Artificial Intelligence and Robotics Commons^™

Fostering Trust In Artificial Intelligence In Commercial Aviation: An Exploratory Study, Leila Halawi, Mark Miller, Sam Holley

Publications

Artificial intelligence (AI) is a transformative force, compelling industries to adapt their operations, management systems, and workforce capabilities. The aviation sector finds itself at the forefront of this transformation, confronted with the imperative to navigate the complex dynamics of trust amidst AI's integration. Through a comprehensive survey involving 310 professionals from across the US commercial aviation sector, the research aims to shed light on the trust construct. The exploratory study provides critical insights for strategic AI adoption within the industry. A crosstabulation explored how employee trust in AI for decision-making differed across various demographic groups. In addition, a onesample T-test …

Understanding Data Through The Lens Of Topology, Quang Truong

Dartmouth College Master’s Theses

Machine learning depends on the ability to learn insightful representations from data. Topology of data offers a rich source of information for constructing such representations, yet its potential remains under-explored by the broader machine learning community. This work investigates the power of applied topology through two complementary projects: Topological Message Passing with Path Complexes and Persistent Homology for Anomaly Detection. In the first project, we extend the topological message passing framework by introducing a novel approach centered on path complexes, where paths form the fundamental building blocks. Our theoretical analysis demonstrates that this model generalizes existing topological deep learning and …

Wave Energy Converter Wave Force Prediction Using A Neural Network, Morgan Kline

Dissertations, Master's Theses and Master's Reports

Due to the unpredictable nature of large bodies of water, wave energy can be a difficult renewable resource to rely on. One way to make Wave Energy Converters (WECs) more efficient is to apply a control strategy. In many control solutions, it is assumed that the wave excitation force is known into the future. In many instances, especially with complex waveforms, this is simply not the case. Simulation studies have shown the promise of wave force prediction using neural networks. This study demonstrates this experimentally and aims to characterize the important factors when designing such a network. Several wave elevation …

Analysis Of Speech Recognition Systems And Error Correction Approaches, Saki Imai

Honors Theses

Despite significant advances in automatic speech recognition (ASR) accuracy, challenges remain. Naturally occurring conversation often involves multiple overlapping speakers, of different ages, accents and genders, as well as noisy environments and suboptimal audio recording equipment, all of which reduce ASR accuracy. In this study, we evaluate the accuracy of state of the art open source ASR systems across diverse conversational speech datasets, examining the impact of audio and speaker characteristics on WER. We then explore the potential of ASR ensembling plus post-ASR correction methods to improve transcription accuracy. Our findings underscore the need for robust error correction techniques and of …

The Educational Affordances And Challenges Of Chatgpt: State Of The Field, Helen Crompton, Diane Burke

STEMPS Faculty Publications

ChatGPT was released to the public in November 30, 2022. This study examines how ChatGPT can be used by educators and students to promote learning and what are the challenges and limitations. This study is unique in providing one of the first systematic reviews using peer review studies to provide an early examination of the field. Using PRISMA principles, 44 articles were selected for review. Grounded coding was then used to reveal trends in the data. The findings show that educators can use ChatGPT for teaching support, task automation, and professional development. These were further delineated further by axial sub …

Tension Control And Interproximation Techniques Forshape Design And Rgb-Depth Segmentation Reconstruction And Modeling, Anastasia Kazadi

Theses and Dissertations--Computer Science

Human eyes possess remarkable capabilities to perceive and interpret a wealth of information about our environment; from discerning colors and depths to identifying object boundaries and navigating obstacles, our eyes serve as invaluable guides in our daily lives. Ongoing research in the fields of computer vision and computer graphics continuously explore the ways to replicate extraordinary human vision abilities in order to develop systems and frameworks which would enable computers to capture, analyze, and act upon discerned information. In this context, this dissertation seeks to investigate and automate various shape control and data processing techniques for 3D modeling and shape …

Finding Hierarchies To Improve Learning In Hierarchical Reinforcement Learning, Roy Mobley

Theses and Dissertations--Computer Science

Reinforcement Learning (RL) is an approach to allowing computer agents to try and learn how to solve problems by learning what actions are best to take in a given situation. RL is effective for learning what to do in an environment, but as the problem grows larger, the amount of information needed grows exponentially, making RL less effective on complex problems. A big challenge, often called the curse of dimensionality, is that the number of states and possible number of actions in an environment can grow too large to sufficiently test every possible combination of state and action. One method …

Integrating Art And Ai: Evaluating The Educational Impact Of Ai Tools In Digital Art History Learning, James Hutson

Faculty Scholarship

This study delves into the burgeoning intersection of Artificial Intelligence (AI) and art history education, an area that has been relatively unexplored. The research focuses on how AI art generators impact learning outcomes in art history for both undergraduate and graduate students enrolled in Ancient Art courses, covering eras from ancient Mesopotamia to the fall of Rome. Utilizing a mixed-methods approach, the study analyzes AI-generated artworks, reflective essays, and survey responses to assess how these generative tools influence students’ comprehension, engagement, and creative interpretation of historical artworks. The study reveals that the use of AI tools in art history not …

Intelligent Capabilities Of Traditional Knowledge Organization Methods, Xinning Su

Journal of Scientific Information Research

[Purpose/significance]By analyzing the system and rules of traditional knowledge organization methods, the intelligent capabilities of traditional knowledge organization methods are refined and integrated into artificial intelligence（AI） technology, to enhance the precision and efficiency of AI in information processing. [Method/process]This paper reviews the development of knowledge organization and analyses the inherit structure and mechanisms of traditional knowledge organization methods. [Result/conclusion]Research suggests that over centuries of development and evolution, knowledge organization has gained the ability to reflect knowledge systems and disciplinary systems across different disciplines from diverse perspectives, establish semantic relations from diverse knowledge associations, and associate and integrate knowledge of different …

Automated In Situ Segmentation Of Sugarcane Roots, Joseph Salas-Leon

Computer Science and Engineering Theses - Archive

Sugarcane roots are not understood and previous methods of collecting and processing data have proved to be laborious and time consuming. Using Minirhizotrons, Researchers observe and photograph roots without disturbing the soil and are useful for studying root growth over time. Software such as Rhyzovision exists to allow quick processing of root images. These software tools require clean or well annotated images of only the roots to provide accurate information. Current annotations of the images are done manually and requires a Scientist with domain knowledge of roots to accurately annotate the root images. We are employing the use of CNN …

Adaptive Neh With Constrained Nearest Neighbor Subtours For The Electric Vehicle Routing Problem With Time Windows, Andrew Struthers

All Master's Theses

The development of electric vehicles is currently considered one of the most innovative areas in manufacturing. Largely driven by the desire to reduce greenhouse emissions, electric vehicles are seen as a viable alternative to internal combustion engine cars. Starting from consumer cars, a dedicated effort is being made to translate this into commercial vehicles for freight and delivery. This research introduces a novel adaptive Nawaz, Enscore, Ham (NEH) algorithm with constrained nearest neighbor subtour (NEH-NN). This algorithm is tested on the standard benchmark problems in literature and used as a seed solution for the Genetic Algorithm (GA). The performance and …

Human-Centered Machine Learning With Interpretable Visual Knowledge Discovery, Lincoln Huber

All Master's Theses

This research advances interpretable machine learning (ML) by introducing hyperblocks (HBs) as a structured, rule-based approach for creating transparent and accurate models using meaningful numeric attributes directly interpretable to end users. Key techniques, including Parallel Hyperblock Creation, Interactive Hyperblock Creation, Level n Hyperblock Creation, and k-Nearest Neighbor Hyperblock, provide a framework that ensures domain experts can meaningfully engage with the model’s decision-making process through lossless visualizations using General Line Coordinates (GLC). Case studies with the Wisconsin Breast Cancer and MNIST datasets demonstrated HBs' effectiveness in handling high-risk and complex classification tasks, offering interpretable accuracy that traditional models struggle to achieve. …

Agentes Artificiales En Las Juntas Corporativas, Sergio Alberto Gramitto Ricci, David Cordero-Heredia, Carlos A. Carillo-Jaramillo

Faculty Works

Miles de afios atras, empresarios romanos gestionaban negocios conjuntos a traves de esclavos altamente inteligentes de propiedad comun. Los esclavos romanos no tenfan plena capacidad legal y eran considerados propiedad de sus duefios comunes. Ahora, las corporaciones buscan delegar la toma de decisiones a maquinas superinteligentes mediante el uso de inteligencia artificial en las juntas corporativas. La inteligencia artificial podrfa asistir, integrar e incluso reemplazar a los directores humanos. Sin embargo, el concepto de usar inteligencia artificial en las juntas directivas esta, en gran medida, inexplorado y plantea varios problemas. Este artfculo arroja luz sobre los desaffos legales y de …

On Generative Models And Joint Architectures For Document-Level Relation Extraction, Aviv Brokman

Theses and Dissertations--Statistics

Biomedical text is being generated at a high rate in scientific literature publications and electronic health records. Within these documents lies a wealth of potentially useful information in biomedicine. Relation extraction (RE), the process of automating the identification of structured relationships between entities within text, represents a highly sought-after goal in biomedical informatics, offering the potential to unlock deeper insights and connections from this vast corpus of data. In this dissertation, we tackle this problem with a variety of approaches.

We review the recent history of the field of document-level RE. Several themes emerge. First, graph neural networks dominate the …

Building A Human Digital Twin (Hdtwin) Using Large Language Models For Cognitive Diagnosis: Algorithm Development And Validation, Gina Sprint, Maureen Schmitter-Edgecombe, Diane Cook

Computer Science Faculty Scholarship

Background: Human digital twins have the potential to change the practice of personalizing cognitive health diagnosis because these systems can integrate multiple sources of health information and influence into a unified model. Cognitive health is multifaceted, yet researchers and clinical professionals struggle to align diverse sources of information into a single model. Objective: This study aims to introduce a method called HDTwin, for unifying heterogeneous data using large language models. HDTwin is designed to predict cognitive diagnoses and offer explanations for its inferences. Methods: HDTwin integrates cognitive health data from multiple sources, including demographic, behavioral, ecological momentary assessment, n-back test, …

Social Networks And Large Language Models For Division I Basketball Game Winner Prediction, Gina Sprint

Computer Science Faculty Scholarship

Sporting event outcome prediction is a well-established and actively researched domain, with a particular focus on college basketball’s March Madness tournament. Researchers, fans, and gamblers alike seek accurate game-level predictions using features such as tournament seeds, season performance, and expert opinions. While machine learning algorithms have been harnessed to build prediction models, no perfect model or human-created bracket has emerged. This paper explores a novel approach to basketball game outcome prediction by utilizing the power of social networks and large language models (LLMs). LLMs are trained to understand and generate text, often eliminating the need for a feature engineering step. …

Deep Transfer Learning-Based Bird Species Classification Using Mel Spectrogram Images, Mrinal Kanti Baowaly, Bisnu Chandra Sarkar, Md.Abul Ala Walid, Md. Martuza Ahamad, Bikash Chandra Singh, Eduardo Silva Alvarado, Imran Ashraf, Md. Abdus Samad

School of Cybersecurity Faculty Publications

The classification of bird species is of significant importance in the field of ornithology, as it plays an important role in assessing and monitoring environmental dynamics, including habitat modifications, migratory behaviors, levels of pollution, and disease occurrences. Traditional methods of bird classification, such as visual identification, were time-intensive and required a high level of expertise. However, audio-based bird species classification is a promising approach that can be used to automate bird species identification. This study aims to establish an audio-based bird species classification system for 264 Eastern African bird species employing modified deep transfer learning. In particular, the pre-trained EfficientNet …

A Systemic Mapping Study On Intrusion Response Systems, Adel Rezapour, Mohammad Ghasemigol, Daniel Takabi

School of Cybersecurity Faculty Publications

With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative …

Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent decades, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high-resolution images remains a major challenge. In this work, we address the challenge of very high-resolution histopathological image (VHRHI) classification using a new information-theoretic discriminative patch selection approach. We show results on a high-resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. Then we address how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. These ``weak labels'' are applied throughout the image but describe tumor regions of variable sizes and …

Foraging Economies: A Market Based Methodology For Robotic Swarm Foraging, John A. Little

Graduate Theses, Dissertations, and Problem Reports (ETD)

Swarm robotics involves coordinating large groups of autonomous agents to accomplish complex tasks through decentralized, adaptive behaviors, providing a robust and scalable approach suited to dynamic and unpredictable environments. While traditional swarm models frequently draw inspiration from biological systems such as ant colonies or bee foraging, other approaches use techniques from physics, control theory, and economics to achieve effective coordination. This study distinguishes itself by applying economic principles—specifically, market-driven mechanisms like auctions, utility functions based on opportunity cost, and supply-demand dynamics based on fluctuating resource values at a central base—to improve task allocation within a swarm foraging context. This approach …