一个上下文无关文法的构造定理及其在推断中的应用

递归概念可以在句子中重复派生、循环出现。对这样的句子推断时,若为递归概念的每一个派生部分引进一个递归概念来描述,将推断出多个与之有相似的产生式结构的递归概念,同时也构造出一个新文法。本文先给出新文法的形式化构造方法,证明了新文法与原文法的等价性。在文章的后部,通过实例,介绍该定理在简化复杂文法推断中的应用。     

面向领域驱动设计的逆向建模支持方法

领域驱动设计作为一种应对领域复杂性的软件开发方法,近年来得到了广泛应用.作为其中的关键步骤,领域建模仍然面临着领域模型与程序设计之间松散的逻辑关系带来的种种问题.为了应对此问题,基于模型驱动的逆向工程,提出了一种面向领域驱动设计的代码到模型的转换方法,以更好地支持领域建模,并实现了自动化工具原型.该方法能够实时抽象出程序设计对应的领域模型,一方面有助于通过对比程序设计与领域模型的分歧来避免设计偏离模型,另一方面减少了知识消化过程中对代码实现细节的依赖,从而促进了程序设计对建模过程的反馈.案例研究部分将该方法应用于实际软件项目,结果充分表明了其有效性.     

基于文法推断的协议逆向工程

要深入了解网络中的各种应用过程,进而对这些应用进行自动分类、识别、跟踪和控制,首先就要获得代表这些应用会话过程的状态机.为此提出一种新的方法从采集的应用层数据中反推协议状态机.它采用基于差错纠正的文法推断方法,利用应用层协议交互过程中出现的标识符状态序列,逆向工程其协议状态机.为充分挖掘和发挥差错纠正的性能,提出了最佳路径匹配标准确定纠正路径,以及基于概率统计的异常入度区分及其剪枝的方法;通过去重的状态合并和相似行为意义的协议结构化简措施解决状态膨胀问题,从而获取最精简的协议状态机.通过在包含多种应用层协议的实际网络中的实验,验证了该方法的有效性.     

Applying Programming Language Evaluation Criteria for Model Transformation Languages

The appraisal of the status quo for the methods of evaluating model transformation languages (MTLs) manifests several shortcomings: they are often either language-specific or feature-specific, they may lack of sufficient discussion on possible values for proposed criteria, few MTLs may be applied in their evaluation, or a combination of these limitations. We have previously proposed a method which utilizes programming language (PL) criteria for evaluation of MTLs. In this paper, an improved method is proposed in which a large family of 11 major criteria with total of 46 sub-criteria, mainly inspired from PL evaluation criteria, is used to evaluate MTLs. Then, an interactive methodology is proposed that consolidates the criteria to establish a \textit{decision-support} system for MTL selection. In order to investigate the effectiveness of the criteria and the proposed methodology, six MTLs were used for studies: ATL, Kermeta, ETL, QVT-O, QVT-R, and TGG. The results of MTL evaluations corroborate that the criteria are highly effective in practice; they provide helpful insights for different users to enable them to choose the most appropriate MTL for the application at hand. With our decision-support methodology, we could have achieved evidence to imply applicability in real-world scenarios.     

基于正反向工程的模型驱动的软件开发过程研究

主要探讨基于正反向工程的模型驱动的软件开发过程。本文把建模过程分为系统建模与数据库建模两个过程,首先介绍了两款主流的建模工具,然后给出这两款建模工具的建模流程,并且阐述在正反向工程中选择建模工具的依据;其次,以PowerDesigner为例叙述了具体的基于建模工具的开发流程;最后,给出结论。     

在线社会网络共演化的结构推断与预测

在线社会网络研究中,关系结构和交互结构的共演化机理是一个十分关键的核心问题,它反映了在线社会网络关系结构变化、行为模式演化及关系结构与交互结构演化的互影响情况,对于社会推荐、网络舆情预警和控制等都具有重要意义.大量交互信息的可见性和真实关系结构的不易见性,使得利用动态交互网络直接推断隐结构和预测未来结构成为当前研究热点,并成为揭示共演化机理的一种途径.从微观尺度对2种重要的社会网络：社会媒体和社交网络中的动态结构推断和预测的研究进展进行了综述.首先对在线社会网络共演化和结构推断及预测进行定义,并对其之间关系进行分析;然后对隐关系强度推断、类型推断、关系结构预测和交互行为预测的关键技术等进行综述和分析,最后对在线社会网络结构推断与预测研究的难点和发展趋势进行分析和展望.     

基于SPEM的CMM软件过程元模型

软件企业在实施CMM(capability maturity modelfor software)的过程中面临最主要的障碍是如何将CMM 软件过程模型转换成可实施的、体现组织过程特征的CMM实施过程模型.可以利用模型驱动架构MDA来支持CMM模型转换,其首要问题是建立CMM软件过程元模型.通过分析CMM软件过程,给出了面向CMM的软件过程工程元模型SPEM的扩展策略,提出了一个基于SPEM的CMM软件过程元模型一-SPM-CMM.该元模型既支持CMM软件过程的抽象语法和规则语义,也支持利用UML CASE工具操作CMM软件过程模型.     

Inference of a class of CFPG by means of semantic rules

An important property of Context-Free Programmed Grammars (CFPG) is that some context-sensitive Languages can be generated by CFPG. To infer a special class of CFPG for syntactic pattern recognition is the motivation of this paper. First an one-dimensional string is transformed into a corresponding binary tree. Then in terms of the structure of subtrees, and the semantic rules corresponding to the context-free productions, a method for the inference of Context-free Programmed Grammar is presented. Inference of Stochastic Context-Free Programmed Grammar (STCFPG) by the maximum-likelihood estimate approach is also discussed.This work was supported in part by the NSF Grant ECS 78-16970.     

自适应神经模糊推理系统用于症候诊断的研究

提出了基于T-S模型(Takagi-Sugeno型)的自适应神经网络模糊推理系统(AdaptiveNetwork-basedFuzzyInferenceSystem,ANFIS),介绍了高木—关野(Takagi-Sugeno型)模型结构和自适应神经网络模糊推理系统的结构和算法。该文采用减法聚类初始化模糊推理系统模型,把神经网络学习机制引入到逻辑推理中,使传统的逻辑推理不仅具有逻辑思维及语言表达能力而且具有自学习和联想能力,通过2型糖尿病症候数据库验证了ANFIS用于症候诊断的合理性和有效性。提示自适应神经网络模糊推理系统适合中医症候诊断的研究。     

VAnDroid: A framework for vulnerability analysis of Android applications using a model-driven reverse engineering technique

Android is extensively used worldwide by mobile application developers. Android provides applications with a message passing system to communicate within and between them. Due to the risks associated with this system, it is vital to detect its unsafe operations and potential vulnerabilities. To achieve this goal, a new framework, called VAnDroid, based on Model Driven Reverse Engineering (MDRE), is presented that identifies security risks and vulnerabilities related to the Android application communication model. In the proposed framework, some security-related information included in an Android app is automatically extracted and represented as a domain-specific model. Then, it is used for analyzing security configurations and identifying vulnerabilities in the corresponding application. The proposed framework is implemented as an Eclipse-based tool, which automatically identifies the Intent Spoofing and Unauthorized Intent Receipt as two attacks related to the Android application communication model. To evaluate the tool, it has been applied to several real-world Android applications, including 20 apps from Google Play and 110 apps from the F-Droid repository. VAnDroid is also compared with several existing analysis tools, and it is shown that it has a number of key advantages over those tools specifically regarding its high correctness, scalability, and usability in discovering vulnerabilities. The results well indicate the effectiveness and capacity of the VAnDroid as a promising approach in the field of Android security.     

Regular Model Checking Using Inference of Regular Languages

Regular model checking is a method for verifying infinite-state systems based on coding their configurations as words over a finite alphabet, sets of configurations as finite automata, and transitions as finite transducers. We introduce a new general approach to regular model checking based on inference of regular languages. The method builds upon the observation that for infinite-state systems whose behaviour can be modelled using length-preserving transducers, there is a finite computation for obtaining all reachable configurations up to a certain length n. These configurations are a (positive) sample of the reachable configurations of the given system, whereas all other words up to length n are a negative sample. Then, methods of inference of regular languages can be used to generalize the sample to the full reachability set (or an overapproximation of it). We have implemented our method in a prototype tool which shows that our approach is competitive on a number of concrete examples. Furthermore, in contrast to all other existing regular model checking methods, termination is guaranteed in general for all systems with regular sets of reachable configurations. The method can be applied in a similar way to dealing with reachability relations instead of reachability sets too.     

动态判断与非单调推理的属性重心坐标表示模型

本文研究动态判断与非单调推理的属性重心坐标表示模型。研究表明：命题真值随基准而变的动态判断及其诱导的非单调推理可用属性重心坐标加以表示,而基于基准变换的 解释及其属性坐标表示模型更符合实际情况,也更合乎逻辑。     

Inference of regular languages using state merging algorithms with search

State merging algorithms have emerged as the solution of choice for the problem of inferring regular grammars from labeled samples, a known NP-complete problem of great importance in the grammatical inference area. These methods derive a small deterministic finite automaton from a set of labeled strings (the training set), by merging parts of the acceptor that corresponds to this training set. Experimental and theoretical evidence have shown that the generalization ability exhibited by the resulting automata is highly correlated with the number of states in the final solution.As originally proposed, state merging algorithms do not perform search. This means that they are fast, but also means that they are limited by the quality of the heuristics they use to select the states to be merged. Sub-optimal choices lead to automata that have many more states than needed and exhibit poor generalization ability.In this work, we survey the existing approaches that generalize state merging algorithms by using search to explore the tree that represents the space of possible sequences of state mergings. By using heuristic guided search in this space, many possible state merging sequences can be considered, leading to smaller automata and improved generalization ability, at the expense of increased computation time.We present comparisons of existing algorithms that show that, in widely accepted benchmarks, the quality of the derived solutions is improved by applying this type of search. However, we also point out that existing algorithms are not powerful enough to solve the more complex instances of the problem, leaving open the possibility that better and more powerful approaches need to be designed.     

单可表示类的特征

择优模型是目前常识推理领域中最常用的语义结构之一。在择优模型中有一类重要的模型－单射择优模型,近年来,众多的学者对该模型类从各种角度进行了研究,但一个重要而基本的问题,即何种类型的择优后承具有单射择优模型,虽经多位学者的研究到现在仍然悬而未解。目前,最好的结果Freund在有限语言限制下证明了“择优后承具有单射择优模型当且仅当它满足弱析取合理性”。文中提出择优模型的一种转换,将此转换应用于Kraus,Leham及Magidor提出的KLM模型上,在一般语言框架下证明了“满足弱析取合理性的择优后承必具有单射择优模型”,从而将该问题的研究推进了一步。     

一种在线推理模糊控制器算法的设计与实现

通过VC＋＋语言实现在线推理模糊控制器的算法,并以微机过程控制系统为研究对象,将液位传感器采集的信号通过PCI-9111HR系列数据采集卡送于在线推理式模糊控制器,实现对上、下水箱液位的控制,并把这种在线推理式模糊控制和人工控制的结果用webaeeess网际组态软件进行实时趋势曲线比较。实际结果验证了其算法的可行性。     

多维度等级评分模型优化技术

研究了多维度等级评分模型的训练学习优化技术.为了解决不同用户之间的评分标注所存在的不一致性,提出两种简单、有效的模型训练优化技术,包括基于容忍度的样本选择方法和基于排序损失的样本选择方法.另外,为了充分利用不同特征的用户评分标注之间的相关性,提出了一个面向属性的协同过滤技术以改善多维度等级评分模型.在两个公开的英语和汉语真实餐馆评论数据集上进行实验验证,实验结果表明,所提出的方法有效地改善了等级评分的性能.     

一种基于真度变换观点的近似推理

以全新的思想和视角,把蕴涵式p→q看作一种真度变换,并提出了真度变换率和真度变换差的概念,然后在此基础上给出了一组称为肯定前件式和否定后件式真度假言推理的推理规则,从而得到了一种命题近似推理的新方法。把该方法推广到谓词逻辑,就得到一种基于谓词逻辑的近似推理新方法。因此,文章的思想和方法可作为模糊推理的理论基础。     

聚类概率矩阵分解的变分推断及应用

概率矩阵分解模型根据用户历史交互信息个性化推荐商品,是协同过滤中的经典方法之一.传统矩阵分解假设下无法利用不同用户之间的相似性,且在面对异常值时常预测失准.根据用户聚类信息,可构建共轭先验分布与类别相关的聚类概率矩阵分解模型,同时改变相关共轭先验分布形式,完成对参数作正则化处理.通过变分推断,理论推导变分参数的显式表达式,从而建立相应评分预测算法.模拟及真实数据集均表明该模型的预测性能优于基准模型,并能对用户评分做出现实解释.     

基于MDA的医院管理系统的研究与设计

模型驱动架构（MDA）是对象管理组织OMG提出的软件开发方法,它具有高效地实现系统集成和互操作、解决遗留系统问题、适应业务逻辑的快速变更以及缩短开发周期等优点。文章以一个医院管理系统项目设计为实例,研究了基于模型驱动架构（MDA）的软件开发方法,实现了领域模型的建立及领域模型到应用模型的变换。