performance evaluation of the devised technique and discussion on the future work for accelerating the pre-processing of the original video data.     

Defining syntax-directed translations by tree bimorphisms

We introduce a class of tree bimorphisms that define exactly the translations performed by syntax-directed translation schemata. We also show that these “quasi-alphabetic” tree bimorphisms preserve recognizability, and that their class is closed under composition and inverses.