基于.NET架构高校教学资源库系统研究

.NET的架构下的高校教师信息管理的实现,接着讲解.NET的架构下的学生信息管理的实现,最后说明.NET的架构的学生作业管理系统的实现,还介绍了NET的架构的跨校区合作、交流的实现。     

Effects of computers on Japanese schools

In this paper I consider how the computer can or should be accepted in Japanese schools. The concept of teaching in Japan stresses learning from a long-term perspective. Whereas in the instructional technology, on which the CAI or the Tutoring System depends, step-by-step attainments in relatively short time are emphasized. The former is reluctant in using the computer, but both share the Platonic perspective which are goal-oriented. However, The Socratic teacher, who intends to activate students' innate disposition to be better, would find another way of teaching and use of the computer.     

Mine,ours, and yours: Whose engagement and prior knowledge affects individual achievement from online collaborative learning?

In computer-supported collaborative learning research, studies examining the combined effects of individual level, group level and within-group differences level measures on individual achievement are scarce. The current study addressed this by examining whether individual, group and within-group differences regarding engagement and prior knowledge predict individual achievement. Engagement was operationalised as group members' exhibited activities in the task space (i.e., discussing domain-content) and social space (i.e., regulating ideas, actions and socioemotional processes). Prior knowledge and achievement were operationalised as group members' performance on a domain-related pre-test and post-test, respectively. Data was collected for 95 triads of secondary education students collaborating on a complex business-economics problem. Subsequently, three different multilevel models were tested to examine the combined effect. First a model with the individual level measures (model 1) was tested and in subsequent models the group level measures (model 2) and within-group levels measures (model 3) were added. Findings indicate model 2 showed the best fit; group members' individual engagement in the social space activities as well as the groups' average prior knowledge positively predicts individual achievement. No effects were found for either group members' or groups' engagement in the task space and for the within-group differences.     

众筹项目推荐:面向隐式反馈的深度学习协同过滤

目前,在推荐系统研究中,用户的隐式反馈,以及极度稀疏的数据,已成为影响协同过滤推荐效果的主要问题.针对这一现象,本文提出了深度学习协同过滤算法,先利用卷积神经网络,对用户-项目矩阵的隐层特征进行学习,再结合协同过滤,对用户-项目的交互信息进行建模,并将两种特征融合预测推荐列表.以众筹平台的数据为实验对象,比较模型中各参数对推荐效果的影响,并设计与基线方法的对比实验.实验结果表明:均匀采集负反馈,并在一定卷积层数的网络中,数据稀疏度越高,效果越好;对比基线方法,本文提出的算法在公开数据集(Yahoo!Movie)上取得了最好的推荐结果.本文提出的算法有助于提高众筹平台的融资成功率,同时也丰富了推荐系统的研究体系.     

Graph-Based Dimensionality Reduction for Hyperspectral Imagery: A Review

Hyperspectral image(HSI) contains a wealth of spectral information, which makes fine classification of ground objects possible. In the meanwhile, overly redundant information in HSI brings many challenges. Specifically, the lack of training samples and the high computational cost are the inevitable obstacles in the design of classifier. In order to solve these problems, dimensionality reduction is usually adopted. Recently, graph-based dimensionality reduction has become a hot topic. In this paper, the graph-based methods for HSI dimensionality reduction are summarized from the following aspects. 1) The traditional graph-based methods employ Euclidean distance to explore the local information of samples in spectral feature space. 2) The dimensionality-reduction methods based on sparse or collaborative representation regard the sparse or collaborative coefficients as graph weights to effectively reduce reconstruction errors and represent most important information of HSI in the dictionary. 3) Improved methods based on sparse or collaborative graph have made great progress by considering global low-rank information, local intra-class information and spatial information. In order to compare typical techniques, three real HSI datasets were used to carry out relevant experiments, and then the experimental results were analysed and discussed.Finally, the future development of this research field is prospected.     

基于BC聚类的差分隐私保护推荐算法

为提高差分隐私保护下推荐算法的准确性,提出了一种考虑差分隐私保护的基于Bhattacharyya系数（BC）的聚类推荐算法.以BC作为项目相似性度量的标准,根据BC相似性对项目进行K-medoids聚类,并在聚类簇中进行私有项目邻居选择.最后,根据最近邻居集信息,对用户的评分进行预测和Top-n推荐.提出的方案有效地克服了已有方法中存在的相似性度量依赖于共同评分的问题,提高了相似性度量的准确性,有效避免了因隐私保护而造成的最近邻居集质量下降的问题.理论分析和实验测试的结果表明,该方法在实现隐私保护的同时还能有效保证推荐的高质量,较好地实现了隐私保护和数据效用之间的平衡,具有良好的应用潜力.     

大型非周期相控阵天线协同设计

针对大型非周期相控阵天线的特点,提出一种多学科、多领域协同设计的方法。然后运用该方法指导两个产品天线设计,围绕协同设计的典型流程和几个关键控制点展开论述。在测试阶段实测天线的主要指标,天线1辐射端面平面度≤1.4 mm,栅瓣电平≤-20 dB,天线2辐射端面平面度≤1.1 mm,栅瓣电平≤-18 dB,实测数据与仿真结果吻合,各项指标优于设计输入要求,使用性能良好,验证协同设计方法的有效性。最后指明大型非周期相控阵天线协同设计的进一步研究方向。     

产品形态设计中的协同工作研究

计算机支持的协同工作CSCW(Computer Supported Cooperrative Work)随着计算机和通信突飞猛进的发展已经成为近些年来的热点课题,在产品形态设计中开展研究协同工作技术就具有非常重要的意义.文章针对产品设计协同工作的需求,结合产品设计的特点,研究建立了产品形态设计协作模型,论述了基于Web的开发应用技术和开发特征.为解产品形态设计的协同问题,提供了可行的新模式,给出了利用Web现有资源以及低成本技术构建产品开发项目协同设计环境的一种新的开发研究方向.     

论我国高校教师的职业生涯规划激励机制

高校教师的特性需要进行职业生涯规划。行之有效的职业生涯规划对实现高校组织的可持续发展和教师个人的发展意义重大。本文首次运用激励理论从高校组织职业规划和教师自我职业规划两个层面构建了高校教师职业生涯规划的激励机制。     

Determination of tetrachloroethylene—Results of a collaborative study and the standardized method

The development, by collaborative study, of a standardized method for the determination of tetrachloroethylene in olive oils is described. The results of the study, which was carried out in 1989 under the direction of the International Olive Oil Council (IOOC), are presented and show that tetrachloroethylene can be readily determined, to an acceptable degree of precision, by gas-liquid chromatography with electron capture detection. A limited number of results obtained for the determination of trichloroethylene and chloroform by the same method indicate that a similar precision could be obtained with the method when determining these solvents also.