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Artificial Intelligence's Ability To Detect Online Predators, Olatilewa Osifeso
Artificial Intelligence's Ability To Detect Online Predators, Olatilewa Osifeso
Electronic Theses, Projects, and Dissertations
Online child predators pose a danger to children who use the Internet. Children fall victim to online predators at an alarming rate, based on the data from the National Center of Missing and Exploited Children. When making online profiles and joining websites, you only need a name, an email and a password without identity verification. Studies have shown that online predators use a variety of methods and tools to manipulate and exploit children, such as blackmail, coercion, flattery, and deception. These issues have created an opportunity for skilled online predators to have fewer obstacles when it comes to contacting and …
Particle Classification Of Electromagnetic Clusters Using The Sphenix Detector, Fredrick J. Melhorn
Particle Classification Of Electromagnetic Clusters Using The Sphenix Detector, Fredrick J. Melhorn
Chancellor’s Honors Program Projects
No abstract provided.
Semantic Segmentation Of Point Cloud Sequences Using Point Transformer V3, Marion Sisk
Semantic Segmentation Of Point Cloud Sequences Using Point Transformer V3, Marion Sisk
Master's Theses
Semantic segmentation of point clouds is a basic step for many autonomous systems including automobiles. In autonomous driving systems, LiDAR sensors are frequently used to produce point cloud sequences that allow the system to perceive the environment and navigate safely. Modern machine learning techniques for segmentation have predominately focused on single-scan segmentation, however sequence segmentation has often proven to perform better on common segmentation metrics. Using the popular Semantic KITTI dataset, we show that by providing point cloud sequences to a segmentation pipeline based on Point Transformer v3, we increase the segmentation performance between seven and fifteen percent when compared …
Predicting Biomolecular Properties And Interactions Using Numerical, Statistical And Machine Learning Methods, Elyssa Sliheet
Predicting Biomolecular Properties And Interactions Using Numerical, Statistical And Machine Learning Methods, Elyssa Sliheet
Mathematics Theses and Dissertations
We investigate machine learning and electrostatic methods to predict biophysical properties of proteins, such as solvation energy and protein ligand binding affinity, for the purpose of drug discovery/development. We focus on the Poisson-Boltzmann model and various high performance computing considerations such as parallelization schemes.
Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry
Time Series Models For Predicting Application Gpu Utilization And Power Draw Based On Trace Data, Dorothy Xiaoshuang Parry
Electrical & Computer Engineering Theses & Dissertations
This work explores collecting performance metrics and leveraging various statistical and machine learning time series predictive models on a memory-intensive application, Inception v3. Trace data collected using nvidia-smi measured GPU utilization and power draw for two runs of Inception3. Experimental results from the statistical and machine learning-based time series predictive algorithms showed that the predictions from statistical-based models were unable to capture the complex changes in the trace data. The Probabilistic TNN model provided the best results for the power draw trace, according to the test evaluation metrics. For the GPU utilization trace, the RNN models produced the most accurate …
Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan
Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan
Doctoral Dissertations
The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven to be an invaluable tool for the mineralogical analysis of the Martian surface. It has been crucial in identifying and mapping the spatial extents of various minerals. Primarily, the identification and mapping of these mineral spectral-shapes have been performed manually. Given the size of the CRISM image dataset, manual analysis of the full dataset would be arduous/infeasible. This dissertation attempts to address this issue by describing an (machine learning based) automated processing pipeline for CRISM data that can be used to identify and map the unique mineral signatures present in …
Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia
Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia
Doctoral Dissertations
AI has the potential to accelerate scientific discovery by enabling scientists to analyze vast datasets more efficiently than traditional methods. For example, this thesis considers the detection of star clusters in high-resolution images of galaxies taken from space telescopes, as well as studying bird migration from RADAR images. In these applications, the goal is to make measurements to answer scientific questions, such as how the star formation rate is affected by mass, or how the phenology of bird migration is influenced by climate change. However, current computer vision systems are far from perfect for conducting these measurements directly. They may …
Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev
Reinforcement Learning: Applying Low Discrepancy Action Selection To Deep Deterministic Policy Gradient, Aleksandr Svishchev
Electronic Theses and Dissertations
Reinforcement learning (RL) is a subfield of machine learning concerned with agents learning to behave optimally by interacting with an environment. One of the most important topics in RL is how the agent should explore, that is, how to choose actions in order to rate their impact on long-term reward. For example, a simple baseline strategy might be uniformly random action selection. This thesis investigates the heuristic idea that agents will learn faster if they explore by factoring the environment’s state into their decision and intentionally choose actions which are as different as possible from what they have previously observed. …
Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry
Adaptive Multi-Label Classification On Drifting Data Streams, Martha Roseberry
Theses and Dissertations
Drifting data streams and multi-label data are both challenging problems. When multi-label data arrives as a stream, the challenges of both problems must be addressed along with additional challenges unique to the combined problem. Algorithms must be fast and flexible, able to match both the speed and evolving nature of the stream. We propose four methods for learning from multi-label drifting data streams. First, a multi-label k Nearest Neighbors with Self Adjusting Memory (ML-SAM-kNN) exploits short- and long-term memories to predict the current and evolving states of the data stream. Second, a punitive k nearest neighbors algorithm with a self-adjusting …
A Comparison Of Lexical Tokenization Methods, Nathan Culmer
A Comparison Of Lexical Tokenization Methods, Nathan Culmer
Williams Honors College, Honors Research Projects
The purpose of this project was to compare tokenization methods, or methods of breaking up a text into meaningful parts for use in natural language processing. The effectiveness of several commonly used tokenization methods were investigated, including morpheme tokenization, which takes into account the linguistic features of the language. In addition, I proposed and implemented a new technique to consider the capitalization pattern of a word in the tokenization process, in order to allow this process to include more natural language features. The effectiveness of these methods was compared by using them in a sentiment analysis model for various datasets, …
Identifying The Origins Of Business’ Data Breaches Utilizing Covert Timing Channels, Gayle L. Frisbie
Identifying The Origins Of Business’ Data Breaches Utilizing Covert Timing Channels, Gayle L. Frisbie
Master's Theses and Doctoral Dissertations
Cybersecurity events and data breaches are on the rise and are very costly to businesses. Businesses rely on connectivity and information systems to conduct business, yet those same information systems can be breached and the organization's data exposed. Today, there is a heavy reliance of organizations upon network connections to connect the entire organization in order to conduct business efficiently and from multiple locations. Covert timing channels are a cybersecurity attack method in which malicious actors embed privileged information into normal network traffic without authorization. Malicious actors, by carefully manipulating timing patterns in covert timing channels, can create a hidden …
Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart
Adaptable And Trustworthy Machine Learning For Human Activity Recognition From Bioelectric Signals, Morgan S. Stuart
Theses and Dissertations
Enabling machines to learn measures of human activity from bioelectric signals has many applications in human-machine interaction and healthcare. However, labeled activity recognition datasets are costly to collect and highly varied, which challenges machine learning techniques that rely on large datasets. Furthermore, activity recognition in practice needs to account for user trust - models are motivated to enable interpretability, usability, and information privacy. The objective of this dissertation is to improve adaptability and trustworthiness of machine learning models for human activity recognition from bioelectric signals. We improve adaptability by developing pretraining techniques that initialize models for later specialization to unseen …