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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Collaborating On Machine Reading: Training Algorithms To Read Complex Collections, Carrie M. Pirmann, Brian R. King, Bhagawat Acharya, Katherine M. Faull
Collaborating On Machine Reading: Training Algorithms To Read Complex Collections, Carrie M. Pirmann, Brian R. King, Bhagawat Acharya, Katherine M. Faull
Bucknell University Digital Scholarship Conference
Interdisciplinary collaboration between two faculty members in the humanities and computer science, a research librarian, and an undergraduate student has led to remarkable results in an ongoing international DH research project that has at its core 18th century manuscripts. The corpus stems from a vast collection of archival materials held by the Moravian Church in the UK, Germany, and the US. The number of pages to be transcribed, differences in handwriting styles, paper quality, and original language pose enormous problems for the feasibility of human transcription. This presentation will review the hypothesis, process, and findings of a summer research project …
Smart Home Simulation In The Virtual World, Thomas Jones-Moore, David Son
Smart Home Simulation In The Virtual World, Thomas Jones-Moore, David Son
Scholars Week
The goal of this project is to produce a 'smart home' by using IoT and RFID like things in the virtual world to help solve problems. Some of these problems can be CPR training, etc. Used as an evaluation platform of suggested hardware to get a desired (or best fit) set of smart objects, or combinations with computer vision. Cost model to determine best fit based on: accuracy, lowest cost, easiest deployment, etc.
Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher
Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods, Christof Teuscher
Student Research Symposium
In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …