一种感知情景的存取控制策略的规范和分析方法

对RBAC的角色激活过程增加情景约束,建立了感知情景的存取控制策略;利用D ata-log逻辑语言来建立感知情景存取控制策略模型的形式化规范;使用一阶逻辑语言描述策略的一致性、完整性等性质,借助D atalog实现了一个控制策略自动决策原型系统。     

一种可动态更新的高可用中间件框架

针对动态更新缺乏对分布式应用支持的缺陷,提出了一套可动态更新的高可用中间件框架,用于分布式环境中高可用服务的开发．它包含了一套灵活的动态插件管理框架,可完成单进程的动态更新,同时提供了一套分布式协作服务管理框架,来完成多进程协同服务的同步动态更新．通过实现服务可用性论坛提出的高可用应用接口规范,验证了该框架具有单进程及多进程同步动态更新的能力．     

论职业教育被看作"次等教育"的缘由与对策

针对职业技术教育被视为“次等教育”的现状,从历史沿革与国际比较等多个视角,直面剖析产生这一现状的根由,追溯近20年政策引导上的缺位,并从转变教育观念,重新认识职业技术教育的重要价值,调整教育结构布局,改变人才培养模式等多个方面,提出了发展职业技术教育的基本对策。     

基于超网络的舆论演化动态模型

针对舆论演化过程中的复杂动力学问题,提出了超网络视觉下的舆论演化动态模型,该模型包括节点的添加、重新连接链路、超边的添加以及节点的老化4个过程,其中节点代表关键词,超边代表关键词所构成的话题.其次,利用非均匀网络的演化机制,分别对该动态演化模型的超度、节点自身关注度与节点间影响力两个因素的超度进行了详细的理论分析,分析...     

过站航班地面保障流程效能评估

为了感知过站航班地面保障流程运行状况和优化机场协同决策信息系统,研究了单航班条件下的保障流程效能评估问题。通过系统分析地面保障流程,得到各场景的效能评估指标。结合评估指标的大小、重要性及聚散状态3个维度,构建了流程效能评估指标体系。设计了一种基于雷达图周长-面积的过站航班地面保障流程动态效能评估方法,并结合实际运行数据开展仿真验证。研究表明,流程效能评估结果整体上直观描述了保障流程效能的动态变化。通过客观性对比和敏感性分析可知,结果一定程度反映了过站航班离港属性,为航班推出控制提供依据。     

Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors

In this paper, we assess the predictive content of latent economic policy uncertainty and data surprise factors for forecasting and nowcasting gross domestic product (GDP) using factor-type econometric models. Our analysis focuses on five emerging market economies: Brazil, Indonesia, Mexico, South Africa, and Turkey; and we carry out a forecasting horse race in which predictions from various different models are compared. These models may (or may not) contain latent uncertainty and surprise factors constructed using both local and global economic datasets. The set of models that we examine in our experiments includes both simple benchmark linear econometric models as well as dynamic factor models that are estimated using a variety of frequentist and Bayesian data shrinkage methods based on the least absolute shrinkage operator (LASSO). We find that the inclusion of our new uncertainty and surprise factors leads to superior predictions of GDP growth, particularly when these latent factors are constructed using Bayesian variants of the LASSO. Overall, our findings point to the importance of spillover effects from global uncertainty and data surprises, when predicting GDP growth in emerging market economies.     

Correcting the January optimism effect

Each month, various professional forecasters give forecasts for next year's real gross domestic product (GDP) growth and unemployment. January is a special month, when the forecast horizon moves to the following calendar year. Instead of deleting the January data when analyzing forecast updates, I propose a periodic version of a test regression for weak-form efficiency. An application of this periodic model for many forecasts across a range of countries shows that in January GDP forecast updates are positive, whereas the forecast updates for unemployment are negative. I document that this January optimism about the new calendar year is detrimental to forecast accuracy. To empirically analyze Okun's law, I also propose a periodic test regression, and its application provides more support for this law.     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

基于图像理解舰船打击效果评估数学模型

研究了基于图像理解的舰船打击效果评估中数学模型的建立问题,提出了舰船打击效果评估的两种模式,讨论了打击效果评估知识库建立问题,给出了打击效果评估的数学模型,并解决了打击效果评估中单项功能打击效果评估与整体效果评估的关系问题,用统计决策理论实现知识库的智能维护,将知识库的智能维护变为一个简单的二次规划问题。     

黄河口凹陷明化镇组下段网状浅水三角洲沉积

为了认识湖盆萎缩期网状浅水三角洲的沉积特征和沉积模式,利用测井、录井、取芯、粒度、地震等资料,对黄河口凹陷明化镇组下段低位域地层进行了分析.研究认为,黄河口凹陷明化镇组下段低位域网状浅水三角洲河道砂体主要是垂向加积作用的产物,河道平面上呈网状、交织状,具有宽深比小、稳定性强等特点,砂体发育程度中等;其形成于低坡降背景,...