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Full-Text Articles in Computer Engineering
A Deep Learning Agent For Games With Hidden Information, Robert A. Mills
A Deep Learning Agent For Games With Hidden Information, Robert A. Mills
Senior Projects Spring 2018
The goal of this project is to develop an agent capable of playing a particular game at an above average human level. In order to do so we investigated reinforcement and deep learning techniques for making decisions in discrete action spaces with hidden information. The methods we used to accomplish this goal include a standard word2vec implementation, an alpha-beta minimax tree search, and an LSTM network to evaluate game states. Given just the rules of the game and a vector representation of the game states, the agent learned to play the game by competitive self play. The emergent behavior from …
Using Static Analysis And Dalvik Bytecode On Android Compass Applications To Detect Operational Anomalies, Arti J. Tripathi
Using Static Analysis And Dalvik Bytecode On Android Compass Applications To Detect Operational Anomalies, Arti J. Tripathi
Senior Projects Spring 2018
The focus of this paper is the functionality of Android applications and the detection of functional anomalies though a basic static analysis approach. The intention of this research is analyzing applications without running them and detecting how application behavior might correlate with method call patterns. We will focus on simple free compass applications because their ostensible simplicity will make high variation in methods calls an interesting phenomenon. We employ clustering algorithms and other statistical methods to isolate a particularly unusual collection of applications and then perform a qualitative analysis of these applications to discover any interesting common operational behavior or …
Training Neural Networks To Pilot Autonomous Vehicles: Scaled Self-Driving Car, Jason Zisheng Chang
Training Neural Networks To Pilot Autonomous Vehicles: Scaled Self-Driving Car, Jason Zisheng Chang
Senior Projects Spring 2018
This project explores the use of deep convolutional neural networks in autonomous cars. Successful implementation of autonomous vehicles has many societal benefits. One of the main benefits is its potential to significantly reduce traffic accidents. In the United States, the National Highway Traffic Safety Administration states that human error is at fault for 93% of automotive crashes. Robust driverless vehicles can prevent many of these collisions. The main challenge in developing autonomous vehicles today is how to create a system that is able to accurately perceive and process the world around it. In 2016, NVIDIA successfully trained a deep convolutional …