基于博弈论视角的稀土矿区环境治理及监管策略研究

针对稀土矿区环境治理的复杂性,本文通过建立地方政府和稀土企业之间的动态演化博弈模型,分析了博弈双方在不同情形下的演化稳定策略及其影响因素,并利用matlab对其进行数值仿真。研究表明,地方政府的监管成本、执行环境规制时对稀土企业的奖惩政策、稀土企业的治污成本以及非法开采时的额外收益都会影响地方政府与稀土企业的演化稳定策略,降低地方政府的监管成本、稀土企业的治污成本等都会引导博弈双方的演化稳定策略趋向于{严格监管,合法开采},从而促进矿区环境的可持续性发展。     

一种基于集成学习的试题多知识点标注方法

个性化试题推荐、试题难度预测、学习者建模等教育数据挖掘任务需要使用到学生作答数据资源及试题知识点标注,现阶段的试题数据都是由人工标注知识点。因此,利用机器学习方法自动标注试题知识点是一项迫切的需求。针对海量试题资源情况下的试题知识点自动标注问题,本文提出了一种基于集成学习的试题多知识点标注方法。首先,形式化定义了试题知识点标注问题,并借助教材目录和领域知识构建知识点的知识图谱作为类别标签。其次,采用基于集成学习的方法训练多个支持向量机作为基分类器,筛选出表现优异的基分类器进行集成,构建出试题多知识点标注模型。最后,以某在线教育平台数据库中的高中数学试题为实验数据集,应用所提方法预测试题考察的知识点,取得了较好的效果。     

大数据背景下基于5S的城市交通拥堵评价模型研究

近几年来,城市交通拥堵问题日益突出,极大制约了城市发展。在大数据背景下,为了准确掌握交通实时拥堵状况,改善城市交通,便利市民出行,本文深入挖掘城市交通拥堵的影响因素,构建了基于交通5S要素的城市拥堵理论模型,运用径向基函数神经网络方法工具,以上海静安寺、上海站、陆家嘴周围三大拥堵路段的交通数据集为例,验证了该模型的有效性。实验结果表明,由该模型获得的城市交通拥堵预测值与上海实际交通路况具有较好的拟合效果,表明交通5S模型与方法能够准确有效地评价城市交通拥堵。     

油砂开发经济性评价方法研究及应用

随着我国油企进入加拿大油砂行业,如何降低油砂开发项目的风险并提高决策的科学性变得愈发重要。油砂开采的经济性作为投资决策的重要着力点,研究相应的经济评价方法也就有了十分重要的意义。本文较为全面的分析了露天开发油砂及蒸汽辅助重力卸油技术(SAGD)开发油砂的技术特点;选择折现现金流理论作为理论基础分别构建了露天开发与SAGD开发油砂项目经济评价模型,并对模型中涉及的收入、投资、成本等参数的估算方法进行了系统的研究;通过对新疆某油砂试采区的实际评价,验证了方法的可行性。该方法作为油砂开发项目经济评价的初步探索,将为加拿大和我国油砂开发项目的经济评价工作提供一定的参考依据。     

从支持向量机到非平行支持向量机

非平行支持向量机是支持向量机的延伸,受到了广泛的关注.非平行支持向量机构造允许非平行的支撑超平面,可以描述不同类别之间的数据分布差异,从而适用于更广泛的问题.然而,对非平行支持向量机模型与支持向量机模型之间的关系研究较少,且尚未有等价于标准支持向量机模型的非平行支持向量机模型.从支持向量机出发,构造出新的非平行支持向量机模型,该模型不仅可以退化为标准支持向量机,保留了支持向量机的稀疏性和核函数可扩展性.同时,可以描述不同类别之间的数据分布差异,适用于更广泛的非平行结构数据等.最后,通过实验初步验证了所提模型的有效性.     

Measuring Lineup Difficulty By Matching Distance Metrics With Subject Choices in Crowd-Sourced Data

Graphics play a crucial role in statistical analysis and data mining. Being able to quantify structure in data that is visible in plots, and how people read the structure from plots is an ongoing challenge. The lineup protocol provides a formal framework for data plots, making inference possible. The data plot is treated like a test statistic, and lineup protocol acts like a comparison with the sampling distribution of the nulls. This article describes metrics for describing structure in data plots and evaluates them in relation to the choices that human readers made during several large Amazon Turk studies using lineups. The metrics that were more specific to the plot types tended to better match subject choices, than generic metrics. The process that we followed to evaluate metrics will be useful for general development of numerically measuring structure in plots, and also in future experiments on lineups for choosing blocks of pictures. Supplementary materials for this article are available online.     

Mining Pareto-optimal modules for delayed product differentiation

This paper presents a framework for finding optimal modules in a delayed product differentiation scenario. Historical product sales data is utilized to estimate demand probability and customer preferences. Then this information is used by a multiple-objective optimization model to form modules. An evolutionary computation approach is applied to solve the optimization model and find the Pareto-optimal solutions. An industrial case study illustrates the ideas presented in the paper. The mean number of assembly operations and expected pre-assembly costs are the two competing objectives that are optimized in the case study. The mean number of assembly operations can be significantly reduced while incurring relatively small increases in the expected pre-assembly cost.     

基于Web挖掘和站点拓扑的自适应网站研究

针对当前自适应网站实现领域研究的不足,尤其是缺乏对网站自身拓扑结构的分析,站在用户的角度,结合Web日志挖掘和站点拓扑分析,提出地标系数这一指标来衡量网站结点的重要性.在此基础上,提出自适应网站的实现架构,并给出智能推荐(包括高亮显示和动态地图)和缓冲预取两种自适应策略.最后,对一个原型系统进行实验分析.结果表明,提出的自适应策略可提高网站访问者的访问效率.     

Lazy structure-activity relationships (lazar) for the prediction of rodent carcinogenicity and Salmonella mutagenicity

lazar is a new tool for the prediction of toxic properties of chemical structures. It derives predictions for query structures from a database with experimentally determined toxicity data. lazar generates predictions by searching the database for compounds that are similar with respect to a given toxic activity and calculating the prediction from their activities. Apart form the prediction, lazar provides the rationales (structural features and similar compounds) for the prediction and a reliable condence index that indicates, if a query structure falls within the applicability domain of the training database. Leave-one-out (LOO) crossvalidation experiments were carried out for 10 carcinogenicity endpoints ({female|male} {hamster|mouse|rat} carcinogenicity and aggregate endpoints {hamster|mouse|rat} carcinogenicity and rodent carcinogenicity) and Salmonella mutagenicity from the Carcinogenic Potency Database (CPDB). An external validation of Salmonella mutagenicity predictions was performed with a dataset of 3895 structures. Leave-one-out and external validation experiments indicate that Salmonella mutagenicity can be predicted with 85% accuracy for compounds within the applicability domain of the CPDB. The LOO accuracy of lazar predictions of rodent carcinogenicity is 86%, the accuracies for other carcinogenicity endpoints vary between 78 and 95% for structures within the applicability domain.     

Tracking customer portfolio composition: A factor analysis approach

In this paper, we address the changing composition of a customer portfolio taking into account actions undertaken by the company to adapt its service offer to market conditions and/or technological innovations. We present a specific methodology to identify clusters of customers in different periods and then compare them over time. The classification process takes into account both qualitative and quantitative aspects of the consumption levels of the services or products offered by the company. The possibility of period‐to‐period variation in the customer portfolio and the service or product offer is also considered, in order to achieve a more realistic scenario. The core of the proposed methodology is related to the family of exploratory factorial and cluster techniques. The customers are classified by using a bicriterial clustering methodology based on ‘tandem’ analysis (multiple factor analysis+cluster analysis of the main factors). The bicriterial approach allows for a compromise between customers' consumption levels (a quantitative criterion) and their consumption/non‐consumption pattern (a qualitative criterion). The evolution of the customer portfolio composition is explored through multiple correspondence analysis. This technique allows visual comparison of the position of different clusters against time and the identification of key changes in customer consumption behavior. The methodology is tested on realistic customer portfolio scenarios for a major telecommunication company. We simulate various scenarios to show the strengths of our proposal. Copyright © 2009 John Wiley & Sons, Ltd.