基于知识点的试卷质量分析研究

通过对现有试卷质量评测标准的介绍,指出其中存在的不足,并以此引入知识点因素对评测标准加以改进。知识点的引入使评测粒度更加细腻,评价依据更加准确有效,能够真正反映出试卷的实际难度。最后根据评测标准的自身特点。利用OLAP技术对其加以实现,通过实践证明评测标准的有效性。     

Al-Si合金活塞金属型铸造工艺和模具设计智能化

为了开发Al-Si合金活塞金属型铸造工艺和模具智能化设计系统，在对国内外先进设计经验总结、归纳的基础上，采用面向对象的知识表达方法，建立了模块化的知识库。系统应用了基于实例的推理技术并建立了实例库和实例原型库。通过开发的文件接口程序，完成了一般设计型专家系统与传统CAD系统的连接，实现了智能化设计。通过与现场使用的设计方案比较，验证了本系统的有效性和实用性。     

地方本科院校计算机类专业人才培养模式的探索与实践

地方本科院校计算机类专业承担着为我国培养计算机应用型人才的重任,探索新时期下计算机应用型人才的培养模式,其根本出路就是坚持"以就业为导向、以服务为宗旨,走产学研结合之路"。在培养目标上,目前应分为以下4个方向:计算机硬件维护与应用、计算机网络安全技术、计算机程序开发技术、计算机信息管理。在理论教学上采取"基本素质模块"+"专业基础模块"+"专业主干模块"+"专业创新模块";在实践教学上采取"基本技能"+"专业技能"+"开发设计技能"+"岗位综合应用技能"。     

知识的属性扰动规律的不等式关系

知识的属性扰动引起知识规律的变化,这些变化的规律称为扰动规律.利用单向S-粗集和单向S-粗集对偶,给出属性扰动的知识规律的上规律.下规律的概念,并对知识规律与属性扰动的知识规律进行了讨论,提出了属性扰动知识规律(上规律,下规律)的不等式定理.     

基于SharePoint平台的知识协同管理系统的设计

本文从湖北广播电视总台技术管理服务中心知识协同管理系统建设的实践出发,阐述了团队协同的重要性,介绍了基于SharePoint开发的知识协同管理系统,包括系统技术架构和知识存储、知识发现和测量、团队协作、权限管理以及系统管理五大功能模块。     

Dominance-based rough set approach and knowledge reductions in incomplete ordered information system

Many methods based on the rough set to deal with incomplete information systems have been proposed in recent years. However, they are only suitable for the incomplete systems with regular attributes whose domains are not preference-ordered. This paper thus attempts to present research focusing on a complex incomplete information system—the incomplete ordered information system. In such incomplete information systems, all attributes are considered as criterions. A criterion indicates an attribute with preference-ordered domain. To conduct classification analysis in the incomplete ordered information system, the concept of similarity dominance relation is first proposed. Two types of knowledge reductions are then formed for preserving two different notions of similarity dominance relations. With introduction of the approximate distribution reduct into the incomplete ordered decision system, the judgment theorems and discernibility matrixes associated with four novel approximate distribution reducts are obtained. A numerical example is employed to substantiate the conceptual arguments.     

Document recommendation for knowledge sharing in personal folder environments

Sharing sustainable and valuable knowledge among knowledge workers is a fundamental aspect of knowledge management. In organizations, knowledge workers usually have personal folders in which they organize and store needed codified knowledge (textual documents) in categories. In such personal folder environments, providing knowledge workers with needed knowledge from other workers’ folders is important because it increases the workers’ productivity and the possibility of reusing and sharing knowledge. Conventional recommendation methods can be used to recommend relevant documents to workers; however, those methods recommend knowledge items without considering whether the items are assigned to the appropriate category in the target user’s personal folders. In this paper, we propose novel document recommendation methods, including content-based filtering and categorization, collaborative filtering and categorization, and hybrid methods, which integrate text categorization techniques, to recommend documents to target worker’s personalized categories. Our experiment results show that the hybrid methods outperform the pure content-based and the collaborative filtering and categorization methods. The proposed methods not only proactively notify knowledge workers about relevant documents held by their peers, but also facilitate push-mode knowledge sharing.     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Conceptual equivalence for contrast mining in classification learning

Learning often occurs through comparing. In classification learning, in order to compare data groups, most existing methods compare either raw instances or learned classification rules against each other. This paper takes a different approach, namely conceptual equivalence, that is, groups are equivalent if their underlying concepts are equivalent while their instance spaces do not necessarily overlap and their rule sets do not necessarily present the same appearance. A new methodology of comparing is proposed that learns a representation of each group’s underlying concept and respectively cross-exams one group’s instances by the other group’s concept representation. The innovation is fivefold. First, it is able to quantify the degree of conceptual equivalence between two groups. Second, it is able to retrace the source of discrepancy at two levels: an abstract level of underlying concepts and a specific level of instances. Third, it applies to numeric data as well as categorical data. Fourth, it circumvents direct comparisons between (possibly a large number of) rules that demand substantial effort. Fifth, it reduces dependency on the accuracy of employed classification algorithms. Empirical evidence suggests that this new methodology is effective and yet simple to use in scenarios such as noise cleansing and concept-change learning.