Presents a technique for building expert systems which combines the fuzzy-set approach with artificial neural network structures. This technique can effectively deal with two types of medical knowledge, a nonfuzzy one and a fuzzy one, which usually contribute to the process of medical diagnosis. Nonfuzzy numerical data are obtained from medical tests. Fuzzy linguistic rules describing the diagnosis process are provided by a human expert. The proposed method has been successfully applied in veterinary medicine as a support system in the diagnosis of canine liver diseases.>

To have broad application or approach the capacity of ordinary human thinking, analogical reasoning programs must become more complex or their semantics must become richer, or both. Little research is being done to discover what a broad collection of semantics can contribute to general-purpose analogical reasoning. From an analysis of a small collection of words, two classes containing over 300 categories are determined representing the semantics for understanding metaphors. A broad-based collection of simple target is source metaphors is sampled with each target and source represented in terms of these categories without known influence from the respective metaphor. A computational model is developed drawing on several disciplines while using rules for the recognition and elaboration phases of reasoning. Recognition primarily involves finding and reorganizing relevant schemata within the source. Elaboration involves reorganizing and possibly adding to the target. Given each metaphor, the TisS computer program creates a new representation of each target and source including several meanings for each metaphor. A test with human subjects making aptness and agreement judgements on the generated statements is discussed, suggesting a methodology for using a rich semantic base in analogical reasoning.

No abstract available.

Partitioning is a highly secure approach to protecting statistical databases. When updates are introduced, security depends on putting restrictions on the sizes of partition sets which may be queried. To overcome this problem, attempts have been made to add “dummy” records. Recent work has shown that this leads to high information loss. This paper reconsiders the restrictions on the size of partitioning sets required to achieve a high level of security. Updates of two records at a time were studied earlier, and security was found to hold if the sizes of the partition sets were kept even. In this paper an extended model is presented, allowing very general updates to be performed. The security problem is thoroughly studied, giving if and only if conditions. The earlier result is shown to be part of a corollary to the main theorem of this paper. Alternatives to adding dummy records are presented and the practical implications of the theory for the database manager are discussed.