Artificial Intelligence and Robotics Commons^™

Abso2luteu-Net: Tissue Oxygenation Calculation Using Photoacoustic Imaging And Convolutional Neural Networks, Kevin Hoffer-Hawlik, Geoffrey P. Luke

ENGS 88 Honors Thesis (AB Students)

Photoacoustic (PA) imaging uses incident light to generate ultrasound signals within tissues. Using PA imaging to accurately measure hemoglobin concentration and calculate oxygenation (sO₂) requires prior tissue knowledge and costly computational methods. However, this thesis shows that machine learning algorithms can accurately and quickly estimate sO₂. absO₂luteU-Net, a convolutional neural network, was trained on Monte Carlo simulated multispectral PA data and predicted sO₂ with higher accuracy compared to simple linear unmixing, suggesting machine learning can solve the fluence estimation problem. This project was funded by the Kaminsky Family Fund and the Neukom Institute.

An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari

Dissertations

One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating learning …

Using Neural Networks To Classify Discrete Circular Probability Distributions, Madelyn Gaumer

HMC Senior Theses

Given the rise in the application of neural networks to all sorts of interesting problems, it seems natural to apply them to statistical tests. This senior thesis studies whether neural networks built to classify discrete circular probability distributions can outperform a class of well-known statistical tests for uniformity for discrete circular data that includes the Rayleigh Test¹, the Watson Test², and the Ajne Test³. Each neural network used is relatively small with no more than 3 layers: an input layer taking in discrete data sets on a circle, a hidden layer, and an output …

Applied Machine Learning For Classification Of Musculoskeletal Inference Using Neural Networks And Component Analysis, Shaswat Sharma

Electronic Theses and Dissertations

Artificial Intelligence (AI) is acquiring more recognition than ever by researchers and machine learning practitioners. AI has found significance in many applications like biomedical research for cancer diagnosis using image analysis, pharmaceutical research, and, diagnosis and prognosis of diseases based on knowledge about patients' previous conditions. Due to the increased computational power of modern computers implementing AI, there has been an increase in the feasibility of performing more complex research.

Within the field of orthopedic biomechanics, this research considers complex time-series dataset of the "sit-to-stand" motion of 48 Total Hip Arthroplasty (THA) patients that was collected by the Human Dynamics …

Application Of Retrograde Analysis To Fighting Games, Kristen Yu

Electronic Theses and Dissertations

With the advent of the fighting game AI competition, there has been recent interest in two-player fighting games. Monte-Carlo Tree-Search approaches currently dominate the competition, but it is unclear if this is the best approach for all fighting games. In this thesis we study the design of two-player fighting games and the consequences of the game design on the types of AI that should be used for playing the game, as well as formally define the state space that fighting games are based on. Additionally, we also characterize how AI can solve the game given a simultaneous action game model, …

[Accepted Article Manuscript Version (Postprint)] Identification And Parasocial Relationships With Characters From Star Wars: The Force Awakens., Alice Hall

Communication and Media Faculty Works

This study investigated identification and parasocial relationships (PSRs) with media characters by examining viewers’ responses to the movie Star Wars: The Force Awakens through an online survey of 113 audience members who saw the film in a theater within a month of its release. Participants reported stronger PSR and identification with the more familiar characters from the first trilogy than with the new characters introduced in the film, although the association with identification was limited to older participants. Star Wars fanship was associated with identification and PSR for old and new characters. Familiarity with the earlier films was associated with …

Artificial Intelligence: How Knowledge Is Created, Transferred, And Used, Jörg Hellwig Phd, Sarah Huggett, Mark Siebert, Bamini Jayabalasingham Phd

Public Reports

This document summarizes Key Findings from the full report "Artificial Intelligence: how knowledge is created, transferred, and used", available alongside other relevant material on the Elsevier Artificial Intelligence resource centre.The RELX group has extensive data assets, powerful computing capabilities, and a vast technological talent base. These allow Elsevier to provide unique insights on AI through this report. We hope these will be of interest to research evaluators, research funders, policy makers, and researchers, as they seek to navigate this complex, evolving, and fast-growing field.

Radically Simplifying Gated Recurrent Architectures Without Loss Of Performance, Jonathan Boardman, Ying Xie

Published and Grey Literature from PhD Candidates

Long Short-Term Memory (LSTM) units are a family of Recurrent Neural Network (RNN) architectures that have proven incredibly effective at learning from sequence data. They are also extremely complex, making them expensive to train and difficult to understand. A recent trend towards simplification has produced the Gated Recurrent Unit (GRU) and the Minimal Gated Unit (MGU), both of which perform as well as the LSTM (or better) on a variety of tasks. The MGU is one of the simplest gated recurrent architectures at the moment. Our study demonstrates that it is possible to radically simplify the MGU without significant loss …

Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh

Electrical and Computer Engineering Publications

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …

Work-In-Progress Reports Submitted To The Library Of Congress As Part Of Digital Libraries, Intelligent Data Analytics, And Augmented Description, Chulwoo Pack, Yi Liu, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Technical Reports

This document includes work-in-progress reports submitted to the Library of Congress as part of the Aida digital libraries research team's work on Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project. These work-in-progress reports provide a snapshot glimpse, as well as underlying rationale and decision-making, at various points in the development of the project and its machine learning explorations. Reports cover explorations on historic newspapers, minimally-processed manuscript collections, materials digitized from physical originals and those digitized from microform surrogates, and investigate challenges related to image segmentation and document zoning, classification, document image quality analysis, metadata generation, and more.

Android Application For Mnist Handwritten Digits Classification, Mina Gabriel

Project Topics and Ideas

Use Neural Network architecture to classify MNIST handwritten digits dataset, student/s should implement a phone application (Android) to demonstrate their work, application will then be published to the app store for other students and CISC faculty members for evaluation and feedback.

Regulation Of Artificial Intelligence In Selected Jurisdictions, Jenny Gesley, Tariq Ahmad, Edouardo Soares, Ruth Levush, Gustavo Guerra, James Martin, Kelly Buchanan, Laney Zhang, Sayuri Umeda, Astghik Grigoryan, Nicolas Boring, Elin Hofverberg, Clare Feikhert-Ahalt, Graciela Rodriguez-Ferrand, George Sadek, Hanibal Goitom

Copyright, Fair Use, Scholarly Communication, etc.

Comparative Summary

This report examines the emerging regulatory and policy landscape surrounding artificial intelligence (AI) in jurisdictions around the world and in the European Union (EU). In addition, a survey of international organizations describes the approach that United Nations (UN) agencies and regional organizations have taken towards AI. As the regulation of AI is still in its infancy, guidelines, ethics codes, and actions by and statements from governments and their agencies on AI are also addressed. While the country surveys look at various legal issues, including data protection and privacy, transparency, human oversight, surveillance, public administration and services, autonomous vehicles, …

The Use Of Deep Learning Distributed Representations In The Identification Of Abusive Text, Susan Mckeever, Hao Chen, Sarah Jane Delany

Conference papers

The selection of optimal feature representations is a critical step in the use of machine learning in text classification. Traditional features (e.g. bag of words and n-grams) have dominated for decades, but in the past five years, the use of learned distributed representations has become increasingly common. In this paper, we summarise and present a categorisation of the stateof-the-art distributed representation techniques, including word and sentence embedding models. We carry out an empirical analysis of the performance of the various feature representations using the scenario of detecting abusive comments. We compare classification accuracies across a range of off-the-shelf embedding models …

Facial Re-Enactment, Speech Synthesis And The Rise Of The Deepfake, Nicholas Gardiner

Theses : Honours

Emergent technologies in the fields of audio speech synthesis and video facial manipulation have the potential to drastically impact our societal patterns of multimedia consumption. At a time when social media and internet culture is plagued by misinformation, propaganda and “fake news”, their latent misuse represents a possible looming threat to fragile systems of information sharing and social democratic discourse. It has thus become increasingly recognised in both academic and mainstream journalism that the ramifications of these tools must be examined to determine what they are and how their widespread availability can be managed.

This research project seeks to examine …

Predictive Modeling Of Webpage Aesthetics, Ang Chen

Masters Theses

"Aesthetics plays a key role in web design. However, most websites have been developed based on designers' inspirations or preferences. While perceptions of aesthetics are intuitive abilities of humankind, the underlying principles for assessing aesthetics are not well understood. In recent years, machine learning methods have shown promising results in image aesthetic assessment. In this research, we used machine learning methods to study and explore the underlying principles of webpage aesthetics"--Abstract, page iii.

Image-Based Roadway Assessment Using Convolutional Neural Networks, Weilian Song

Theses and Dissertations--Computer Science

Road crashes are one of the main causes of death in the United States. To reduce the number of accidents, roadway assessment programs take a proactive approach, collecting data and identifying high-risk roads before crashes occur. However, the cost of data acquisition and manual annotation has restricted the effect of these programs. In this thesis, we propose methods to automate the task of roadway safety assessment using deep learning. Specifically, we trained convolutional neural networks on publicly available roadway images to predict safety-related metrics: the star rating score and free-flow speed. Inference speeds for our methods are mere milliseconds, enabling …

Healthcare Robotics: Key Factors That Impact Robot Adoption In Healthcare, Sujatha Alla, Pilar Pazos

Engineering Management & Systems Engineering Faculty Publications

In the current dynamic business environment, healthcare organizations are focused on improving patient satisfaction, performance, and efficiency. The healthcare industry is considered a complex system that is highly reliant of new technologies to support clinical as well as business processes. Robotics is one of such technologies that is considered to have the potential to increase efficiency in a wide range of clinical services. Although the use of robotics in healthcare is at the early stages of adoption, some studies have shown the capacity of this technology to improve precision, accessibility through less invasive procedures, and reduction of human error during …

Anomaly Detection In Bacnet/Ip Managed Building Automation Systems, Matthew Peacock

Theses: Doctorates and Masters

Building Automation Systems (BAS) are a collection of devices and software which manage the operation of building services. The BAS market is expected to be a $19.25 billion USD industry by 2023, as a core feature of both the Internet of Things and Smart City technologies. However, securing these systems from cyber security threats is an emerging research area. Since initial deployment, BAS have evolved from isolated standalone networks to heterogeneous, interconnected networks allowing external connectivity through the Internet. The most prominent BAS protocol is BACnet/IP, which is estimated to hold 54.6% of world market share. BACnet/IP security features are …

A Review Of Reasons For Failure In Applying Machine Learning To Financial Trading And An Experiment Investigating Combinatorial Purged Cross Validation’S Merit In Preventing The Most Prominent Of These Reasons, Multiple Testing Bias, Colin Fritz

Graduate Research Theses & Dissertations

The interest in applying machine learning to financial trading in the hedge fund industry has exploded in the last five years due to the massive success of a handful of ‘quantitative’ investment firms like Renaissance Technologies who has pioneered the use of machine learning techniques in investment since the 1980s. The failure rate of such firms attempting to deploy financial machine learning strategies is very high. This thesis reviews many of the causes for failure such as harmful correlations between examples in the dataset, redundant observations, improper data sampling paradigm, and multiple testing bias. Of these, multiple testing bias is …

Determining Political Inclination In Tweets Using Transfer Learning, Mehtab Iqbal

College of Graduate Studies: Theses & Dissertations

Last few years have seen tremendous development in neural language modeling for transfer learning and downstream applications. In this research, I used Howard and Ruder’s Universal Language Model Fine Tuning (ULMFiT) pipeline to develop a classifier that can determine whether a tweet is politically left leaning or right leaning by likening the content to tweets posted by @TheDemocrats or @GOP accounts on Twitter. We achieved 87.7% accuracy in predicting political ideological inclination.