A language-independent approach to the extraction of dependencies between source code entities

ContextSoftware networks are directed graphs of static dependencies between source code entities (functions, classes, modules, etc.). These structures can be used to investigate the complexity and evolution of large-scale software systems and to compute metrics associated with software design. The extraction of software networks is also the first step in reverse engineering activities.ObjectiveThe aim of this paper is to present SNEIPL, a novel approach to the extraction of software networks that is based on a language-independent, enriched concrete syntax tree representation of the source code.MethodThe applicability of the approach is demonstrated by the extraction of software networks representing real-world, medium to large software systems written in different languages which belong to different programming paradigms. To investigate the completeness and correctness of the approach, class collaboration networks (CCNs) extracted from real-world Java software systems are compared to CCNs obtained by other tools. Namely, we used Dependency Finder which extracts entity-level dependencies from Java bytecode, and Doxygen which realizes language-independent fuzzy parsing approach to dependency extraction. We also compared SNEIPL to fact extractors present in language-independent reverse engineering tools.ResultsOur approach to dependency extraction is validated on six real-world medium to large-scale software systems written in Java, Modula-2, and Delphi. The results of the comparative analysis involving ten Java software systems show that the networks formed by SNEIPL are highly similar to those formed by Dependency Finder and more precise than the comparable networks formed with the help of Doxygen. Regarding the comparison with language-independent reverse engineering tools, SNEIPL provides both language-independent extraction and representation of fact bases.ConclusionSNEIPL is a language-independent extractor of software networks and consequently enables language-independent network-based analysis of software systems, computation of design software metrics, and extraction of fact bases for reverse engineering activities.     

A hybrid intelligent system for medical data classification

In this paper, a hybrid intelligent system that consists of the Fuzzy Min–Max neural network, the Classification and Regression Tree, and the Random Forest model is proposed, and its efficacy as a decision support tool for medical data classification is examined. The hybrid intelligent system aims to exploit the advantages of the constituent models and, at the same time, alleviate their limitations. It is able to learn incrementally from data samples (owing to Fuzzy Min–Max neural network), explain its predicted outputs (owing to the Classification and Regression Tree), and achieve high classification performances (owing to Random Forest). To evaluate the effectiveness of the hybrid intelligent system, three benchmark medical data sets, viz., Breast Cancer Wisconsin, Pima Indians Diabetes, and Liver Disorders from the UCI Repository of Machine Learning, are used for evaluation. A number of useful performance metrics in medical applications which include accuracy, sensitivity, specificity, as well as the area under the Receiver Operating Characteristic curve are computed. The results are analyzed and compared with those from other methods published in the literature. The experimental outcomes positively demonstrate that the hybrid intelligent system is effective in undertaking medical data classification tasks. More importantly, the hybrid intelligent system not only is able to produce good results but also to elucidate its knowledge base with a decision tree. As a result, domain users (i.e., medical practitioners) are able to comprehend the prediction given by the hybrid intelligent system; hence accepting its role as a useful medical decision support tool.     

FA composition and regiospecific analysis of Acer saccharum (sugar maple tree) and Acer saccharinum (silver maple tree) seed oils

GC analysis was performed to determine regiospecific distribution and FA composition in seed oils of the Aceraceae species, Acer saccharum and A. saccharinum. The oil content in the seeds was low at 5.0% in A. saccharum and 5.8% in A. saccharinum, and the main FA were linoleic (30.8 and 29.4%), oleic (21.3 and 27.6%), palmitic (10.1 and 10.5%), and cis-vaccenic (9.4 and 7.9%) acids, respectively. In addition, both oils contained long-chain monoenes of the n−9 and n−7 groups, including 11-eicosenoic, 13-docosenoic, 15-tetracosenoic, 13-eicosenoic, and 15-docosenoic acids, whereas γ-linolenic acid accounted for 0.8% of total FA in A. saccharum, and 0.5% in A. saccharinum. Regiospecific analysis, performed using the methodology of dibutyroyl derivatives of MAG, indicated that linoleic, oleic, and linolenic acids were mainly esterified at the internal position of TAG in both seed oils, whereas long-chain monoenes of the n−7 group were almost exclusively esterified on the external positions.     

一种应用层组播可靠性技术

组播是一种高效的多点通信方式,IP组播效率高,但需专门的组播路由器,代价大,至今未能大规模部署.应用层组播数据的复制转发功能由终端主机完成,不涉及现有网络基础设施的更新,在Internet上容易实现,但终端主机的稳定性和安全性不如组播路由器,容易导致单点失效,且数据恢复困难.为解决应用层组播存在的可靠性问题,提出了一种树环结构的应用层组播方案,通过环结构来增加数据传输的可靠性,并采用主动随机转发技术来解决树环结构的多点失效问题.该方案既有效解决了应用层组播可靠传输问题,又降低了恢复时延和系统开销.     

粒径对膨胀土无荷膨胀率的影响研究

为了研究粒径大小对重塑土无荷膨胀率试验结果的影响,在符合现有规范的试验条件下,将某地基泥岩碾碎成1~2 mm、0.1~1 mm、0.1 mm的三种不同粒径,进行不同比例组合搭配的重塑土无荷膨胀率试验。试验结果表明:不同粒径大小的膨胀土样的无荷膨胀率试验结果有很大差异,并且不同粒径组合下的无荷膨胀率试验结果呈正态分布趋势,且峰值处的无荷膨胀率值为该重塑土稳定、可靠的无荷膨胀率值,无荷膨胀率试验结果接近峰值处无荷膨胀率值的粒径级配为该重塑土的最佳级配。经过分析得到使重塑土无荷膨胀率结果稳定、可靠的粒径级配是1~2 mm、0.1~1 mm、0.1 mm的土粒质量比为0∶100∶0,即使重塑土无荷膨胀率试验结果稳定可靠的粒径范围为0.1~1 mm。在该粒径范围内的重塑土无荷膨胀率试验结果最为真实、准确。     

Tactile display with tangential and normal skin displacement for robot-assisted surgery

This paper proposes a tactile display providing both shear and normal feedback to the fingertip for generating three-axis tactile feedback during teleoperation of a surgical robot. The display is composed of five balloons actuated by controlling the pneumatic pressure. The implemented display is 18?mm?×?18?mm?×?15?mm. This size is suitable for mounting the display onto the master controls of a surgical robot. The maximum normal and shear displacements are 2 and 1.3?mm, respectively. The proposed tactile display may provide perceivable stimuli to a human finger pad in all five directions: normal, distal, proximal, radial, and ulnar. This paper also reports on the results of psychophysical measurement of the minimum perceivable movement of the developed tactile display for each of the five directions.     

前向树状紧急疏散网络的自适应性控制策略分析

针对前向树状的紧急疏散网络,提出了一种不需要进行需求预测且具有适应性和强鲁棒性的分散式控制疏散策略.通过分析如何确定典型匝道与快速路交汇点处的最优疏散路线,明确了构建可控疏散网络的基本原则.定义了前向树状紧急疏散网络,并对所构建网络的适用范围和具体风险水平标定处理作了明确说明.针对前向树状网络的典型子结构,给出了可以始终充分利用下游通行能力,且充分考虑上游来流具有较高风险水平特点的具体疏散路线选择及放流策略.在上述典型子结构分析基础上,给出了包括如何逆流向确定路段限制性通行能力、顺流向确定路段最大可行流量以及实时动态调整疏散策略等基本步骤的网络疏散策略.最后,通过一个数值算例,比较了新策略与纯粹上游优先策略、纯粹下游优先策略的运行效率,证明了新的适应控制策略的可行性与有效性.     

基于不确定决策树分类算法在滑坡危险性预测的应用

滑坡灾害预测受多种因素影响,其中降雨等不确定因素存在难以获取数据及有效处理等难题,为提高滑坡危险性预测的准确率,根据滑坡灾害发生相关理论及决策树分类原理,提出了基于不确定决策树算法在滑坡危险性预测的方法.该方法引入不确定因子降雨,并将不确定因子和其余评价因子一起,根据不确定决策树算法理论构建出不确定决策树,建立滑坡危险性等级预测模型,并用延安市宝塔区的实例进行验证.实验结果表明,该预测方法取得了较高的总体精度和有效精度,达到了滑坡预测的精度标准,且两项预测精度均高于传统C4.5决策树方法.     

基于多决策树的RFID入侵检测模型

为了能够有效对RFID系统的已知攻击和未知攻击进行检测和预警,在分析大量RFID安全协议的基础上,提出了利用构建多决策树的方法对RFID系统的入侵行为进行检测。通过分析,得到所构造出的多决策树能够发现针对RFID系统的攻击之间的关系,并且具有可扩展性和高效性,使之能够适用于各种实际的RFID系统的应用。     

近红外分析网络共享平台中复杂工作流实现算法

近红外分析共享平台中的采集工作流是一个以数据为中心的工作流。对系统中复杂采集工作流的跳转执行过程进行了详细分析,针对自由工作流跳转造成的数据和状态不一致性的问题,提出了一个任务执行优先关系算法,计算采集工作流中各个活动任务执行次序关系值,并构造一个执行次序关系树,记录各任务活动的状态信息;在流程回退、撤销、跳转时保证了数据和状态的一致性,较好地实现了跨任务活动的跳转。应用结果表明,此算法能够正确高效地实现复杂流程的跳转。