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.     

基于入侵容忍的证书撤销列表机制研究

公钥基础设施(PKI)系统中,认证机构(CA)签名不易伪造,对基于证书撤销列表(CRL)的证书撤销系统的入侵通常是破坏系统的可用性和数据的完整性,针对这一特点,设计了入侵容忍CRL服务系统。系统利用冗余的多台服务器存储CRL,在进行多机之间的数据复制和使用时,采取随机选择主服务器的被动复制算法及选择最近更新的CRL简单表决算法。在实验给定的入侵攻击条件下,入侵容忍的CRL系统比无容忍系统的证书撤销查询正确率提高了近20%,但也增加了系统的开销。实验结果表明,适当地增加CRL服务器的数量能够提高证书撤销查询的正确率且控制系统的开销。     

基于多方向显著子区域置信表决的多目标检测

多目标检测是智能交通监控系统的一项重要和挑战性的工作。提出一种基于多方向显著子区域置信表决的多目标检测方法。利用Gabor滤波提取目标四方向显著特征像素;根据多阈值条件确定每个目标的有效子区域及尺度;判断有效交集子区域和交集状态;加权融合交集面积占空比和有效交集子区域相对面积占空比,获得各个有效交集子区域置信表决系数;根据最大置信表决系数获得目标最优检测。采用实际交通场景采集的视频图像序列进行实验,结果表明该方法在邻近目标交互干扰和遮挡、目标阴影、非均匀光照及背景中相似色彩干扰等情况下,均具有鲁棒、准确的检测性能。     

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.     

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。     

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。     

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.     

基于自产生投票的手写汉字识别

在模式识别领域,投票策略是非常有效的,而且已被成功应用到人脸检测、识别等领域. 然而,在手写汉字识别 (Handwritten Chinese character recognition, HCCR)中,由于类别集很大、训练样本少等特点,现有的很多分类器集成方法方法都很难直接应用于此领域. 本文提出一种自产生式投票的方法,该方法通过事先学习得到的参数集产生一个测试集合,然后用一个分类器去识别 测试集合中的每个样本,得到属于各个类别的概率,最后通过加权投票得到识别结果. 实验结果表明,本文提出的方法是实用和有效的.     

利用信源残留冗余实现高容错数字分接

正确的数字分接是PDH数字群路信号信息还原的前提。在高误码信道环境下,为实现可靠通信,提出了利用信源残留冗余来实现高容错数字同步分接的改进算法。算法通过挖掘PDH数据最底层的PCM信号中所残留的冗余,根据码速调整对PCM残留冗余的影响,来增强码速恢复的判断依据。实验的仿真结果显示算法较为彻底地解决了群路分接的滑帧问题,在容错性和准确性上比ITU推荐的多数判决法有显著提高,且较低的计算复杂性使其易于用软硬件实现。由于揭示的规律在大容量PDH通信中是普遍存在的,因此提出的算法具有很大的实用性。