具有时滞的二阶Hopfield神经网络的全局动力性

通过正不变集、吸引集与系统稳定性的关系,并利用非负矩阵的性质和微分不等式的技巧,对具有时滞的二阶Hopfield神经网络全局动力性进行研究.得到了易于验证的关于平衡点一致稳定和全局渐近稳定的充分条件.     

网络安全分析与技术解决方案探究

计算机网络的发展给网络安全带来了很大的冲击,网络的安全成了信息安全的热点。针对计算机网络系统存在的安全性和可靠性问题,探讨了网络常见的安全隐患,以及解决此类隐患的一些方法,以使网络可以更加安全、可靠、稳定的运行。     

BulkTree: an overlay network architecture for live media streaming

Peer-to-peer (P2P) systems are now very popular. Current P2P systems are broadly of two kinds, structured and unstructured. The tree structured P2P systems used technologies such as distributed hash tables (DHT) and hierarchical clustering can search the required target quickly, however, in a tree, the internal node has a higher load and its leave or crash often causes a large population of its offspring's problems, so that in the highly dynamic Internet environment the tree structure may still suffer frequent breaks. On the other hand, most widely used unstructured P2P networks rely on central directory servers or massive message flooding, clearly not scalable. So, we consider both of the above systems' advantages and disadvantages and realize that in the P2P systems one node may fail easily, but that when a number of nodes organized as a set, which we call "super node", the set is robust. Super nodes can be created and updated aware of topology-aware, and used with simple protocol such as flooding or "servers" to exchange information. Furthermore the entire robust super node can be organized into exquisite tree structure. By using this overlay network architecture, P2P systems are robust, efficient, scalable and secure. The simulation results demonstrated that our architecture greatly reduces the alteration time of the structure while decreasing the average delay time, compared to the common tree structure.     

基于随机游走算法的社交网络构建

为了挖掘社交网络的用户信息,从复杂的社交网络提取有价值的数据,需要对社交用户网络进行可视化处理和层次构建,本文采取随机游走算法来实现社交用户网络的层次生成。首先,从社交网络的网络营销方式和商业价值两方面阐述了社交网络研究价值,接着提出了随机游走算法数学模型,最后进行实例仿真,生成社交用户的层次网络模型。     

Temporal networks in collaborative learning: A case study

Social Network Analysis (SNA) has enabled researchers to understand and optimize the key dimensions of collaborative learning. A majority of SNA research has so far used static networks, ie, aggregated networks that compile interactions without considering when certain activities or relationships occurred. Compressing a temporal process by discarding time, however, may result in reductionist oversimplifications. In this study, we demonstrate the potentials of temporal networks in the analysis of online peer collaboration. In particular, we study: (1) social interactions by analysing learners' collaborative behaviour, part of a case study in which they worked on academic writing tasks, and (2) cognitive interactions through the analysis of students' self-regulated learning tactics. The study included 123 students and 2550 interactions. By using temporal networks, we show how to analyse the longitudinal evolution of a collaborative network visually and quantitatively. Correlation coefficients with grades, when calculated with time-respecting temporal measures of centrality, were more correlated with learning outcomes than traditional centrality measures. Using temporal networks to analyse the co-temporal and longitudinal development, reach, and diffusion patterns of students' learning tactics has provided novel insights into the complex dynamics of learning, not commonly offered through static networks.     

基于复杂适应系统理论的中国当代大学生职业生涯辅导模型研究

随着"80后"大学生步入职场,职业观等方面的许多问题已经凸现,在这种背景下,大学生职业生涯辅导教育就显得十分迫切。通过将复杂适应系统(CAS)理论中的7要素与大学生职业生涯辅导过程相结合,为中国当代大学生职业生涯辅导提供了一个理论模型。     

Connecting Marginal Rice Farmers to Agricultural Knowledge and Information Systems in Vietnam Uplands

Abstract

In Vietnam, agricultural extension has contributed to rural development and poverty alleviation over the past two decades of agricultural decollectivization, but it was not very effective in reducing disparities within farmer communities. The study examined how better interactions of extension services with other agencies and information sources may help marginal farmers in catching up with the general improvement of living conditions in a mountainous area in northern Vietnam. It combined three complementary viewpoints on this issue: that of the agricultural extension staff, that of farmers and that of development experts with a long working experience in the mountains of Vietnam. The analysis of existing structures and functions of the extension system revealed a number of obstacles to the participation of marginal farmers in extension programmes and helped to identify relevant domains of intervention.     

基于认知负荷理论的复杂学习整体任务设计探讨

目前的教学设计趋向于聚焦真实的学习任务,但由于这些任务的复杂性,使学习者的学习受到阻碍。文章基于认知负荷理论介绍了复杂学习任务的设计,并结合认知负荷理论具体探讨了如何通过降低认知负荷来进行复杂学习的整体任务设计。     

产业集聚、创新网络与集群企业技术能力——基于绍兴市14262份问卷的调查与分析

从产业集聚、创新网络两重视角,分析了集群企业技术能力的影响因素,并在此基础上,运用绍兴市纺织产业集群的14262份问卷实证检验了产业集群、创新网络对集群企业技术能力的影响。结果表明,产业集聚、创新网络对集群企业的技术能力有明显的促进作用;产业集聚与创新网络的所有子效应,对不同技术子能力的影响程度均存在明显差异。因此,提高产业集聚程度、构建和完善创新网络平台对于提升集群企业竞争力尤为重要。     

Utilizing statistical physics and machine learning to discover collective behavior on temporal social networks

Computational social science has become a branch of social science that uses computationally intensive ways to investigate and model social phenomena. Exploitation on mathematics, physics, and computer sciences, and analytic approaches like Social Network Analysis (SNA), Machine Learning (ML), etc, develops and tests the theories of complex social phenomena. In the emerging environment of social media, the new characteristics of social collective behavior and its extensive phenomena have become the hot spot of common concern across many disciplines. In this paper, we propose a general quantitative framework to discover the social collective behavior in temporal social networks. The general framework incorporates the Time-Correlation Function (T.C.F.) in statistical physics and evolutionary approach in Machine Learning, and provides the quantitative evidence of the existence of social collective behavior. Results show collective behaviors are observed and there exists a tiny fraction of users whose behavior are constantly replicated by public, disregard of the behavior itself. Our method is assumption-independent and has the potential to be applied to various temporal systems.