安全事件模糊等量约束的因果关联算法研究

通过对异构网络的安全事件特点的深入分析,指出在大量不完整、模糊的安全事件中准确地定位潜在的安全威胁,对态势和威胁及其重要程度进行全面及时的评估,是当前安全管理的一个技术难题.提出一种基于时间关联窗口的安全事件模糊关联算法,在一定程度上解决安全事件的漏报与领域知识的不完备性问题,对当前的安全态势、安全威胁及其重要程度进行全面及时评估提供有力的支持.     

一种结合了纹理和颜色的运动目标跟踪算法

运动目标跟踪是智能视频监控中的关键技术之一,怎样描述运动目标是该技术的一个关键问题。提出一种新的运动目标跟踪算法,结合改进的LBP纹理和色度来描述运动目标,并采用Camshift算法的思想进行目标跟踪。为了降低算法的复杂性,在保证跟踪精确度的前提下,LBP纹理和色度的种类被极大地精简。实验证明提出的方法能有效地跟踪运动目标,在常规的视频分辨率下能达到实时性,比同类的其它算法性能更好。     

一种安全高效的Java操作系统IPC机制的设计与实现

安全高效的进程间通信机制是操作系统中实现进程间协同工作的关键.针对现有Java操作系统普遍使用的共享堆和Java远程过程调用(RMI)等进程间通信机制在安全性、高效性等方面的缺陷,面向Java操作系统的结构特点和进程间通信需求,设计和实现了一种新的安全、高效的进程间通信机制MSP.MSP机制通过区分小尺寸和大尺寸消息,...     

Online deadline scheduling with preemption penalties

This paper presents a study of the problem of online deadline scheduling under the preemption penalty model of Zheng, Xu, and Zhang (2007). In that model, each preemption incurs a penalty of ρ times the weight of the preempted job, where ρ ? 0 is the preemption penalty parameter. The objective is to maximise the total weight of jobs completed on time minus the total penalty.     

Disc cutters plane layout design of the full-face rock tunnel boring machine (TBM) based on different layout patterns

The quality of plane layout design of the disc cutters for the full-face rock tunnel boring machine (TBM) directly affects the balance of force distribution on the cutter head during the excavating. Various layout patterns have been adopted in practice during the layout design of the disc cutters. Considering the engineering technical requirements and the corresponding structure design requirements of the cutter head, this study formulates a nonlinear multi-objective mathematical model with multiple constraints for the disc cutters plane layout design, and analyses the characteristics of a multi-spiral layout pattern, a dynamic star layout pattern and a stochastic layout pattern. And then a genetic algorithm is employed to solve a disc cutters’ multi-spiral layout problem, and a cooperative co-evolutionary genetic algorithm (CCGA) is utilized to solve a disc cutters’ star or stochastic layout problems. The emphasis was put on the study of superiority of three different layout patterns. Finally, an instance of the disc cutters’ plane layout design was solved by the proposed methods using three different kinds of layout patterns. Experimental results showed the effectiveness of the method of combining the mathematical model with the algorithms, and the pros and cons of the three layout patterns.     

On group decision making with four formats of incomplete preference relations

In this paper, a new approach is proposed to solve group decision making (GDM) problems where the preference information on alternatives provided by decision makers (DMs) is represented in four formats of incomplete preference relations, i.e., incomplete multiplicative preference relations, incomplete fuzzy preference relations, incomplete additive linguistic preference relations, incomplete multiplicative linguistic preference relations. In order to make the collective opinion close each decision maker’s opinion as near as possible, an optimization model is constructed to integrate the four different formats of incomplete preference relations and to compute the collective ranking values of the alternatives. The ranking of alternatives or selection of the most desirable alternative(s) is directly obtained from the derived collective ranking values. A numerical example is also used to illustrate the applicability of the proposed approach.     

Piecewise Pseudo-Maximum Likelihood Estimation for Risk Aversion Case in First-Price Sealed-Bid Auction

In the literature, more and more clues show that the bidders indeed tend to risk averse. But as a good structure estimation procedure in first-price sealed-bid auctions, the traditional PPMLE (Piecewise Pseudo-Maximum Likelihood Estimation) approach is only applicable to the risk neutrality case. This study generalizes this approach to the risk aversion case, and obtains a simple estimation procedure for the Pareto family of private costs. In the procedure, the estimation order of distribution parameters of private costs is not important, which is different from the traditional PPMLE method. Though there is no unique estimator for θ ₁ and γ, some guidance is given for real-world applications on the basis of our Monte Carlo simulation experiments. What’s more, this procedure can be reduced further, if one is only interested in estimating the private costs. Extensive simulation experimental results indicate that our approach outperforms or matches at least the traditional one in the case of risk neutrality, and is applicable to the risk aversion case.     

Accelerating the kernel-method-based feature extraction procedure from the viewpoint of numerical approximation

The kernel method suffers from the following problem: the computational efficiency of the feature extraction procedure is inversely proportional to the size of the training sample set. In this paper, from a novel viewpoint, we propose a very simple and mathematically tractable method to produce the computationally efficient kernel-method-based feature extraction procedure. We first address the issue that how to make the feature extraction result of the reformulated kernel method well approximate that of the naïve kernel method. We identify these training samples that statistically contribute much to the feature extraction results and exploit them to reformulate the kernel method to produce the computationally efficient kernel-method-based feature extraction procedure. Indeed, the proposed method has the following basic idea: when one training sample has little effect on the feature extraction result and statistically has the high correlation with regard to all the training samples, the feature extraction term associated with this training sample can be removed from the feature extraction procedure. The proposed method has the following advantages: First, it proposes, for the first time, to improve the kernel method through formal and reasonable evaluation on the feature extraction term. Second, the proposed method improves the kernel method at a low extra cost and thus has a much more computationally efficient training phase than most of the previous improvements to the kernel method. The experimental comparison shows that the proposed method performs well in classification problems. This paper also intuitively shows the geometrical relation between the identified training samples and other training samples.     

Exploiting effective features for chinese sentiment classification

Features play a fundamental role in sentiment classification. How to effectively select different types of features to improve sentiment classification performance is the primary topic of this paper. Ngram features are commonly employed in text classification tasks; in this paper, sentiment-words, substrings, substring-groups, and key-substring-groups, which have never been considered in sentiment classification area before, are also extracted as features. The extracted features are then compared and analyzed. To demonstrate generality, we use two authoritative Chinese data sets in different domains to conduct our experiments. Our statistical analysis of the experimental results indicate the following: (1) different types of features possess different discriminative capabilities in Chinese sentiment classification; (2) character bigram features perform the best among the Ngram features; (3) substring-group features have greater potential to improve the performance of sentiment classification by combining substrings of different lengths; (4) sentiment words or phrases extracted from existing sentiment lexicons are not effective for sentiment classification; (5) effective features are usually at varying lengths rather than fixed lengths.