Open Access. Powered by Scholars. Published by Universities.®
Artificial Intelligence and Robotics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Engineering (444)
- Computer Engineering (182)
- Data Science (158)
- Other Computer Sciences (151)
- Electrical and Computer Engineering (137)
-
- Theory and Algorithms (135)
- Social and Behavioral Sciences (107)
- Information Security (105)
- Databases and Information Systems (104)
- Statistics and Probability (97)
- Graphics and Human Computer Interfaces (87)
- Software Engineering (82)
- Life Sciences (81)
- Medicine and Health Sciences (72)
- Numerical Analysis and Scientific Computing (71)
- Robotics (71)
- Arts and Humanities (49)
- Mechanical Engineering (49)
- Operations Research, Systems Engineering and Industrial Engineering (49)
- Applied Mathematics (47)
- Mathematics (44)
- Biomedical Engineering and Bioengineering (39)
- Business (38)
- Aerospace Engineering (36)
- Bioinformatics (34)
- Applied Statistics (33)
- Civil and Environmental Engineering (33)
- Institution
-
- San Jose State University (218)
- Western University (125)
- University of Massachusetts Amherst (118)
- California Polytechnic State University, San Luis Obispo (64)
- Air Force Institute of Technology (63)
-
- Old Dominion University (63)
- University of Arkansas, Fayetteville (54)
- University of Kentucky (52)
- City University of New York (CUNY) (49)
- University of South Florida (38)
- New Jersey Institute of Technology (33)
- University of Tennessee, Knoxville (33)
- University of Denver (30)
- Missouri University of Science and Technology (28)
- West Virginia University (26)
- University of Texas at El Paso (25)
- Virginia Commonwealth University (24)
- Michigan Technological University (23)
- University of New Orleans (23)
- University of Windsor (21)
- University of Nevada, Las Vegas (20)
- Washington University in St. Louis (19)
- Bard College (18)
- Clemson University (18)
- Dartmouth College (18)
- Louisiana State University (17)
- Portland State University (17)
- University of Central Florida (17)
- University of Louisville (17)
- East Tennessee State University (16)
- Keyword
-
- Machine learning (263)
- Machine Learning (210)
- Deep learning (142)
- Artificial intelligence (114)
- Deep Learning (109)
-
- Artificial Intelligence (93)
- Computer vision (55)
- Robotics (51)
- Computer Vision (42)
- Natural language processing (41)
- Reinforcement Learning (41)
- Neural networks (40)
- Neural Networks (38)
- Computer Science (35)
- Natural Language Processing (35)
- Classification (31)
- Reinforcement learning (30)
- Clustering (28)
- Daniel Felix Ritchie School of Engineering and Computer Science (28)
- AI (27)
- Neural network (26)
- Optimization (22)
- Neural Network (21)
- #antcenter (18)
- Applied sciences (17)
- NLP (17)
- Convolutional Neural Network (16)
- Image processing (15)
- Object detection (15)
- Convolutional neural network (14)
- Publication Year
- Publication
-
- Master's Projects (218)
- Doctoral Dissertations (161)
- Electronic Thesis and Dissertation Repository (125)
- Theses and Dissertations (125)
- Electronic Theses and Dissertations (112)
-
- Master's Theses (52)
- Dissertations (43)
- Dissertations, Theses, and Capstone Projects (41)
- Theses and Dissertations--Computer Science (40)
- USF Tampa Graduate Theses and Dissertations (38)
- Graduate Theses and Dissertations (31)
- Honors Theses (27)
- Masters Theses (27)
- Graduate Theses, Dissertations, and Problem Reports (26)
- Open Access Theses & Dissertations (25)
- Theses (25)
- University of New Orleans Theses and Dissertations (23)
- Computer Science and Computer Engineering Undergraduate Honors Theses (21)
- Dissertations, Master's Theses and Master's Reports (21)
- UNLV Theses, Dissertations, Professional Papers, and Capstones (20)
- Dissertations and Theses (19)
- McKelvey School of Engineering Theses & Dissertations (17)
- Electrical & Computer Engineering Theses & Dissertations (16)
- Computer Science Theses & Dissertations (13)
- Dissertations and Theses Collection (Open Access) (13)
- Walden Dissertations and Doctoral Studies (13)
- Legacy Theses & Dissertations (2009 - 2024) (12)
- CCE Theses and Dissertations (11)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (11)
- Dissertations, Theses, and Masters Projects (11)
- File Type
Articles 1 - 30 of 1786
Full-Text Articles in Artificial Intelligence and Robotics
Evaluation Of An End-To-End Radiotherapy Treatment Planning Pipeline For Prostate Cancer, Mohammad Daniel El Basha, Court Laurence, Carlos Eduardo Cardenas, Julianne Pollard-Larkin, Steven Frank, David T. Fuentes, Falk Poenisch, Zhiqian H. Yu
Evaluation Of An End-To-End Radiotherapy Treatment Planning Pipeline For Prostate Cancer, Mohammad Daniel El Basha, Court Laurence, Carlos Eduardo Cardenas, Julianne Pollard-Larkin, Steven Frank, David T. Fuentes, Falk Poenisch, Zhiqian H. Yu
Dissertations & Theses (Open Access)
Radiation treatment planning is a crucial and time-intensive process in radiation therapy. This planning involves carefully designing a treatment regimen tailored to a patient’s specific condition, including the type, location, and size of the tumor with reference to surrounding healthy tissues. For prostate cancer, this tumor may be either local, locally advanced with extracapsular involvement, or extend into the pelvic lymph node chain. Automating essential parts of this process would allow for the rapid development of effective treatment plans and better plan optimization to enhance tumor control for better outcomes.
The first objective of this work, to automate the treatment …
Comprehensive Question And Answer Generation With Llama 2, Matous Hybl
Comprehensive Question And Answer Generation With Llama 2, Matous Hybl
MS in Computer Science Theses
Since the introduction of transformers, large language models have proven capable in many natural language processing fields. However, existing systems still face challenges in generating high-quality extractive questions. Base models and public chatbots fall short if the question source or quantity are critical. Our contribution is a question and answer generator for generating comprehensive, extractive questions and answers. This approach includes fine-tuning a LLaMA 2 base model for answer extraction (AE) and question generation (QG). We evaluate the resulting system using common automated metrics and a manual evaluation. We find that our system is comparable to the latest research and …
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim
Masters Theses
Due to significant investment, research, and development efforts over the past decade, deep neural networks (DNNs) have achieved notable advancements in classification and regression domains. As a result, DNNs are considered valuable intellectual property for artificial intelligence providers. Prior work has demonstrated highly effective model extraction attacks which steal a DNN, dismantling the provider’s business model and paving the way for unethical or malicious activities, such as misuse of personal data, safety risks in critical systems, or spreading misinformation. This thesis explores the feasibility of model extraction attacks on mobile devices using aggregated runtime profiles as a side-channel to leak …
Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota
Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota
Doctoral Dissertations
Policy gradient methods are a class of reinforcement learning algorithms that optimize a parametric policy by maximizing an objective function that directly measures the performance of the policy. Despite being used in many high-profile applications of reinforcement learning, the conventional use of policy gradient methods in practice deviates from existing theory. This thesis presents a comprehensive mathematical analysis of policy gradient methods, uncovering misconceptions and suggesting novel solutions to improve their performance. We first demonstrate that the update rule used by most policy gradient methods does not correspond to the gradient of any objective function due to the way the …
Multi-Slam Systems For Fault-Tolerant Simultaneous Localization And Mapping, Samer Nashed
Multi-Slam Systems For Fault-Tolerant Simultaneous Localization And Mapping, Samer Nashed
Doctoral Dissertations
Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all' solutions are thus exceedingly …
Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer
Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer
Master's Theses
Digital Democracy is a CalMatters and California Polytechnic State University initia-
tive to promote transparency in state government by increasing access to the Califor-
nia legislature. While Digital Democracy is made up of many resources, one founda-
tional step of the project is obtaining accurate, timely transcripts of California Senate
and Assembly hearings. The information extracted from these transcripts provides
crucial data for subsequent steps in the pipeline. In the context of Digital Democracy,
upleveling is when humans verify, correct, and annotate the transcript results after
the legislative hearings have been automatically transcribed. The upleveling process
is done with the …
Future-Proofing The Past: Artificial Intelligence In The Restoration Of Andalusian Architectural Heritage: A Case Study Of The Alhambra Palace, Granada, Spain, Kholoud Bader Hasan Ghaith
Future-Proofing The Past: Artificial Intelligence In The Restoration Of Andalusian Architectural Heritage: A Case Study Of The Alhambra Palace, Granada, Spain, Kholoud Bader Hasan Ghaith
Theses
This thesis explains the contribution of artificial intelligence in heritage restoration as an icon of Andalusian architecture by using the Alhambra as an example. The task of sustaining heritage is increasing dramatically due to the accumulation of heritage assets and the need for modern and innovative operations to cope with preservation tasks. Therefore, this thesis reviews the role of artificial intelligence in improving the restoration operation to improve accuracy and efficiency. I applied the case study as a scientific methodology to explain this work to overcome scientific and subjective obstacles, such as scarce data and software integration while explaining the …
Attribution Robustness Of Neural Networks, Sunanda Gamage
Attribution Robustness Of Neural Networks, Sunanda Gamage
Electronic Thesis and Dissertation Repository
While deep neural networks have demonstrated excellent learning capabilities, explainability of model predictions remains a challenge due to their black box nature. Attributions or feature significance methods are tools for explaining model predictions, facilitating model debugging, human-machine collaborative decision making, and establishing trust and compliance in critical applications. Recent work has shown that attributions of neural networks can be distorted by imperceptible adversarial input perturbations, which makes attributions unreliable as an explainability method. This thesis addresses the research problem of attribution robustness of neural networks and introduces novel techniques that enable robust training at scale.
Firstly, a novel generic framework …
Deep Learning-Based Human Action Understanding In Videos, Elahe Vahdani
Deep Learning-Based Human Action Understanding In Videos, Elahe Vahdani
Dissertations, Theses, and Capstone Projects
The understanding of human actions in videos holds immense potential for technological advancement and societal betterment. This thesis explores fundamental aspects of this field, including action recognition in trimmed clips and action localization in untrimmed videos. Trimmed videos contain only one action instance, with moments before or after the action excluded from the video. However, the majority of videos captured in unconstrained environments, often referred to as untrimmed videos, are naturally unsegmented. Untrimmed videos are typically lengthy and may encompass multiple action instances, along with the moments preceding or following each action, as well as transitions between actions. In the …
A Target-Based And A Targetless Extrinsic Calibration Methods For Thermal Camera And 3d Lidar, Farhad Dalirani
A Target-Based And A Targetless Extrinsic Calibration Methods For Thermal Camera And 3d Lidar, Farhad Dalirani
Electronic Thesis and Dissertation Repository
This thesis introduces two novel methods for the extrinsic calibration of a thermal camera and a 3D LiDAR sensor, which are crucial for seamless data integration. The first method employs a distinctive calibration target, leveraging lines and plane equations correspondence in both modalities for a single pose, and incorporating more poses by matching the target's edges. It achieves reliable results, even with just one pose yielding 10.82% translation and 0.51-degree rotation errors. This outperforms alternative methods, which require eight pairs for similar results. The second method eliminates the need for a dedicated target. Instead, by collecting data during the sensor …
Poster, Performed: Understanding Public Opinions Of Authorship In Generative Artificial Intelligence Models Via Analogy, Wylie Z. Kasai
Poster, Performed: Understanding Public Opinions Of Authorship In Generative Artificial Intelligence Models Via Analogy, Wylie Z. Kasai
Dartmouth College Master’s Theses
Over the last decade, generative artificial intelligence models have advanced significantly and provided the public with several tools to create new works of art. However, the true authorship of these works has been debated due to their training on web-scraped data. Serving as an analogy to these larger models, Poster, Performed is an interactive artificial intelligence exhibition project that uses image assets submitted by the public to create poster compositions with custom image processing algorithms. During the course of a four-day exhibition, visitors were asked to identify the exhibition’s primary artist from five options: (1) participants who submitted image assets, …
Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim
CMC Senior Theses
Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …
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 …
Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso
Nonuniform Sampling-Based Breast Cancer Classification, Santiago Posso
Theses and Dissertations--Electrical and Computer Engineering
The emergence of deep learning models and their success in visual object recognition have fueled the medical imaging community's interest in integrating these algorithms to improve medical diagnosis. However, natural images, which have been the main focus of deep learning models and mammograms, exhibit fundamental differences. First, breast tissue abnormalities are often smaller than salient objects in natural images. Second, breast images have significantly higher resolutions but are generally heavily downsampled to fit these images to deep learning models. Models that handle high-resolution mammograms require many exams and complex architectures. Additionally, spatially resizing mammograms leads to losing discriminative details essential …
Using Pose Estimation Software To Predict Actions In Sabre Fencing, Micah Edwin Peters Ii
Using Pose Estimation Software To Predict Actions In Sabre Fencing, Micah Edwin Peters Ii
Honors College Theses
Fencing is a combat sport that uses three different swords: epee, foil, and sabre. Due to its fast-paced nature and employment of right of way, sabre fencing is often considered the most difficult of the three to learn. Computer vision and pose estimation software can be used to lower the barrier of entry to sabre fencing by identifying the different actions in sabre fencing. This project focuses on using open-source software to design a program that can identify the sabre parries as well as the main sabre movements. This program could be used to help newer fencers and spectators better …
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. …
Smart Applications And Resource Management In Internet Of Things, Zeinab Akhavan
Smart Applications And Resource Management In Internet Of Things, Zeinab Akhavan
Computer Science ETDs
Internet of Things (IoT) technologies are currently the principal solutions driving smart cities. These new technologies such as Cyber Physical Systems, 5G and data analytic have emerged to address various cities' infrastructure issues ranging from transportation and energy management to healthcare systems. An IoT setting primarily consists of a wide range of users and devices as a massive network interacting with different layers of the city infrastructure resulting in generating sheer volume of data to enable smart city services. The goal of smart city services is to create value for the entire ecosystem, whether this is health, education, transportation, energy, …
Context-Driven Behavior: Improved Contextual Reasoning For Context-Aware Agents, Christian L. Wilson
Context-Driven Behavior: Improved Contextual Reasoning For Context-Aware Agents, Christian L. Wilson
Electronic Theses and Dissertations
Over the last three decades, a considerable amount of research has been dedicated to improving an artificial agent's ability to recognize and deal effectively with context. In this paper, I discuss a framework for a novel form of contextual reasoning. Unlike existing contextual reasoning frameworks, which allow an agent to apply its contextual knowledge after it is operating in an instance of a known context, the model I discuss allows an agent to reason about context proactively. With a proactive model, an agent forecasts the future contexts it will encounter, then takes steps to ensure its behaviors are appropriate for …
Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang
Learning Mortality Risk For Covid-19 Using Machine Learning And Statistical Methods, Shaoshi Zhang
Electronic Thesis and Dissertation Repository
This research investigates the mortality risk of COVID-19 patients across different variant waves, using the data from Centers for Disease Control and Prevention (CDC) websites. By analyzing the available data, including patient medical records, vaccination rates, and hospital capacities, we aim to discern patterns and factors associated with COVID-19-related deaths.
To explore features linked to COVID-19 mortality, we employ different techniques such as Filter, Wrapper, and Embedded methods for feature selection. Furthermore, we apply various machine learning methods, including support vector machines, decision trees, random forests, logistic regression, K-nearest neighbours, na¨ıve Bayes methods, and artificial neural networks, to uncover underlying …
Probing And Enhancing The Reliance Of Transformer Models On Poetic Information, Almas Abdibayev
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 …
Cm-Ii Meditation As An Intervention To Reduce Stress And Improve Attention: A Study Of Ml Detection, Spectral Analysis, And Hrv Metrics, Sreekanth Gopi
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 …
Designing An Artificial Immune Inspired Intrusion Detection System, William Hosier Anderson
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 …
Study Of Augmentations On Historical Manuscripts Using Trocr, Erez Meoded
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 16th-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
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
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 …
Overcoming Foreign Language Anxiety In An Emotionally Intelligent Tutoring System, Daneih Ismail
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 …
A Bridge Between Graph Neural Networks And Transformers: Positional Encodings As Node Embeddings, Bright Kwaku Manu
A Bridge Between Graph Neural Networks And Transformers: Positional Encodings As Node Embeddings, Bright Kwaku Manu
Electronic Theses and Dissertations
Graph Neural Networks and Transformers are very powerful frameworks for learning machine learning tasks. While they were evolved separately in diverse fields, current research has revealed some similarities and links between them. This work focuses on bridging the gap between GNNs and Transformers by offering a uniform framework that highlights their similarities and distinctions. We perform positional encodings and identify key properties that make the positional encodings node embeddings. We found that the properties of expressiveness, efficiency and interpretability were achieved in the process. We saw that it is possible to use positional encodings as node embeddings, which can be …
Generalized Differentiable Neural Architecture Search With Performance And Stability Improvements, Emily J. Herron
Generalized Differentiable Neural Architecture Search With Performance And Stability Improvements, Emily J. Herron
Doctoral Dissertations
This work introduces improvements to the stability and generalizability of Cyclic DARTS (CDARTS). CDARTS is a Differentiable Architecture Search (DARTS)-based approach to neural architecture search (NAS) that uses a cyclic feedback mechanism to train search and evaluation networks concurrently, thereby optimizing the search process by enforcing that the networks produce similar outputs. However, the dissimilarity between the loss functions used by the evaluation networks during the search and retraining phases results in a search-phase evaluation network, a sub-optimal proxy for the final evaluation network utilized during retraining. ICDARTS, a revised algorithm that reformulates the search phase loss functions to ensure …
Leveraging Artificial Intelligence For Team Cognition In Human-Ai Teams, Beau Schelble
Leveraging Artificial Intelligence For Team Cognition In Human-Ai Teams, Beau Schelble
All Dissertations
Advances in artificial intelligence (AI) technologies have enabled AI to be applied across a wide variety of new fields like cryptography, art, and data analysis. Several of these fields are social in nature, including decision-making and teaming, which introduces a new set of challenges for AI research. While each of these fields has its unique challenges, the area of human-AI teaming is beset with many that center around the expectations and abilities of AI teammates. One such challenge is understanding team cognition in these human-AI teams and AI teammates' ability to contribute towards, support, and encourage it. Team cognition is …
Decoding Usage And Adoption Behavior Of The Low-Carbon Transportation Market: An Ai-Driven Exploration, Vuban Chowdhury
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 …