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Institution

University of New Brunswick

EducationFredericton, New Brunswick, Canada
About: University of New Brunswick is a education organization based out in Fredericton, New Brunswick, Canada. It is known for research contribution in the topics: Population & Context (language use). The organization has 10498 authors who have published 20654 publications receiving 474448 citations.


Papers
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Proceedings ArticleDOI
21 May 2007
TL;DR: In this article, a photovoltaic array (PVA) simulation model to be used in Matlab-Simulink GUI environment is developed and presented in this paper, the model is developed using basic circuit equations of the PV solar cells including the effects of solar irradiation and temperature changes.
Abstract: A photovoltaic array (PVA) simulation model to be used in Matlab-Simulink GUI environment is developed and presented in this paper. The model is developed using basic circuit equations of the photovoltaic (PV) solar cells including the effects of solar irradiation and temperature changes. The new model was tested using a directly coupled dc load as well as ac load via an inverter. Test and validation studies with proper load matching circuits are simulated and results are presented here.

457 citations

Journal ArticleDOI
TL;DR: A comprehensive overview of the finding to date relating to fake news is presented, characterized the negative impact of online fake news, and the state-of-the-art in detection methods are characterized.
Abstract: Over the recent years, the growth of online social media has greatly facilitated the way people communicate with each other. Users of online social media share information, connect with other people and stay informed about trending events. However, much recent information appearing on social media is dubious and, in some cases, intended to mislead. Such content is often called fake news. Large amounts of online fake news has the potential to cause serious problems in society. Many point to the 2016 U.S. presidential election campaign as having been influenced by fake news. Subsequent to this election, the term has entered the mainstream vernacular. Moreover it has drawn the attention of industry and academia, seeking to understand its origins, distribution and effects. Of critical interest is the ability to detect when online content is untrue and intended to mislead. This is technically challenging for several reasons. Using social media tools, content is easily generated and quickly spread, leading to a large volume of content to analyse. Online information is very diverse, covering a large number of subjects, which contributes complexity to this task. The truth and intent of any statement often cannot be assessed by computers alone, so efforts must depend on collaboration between humans and technology. For instance, some content that is deemed by experts of being false and intended to mislead are available. While these sources are in limited supply, they can form a basis for such a shared effort. In this survey, we present a comprehensive overview of the finding to date relating to fake news. We characterize the negative impact of online fake news, and the state-of-the-art in detection methods. Many of these rely on identifying features of the users, content, and context that indicate misinformation. We also study existing datasets that have been used for classifying fake news. Finally, we propose promising research directions for online fake news analysis.

449 citations

Proceedings ArticleDOI
22 May 2008
TL;DR: RevLib is introduced, an online resource for reversible functions and reversible circuits that provides a large database of functions with respective circuit realizations and tools are introduced to support researchers in evaluating their algorithms and documenting their results.
Abstract: Synthesis of reversible logic has become an active research area in the last years. But many proposed algorithms are evaluated with a small set of benchmarks only. Furthermore, results are often documented only in terms of gate counts or quantum costs, rather than presenting the specific circuit. In this paper RevLib (www.revlib.org) is introduced, an online resource for reversible functions and reversible circuits. RevLib provides a large database of functions with respective circuit realizations. RevLib is designed to ease the evaluation of new methods and facilitate the comparison of results. In addition, tools are introduced to support researchers in evaluating their algorithms and documenting their results.

449 citations

Journal ArticleDOI
TL;DR: An algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations outperformed methods that used only EMG signals or mechanical information.
Abstract: In this study, we developed an algorithm based on neuromuscular-mechanical fusion to continuously recognize a variety of locomotion modes performed by patients with transfemoral (TF) amputations. Electromyographic (EMG) signals recorded from gluteal and residual thigh muscles and ground reaction forces/moments measured from the prosthetic pylon were used as inputs to a phase-dependent pattern classifier for continuous locomotion-mode identification. The algorithm was evaluated using data collected from five patients with TF amputations. The results showed that neuromuscular-mechanical fusion outperformed methods that used only EMG signals or mechanical information. For continuous performance of one walking mode (i.e., static state), the interface based on neuromuscular-mechanical fusion and a support vector machine (SVM) algorithm produced 99% or higher accuracy in the stance phase and 95% accuracy in the swing phase for locomotion-mode recognition. During mode transitions, the fusion-based SVM method correctly recognized all transitions with a sufficient predication time. These promising results demonstrate the potential of the continuous locomotion-mode classifier based on neuromuscular-mechanical fusion for neural control of prosthetic legs.

447 citations

Proceedings ArticleDOI
17 Apr 2009
TL;DR: In this paper, the authors describe and classify both the types of people who volunteer geospatial information and the nature of their contributions, and offer different taxonomies that can help researchers clarify what is at stake with respect to the contributors.
Abstract: Advances in positioning, Web mapping, cellular communications and wiki technologies have surpassed the original visions of GSDI programs around the world. By tapping the distributed knowledge, personal time and energy of volunteer contributors, GI voluntarism is beginning to relocate and redistribute selected GI productive activities from mapping agencies to networks of non-state volunteer actors. Participants in the production process are both users and producers, or ‘produsers’ to use a recent neologism. Indeed, GI voluntarism ultimately has the potential to redistribute the rights to define and judge the value of the produced geographic information and of the new production system in general. The concept and its implementation present a rich collection of both opportunities and risks now being considered by leaders of public and private mapping organizations world-wide. In this paper, the authors describe and classify both the types of people who volunteer geospatial information and the nature of their contributions. Combining empirical research dealing with the Open Source software and Wikipedia communities with input from selected national mapping agencies and private companies, the authors offer different taxonomies that can help researchers clarify what is at stake with respect to geospatial information contributors. They identify early lessons which may be drawn from this research, and suggest questions which may be posed by large mapping organizations when considering the potential opportunities and risks associated with encouraging and employing Volunteered Geographic Information in their programs.

445 citations


Authors

Showing all 10596 results

NameH-indexPapersCitations
David Scott124156182554
Wei Lu111197361911
Richard J. Hobbs10859268141
Wei Zhang104291164923
Chris M. Wood10279543076
Mark S. Tremblay10054143843
James Taylor95116139945
Johan Richard9549925915
Chun Li9351741645
Bin Li92175542835
Robert J. Blanchard8324122316
Robie W. Macdonald7929223460
Serge Kaliaguine7646521443
Ravin Balakrishnan7218215970
Min Wang7271619197
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202341
2022145
20211,008
20201,066
2019989