• Machine Learning Model to Detect Emergency in the Global Pandemic 

  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 ...
• Sentiment Analysis Methods to Mitigate Negative Effect of the COVID-19 Pandemic 

  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 ...
• ReSounding the Archives 

  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 ...
• Scripto 

  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 ...
• A Technical Report on Security Evaluation of Logic Locking: Attacks and Defenses 

  Zamiri Azar, Kimia (2020)
• Conditional Classification: A Solution for Computational Energy Reduction 

  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 ...
• Diverse Knowledge Distillation (DKD): A Solution for Improving The Robustness of Ensemble Models Against Adversarial Attacks 

  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 ...
• A Technical Report On Accelerator Design For Deep Neural Networks 

  Mirzaeian, Ali (2019)

View more