Logistic Regression Trust–A Trust Model for Internet-of-Things Using Regression Analysis

Internet of Things (IoT) is a popular social network in which devices are virtually connected for communicating and sharing information. This is applied greatly in business enterprises and government sectors for delivering the services to their customers, clients and citizens. But, the interaction is successful only based on the trust that each device has on another. Thus trust is very much essential for a social network. As Internet of Things have access over sensitive information, it urges to many threats that lead data management to risk. This issue is addressed by trust management that help to take decision about trustworthiness of requestor and provider before communication and sharing. Several trust-based systems are existing for different domain using Dynamic weight method, Fuzzy classification, Bayes inference and very few Regression analysis for IoT. The proposed algorithm is based on Logistic Regression, which provide strong statistical background to trust prediction. To make our stand strong on regression support to trust, we have compared the performance with equivalent sound Bayes analysis using Beta distribution. The performance is studied in simulated IoT setup with Quality of Service (QoS) and Social parameters for the nodes. The proposed model performs better in terms of various metrics. An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services. The most salient features of IoT system is to design it with scalability, extendibility, compatibility and resiliency against attack. The existing works finds a way to integrate direct and indirect trust to converge quickly and estimate the bias due to attacks in addition to the above features.     

基于Logistic回归的电梯健康评估

电梯的隐患故障与其运行状态存在着一定的关联性.根据电梯的组成要素、体现电梯健康状况表征,合理选择评价参数,基于logistic回归方法建立评价模型.通过分析评价模型基本原理,整合原始数据、引入惩罚因子、交叉验证、高阶拟线性等方法,解决了数据不均衡现状和差异性,提高了评价模型准确度,达到了对电梯健康实时监控并预警.     

如何用经验Logistic回归方法估计Rasch模型中参数

将经验Logistic回归方法用于项目反应理论中Rasch模型的参数估计，通过蒙特卡洛模拟表明这种方法对项目参数的估计较好，这种方法是对极大似然估计方法的不足的一种补充。     

基于同态加密的多分类Logistic回归模型

随着计算能力的发展,机器学习得到了广泛的应用,数据的安全问题也成为一个重要问题.同态加密技术可以在不泄露明文信息的情况下,对密文进行运算并在解密后得到与在明文上执行相应运算一致的结果.因此,同态加密是一种可行的有潜力的数据安全外包解决方案.为了解决现实生活中出现的多分类问题,本文基于Cheon等提出的HEAAN同态加密方案,提出了一种能有效保护数据隐私的多分类Logistic回归模型,采用"一对其余"的拆解策略,通过训练多个分类器,将二分类Logistic回归模型推广到多分类.数据持有者可以将数据加密后发送给服务器,服务器使用多分类Logistic回归模型对加密数据进行训练,并将结果传回数据持有者,数据持有者解密结果后可以用来对多分类数据进行预测,整个过程中不会有隐私被泄露.本文通过对UCI的Dermatology和Iris数据集进行了实验,测试模型的性能.Dermatology数据集包含358条样本, 34个特征属性,分为6个类别,训练时间约为36.70分钟,准确率达到77.18%,与明文计算的准确率一致.实验验证了本文的模型在效率和准确率方面的可行性.     

多元Logistic回归分析在基于黑客脚印的入侵检测中的应用

针对以往基于主机的入侵检测方法算法复杂、占用系统资源大的不足,介绍了一种轻量级的基于黑客脚印的检测方法,并将多元Logistic回归分析应用到远程攻击的检测中去,不仅可以有效地检测出入侵行为,而且可以降低入侵检测系统对系统资源的占用。     

一种基于多项式逻辑回归高光谱影像分类方法的改进

针对多项式逻辑回归分类器在参数寻优过程中精度提升不明显和运行速度缓慢的问题,提出一种利用DFP修正拟牛顿算法进行多项式逻辑回归参数求解的方法来提高多项式逻辑回归参数优化的效率,该算法以弦截法代替牛顿算法中的二阶Hessian矩阵,并采用DFP修正法不断修正。利用该算法对两幅高光谱影像进行分类实验,结果表明:与传统的多项式回归分类方法相比,该算法在分类精度和效率方面都有明显的改进。     

面向对象软件回归测试技术研究

回归测试的目标之一是在程序修改后,只对进行修改的部分重新测试,从而达到与完全测试相同的测试覆盖.利用数据流或部分数据流技术,对结构化程序进行回归测试的技术已相继提出.随着面向对象方法的逐渐成熟,对面向对象软件测试技术的研究有了迫切的需求.通过分析对象系统的特性,定义了对象之间的依赖关系,通过这个依赖关系,导出测试对象的方法序列,并应用程序切片技术,标识那些受到程序修改影响的测试用例,只有这些测试用例才需要在回归测试中重新执行.最后给出了一完整的对象系统的回归测试解决方案.     

基于密度估计的逻辑回归模型

介绍了一种基于密度的逻辑回归（Density-based logistic regression,DLR）分类模型以解决逻辑回归中非线性分类的问题. 其主要思想是根据Nadarays-Watson密度估计将训练数据映射到特定的特征空间,然后组建优化模型优化特征权重以及Nadarays-Watson 密度估计算法的宽度. 其主要优点在于：它不仅优于标准的逻辑回归,而且优于基于径向基函数（Radial basis function,RBF）内核的核逻辑回归（Kernel logistic regression,KLR）. 特别是与核逻辑回归分析和支持向量机（Support vector machine,SVM）相比,该方法不仅达到更好的分类精度,而且有更好的时间效率. 该方法的另一个显著优点是,它可以很自然地扩展到数值类型和分类型混合的数据集中. 除此之外,该方法和逻辑回归（Logistic regression,LR）一样,有同样的模型可解释的优点,这恰恰是其他如核逻辑回归分析和支持向量机所不具备的.     

基于贝叶斯Logistic回归的软件缺陷预测研究

在软件开发初期及时识别出软件存在的缺陷,可以帮助项目管理团队及时优化开发测试资源分配,以便对可能含有缺陷的软件进行严格的质量保证活动,这对于软件的高质量交付有着重要的作用,因此,软件缺陷预测成为软件工程领域内一个研究热点。虽然人们已经使用多种机器学习算法建立了缺陷预测模型,但还没有对这些模型的贝叶斯方法进行研究。提出了无信息先验和信息先验的贝叶斯Logistic回归方法来建立缺陷预测模型,并对贝叶斯Logistic回归的优势以及先验信息在贝叶斯Logistic回归中的作用进行了研究。最后,在PROMISE数据集上与其他已有缺陷预测方法（LR、NB、RF、SVM）进行了比较研究,结果表明：贝叶斯Logistic回归方法可以取得很好的预测性能。     

结合差分演化和逻辑回归的构音障碍自动识别方法

针对传统的构音障碍诊断方法存在耗时高、成本高等问题，提出一种构音障碍语音的计算机自动识别方法。结合Gammatone频率倒谱系数(Gammatone Frequency Cepstrum Coefficients, GFCC)与常用声学特征形成组合声学特征，应用差分演化算法进行特征选择，并使用逻辑回归分类器对构音障碍语音进行识别。将Torgo构音障碍语音数据库分成3个语音子集，分别是非词、短词语、限制句子集，提取24维GFCC和37维常用的声学特征构成组合声学特征，最后使用差分演化算法和逻辑回归分类器进行分类识别。实验表明：使用差分演化算法可以有效选择出具有更佳识别能力的特征，从而显著提高构音障碍识别率。在非词子集上的实验准确率达到98.18%，召回率为98.3%，精确率为98.3%。     

基于Web服务的软件复用研究

软件复用技术是提高软件开发效率的重要方法,介绍了软件复用技术和Web Service的开放体系结构,提出了基于Web服务的软件复用方法,并针对如何确定Web服务的功能模块粒度提出了相应的解决方案。通过实际应用,证明了该解决方案能够有效地提高项目开发效率。     

基于逻辑回归的汽车造型品牌意象研究

针对汽车造型的品牌辨识需求,提出基于逻辑回归的品牌意象研究方法。以现代 品牌为例,首先进行设计师和消费者意象评价调研,创建样本车型的品牌意象评价是非和等级 矩阵。然后以造型元素和造型形式为自变量,以评价是非二元值和评价等级为因变量,分别创 建样本车型的二元逻辑回归和有序逻辑回归模型,确定现代品牌的特色造型元素和特征造型形 式。最后通过该品牌其他车型样本的消费者调研验证了结论的正确性。     

基于数据纵向分布的隐私保护逻辑回归

逻辑回归是机器学习的重要算法之一,为解决集中式训练方式不能保护隐私的问题,提出隐私保护的逻辑回归解决方案,该方案适用于数据以特征维度进行划分,纵向分布在两方情况下,两方进行协作式训练学习到共享的模型结构.两方在本地数据集上进行训练,通过交换中间计算结果而不直接暴露私有数据,利用加法同态加密算法在密文下进行运算保证计算安全,保证在交互中不能获取对方的敏感信息.同时,提供隐私保护的预测方法,保证模型部署服务器不能获取询问者的私有数据.经过分析与实验验证,在几乎不损失精度的前提下,该案可以在两方均是半诚实参与者情况下提供隐私保护.     

Comparative Assessment of Software Quality Classification Techniques: An Empirical Case Study

Software metrics-based quality classification models predict a software module as either fault-prone (fp) or not fault-prone (nfp). Timely application of such models can assist in directing quality improvement efforts to modules that are likely to be fp during operations, thereby cost-effectively utilizing the software quality testing and enhancement resources. Since several classification techniques are available, a relative comparative study of some commonly used classification techniques can be useful to practitioners. We present a comprehensive evaluation of the relative performances of seven classification techniques and/or tools. These include logistic regression, case-based reasoning, classification and regression trees (CART), tree-based classification with S-PLUS, and the Sprint-Sliq, C4.5, and Treedisc algorithms. The use of expected cost of misclassification (ECM), is introduced as a singular unified measure to compare the performances of different software quality classification models. A function of the costs of the Type I (a nfp module misclassified as fp) and Type II (a fp module misclassified as nfp) misclassifications, ECM is computed for different cost ratios. Evaluating software quality classification models in the presence of varying cost ratios is important, because the usefulness of a model is dependent on the system-specific costs of misclassifications. Moreover, models should be compared and preferred for cost ratios that fall within the range of interest for the given system and project domain. Software metrics were collected from four successive releases of a large legacy telecommunications system. A two-way ANOVA randomized-complete block design modeling approach is used, in which the system release is treated as a block, while the modeling method is treated as a factor. It is observed that predictive performances of the models is significantly different across the system releases, implying that in the software engineering domain prediction models are influenced by the characteristics of the data and the system being modeled. Multiple-pairwise comparisons are performed to evaluate the relative performances of the seven models for the cost ratios of interest to the case study. In addition, the performance of the seven classification techniques is also compared with a classification based on lines of code. The comparative approach presented in this paper can also be applied to other software systems.     

基于CMMI的软件风险管理

大型软件项目在开发过程中经常会伴随一些风险,而这些风险往往会对项目造成不利的影响,因此需要对风险进行管理。在CMMI模型中,在严格定义级给出了一个软件风险管理的过程域,本文介绍的软件风险管理实现就是基于这个规范的。该实现把软件风险管理分成五个步骤：风险识别、风险评估、风险计划、风险跟踪和风险控制,进而对风险进行全面管理。     

浅谈软件项目人力资源管理的重要性

随着科学技术的进步与知识经济的发展,现代企业的核心竞争力往往是由企业所拥有的人力资源决定,在软件开发这样的行业当中,人才的作用更是显得关键。本文通过对IT项目人力资源管理的详细阐述,论证软件项目人力资源管理在促进我国知识与经济发展中的重要性。     

Software Project Duration and Effort: An Empirical Study

Software projects frequently finish late and over budget. Much of the research to date has characterized this problem in terms of inadequate project estimation or incomplete requirements determination. In this study, we concentrate instead on understanding the relationship between project duration and project effort. Over time, a dynamic environment contributes to the expansion of project requirements, thus increasing the scope and effort required to complete the project, irrespective of initial requirements and anticipated project size. Further, frequent delays and interruptions in a project contribute to greater effort each time work is resumed. We develop and empirically evaluate a two-stage model to relate project duration and effort. Our results indicate a significant and positive relationship between project duration and effort, controlling for anticipated project size and other project characteristics. Our model also provides an estimate for the rate of environmental change while projects are in progress. We demonstrate the practical implications of our model by showing how it can be used in conjunction with time boxing techniques and new development methodologies to better scope software projects.     

软件项目外包采购管理的探讨

分析了目前软件外包采购管理的重要意义和目前的形势，提出基于“双赢”策略的软件外包采购思想，在项目管理理论、CMM和ISO9000的基础上，提出和细化了软件项目外包采购管理的总体框架和具体操作内容，旨在通过对软件外包项目采购的选择购买、跟踪与控制、评估验收和项目后处理等过程的研究，来提高软件外包采购的项目管理水平，满足承包方对分承制方产品在质量，进度和成本等方面的要求和对外包过程的有效控制，为软件项目外包采购管理人员提供具体的操作过程。     

Software Process Improvement Problems in Twelve Software Companies: An Empirical Analysis

In this paper we discuss our study of the problems 12 software companies experienced in software development. In total we present qualitative data collected from 45 focus groups that involved over 200 software staff. We look at how different practitioner groups respond to software process improvement problems. We show our classification and analysis of this data using correspondence analysis. Correspondence analysis is a graphical data representation method new to software development research. The aim of the work we present is to develop a more holistic understanding of the problems practitioners are experiencing in their attempts to improve their software processes. Our main finding is that there is an association between a company's capability maturity and patterns of reported problems. Organizational problems are more associated with high maturity companies than with low maturity companies. Low maturity companies are closely linked to problems relating directly to projects such as documentation, timescales, tools and technology. Our findings also confirm differences in practitioner group problems. Senior managers cite problems with goals, culture and politics. Project managers are concerned with timescales, change management, budgets and estimates. Developers are experiencing problems with requirements, testing, documentation, communication, tools and technology. These associations are displayed graphically through correspondence analysis maps.     

一个更容易应用软件模式的方法

当前的软件模式都是解决某个特定问题的详细设计方案，一般都用类结构图来表示，无法实现从分析到设计的平滑过渡，从而导致了在模式的应用中对模式的生搬硬套，达不到应有的效果。该文使用了Role、Meta-pattern等技术，获得了一个更加容易的使用方法.