Exponential Continuous Non-Parametric Neural Identifier With Predefined Convergence Velocity

This paper addresses the design of an exponential function-based learning law for artificial neural networks (ANNs) with continuous dynamics. The ANN structure is used to obtain a non-parametric model of systems with uncertainties, which are described by a set of nonlinear ordinary differential equations. Two novel adaptive algorithms with predefined exponential convergence rate adjust the weights of the ANN. The first algorithm includes an adaptive gain depending on the identification error which accelerated the convergence of the weights and promotes a faster convergence between the states of the uncertain system and the trajectories of the neural identifier. The second approach uses a time-dependent sigmoidal gain that forces the convergence of the identification error to an invariant set characterized by an ellipsoid. The generalized volume of this ellipsoid depends on the upper bounds of uncertainties, perturbations and modeling errors. The application of the invariant ellipsoid method yields to obtain an algorithm to reduce the volume of the convergence region for the identification error. Both adaptive algorithms are derived from the application of a non-standard exponential dependent function and an associated controlled Lyapunov function. Numerical examples demonstrate the improvements enforced by the algorithms introduced in this study by comparing the convergence settings concerning classical schemes with non-exponential continuous learning methods. The proposed identifiers overcome the results of the classical identifier achieving a faster convergence to an invariant set of smaller dimensions.      

基于底层虚拟机的标识符混淆方法

针对现有代码混淆仅限于某一特定编程语言或某一平台,并不具有广泛性和通用性,以及控制流混淆和数据混淆会引入额外开销的问题,提出一种基于底层虚拟机（LLVM）的标识符混淆方法。该方法实现了4种标识符混淆算法,包括随机标识符算法、重载归纳算法、异常标识符算法以及高频词替换算法,同时结合这些算法,设计新的混合混淆算法。所提混淆方法首先在前端编译得到的中间文件中候选出符合混淆条件的函数名,然后使用具体的混淆算法对这些函数名进行处理,最后使用具体的编译后端将混淆后的文件转换为二进制文件。基于LLVM的标识符混淆方法适用于LLVM支持的语言,不影响程序正常功能,且针对不同的编程语言,时间开销在20%内,空间开销几乎无增加;同时程序的平均混淆比率在77.5%,且相较于单一的替换算法和重载算法,提出的混合标识符算法理论分析上可以提供更强的隐蔽性。实验结果表明,所提方法具有性能开销小、隐蔽性强、通用性广的特点。     

基于工业互联网标识体系的协同制造研究

为了实现全生产要素的网络化协同以及智能化生产,解决生产过程中因不同地区、不同企业、不同编码体系而导致的无法协同设计、生产,以及产品追溯不完整,设计了基于工业互联网平台的标识解析体系,对不同企业、订单、设备、物料、生产过程、产品等进行统一分级注册,按需实时解析;设计了相应的智能示范产线,实现了在个性化定制模式下的全程自主控制;融合数字孪生与图像识别等先进技术,测试验证了基于工业互联网标识体系在异主异地异构生产时的有效性。     

基于二次传播的开源软件缺陷定位方法

为了提高开源软件缺陷定位的准确度,提出一种基于二次传播的开源软件缺陷定位方法TPA。将重启随机游走算法与标签传播算法相结合,在基于信息检索方法的基础上,从开源软件中各个模块的调用关系、模块风险度和历史缺陷报告三个方面分析缺陷存在的具体位置,将缺陷定位分为风险度传播和标签传播两个部分。通过软件模块之间的相关度和调用关系,结合开源软件的历史修改信息评估模块风险度;通过已修改的缺陷报告构造缺陷标签,并采用标签传播定位软件缺陷。在Eclipse 3.1缺陷报告数据集中,top-◢N◣ rank和MRR指标均高于同类缺陷定位方法。实验表明,TPA方法具有较高的准确度。     

基于区块链的持久标识符系统

随着信息产业的发展, 数据生产者产生了大量价值数据. 为了进行数据共享, 赋予数据相应的标识符用于解析数据所在位置,同时为使数据可长期通过标识符访问, 还需保证标识符解析服务长期可用, 但现有标识符系统多数采用半去中心化结构, 由于过于依赖最终解析服务, 其中部分系统由于各种原因逐渐丧失解析能力. 本文基于区块链系统的分布式账本数据一致性, 提出了一种基于区块链的持久标识符系统, 在兼容现有标识符系统访问层的基础上, 提供存储层以保证标识符解析服务持久性及数据的长期正确保存. 基于Handle系统及 Hyperledger Fabric的测试结果表明, 该系统能够在提供可接受的请求响应速度与存储占用率的前提下, 为持久标识符服务提供更好的数据完整 性与解析服务长期可用性.     

Improving quality of software product line by analysing inconsistencies in feature models using an ontological rule‐based approach

In software product line engineering, feature models (FMs) represent the variability and commonality of a family of software products. The development of FMs may introduce inaccurate feature relationships. These relationships may cause various types of defects such as inconsistencies, which deteriorate the quality of software products. Several researchers have worked on the identification of defects due to inconsistency in FMs, but only a few of them have explained their causes. In this paper, FM is transformed to predicate‐based feature model ontology using Prolog. Further, first‐order logic is employed for defining rules to identify defects due to inconsistency, the explanations for their causes, and suggestions for their corrections. The proposed approach is explained using an FM available in Software Product Line Online Tools repository. It is validated using 26 FMs of discrete sizes up to 5,543 features, generated using the FeatureIDE tool and real‐world FMs. Results indicate that the proposed methodology is effective, accurate, and scalable and improves software product line.     

热拌沥青混合料四点弯曲梁疲劳试验结果的不一致性及修正

设计了室内试验,对相同频率、相同应变、相同温度条件下的HMA试件在ASTM D7460和AASHTO T321两种规范下的四点弯曲梁疲劳试验结果进行了对比,发现存在较大的不一致性;从HMA的粘弹特性以及两种规范下的输入应变和输入荷载在试验中的实际响应出发,分析了实验结果出现不一致性的原因,即输入应变和实际应变的不一致导致了试验结果的不一致;提出了一种简便而有效的修正方法并进行了验证。研究结果有助于缩小在ASTM D7460和AASHTO T321两种规范下,同种HMA、相同试验条件得出的疲劳结果的不一致性,为路面设计、施工提供更加客观、准确的数据支撑。     

基于缺陷相似度与再分配图的软件缺陷分配方法

准确地将缺陷分配给最合适的修复者对大型软件项目的缺陷修复具有重要意义。当前缺陷自动分配技术的研究主要利用历史缺陷报告的描述信息、缺陷关联信息、历史分派信息等,但这些方法都没有将缺陷报告信息充分挖掘。提出在缺陷报告分配时将缺陷历史分派信息和缺陷文本相似信息相结合。首先根据缺陷历史分派信息生成再分配图;然后计算新缺陷报告与历史缺陷报告缺陷的文本相似度,找出相似度最高的前K个缺陷报告所对应的修复者;最后,根据这些修复者在再分配图中的依赖关系生成预测再分配路径。为了验证该方法的有效性,利用Eclipse和Mozilla的缺陷报告集进行实验,实验表明提出的方法在预测的准确度上明显优于其他方法。     

From source code to test cases: A comprehensive benchmark for resource leak detection in Android apps

Android apps share resources, such as sensors, cameras, and Global Positioning System, that are subject to specific usage policies whose correct implementation is left to programmers. Failing to satisfy these policies may cause resource leaks, that is, apps may acquire but never release resources. This might have different kinds of consequences, such as apps that are unable to use resources or resources that are unnecessarily active wasting battery. Researchers have proposed several techniques to detect and fix resource leaks. However, the unavailability of public benchmarks of faulty apps makes comparison between techniques difficult, if not impossible, and forces researchers to build their own data set to verify the effectiveness of their techniques (thus, making their work burdensome). The aim of our work is to define a public benchmark of Android apps affected by resource leaks. The resulting benchmark, called AppLeak, is publicly available on GitLab and includes faulty apps, versions with bug fixes (when available), test cases to automatically reproduce the leaks, and additional information that may help researchers in their tasks. Overall, the benchmark includes a body of 40 faults that can be exploited to evaluate and compare both static and dynamic analysis techniques for resource leak detection.     

基于加密传输的标识解析模型研究

标识解析系统是互联网稳定运行和发展的基石,其隐私保护和数据安全问题日益引起关注。基于签名技术的DNSSEC和基于加密技术的DoT等机制解决了部分安全问题,但无法实现标识解析全流程的用户隐私保护。结合当前技术研究现状,提出一种新的基于加密传输的标识解析信任模型,建立信任链实现标识解析系统各节点的信任传递,通过全流程加密通信,保护标识解析过程中用户隐私和数据安全。首先简要介绍域名领域安全技术的研究现状,然后详细描述模型的整体架构、信任链模型和工作流程,最后通过5组实验,对模型在不同加密方法和传输协议下的时延、性能、安全性进行测试和分析,并结合现网DNS测试结果对模型的现实可行性进行验证。