Challenges of measuring progress in Afghanistan using violence trends: the effects of aggregation,military operations,seasonality, weather,and other causal factors

Measuring nationwide progress of counterinsurgency operations in Afghanistan using violence trends is difficult due to several factors: aggregation of data to the national level may obfuscate disparate local trends; the observed seasonality in violence makes comparisons difficult and may obscure progress; and short-term spikes or troughs – attributable to weather, military operations and tempo, or holiday periods – heavily influence simple averaging schemes. Despite these challenges, proper understanding of violence statistics is critical to estimating the effectiveness of military forces added during a surge or redeployed as part of transition. This article explores methods for analyzing observed violence trends to identify causal factors, to provide a comparable baseline, and to inform assessments at appropriate levels of aggregation. One methodology for seasonal adjustment of violence data is discussed and shown to provide a logical baseline for examining trends. An ordinary least squares regression model is developed and implemented using time-series violence data.     

基于组织协同进化的软件缺陷预测方法

针对传统有标识软件度量元数据存在软件缺陷预测精度低的问题,首先对比选择合适的离散化方法,然后将组织协同进化分类算法引入并应用到航天软件缺陷预测领域,给出了一种基于组织协同进化的软件缺陷预测方法。该方法根据预测目标将离散后的软件度量元数据划分为不同种群,在各种群内部形成进化个体(组织)。组织在增减算子、交换算子、合并算子和组织选择机制的作用下不断进化,并基于属性重要度协同进化的方式进行适应度函数的计算,实现了有标识软件度量元数据缺陷预测精度的提高。最后通过两组仿真实验,验证了基于组织协同进化航天软件缺陷预测方法的有效性。     

基于复杂性的战场损伤定位流程评价模型研究

损伤定位流程制定的合理与否是衡量一个评估员水平高低的重要指标之一。对损伤定位流程的评价涉及到所用时间、工具、操作的难度等指标都可以用其复杂性来表示,目前鲜有关于损伤定位流程评价的研究,因此提出战场损伤定位流程的复杂性,并借鉴软件工程领域的程序复杂性量化方法——图熵法对其进行量化。经过数据验证,复杂性与评估时间之间存在较好的拟合关系,并用实例说明了模型的应用效果。