Artificial Intelligence and Robotics Commons^™

Automatic Identification Of Animals In The Wild: A Comparative Study Between C-Capsule Networks And Deep Convolutional Neural Networks., Joel Kamdem Teto, Ying Xie

Master of Science in Computer Science Theses

The evolution of machine learning and computer vision in technology has driven a lot of

improvements and innovation into several domains. We see it being applied for credit decisions, insurance quotes, malware detection, fraud detection, email composition, and any other area having enough information to allow the machine to learn patterns. Over the years the number of sensors, cameras, and cognitive pieces of equipment placed in the wilderness has been growing exponentially. However, the resources (human) to leverage these data into something meaningful are not improving at the same rate. For instance, a team of scientist volunteers took 8.4 ...

Extraction Of Information Related To Adverse Drug Events From Electronic Health Record Notes: Design Of An End-To-End Model Based On Deep Learning, Fei Li, Weisong Liu, Hong Yu

Open Access Articles

BACKGROUND: Pharmacovigilance and drug-safety surveillance are crucial for monitoring adverse drug events (ADEs), but the main ADE-reporting systems such as Food and Drug Administration Adverse Event Reporting System face challenges such as underreporting. Therefore, as complementary surveillance, data on ADEs are extracted from electronic health record (EHR) notes via natural language processing (NLP). As NLP develops, many up-to-date machine-learning techniques are introduced in this field, such as deep learning and multi-task learning (MTL). However, only a few studies have focused on employing such techniques to extract ADEs.

OBJECTIVE: We aimed to design a deep learning model for extracting ADEs and ...

Validation Study Of Image Recognition Algorithms, Jacob Miller, Jeremy Evert

Student Research

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 ...

Multi-Resolution Analysis Of Large Molecular Structures And Interactions, Kasra Manavi

Computer Science ETDs

Simulation of large molecular structures and their interactions has become a major component of modern biomolecular research. Methods to simulate these type of molecules span a wide array of resolutions, from all atom molecular dynamics to model interaction energetics to systems of linear equations to evaluate population kinetics. In recent years, there has been an acceleration of molecular structural information production, primarily from x-ray crystallography and electron microscopy. This data has provided modelers the ability to produce better representations of these molecular structures. The purpose of this research is to take advantage of this information to develop multi-resolution models for ...

A Model-Based Ai-Driven Test Generation System, Dionny Santiago

FIU Electronic Theses and Dissertations

Achieving high software quality today involves manual analysis, test planning, documentation of testing strategy and test cases, and development of automated test scripts to support regression testing. This thesis is motivated by the opportunity to bridge the gap between current test automation and true test automation by investigating learning-based solutions to software testing. We present an approach that combines a trainable web component classifier, a test case description language, and a trainable test generation and execution system that can learn to generate new test cases. Training data was collected and hand-labeled across 7 systems, 95 web pages, and 17,360 ...

Performance Of The Parallelized Monte-Carlo Tree Search Approach For Dots And Boxes, Pranay Agrawal, Uta Ziegler

Posters-at-the-Capitol

The Monte-Carlo tree search (MCTS) is a method designed to solve difficult learning problems. MCTS performs random simulations from the current situation and stores the results in order to distinguish decisions based on their past success. MCTS will then select the best decision and finally repeat the process. Parallelizing the MCTS means to divide the learning process among independent learners. Then, after a fixed number of simulations, the data is shared and combined. Past research has shown that this approach is faster than non-parallelized approaches. Therefore, we anticipated that the time reduced from dividing the learning outweighs the potential costs ...

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, 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 ...

A Mathematical Framework On Machine Learning: Theory And Application, Bin Shi

FIU Electronic Theses and Dissertations

The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex optimization algorithms and improve them into nonconvex objective? For application ...

Emotion Recognition Using Deep Convolutional Neural Network With Large Scale Physiological Data, Astha Sharma

Graduate Theses and Dissertations

Classification of emotions plays a very important role in affective computing and has real-world applications in fields as diverse as entertainment, medical, defense, retail, and education. These applications include video games, virtual reality, pain recognition, lie detection, classification of Autistic Spectrum Disorder (ASD), analysis of stress levels, and determining attention levels. This vast range of applications motivated us to study automatic emotion recognition which can be done by using facial expression, speech, and physiological data.

A person’s physiological signals such are heart rate, and blood pressure are deeply linked with their emotional states and can be used to identify ...

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, 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, Vern R. Walker

Legal Tech Boot Camp

No abstract provided.

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, Da Shen, Yuliya Lierler

Yuliya Lierler

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 ...

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 ...

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, Yuliya Lierler

Yuliya Lierler

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 ...