Community Networks: Rules of Behavior and Social Structure

This article investigates the intertwining relationships between the Gemeinschaft and the Gesellschaft dimension of a community network (CN)—that is, of a virtual community whose members' common interest is a locality, in our case the city of Milan, Italy. The article looks through the experience of the last 7 years of life on RCM—the Milan Community Network—to reflect on the role that the sharing of rules of behavior has on the evolution of social structure in a virtual community. It identifies three phases of development—the design phase, the first growth phase, and the most recent phase—and presents the social structure set up to guarantee governance of the CN. The conclusions show that the third phase is not an actual stable state, as the continuous evolution of the societal and technological context requires continuous redesign of the CN itself in order to meet the renewed demands of a new context.     

技术创新网络分裂断层对子群极化的影响研究——基于网络嵌入性视角

将分裂断层理论引入技术创新网络,能够为探索中观网络子群结构的动态过程提供重要依据。本研究以2000年至2012年我国电子信息产业联合申请专利为样本构建组织间技术创新网络,探索网络嵌入性视角下分裂断层对子群极化的影响机理。结果表明:嵌入于组织间的二元关系强度在不同成员对间分布不均匀时会使整体网络产生潜在的分裂断层,但分裂断层对子群极化并不存在显著的直接影响;当结构嵌入性水平和位置嵌入性水平较高时,分裂断层对子群极化的作用显著提升。说明潜在的分裂断层在没有被触发的情况下,可能保持休眠状态且不会产生直接影响,当一些关键机制触发分裂断层时,其作用得到显著加强。研究结论为解释网络中观子群结构形成与变化的微观机制提供了新的理论视角,对进一步研究技术创新网络的稳定性和知识共享具有重要的借鉴作用。     

一种前馈神经网络算法

神经网络由于其非线性处理能力强，性能稳定等特点得到了广泛应用和研究，主要应用于模式识别、信号处理、知识工程、专家系统、优化组合、机器人控制等。神经网络中使用最为广泛的就是前馈神经网络，其网络权值学习算法中影响最大的就是误差反向传播算法(back-propagation简称BP算法)。BP算法存在局部极小点，收敛速度慢等缺点，基于优化理论的Levenberg-Marquardt算法忽略了二阶项，该文讨论当误差不为零或者不为线性函数即二阶项S(W)不能忽略时的Hesse矩阵的近似计算，进而训练网络。     

拷贝硅谷:一种聚集网络理论解释

本文介绍了目前在关于拷贝硅谷、建设高新科技园区问题上国内外不同的观点，提出聚集网络理论。在此基础上，通过对韩国大德科学城和我国台湾新竹科学工业园的成功实践考察，提出了它们各自成功发展高技术产业的原因，在于根据各自的实际，市场性和建构性、网络资本和风险资本网络的有机结合，适应了高技术发展的本质要求。认为我国应注重网络资源管理，营造风险资本网络体系发展的环境。     

类脑智能研究现状与发展思考

近年来人工智能研究的许多重要进展反映了一个趋势:来自脑科学的启发,即使是局部的借鉴都能够有效地提升现有人工智能模型与系统的智能水平。然而,想要真正逼近乃至超越人类水平的人工智能,还需要对脑信息处理机制更为深入的研究和借鉴。类脑智能研究的目标就是通过借鉴脑神经结构及信息处理机制,实现机制类脑、行为类人的下一代人工智能系统。文章从受脑启发的新一代人工神经网络、基于记忆、注意和推理的认知功能模型、基于生物脉冲神经网络的多脑区协同认知计算模型等角度,并结合研究团队在类脑智能领域的研究进展,论述类脑智能的研究进展、发展方向和对未来发展的思考。     

基于人工神经网络的住宅性能综合评价研究

住宅性能的评价是住宅商品化的基础性工作,是住宅商品化前提。本文在分析住宅性能影响因素的基础上,将人工神经网络应用于住宅性能等级的综合评价中,提供了一条定量、客观评价住宅性能等级的智能化新方法。     

高技术企业协作R＆D网络构建活动和路径研究

对高技术企业协作R＆D网络的构建活动和路径进行了探讨，其中构建活动包括三个：建立共同协作基础、伙伴成员的搜寻与选择、组织协作网络;构建路径分为三种：自组织路径、被组织路径和混合路径。     

The social strategy cone: Towards a framework for evaluating social media strategies

Social media is growing rapidly. Providing both risks and opportunities for organizations as it does. The social strategy cone is developed for evaluating social media strategies. This framework comprises of seven key elements of social media strategies as based on a systematic literature review and case studies. The results of 21 interviews have contributed to the construction of the social media strategy cone for analyzing social media strategies. Three levels of maturity of social media strategy are proposed: initiation, diffusion and maturity. Initiation includes the key elements: ‘target audience’ and ‘channel choice’ while all case organizations studied payed attention to these elements. Diffusion includes the elements: ‘goals’, ‘resources’ and ‘policies’. Maturity adds the elements of ‘monitoring’ and ‘content activities’. Only 3 of the 9 organizations studied are in this phase of maturity. Although, theory suggests the importance of the element of ‘monitoring’ our research shows the need for more attention in practice.     

Multi-layer self-organizing polynomial neural networks and their development with the use of genetic algorithms

In this paper, we introduce a new architecture of genetic algorithms (GA)-based self-organizing polynomial neural networks (SOPNN) and discuss a comprehensive design methodology. Let us recall that the design of the “conventional” PNNs uses an extended group method of data handling (GMDH) and exploits polynomials (such as linear, quadratic, and modified quadratic functions) as well as considers a fixed number of input nodes (as being selected in advance by a network designer) at polynomial neurons (or nodes) located in each layer. The proposed GA-based SOPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional PNNs. The design procedure applied in the construction of each layer of a PNN deals with its structural optimization involving the selection of preferred nodes (or PNs) with specific local characteristics (such as the number of input variables, the order of the polynomial, and a collection of the specific subset of input variables) and addresses specific aspects of parametric optimization. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the network. To evaluate the performance of the GA-based SOPNN, the model is experimented with using chaotic time series data. A comparative analysis reveals that the proposed GA-based SOPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.     

发现抑或人工生成:科学实验与规律的双重关系

自然规律是随着宇宙的演化而自然生成的,而人类能够通过自己的实践活动实现新规律的人工生成。科学实验作为科学研究的重要基础和手段,人们能够通过纯化简化实验发现自然界原本存在的自然规律,也能够通过强化激化及特殊实验和技术科学实验人工生成自然界原本不存在的新规律。人工生成新规律,改变了人与规律关系的传统认识,并使人们充分认识到在科学研究和技术开发活动中,设定新技术目标、加强实验仪器和设备的研制、加强稀有资源的研究开发与保护、加强技术科学研究等具有特别重要的意义。