带多比特记忆组合生成器的密码分析

带记忆的组合生成器可以提高生成器的代数次数和相关免疫阶数,是一种重要的密钥流生成器。为此,研究带多比特记忆的组合生成器的概率模型,分析带多比特记忆的组合生成器的k阶相关免疫的充分必要条件,计算其输出序列与输入序列相关系数的平方和,并给出平方和的一般计算公式。分析结果表明,满足一定条件的带多比特记忆的组合生成器可以较好地抵抗相关攻击。     

六边形覆盖条件下的HWSN最大寿命模型

提出一种在六边形覆盖约束条件下异构无线传感器网络的“突然死亡”能量消耗模型,该模型可以保证能量均衡消耗和网络寿命最大化。在该能耗模型基础上给出一种考虑节点能耗约束和数据完整性等因素的网络寿命模型,并求得网络寿命模型的最优解。仿真结果表明,构建在六边形覆盖条件下的异构监测传感器模型可以均衡网络能耗,且使网络寿命最大化。     

面向轨迹数据流的KNN近似查询

提出一种基于滑动窗口的K-最近邻(KNN)近似查询算法。将滑动窗口内数据通过聚类划分成若干大小不一的基本窗口,针对每个基本窗口给定一个采样率,对窗口内数据进行偏倚采样,形成数据流摘要,并基于该摘要,采用计算几何平面扫描算法执行分布式最近邻查询。仿真实验结果表明该算法有效,且具有较好的可扩展性。     

基于约束的可能性XML条件计算

将约束引入到可能性XML时,因分布节点值的不确定性,XML文档无法检验对涉及分布节点的约束的合法性。在一些场景中,要返回给用户符合约束的查询结果和概率,通常没有考虑去除不可能域分布来做条件计算,得出的并不是准确概率。为此,提出一种可能性XML的概念,给出可能性XML中可能域和约束的有效表达,通过提出的方案解决条件计算和准确概率计算的问题。实验结果表明,该算法的效率较高,条件计算后的结果更能被用户接受。     

基于法向量的非线性逼近型细分格式

提出一种二进制的几何非线性逼近型细分格式。在该格式中,新点不全是旧点的线性组合,其中一个新点是通过在法向量方向偏移所产生,且法向量在每次细分中能自适应计算。引入一些参数来控制细分过程,且参数对曲线形状的影响是局部的。实例证明,通过选择适当的参数值,产生的细分曲线具有保凸性和 连续性。     

基于骨架关节点约束的交互式局部变形

利用广义元球变形技术,提出一种基于骨架关节点约束的交互式局部变形方法。该方法提取多边形网格模型的骨架关节点并结合模型骨架图结构确定骨架关节点对应的局部区域,计算三维网格点到骨架节点的欧式距离,将约束区域的最大欧氏距离作为约束半径,得到各骨架节点对应的势函数值,通过控制骨架节点的空间位置给出三角形面片点的新坐标位置。实验结果表明,该方法有效保持多边形网络模型的局部特征,并确保了模型变形的直观性和高效性。     

基于ESCA系统的层次化显式访存机制研究

针对高性能混合计算系统中的存储墙问题,在分析其计算模式特点及传统访存机制局限性的基础上,提出适用于混合计算系统的层次化显式存储访问机制,并基于ESCA多核处理器系统进行实现和评测。实验结果显示,针对核心应用程序DGEMM,延迟隐藏能够占据整体运行时间的56%,并获得1.5倍的加速比,能弥补计算与存储访问间的速度差异,提高系统计算效率。     

带权定性影响图

定性影响图是具有精确概率和效用的影响图的定性抽象。在定性影响图中,节点之间的影响关系使用定性符号描述,这种符号描述简化了不确定知识的表示,降低了影响图建模的难度,加速了不确定知识的推理。但是,定性影响图在抽象过程中损失了部分信息,导致定性影响图在评价过程中会产生不确定结果,阻碍了定性影响图的广泛应用。以加权的思想扩展定性影响图,使扩展的定性影响图中每个定性影响都带有一个表示节点间影响强弱的数值权值,在评价过程中根据定性影响权值的比较来减少不确定结果的产生,从而扩大定性影响图的应用范围。     

Corrective classification: Learning from data imperfections with aggressive and diverse classifier ensembling

Learning from imperfect (noisy) information sources is a challenging and reality issue for many data mining applications. Common practices include data quality enhancement by applying data preprocessing techniques or employing robust learning algorithms to avoid developing overly complicated structures that overfit the noise. The essential goal is to reduce noise impact and eventually enhance the learners built from noise-corrupted data. In this paper, we propose a novel corrective classification (C2) design, which incorporates data cleansing, error correction, Bootstrap sampling and classifier ensembling for effective learning from noisy data sources. C2 differs from existing classifier ensembling or robust learning algorithms in two aspects. On one hand, a set of diverse base learners of C2 constituting the ensemble are constructed via a Bootstrap sampling process; on the other hand, C2 further improves each base learner by unifying error detection, correction and data cleansing to reduce noise impact. Being corrective, the classifier ensemble is built from data preprocessed/corrected by the data cleansing and correcting modules. Experimental comparisons demonstrate that C2 is not only more accurate than the learner built from original noisy sources, but also more reliable than Bagging [4] or aggressive classifier ensemble (ACE) [56], which are two degenerated components/variants of C2. The comparisons also indicate that C2 is more stable than Boosting and DECORATE, which are two state-of-the-art ensembling methods. For real-world imperfect information sources (i.e. noisy training and/or test data), C2 is able to deliver more accurate and reliable prediction models than its other peers can offer.     

Project scheduling under resource constraints: Application of the cumulative global constraint in a decision support framework

This paper concerns project scheduling under resource constraints. Traditionally, the objective is to find a unique solution that minimizes the project makespan, while respecting the precedence constraints and the resource constraints. This work focuses on developing a model and a decision support framework for industrial application of the cumulative global constraint. For a given project scheduling, the proposed approach allows the generation of different optimal solutions relative to the alternate availability of outsourcing and resources. The objective is to provide a decision-maker an assistance to construct, choose, and define the appropriate scheduling program taking into account the possible capacity resources. The industrial problem under consideration is modeled as a constraint satisfaction problem (CSP). It is implemented under the constraint programming language CHIP V5. The provided solutions determine values for the various variables associated to the tasks realized on each resource, as well as the curves with the profile of the total consumption of resources on time.