Artificial Intelligence and Robotics Commons^™

Automs: Automatic Model Selection For Novelty Detection With Error Rate Control, Yifan Zhang, Haiyan Jiang, Haojie Ren, Changliang Zou, Dejing Dou

Machine Learning Faculty Publications

Given an unsupervised novelty detection task on a new dataset, how can we automatically select a “best” detection model while simultaneously controlling the error rate of the best model? For novelty detection analysis, numerous detectors have been proposed to detect outliers on a new unseen dataset based on a score function trained on available clean data. However, due to the absence of labeled anomalous data for model evaluation and comparison, there is a lack of systematic approaches that are able to select the “best” model/detector (i.e., the algorithm as well as its hyperparameters) and achieve certain error rate control simultaneously. …

Factored Adaptation For Non-Stationary Reinforcement Learning, Fan Feng, Biwei Huang, Kun Zhang, Sara Magliacane

Machine Learning Faculty Publications

Dealing with non-stationarity in environments (e.g., in the transition dynamics) and objectives (e.g., in the reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL). While most current approaches model the changes as a single shared embedding vector, we leverage insights from the recent causality literature to model non-stationarity in terms of individual latent change factors, and causal graphs across different environments. In particular, we propose Factored Adaptation for Non-Stationary RL (FANS-RL), a factored adaption approach that learns jointly both the causal structure in terms of a factored MDP, and a factored representation of …

Eureka: Euphemism Recognition Enhanced Through Knn-Based Methods And Augmentation, Sedrick Scott Keh, Rohit Bharadwaj, Emmy Liu, Simone Tedeschi, Varun Gangal, Roberto Navigli

Computer Vision Faculty Publications

We introduce EUREKA, an ensemble-based approach for performing automatic euphemism detection. We (1) identify and correct potentially mislabelled rows in the dataset, (2) curate an expanded corpus called EuphAug, (3) leverage model representations of Potentially Euphemistic Terms (PETs), and (4) explore using representations of semantically close sentences to aid in classification. Using our augmented dataset and kNN-based methods, EUREKA was able to achieve state-of-the-art results on the public leaderboard of the Euphemism Detection Shared Task, ranking first with a macro F1 score of 0.881.

Rare Gems: Finding Lottery Tickets At Initialization, Kartik Sreenivasan, Jy Yong Sohn, Liu Yang, Matthew Grinde, Alliot Nagle, Hongyi Wang, Eric Xing, Kangwook Lee, Dimitris Papailiopoulos

Machine Learning Faculty Publications

Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming “train, prune, re-train” approach. Frankle & Carbin [9] conjecture that we can avoid this by training lottery tickets, i.e., special sparse subnetworks found at initialization, that can be trained to high accuracy. However, a subsequent line of work [11, 41] presents concrete evidence that current algorithms for finding trainable networks at initialization, fail simple baseline comparisons, e.g., against training random sparse subnetworks. Finding lottery tickets that train to better accuracy compared to simple baselines remains an open …

Independence Testing-Based Approach To Causal Discovery Under Measurement Error And Linear Non-Gaussian Models, Haoyue Dai, Peter Spirtes, Kun Zhang

Machine Learning Faculty Publications

Causal discovery aims to recover causal structures generating the observational data. Despite its success in certain problems, in many real-world scenarios the observed variables are not the target variables of interest, but the imperfect measures of the target variables. Causal discovery under measurement error aims to recover the causal graph among unobserved target variables from observations made with measurement error. We consider a specific formulation of the problem, where the unobserved target variables follow a linear non-Gaussian acyclic model, and the measurement process follows the random measurement error model. Existing methods on this formulation rely on non-scalable over-complete independent component …

An Ai-Based Framework For Studying Visual Diversity Of Urban Neighborhoods And Its Relationship With Socio-Demographic Variables, Md Amiruzzaman, Ye Zhao, Stefanie Amiruzzaman, Aryn C. Karpinski, Tsung Heng Wu

Computer Science Faculty Publications

This study presents a framework to study quantitatively geographical visual diversities of urban neighborhood from a large collection of street-view images using an Artificial Intelligence (AI)-based image segmentation technique. A variety of diversity indices are computed from the extracted visual semantics. They are utilized to discover the relationships between urban visual appearance and socio-demographic variables. This study also validates the reliability of the method with human evaluators. The methodology and results obtained from this study can potentially be used to study urban features, locate houses, establish services, and better operate municipalities.

Synthetic Data Generation For Intelligent Inspection Of Structural Environments, Noshin Habib

Open Access Theses & Dissertations

Automated detection of cracks and corrosion in pavements and industrial settings is essential to a cost-effective approach to maintenance. Deep learning has paved the path for vast levels of improvement in the area. Such models require a plethora of data with accurate ground truth and enough variation for the model to generalize to the data, which is notwidely available. There has been recent progress in computer graphics being used for the creation of synthetic data to address the issue of deficient data availability, but it is limited to specific objects, such as cars and human beings. Textures and deformities within …

Identity Term Sampling For Measuring Gender Bias In Training Data, Nasim Sobhani, Sarah Jane Delany

Conference Papers

Predictions from machine learning models can reflect biases in the data on which they are trained. Gender bias has been identified in natural language processing systems such as those used for recruitment. The development of approaches to mitigate gender bias in training data typically need to be able to isolate the effect of gender on the output to see the impact of gender. While it is possible to isolate and identify gender for some types of training data, e.g. CVs in recruitment, for most textual corpora there is no obvious gender label. This paper proposes a general approach to measure …

Motion Planning Under Uncertainties, Sourav Dutta

Legacy Theses & Dissertations (2009 - 2024)

A robot is an agent that can bring some changes to the environment around it. Motion planning is the problem of carrying out specialized tasks by a robot by either moving itself or some other object (usually called \textit{payload}) from one place to another. In a real-world scenario, a robot is faced with constraints such as momentum, friction, sensor inaccuracies, etc., that can affect its decision-making while performing specialized tasks. These constraints are identified as uncertainties, and successful planning involves making provisions for such uncertainties. In this work, we present methods like stochastic processes, sequential inference, and pattern recognition to …

Development Of Nucleic Acid Diagnostics For Targeted And Non-Targeted Biosensing, Christopher William Smith

Legacy Theses & Dissertations (2009 - 2024)

The field of nucleic acid technology is rapidly expanding with new impactful discoveriesbeing made each year. Starting from the discovery of the double-helix structure, cloning, gene editing, polymerase chain reaction (PCR), CRISPR technology, and even the late mRNA vaccines; nucleic acid technology is at the forefront of improving medicine. Nucleic acid technology is extremely versatile due to its easy programmability, automated cheap synthesis, and even its catalog for numerous chemical modifications that can be used to alter structure stability. For example, the number of permutations that can be made with DNA just by altering the code for adenine (A), cytosine …

Probabilistic Forecasting Of Winter Mixed Precipitation Types In New York State Utilizing A Random Forest, Brian Chandler Filipiak

Legacy Theses & Dissertations (2009 - 2024)

Operational forecasters face a plethora of challenges when making a forecast; they must consider multiple data sources ranging from radar and satellites to surface and upper air observations, to numerical weather prediction output. Forecasts must be done in a limited window of time, which adds an additional layer of difficulty to the task. These challenges are exacerbated by winter mixed precipitation events where slight differences in thermodynamic profiles or changes in terrain create different precipitation types across small areas. In addition to being difficult to forecast, mixed precipitation events can have large-scale impacts on our society.

Hard-Real-Time Computing Performance In A Cloud Environment, Alvin Cornelius Murphy

Engineering Management & Systems Engineering Theses & Dissertations

The United States Department of Defense (DoD) is rapidly working with DoD Services to move from multi-year (e.g., 7-10) traditional acquisition programs to a commercial industrybased approach for software development. While commercial technologies and approaches provide an opportunity for rapid fielding of mission capabilities to pace threats, the suitability of commercial technologies to meet hard-real-time requirements within a surface combat system is unclear. This research establishes technical data to validate the effectiveness and suitability of current commercial technologies to meet the hard-real-time demands of a DoD combat management system. (Moreland Jr., 2013) conducted similar research; however, microservices, containers, and container …

Towards Privacy And Security Concerns Of Adversarial Examples In Deep Hashing Image Retrieval, Yanru Xiao

Computer Science Theses & Dissertations

With the explosive growth of images on the internet, image retrieval based on deep hashing attracts spotlights from both research and industry communities. Empowered by deep neural networks (DNNs), deep hashing enables fast and accurate image retrieval on large-scale data. However, inheriting from deep learning, deep hashing remains vulnerable to specifically designed input, called adversarial examples. By adding imperceptible perturbations on inputs, adversarial examples fool DNNs to make wrong decisions. The existence of adversarial examples not only raises security concerns for real-world deep learning applications, but also provides us with a technique to confront malicious applications.

In this dissertation, we …

Identification Of Factors Contributing To Traffic Crashes By Analysis Of Text Narratives, Cristian D. Arteaga-Sanchez

UNLV Theses, Dissertations, Professional Papers, and Capstones

The fatalities, injuries, and property damage that result from traffic crashes impose a significant burden on society. Current research and practice in traffic safety rely on analysis of quantitative data from crash reports to understand crash severity contributors and develop countermeasures. Despite advances from this effort, quantitative crash data suffers from drawbacks, such as the limited ability to capture all the information relevant to the crashes and the potential errors introduced during data collection. Crash narratives can help address these limitations, as they contain detailed descriptions of the context and sequence of events of the crash. However, the unstructured nature …

A Mechanically Intelligent Hosing-Drone, Blake Hament

UNLV Theses, Dissertations, Professional Papers, and Capstones

This manuscript presents a ”mechanically intelligent” approach to designing a Hosing-Drone for heavy-duty pressure washing. Spraying a hose creates strong reaction forces and torques. Previously demonstrated spraying robots are over-engineered to be very massive with huge inertias. These high inertias ”wash out” the reaction from the spraying. In the proposed approach, the contributions from all observable fluid dynamics, fluid structure interactions, and aerodynamics are studied individually and for the coupled system. Experimental data is collected and fit to dynamic models. These models are used to design a smaller, lighter, more agile vehicle than has been previously demonstrated. An impedance controller …

Wildfire Spread Prediction Using Attention Mechanisms In U-Net, Kamen Haresh Shah, Kamen Haresh Shah

Master's Theses

An investigation into using attention mechanisms for better feature extraction in wildfire spread prediction models. This research examines the U-net architecture to achieve image segmentation, a process that partitions images by classifying pixels into one of two classes. The deep learning models explored in this research integrate modern deep learning architectures, and techniques used to optimize them. The models are trained on 12 distinct observational variables derived from the Google Earth Engine catalog. Evaluation is conducted with accuracy, Dice coefficient score, ROC-AUC, and F1-score. This research concludes that when augmenting U-net with attention mechanisms, the attention component improves feature suppression …

Expressiveness Of Real-Time Motion Captured Avatars Influences Perceived Animation Realism And Perceived Quality Of Social Interaction In Virtual Reality, Alan D. Fraser, Isabella Branson, Ross C. Hollett, Craig P. Speelman, Shane L. Rogers

Research outputs 2022 to 2026

Using motion capture to enhance the realism of social interaction in virtual reality (VR) is growing in popularity. However, the impact of different levels of avatar expressiveness on the user experience is not well understood. In the present study we manipulated levels of face and body expressiveness of avatars while investigating participant perceptions of animation realism and interaction quality when disclosing positive and negative experiences in VR. Moderate positive associations were observed between perceptions of animation realism and interaction quality. Post-experiment questions revealed that many of our participants (approximately 40 %) indicated the avatar with the highest face and body …

Data-Driven Deep Learning-Based Analysis On Thz Imaging, Haoyan Liu

Graduate Theses and Dissertations

Breast cancer affects about 12.5% of women population in the United States. Surgical operations are often needed post diagnosis. Breast conserving surgery can help remove malignant tumors while maximizing the remaining healthy tissues. Due to lacking effective real-time tumor analysis tools and a unified operation standard, re-excision rate could be higher than 30% among breast conserving surgery patients. This results in significant physical, physiological, and financial burdens to those patients. This work designs deep learning-based segmentation algorithms that detect tissue type in excised tissues using pulsed THz technology. This work evaluates the algorithms for tissue type classification task among freshly …

Movie Reviews Sentiment Analysis Using Bert, Gibson Nkhata

Graduate Theses and Dissertations

Sentiment analysis (SA) or opinion mining is analysis of emotions and opinions from texts. It is one of the active research areas in Natural Language Processing (NLP). Various approaches have been deployed in the literature to address the problem. These techniques devise complex and sophisticated frameworks in order to attain optimal accuracy with their focus on polarity classification or binary classification. In this paper, we aim to fine-tune BERT in a simple but robust approach for movie reviews sentiment analysis to provide better accuracy than state-of-the-art (SOTA) methods. We start by conducting sentiment classification for every review, followed by computing …

The Role Of Generative Adversarial Networks In Bioimage Analysis And Computational Diagnostics., Ahmed Naglah

Electronic Theses and Dissertations

Computational technologies can contribute to the modeling and simulation of the biological environments and activities towards achieving better interpretations, analysis, and understanding. With the emergence of digital pathology, we can observe an increasing demand for more innovative, effective, and efficient computational models. Under the umbrella of artificial intelligence, deep learning mimics the brain’s way in learn complex relationships through data and experiences. In the field of bioimage analysis, models usually comprise discriminative approaches such as classification and segmentation tasks. In this thesis, we study how we can use generative AI models to improve bioimage analysis tasks using Generative Adversarial Networks …

Wordmuse, John M. Nelson

Computer Science and Software Engineering

Wordmuse is an application that allows users to enter a song and a list of keywords to create a new song. Built on Spotify's API, this project showcases the fusion of music composition and artificial intelligence. This paper also discusses the motivation, design, and creation of Wordmuse.

Region Detection & Segmentation Of Nissl-Stained Rat Brain Tissue, Alexandro Arnal

Open Access Theses & Dissertations

People who analyze images of biological tissue rely on the segmentation of structures as a preliminary step. In particular, laboratories studying the rat brain delineate brain regions to position scientific findings on a brain atlas to propose hypotheses about the rat brain and, ultimately, the human brain. Our work intersects with the preliminary step of delineating regions in images of brain tissue via computational methods.

We investigate pixel-wise classification or segmentation of brain regions using ten histological images of brain tissue sections stained for Nissl substance. We present a deep learning approach that uses the fully convolutional neural network, U-Net, …

Fiscal And Monetary Policy Interactions In A Developing Economy: A Dsge-Based Evidence From Nigeria, Queen E. Oye, Philip O. Alege

CBN Journal of Applied Statistics (JAS)

This study characterizes the nature of fiscal-monetary interaction in Nigeria and gauges its macroeconomic effects by estimating a New Keynesian Dynamic Stochastic General Equilibrium (NK DSGE) model. Two policy simulations were also conducted. The first experiment considers the desirable active-passive policy mix while the second experiment ranks alternative monetary policy rules among the differing objectives of price, output and exchange rate stabilization. The study finds that fiscal and monetary policies interact as complements in an active monetary and passive fiscal policy mix over the sample period. The result from the first policy simulation reveals that the active monetary and passive …

Stuck-At-Fault Immunity Enhancement Of Memristor-Based Edge Ai Systems, Md. Oli-Uz-Zaman

Graduate Theses and Dissertations (2019 - present)

Deep Neural Networks (DNN) are widely used in edge AI. But the complex perception and decision-making demand the overlarge computation and make the DNN architecture very sophisticated. Memristors have multilevel resistance property that enables faster in-memory DNN computation to remove the bottleneck caused by the von Neumann architecture and CMOS technology. However, the Stuck-At-Fault (SAF) defect of memristor generated from immature fabrication and heavy device utilization makes the memristor-based edge AI commercially unavailable. To mitigate this problem, an Adaptive Mapping Method (AMM) is proposed in this project. Based on the analysis for the VGG8 model with CIFAR10 dataset, the experiment …

Predicting Startup Success Using Publicly Available Data, Emily Gavrilenko

Master's Theses

Predicting the success of an early-stage startup has always been a major effort for investors and venture funds. Statistically, there are about 305 million total startups created in a year, but less than 10% of them succeed to become profitable businesses. Accurately identifying the signs of startup growth is the work of countless investors, and in recent years, research has turned to machine learning in hopes of improving the accuracy and speed of startup success prediction.

To learn about a startup, investors have to navigate many different internet sources and often rely on personal intuition to determine the startup’s potential …

Causal Inference In Psychology And Neuroscience: From Association To Causation, Dehua Liang

Computational and Data Sciences (PhD) Dissertations

In psychology and neuroscience, inferring causality in non-experimental studies is almost taboo, because data in these studies, e.g., survey data and resting-state neuroimaging data, are often contaminated by unmeasured confounders. Psychologists and neuroscientists are often cautious about their results, and reluctant to make false claims about causality in non-experimental studies. Therefore, they adopt less stringent statistical analysis techniques that can only infer associational relations. However, the ambiguity about causality in traditional statistical analysis creates much confusion in interpreting analytical results - some studies make implicit causal claims about their results using words such as “impacts”, “lead to” and “affects”. This …

Machine Learning For Early Detection Of Pediatric Sepsis, Glory Manson-Endeboh

Graduate Theses and Dissertations

Sepsis is a host response to infection in both adults and children. It contributes to approximately 1.7 million cases annually with nearly 270,000 annual deaths in the United States. In the United States, the financial burden of sepsis on survivors and their families including the hospitals is over $4.8 billion, at approximately $64,280 per hospitalization. The first goal of this thesis research is to develop efficient machine learning models to predict pediatric sepsis accurately for inpatients. The second objective is to develop machine learning methods to determine how early sepsis can be detected to mitigate mortality. We examine data collected …

Divide-And-Conquer Distributed Learning: Privacy-Preserving Offloading Of Neural Network Computations, Lewis C.L. Brown

Graduate Theses and Dissertations

Machine learning has become a highly utilized technology to perform decision making on high dimensional data. As dataset sizes have become increasingly large so too have the neural networks to learn the complex patterns hidden within. This expansion has continued to the degree that it may be infeasible to train a model from a singular device due to computational or memory limitations of underlying hardware. Purpose built computing clusters for training large models are commonplace while access to networks of heterogeneous devices is still typically more accessible. In addition, with the rise of 5G networks, computation at the edge becoming …

On The Merge Of K-Nn Graph, Wan-Lei Zhao, Hui Wang, Peng-Cheng Lin, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

k-nearest neighbor graph is a fundamental data structure in many disciplines such as information retrieval, data-mining, pattern recognition, and machine learning, etc. In the literature, considerable research has been focusing on how to efficiently build an approximate k-nearest neighbor graph (k-NN graph) for a fixed dataset. Unfortunately, a closely related issue of how to merge two existing k-NN graphs has been overlooked. In this paper, we address the issue of k-NN graph merging in two different scenarios. In the first scenario, a symmetric merge algorithm is proposed to combine two approximate k-NN graphs. The algorithm facilitates large-scale processing by the …

Enhancing Literacy Education With Narrative Richness In The Metaverse, Fiona Fui-Hoon Nah, Daniel Jiandong Shen, Umawathy Techanamurthy

Research Collection School Of Computing and Information Systems

Through an education-centric metaverse learning application, this research aims to assess the use of narrative richness to deliver media, language, and sustainability literacy education. The 21st-century learning needs require teaching and learning resources to be shared and managed more effectively across institutions. The use of metaverse features can help to manage varying narrative richness to boost learning reflection and attitude. Despite its potential, it is unclear how narrative richness in the metaverse can enhance teaching and learning. The study proposed in this research, which includes institutions from four Asian countries, is driven by this knowledge and evidence gap. Module leaders …