浅谈视觉传达设计基础课“书法艺术”教学

视觉传达设计中字体形式主要有装饰性美术字和中国书法两大基本类型,而书法是中国文化的主要载体,也是汉字表达情感的一门艺术。建立书法艺术观,辅之以艺术构成理论和相关美学原理,引导学生对书法内在规律和技巧能动地理解和能动地接受,强化视觉感悟。     

基于类间分散度和类内集中度的文本特征选择。

特征选择是文本分类的关键步骤之一,所选特征子集的优劣直接影响文本分类的结果。论文首先定义了两种特征分类能力：一种是特征对类间文档的分散程度,该分散度越大越好;另一种是特征对类内文档的聚集程度,该集中度越大越好。然后把这两种特征影响度有机地结合起来设计了一个新的特征选择方法,该方法能够对所选特征进行综合考虑,从而使获得的特征集具有较好的代表性。仿真实验表明所提特征选择方法在一定程度上能够提高文本分类性能。     

论理工类工业设计的素描教学改革

理工类学生由于入学前很少经过系统的美术专业训练,造型基础十分薄弱,但其在毕业时,其设计能力并不比文科生艺术生要求低,甚至在当今社会中,很大一部分企业对有着理工背景的专业设计人才的要求日益迫切。如何尽快地使其在长期固化的理性思维向活泼开放的感性思维,这是一个新的领域和挑战。素描作为其学习的第一门专业基础课,其改革势在必行。     

基于Sql Server BI平台和决策树技术的CET-4成绩分析

文章首先对Sql Server 2008商业智能平台及决策树技术相关理论作了介绍,然后对挖掘数据源数据进行了一系列预处理。利用Sql Server 2008商业智能平台下的决策树技术并采用数据挖掘扩展语言即DMX语言创建了CET-4成绩分析决策树模型。分类矩阵和挖掘提升图分别对该模型的评估结果表明了模型具有较高的可靠性和分类准确度,同时模型对应的一些规则可作为英语教学管理和改革的重要参考依据。     

Intranet搜索引擎设计与实现

随着企业内部网络应用的深入,Intranet内部的信息资源越来越庞大,怎样为这些数据信息构建索引是我们面If缶的主要任务,而全文检索的产生解决了这一问题。目前越来越多的中小企业采用Linux系统作为Web平台,并且利用检索系统来管理内部繁多的文本和HTML文件。文章对www搜索引擎的全文检索及其相关技术进行了分析和讨论,实现了一个基于Linux环境的Intranet搜索引擎LISE（Linux Intranet Search Engine）。LISE利用了基于词表的索引方法,为用户提供了更加准确的信息,能满足多种中小企业用户的需求。     

基于B/S网上教学资源管理平台的分析与设计

随着信息化技术的不断发展,网上教学资源管理平台已经成为目前高校教学中非常重要的工具,尤其是在当前信息技术和计算机网络技术飞速发展的时代,高校开始充分利用现有网络资源来进行教学资源管理。区别于传统的C/S模式,B/S开发模式使得高校内部用户通过校园网或者互联网就可访问本平台。本文就基于B/S模式的网上教学资源管理平台进行详细分析。     

利用二进制数算法寻找5个点的Ramsey图

用二进制数来表示完全图的各种组合,借助计算机编程可从完全图的各种组合中寻找出ramsey图。5个顶点的Ramsey图是最基本的Ramsey图,这种基本方法为寻找更复杂的Ramsey图提供了基础。     

Parametric 3D modeling of young women's lower bodies based on shape classification

Three-dimensional (3D) human body modeling is an important research direction in the field of clothing virtual design. On the basis of 3D human body scanning, this paper studied a method to build a 3D parametric lower body model according to body classification. The research includes three main parts. (1) Anthropometry and body shape classification. We randomly selected 333 young women ages 18–25 years old in Northeast China as the experimental sample. Then we divided the lower body shape into three categories using principal component analysis and K-means clustering. (2) Determination of feature cross sections and points, and reconstruction of feature curves. According to the average values of each body type, we obtained the mean reference body by Euclidean distance method. We determined feature cross sections and points, and extracted the 3D coordinates of the feature points of the mean reference body to reconstruct the feature curves. (3) The surface lofting and establishment of parametric 3D lower body model. According to the shape characteristics of the lower body, we constructed the guiding lines for the crotch and lower limbs, and established parametric lower body models for three body types.Relevance to industry3D human modeling is an important part of garment industry digitization. This research provides an effective way to construct a parametric 3D lower body model. The method offers a reference for the parametric virtual human modeling and virtual fitting of trousers.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.     

浅析管理在信息系统安全中的必要性

在信息系统安全规划和设计中,管理作为信息系统的安全基础和信息系统各安全层面的基本要素,已经成为构建安全、可用信息系统不可或缺的必要条件。文章针对管理在信息系统安全中的作用、特点进行了论述,并结合等级保护政策提出了相应的管理建议。