HLA仿真系统因果追溯性设计

随着仿真系统变得日益复杂，基于仿真数据人为的进行因果追溯已经变得越来越凼难，所以必须对原有的仿真系统进行因果追溯性设计，使自动或半自动追溯凶果成为可能。为此，首先确定了因果关系追溯的整体思路，然后面对普遍使用的高层体系结构HLA分印仿直系统，深入分析对象类和交互类两类交互数据以及交互特点，井详细划分行为事件及因果关系类型；对HLA仿真系统中关键的联邦对象模型FOM以及仿真器进行扩展设计，从而实现系统的因果追溯性，为仿真因果追溯分析提供必要的数据。最后提出保证因果追溯需要注意的几项设计原则，并对设计所能够支持的应用做出展望。     

A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm

The estimation of the differences among groups in observational studies is frequently inaccurate owing to a bias caused by differences in the distributions of covariates. In order to estimate the average treatment effects when the treatment variable is binary, Rosenbaum and Rubin [1983. The central role of the propensity score in observational studies for causal effects. Biometrika 70, 41-55] proposed an adjustment method for pre-treatment variables using propensity scores. Imbens [2000. The role of the propensity score in estimating dose-response functions. Biometrika 87, 706-710] extended the propensity score methodology for estimation of average treatment effects with multivalued treatments.However, these studies focused only on estimating the marginal mean structure. In many substantive sciences such as the biological and social sciences, a general estimation method is required to deal with more complex analyses other than regression, such as testing group differences on latent variables. For latent variable models, the EM algorithm or the traditional Monte Carlo methods are necessary. However, in propensity score adjustment, these methods cannot be used because the full distribution is not specified.In this paper, we propose a quasi-Bayesian estimation method for general parametric models that integrate out the distributions of covariates using propensity scores. Although the proposed Bayes estimates are shown to be consistent, they can be calculated by existing Markov chain Monte Carlo methods such as Gibbs sampler. The proposed method is useful to estimate parameters in latent variable models, while the previous methods were unable to provide valid estimates for complex models such as latent variable models.We also illustrated the procedure using the data obtained from the US National Longitudinal Survey of Children and Youth (NLSY1979-2002) for estimating the effect of maternal smoking during pregnancy on the development of the child's cognitive functioning.     

无机物——聚醋酸乙烯复合乳液粒子结构及主要性能

本文对均聚醋酸乙烯乳液及其与无机物的混合物,无机物——聚醋酸乙烯复合乳液的粒子结构进行了比较,阐述了复合乳液的主要性能。     

可信机器学习的公平性综述

人工智能在与人类生活息息相关的场景中自主决策时,正逐渐面临法律或伦理的问题或风险.可信机器学习是建立安全人工智能系统的核心技术,是人工智能领域的热门研究方向,而公平性是可信机器学习的重要考量.公平性旨在研究机器学习算法决策对个人或群体不存在因其固有或后天属性所引起的偏见或偏爱.本文从公平表征、公平建模和公平决策三个角度出发,以典型案例中不公平问题及其危害为驱动,分析数据和算法中造成不公平的潜在原因,建立机器学习中的公平性抽象定义及其分类体系,进一步研究用于消除不公平的机制.可信机器学习中的公平性研究在人工智能多个领域中处于起步阶段,如计算机视觉、自然语言处理、推荐系统、多智能体系统和联邦学习等.建立具备公平决策能力的人工智能算法,是加速推广人工智能落地的必要条件,且极具理论意义和应用价值.     

基于混合自适应遗传算法的工作流挖掘优化

针对目前工作流挖掘算法采用局部策略而无法保证最优挖掘以及算法对噪声敏感的情况,提出了基于混合自适应遗传算法的工作流挖掘优化算法。首先定义了基本工作流网以及变迁的使能和点火规则,描述了过程模型;然后提出了过程模型转换成基本工作流网的算法,给出了衡量事件日志与过程模型的符合性的适应值评价函数;最后根据进化阶段以及个体相似度设计了混合自适应的交叉率和变异率。仿真试验结果表明,该算法与α算法相比具有更高的鲁棒性和对噪声的抗干扰性;与基本遗传算法相比,该算法能显著提高解的质量和收敛速度。     

Improvement of causal analysis using multivariate statistical process control

Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems, where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems requires significant effort due to the large number of possible causes. This study presents an approach to detect problems and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices can be monitored simultaneously to facilitate the causal analysis of a software process.

基于多通道脑电信号的因果网络情绪识别

情绪是由大脑内多个通道共同作用产生的,格兰杰因果检验作为情绪识别的主流方法,在计算任意2个通道之间的因果关系时容易忽略其他通道的影响。面向多通道脑电信号,提出一种基于条件格兰杰因果检验（CGC）的因果网络情绪识别方法。利用CGC算法计算不同情绪下大脑全通道的因果关系,据此构建因果网络,并通过分析各通道的入/出度和介数拓扑属性找到关键通道,得到简化的因果网络进行情绪识别。将节点之间的因果连接关系作为特征分别输入SVM和KNN分类器进行分类训练,实验结果表明,简化网络的识别率分别为75.3%和78.4%,验证了所提方法的有效性。     

Dependency mining-based causal message logging

In fault-tolerant computing, the approach of causal message logging provides on-demand stable logging and enables the independent recovery of nodes. It imposes the requirement that the dependency between non-deterministic events needs to be known for nodes. The dependency information is disseminated through messages. A central problem in traditional causal message logging is that if the message logging progress of nodes cannot be effectively tracked, some redundant dependency information will be piggybacked on the messages. In this paper, a dependency mining based approach is proposed. It tries to detect the message logging progress of nodes only from the dependency between non-deterministic events, without needing to piggyback an additional dependency vector or dependency matrix on each message.     

The moderating effects of psychological reactance and product involvement on online shopping recommendation mechanisms based on a causal map

The use of the Internet in the daily activities of individuals and firms has become entrenched, and online shopping has become commonplace. However, debates about how online shopping recommendation mechanisms (OREMs) should be designed have not been completely resolved. The challenge with traditional online shopping recommendation mechanisms (TR-OREMs) is that they focus too much on quantitative factors. Thus, they ignore causal interrelationships with qualitative factors that are believed to significantly impact quantitative factors. Considering only quantitative factors and ignoring qualitative ones likely distorts the final recommendation results. Another problem with TR-OREMs is that they ignore the perceived psychological reactance of consumers against the recommended products. Such consumer reactance may be reduced when the causal interrelationships among all the quantitative and qualitative factors are analyzed and incorporated properly into the OREM. To overcome these problems, we propose a causal map – online shopping recommendation mechanisms (CM-OREMs) based on a causal map. We analyzed possible causal relationships among quantitative and qualitative factors and incorporated them in the recommendation process to reduce consumer reactance against the recommendation results. Furthermore, an elaboration likelihood model (ELM) was used to build hypotheses about how the online shopping behavior of consumers is affected by OREMs based on the proposed causal map. Specifically, the performance of the proposed OREM was empirically analyzed by gathering experiment data from qualified respondents who were asked to refer to the proposed OREM before making purchasing decisions via mobile phones. Statistical results showed that the proposed OREMs could enhance consumer decision satisfaction, decision confidence, and attitude toward the recommended products. It could also positively affect consumer purchasing intentions. The OREM had a greater effect on the high-reactance group of participants than on the low-reactance group as well as on a high-involvement product versus a low-involvement product.     

Moving objects forecast in image sequences using autoregressive algorithms

The objective of this paper is to present an overall approach to forecasting the future position of the moving objects of an image sequence after processing the images previous to it. The proposed method makes use of classical techniques such as optical flow to extract objects’ trajectories and velocities, and autoregressive algorithms to build the predictive model. Our method can be used in a variety of applications, where videos with stationary cameras are used, moving objects are not deformed and change their position with time. One of these applications is traffic control, which is used in this paper as a case study with different meteorological conditions to compare with.