no code implementations • 27 Aug 2020 • Isha Ghodgaonkar, Subhankar Chakraborty, Vishnu Banna, Shane Allcroft, Mohammed Metwaly, Fischer Bordwell, Kohsuke Kimura, Xinxin Zhao, Abhinav Goel, Caleb Tung, Akhil Chinnakotla, Minghao Xue, Yung-Hsiang Lu, Mark Daniel Ward, Wei Zakharov, David S. Ebert, David M. Barbarash, George K. Thiruvathukal
This research team has created methods that can discover thousands of network cameras worldwide, retrieve data from the cameras, analyze the data, and report the sizes of crowds as different countries issued and lifted restrictions (also called ''lockdown'').
no code implementations • 11 Oct 2019 • Morteza Karimzadeh, Luke S. Snyder, David S. Ebert
The first responder community has traditionally relied on calls from the public, officially-provided geographic information and maps for coordinating actions on the ground.
no code implementations • 5 Oct 2019 • Luke S. Snyder, Morteza Karimzadeh, Ray Chen, David S. Ebert
In this paper, we adapt, improve, and evaluate a state-of-the-art deep learning model for city-level geolocation prediction, and integrate it with a visual analytics system tailored for real-time situational awareness.
no code implementations • 1 Aug 2019 • Luke S. Snyder, Yi-Shan Lin, Morteza Karimzadeh, Dan Goldwasser, David S. Ebert
We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements.
no code implementations • 1 Aug 2018 • Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li, David S. Ebert
We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner.