一种改进的基于抽象语法树的软件源代码比对算法

在软件同源性检测方法中,基于抽象语法树的比对方法能够有效地检测出基于代码全文拷贝、修改变量名、调整代码顺序等的抄袭手段,被广泛用于抄袭检测工具中。但基于抽象语法树的比对方法对于修改变量类型和添加无意义变量的抄袭手段束手无策。针对这种情况,提出了一种基于抽象语法树的改进思想,该思想通过剪去语法树中影响判断的叶子节点的手段来还原检测原文抄袭,能够达到有效检测修改变量类型和添加无意义变量等抄袭的目的。     

VAX/VMS Ada编译中存贮空间的动态分配

VAX/VMS编译程序在编译过程中要动态地请求大量的存贮空间。在语法分析阶段构造抽象语法树(AST),要求分配工作空间,在形成树节点时,要为树节点分配空间;语义分析阶段将抽象语法树转换成Diana树,必须对抽象语法树进行扩充,此时也需要动态地分配空间;在编译后端,线性化和代码生成阶段同样需要申请存贮空间。系统一切资源     

融合滑动窗口和哈希函数的代码漏洞检测模型

针对传统漏洞检测分类需要定义人工特征以及相似度匹配算法不能检测非克隆漏洞、现有深度学习漏洞检测的方法特征维度过大以及只针对函数调用的问题,提出一种融合滑动窗口和哈希函数的深度学习方法,对源代码进行静态漏洞检测分类.首先抽取源代码的方法体,形成正负样本集,对样本集中的每个样本构建抽象语法树,根据语法树中的节点类型替换程序员自定义的变量名以及方法名,并以先序遍历的方式序列化抽象语法树;然后对抽象语法树节点中的节点信息进行分词,为每个词分配一个独立的节点编号;其次对树节点进行进一步的拆分,形成词序列,基于滑动窗口与哈希函数训练出相应的漏洞检测分类模型.最后,在SARD数据集中选取CWE190整数上溢和CWE191整数下溢两类漏洞进行实验,该模型在CWE190、CWE191中的分类准确率和召回率分别达到97.4％、94.2％和97.6％、95.1％.实验结果表明,提出方法能够检测到代码中的安全漏洞类型,并且在分类准确率和召回率上优于现有的方法.     

基于多源异构数据的查询分解算法

为解决全局查询无法直接查询异构数据源问题,提出将全局查询分解为针对异构数据源子查询的解决方案。设计面向多数据源异构数据的全局查询分解算法和子查询重构算法。全局查询分解算法基于JavaCC及JJTree工具将全局查询语句抽象成语法树。子查询重构算法遍历语法树,根据节点类型采用不同的重构算法,构造出针对不同类型数据源的子查询语句。     

一种新的XML文档更新计算

当对XML文档进行插入操作时面临调整编码问题,目前提出的很多编码方案不能同时很好地支持XPath查询和XML文档更新。在分析现有编码方案的基础上,提出了基于完全树的编码方案,该编码方案采用序号冗余和虚拟节点两种冗余方式,不仅支持XPath的查询,而且能有效降低因插入节点需要对XML文档进行二次编码率。实验结果表明,完全树以及相应编码有效提高了XML文档插入节点的效率。     

基于增量分析技术的TTCN-3语言语法制导编辑器

深入介绍TTCN-3语言的语法制导编辑技术,详细描述模板驱动的编辑方式以及在该编辑环境中采用的增量语法分析技术.在传统的模板编辑中,编辑操作是直接对语法树节点进行的,导致编辑过程不仅复杂且缺乏灵活性.针对这一问题,采用了基于文本模式的模板驱动编辑方法,编辑过程中不再对语法树节点进行操作,使之更好地与增量语法分析技术相结合,在改善编辑灵活性的同时提高了语法分析的效率.     

融合多结构信息的中文句法分析方法

句法分析是自然语言理解的一项基础技术,是迈向深层语言理解的基石。目前常用的句法分析方法的语法模型建立在上下文无关文法的假设上。事实上,短语结构树的节点之间具有很强的上下文相关性,充分利用结构信息,可进一步提高句法分析的准确性。融合了句法结构树中的多结构信息(在非终节点中增加父亲节点及左、右姐妹节点等标记)以加强语法规则的上下文约束,并采用结构化支持向量机的方法对句法进行了分析。实验表明,该融合多结构信息的句法分析方法可以消解结构歧义,提升句法分析精确率和F1值。     

基于簇的无线传感器网络密钥管理协议

无线传感器网络密钥管理协议的研究是一个热点问题.针对无线传感器网络体系结构的特征,提出一种基于无线传感器网络簇路由选择协议的集中控制密钥管理协议.将逻辑密钥树协议应用于无线传感器网络中,降低基站节点和普通传感器节点的存储开销和降低普通传感器节点的密钥计算开销,各个簇内采用逻辑密钥树协议作为密钥管理协议,命令控制节点采用Kerberos方法对基站节点实行安全管理.最后对提出的密钥管理协议进行了性能分析.     

基于图的MC/DC 最小测试用例集快速生成算法

依针对MC／DC最4,N试用例集生成效率的问题,依据MC／DC准则,以条件判定组合的复杂逻辑关系为研究对象,采用将原始布尔表达式转化为语法树,再将语法树转化为图,分析图中节点对判定结果的影响的方法,研究了基于图快速生成最小测试用例集的算法．最后结合实倒对该算法进行了验证,并与其他算法进行了比较,结果表明该算法在手动和自动生成最小测试用例集时具有实用性和优越性．     

一种无"热点"的覆盖网协同缓存策略

覆盖网协同缓存(overlay cooperative caching,简称OCC)聚集客户节点的资源来提供可扩展、经济有效的缓存服务.在典型的OCC系统中,节点的异构性和工作负载的不对称,容易造成节点资源使用的不平衡,形成一些负载过重的"热点"节点.但对于这一问题,已有的OCC系统缺乏有效的负载平衡机制.针对多媒体内容分发服务,设计了一种无热点的OCC策略——HFOCC(hotspots free overlay cooperative caching).通过将"热点"对象复制到低负载节点,分散服务请求,达到消除热点的目的.为了提高缓存空间的利用率,HFOCC将一个节点的缓存空间动态地划分为home cache和replica cache两部分,并实施统一的缓存管理策略;基于一种"软"副本生命期控制机制,当工作负载发生变化时,冗余副本被及时删除,系统表现出了良好的自适应性.实验证明,HFOCC有效地提高了系统吞吐率和资源利用率.     

一种改进的异构链路协同预测算法研究

现有的链路预测方法仅考虑单种链路类型预测或多种链路类型的独立预测,经常使得预测结果不够准确。为此,研究了异构信息网络中多种链路类型的协同预测问题。根据源节点的相似节点和目标节点的相似节点之间的当前链路信息,提出了同质连接原理,设计了一种针对不同类型节点的相关性指标,用于描述不同类型节点间的链路存在概率,并将其与传统的邻近性指标相结合拓展到异构链路预测中。然后,将异构信息网络中的被标记数据和无标记数据融合起来,提出一种异构链路协同预测算法(Heterogeneous Collective Link Prediction, HCLP),通过获得不同类型链路间的各种复杂关系,结合互补性预测信息,实现多种链路类型的协同预测。基于真实场景的实验结果表明,所提的链路协同预测方法可有效提升异构信息网络的链路预测性能。     

Analytical modeling and mitigation techniques for the energy hole problem in sensor networks

In this paper we investigate the problem of uneven energy consumption in a large class of many-to-one sensor networks. In a many-to-one sensor network, all sensor nodes generate constant bit rate (CBR) data and send them to a single sink via multihop transmissions. This type of sensor network has many potential applications such as environmental monitoring and data gathering. Based on the observation that sensor nodes sitting around the sink need to relay more traffic compared to other nodes in outer sub-regions, our analysis verifies that nodes in inner rings suffer much faster energy consumption rates (ECR) and thus have much shorter expected lifetimes. We term this phenomenon of uneven energy consumption rates as the “energy hole” problem, which may result in severe consequences such as early dysfunction of the entire network. We proposed analytical modeling for this problem, which can help understand the relevance of different factors on energy consumption rates. Using this model, we study the effectiveness of several existing approaches towards mitigating the “energy hole” problem, including deployment assistance, traffic compression and aggregation. We have used simulation results to validate our analysis.     

一种基于流作业的网格作业调度研究

在基于网格环境的一些网格应用中,用户需要提交一种作业类型,该作业可以被分解为逻辑上独立的元作业,这些元作业不存在依赖和通讯关系,并且它们的执行需要大量的数据移动。针对这种作业类型,本文提出了一种基于流作业的网格调度模型。在该模型中,这些独立的元作业像"流"一样自主地流向各个计算节点去执行,各计算节点接收的流量取决于其计算能力,并避免"断流"问题。同时,该模型还分离了作业流和数据流,实现了作业逻辑控制和数据控制的分离,提高了调度的灵活性。本文将该调度模型应用于药物虚拟筛选应用中,该模型能够充分利用计算节点的计算能力。     

Graffiti: graph-based classification in heterogeneous networks

We address the problem of multi-label classification in heterogeneous graphs, where nodes belong to different types and different types have different sets of classification labels. We present a novel approach that aims to classify nodes based on their neighborhoods. We model the mutual influence of nodes as a random walk in which the random surfer aims at distributing class labels to nodes while walking through the graph. When viewing class labels as “colors”, the random surfer is essentially spraying different node types with different color palettes; hence the name Graffiti of our method. In contrast to previous work on topic-based random surfer models, our approach captures and exploits the mutual influence of nodes of the same type based on their connections to nodes of other types. We show important properties of our algorithm such as convergence and scalability. We also confirm the practical viability of Graffiti by an experimental study on subsets of the popular social networks Flickr and LibraryThing. We demonstrate the superiority of our approach by comparing it to three other state-of-the-art techniques for graph-based classification.     

Latency based group discovery algorithm for network aware cloud scheduling

Cloud computing is a big paradigm shift of computing mechanism. It provides high scalability and elasticity with a range of on-demand services. We can execute a variety of distributed applications on cloud’s virtual machines (computing nodes). In a distributed application, virtual machine nodes need to communicate and coordinate with each other. This type of coordination requires that the inter-node latency should be minimal to improve the performance. But in the case of nodes belonging to different clusters of the same cloud or in a multi-cloud environment, there can be a problem of higher network latency. So it becomes more difficult to decide, which node(s) to choose for the distributed application execution, to keep inter-node latency at minimum. In this paper, we propose a solution for this problem. We propose a model for the grouping of nodes with respect to network latency. The application scheduling is done on the basis of network latency. This model is a part of our proposed Cloud Scheduler module, which helps the scheduler in scheduling decisions on the basis of different criteria. Network latency and resultant node grouping on the basis of this latency is one of those criteria. The main essence of the paper is that our proposed latency grouping algorithm not only has no additional network traffic overheads for algorithm computation but also works well with incomplete latency information and performs intelligent grouping on the basis of latency. This paper addresses an important problem in cloud computing, which is locating communicating virtual machines for minimum latency between them and group them with respect to inter-node latency.     

物联网中节点的动态调整算法简

为了解决无线传感器网络中,经常出现的冗余路由节点和孤立节点的问题,本文提出了一种基于设备节点类型转换的动态调整算法。该算法根据对网络中路由节点邻居表中节点关系的分析,来确定网络中节点类型转换的方式和时间,最终实现网络中所有节点能够动态调整本身的类型转换,达到网络性能最优化的目的。     

节点分类及失效对网络能控性的影响

复杂系统间的相互作用能够用复杂网络描述.复杂网络中某些节点遭受攻击或破坏会造成网络故障,导致整个网络能控性变化.不同节点失效会对网络能控性有不同的影响.本文提出一种网络节点的分类方式,将网络中的节点根据边的方向和匹配关系分成九种类型,并给出了辨识节点类型的算法.另外,本文给出了基于此分类方式下复杂网络中某类节点失效时,...     

基于改进秃鹰搜索算法的Kubernetes资源调度应用

Kubernetes是一个管理容器化应用的开源平台,其默认的调度算法在优选阶段仅把CPU和内存两种资源来作为计算节点的评分指标,同时还忽略了不同类型的Pod对节点资源的占用比例是不同的,容易导致某一资源达到性能瓶颈,从而造成节点对资源使用失衡.针对上述问题,本文在Kubernetes原有的资源指标基础上增加了带宽和磁盘容量,考虑到CPU、内存、带宽和磁盘容量这4类资源在节点上的占用比例对节点的性能的影响,可能造成Pod中应用的非正常运行,甚至杀死Pod,从而影响集群整体的高可靠性.本文将等待创建的Pod区分为可压缩消耗型、不可压缩消耗型以及均衡型,并为每种类型的Pod设置相应的权重,最后通过改进的秃鹰搜索算法(TBESK)来寻找出最优节点进行调度.实验结果表明,随着集群中Pod的数量在不断增加,在集群负载较大的情况下, TBESK算法的综合负载标准差和默认的调度算法相比提升了24%.     

基于概率神经网络的蛋白质相互作用分类器

蛋白质-蛋白质作用面上的结构特征对于研究蛋白质功能具有重要意义。提出了一种新的、基于统计直方图提取蛋白质作用面特征的方法,并且利用提取出的作用面特征,结合概率神经网络,实现了对作用面结构类型的分类预测。从预测结果来看,统计直方图提取出的特征,对蛋白质作用面结构具有很好的区分能力,而且可以通过调节划分的区间个数和节点的选取方式,达到对作用面结构的不同粒度的描述,以适用于不同目的的研究,这可能对与结构有关的某些生物信息学问题的研究具有启发性。利用概率神经网络对作用面结构进行分类预测,避开了费时的结构比对和数据库搜索,且训练快速,扩展能力强,正确率高,对独立测试集的911个蛋白复合物视在正确率达到90.67%。基于该算法的MATLAB分类器软件可以通过E-Mail与作者联系获取。     

ABACUS: frequent pAttern mining-BAsed Community discovery in mUltidimensional networkS

Community discovery in complex networks is the problem of detecting, for each node of the network, its membership to one of more groups of nodes, the communities, that are densely connected, or highly interactive, or, more in general, similar, according to a similarity function. So far, the problem has been widely studied in monodimensional networks, i.e. networks where only one connection between two entities may exist. However, real networks are often multidimensional, i.e., multiple connections between any two nodes may exist, either reflecting different kinds of relationships, or representing different values of the same type of tie. In this context, the problem of community discovery has to be redefined, taking into account multidimensional structure of the graph. We define a new concept of community that groups together nodes sharing memberships to the same monodimensional communities in the different single dimensions. As we show, such communities are meaningful and able to group nodes even if they might not be connected in any of the monodimensional networks. We devise frequent pAttern mining-BAsed Community discoverer in mUltidimensional networkS (ABACUS), an algorithm that is able to extract multidimensional communities based on the extraction of frequent closed itemsets from monodimensional community memberships. Experiments on two different real multidimensional networks confirm the meaningfulness of the introduced concepts, and open the way for a new class of algorithms for community discovery that do not rely on the dense connections among nodes.