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
Bots, Bias And Big Data: Artificial Intelligence, Algorithmic Bias And Disparate Impact Liability In Hiring Practices, 2018 University of Arkansas, Fayetteville
Bots, Bias And Big Data: Artificial Intelligence, Algorithmic Bias And Disparate Impact Liability In Hiring Practices, Mckenzie Raub
Arkansas Law Review
No abstract provided.
Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, 2018 California State University, San Bernardino
Sensor-Based Human Activity Recognition Using Bidirectional Lstm For Closely Related Activities, Arumugam Thendramil Pavai
Electronic Theses, Projects, and Dissertations
Recognizing human activities using deep learning methods has significance in many fields such as sports, motion tracking, surveillance, healthcare and robotics. Inertial sensors comprising of accelerometers and gyroscopes are commonly used for sensor based HAR. In this study, a Bidirectional Long Short-Term Memory (BLSTM) approach is explored for human activity recognition and classification for closely related activities on a body worn inertial sensor data that is provided by the UTD-MHAD dataset. The BLSTM model of this study could achieve an overall accuracy of 98.05% for 15 different activities and 90.87% for 27 different activities performed by 8 persons ...
Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, 2018 University of Arkansas, Fayetteville
Learning-Based Analysis On The Exploitability Of Security Vulnerabilities, Adam Bliss
Computer Science and Computer Engineering Undergraduate Honors Theses
The purpose of this thesis is to develop a tool that uses machine learning techniques to make predictions about whether or not a given vulnerability will be exploited. Such a tool could help organizations such as electric utilities to prioritize their security patching operations. Three different models, based on a deep neural network, a random forest, and a support vector machine respectively, are designed and implemented. Training data for these models is compiled from a variety of sources, including the National Vulnerability Database published by NIST and the Exploit Database published by Offensive Security. Extensive experiments are conducted, including testing ...
Validation Study Of Image Recognition Algorithms, 2018 Southwestern Oklahoma State University
Validation Study Of Image Recognition Algorithms, Jacob Miller, Jeremy Evert
Developments in machine learning in recent years have created opportunities that previously never existed. One such field with an explosion of opportunity is image recognition, also known as computer vision; the process in which a machine analyzes a digital image.
In order for a machine to ‘see’ as a human does, it must break down the image in a process called image segmentation. The way the machine goes about doing this is important, and many algorithms exist to determine just how a machine will decide to group the pixels in an image.
This research is a validation study of related ...
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 ...
Linking Gait Dynamics To Mechanical Cost Of Legged Locomotion, 2018 University of Nevada, Las Vegas; University Medical Center of Southern Nevada
Linking Gait Dynamics To Mechanical Cost Of Legged Locomotion, David V. Lee, Sarah L. Harris
Life Sciences Faculty Publications
For millenia, legged locomotion has been of central importance to humans for hunting, agriculture, transportation, sport, and warfare. Today, the same principal considerations of locomotor performance and economy apply to legged systems designed to serve, assist, or be worn by humans in urban and natural environments. Energy comes at a premium not only for animals, wherein suitably fast and economical gaits are selected through organic evolution, but also for legged robots that must carry sufficient energy in their batteries. Although a robot's energy is spent at many levels, from control systems to actuators, we suggest that the mechanical cost ...
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 ...
Higher-Level Consistencies: Where, When, And How Much, 2018 University of Nebraska-Lincoln
Higher-Level Consistencies: Where, When, And How Much, Robert J. Woodward
Computer Science and Engineering: Theses, Dissertations, and Student Research
Determining whether or not a Constraint Satisfaction Problem (CSP) has a solution is NP-complete. CSPs are solved by inference (i.e., enforcing consistency), conditioning (i.e., doing search), or, more commonly, by interleaving the two mechanisms. The most common consistency property enforced during search is Generalized Arc Consistency (GAC). In recent years, new algorithms that enforce consistency properties stronger than GAC have been proposed and shown to be necessary to solve difficult problem instances.
We frame the question of balancing the cost and the pruning effectiveness of consistency algorithms as the question of determining where, when, and how much of ...
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 ...