Artificial Intelligence and Robotics Commons^™

Probing And Enhancing The Reliance Of Transformer Models On Poetic Information, Almas Abdibayev

Dartmouth College Ph.D Dissertations

Transformer models have achieved remarkable success in the widest variety of domains, spanning not just a multitude of tasks within natural language processing, but also those in computer vision, speech, and reinforcement learning. The key to this success is largely attributed to the self-attention mechanism, particularly its ability to scale in performance as it grows in the number of parameters. Extensive effort has been underway to study the major linguistic properties learned by these models during the course of their pretraining. However, the role of certain finer linguistic phenomena present in language and their utilization by Transformers has not been …

Deep Learning Uncertainty Quantification For Clinical Text Classification, Alina Peluso, Ioana Danciu, Hong-Jun Yoon, Jamaludin Mohd Yusof, Tanmoy Bhattacharya, Adam Spannaus, Noah Schaefferkoetter, Eric B. Durbin, Xiao-Cheng Wu, Antoinette Stroup, Jennifer Doherty, Stephen Schwartz, Charles Wiggins, Linda Coyle, Lynne Penberthy, Georgia D. Tourassi, Shang Gao

School of Public Health Faculty Publications

INTRODUCTION: Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network's confidence, in-depth analyses are needed to establish whether they are well calibrated. METHOD: In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National …

Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi

Master of Science in Computer Science Theses

Students frequently face heightened stress due to academic and social pressures, particularly in de- manding fields like computer science and engineering. These challenges are often associated with serious mental health issues, including ADHD (Attention Deficit Hyperactivity Disorder), depression, and an increased risk of suicide. The average student attention span has notably decreased from 21⁄2 minutes to just 47 seconds, and now it typically takes about 25 minutes to switch attention to a new task (Mark, 2023). Research findings suggest that over 95% of individuals who die by suicide have been diagnosed with depression (Shahtahmasebi, 2013), and almost 20% of students …

The Transformative Integration Of Artificial Intelligence With Cmmc And Nist 800-171 For Advanced Risk Management And Compliance, Mia Lunati

Cybersecurity Undergraduate Research Showcase

This paper explores the transformative potential of integrating Artificial Intelligence (AI) with established cybersecurity frameworks such as the Cybersecurity Maturity Model Certification (CMMC) and the National Institute of Standards and Technology (NIST) Special Publication 800-171. The thesis argues that the relationship between AI and these frameworks has the capacity to transform risk management in cybersecurity, where it could serve as a critical element in threat mitigation. In addition to addressing AI’s capabilities, this paper acknowledges the risks and limitations of these systems, highlighting the need for extensive research and monitoring when relying on AI. One must understand boundaries when integrating …

Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson

Theses and Dissertations

The domain of Intrusion Detection Systems (IDS) has witnessed growing interest in recent years due to the escalating threats posed by cyberattacks. As Internet of Things (IoT) becomes increasingly integrated into our every day lives, we widen our attack surface and expose more of our personal lives to risk. In the same way the Human Immune System (HIS) safeguards our physical self, a similar solution is needed to safeguard our digital self. This thesis presents the Artificial Immune inspired Intrusion Detection System (AIS-IDS), an IDS modeled after the HIS. This thesis proposes an architecture for AIS-IDS, instantiates an AIS-IDS model …

Scalable And Explainable Self-Supervised Motif Discovery In Temporal Data, Somayeh Bakhtiari Ramezani

Theses and Dissertations

The availability of a scalable and explainable rule extraction technique via motif discovery is crucial for identifying the health states of a system. Such a technique can enable the creation of a repository of normal and abnormal states of the system and identify the system’s state as we receive data. In complex systems such as ECG, each activity session can consist of a long sequence of motifs that form different global structures. As a result, applying machine learning algorithms without first identifying the local patterns is not feasible and would result in low performance. Thus, extracting unique local motifs and …

Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded

Theses and Dissertations

Historical manuscripts are an essential source of original content. For many reasons, it is hard to recognize these manuscripts as text. This thesis used a state-of-the-art Handwritten Text Recognizer, TrOCR, to recognize a 16^th-century manuscript. TrOCR uses a vision transformer to encode the input images and a language transformer to decode them back to text. We showed that carefully preprocessed images and designed augmentations can improve the performance of TrOCR. We suggest an ensemble of augmented models to achieve an even better performance.

Phenotyping Cotton Compactness Using Machine Learning And Uas Multispectral Imagery, Joshua Carl Waldbieser

Theses and Dissertations

Breeding compact cotton plants is desirable for many reasons, but current research for this is restricted by manual data collection. Using unmanned aircraft system imagery shows potential for high-throughput automation of this process. Using multispectral orthomosaics and ground truth measurements, I developed supervised models with a wide range of hyperparameters to predict three compactness traits. Extreme gradient boosting using a feature matrix as input was able to predict the height-related metric with R2=0.829 and RMSE=0.331. The breadth metrics require higher-detailed data and more complex models to predict accurately.

Brain-Inspired Spatio-Temporal Learning With Application To Robotics, Thiago André Ferreira Medeiros

USF Tampa Graduate Theses and Dissertations

The human brain still has many mysteries and one of them is how it encodes information. The following study intends to unravel at least one such mechanism. For this it will be demonstrated how a set of specialized neurons may use spatial and temporal information to encode information. These neurons, called Place Cells, become active when the animal enters a place in the environment, allowing it to build a cognitive map of the environment. In a recent paper by Scleidorovich et al. in 2022, it was demonstrated that it was possible to differentiate between two sequences of activations of a …

Is Your Computer Lying? Ai And Deception, Noreen L. Herzfeld

Reuter Professorship of Science and Religion Publications

Recent developments in AI, especially the spectacular success of Large Language models, have instigated renewed questioning of what remains distinctively human. As AI stands poised to take over more and more human tasks, what is left that distinguishes humans? One way we might identify a humanlike intelligence would be when we detect it telling lies. Yet AIs lack both the intention and the motivation to truly tell lies, instead producing merely bullshit. With neither emotions, embodiment, nor the social awareness that leads to a theory of mind, AIs lack the internal referents on which to judge truth or falsity. When …

Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail

College of Computing and Digital Media Dissertations

Learning a foreign language entails cognitive and emotional obstacles. It involves complicated mental processes that affect learning and emotions. Positive emotions such as motivation, encouragement, and satisfaction increase learning achievement, while negative emotions like anxiety, frustration, and confusion may reduce performance. Foreign Language Anxiety (FLA) is a specific type of anxiety accompanying learning a foreign language. It is considered a main impediment that hinders learning, reduces achievements, and diminishes interest in learning.

Detecting FLA is the first step toward reducing and eventually overcoming it. Previously, researchers have been detecting FLA using physical measurements and self-reports. Using physical measures is direct …

The Holistic Archival Personality Profiling Model (Happm): Comprehensive Data Integration For Personality Analysis, James Hutson, Pace Ellsworth

Faculty Scholarship

The traditional approach to biographical profiling, predominantly reliant on limited and fragmented datasets, has frequently resulted in superficial personality understandings. This is largely due to an overemphasis on official records and notable events, neglecting the rich tapestry of everyday experiences and personal interactions that significantly shape personalities. To address this shortcoming, this article introduces a multi-disciplinary methodology, The Holistic Archival Personality Profiling Model (HAPPM), which integrates a diverse array of archival materials, including personal correspondences, social media footprints, and family memorabilia. This approach involves digitizing various data forms, including handwritten documents, into machine-readable text, and then semantically classifying this data …

Mgmt Promoter Methylation Status Prediction Using Mri Scans? An Extensive Experimental Evaluation Of Deep Learning Models, Numan Saeed, Muhammad Ridzuan, Hussain Alasmawi, Ikboljon Sobirov, Mohammad Yaqub

Computer Vision Faculty Publications

The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only a few systems have shown medical efficacy. From this perspective, we examine a wide range of deep learning algorithms for the assessment of glioblastoma - a common brain tumor in older adults that is lethal. Surgery, chemotherapy, and radiation are the standard treatments for glioblastoma patients. The methylation status of the MGMT promoter, a specific genetic sequence found in the tumor, affects chemotherapy's effectiveness. MGMT promoter methylation improves chemotherapy response and survival in several cancers. MGMT promoter …

Predictive Machine Learning And Its Future In Professional Basketball, Zachary Harmon

Honors College Theses

Artificial Intelligence (AI) is an ever-evolving field, transforming various aspects of contemporary life. From language models to immersive gaming experiences, AI technologies have become integral to our daily existence. Among the most promising arenas for AI integration is the world of sports. This research delves into the application of machine learning models to predict NBA game outcomes, shedding light on the profound impact of machine learning in the realm of professional basketball. Beyond the scope of game prediction, this study explores the broader implications, such as optimizing the selection of televised games, assisting players in showcasing their skills, and much …

Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury

Graduate Theses and Dissertations

The transportation sector stands as a significant contributor to greenhouse gas emissions in the United States, with its environmental impact steadily escalating over the past few decades. This has prompted government agencies to facilitate the adoption and usage of low-carbon transportation (LCT) options as alternatives to fossil-fuel-powered transportation. LCTs include modes of transportation that minimize the overall carbon footprint of the transportation sector by relying on energy sources that are environmentally sustainable. These sustainable transportation options have also garnered significant interest in the transportation research community. For government agencies and researchers alike, a comprehensive understanding of the adoption and usage …

Rising Threat - Deepfakes And National Security In The Age Of Digital Deception, Dougo Kone-Sow

Cybersecurity Undergraduate Research Showcase

This paper delves into the intricate landscape of deepfakes, exploring their genesis, capabilities, and far-reaching implications. The rise of deepfake technology presents an unprecedented threat to American national security, propagating disinformation and manipulation across various media formats. Notably, deepfakes have evolved from a historical backdrop of disinformation campaigns, merging with the advancements of artificial intelligence (AI) and machine learning to craft convincing but false multimedia content.

Examining the capabilities of deepfakes reveals their potential for misuse, evidenced by instances targeting individuals, companies, and even influencing political events like the 2020 U.S. elections. The paper highlights the direct threats posed by …

New Paths Of Attacks: Revealing The Adaptive Integration Of Artificial Intelligence In Evolving Cyber Threats Targeting Social Media Users And Their Data, Larry Teasley

Cybersecurity Undergraduate Research Showcase

The intersection between artificial intelligence tools and social media has opened doors to numerous opportunities and risks. This research delves into the escalating threat landscape in a society heavily dependent on social media. Despite the efforts by social media companies and cybersecurity professionals to mitigate cyber-attacks, the constant advancements of new technologies render social media platforms increasingly vulnerable. Malicious actors exploit generative AI to collect user data, enhancing cyber threats on social media. Notably, generative AI amplifies phishing attacks, disseminates false information, and propagates propaganda, posing substantial challenges to platform security. Ease access to large language models (LLMs) further complicates …

Integrating Ai Into Uavs, Huong Quach

Cybersecurity Undergraduate Research Showcase

This research project explores the application of Deep Learning (DL) techniques, specifically Convolutional Neural Networks (CNNs), to develop a smoke detection algorithm for deployment on mobile platforms, such as drones and self-driving vehicles. The project focuses on enhancing the decision-making capabilities of these platforms in emergency response situations. The methodology involves three phases: algorithm development, algorithm implementation, and testing and optimization. The developed CNN model, based on ResNet50 architecture, is trained on a dataset of fire, smoke, and neutral images obtained from the web. The algorithm is implemented on the Jetson Nano platform to provide responsive support for first responders. …

Demystifying Artificial Intelligence (Ai) For Early Childhood And Elementary Education: A Case Study Of Perceptions Of Ai Of State Of Missouri Educators, Kathryn Arnone, James Hutson, Karen Woodruff

Faculty Scholarship

Artificial intelligence (AI) and its impact on society have received a great deal of attention in the past five years since the first Stanford AI100 report. AI already globally impacts individuals in critical and personal ways, and many industries will continue to experience disruptions as the full algorithmic effects are understood. However, with regard to education, adopting in disciplines remains limited largely to Computer Science and Information Technology in postsecondary education. Recent advances with technology are especially promising for their potential to create and scale personalized learning for students, to optimize strategies for learning outcomes, and to increase access to …

A Hybrid Cognitive Model For Machine Agents In Project And Action Teams, Joshua A. Lapso

Theses and Dissertations

High performing human teams transcend complex domain uncertainty by achieving an emergent state of shared cognition, in which knowledge is organized, represented, and distributed to team members for rapid execution. However, this requires that individuals emit perceivable qualities upon which other members can make inferences about intent. In pursuit of future human and machine team studies, this research presents a hybrid cognitive model for machine agents in fully cooperative and semi-cooperative action and project teams. The hybrid cognitive model unifies the characteristics of the shared mental model and transactive memory system. The resultant model facilitates anytime selection over the two …

Marss: Multi-Agent Reinforcement Learning For Satellite Swarms, Nicholas J. Yielding

Theses and Dissertations

Multi-agent systems and swarms in spacecraft formation flying are of ever-increasing importance in a contested space environment—use of multiple spacecraft to contribute to a cooperative mission potentially increases positive outcomes on orbit, while autonomy becomes an ever more important requirement to reduce reaction time in dynamic situations and lower the burden on space operators. This research explores difficult swarm Guidance Navigation and Control (GNC) scenarios using Deep Reinforcement Learning (DRL). DRL polices are trained to provide guidance inputs to agents in multi-agent swarm environments for completing complex, teamwork focused objectives in geosynchronous orbit. An example scenario is explored for a …

Cvii: Enhancing Interpretability In Intelligent Sensor Systems Via Computer Vision Interpretability Index, Hossein Mohammadi, Krishnaprasad Thirunarayan, Lingwei Chen

Computer Science and Engineering Faculty Publications

In the realm of intelligent sensor systems, the dependence on Artificial Intelligence (AI) applications has heightened the importance of interpretability. This is particularly critical for opaque models such as Deep Neural Networks (DNN), as understanding their decisions is essential, not only for ethical and regulatory compliance, but also for fostering trust in AI-driven outcomes. This paper introduces the novel concept of a Computer Vision Interpretability Index (CVII). The CVII framework is designed to emulate human cognitive processes, specifically in tasks related to vision. It addresses the intricate challenge of quantifying interpretability, a task that is inherently subjective and varies across …

Leveraging Generative Agents: Autonomous Ai With Simulated Personas For Interactive Simulacra And Collaborative Research, James Hutson, Jay Ratican

Faculty Scholarship

The advent of large language models (LLMs) and AI learning have fundamentally reshaped the research landscape, paving the way for novel problem-solving approaches. This paper introduces a unique framework that leverages the capabilities of autonomous AI agents with simulated personas to drive collaborative research in groundbreaking ways. Inspired by a recent study of autonomous agents mirroring human behavior, this concept encourages the use of a cadre of AI agents, each possessing specialized expertise for collective endeavors. By replicating human diversity in teamwork, this approach targets complex and hitherto unsolvable issues. The key to this strategy is persona and emotional simulation, …

The Analysis And Impact Of Artificial Intelligence On Job Loss, Ava Baratz

Cybersecurity Undergraduate Research Showcase

This paper illustrates the analysis and impact of Artificial Intelligence (AI) on job loss across various industries. This paper will discuss an overview of AI technology, a brief history of AI in industry, the positive impacts of AI, the negative impacts of AI on employment, AI considerations that contribute to job loss, the future outlook of AI, and employment loss mitigation strategies Various professional source articles and reputable blog posts will be used to finalize research on this topic.

Perception Of Bias In Chatgpt: Analysis Of Social Media Data, Abdullah Wahbeh, Mohammad A. Al-Ramahi, Omar El-Gayar, Ahmed El Noshokaty, Tareq Nasralah

Computer Information Systems Faculty Publications (Archived)

In this study, we aim to analyze the public perception of Twitter users with respect to the use of ChatGPT and the potential bias in its responses. Sentiment and emotion analysis were also analyzed. Analysis of 5,962 English tweets showed that Twitter users were concerned about six main types of biases, namely: political, ideological, data & algorithmic, gender, racial, cultural, and confirmation biases. Sentiment analysis showed that most of the users reflected a neutral sentiment, followed by negative and positive sentiment. Emotion analysis mainly reflected anger, disgust, and sadness with respect to bias concerns with ChatGPT use.

Discourses Across Periods Of Time, Amanda Raquel Moreno

Theses

This literature review explores the revolutionary effect of generative artificial intelligence (AI) and virtual reality (VR) on digital art history, specifically concentrating on their capacity to enable dialogical exchanges with historical figures and deepen the understanding of artworks. This study considers the current state of research, detecting key methodologies, areas of improvement, and possible challenges and ethical concerns. The example historical figure used in this analysis is the iconic Mexican artist Frida Kahlo. Kahlo’s refusal to correspond to a specific artistic style makes her an ideal subject for generative AI and VR-based investigation, offering fresh insights into her work. The …

Dropped Objects Recognition In Offshore Operations Based On Computer Vision And Artificial Intelligence, Xiaochuan Yu

LSU New Orleans Theses and Dissertations

We are more interested in recovering and salvaging containers which may be loaded with either valuable or harmful substances. Therefore, we designed various container models to account for different loading conditions and then exported them to a 3D printer. Six small container models were dropped into the trailer pool at the University of New Orleans (UNO) as planned at angles of 0°, 45°, and 90°.

After collecting all the videos using a high-definition camera set outside the tank, we perform pre-processing tasks on the videos in preparation for model training. The two-phase angle classification method uses a pretrained ResNet50 model …

Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.

Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …

Responsibility Gaps And Black Box Healthcare Ai: Shared Responsibilization As A Solution, Benjamin H Lang, Sven Nyholm, Jennifer Blumenthal-Barby

Center for Medical Ethics and Health Policy Staff Publications

As sophisticated artificial intelligence software becomes more ubiquitously and more intimately integrated within domains of traditionally human endeavor, many are raising questions over how responsibility (be it moral, legal, or causal) can be understood for an AI’s actions or influence on an outcome. So called “responsibility gaps” occur whenever there exists an apparent chasm in the ordinary attribution of moral blame or responsibility when an AI automates physical or cognitive labor otherwise performed by human beings and commits an error. Healthcare administration is an industry ripe for responsibility gaps produced by these kinds of AI. The moral stakes of healthcare …

Use Of Ai To Recreate And Repatriate Lost, Destroyed Or Stolen Paintings: The 1785 Parisian Salon Case Study, Charles E. O'Brien, James Hutson, Trent Olsen, Jay Ratican

Faculty Scholarship

This study investigates the efficacy of artificial intelligence (AI) in the field of artwork restoration, focusing on lost, stolen, or destroyed artworks. Employing a dual approach that combines traditional manual restoration techniques with advanced generative AI tools, the research centers on a case study of the 1785 Parisian Salon. It specifically examines the reconstitution of Antoine François Callet's painting, Achilles Dragging the Body of Hector, unveiled alongside Jacques-Louis David's Oath of the Horatii. The study utilizes Easy Diffusion and Stable Diffusion 2.1 technologies for inpainting and colorization processes. These AI tools are employed in concert with manual restoration practices to …