Showing papers in "Lecture Notes in Computer Science in 1988"

Belief functions versus probability functions

Abstract: Two models are proposed to quantify someone's degree of belief, based respectively on probability functions, the Bayesian model, and on belief functions, the transferable belief model (Shafer 1976). The first, and by far the oldest, is well established and supported by excellent axiomatic and behaviour arguments. The model based on belief functions is often understood as some kind of generalization either of the Bayesian model or of the upper and lower probabilities model. Therefore we present our interpretation of the model developed initially by Shafer in his book (1977) and called here the transferable belief model. The major point of our interpretation is the fact-we try to dissociate completely the transferable belief model from any model based on probability functions.

Notes on the elimination of conditions

Abstract: In these notes we have described a method for eliminating equality tests from conditions in ctrs. Two applications have been shown: the first is the transformation of a conditional trs into an equivalent unconditional one; the second is the extension of the user syntax of K-LEAF, a logic+functional programming language.

Fast RSA-hardware : dream or reality?

Abstract: This paper describes a successful hardware implementation of the RSA algorithm. It is implemented as an 120-bit bit-slice processor, which may be interconnected without additional circuitry to obtain arbitrary word lengths. With 512-bit operands, exponentiation takes less than 30 milliseconds.

An Application of Abstract Interpretation in Source Level Program Transformation

Abstract: We describe an application of abstract interpretation within the field of source-to-source program transformation for pure Horn clause logic programs. Using a very concrete setting, we aim to provide a comprehensible introduction to the technique of abstract interpretation, particularly suited for the novice in the field. Also, we argue that abstract interpretation is not only suited for applications in code optimization, but provides an excellent tool to support techniques in source level program transformation.

Knowledge-based road network extraction on spot satellite images

Abstract: Automated delineation of linear cultural structures can help to improve the classification of remotely-sensed images. This topic also provides an excellent testbed for knowledge-based computer vision research. In this paper, a road network extraction system, useful on SPOT satellite images, is described. By applying semantic model-fitting operators, an initial spatial segmentation is obtained. To analyse the resulting primal road network, declaratively stated knowledge about generic appearances of roads and crossroads on SPOT images is used. As a result a more detailed description of the network can be obtained. Advantages and drawbacks of the use of existing knowledge base tools for this computer vision task are reported.

Software Support for the Refinement of VDM Specifications

Abstract: A tool set is described which supports the use of VDM in software development. The tool set aids the user both in the construction of VDM specifications and in the refinement of such specifications. A proof checker is included which checks the correctness of the associated proofs. Design decisions necessary to allow automated support of the refinement process are discussed.

ACP with signals

Abstract: New operators are introduced on top of ACP [BK 84] in order to incorporate stable signals in process algebra. Semantically this involves assigning labels to nodes of process graphs. The labels of nodes are called signals. In combination with the operators of BPA, a signal insertion operator allows to describe each finite tree labeled with actions and signals, provided the signals do not occur at leaves of the tree. In a merge processes can observe the signals of concurrent processes. This research was sponsored in part by ESPRIT under contract 432, METEOR.