University of Windsor

About: University of Windsor is a education organization based out in Windsor, Ontario, Canada. It is known for research contribution in the topics: Population & Argumentation theory. The organization has 10654 authors who have published 22307 publications receiving 435906 citations. The organization is also known as: UWindsor & Assumption University of Windsor.

Metal-organic frameworks with dynamic interlocked components.

Abstract: The dynamics of mechanically interlocked molecules such as catenanes and rotaxanes have been studied in solution as examples of rudimentary molecular switches and machines. A metal–organic framework with a [2]rotaxane as a building block demonstrates that such dynamic processes can also operate inside a solid-state material.

Detecting opinion spams and fake news using text classification

Abstract: *Correspondence Traore, Issa, ECE Department, University of Victoria, Victoria, BC, Canada. Email: itraore@ece.uvic.ca In recent years, deceptive content such as fake news and fake reviews, also known as opinion spams, have increasingly become a dangerous prospect for online users. Fake reviews have affected consumers and stores alike. Furthermore, the problem of fake news has gained attention in 2016, especially in the aftermath of the last U.S. presidential elections. Fake reviews and fake news are a closely related phenomenon as both consist of writing and spreading false information or beliefs. The opinion spam problem was formulated for the first time a few years ago, but it has quickly become a growing research area due to the abundance of user-generated content. It is now easy for anyone to either write fake reviews or write fake news on the web. The biggest challenge is the lack of an efficient way to tell the difference between a real review and a fake one; even humans are often unable to tell the difference. In this paper, we introduce a new n-gram model to detect automatically fake contents with a particular focus on fake reviews and fake news. We study and compare 2 different features extraction techniques and 6 machine learning classification techniques. Experimental evaluation using existing public datasets and a newly introduced fake news dataset indicate very encouraging and improved performances compared to the state-of-the-art methods.

Organizational structure, context, customer orientation, and performance: lessons from Chinese state-owned enterprises

Abstract: This study develops a model grounded in the contingency theory (i.e., context–structure–performance) applicable to Chinese state-owned enterprises (SOEs). Using data from a sample of 205 industrial SOEs, the study shows that SOE growth performance relative to the industry is positively predicted by formal control, inversely predicted by decentralization, and positively predicted by the interaction of the two. Customer product knowledge utilization, unrelated to growth performance relative to the industry, is positively predicted by formal control and the interaction of formal control with decentralization. Foreign induced industry competitiveness, technological turbulence, size, and production technology routineness are treated as context variables and modeled accordingly. Copyright © 2003 John Wiley & Sons, Ltd.