Decision support for Cybersecurity risk planning

Security countermeasures help ensure the confidentiality, availability, and integrity of information systems by preventing or mitigating asset losses from Cybersecurity attacks. Due to uncertainty, the financial impact of threats attacking assets is often difficult to measure quantitatively, and thus it is difficult to prescribe which countermeasures to employ. In this research, we describe a decision support system for calculating the uncertain risk faced by an organization under cyber attack as a function of uncertain threat rates, countermeasure costs, and impacts on its assets. The system uses a genetic algorithm to search for the best combination of countermeasures, allowing the user to determine the preferred tradeoff between the cost of the portfolio and resulting risk. Data collected from manufacturing firms provide an example of results under realistic input conditions.     

An evolutionary real options framework for the design and management of projects and systems with complex real options and exercising conditions

To address the issue of decision support for designing and managing flexible projects and systems in the face of uncertainties, this paper integrates real options valuation, decision analysis techniques, Monte Carlo simulations and evolutionary algorithms in an evolutionary real options framework. The proposed evolutionary real options framework searches for an optimized portfolio of real options and makes adaptive plans to cope with uncertainties as the future unfolds. Exemplified through a test case, the evolutionary framework not only compares favorably with traditional fixed design approaches but also delivers considerable improvements over prevailing real options practices.     

A framework for dynamic multiple-criteria decision making

The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value.However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary.In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility.     

Training linear ranking SVMs in linearithmic time using red-black trees

We introduce an efficient method for training the linear ranking support vector machine. The method combines cutting plane optimization with red-black tree based approach to subgradient calculations, and has O(ms + mlog (m)) time complexity, where m is the number of training examples, and s the average number of non-zero features per example. Best previously known training algorithms achieve the same efficiency only for restricted special cases, whereas the proposed approach allows any real valued utility scores in the training data. Experiments demonstrate the superior scalability of the proposed approach, when compared to the fastest existing RankSVM implementations.     

基于自适应增强算法的支持向量机组合模型

为了提高软测量模型的泛化能力,提出一种基于AdaBoosting算法的组合支持向量机(SVM)模型.该方法在贝叶斯分析的基础上,利用样本概率初始化惩罚系数,依据回归过程中的损失函数更新惩罚系数权重,使得SVM训练模型有强、弱之分,突出一些重要样本的作用,以提高模型的估计精度和泛化能力.仿真结果表明,依据该方法建立的组合模型明显改善了软测量模型的估计能力和泛化能力.     

求解双边加权模糊支持向量机的序贯最小优化算法

高的计算复杂度限制了双边加权模糊支持向量机在实际分类问题中的应用。为了降低计算复杂度,提出了应用序贯最小优化算法（SMO）解该模型,该模型首先将整个二次规划问题分解成一系列规模为2的二次规划子问题,然后求解这些二次规划子问题。为了测试SMO算法的性能,在三个真实数据集和两个人工数据集上进行了数值实验。结果表明：与传统的内点算法相比,在不损失测试精度的情况下,SMO算法明显地降低了模型的计算复杂度,使其在实际中的应用成为可能。     

基于差分进化支持向量机的移动机器人可通过度预测

提出了一种移动机器人可通过度预测方法.给出了基于相对震动强度的可通度描述.通过提取典型地表图像的色彩和纹理特征并测量机器人通过该地表的相对震动强度建立训练样本集.使用差分进化算法优化支持向量机模型参数形成差分进化支持向量机对训练样本和相对震动强度进行拟合.在移动机器人运行过程中,线性分割前方地表图像形成预测子区域,通过...     

Electronic Meeting Support for Councils

City councils hold meetings several times a week. There is a need for computer support at certain meetings. This paper examines the potential for group support systems for use in city council meetings and shows in what ways they can be helpful in pre-meeting and post-meeting activities. The study is based on 17 computer-supported city council meetings, carried out in Stuttgart, Kornwestheim and other cities as part of the Cuparla Project between 1996 and 1998. Three of these meetings are described in the paper as brief case studies. Following this, all 17 meeting sequences are evaluated and analysed. Guidelines have been developed from the results of the study for the introduction of group support systems in city council meetings.     

一种基于Rough集的缺省规则挖掘算法

Rough集方法是一种处理不确定或模糊知识的重要工具,对基于Rough集理论的缺省规则挖掘算法进行研究,在引入规则支持度概念后,提出了一种基于Rough集的缺省规则挖掘算法MDRBR。实验结果表明,该算法能较好地过滤噪音,提高规则的挖掘效率。     

一个基于数据仓库的企业财务管理决策支持系统框架初探

基于Internet环境,应用数据仓库和数据挖掘技术,本文构建了一个企业财务管理决策支持系统框架,提出了系统的体系结构。