基于语义的Web服务发现算法

传统的Web服务发现只是简单的基于关键字的语法匹配,查询得到的服务往往不是用户想要的.在基于接口的Web服务发现的基础上,改进了Web服务描述模型,增加了服务质量(QoS),提出了分步过滤匹配算法.先通过服务类别过滤器进行语义过滤筛选,去除不相关的Web服务,然后通过服务相似度度量候选服务和请求服务之间的相似程度.候选服务和请求服务之间的相似度是通过服务功能相似度和服务质量相似度两个方面进行综合评估的.最后,通过实验证明了该匹配算法的可行性和有效性.     

加权3D-Matching的改进算法

Matching问题构成了一类重要的NP难问题.此类问题在诸多领域中有着重要的应用,如调度、代码优化等领域.对于加权3D-matching问题,通过深入分析问题的结构特性,可以转化成加权3D-matching augmentation问题进行求解,即从一个最大加权的k-matching着手构造权值最大的(k+1)-matching.从问题的特殊结构特性出发,给出了加权3D-matching augmentation问题特有的性质: k- matching中存在2列使得该2列至少有2k/3元素被包含在(k+1)-matching中所对应的2列中.基于给出的性质,通过运用color-coding和动态规划技术,给出了一个时间复杂度为O* (4.823k)的参数算法,最终求解加权3D-matching问题.该算法较目前文献中的最好结果O* (5.473k)有了极大的改进.     

一种基于嵌入式平台的原位测试设备研制

针对现有发动机状态检测设备体积大、重量大,集成度低,导致机动性较差的实际,提出了一种基于嵌入式平台的原位测试设备的设计方案,构建了硬件、软件平台,给出了相关的硬件电路和软件设计.实际应用表明,新研制设备不仅功能齐全,工作稳定可靠,便于地勤人员的维护保障,而且具有使用、携带方便、智能化、集成度高的特点.     

QoS-aware placement of stream processing service

Many emerging online stream processing services require the consideration of quality of service (QoS), which is highly dependent on the placement of services at various hosts. This paper investigates the QoS-aware placement problems of stream processing services under different contexts. On condition that the client demands are stable, the QoS-aware placement problem aiming to minimize the cost when servers are CPU-uncapacitated, is equivalent to the set cover problem, and can be solved by a greedy algorithm with approximation factor O(log?n), where n is the number of clients. However, when CPU capacity constraints on servers are taken into account, the QoS-aware placement problem cannot be approximated unless P=NP. Therefore, we propose two heuristic algorithms: (1) ISCA (Iterated Set Cover-based Algorithm) and (2) KBA (Knapsack-Based Algorithm). We also consider the placement problem of client demands increasing over time. Two objectives, called extension factor and system lifetime, are proposed for demand increment-blind and increment-aware models respectively. Both of them can be solved by extending ISCA and KBA. The experimental results show that ISCA and KBA have distinct effects on different demand sizes. ISCA is more efficient when client demands are relatively small, while KBA performs better for larger demands.     

基于Petri网的联锁软件测评仿真建模

车站联锁软件一直有着严格的测试要求,仿真模块是软件测试的重要组成部分,Petri网作为形式化语言的一种,有着准确与完备的特点,对于联锁软件这类安全苛求软件的测试尤为适合,提出了一种使用Petri网对联锁软件测试中仿真模块建模的方式,并分别给出了用基本Petri网和有色Petri网的2个建模实例.该模型有助于提高铁路联锁软件测试系统的安全性与精确性.     

基于状态预测的网格工作流资源选择策略

考虑到网格中存在着大量功能相同但可靠性各不相同的资源,针对执行时间限制严格类型的网格工作流提出了一种新的基于状态预"311的资源选择策略.该策略以工作流DAG图中的关键路径上的前驱任务资源收到输入转移数据而被触发执行的时刻作为预测时刻,利用Markov过程计算出后继任务的各候选资源在前驱任务结束时刻处于"闲状态"的概率大小,然后选择这一概率较大的资源作为此后继任务的执行资源,从而了保证了工作流关键路径任务执行时问的要求.最后通过实验验证了该策略的有效性.     

快速三维人耳提取与识别

针对现有三维人耳提取与识别算法中存在处理时间长、识别率低的问题,提出一种快速三维人耳提取方法和2种三维人耳识别方法.三维人耳提取时,使用不变特征迭代最近点算法使人耳与平均耳对齐,完成位置和姿态的归一化,然后用掩膜提取出三维人耳.第一种=三维人耳识别方法结合人耳深度和曲率信息,采用主元分析算法进行降维.然后用最近邻分类完成识别;第二种三维人耳识别方法则使用不变特征迭代最近点算法对齐测试耳与原型耳,利用配准误差完成人耳识别.实验结果表明,第一种人耳识别方法识别率较高、计算速度很快,第二种人耳识别方法可达到很高的识别率.     

Is my model any good: differentially private regression diagnostics

Linear and logistic regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the regression model, estimate its parameters, and use the results for inference or prediction. Instead, they first use a variety of diagnostic techniques to assess how well the model fits the relationships in the data and how well it can be expected to predict outcomes for out-of-sample records, revising the model as necessary to improve fit and predictive power. In this article, we develop \(\epsilon \)-differentially private diagnostics tools for regression, beginning to fill a gap in privacy-preserving data analysis. Specifically, we create differentially private versions of residual plots for linear regression and of receiver operating characteristic (ROC) curves as well as binned residual plot for logistic regression. The residual plot and binned residual plot help determine whether or not the data satisfy the assumptions underlying the regression model, and the ROC curve is used to assess the predictive power of the logistic regression model. These diagnostics improve the usefulness of algorithms for computing differentially private regression output, which alone does not allow analysts to assess the quality of the posited model. Our empirical studies show that these algorithms can be effective tools for allowing users to evaluate the quality of their models.     

基于特征映射的互联网频繁模式识别算法

提出了一种基于特征映射的互联网频繁模式识别算法,采用特征映射与聚类相结合的方法,解决了SPADE算法不能反映类信息的问题与大量数据聚类时的效率低下,同时,提出了比较合理的频繁模式定量评价体系,从而完成了识别系统的整体设计,对于处理互联网海量用户数据有着相当重要的作用,同时,可以根据此算法提供个性化推荐等服务来提升用户体验.