Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, 2019 Selected Works
Generative Linguistics And Neural Networks At 60: Foundation, Friction, And Fusion, Joe Pater
Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, 2018 Department of Compter Science
Strong Equivalence And Program's Structure In Arguing Essential Equivalence Between First-Order Logic Programs, Yuliya Lierler
Automatic Program Rewriting In Non-Ground Answer Set Programs, 2018 University of Nebraska at Omaha
Automatic Program Rewriting In Non-Ground Answer Set Programs, Nicholas Hippen, Yuliya Lierler
End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, 2018 University of New Mexico
End-To-End Deep Learning Systems For Scene Understanding, Path Planning And Navigation In Fire Fighter Teams, Manish Bhattarai
Shared Knowledge Conference
Firefighting is a dynamic activity with many operations occurring simultaneously. Maintaining situational awareness, defined as knowledge of current conditions and activities at the scene, are critical to accurate decision making. Firefighters often carry various sensors in their personal equipment, namely thermal cameras, gas sensors, and microphones. Improved data processing techniques can mine this data more effectively and be used to improve situational awareness at all times thereby improving real-time decision making and minimizing errors in judgment induced by environmental conditions and anxiety levels. This objective of this research employs state of the art Machine Learning (ML) techniques to create an ...
Becoming Human: The Darwinian Evolution Of Ai, 2018 University of North Georgia
Becoming Human: The Darwinian Evolution Of Ai, Alexander Aviles
Georgia Undergraduate Research Conference (GURC)
Public fear about the rise of artificial intelligence (AI) has created growing interest in understanding the differences from what created humans and machines. This project contrasts the two major models for creating intelligent life, Darwin’s Theory of Evolution and the neural mapping use to construct AI. Beginning by defining the particulars of Darwinian evolution, the paper explains how evolution relies upon interaction between populations and environmental factors. Turning to research in computer science by the likes of Alan Turing and John McCarthy, the paper then explains how artificial neural networks are programmed to work locally to accomplish a set ...
Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, 2018 Stanford University
Cross-Referencing Social Media And Public Surveillance Camera Data For Disaster Response, Chittayong Surakitbanharn,, Calvin Yau, Guizhen Wang, Aniesh Chawla, Yinuo Pan, Zhaoya Sun, Sam Yellin, David Ebert, Yung-Hsiang Lu, George K. Thiruvathukal
Computer Science: Faculty Publications and Other Works
Physical media (like surveillance cameras) and social media (like Instagram and Twitter) may both be useful in attaining on-the-ground information during an emergency or disaster situation. However, the intersection and reliability of both surveillance cameras and social media during a natural disaster are not fully understood. To address this gap, we tested whether social media is of utility when physical surveillance cameras went off-line during Hurricane Irma in 2017. Specifically, we collected and compared geo-tagged Instagram and Twitter posts in the state of Florida during times and in areas where public surveillance cameras went off-line. We report social media content ...
Applied Cognitive Computing And Artificial Intelligence: How Machines Learn To “Read” The Law, 2018 Maurice A. Deane School of Law at Hofstra University
Applied Cognitive Computing And Artificial Intelligence: How Machines Learn To “Read” The Law, Vern R. Walker
Legal Tech Boot Camp
No abstract provided.
March Of The Silent Bots, 2018 Singapore Management University
March Of The Silent Bots, Paul Robert Griffin
MITB Thought Leadership Series
Self-intelligent software robots, or ‘bots’ are everywhere. These small pieces of code run automated tasks when you order a taxi, search for a restaurant or check the weather. Quietly beavering away, it is unknown how many bots exist, but undoubtedly this number is set to surge over time. Already, bots comprise roughly half of all internet traffic.
Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, 2018 University of Nebraska at Omaha
Smt-Based Constraint Answer Set Solver Ezsmt+ For Non-Tight Programs, Da Shen, Yuliya Lierler
Evaluating Prose Style Transfer With The Bible, 2018 Dartmouth College
Evaluating Prose Style Transfer With The Bible, Keith Carlson, Allen Riddell, Daniel Rockmore
Open Dartmouth: Faculty Open Access Articles
In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few publicly available corpora for this task. In this work, we identify a high-quality source of aligned, stylistically distinct text in different versions of the Bible. We provide a standardized split, into training, development and testing data, of the public domain versions in our corpus. This corpus is ...
Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, 2018 The Graduate Center, City University of New York
Enhancing 3d Visual Odometry With Single-Camera Stereo Omnidirectional Systems, Carlos A. Jaramillo
All Dissertations, Theses, and Capstone Projects
We explore low-cost solutions for efficiently improving the 3D pose estimation problem of a single camera moving in an unfamiliar environment. The visual odometry (VO) task -- as it is called when using computer vision to estimate egomotion -- is of particular interest to mobile robots as well as humans with visual impairments. The payload capacity of small robots like micro-aerial vehicles (drones) requires the use of portable perception equipment, which is constrained by size, weight, energy consumption, and processing power. Using a single camera as the passive sensor for the VO task satisfies these requirements, and it motivates the proposed solutions ...
Strong Equivalence And Conservative Extensions Hand In Hand For Arguing Correctness Of New Action Language C Formalization, 2018 Department of Compter Science
Strong Equivalence And Conservative Extensions Hand In Hand For Arguing Correctness Of New Action Language C Formalization, Yuliya Lierler
Natural Language Understanding: Deep Learning For Abstract Meaning Representation, 2018 University of Colorado at Boulder
Natural Language Understanding: Deep Learning For Abstract Meaning Representation, William Roger Foland Jr.
Computer Science Graduate Theses & Dissertations
In the last few years there have been major improvements in the performance of hard nat- ural language processing tasks due to the application of artificial neural network models. These models replace complex hand-engineered systems for extracting and representing the meaning of human language with systems which learn features based on processing examples of language. In this dissertation, I present deep neural networks for semantic role labeling, and then for Abstract Meaning Representation parsing, and a novel Distributed Abstract Meaning Representation, or DAMR. I then describe a model used to create fixed vector representations of sentence meaning from DAMR. Finally ...
Fake News Detection: A Deep Learning Approach, 2018 Southern Methodist University
Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia
SMU Data Science Review
Fake news is defined as a made-up story with an intention to deceive or to mislead. In this paper we present the solution to the task of fake news detection by using Deep Learning architectures. Gartner research  predicts that “By 2022, most people in mature economies will consume more false information than true information”. The exponential increase in production and distribution of inaccurate news presents an immediate need for automatically tagging and detecting such twisted news articles. However, automated detection of fake news is a hard task to accomplish as it requires the model to understand nuances in natural ...
Investigating Dataset Distinctiveness, 2018 Purdue University
Investigating Dataset Distinctiveness, Andrew Ulmer, Kent W. Gauen, Yung-Hsiang Lu, Zohar R. Kapach, Daniel P. Merrick
The Summer Undergraduate Research Fellowship (SURF) Symposium
Just as a human might struggle to interpret another human’s handwriting, a computer vision program might fail when asked to perform one task in two different domains. To be more specific, visualize a self-driving car as a human driver who had only ever driven on clear, sunny days, during daylight hours. This driver – the self-driving car – would inevitably face a significant challenge when asked to drive when it is violently raining or foggy during the night, putting the safety of its passengers in danger. An extensive understanding of the data we use to teach computer vision models – such as ...
Deep Machine Learning For Mechanical Performance And Failure Prediction, 2018 Penn State University
Deep Machine Learning For Mechanical Performance And Failure Prediction, Elijah Reber, Nickolas D. Winovich, Guang Lin
The Summer Undergraduate Research Fellowship (SURF) Symposium
Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at how effective a deep neural network is at predicting the failure or energy output of a wind turbine. A data set was obtained that contained sensor data from 17 wind turbines over 13 months, measuring numerous variables, such as spindle speed and blade position and whether or not the wind turbine experienced ...
Deep Neural Network Architectures For Modulation Classification Using Principal Component Analysis, Sharan Ramjee, Shengtai Ju, Diyu Yang, Aly El Gamal
The Summer Undergraduate Research Fellowship (SURF) Symposium
In this work, we investigate the application of Principal Component Analysis to the task of wireless signal modulation recognition using deep neural network architectures. Sampling signals at the Nyquist rate, which is often very high, requires a large amount of energy and space to collect and store the samples. Moreover, the time taken to train neural networks for the task of modulation classification is large due to the large number of samples. These problems can be drastically reduced using Principal Component Analysis, which is a technique that allows us to reduce the dimensionality or number of features of the samples ...
Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, 2018 Boise State University
Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, Kiran Thapa
Boise State University Theses and Dissertations
Describing scenes such as rooms, city streets, or routes, is a very common human task that requires the ability to identify and describe the scene sufficiently for a hearer to develop a mental model of the scene. When people talk about such scenes, they mention some objects of the scene at the exclusion of others. We call the mentioned objects salient objects as people consider them noticeable or important in comparison to other non-mentioned objects. In this thesis, we look at saliency of visual scenes and how visual saliency informs what can and should be said about a scene when ...
Thermal Fluid Model Development Of Steam Methane Reformer Using Artificial Neural Network, 2018 University of Texas at Tyler
Thermal Fluid Model Development Of Steam Methane Reformer Using Artificial Neural Network, M. A. Rafe Biswas
M. A. Rafe Biswas
A Novel Multirobot System For Distributed Phenotyping, 2018 Iowa State University
A Novel Multirobot System For Distributed Phenotyping, Tianshuang Gao, Homagni Saha, Hamid Emadi, Jiaoping Zhang, Alec Lofquist, Arti Singh, Baskar Ganapathysubramanian, Soumik Sarkar, Asheesh Singh, Sourabh Bhattacharya
Mechanical Engineering Publications
Phenotypic studies require large datasets for accurate inference and prediction. Collecting plant data in a farm can be very labor intensive and costly. This paper presents the design, architecture (hardware and software) and deployment of a distributed modular agricultural multi-robot system for row crop field data collection. The proposed system has been deployed in a soybean research farm at Iowa State University.