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Articles 1 - 5 of 5
Full-Text Articles in Artificial Intelligence and Robotics
Human Trafficking And Machine Learning: A Data Pipeline From Law Agencies To Research Groups, Nathaniel Hites
Human Trafficking And Machine Learning: A Data Pipeline From Law Agencies To Research Groups, Nathaniel Hites
Computer Science and Engineering Theses and Dissertations
Human trafficking is a form of modern-day slavery that, while highly illegal, is more dangerous with the advancements of modern technology (such as the Internet), which allows such a practice to spread more easily and quickly all over the world. While the number of victims of human trafficking is large (according to non-profit organization Safe House, there are estimated to be about 20.5 million human trafficking victims, worldwide (“Human Trafficking Statistics & Facts.” Safe Horizon)- co-erced or manipulated by traffickers into either forced labor, or sexual exploitation and encounters), the number of heard cases is proportionally low- several thousand successful …
A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad
A Modeling Framework For Urban Growth Prediction Using Remote Sensing And Video Prediction Technologies: A Time-Dependent Convolutional Encoder-Decoder Architecture, Ahmed Hassan Jaad
Civil and Environmental Engineering Theses and Dissertations
Studying the growth pattern of cities/urban areas has received considerable attention during the past few decades. The goal is to identify directions and locations of potential growth, assess infrastructure and public service requirements, and ensure the integration of the new developments with the existing city structure. This dissertation presents a novel model for urban growth prediction using a novel machine learning model. The model treats successive historical satellite images of the urban area under consideration as a video for which future frames are predicted. A time-dependent convolutional encoder-decoder architecture is adopted. The model considers as an input a satellite image …
Cognition And Context-Aware Computing: Towards A Situation-Aware System With A Case Study In Aviation, Justin C. Wilson
Cognition And Context-Aware Computing: Towards A Situation-Aware System With A Case Study In Aviation, Justin C. Wilson
Computer Science and Engineering Theses and Dissertations
In aviation, flight instructors seek to comprehend the intent and awareness of their students. With this awareness, derived from in-flight observation and post-flight examination, a human instructor can infer the internal contexts of their student aviators as they perform. It is this understanding that is fundamental for evaluating student development. Further, a well-understood construct for describing the state of knowledge about a dynamic environment is known as situational awareness (SA). Often pilot error is associated with SA [80], and it is fundamental to flight safety and mission execution. If these contexts can be automatically inferred, instructors and students can more …
Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong
Stochastic Orthogonalization And Its Application To Machine Learning, Yu Hong
Electrical Engineering Theses and Dissertations
Orthogonal transformations have driven many great achievements in signal processing. They simplify computation and stabilize convergence during parameter training. Researchers have introduced orthogonality to machine learning recently and have obtained some encouraging results. In this thesis, three new orthogonal constraint algorithms based on a stochastic version of an SVD-based cost are proposed, which are suited to training large-scale matrices in convolutional neural networks. We have observed better performance in comparison with other orthogonal algorithms for convolutional neural networks.
Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems
Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems
Computer Science and Engineering Theses and Dissertations
Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …