用于挖掘关联规则的可视化人机接口

讨论了一种接口技术，用于实现在关联规则挖掘时用户可以通过接口的界面选择要执行的算法，以及设置关联规则的“置信度”和“支持度”参数，并将挖掘的结果以容易理解的方式通过接口的界面显示出来。该接口以COM技术和VB、VC语言为基础设计开发，能对SQL Server创建的数据库进行挖掘。通过以Apriori算法和FP-Tree算法的实验，证明接口的工作是正确有效的。     

二叉比较树实现规则引擎的实践

在各种规模的系统中，由于规则存在很大不确定性，使得规则的查询变得复杂且多变。本文试图通过构建二叉比较树的方式为规则的查询提供一个统一和简单的接口，从而实现一个简单适用的规则引擎。     

有关MIS的智能检索接口与规则处理探讨

随着管理信息系统的发展，人们对检索接口的灵活性要求越来越高，文中提出了一种自然语言检索接口的方法，并设计了一个产生式规则索引算法，以提高管理信息系统的检索速度。     

基于XML数据的关联规则挖掘研究

XML凭借其诸多优点，在短短的时间内迅速成为表示和交换信息的标准。大量XML数据的涌现给数据挖掘提出了新的挑战。传统关联规则挖掘是基于关系数据库的，因此现有许多XML数据关联规则挖掘的方法都或多或少地利用关系数据库-即把XML数据文档映射成关系数据库来完成的。在仔细研究了XML数据的访问接口后，给出了一个基于Apriori算法可直接从XML文档挖掘关联规则的类接口，并且在．NET平台下用C#语言实现了。     

基于数据模型的接口用例自动生成

在嵌入式软件设计过程中，接口是非常重要的设计元素，对接口协议进行验证是接口测试中的主要内容。但现在软件接口类型不同，需要人工识别与组装各种接口的测试用例报文，接口协议的复杂度直接影响测试用例编制的难度与工作量。为实现接口测试用例的自动生成，提出一种基于数据模型的接口用例自动生成技术，根据复杂的接口协议文件，建立被测软件的接口协议形式化模型，准确、高效地表达接口协议规则及要素，并使用动态计算与替换算法得到相应业务场景下的预期结果，生成标准化的测试用例，更可兼容多种类型的接口，有效提升了接口测试效率。     

微机关系数据库的递归查询接口

为满足大型企业中产品零部件数据管理的需要，解决关系数据库所不能解决的递归查询问题，为微机关系数据库设计并实现了一个递归查询接口，该接口能够计算了Ｄａｔａｌｏｇ逻辑程序，通过允许规划头部包含函数符号，使规则增加了数值计算能力。     

通用文档程序架构中操作行为的研究与设计

随设计描述通用文档程序架构中操作行为，使开发文档处理程序简化为面向固定接口的开发。通过实现一套统一的接口来实现不同文档类型（单文档和多文档）的统一操作行为。利用该方法进行文档的操作，验证了接口规则的合理性和有效性，同时能给用户带来一致交互体验。     

一种面向内核接口的顺序依赖规则挖掘与违例检测方法

内核扩展函数以接口的形式提供给驱动,用于管理设备和申请相关的资源.这些接口中存在大量的顺序依赖规则,如自旋锁必须经过初始化才能加锁,然后才能解锁;驱动在加载时申请的内存,卸载时必须予以释放等.然而,驱动开发者常常不熟悉或疏忽内核接口的使用规则,导致驱动中存在大量的接口使用违例,影响驱动及系统的可靠运行.文中提出了一种面向内核接口的顺序依赖规则挖掘与违例检测方法(SD-Miner).该方法结合驱动源码的结构特征,对驱动代码使用的内核接口进行统计分析,挖掘并提取内核接口的顺序依赖规则,并利用提取的规则检测现有的驱动源码中的使用违例.SD-Miner对Linux 3.10.10和2.6.38的驱动源码分别进行了规则挖掘和违例检测.对比检测结果发现,在2.6.38中检测出的错误中,有64处在3.10.10中得到了修正.SD-Miner检测和分析Linux 3.10.10的3781款驱动的过程仅耗费5 min,共计提取出了220个顺序依赖相关的接口使用规则,并检测到了756个使用违例,作者将其中50个提交给了开发者,累计有25个回复者对20个使用违例进行了确认.实验结果表明,SD-Miner能够有效地挖掘出内核接口的顺序依赖规则,并检测出使用违例,进而辅助开发人员对驱动进行修正来提高驱动可靠性.此外,规则的挖掘是基于驱动的结构信息和统计信息,不需要开发者在源码中提供额外的注释及标注.     

基于CORBA技术的GDMO接口构建的研究

用CORBA技术构建TMN中的被管对象的接口，可以加快实现异构环境下电信网的分布式管理。通过研究TMN中的GDMO模板和CORBA中的IDL接口，可以建立二者之间的映射规则。利用这种映射关系，有助于实现GDMO中的动作、属性、通知、参数、行为等模板到IDL接口中的操作、属性、参数和注释的自动映射。     

COM接口的Z规范

对微软公司提出的COM组件模型中的接口规则给出了一个基于Z语言规范化描述，使其具有严格的语义基础，能通过形式化的分析来保证规则的相容性，在此基础之上对组件也给出了Z语言的定义，并对合法组件的性质作出了形式化分析。     

NOTE ON THE ARITHMETIC RULE BY ZADEH FOR FUZZY CONDITIONAL INFERENCE

Abstract

This paper shows that Zadeh's arithmetic rule for fuzzy conditional propositions “If x is A then y is B” and “If x is A then y is B else y is C” can infer quite reasonable consequences in a fuzzy conditional inference if new compositions of “max-[Odot] composition” and “max- composition” are used in the compositional rule of inference, though, as was pointed out before, this arithmetic rule cannot get suitable consequences in the compositional rule of inference which uses max-min composition. Moreover, it is shown that the arithmetic rule satisfies a syllogism under these two compositions.     

模糊推理的合成规则及其合成运算的选择研究

本文首先提出当知识库中含有多条取自于同论域上的模糊推理规则时，应用通常的六条合成推理规则所推得的结论将随着模糊推理规则数的增多而越来越偏离真实度这一问题。本文针对该问题对通常的六条合成推理规则及其三角模下的六条推广合成推理规则进行了理论比较研究，并对合成运算进行了比较研究。研究表明：合成推理规则（或Ｒ￣４）以合成运算ｍａｘ－Ｔ０是克服上述问题的最佳选择。这一重要结果对于模糊知识库的设计具有指导意义。     

On the extension of the compositional rule of inference

In this article we introduce an extension of Zadeh's compositional rule of inference in terms of the general rule of inference using a triangular norm extended to n arguments. Using this extension, all inferences schemes, crisp as well as fuzzy, based on the compositional rule of inference can be obtained in a uniform way. © 1993 John Wiley & Sons, Inc.     

Fuzzy modus ponens: A new model suitable for applications in knowledge-based systems

Models for fuzzy deductive reasoning are becoming important in the field of knowledge-based systems. Zadeh suggested a compositional rule of inference, also called Generalized Modus Ponens. In order to study the fine structure of this model, we test seven rules of implication, usually found in the fuzzy literature, and two kinds of compositional rule with several properties which seems to be required by commonsense reasoning. But, as no rule is shown to satisfy these properties, we propose a new model for fuzzy Modus Ponens. the main idea of this model is to find a fuzzy extension of the meta-level rule of inference, in comparison with the object-level extension of the disjunctive syllogism such as the Zadeh's model. It is shown that our model satisfies all the intuitively required properties, and moreover is computationally very easy to apply.     

Learning-based symbolic assume-guarantee reasoning for Markov decision process by using interval Markov process

Many real-life critical systems are described with large models and exhibit both probabilistic and non-deterministic behaviour. Verification of such systems requires techniques to avoid the state space explosion problem. Symbolic model checking and compositional verification such as assume-guarantee reasoning are two promising techniques to overcome this barrier. In this paper, we propose a probabilistic symbolic compositional verification approach (PSCV) to verify probabilistic systems where each component is a Markov decision process (MDP). PSCV starts by encoding implicitly the system components using compact data structures. To establish the symbolic compositional verification process, we propose a sound and complete symbolic assume-guarantee reasoning rule. To attain completeness of the symbolic assume-guarantee reasoning rule, we propose to model assumptions using interval MDP. In addition, we give a symbolic MTBDD-learning algorithm to generate automatically the symbolic assumptions. Moreover, we propose to use causality to generate small counterexamples in order to refine the conjecture assumptions. Experimental results suggest promising outlooks for our probabilistic symbolic compositional approach.     

POPFNN-CRI(S): pseudo outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier

A pseudo-outer product based fuzzy neural network using the compositional rule of inference and singleton fuzzifier [POPFNN-CRI(S)] is proposed in this paper. The correspondence of each layer in the proposed POPFNN-CRI(S) to the compositional rule of inference using standard T-norm and fuzzy relation gives it a strong theoretical foundation. The proposed POPFNN-CRI(S) training consists of two phases; namely: the fuzzy membership derivation phase using the novel fuzzy Kohonen partition (FKP) and pseudo Kohonen partition (PFKP) algorithms, and the rule identification phase using the novel one-pass POP learning algorithm. The proposed two-phase learning process effectively constructs the membership functions and identifies the fuzzy rules. Extensive experimental results based on the classification performance of the POPFNN-CRI(S) using the Anderson's Iris data are presented for discussion. Results show that the POPFNN-CRI(S) has taken only 15 training iterations and misclassify only three out of all the 150 patterns in the Anderson's Iris data.     

Automated circular assume-guarantee reasoning

Model checking is a successful approach for verifying hardware and software systems. Despite its success, the technique suffers from the state explosion problem which arises due to the large state space of real-life systems. One solution to the state explosion problem is compositional verification, that aims to decompose the verification of a large system into the more manageable verification of its components. To account for dependencies between components, assume-guarantee reasoning defines rules that break-up the global verification of a system into local verification of individual components, using assumptions about the rest of the system. In recent years, compositional techniques have gained significant successes following a breakthrough in the ability to automate assume-guarantee reasoning. However, automation has been restricted to simple acyclic assume-guarantee rules. In this work, we focus on automating circular assume-guarantee reasoning in which the verification of individual components mutually depends on each other. We use a sound and complete circular assume-guarantee rule and we describe how to automatically build the assumptions needed for using the rule. Our algorithm accumulates joint constraints on the assumptions based on (spurious) counterexamples obtained from checking the premises of the rule, and uses a SAT solver to synthesize minimal assumptions that satisfy these constraints. To the best of our knowledge, our work is the first to fully automate circular assume-guarantee reasoning. We implemented our approach and compared it with established non-circular compositional methods that use learning or SAT-based techniques. The experiments show that the assumptions generated for the circular rule are generally smaller, and on the larger examples, we obtain a significant speedup.     

Groupwise geometric and photometric direct image registration

Image registration consists in estimating geometric and photometric transformations that align two images as best as possible. The direct approach consists in minimizing the discrepancy in the intensity or color of the pixels. The inverse compositional algorithm has been recently proposed by Baker et al. for the direct estimation of groupwise geometric transformations. It is efficient in that it performs several computationally expensive calculations at a pre-computation phase. Photometric transformations act on the value of the pixels. They account for effects such as lighting change. Jointly estimating geometric and photometric transformations is thus important for many tasks such as image mosaicing. We propose an algorithm to jointly estimate groupwise geometric and photometric transformations while preserving the efficient pre-computation based design of the original inverse compositional algorithm. It is called the dual inverse compositional algorithm. It uses different approximations than the simultaneous inverse compositional algorithm and handles groupwise geometric and global photometric transformations. Its name stems from the fact that it uses an inverse compositional update rule for both the geometric and the photometric transformations. We demonstrate the proposed algorithm and compare it to previous ones on simulated and real data. This shows clear improvements in computational efficiency and in terms of convergence.     

Fuzzy conditional inference under max-⊙ composition

This paper shows that the majority of fuzzy inference methods for a fuzzy conditional proposition “If x is A then y is B,” with A and B fuzzy concepts, can infer very reasonable consequences which fit our intuition with respect to several criteria such as modus ponens and modus tollens, if a new composition called “max-⊙ composition” is used in the compositional rule of inference, though reasonable consequences cannot always be obtained when using the max-min composition, which is used usually in the compositional rule of inference. Furthermore, it is shown that a syllogism holds for the majority of the methods under the max-⊙ composition, though they do not always satisfy the syllogism under the max-min composition.     

密码协议的一种安全模型

将密码协议与密码算法视为一个系统,建立了密码协议系统的一种安全模型.基于假设/保证的组合推理技术提出了新的假设/保证推理规则和假设/保证推理算法,证明了该规则的完备性,实现了密码协议系统的模型检查,并重点解决了系统分解问题、假设函数的设定问题、进程+逻辑的系统特性描述问题等难题.以kerberos密码协议系统为例,利用该安全模型和假设/保证推理技术对密码协议系统进行了安全验证.