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Graph Neural Network Guided By Feature Selection And Centrality Measures For Node Classification On Homophilic And Heterophily Graphs, Asmaa M. Mahmoud, Heba F. Eid, Abeer S. Desuky, Hoda A. Ali 2024 Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt.

Graph Neural Network Guided By Feature Selection And Centrality Measures For Node Classification On Homophilic And Heterophily Graphs, Asmaa M. Mahmoud, Heba F. Eid, Abeer S. Desuky, Hoda A. Ali

Al-Azhar Bulletin of Science

One of the most recent developments in the fields of deep learning and machine learning is Graph Neural Networks (GNNs). GNNs core task is the feature aggregation stage, which is carried out over the node's neighbours without taking into account whether the features are relevant or not. Additionally, the majority of these existing node representation techniques only consider the network's topology structure while completely ignoring the centrality information. In this paper, a new technique for explaining graph features depending on four different feature selection approaches and centrality measures in order to identify the important nodes and relevant node features is …


Environmental, Social, And Governance (Esg) And Artificial Intelligence In Finance: State-Of-The-Art And Research Takeaways, Trstan LIM 2024 Singapore Management University

Environmental, Social, And Governance (Esg) And Artificial Intelligence In Finance: State-Of-The-Art And Research Takeaways, Trstan Lim

Research Collection School Of Computing and Information Systems

The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical …


Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy 2024 Missouri University of Science and Technology

Convolutional Spiking Neural Networks For Intent Detection Based On Anticipatory Brain Potentials Using Electroencephalogram, Nathan Lutes, V. Sriram Siddhardh Nadendla, K. Krishnamurthy

Computer Science Faculty Research & Creative Works

Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of convolutional networks with the computational efficiency of SNNs has been introduced. This paper studies the feasibility of using a convolutional spiking neural network (CSNN) to detect anticipatory slow cortical potentials (SCPs) related to braking intention in human participants using an electroencephalogram (EEG). Data was collected during an experiment wherein participants operated a remote-controlled vehicle on a testbed …


Auditory Vigilance Decrement In Drivers Of A Partially Automated Vehicle: A Pilot Study Using A High-Fidelity Driving Simulator, Luca Brooks, Jeffrey Glassman, Yusuke Yamani 2024 Old Dominion University

Auditory Vigilance Decrement In Drivers Of A Partially Automated Vehicle: A Pilot Study Using A High-Fidelity Driving Simulator, Luca Brooks, Jeffrey Glassman, Yusuke Yamani

Undergraduate Research Symposium

Vigilance decrement is the decline in the ability to monitor and detect behaviorally important signals over time, a phenomenon that can arise even after 30 minutes of watch (Mackworth, 1948). Recently, McCarley & Yamani (2021) found bias shifts, sensitivity losses, and attentional lapses contribute to vigilance decrement, but when each effect is isolated, there was little evidence that sensitivity loss affected vigilance decrement. With the introduction of partially autonomous vehicles, vigilance decrement may be problematic for drivers who must monitor the autonomous system for failures and takeover requests. Thus, this pilot study aims to extend McCarley and Yamani (2021) and …


Improving Educational Delivery And Content In Juvenile Detention Centers, Yomna Elmousalami 2024 Old Dominion University

Improving Educational Delivery And Content In Juvenile Detention Centers, Yomna Elmousalami

Undergraduate Research Symposium

Students in juvenile detention centers have the greatest need to receive improvements in educational delivery and content; however, they are one of the “truly disadvantaged” populations in terms of receiving those improvements. This work presents a qualitative data analysis based on a focus group meeting with stakeholders at a local Juvenile Detention Center. The current educational system in juvenile detention centers is based on paper worksheets, single-room style teaching methods, outdated technology, and a shortage of textbooks and teachers. In addition, detained students typically have behavioral challenges that are deemed "undesired" in society. As a result, many students miss classes …


Extracting Dnn Architectures Via Runtime Profiling On Mobile Gpus, Dong Hyub Kim 2024 University of Massachusetts Amherst

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 …


An Analysis And Ontology Of Teaching Methods In Cybersecurity Education, Sarah Buckley 2024 Louisiana State University

An Analysis And Ontology Of Teaching Methods In Cybersecurity Education, Sarah Buckley

LSU Master's Theses

The growing cybersecurity workforce gap underscores the urgent need to address deficiencies in cybersecurity education: the current education system is not producing competent cybersecurity professionals, and current efforts are not informing the non-technical general public of basic cybersecurity practices. We argue that this gap is compounded by a fundamental disconnect between cybersecurity education literature and established education theory. Our research addresses this issue by examining the alignment of cybersecurity education literature concerning educational methods and tools with education literature.

In our research, we endeavor to bridge this gap by critically analyzing the alignment of cybersecurity education literature with education theory. …


The Social Pot: A Social Media Application, Reid Long 2024 Bridgewater College

The Social Pot: A Social Media Application, Reid Long

ASPIRE 2024

The Social Pot is a web application that allows a user to post to Instagram and X simultaneously from one place. The user creates a Social Pot Account and from there can set their Instagram username and password within the home page. Once the user attempts to post, it will redirect them to login to X which once successful will make the tweet. Used the API 'instagram-private-api'. User needed to give access to my X Project which in turn gave an Auth token (via X redirect URL). The auth token was then sent to my endpoint in order to get …


Sensor Analytics For Subsea Pipeline And Cable Inspection: A Review, Connor R. Vincent 2024 Louisiana State University and Agricultural and Mechanical College

Sensor Analytics For Subsea Pipeline And Cable Inspection: A Review, Connor R. Vincent

LSU Master's Theses

Submarine pipelines and cables are vital for transmitting physical and digital resources across bodies of water, necessitating regular inspection to assess maintenance needs. The safety of subsea pipelines and cables is paramount for sustaining industries such as telecommunications, power transmission, water supply, waste management, and oil and gas. Incidents like those involving the Nord Stream subsea pipeline and the SEA-ME-WE 4 subsea communications cable exemplify the severe economic and environmental consequences of damage to these critical infrastructures. Existing inspection methods often fail to meet accuracy requirements, emphasizing the need for advancements in inspection technologies. This comprehensive survey covers the sensors …


An Efficient Privacy-Preserving Framework For Video Analytics, Tian Zhou 2024 University of Massachusetts Amherst

An Efficient Privacy-Preserving Framework For Video Analytics, Tian Zhou

Doctoral Dissertations

With the proliferation of video content from surveillance cameras, social media, and live streaming services, the need for efficient video analytics has grown immensely. In recent years, machine learning based computer vision algorithms have shown great success in various video analytic tasks. Specifically, neural network models have dominated in visual tasks such as image and video classification, object recognition, object detection, and object tracking. However, compared with classic computer vision algorithms, machine learning based methods are usually much more compute-intensive. Powerful servers are required by many state-of-the-art machine learning models. With the development of cloud computing infrastructures, people are able …


Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan 2024 University of Massachusetts Amherst

Automated Identification And Mapping Of Interesting Mineral Spectra In Crism Images, Arun M. Saranathan

Doctoral Dissertations

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) has proven to be an invaluable tool for the mineralogical analysis of the Martian surface. It has been crucial in identifying and mapping the spatial extents of various minerals. Primarily, the identification and mapping of these mineral spectral-shapes have been performed manually. Given the size of the CRISM image dataset, manual analysis of the full dataset would be arduous/infeasible. This dissertation attempts to address this issue by describing an (machine learning based) automated processing pipeline for CRISM data that can be used to identify and map the unique mineral signatures present in …


Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia 2024 University of Massachusetts Amherst

Data To Science With Ai And Human-In-The-Loop, Gustavo Perez Sarabia

Doctoral Dissertations

AI has the potential to accelerate scientific discovery by enabling scientists to analyze vast datasets more efficiently than traditional methods. For example, this thesis considers the detection of star clusters in high-resolution images of galaxies taken from space telescopes, as well as studying bird migration from RADAR images. In these applications, the goal is to make measurements to answer scientific questions, such as how the star formation rate is affected by mass, or how the phenology of bird migration is influenced by climate change. However, current computer vision systems are far from perfect for conducting these measurements directly. They may …


Policy Gradient Methods: Analysis, Misconceptions, And Improvements, Christopher P. Nota 2024 University of Massachusetts Amherst

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 2024 University of Massachusetts Amherst

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 …


Preserving Linguistic Diversity In The Digital Age: A Scalable Model For Cultural Heritage Continuity, James Hutson, Pace Ellsworth, Matt Ellsworth 2024 Lindenwood University

Preserving Linguistic Diversity In The Digital Age: A Scalable Model For Cultural Heritage Continuity, James Hutson, Pace Ellsworth, Matt Ellsworth

Faculty Scholarship

In the face of the rapid erosion of both tangible and intangible cultural heritage globally, the urgency for effective, wide-ranging preservation methods has never been greater. Traditional approaches in cultural preservation often focus narrowly on specific niches, overlooking the broader cultural tapestry, particularly the preservation of everyday cultural elements. This article addresses this critical gap by advocating for a comprehensive, scalable model for cultural preservation that leverages machine learning and big data analytics. This model aims to document and archive a diverse range of cultural artifacts, encompassing both extraordinary and mundane aspects of heritage. A central issue highlighted in the …


Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch 2024 Missouri University of Science and Technology

Lrs: Enhancing Adversarial Transferability Through Lipschitz Regularized Surrogate, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Transferability of Adversarial Examples is of Central Importance to Transfer-Based Black-Box Adversarial Attacks. Previous Works for Generating Transferable Adversarial Examples Focus on Attacking Given Pretrained Surrogate Models While the Connections between Surrogate Models and Adversarial Trasferability Have Been overlooked. in This Paper, We Propose Lipschitz Regularized Surrogate (LRS) for Transfer-Based Black-Box Attacks, a Novel Approach that Transforms Surrogate Models towards Favorable Adversarial Transferability. using Such Transformed Surrogate Models, Any Existing Transfer-Based Black-Box Attack Can Run Without Any Change, Yet Achieving Much Better Performance. Specifically, We Impose Lipschitz Regularization on the Loss Landscape of Surrogate Models to Enable a Smoother …


Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch 2024 Missouri University of Science and Technology

Cr-Sam: Curvature Regularized Sharpness-Aware Minimization, Tao Wu, Tony Tie Luo, Donald C. Wunsch

Computer Science Faculty Research & Creative Works

The Capacity to Generalize to Future Unseen Data Stands as One of the Utmost Crucial Attributes of Deep Neural Networks. Sharpness-Aware Minimization (SAM) Aims to Enhance the Generalizability by Minimizing Worst-Case Loss using One-Step Gradient Ascent as an Approximation. However, as Training Progresses, the Non-Linearity of the Loss Landscape Increases, Rendering One-Step Gradient Ascent Less Effective. on the Other Hand, Multi-Step Gradient Ascent Will Incur Higher Training Cost. in This Paper, We Introduce a Normalized Hessian Trace to Accurately Measure the Curvature of Loss Landscape on Both Training and Test Sets. in Particular, to Counter Excessive Non-Linearity of Loss Landscape, …


Deep Learning Can Be Used To Classify And Segment Plant Cell Types In Xylem Tissue, Reem Al Dabagh, Benjamin Shin, Sean Wu, Fabien Scalzo, Helen Holmlund, Jessica Lee, Chris Ghim, Samuel Fitzgerald, Marinna Grijalva 2024 Pepperdine University

Deep Learning Can Be Used To Classify And Segment Plant Cell Types In Xylem Tissue, Reem Al Dabagh, Benjamin Shin, Sean Wu, Fabien Scalzo, Helen Holmlund, Jessica Lee, Chris Ghim, Samuel Fitzgerald, Marinna Grijalva

Seaver College Research And Scholarly Achievement Symposium

Studies of plant anatomical traits are essential for understanding plant physiological adaptations to stressful environments. For example, shrubs in the chaparral ecosystem of southern California have adapted various xylem anatomical traits that help them survive drought and freezing. Previous studies have shown that xylem conduits with a narrow diameter allows certain chaparral shrub species to survive temperatures as low as -12 C. Other studies have shown that increased cell wall thickness of fibers surrounding xylem vessels improves resistance to water stress-induced embolism formation. Historically, these studies on xylem anatomical traits have relied on hand measurements of cells in light micrographs, …


Mechanistic Investigation Of C—C Bond Activation Of Phosphaalkynes With Pt(0) Complexes, Roberto M. Escobar, Abdurrahman C. Ateşin, Christian Müller, William D. Jones, Tülay Ateşin 2024 The University of Texas Rio Grande Valley

Mechanistic Investigation Of C—C Bond Activation Of Phosphaalkynes With Pt(0) Complexes, Roberto M. Escobar, Abdurrahman C. Ateşin, Christian Müller, William D. Jones, Tülay Ateşin

Research Symposium

Carbon–carbon (C–C) bond activation has gained increased attention as a direct method for the synthesis of pharmaceuticals. Due to the thermodynamic stability and kinetic inaccessibility of the C–C bonds, however, activation of C–C bonds by homogeneous transition-metal catalysts under mild homogeneous conditions is still a challenge. Most of the systems in which the activation occurs either have aromatization or relief of ring strain as the primary driving force. The activation of unstrained C–C bonds of phosphaalkynes does not have this advantage. This study employs Density Functional Theory (DFT) calculations to elucidate Pt(0)-mediated C–CP bond activation mechanisms in phosphaalkynes. Investigating the …


Artificial Intelligence Usage And Data Privacy Discoveries Within Mhealth, Jennifer Schulte 2024 Dakota State University

Artificial Intelligence Usage And Data Privacy Discoveries Within Mhealth, Jennifer Schulte

Faculty Research & Publications

Advancements in artificial intelligence continue to impact nearly every aspect of human life by providing integration options that aim to supplement or improve current processes. One industry that continues to benefit from artificial intelligence integration is healthcare. For years now, elements of artificial intelligence have been used to assist in clinical decision making, helping to identify potential health risks at earlier stages, and supplementing precision medicine. An area of healthcare that specifically looks at wearable devices, sensors, phone applications, and other such devices is mobile health (mHealth). These devices are used to aid in health data collection and delivery. This …


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