material that, unlike traditional cryptographic keys, is (1) not reproducible precisely and (2) not distributed uniformly. We propose two primitives: a fuzzy extractor reliably extracts nearly uniform randomness R from its input; the extraction is error-tolerant in the sense that R will be the same even

Decision trees are one of the most popular choices for learning and reasoning from feature-based examples. They have undergone a number of alterations to deal with language and measurement uncertainties. In this paper, we present another modification, aimed at combining symbolic deci-sion trees

Fuzzy Description Logics have been widely studied as a formalism for representing and reasoning with vague knowledge. One of the most basic reasoning tasks in (fuzzy) Description Logics is to decide whether an ontology representing a knowledge domain is consistent. Surprisingly, not much is known

The Dortmund Electron Accelerator (DELTA) is a 1.5 GeV synchrotron light source. DELTA uses a strong focus-ing magnet structure. The distance of magnets is small and quadrupoles, steerers and sextupoles use the same magnet yoke. Magnet fields were measured taking into account the interference

using multiple-valued logics, it is usual to order the truth values in a lattice structure. In most cases such a partial order intuitively reflects differences in the "measure of truth" that the lattice elements are supposed to represent. There exist, however, other intuitive criteria

Systems based on interpolative fuzzy reasoning work with sparse rule bases. In case of some input values the system should approximate the output value. Carrying out this task depends on the right selection of the suitable fuzzy similarity measure. The goal of this paper is presenting two

This paper is concerned with the foundations of fuzzy reasoning and its relationships with other logics of uncertainty. The definitions of fuzzy logics are first examined and the role of fuzzification discussed. It is shown that fuzzification of PC gives a known multivalued logic

The fuzzy lattice reasoning (FLR) classifier is presented for inducing descriptive, decision-making knowledge (rules) in a mathematical lattice data domain including space RN. Tunable generalization is possible based on non-linear (sigmoid) positive valuation functions; moreover, the FLR classifier

variables whose linguistic values are spatial relations. The work reported here addresses spatial reasoning in situations where numerical or geometric precision is unlikely; it is particularly suited for dynamic situations. Keywords : Spatial Reasoning, Fuzzy Logic, Ontology, Mereology, Topology

an altogether different mechanism. An important observation is that similarity is inherent in fuzzy set theory. In approximate reasoning methodology-similarity relation is used in fuzzification while, similarity measure is used in fuzzy inference mechanism. This research proposes that simi-larity based