Signature verification using holistic measures

Most signature verification systems today are based on the evaluation of dynamic features. They verify the identity of a person by having him sign his signature but cannot verify a signature that has already been written. However, there are no such systems sufficiently reliable for general use. Recent measurements at our laboratory on both static and dynamic characteristics of signatures show that excellent results may be obtained using a small number (only 4) of qualifying characteristics or parameters. The system software gives individual weights to these parameters depending on the sample-to-sample variations in the person's set of reference signatures. An additional fixed weighting is given which depends on the average success that forgers are found to have in duplicating that particular parameter value.The 4 parameters chosen are all static, i.e. they depend only on shape and size of the signature. This means that forged cheques may be identified. In the final system there should be no need for a special pen or tablet although for the present work we have used a digitizer tablet. With a database comprising about 230 signatures from 11 persons we have achieved an error rate of less than 5% using only 4 weighted parameters.     

Intelligent fault inference for rotating flexible rotors using Bayesian belief network

Flexible rotor is a crucial mechanical component of a diverse range of rotating machineries and its condition monitoring and fault diagnosis are of particular importance to the modern industry. In this paper, Bayesian belief network (BBN) is applied to the fault inference for rotating flexible rotors with attempt to enhance the reasoning capacity under conditions of uncertainty. A generalized three-layer configuration of BBN for the fault inference of rotating machinery is developed by fully incorporating human experts’ knowledge, machine faults and fault symptoms as well as machine running conditions. Compared with the Naive diagnosis network, the proposed topological structure of causalities takes account of more practical and complete diagnostic information in fault diagnosis. The network tallies well with the practical thinking of field experts in the whole processes of machine fault diagnosis. The applications of the proposed BBN network in the uncertainty inference of rotating flexible rotors show good agreements with our knowledge and practical experience of diagnosis.     

一种基于贝叶斯网络的随机测试方法在Cache一致性验证中的设计与实现

随着集成电路设计复杂度指数级增长,功能验证已经越来越成为大规模芯片设计的瓶颈,而在多核处理器中,Cache一致性协议十分复杂,验证难度大。针对Cache一致性协议验证提出基于模拟验证的一种基于贝叶斯网络的随机测试生成方法,解决Cache一致性协议状态空间爆炸的问题。首先分析了Cache一致性协议及基于贝叶斯网络推理的CDG方法,并将CDG方法应用于Cache一致性的验证。以FT处理器中的Cache一致性协议验证为例,对比伪随机测试,使用CDG方法将覆盖率提高近30%。     

Signature verification using global and grid features

In this work, algorithms for extracting global geometric and local grid features of signature images were developed. These features were combined to build a multi-scale verification function. This multi-scale verification function was evaluated using statistical procedures. Results indicated that the multi-scale verification function yielded a lower verification error rate and higher reliability than the single-scale verification function using either global geometric or local grid feature representation. The correct verification rate of the multi-scale system was more than 90% in rejecting skilled forgeries and was perfect in rejecting simple forgeries based on a limited database.     

A Multi-stage Online Signature Verification System

: This paper presents a new three-stage verification system which is based on three types of features: global features; local features of the corner points; and function features that contain information of each point of the signatures. The first verification stage implements a parameter-based method, in which the Mahalanobis distance is used as a dissimilarity measure between the signatures. The second verification stage involves corner extraction and corner matching. It also performs signature segmentation. The third verification stage implements a function-based method, which is based on an elastic matching algorithm establishing a point-to-point correspondence between the compared signatures. By combining the three different types of verification, a high security level can be reached. According to our experiments, the rates of false rejection and false acceptance are, respectively, 5.8% and 0%. Received: 12 Febuary 2001, Received in revised form: 24 May 2001, Accepted: 03 July 2001     

Online signature verification using a new extreme points warping technique

There are two common methodologies to verify signatures: the functional approach and the parametric approach. In this paper, we propose a new warping technique for the functional approach in signature verification. The commonly used warping technique is dynamic time warping (DTW). It was originally used in speech recognition and has been applied in the field of signature verification with some success since two decades ago. The new warping technique we propose is named as extreme points warping (EPW). It proves to be more adaptive in the field of signature verification than DTW, given the presence of the forgeries. Instead of warping the whole signal as DTW does, EPW warps a set of selected important points. With the use of EPW, the equal error rate is improved by a factor of 1.3 and the computation time is reduced by a factor of 11.     

Stock price forecast using Bayesian network

Bayesian network is a probabilistic graphical model that represents a set of random variables and their conditional dependencies via a directed acyclic graph. This paper describes the price earnings ratio (P/E ratio) forecast by using Bayesian network. Firstly, the use of clustering algorithm transforms the continuous P/E ratio to the set of digitized values. The Bayesian network for the P/E ratio forecast is determined from the set of the digitized values. NIKKEI stock average (NIKKEI225) and Toyota motor corporation stock price are considered as numerical examples. The results show that the forecast accuracy of the present algorithm is better than that of the traditional time-series forecast algorithms in comparison of their correlation coefficient and the root mean square error.     

Writer-independent off-line signature verification using surroundedness feature

The paper presents a novel set of features based on surroundedness property of a signature (image in binary form) for off-line signature verification. The proposed feature set describes the shape of a signature in terms of spatial distribution of black pixels around a candidate pixel (on the signature). It also provides a measure of texture through the correlation among signature pixels in the neighborhood of that candidate pixel. So the proposed feature set is unique in the sense that it contains both shape and texture property unlike most of the earlier proposed features for off-line signature verification. Since the features are proposed based on intuitive idea of the problem, evaluation of features by various feature selection techniques has also been sought to get a compact set of features. To examine the efficacy of the proposed features, two popular classifiers namely, multilayer perceptron and support vector machine are implemented and tested on two publicly available database namely, GPDS300 corpus and CEDAR signature database.     

Bayesian network modelling through qualitative patterns

In designing a Bayesian network for an actual problem, developers need to bridge the gap between the mathematical abstractions offered by the Bayesian-network formalism and the features of the problem to be modelled. Qualitative probabilistic networks (QPNs) have been put forward as qualitative analogues to Bayesian networks, and allow modelling interactions in terms of qualitative signs. They thus have the advantage that developers can abstract from the numerical detail, and therefore the gap may not be as wide as for their quantitative counterparts. A notion that has been suggested in the literature to facilitate Bayesian-network development is causal independence. It allows exploiting compact representations of probabilistic interactions among variables in a network. In the paper, we deploy both causal independence and QPNs in developing and analysing a collection of qualitative, causal interaction patterns, called QC patterns. These are endowed with a fixed qualitative semantics, and are intended to offer developers a high-level starting point when developing Bayesian networks.     

基于动态贝叶斯网络的社交圈归属匹配模型

为了帮助社交网中新成员寻找与之最为合适的社交圈,尝试采用动态贝叶斯网（DBN）理论解决社交网分析应用中成员（节点）与社交圈（集合）的匹配问题。将圈内成员个人的多项兴趣爱好程度作为描述社交圈基本属性的特征向量,对每一类圈子建立了带有辅助信息形式的DBN模型,求解最大输出概率即为最佳匹配对象。结果表明,在客观测试和主观评价两方面,该模型都收到了较为满意的结果。     

使用贝叶斯网络的社交网络信任驱动推荐方法

为处理推荐行为来源复杂、路径多样、不信任陌生推荐等问题,提出一种在社交网络中信任驱动推荐方法。该方法利用贝叶斯网络,计算用户评分的先验概率分布以及朋友之间的联合条件概率,预测用户在该环境下的评分并将推荐给用户。在信任驱动推荐过程中,预测评分既考虑到用户的偏好,也考虑到用户的社会关系;此外,用户的信息交换只限于朋友之间,能够有效保护用户的隐私。实验结果表明,所提出的推荐方法在预测准确率和推荐覆盖率上具有良好的性能。     

贝叶斯网络在电子系统故障诊断中的应用研究

电子系统大多结构复杂,各组成模块存在错综复杂、相互影响的关系,另外测点较少且测点数据常常是不完备的。针对此类情况,以某电源系统为研究对象,提出了基于贝叶斯网络的电子系统故障诊断方法。首先依据系统的结构获得其因果图,并对各测点信号进行离散化处理;其次建立用于故障诊断的贝叶斯网络模型,并且根据历史数据完成该网络的参数学习,最后利用获得的事实来实现故障的诊断。仿真结果验证了该方法的有效性,为电子系统的故障诊断提出了一种新的思路。     

手写签名的快速认证

提出了一种新的手写签名的快速认证方法，采用包括笔迹起点、终点信息、笔迹方向信息及笔画数等信息在内的匹配函数，将测试签名的特征和参考样板签名的特征进行匹配，判断真伪。在签名对准过程中，根据曲折点的曲率、方向确定旋转、平移变换，大大减少了对准时间。采用大量签名进行实验，该方法识别效果良好。     

Mobile cloud-based depression diagnosis using an ontology and a Bayesian network

Recently, depression has becomes a widespread disease throughout the world. However, most people are not aware of the possibility of becoming depressed during their daily lives. Therefore, obtaining an accurate diagnosis of depression is an important issue in healthcare. In this study, we built an inference model based on an ontology and a Bayesian network to infer the possibility of becoming depressed, and we implemented a prototype using a mobile agent platform as a proof-of-concept in the mobile cloud. We developed an ontology model based on the terminology used to describe depression and we utilized a Bayesian network to infer the probability of becoming depressed. We also implemented the system using multi-agents to run on the Android platform, thereby demonstrating the feasibility of this method, and we addressed various implementation issues. The results showed that our method may be useful for inferring a diagnosis of depression.     

基于模糊模式识别的离线签名验证技术

签名验证有着重要的社会价值，长期以来一直是由人类专家来验证签名。但有时待验证的签名很多，验证的工作量非常大，而其中多数伪造的签名并不是高超的赝品。该文介绍了一种基于模糊模式识别的离线签名验证技术，可以用计算机来帮助人类专家进行粗筛，大大减少专家的工作量。     

一种基于MapReduce的OpenFlow网络属性并行验证算法

针对OpenFlow网络中流表配置错误引起转发回路、路由黑洞和访问控制规则失效等问题,提出一种并行的基于MapReduce的OpenFlow网络属性验证算法。通过在Map阶段划分规则等价类,在Reduce阶段为规则等价类构建基于交换机端口谓词的网络转发图并分析可达性,实现对网络属性的并行验证。同时,通过采用原子谓词将传统可达性分析中的规则匹配域多维集合运算转换为整数集合运算,以进一步提高可达性分析效率。此外,基于原子谓词的谓词表达方式可消除交换机端口谓词集合中的冗余项,降低存储开销。最后,通过理论分析和仿真实验验证了算法的正确性及在时间和存储开销方面的优越性。