小学语文课外阅读中协同知识建构的特征研究

小学阶段是培养学生阅读兴趣的重要阶段,这一阶段应该以多种形式开展课外阅读以培养学生的阅读素养。知识建构在培养学生语言能力、审美、鉴赏、创造思维、文化传承与理解方面发挥着重要的作用,在小学语文课外阅读中有广泛的应用空间。研究以小学五年级所开展的课外阅读为例,对学生在课外阅读中协同知识建构过程进行了分析,发现课外阅读过程中的协同知识建构具有过程性、自发性、可持续发展性的特征,对培养学生的口语表达能力、审美能力、创作能力和人际交往能力有积极的作用。     

我国青少年亲社会行为培养研究

青少年亲社会行为培养关涉一个民族、国家的未来和兴衰.青少年亲社会行为的深层次价值体现,乃是青少年对民族、国家的认同意识、忧患意识和担当意识的自觉与觉醒.对于青少年的亲社会行为培养,主要以坚持社会主义核心价值观为思想前提;以因循传统“知行合一”理论为培养的基本原则;以面向现实人生为培养的关键和落脚点,共同构建青少年的亲社会行为培养模式。     

基于非负张量表示的扩展LDA标注模型

由于“语义鸿沟”的存在,自动图像标注是一项极具挑战性的工作。考虑到图像低层视觉特征与高层语义概念的差异,分别从图像表示与语义建模两个方面来实现自动图像标注。在图像表示方面,提出了一种正则化约束下的非负张量表示方法,用以提取符合人眼视觉直观理解的图像高阶结构特征。在语义建模方面,提出了一种三层贝叶斯模型——扩展隐Dirichlet分配。该模型利用隐变量来实现图像与标注词的关联,并通过一种基于变分推理的期望最大值方法来估计其参数。实验结果表明,ELDA模型在大规模数据库NUS-WIDE上的标注结果相较于现有方法有了显著的提高。     

The graph association problem: Mathematical models and a lagrangian heuristic

Graph association is the problem of merging many graphs that collectively describe a set of possibly repetitive entities and relationships into a single graph that contains unique entities and relationships. As a form of data association, graph association can be used to identify when two sensors are observing the same object so information from both sensors can be combined and analyzed in a meaningful and consistent way. Graph association between two graphs is related to the problem of graph matching, and between multiple graphs it is related to the common labeling of a graph set (also known as multiple graph matching) problem. This article contribution is to formulate graph association as a binary linear program and introduce a heuristic for solving multiple graph association using a Lagrangian relaxation approach to address issues with between‐graph transitivity requirements. The algorithms are tested on a representative dataset. The developed model formulation was found to accurately solve the graph association problem. Furthermore, the Lagrangian heuristic was found to solve the developed model within 3% of optimal on many problem instances, and found better solutions to large problems than is possible by directly using CPLEX. © 2013 Wiley Periodicals, Inc. Naval Research Logistics, 2013     

The Israel Defense Forces as an epistemic authority: An intellectual challenge in the reality of the Israeli – Palestinian conflict

The changes in the nature of warfare and its transformation toward Low Intensity Conflict (LIC) intrastate conflict have challenged the patterns of interaction between the political and the military echelons in Israel. It seems that the political echelon's superiority is maintained at the institutional and formal levels, but on the substantive level, which demands relying on knowledge and systematic staff work, the political echelon's position is weakened and loses its validity.

Introducing the military echelon in Israel as an epistemic authority regarding the violent confrontation and the main outlines of the military knowledge development process might clarify why the absence of the required dialogue between the echelons and the weakness of the intellectual effort increased the military's influence over the shaping of Israeli conflict-management strategy. The argument's validity and its explanatory power can be found relevant for other countries whose militaries are deeply involved in the management of LIC.     

新一代指挥信息系统中的语义网应用能力分析

随着现代战争形式的演变,提升指挥信息系统能力的关键在于信息处理.语义网是一种先进的信息表示方法,能够有效地提升机器的信息理解和处理能力.简要介绍了语义网技术的组成和本质能力,并结合美军的研究实例,分析了其在新一代指挥信息系统中的应用能力,指出了发展机遇和挑战,并对未来研究提出了建议.     

基于粗糙集理论的武器系统效能评定

简述了各种武器效能评定方法。建立武器参数效能模型,首先要挑选特征参数,这里采用知识约简方法选择武器的特征参数。利用神经网络理论建立了参数效能模型,武器系统的效能与武器特征参数的关系可通过神经网络的阈值和权值得到体现。通过实例对神经网络法与指数法所得的结果进行了比较,结果表明神经网络法比指数法精确。     

战略联盟中知识转移影响因素概念模型构建：网络角度的思考

根据知识转移的一般过程,从社会网络视角出发,不同于以往研究中二元维度的分析,从网络结点能力、网络关系和认知、网络结构三个维度提出了影响战略联盟网络组织中知识转移绩效的关键影响因素,并深入分析各个关键因素的细分变量对知识转移绩效的影响作用机理,最后提出战略联盟网络知识转移影响因素的相关假设和概念模型,为下一步对假设的验证做铺垫。     

基于专家系统的防化保障预案评估研究

人工智能技术在众多领域获得广泛应用．为满足现代战争对防化指挥手段的现代化要求,针对不易量化的防化指挥决策问题的评估,提出了构建知识库并应用模糊推理机制建立专家系统,据此进行防化保障预案的评估选优方法,并对该方法的求解过程进行了探讨．     

基于FCM与粗糙集的连续数据知识挖掘方法

对连续数据知识挖掘可采用模糊C均值聚类将其离散化,再应用粗糙集进行约简.文中针对该过程中样本数据离散化后出现多个聚类中心隶属度值较接近的情况,提出了隶属度重叠度的方法来扩展对应离散类别的选取.离散类别确定后再应用粗糙集理论对其进行知识挖掘,可得到连续数据的本质特性.此方法应用于汽轮机轴系振动数据的知识挖掘.与采用最大隶属度确定对应类别的方法相比,该方法不影响对连续数据的知识挖掘,并能真实反映连续数据的特性.