Concordia University

About: Concordia University is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Context (language use) & Control theory. The organization has 13565 authors who have published 31084 publications receiving 783525 citations. The organization is also known as: Sir George Williams University & Loyola College, Montreal.

Development of dynamic recrystallization theory

Abstract: Although hot working had been defined as deformation above the recrystallization temperature (determined after cold working), it was only about 1965 that dynamic recrystallization (DRX) was confirmed to be occurring during the deformation; two decades were required to clarify the similarities to, and the differences from, static recrystallization. In classical discontinuous DRX in Cu, Ni, and γ-Fe, successive necklaces of new grains cause work softening; however in steady-state, the nuclei are uniformly distributed as reestablished dislocation structure limits growth. In high recovery metals at high strains, the grain boundary (GB) serrations meet across the elongated thinned grains thus pinching them off into almost equiaxed grains containing a substructure, thus geometric DRX.

Differentially private data release for data mining

Abstract: Privacy-preserving data publishing addresses the problem of disclosing sensitive data when mining for useful information. Among the existing privacy models, ∈-differential privacy provides one of the strongest privacy guarantees and has no assumptions about an adversary's background knowledge. Most of the existing solutions that ensure ∈-differential privacy are based on an interactive model, where the data miner is only allowed to pose aggregate queries to the database. In this paper, we propose the first anonymization algorithm for the non-interactive setting based on the generalization technique. The proposed solution first probabilistically generalizes the raw data and then adds noise to guarantee ∈-differential privacy. As a sample application, we show that the anonymized data can be used effectively to build a decision tree induction classifier. Experimental results demonstrate that the proposed non-interactive anonymization algorithm is scalable and performs better than the existing solutions for classification analysis.

Mining frequent itemsets with convertible constraints

Abstract: Recent work has highlighted the importance of the constraint based mining paradigm in the context of frequent itemsets, associations, correlations, sequential patterns, and many other interesting patterns in large databases. The authors study constraints which cannot be handled with existing theory and techniques. For example, avg(S) /spl theta/ /spl nu/, median(S) /spl theta/ /spl nu/, sum(S) /spl theta/ /spl nu/ (S can contain items of arbitrary values) (/spl theta//spl isin/{/spl ges/, /spl les/}), are customarily regarded as "tough" constraints in that they cannot be pushed inside an algorithm such as a priori. We develop a notion of convertible constraints and systematically analyze, classify, and characterize this class. We also develop techniques which enable them to be readily pushed deep inside the recently developed FP-growth algorithm for frequent itemset mining. Results from our detailed experiments show the effectiveness of the techniques developed.