Systems failure: the US way of irregular warfare

ABSTRACT

Since 9/11, the United States has achieved notable gains against al Qaeda, and also Islamic State (IS), all while avoiding another mass-casualty attack at home. Yet, institutionally, culturally, and in its capabilities, the US government remains seriously ill-equipped for the task of countering irregular threats. Partly as a result, Islamist extremism shows no sign of being defeated, having instead metastasized since 9/11 and spread. Why, given the importance accorded to counterterrorism, has the US approach remained inadequate? What is impeding more fundamental reforms? The article evaluates the United States’ way of irregular warfare: its troubled engagement with counterinsurgency and its problematic search for lower cost and lower risk ways of combating terrorism. It suggests needed reforms but acknowledges also the unlikelihood of change.     

英语学习中的文化背景知识

分析了英语学习过程中文化背景知识的种类及其特性 ,阐明了这些背景知识对英语学习的影响 ,并介绍了获取这些文化背景知识的主要途径 .     

如何激发学生外语学习动机

学习动机是直接推动学生学习的内部动因，是影响学习效果的主要因素。学生只有了解外语学习动机的分类及其作用，才能确立正确的学习动机，才能有效地提高外语学习成绩。     

现代教学原则在多媒体教学中的贯彻初探

现代教学原则是教师进行教学活动所应遵循的准则。从多媒体电子教案的教学内容选择、多媒体电子教案的演示设计与运用,以及多媒体教学中的教师主导作用和课堂交流3个方面,探讨了如何在多媒体教学中贯彻现代高等学校的教学原则,以充分发挥多媒体教学的优势、弥补其不足,提高课堂教学效果。     

强化学习框架下移动自组织网络分步路由算法

移动自组织网络是一种无基础设施、由移动通信节点组成的无线网络,具有高动态特性。传统的路由协议并不能适应节点移动性带来的频繁拓扑变化,简单的洪泛路由也会因开销过大降低网络的性能。针对如何在移动自组织网络中自适应地进行路由选择,提出强化学习框架下的分步路由选择算法。该算法以最小链路总往返时延为目标,基于强化学习进行路由搜寻,在筛选出符合目标需求节点集合的基础上,结合置信度选择路由。在链路变得不可靠时,数据包被广播给筛选出的邻居节点集以提升路由可靠性并降低开销。对提出的算法在分组到达率和路由开销等主要性能指标进行数值仿真分析。仿真结果表明,提出的分步路由算法相比于基于强化学习的智能鲁棒路由,在降低开销的同时,保持着相当的吞吐率。     

在线学习的主用户仿冒攻击策略

在认知无线网络中,次用户通过频谱感知来学习频谱环境,从而接入那些没有被主用户占用的频谱空隙。事实上,多种恶意攻击的存在会影响次用户频谱感知的可靠性。只有深入研究恶意攻击策略,才能确保认知无线网络的安全。基于此,研究了一种认知无线网络中的欺骗性干扰策略,即主用户仿冒攻击策略,该攻击策略通过在信道上传输伪造的主用户信号来降低次用户频谱感知的性能。具体来说,将攻击策略问题建模为在线学习问题,并提出基于汤普森采样的攻击策略以实现在探索不确定信道和利用高性能信道间的权衡。仿真结果表明,与现有的攻击策略相比,提出的攻击策略能更好地通过在线学习优化攻击决策以适应非平稳的认知无线网络。     

基于科学计量的机器学习技术发展评估分析

机器学习是通过数据训练使机器获取新的知识和技能的计算机技术，是人工智能技术的核心和前沿领域之一。本文以Web of Science?核心合集收录的机器学习领域相关SCI论文和incoPat全球专利数据库收录的机器学习领域相关专利为数据源，运用科学计量方法对文献数据和专利数据进行时间、地域和机构分布分析，展示了机器学习领域研究实力的分布情况，并对机器学习的发展趋势进行了分析；然后利用主题聚类分析方法及可视化软件VOSviewer，挖掘出机器学习领域的关键技术、技术热点；最后对结果进行了总结分析，以期为我国机器学习发展布局提供参考和借鉴。     

The military as a learning organisation: establishing the fundamentals of best-practice in lessons-learned

The post-Cold War era has witnessed the rapid expansion of organisational learning initiatives within NATO militaries, especially formal “lessons-learned” processes. The effectiveness of national lessons-learned processes in recalibrating military activity to the demands of ongoing operations has been highly-differentiated. However, the academic literature on military change and practitioner guidance has been slow to investigate the key features of best-practice in military learning. This article breaks new ground by drawing upon the literature on dynamic organisational capabilities to explore the fundamental organisational processes and activities which are necessary to implement successful lessons-learned. It examines, in particular, the organisational features which facilitate “knowledge transformation”: the effective combination of new knowledge gained from intra- and inter-organisational learning with existing organisational knowledge. The article concludes by highlighting several potential future empirical and theoretical research agendas in military learning and the importance of engagement between lessons-learned practitioners and organisational learning scholars.