Artificial Intelligence and Robotics Commons^™

Deep Learning Approach For Accurate Segmentation Of Oil Spills In Marine Systems, Mohamed Elsheref

LSU New Orleans Theses and Dissertations

Oil spills present critical environmental hazards, threatening marine ecosystems and necessitating fast, accurate detection for effective mitigation. Synthetic Aperture Radar (SAR) imagery has been instrumental in detecting oil spills, but manual interpretation is often inefficient and prone to errors. This study addresses the limitations of manual methods by proposing a deep learning approach for automated oil spill detection and segmentation.

Utilizing a novel transfer learning-based semantic segmentation model, this research focuses on detecting oil slicks on the sea surface with higher accuracy and efficiency. The model leverages pre-trained networks and incorporates U-Net variants, including UNet++ and MultiResUNet, to optimize spatial …

Enhancing Password Security And Memorability Using Machine Learning And Linguistic Patterns, Jared Wise

LSU New Orleans Theses and Dissertations

In the digital age, text-based passwords remain a primary method for securing online accounts. Yet, users frequently face a dilemma between creating passwords that are easy to remember and sufficiently secure against cyberattacks. This research introduces an approach to password generation that bridges this gap by utilizing linguistic patterns, particularly song lyrics, to develop highly secure and naturally memorable passwords. Using large lyric datasets gained from web scrapes from popular song lyric websites (AZ Lyrics, Genius), features are extracted from a corpus of over 5 million lyrics using sentence structure and natural language processing in a novel way. In using …

Detecting Anomalies In Dynamic Attributed Graphs: An Unsupervised Learning Approach, Austin Hamilton

Electronic Theses and Dissertations

Dynamic attributed graphs, which evolve over time and hold node-specific attributes, are essential in fields like social network analysis, where anomalous node detection is a growing area. Vehicular social networks (VSNs), a subset of these graphs, are ad hoc networks in which vehicles exchange data with one another and with infrastructure. In this dynamic context, identifying anomalous nodes is challenging but crucial for maintaining trust within the network. This work presents an unsupervised deep learning approach for anomalous node detection in VSNs. This model achieved an accuracy of 71% while detecting synthetic anomalies in a simulated network based on real-world …

Basic Safety Message Generation Through A Video-Based Analytics For Potential Safety Application, Abyad Enan

All Theses

With the advancement of modern artificial intelligence techniques, computer vision can play a vital role in enhancing roadway safety by reducing the risk of imminent collisions. To do so, a vision-based safety application is required, where a roadside camera can monitor the roadway traffic and predict potential risks of crashes in real-time. If any risky situation or behavior is observed that may lead to a crash, then a safety application can send warnings to the vehicles at risk. For vision-based safety applications on a roadway section, it is important to accurately monitor each vehicle’s location, speed, acceleration, heading direction, etc. …

Regulating Robo-Advisors In An Age Of Generative Artificial Intelligence, Daniel Schwarcz, Tom Baker

Law & Economics Working Papers

New generative Artificial Intelligence (AI) tools can increasingly engage in personalized, sustained and natural conversations with users. This technology has the capacity to reshape the financial services industry, making customized expert financial advice broadly available to consumers. However, AI’s ability to convincingly mimic human financial advisors also creates significant risks of large-scale financial misconduct. Which of these possibilities becomes reality will depend largely on the legal and regulatory rules governing “robo-advisors” that supply fully automated financial advice to consumers. This Article consequently critically examines this evolving regulatory landscape, arguing that current U.S. rules fail to adequately limit the risk that …

Mechanically Cost-Effective Approach For Bipedal Walking In Robots Using Instantaneous Collision Angle, Smit R. Patel

UNLV Theses, Dissertations, Professional Papers, and Capstones

Humans, as bipedal locomotors, are effective at reducing the mechanical cost of transport (CoTmech) by adopting movement strategies and gaits that minimize energy expenditure for a given distance. By using different gaits at different speeds, leveraging their long spring-like tendons and muscle elasticity which store and release energy during movement, humans reduce the mechanical effort required for locomotion. Current locomotion solutions offered in bipedal robots, based on legacy walking and running gait models, are not great at energy efficiency unless walking at very low speeds. Additionally, the control system of robots, designed to ensure stability and adaptability, requires substantial resources, …

Enhancing Assessment And Feedback In Game Design Programs: Leveraging Generative Ai For Efficient And Meaningful Evaluation, James Hutson, Ben Fulcher, Jay Ratican

Faculty Scholarship

The integration of generative AI tools in game design education offers promising ways to streamline the grading, assessment, and feedback processes that are typically labor-intensive. In game design programs, faculty often deal with varied file formats, including 3D models, executable prototypes, videos, and complex game design documents. Traditional methods of assessment and feedback, primarily text-based, struggle to provide timely and actionable insights for students. Furthermore, only a small percentage of top students consistently review and apply feedback, leading to inefficiencies. This article explores how generative AI tools can augment these processes by automating aspects of grading, generating more personalized and …

Universal Shape Replication Via Self-Assembly With Signal-Passing Tiles, Andrew Alseth, Daniel Hader, Matthew J. Patitz

Computer Science and Computer Engineering Faculty Publications and Presentations

In this paper, we investigate shape-assembling power of a tile-based model of self-assembly called the Signal-Passing Tile Assembly Model (STAM). In this model, the glues that bind tiles together can be turned on and off by the binding actions of other glues via “signals”. Specifically, the problem we investigate is “shape replication” wherein, given a set of input assemblies of arbitrary shape, a system must construct an arbitrary number of assemblies with the same shapes and, with the exception of size-bounded junk assemblies that result from the process, no others. We provide the first fully universal shape replication result, namely …

Leveraging Ai Tools In University Writing Instruction: Enhancing Student Success While Upholding Academic Integrity, Daisuke Akiba, Rebecca Garte

Publications and Research

The emergence of AI-powered Large Language Models (LLMs), such as ChatGPT and Google Gemini, presents both opportunities and challenges for higher education, particularly regarding academic integrity in writing instruction. This exploratory study examines a novel pedagogical approach that integrates LLMs as required feedback tools in a university-level psychology writing assignment. The exclusive online approach emphasizes improvement through revision, requiring students to obtain AI-generated feedback on ungraded initial drafts based on an instructor-provided rubric, with final assessment focused on the quality of subsequent revisions. Analysis of survey data from 39 undergraduate students, incorporating both quantitative measures and qualitative responses, revealed several …

Flexfl: Heterogeneous Federated Learning Via Apoz-Guided Flexible Pruning In Uncertain Scenarios, Zekai Chen, Chentao Jia, Ming Hu, Xiaofei Xie, Anran Li, Mingsong Chen

Research Collection School Of Computing and Information Systems

Along with the increasing popularity of Deep Learning (DL) techniques, more and more Artificial Intelligence of Things (AIoT) systems are adopting federated learning (FL) to enable privacy-aware collaborative learning among AIoT devices. However, due to the inherent data and device heterogeneity issues, existing FL-based AIoT systems suffer from the model selection problem. Although various heterogeneous FL methods have been investigated to enable collaborative training among heterogeneous models, there is still a lack of i) wise heterogeneous model generation methods for devices, ii) consideration of uncertain factors, and iii) performance guarantee for large models, thus strongly limiting the overall FL performance. …

Converting Vocal Performances Into Sheet Music Leveraging Large Language Models, Jinjing Jiang, Nicole Teo, Haibo Pen, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Advanced natural language processing (NLP) models are increasingly applied in music composition and performance, particularly for generating vocal melodies and simulating singing voices. While NLP techniques have been effective in analyzing vocal performance data to assess quality and style, the automatic transcription of vocal performances into sheet music remains a significant challenge. Manual transcription tools often fall short due to the intricate dynamics of vocal expression. This study tackles the automation of vocal performance transcription into sheet music using innovative techniques, including large language models (LLMs). We propose a method to translate vocal audio input into display-ready sheet music effectively. …

Real-Time Network Simulations For Ml/Dl Ddos Detection Using Docker, Luis D. Garcia

Master's Theses

As the integration of artificial intelligence (AI) within cybersecurity continues to

grow, machine learning (ML) and deep learning (DL) models are increasingly used to

detect cyber attacks. However, these models are rarely evaluated in real-time attack

scenarios to see how subtle changes from the real networking environment can affect

their predictions. To address this issue, we propose a scalable, platform-independent

Docker testbed specifically designed for simulating real-time Distributed Denial of

Service (DDoS) attack scenarios that allows researchers to deploy and evaluate their

pre-trained, ML and DL detection models. Our framework is simple to configure

and can run across Intel and …

Unsupervised Moving Object Segmentation With Atmospheric Turbulence, Dehao Qin

All Theses

Moving object segmentation in the presence of atmospheric turbulence is a highly challenging task due to the irregular and time-varying distortions induced by the atmospheric turbulence. This thesis presents an unsupervised approach for segmenting moving objects in videos affected by such atmospheric turbulence. The proposed methodology is grounded in a detect-then-grow scheme: the algorithm begins by identifying a small set of moving object pixels (seed points) with high confidence and progressively expanding a foreground mask from these seed points to segment all moving objects. The proposed approach capitalizes on rigid geometric consistency across video frames to disentangle different types of …

Patterns Of Interactions In Human-Machine Teams, Kazuhiko Momose

Theses and Dissertations

Increasingly capable machines, including Artificial Intelligence (AI) agents are playing a more important role in a wide range of applications, including human daily activities and safety-critical systems. They can benefit even more when humans and such machines agents work together as a team by leveraging each other's strengths and complementing each other to enhance overall performance. To design high-performing teams, it is critical to analyze the team dynamics and understand how humans and machines interact with each other. Collaboration, Coordination, and Cooperation (3Cs) are terms typically used to describe the behavior of teams. However, these terms tend to be used …

Exploring Post-Covid-19 Health Effects And Features With Advanced Machine Learning Techniques, Muhammad N. Islam, Md S. Islam, Nahid H. Shourav, Iftiaqur Rahman, Faiz A. Faisal, Md M. Islam, Iqbal H. Sarker

Research outputs 2022 to 2026

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state. To achieve these objectives, we have identified the post-COVID-19 health factors and …

Llm Potentiality And Awareness: A Position Paper From The Perspective Of Trustworthy And Responsible Ai Modeling, Iqbal H. Sarker

Research outputs 2022 to 2026

Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, …

Phypo: Priority-Based Hybrid Task Partitioning And Offloading In Mobile Computing Using Automated Machine Learning, Shehr Bano, Ghulam Abbas, Muhammad Bilal, Ziaul Haq Abbas, Zaiwar Ali, Muhammad Waqas

Research outputs 2022 to 2026

With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, it has limited resources and cannot process bigger tasks. To resolve these issues, a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme is introduced in this paper, which prioritizes the tasks with high time sensitivity and offloads them intelligently. It also calculates the optimal number of partitions a task can be divided into. The utility of …

Neural Network Architecture Search Enabled Wide-Deep Learning (Nas-Wd) Integrated Hyperspectral Imaging Understanding For Woody Breast In Poultry Processing, Chaitanya Kumar Reddy Pallerla

Graduate Theses and Dissertations

The development and implementation of a Wide & Deep (WD) learning model tailored for classification and regression tasks utilizing spectral data provides a robust solution to evaluate woody breast (WB) conditions in poultry fillets. This process begins with thorough data preprocessing, which includes loading spectral and classification datasets, imputing missing values with medians, and splitting the data into training and testing sets to ensure rigorous model evaluation. The WD model architecture integrates wide linear models and deep neural networks to harness the strengths of both approaches. The wide component excels at memorizing sparse feature interactions, while the deep component captures …

Enhancing Low-Resource Language Performance In Multilingual Large Language Models, Mingqi Li

All Dissertations

The large language models play an important role in many natural language tasks. However, training these models requires large amounts of data, which is not available for many languages. A noticeable performance gap exists between English and other languages, with low-resource languages showcasing this gap prominently. Therefore, it becomes imperative to improve large language models for low-resource languages. To address these challenges, we developed knowledge distillation and strategic prompt-learning, and attention alignment methods to improve the representation capabilities of large language models for low-resource language, and then enhanced their performance in downstream tasks.

In our first study, we developed a …

Addressing Ethical Issues In Healthcare Artificial Intelligence Using A Lifecycle-Informed Process, Xiaoqian Jiang, Camille Nebeker, Laurie Novak, Kirk Roberts, Martin Were, Zhijun Yin, Vardit Ravitsky, Joseph Coco, Rachele Hendricks-Sturrup, Ishan Williams, Ellen W Clayton, Bradley A Malin, Bridge2ai Ethics And Trustworthy Ai Working Group

Faculty, Staff and Student Publications

OBJECTIVES: Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

MATERIALS AND METHODS: We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle …