一种信息门户中基于本体的信息查询模型

提出了一种信息门户中的基于本体的信息查询模型，该模型主要由两个模块组成：基于领域本体的改进的查询模块和基于模糊本体的逐步精确的查询模块。前者对应于用户能够清楚地表达自己需要的信息，它可以基于相似度对查询结果进行排序。后者则对应于用户不能够清楚地表达自己需要的信息，它可以向用户提出查询建议，逐步精确用户的查询。     

Exits in the refinement calculus

Although many programming languages contain exception handling mechanisms, their formal treatment — necessary for rigorous development — can be complex. Nevertheless, this paper presents a simple incorporation ofexit commands and exception blocks into a rigorous program development method. The refinement calculus, chosen for the exercise, is a method of developing imperative programs. It is based on weakest preconditions, although they are not used explicitly during program construction; they merely justify the general method. In the style of the refinement calculus, program development laws are given that introduce and allow the manipulation ofexits. The soundness of the new laws is shown using weakest preconditions (as for the existing refinement calculus laws). The extension of weakest preconditions needed to handleexits is a variation on earlier work of Cristian; the variation is necessary to handle nondeterminism.     

用Petri网描述程序精化中的循环不变式

形式化方法把程序看成规范,形式化开发方法包括形式规范和规范（程序）的精化。精化演算方法能够通过演算的方式,把规范逐步精化为程序。然而,演化的过程依赖于开发人员的经验,整个过程全部都是手动的。形式化方法的最高目标是软件自动化,使得能从规范自动开发出正确的程序。因而用Petri网来描述程序精化中的循环不变式,希望以此作为软件自动化的一个探索。     

Metric-based reasoning about pseudocode design in the partial metrics system

The goal of the PMS project is to produce an environment in which the intelligent online assessment of the design for large-scale ADA programming projects is provided. The focus is on the representation of knowledge about the design process for an individual module. Changes in pseudocode complexity are measured in terms of partial metrics. These metrics can take the designers inferences about the pseudocode program structure into account when assessing module complexity. Next, a model of the stepwise refinement process is given which demonstrates how pseudocode elaboration decisions can be modelled in partial metric terms. Finally, the decisions associated with each refinement step for 17 example refinements taken from the computer science literature are described using partial metrics.     

A Transportable Programming Language (TPL) System. I. overview

A Transportable Programming Language, TPL, is defined as one that can be used to codeany program that will compile and correctly execute onany machine in a designated range. The range specifies only the minimum software and hardware needed to support the TPL. A HLL-TPL System is one that, in addition to supporting the TPL, can be used to transport any program coded in a dialect of the High-Level Language, HLL, so that it will compile and correctly execute on any machine in a designated range.Methods of implementing a HLL-TPL System are reviewed and it is demonstrated that theSuperset Method, in which the Hypothetical Parent of the HLL (HPHLL) acts as the intermediate language between the dialects of a HLL, leads to a fully transportable High-Level Language—the HPHLL—whose only parameters are: The amount of memory available; and the maximum module size exclusive of arrays. The ongoing work to realize a production version of the FORTRAN-TPL system is described. Its essential characteristic is a fully transportable bifunctional FORTRAN-TPL compiler that converts a dialect of FORTRAN to the intermediate language, HPFORTRAN, and visa-versa.     

一种从面向对象Z规约到代码的精化演算方法

COOZ(complete object-oriented Z)的优势在于精确描述大型程序的规约.COOZ本身的结构 不支持精化演算,这限制了COOZ的应用能力,使COOZ难以作为完整的方法应用于软件的开发. 将精化演算引入COOZ,弥补了COOZ在设计和实现阶段的不足,同时也消除了规约与实现之间在 结构和表示方法上的完全分离,使程序开发在一个完整的框架下平滑进行.该文提出了基于CO OZ和精化演算的软件开发模型,通过实例讨论了数据精化和操作精化问题.在精化演算实现技 术方面构造了一种数据精化算子,提出一     

The Automated Refinement of a Requirements Domain Theory

The specification and management of requirements is widely considered to be one of the most important yet most problematic activities in software engineering. In some applications, such as in safety critical areas or knowledge-based systems, the construction of a requirements domain theory is regarded as an important part of this activity. Building and maintaining such a domain theory, however, requires a large investment and a range of powerful validation and maintenance tools. The area of theory refinement is concerned with the use of training data to automatically change an existing theory so that it better fits the data. Theory refinement techniques, however, have not been extensively used in applications because of the problems in scaling up their underlying algorithms.In our work we have applied theory refinement to assist in the problem of validation and maintenance of a requirements theory concerning separation standards in the North East Atlantic. In this paper we describe an implemented refinement algorithm, which processes a logic program automatically generated from the theory. We overcame the size and expressiveness problems typically encountered when applying theory refinement to a logic program of this kind by designing focused, composite refinement operators within the algorithm. These operators modify the auto-generated logic program by generalising or specialising clauses containing ordinal relations—that is relations which operate on totally ordered data.     

Blaming the client: on data refinement in the presence of pointers

Data refinement is a common approach to reasoning about programs, based on establishing that a concrete program indeed satisfies all the required properties imposed by an intended abstract pattern. Reasoning about programs in this setting becomes complex when use of pointers is assumed and, moreover, a well-known method for proving data refinement, namely the forward simulation method, becomes unsound in presence of pointers. The reason for unsoundness is the failure of the “lifting theorem” for simulations: that a simulation between abstract and concrete modules can be lifted to all client programs. The result is that simulation does not imply that a concrete can replace an abstract module in all contexts. Our diagnosis of this problem is that unsoundness is due to interference from the client programs. Rather than blame a module for the unsoundness of lifting simulations, our analysis places the blame on the client programs which cause the interference: when interference is not present, soundness is recovered. Technically, we present a novel instrumented semantics which is capable of detecting interference between a module and its client. With use of special simulation relations, namely growing relations, and interpreting the simulation method using the instrumented semantics, we obtain a lifting theorem. We then show situations under which simulation does indeed imply refinement.     

Combining Symbolic and Neural Learning

Conclusion Connectionist machine learning has proven to be a fruitful approach, and it makes sense to investigate systems that combine the strengths of the symbolic and connectionist approaches to AI. Over the past few years, researchers have successfully developed a number of such systems. This article summarizes one view of this endeavor, a framework that encompasses the approaches of several different research groups. This framework (see Figure 1) views the combination of symbolic and neural learning as a three-stage process: (1) the insertion of symbolic information into a neural network, thereby (partially) determining the topology and initial weight settings of a network, (2) the refinement of this network using a numeric optimization method such as backpropagation, possibly under the guidance of symbolic knowledge, and (3) the extraction of symbolic rules that accurately represent the knowledge contained in a trained network. These three components form an appealing, complete picture—approximately-correct symbolic information in, more-accurate symbolic information out—however, these three stages can be independently studied. In conclusion, the research summarized in this paper demonstrates that combining symbolic and connectionist methods is a promising approach to machine learning.     

A comparison of refinement orderings and their associated simulation rules

In this paper we compare the refinement orderings, and their associated simulation rules, of state-based specification languages such as Z and Object-Z with the refinement orderings of event-based specification languages such as CSP. We prove with a simple counter-example that data refinement, established using the standard simulation rules for Z, does not imply failures refinement in CSP. This contradicts accepted results.Having explored the differences between the simulation rules for establishing data refinement and those for establishing the refinement of action systems and state-transition systems—models in which refinement is equivalent to failures refinement within CSP—we present a new set of simulation rules for data types. These alternative rules are both sound and jointly complete with respect to the stable failures refinement ordering. Furthermore we present an alternative refinement ordering for CSP, one in which refinement is equivalent to data refinement in Z.