Analyzing User-Perceived Dependability and Performance Characteristics of Voting Algorithms for Managing Replicated Data

User-perceived dependability and performance metrics are very different from conventional ones in that the dependability and performance properties must be assessed from the perspective of users accessing the system. In this paper, we develop techniques based on stochastic Petri nets (SPN) to analyze user-perceived dependability and performance properties of quorum-based algorithms for managing replicated data. A feature of the techniques developed in the paper is that no assumption is made regarding the interconnection topology, the number of replicas, or the quorum definition used by the replicated system, thus making it applicable to a wide class of quorum-based algorithms. We illustrate this technique by comparing conventional and user-perceived metrics in majority voting algorithms. Our analysis shows that when the user-perceiveness is taken into consideration, the effect of increasing the network connectivity and number of replicas on the availability and dependability properties perceived by users is very different from that under conventional metrics. Thus, unlike conventional metrics, user-perceived metrics allow a tradeoff to be exploited between the hardware invested, i.e., higher network connectivity and number of replicas, and the performance and dependability properties perceived by users.     

Analogy-Based Practical Classification Rules for Software Quality Estimation

Software metrics-based quality estimation models can be effective tools for identifying which modules are likely to be fault-prone or not fault-prone. The use of such models prior to system deployment can considerably reduce the likelihood of faults discovered during operations, hence improving system reliability. A software quality classification model is calibrated using metrics from a past release or similar project, and is then applied to modules currently under development. Subsequently, a timely prediction of which modules are likely to have faults can be obtained. However, software quality classification models used in practice may not provide a useful balance between the two misclassification rates, especially when there are very few faulty modules in the system being modeled.This paper presents, in the context of case-based reasoning, two practical classification rules that allow appropriate emphasis on each type of misclassification as per the project requirements. The suggested techniques are especially useful for high-assurance systems where faulty modules are rare. The proposed generalized classification methods emphasize on the costs of misclassifications, and the unbalanced distribution of the faulty program modules. We illustrate the proposed techniques with a case study that consists of software measurements and fault data collected over multiple releases of a large-scale legacy telecommunication system. In addition to investigating the two classification methods, a brief relative comparison of the techniques is also presented. It is indicated that the level of classification accuracy and model-robustness observed for the case study would be beneficial in achieving high software reliability of its subsequent system releases. Similar observations are made from our empirical studies with other case studies.     

Feature detection of triangular meshes based on tensor voting theory

This paper presents n-dimensional feature recognition of triangular meshes that can handle both geometric properties and additional attributes such as color information of a physical object. Our method is based on a tensor voting technique for classifying features and integrates a clustering and region growing methodology for segmenting a mesh into sub-patches. We classify a feature into a corner, a sharp edge and a face. Then, finally we detect features via region merging and cleaning processes. Our feature detection shows good performance with efficiency for various dimensional models.     

多数据库中例外模式挖掘方法研究

首先比较了现有的两种挖掘方法,提出了一种改进技术.综合考虑例外的局部和全局兴趣度,剔除非真正有趣的局部例外;增加两种客观度量并按模式重要度排序.实验表明该方法不仅可以有效挖掘多数据库中例外模式,而且还大大减少了用户负担.     

余度系统中的动态同步和表决控制

在多通道余度系统的研究和应用中,同步问题是整个余度系统正确运行的基本保证。基于多交叉通道模型和同步时间的静态分配策略,提出动态时间容错方法。针对该方法,设计基于时间容错的动态表决控制算法,实现多通道系统中的动态同步。给出动态同步表决控制逻辑,用于控制表决时间并监测表决数据。理论分析和实验结果表明,时间容错的动态表决控制算法扩展了任务处理的时间窗口,与静态同步算法相比,可有效降低通道故障率,提高对瞬时干扰的抵御能力以及余度系统的可靠性。     

一个基于OT协议的电子选举方案

将OT协议应用于电子选举中,提出一个新的电子选举方案。该方案能较好地解决电子选举对多选性的要求,同时满足安全电子选举方案的基本特性,且效率较高,可适用于较大规模的选举,进一步改进和完善了现有的电子选举方案。     

基于异常值的拟态裁决优化方法

针对拟态裁决器多数一致性表决算法的优化方法,提出用异常检测的方法直接量化数据可靠性来提升表决正确率.基于异常值的表决算法,通过构建拟态系统异构执行体输出数据集和训练深度学习异常检测模型量化了执行体输出数据异常值;使用权值优化算法优化加权分配,在表决时选择最优加权结果作为表决输出结果.实验结果表明,该方法能够提升拟态裁决器的表决输出正确率,具有一定共模逃逸检测能力,提升了系统的安全性和可靠性.     

Dust particle detection in traffic surveillance video using motion singularity analysis

Dust particle detection in video aims to automatically determine whether the video is degraded by dust particle or not. Dust particles are usually stuck on the camera lends and typically temporally static in the images of a video sequence captured from a dynamic scene. The moving objects in the scene can be occluded by the dusts; consequently, the motion information of moving objects tends to yield singularity. Motivated by this, a dust detection approach is proposed in this paper by exploiting motion singularity analysis in the video. First, the optical model of dust particle is theoretically studied in by simulating optical density of artifacts produced by dust particles. Then, the optical flow is exploited to perform motion singularity analysis for blind dust detection in the video without the need for ground truth dust-free video. More specifically, a singularity model of optical flow is proposed in this paper using the direction of the motion flow field, instead of the amplitude of the motion flow field. The proposed motion singularity model is further incorporated into a temporal voting mechanism to develop an automatic dust particle detection in the video. Experiments are conducted using both artificially-simulated dust-degraded video and real-world dust-degraded video to demonstrate that the proposed approach outperforms conventional approaches to achieve more accurate dust detection.     

基于图学习的社会网络图像标签排序算法

提出一种社会网络图像标签排序算法。将SIFT特征、卷积神经网络特征以及视觉词袋模型相结合,从图像训练集中获取目标图像的视觉近邻图像集;令所有视觉近邻图像为目标图像的初始标签进行加权投票,通过对图像视觉相似度和标签语义相似度的线性融合,计算投票权值;利用目标图像及其视觉近邻图像的标签,构造标签图模型;利用加权投票结果在标签图上执行随机漫步,完成标签排序任务。实验结果验证了提出方法的有效性。