Web 2.0时代,在线知识社区(OKC)成为生产、分享和获取知识的重要平台。如何更高效快速地完成知识序化,为人们及时提供高质量的信息和知识成为OKC平台关心的问题。本文基于互动团队认知理论(ITC),提出了OKC知识序化效率的影响因素模型,...Web 2.0时代,在线知识社区(OKC)成为生产、分享和获取知识的重要平台。如何更高效快速地完成知识序化,为人们及时提供高质量的信息和知识成为OKC平台关心的问题。本文基于互动团队认知理论(ITC),提出了OKC知识序化效率的影响因素模型,探究团队异质性和团队互动过程对知识序化效率的影响。本文以英文版维基百科为研究平台,使用其自带的"Random featured article"功能随机抽样100个特色词条,通过API爬取词条和编辑者客观真实数据,使用偏最小二乘(PLS)路径分析对模型进行了验证。研究发现,知识异质性和经验异质性对知识序化效率无显著直接影响。知识异质性对知识协作和认知冲突存在正向影响。经验异质性对认知冲突有负向影响。知识协作和认知冲突都正向影响沟通协调,而沟通协调对知识序化效率呈U形影响。本研究扩展了OKC序化相关研究和理论,同时也为OKC平台建设和管理提供了实践启示。展开更多
ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the kno...ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the knowledge of an expert on the application. In this paper, a technique for automatic configuration for two types of neural networks is presented. The multilayer perceptron and recurrent Elman are the neural networks used here. The determination of optimal parameters for the neural network is formulated as an optimization problem, solved with the use of meta-heuristic MPCA （multiple particle collision algorithm）. The self-configuring networks are applied to perform data assimilation.展开更多
文摘Web 2.0时代,在线知识社区(OKC)成为生产、分享和获取知识的重要平台。如何更高效快速地完成知识序化,为人们及时提供高质量的信息和知识成为OKC平台关心的问题。本文基于互动团队认知理论(ITC),提出了OKC知识序化效率的影响因素模型,探究团队异质性和团队互动过程对知识序化效率的影响。本文以英文版维基百科为研究平台,使用其自带的"Random featured article"功能随机抽样100个特色词条,通过API爬取词条和编辑者客观真实数据,使用偏最小二乘(PLS)路径分析对模型进行了验证。研究发现,知识异质性和经验异质性对知识序化效率无显著直接影响。知识异质性对知识协作和认知冲突存在正向影响。经验异质性对认知冲突有负向影响。知识协作和认知冲突都正向影响沟通协调,而沟通协调对知识序化效率呈U形影响。本研究扩展了OKC序化相关研究和理论,同时也为OKC平台建设和管理提供了实践启示。
文摘ANN （artificial neural network） is a technique successfully employed in many applications on several research fields. An appropriate configuration for neural networks is a tedious task, and it often requires the knowledge of an expert on the application. In this paper, a technique for automatic configuration for two types of neural networks is presented. The multilayer perceptron and recurrent Elman are the neural networks used here. The determination of optimal parameters for the neural network is formulated as an optimization problem, solved with the use of meta-heuristic MPCA （multiple particle collision algorithm）. The self-configuring networks are applied to perform data assimilation.
