Web Information-Extraction Based on Vision Block and Multi-Features

随着信息社会的快速发展，web数据已经发展成为一种巨大的信息资源。Web信息抽取作为一种从web数据中抽取主题信息的研究内容，是数据分类、自然语言处理等研究领域的基础。因此，如何准确快速的从海量的web数据中抽取关注的信息变得越来越重要。本文对web信息抽取的方法进行了研究，并针对研究过程中遇到的问题，提出相应的解决方法。本文的主要研究内容如下： （1）对已存在的各种web信息抽取算法做出了详细的研究比较。 （2）本文的主要目的是对具有主题信息的主题型网页进行正文抽取，而对于链接型网页不予处理。因此要先判断输入网址的网页类型。本文对两种网页进行了详细的比较，提炼出五个明显的特征，并提出一种...With the rapid development of information society, the web data has developed into a huge information resource. Web information-extraction is one Research based on extracting theme information from web data set, it is the basis of data classification, natural language processing and some other research areas. Therefore, how extract concerned information from the vast amounts of web data fast and a...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机应用技术学号：2302009115276

Beauty, Symmetry, and Magnetocaloric Effect-Four-Shell Keplerates with 104 Lanthanide Atoms

通讯作者地址: Kong, XJThe hydrolysis of Ln(ClO4)(3) in the presence of acetate leads to the assembly of the three largest known lanthanide-exclusive cluster complexes, [Nd-104(ClO4)(6)(CH3COO)(60)(mu(3)-OH)(168)(mu(4)-O)(30)(H2O)(112)].(ClO4)(18).(CH3CH2OH)(8).xH(2)O (1, x approximate to 158) and [Ln(104)(ClO4)(6)(CH3COO)(56)(mu(3)-OH)(168)(mu(4)-O)(30)(H2O)(112)].(ClO4)(22).(CH3CH2OH)(2).xH(2)O (2, Ln = Nd; 3, Ln = Gd; x approximate to 140). The structure of the common 104-lanthanide core, abbreviated as Ln(8)@Ln(48)@Ln(24)@Ln(24), features a four-shell arrangement of the metal atoms contained in an innermost cube (a Platonic solid) and, moving outward, three Archimedean solids: a truncated cuboctahedron, a truncated octahedron, and a rhombicuboctahedron. The magnetic entropy change of Delta Sm = 46.9 J kg(-1) K-1 at 2 K for Delta H = 7 T in the case of the Gd-104 cluster is the largest among previously known lanthanide-exclusive cluster compounds.973 Project from the Ministry of Science and Technology of China 2012CB821704 2014CB845601 National Natural Science Foundation of China 21422106 21371144 21431005 21390391 Foundation for the Author of National Excellent Doctoral Dissertation of China 201219 U.S. NSF APVV-0132-11 3001690 Czech Research Infrastructures LM201102

《大学生心理健康》课程考核改革与创新的研究——以仰恩大学教学实践为例

摘要：《大学生心理健康》是各高校有效推进心理健康教育工作的主要途径,是以提高学生的心理健康水平和培养积极的心理品质为课程目标的一门课程。忽略本课程的特殊性,采取如同其他课程的常规考核方法不能真实、可靠地把握学生的心理健康水平,也没有遵循本课程的科学性与发展性的课程目标。积极心理学的理念是通过不断地对人自身的积极因素的激发,并帮助学生利用这些积极因素来最大限度地挖掘自己的积极力量和培养积极的心理品质,从而促进学生顺利适应与主动发展。因此,我们《大学生心理健康》课程是以积极心理学为课程的核心理念,思考与讨论在实际教学中针对常规课程考核方法存在的诸多不足,来探究一套切实可行的课程考核方法,真正实现《大学生心理健康》的科学性与发展性的课程目标。</p

A Text Feature Selection Method Based on TongYiCi CiLin

特性选择是文本分类、机器学习以及模式识别领域的重要问题之一.特征选择能在保证数据完整性的情况下减少高维数据的特征维数,同时提高分类的精度.以往提出的基于同义词词林的特征选择方法虽然能有效避免提取出的特征值在概念上的重复性,但并未考虑到权值最优的特征向量构成的子集可能并非是最优的.为了解决此问题,结合同义词和遗传算法,提出了一种新的基于同义词词林的文本特征选择方法.该方法首先对特征词进行同义词过滤、合并,在降低特征向量维度的同时避免了同义词带来的影响.然后采用改进的遗传算法选出具有较好适应度值的特征向量.实验结果表明,这种方法较之以往提出的方法,在保证特征选择准确率的基础上能明显地减小特征向量的维度.Feature selection is one of important problems in text categorization,machine learning and pattern recognition.In particular,with the rapid development of network and cloud computing,the massive data analysis methods are vitally important.Feature selection can reduce high dimension data′s feature dimension under the condition of ensuring data integrity and classification accuracy.Previously proposed feature selection method based on TongYiCi CiLin can effectively avoid the eigenvalue repetitive in concept,but they did′t consider about that subset composed by the optimal weight of feature vectors may not the best one.To solve this problem,this article combine the TongYiCi and Genetic Algorithm,proposed a text feature selection method based on TongYiCi CiLin.The experiment results show that the method can reduce feature vector′s dimension and improve the efficiency of feature selection.国家自然科学基金项目(50604012