Artificial Intelligence and Robotics Commons^™

Markov Decision Processes With Embedded Agents, Luke Harold Miles

Theses and Dissertations--Computer Science

We present Markov Decision Processes with Embedded Agents (MDPEAs), an extension of multi-agent POMDPs that allow for the modeling of environments that can change the actuators, sensors, and learning function of the agent, e.g., a household robot which could gain and lose hardware from its frame, or a sovereign software agent which could encounter viruses on computers that modify its code. We show several toy problems for which standard reinforcement-learning methods fail to converge, and give an algorithm, `just-copy-it`, which learns some of them. Unlike MDPs, MDPEAs are closed systems and hence their evolution over time can be treated as …

Multi-Stream Longitudinal Data Analysis Using Deep Learning, Sajjad Fouladvand

Theses and Dissertations--Computer Science

Longitudinal healthcare data encompasses all tasks where patients information are collected at multiple follow-up times. Analyzing this data is critical in addressing many real world problems in healthcare such as disease prediction and prevention. In this thesis, technical challenges in analyzing longitudinal administrative claims data are addressed and novel deep learning based models are proposed for multi-stream data analysis and disease prediction tasks. These algorithms and frameworks are assessed mainly on substance use disorders prediction tasks and specifically designed to tackled these disorders. Substance use disorder is a public health crisis costing the US an estimated $740 billion annually in …

Zero-Shot Action Recognition With Knowledge Enhanced Generative Adversarial Networks, Kaiqiang Huang, Luis Miralles-Pechuán, Susan Mckeever

Conference papers

Zero-Shot Action Recognition (ZSAR) aims to recognise action classes in videos that have never been seen during model training. In some approaches, ZSAR has been achieved by generating visual features for unseen classes based on the semantic information of the unseen class labels using generative adversarial networks (GANs). Therefore, the problem is converted to standard supervised learning since the unseen visual features are accessible. This approach alleviates the lack of labelled samples of unseen classes. In addition, objects appearing in the action instances could be used to create enriched semantics of action classes and therefore, increase the accuracy of ZSAR. …

K-Nearest Neighbour Classifiers - A Tutorial, Padraig Cunningham, Sarah Jane Delany

Conference papers

Perhaps the most straightforward classifier in the arsenal or Machine Learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of …

Light Field Compression And Manipulation Via Residual Convolutional Neural Network, Eisa Hedayati

Dissertations, Master's Theses and Master's Reports

Light field (LF) imaging has gained significant attention due to its recent success in microscopy, 3-dimensional (3D) displaying and rendering, augmented and virtual reality usage. Postprocessing of LF enables us to extract more information from a scene compared to traditional cameras. However, the use of LF is still a research novelty because of the current limitations in capturing high-resolution LF in all of its four dimensions. While researchers are actively improving methods of capturing high-resolution LF's, using simulation, it is possible to explore a high-quality captured LF's properties. The immediate concerns following the LF capture are its storage and processing …

Modeling Human Visual Detection Using Deep Networks, Zach Dekraker

Dissertations, Master's Theses and Master's Reports

The work in this report describes the use of machine learning to model human visual detection. This is in contrast to typical machine learning models, which seek to optimize detection performance overall, e.g., precision versus recall or F1 scores. Instead the goal is to develop models that can accurately match humans' abilities to detect objects in images. There are many AI algorithms that have far surpassed humans in, for example, object detection in large image databases or games such as Go. What is different about this work is that the objective is to accurately model humans' performance in visual detection …

Representing And Learning Preferences Over Combinatorial Domains, Michael Huelsman

Theses and Dissertations--Computer Science

Agents make decisions based on their preferences. Thus, to predict their decisions one has to learn the agent's preferences. A key step in the learning process is selecting a model to represent those preferences. We studied this problem by borrowing techniques from the algorithm selection problem to analyze preference example sets and select the most appropriate preference representation for learning. We approached this problem in multiple steps.

First, we determined which representations to consider. For this problem we developed the notion of preference representation language subsumption, which compares representations based on their expressive power. Subsumption creates a hierarchy of preference …

Personality And Emotion For Virtual Characters In Strong-Story Narrative Planning, Alireza Shirvani

Theses and Dissertations--Computer Science

Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. Virtual characters are an essential part of every interactive narrative. The interaction of rich virtual characters can produce interesting narratives and enhance user experience in virtual environments. I propose models of personality and emotion that are highly domain independent and integrate those models into multi-agent strong-story narrative planning systems. I demonstrate the value of the strong-story properties of the model by generating story conflicts intelligently. My models of emotion and personality enable the narrative generation system to create more opportunities for players to resolve conflicts …

Revisiting Absolute Pose Regression, Hunter Blanton

Theses and Dissertations--Computer Science

Images provide direct evidence for the position and orientation of the camera in space, known as camera pose. Traditionally, the problem of estimating the camera pose requires reference data for determining image correspondence and leveraging geometric relationships between features in the image. Recent advances in deep learning have led to a new class of methods that regress the pose directly from a single image.

This thesis proposes methods for absolute camera pose regression. Absolute pose regression estimates the pose of a camera from a single image as the output of a fixed computation pipeline. These methods have many practical benefits …

"Who Can Help Me?'': Knowledge Infused Matching Of Support Seekers And Support Providers During Covid-19 On Reddit, Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth

Publications

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to …

Law Library Blog (January 2021): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Converting Optical Videos To Infrared Videos Using Attention Gan And Its Impact On Target Detection And Classification Performance, Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li

Electrical & Computer Engineering Faculty Publications

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. …

Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Model continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main …

Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi

Theses and Dissertations (Comprehensive)

This thesis addresses feature selection (FS) problems, which is a primary stage in data mining. FS is a significant pre-processing stage to enhance the performance of the process with regards to computation cost and accuracy to offer a better comprehension of stored data by removing the unnecessary and irrelevant features from the basic dataset. However, because of the size of the problem, FS is known to be very challenging and has been classified as an NP-hard problem. Traditional methods can only be used to solve small problems. Therefore, metaheuristic algorithms (MAs) are becoming powerful methods for addressing the FS problems. …

Uncovering Object Categories In Infant Views, Naiti S. Bhatt

Scripps Senior Theses

While adults recognize objects in a near-instant, infants must learn how to categorize the objects in their visual environments. Recent work has shown that egocentric head-mounted camera videos contain rich data that illuminate the infant experience (Clerkin et al., 2017; Franchak et al., 2011; Yoshida & Smith, 2008). While past work has focused on the social information in view, in this work, we aim to characterize the objects in infants’ at-home visual environments by modifying modern computer vision models for the infant view. To do so, we collected manual annotations of objects that infants seemed to be interacting within a …

Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi

Theses and Dissertations

Language models employ a very large number of trainable parameters. Despite being highly overparameterized, these networks often achieve good out-of-sample test performance on the original task and easily fine-tune to related tasks. Recent observations involving, for example, intrinsic dimension of the objective landscape and the lottery ticket hypothesis, indicate that often training actively involves only a small fraction of the parameter space. Thus, a question remains how large a parameter space needs to be in the first place — the evidence from recent work on model compression, parameter sharing, factorized representations, and knowledge distillation increasingly shows that models can be …

Single And Differential Morph Attack Detection, Baaria Chaudhary

Graduate Theses, Dissertations, and Problem Reports (ETD)

Face recognition systems operate on the assumption that a person's face serves as the unique link to their identity. In this thesis, we explore the problem of morph attacks, which have become a viable threat to face verification scenarios precisely because of their inherent ability to break this unique link. A morph attack occurs when two people who share similar facial features morph their faces together such that the resulting face image is recognized as either of two contributing individuals. Morphs inherit enough visual features from both individuals that both humans and automatic algorithms confuse them. The contributions of this …

Interpretable, Not Black-Box, Artificial Intelligence Should Be Used For Embryo Selection, Michael Anis Mihdi Afnan, Yanhe Liu, Vincent Conitzer, Cynthia Rudin, Abhishek Mishra, Julian Savulescu, Masoud Afnan

Research outputs 2014 to 2021

Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the potential to process complex data sets, to be better at identifying subtle but important patterns, and to be more objective than humans when evaluating embryos. However, a current review of the literature shows much work is still needed before AI can be ethically implemented for this purpose. No randomized controlled trials (RCTs) have been published, and the efficacy studies which exist demonstrate that algorithms can broadly differentiate well between ‘good-’ and ‘poor-’ quality embryos but …

Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya

Honors Theses

Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …

Values Of Trust In Ai In Autonomous Driving Vehicles, Ru Lian

Masters Theses

“Automation with artificial intelligence technology is an emerging field and is widely used in various industries. With the increasing autonomy, learning, and adaptability of intelligent machines such as self-driving cars, it is difficult to regard them as simple tools in human hands. At the same time, a series of problems and challenges such as predictability, interpretability, and causality arise. Trust in self-driving technology will impact the adoption and utilization of autonomous driving technology. A qualitative research methodology, Value-Focused Thinking, is used to identify the values of trust in autonomous driving vehicles and analyze the relationship between these values”--Abstract, page iii.

Algorithmic Responsibility, Algorithmic Bias, Darla Jackson

Other Faculty Publications

No abstract provided.

On Studying Distributed Machine Learning, Simeon Eberz

Senior Honors Theses

The Internet of Things (IoT) is utilizing Deep Learning (DL) for applications such as voice or image recognition. Processing data for DL directly on IoT edge devices reduces latency and increases privacy. To overcome the resource constraints of IoT edge devices, the computation for DL inference is distributed between a cluster of several devices. This paper explores DL, IoT networks, and a novel framework for distributed processing of DL in IoT clusters. The aim is to facilitate and simplify deployment, testing, and study of a distributed DL system, even without physical devices. The contributions of this paper are a deployment …

Classification Of Chess Games: An Exploration Of Classifiers For Anomaly Detection In Chess, Masudul Hoque

All Graduate Theses, Dissertations, and Other Capstone Projects

Chess is a strategy board game with its inception dating back to the 15th century. The Covid-19 pandemic has led to a chess boom online with 95,853,038 chess games being played during January, 2021 on lichess.com. Along with the chess boom, instances of cheating have also become more rampant. Classifications have been used for anomaly detection in different fields and thus it is a natural idea to develop classifiers to detect cheating in chess. However, there are no specific examples of this, and it is difficult to obtain data where cheating has occurred. So, in this paper, we develop 4 …

Deapsecure Computational Training For Cybersecurity Students: Improvements, Mid-Stage Evaluation, And Lessons Learned, Wirawan Purwanto, Yuming He, Jewel Ossom, Qiao Zhang, Liuwan Zhu, Karina Arcaute, Masha Sosonkina, Hongyi Wu

University Administration Publications

DeapSECURE is a non-degree computational training program that provides a solid high-performance computing (HPC) and big-data foundation for cybersecurity students. DeapSECURE consists of six modules covering a broad spectrum of topics such as HPC platforms, big-data analytics, machine learning, privacy-preserving methods, and parallel programming. In the second year of this program, to improve the learning experience, we implemented a number of changes, such as grouping modules into two broad categories, "big-data" and "HPC"; creating a single cybersecurity storyline across the modules; and introducing post-workshop (optional) "hackshops." Two major goals of these changes are, firstly, to effectively engage students to maintain …

Ship Deck Segmentation In Engineering Document Using Generative Adversarial Networks, Mohammad Shahab Uddin, Raphael Pamie-George, Daron Wilkins, Andres Sousa Poza, Mustafa Canan, Samuel Kovacic, Jiang Li

Engineering Management & Systems Engineering Faculty Publications

Generative adversarial networks (GANs) have become very popular in recent years. GANs have proved to be successful in different computer vision tasks including image-translation, image super-resolution etc. In this paper, we have used GAN models for ship deck segmentation. We have used 2D scanned raster images of ship decks provided by US Navy Military Sealift Command (MSC) to extract necessary information including ship walls, objects etc. Our segmentation results will be helpful to get vector and 3D image of a ship that can be later used for maintenance of the ship. We applied the trained models to engineering documents provided …

Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii

Graduate Student Theses, Dissertations, & Professional Papers

Using time dependent observations derived from terrestrial LiDAR and oblique
time-lapse imagery, we demonstrate that a Bayesian approach to glacial motion es-
timation provides a concise way to incorporate multiple data products into a single
motion estimation procedure effectively producing surface velocity estimates with
an associated uncertainty. This approach brings both improved computational effi-
ciency, and greater scalability across observational time-frames when compared to
existing methods. To gauge efficacy, we apply these methods to a set of observa-
tions from the Helheim Glacier, a critical actor in contemporary mass loss trends
observed in the Greenland Ice Sheet. We find that …

Clustering Data To Classify Hearthstone Decks, Tim Inzitari

Williams Honors College, Honors Research Projects

The esports game of "Hearthstone" is a collectible card game with a competitive format that has every team submit 4 decks of 30 cards each. Using K-Means clustering an adaptable way to group data for classifying can be made that works well in every update of the game. This system will take in a list of decks and cluster them to easily classify large amounts of information in a timely fashion. This system will be able to be used by the Universities esports department for years to come to aid the preparation of "Hearthstone" matches. This model uses qualities about …

Source Code Comment Classification Artificial Intelligence, Cole Sutyak

Williams Honors College, Honors Research Projects

Source code comment classification is an important problem for future machine learning solutions. In particular, supervised machine learning solutions that have largely subjective data labels but are difficult to obtain the labels for. Machine learning problems are problems largely because of a lack of data. In machine learning solutions, it is better to have a large amount of mediocre data than it is to have a small amount of good data. While the mediocre data might not produce the best accuracy, it produces the best results because there is much more to learn from the problem.

In this project, data …

Learning From Multi-Class Imbalanced Big Data With Apache Spark, William C. Sleeman Iv

Theses and Dissertations

With data becoming a new form of currency, its analysis has become a top priority in both academia and industry, furthering advancements in high-performance computing and machine learning. However, these large, real-world datasets come with additional complications such as noise and class overlap. Problems are magnified when with multi-class data is presented, especially since many of the popular algorithms were originally designed for binary data. Another challenge arises when the number of examples are not evenly distributed across all classes in a dataset. This often causes classifiers to favor the majority class over the minority classes, leading to undesirable results …

Detecting Surface Interactions Via A Wearable Microphone To Improve Augmented Reality Text Entry, R. Habibi

Dissertations, Master's Theses and Master's Reports

This thesis investigates whether we can detect and distinguish between surface interaction events such as tapping or swiping using a wearable mic from a surface. Also, what are the advantages of new text entry methods such as tapping with two fingers simultaneously to enter capital letters and punctuation? For this purpose, we conducted a remote study to collect audio and video of three different ways people might interact with a surface. We also built a CNN classifier to detect taps. Our results show that we can detect and distinguish between surface interaction events such as tap or swipe via a …