MARS
MARS is a service of Mason Publishing and the Digital Scholarship Center at the George Mason University Libraries. Home of the Electronic Theses and Dissertations, as well as faculty research publications and data files, MARS provides access to the intellectual work of the Mason community.
Whether you want to increase the circulation of your scholarship or you need help complying with Open Access mandates for your research data and publications, we are here to help. To start publishing your content in MARS, please contact us by using our online form.
Communities in MARS
Recently Added
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Raju, Rini
(2021-01-11)
It is crucial to use advanced machine learning models to improve disaster and emergency response in critical events around the world. In this paper, we introduce a new model, which can highlight the essential help that ...
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Mohamud, Sofia A
(2021-01-11)
The goal of this research is to determine crucial factors that played a role in the number of confirmed COVID-19 infections within a given location. We hypothesize that political bias plays a significant role in the rise ...
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Dauterive, Jessica; Schrum, Kelly
(Roy Rosenzweig Center for History and New Media, 2018-05-21)
ReSounding the Archives is an interdisciplinary collaboration that brings together digital humanities, history, and music. The project’s goal is literally to “re-sound” the archives — to bring World War I sheet music to ...
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Hamner, Christopher; Safley, Jim; Nguyen, Kim; Brett, Megan; Leon, Sharon; Fahringer, Alyssa Toby; Dauterive, Jessica; Brennan, Sheila A.; Albers, Ken; Ghajar, Lee Ann; Halabuk, James
(Roy Rosenzweig Center for History and New Media, 2020-10-08)
Scripto is an open-source tool that permits registered users to view digital files and transcribe them with an easy-to-use toolbar, rendering that text searchable. The tool includes a versioning history and editorial ...
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Zamiri Azar, Kimia
(2020)
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Mirzaeian, Ali; Manoj P D, Sai; Vakil, Ashkan; Homayoun, Houman; Sasan, Avesta
(2021)
Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In ...
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Mirzaeian, Ali; Kosecka, Jana; Homayoun, Houman; Mohsenin, Tinoosh; Sasan, Avesta
(2021)
This paper proposes an ensemble learning model that is resistant to adversarial attacks. To build resilience, we introduced a training process where each member learns a radically distinct latent space. Member models are ...
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