A subject who avows being tired, or scared of something, or thinking that p, is normally presumed to have the last word on the relevant matters; we would not presume to criticize her self-ascription or to reject it on the basis of our contrary judgement. Furthermore, unlike ordinary empirical reports, and somewhat like apriori statements, avowals are issued with a very high degree of confidence...

This lecture is concerned with apriori bounds on the size of solutions to Diophantine equations. We will present a rather random collection of results with the intention to provide a flavor of the subject, with only a glimpse of something like a unifying principle. In the next lecture, we will make a better attempt to give coherence to our exposition, by describing the relation to canonical cla...

Two paradigmatic approaches to the normalisation of citation-impact measures are discussed. The results of the mathematical manipulation of standard indicators such as citation means, notably journal Impact Factors, (called aposteriori normalisation) are compared with citation measures obtained from fractional citation counting (called apriori normalisation). The distributions of two subfields ...

A subject who avows being tired, or scared of something, or thinking that p, is normally presumed to have the last word on the relevant matters; we would not presume to criticize her self-ascription or to reject it on the basis of our contrary judgement. Furthermore, unlike ordinary empirical reports, and somewhat like apriori statements, avowals are issued with a very high degree of confidence...

0 Introduction: main objects and results 3 0.1 Main results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 0.2 An application of T1-heorem: electric intensity capacity . . . . . . . . . . . . 7 0.3 How to interpret Calderón–Zygmund operator T? . . . . . . . . . . . . . . . 9 0.3.1 Bilinear form is defined on Lipschitz functions . . . . . . . . . . . . . 10 0.3.2 Bilin...

Accumulation of abundant data from different sources of the society but a little knowledge situation has lead to Knowledge Discovery from Databases or Data Mining. Data Mining techniques use the existing data and retrieve the useful knowledge from it which is not directly visible in the original data. As Data Mining algorithms deal with huge data, the primary concerns are how to store the data ...

The Apriori Algorithm examines baskets of items to determine which subsets of the items occur in lots of baskets. Suppose we wish to determine which items sets occur in at least k baskets. The algorithm considers item sets of size l in the order l = 1, 2, : : :. The only way this algorithm can determine that a set occurs at least k times is to count the k occurrences, but it sometimes determine...

This paper presents a set of programme to extract the application-layer protocol features. Based on frequent itemsets mining, the program automatically extracts four most common features of a protocol: characteristic string, session tag, packet length, and packet order. It is experimentally demonstrated that this progran can significantly improve the efficiency of feature extraction, and can be...

Résumé. L’extraction de toutes les requêtes fréquentes dans une base de données relationnelle est un problème difficile, même si l’on ne considère que des requêtes conjonctives. Nous montrons que ce problème devient possible dans le cas suivant : le schéma de la base est un schéma en étoile, et les données satisfont un ensemble de dépendances fonctionnelles et de contraintes référentielles. De ...

The Apriori algorithm is a popular correlation-based datamining kernel. However, it is a computationally expensive algorithm and the running times can stretch up to days for large databases, as database sizes can extend to Gigabytes. Through the use of a new extension to the systolic array architecture, time required for processing can be significantly reduced. Our array architecture implementa...