Artificial Intelligence and Robotics Commons^™

Sparse Representer Theorems For Learning In Reproducing Kernel Banach Spaces, Rui Wang, Yuesheng Xu, Mingsong Yan

Mathematics & Statistics Faculty Publications

Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions. We consider two typical learning models in an RKBS: the minimum norm interpolation (MNI) problem and the regularization problem. We first establish an explicit representer theorem for solutions of these problems, which represents the extreme points of the solution set by a linear combination of the extreme points of the subdifferential set, of the norm function, …

Generative Artificial Intelligence: Basic Terminology And Concepts, Kincaid Brown

Law Librarian Scholarship

Generative artificial intelligence (GenAI) has been a hard topic to avoid in the media for more than a year. But what do all of the terms mean and what are areas of concern with GenAI tools?

This column aims to provide a baseline explanation of terminology and concepts that are frequently in the media.

Predicting The Need For Cardiovascular Surgery: A Comparative Study Of Machine Learning Models, Arman Ghavidel, Pilar Pazos, Rolando Del Aguila Suarez, Alireza Atashi

Engineering Management & Systems Engineering Faculty Publications

This research examines the efficacy of ensemble Machine Learning (ML) models, mainly focusing on Deep Neural Networks (DNNs), in predicting the need for cardiovascular surgery, a critical aspect of clinical decision-making. It addresses key challenges such as class imbalance, which is pivotal in healthcare settings. The research involved a comprehensive comparison and evaluation of the performance of previously published ML methods against a new Deep Learning (DL) model. This comparison utilized a dataset encompassing 50,000 patient records from a large hospital between 2015-2022. The study proposes enhancing the efficacy of these models through feature selection and hyperparameter optimization, employing techniques …

Improving Neuropathological Analysis With Aβgan: Addressing Morphology Imbalance For Efficient Alzheimer's Disease Diagnosis, Sujatha Alla, Prasanthi Chidipudi, Nagesh Bheesetty, Vedvikash Reddy Velur, Joshit Mohanty, Puneeth Bheesetty, Marisha Jmukhadze, Narendra Lakshmana Gowda, Sai Gireesh Komaragiri

Engineering Management & Systems Engineering Faculty Publications

Histopathologists are experiencing a digital revolution in their field thanks to the digitization of Whole Slide Images (WSIs), which are microscope slides of tissue that can measure gigapixels in size. With so much high resolution data at their disposal, computer vision techniques can now be used to automate laboratory processes, create visual standards, and increase analysis throughput, all of which reduce the workload of pathologists [1]. The "gold" standard in neuropathology, particularly for Alzheimer's Disease- is pathological diagnosis made by looking at White Matter Inclusions (WSIs) in brain tissue. Semi-quantitative scoring in accordance with the standards established by the Consortium …

Beyond Binary: Revealing Variations In Islamophobic Content With Hierarchical Multi-Class Classification, Esraa Aldreabi, Khawlah M. Harahsheh, Mukul Dev Chhangani, Chung-Hao Chen, Jeremy Blackburn

Electrical & Computer Engineering Faculty Publications

In the digital age, the rise of Islamophobia-marked by an irrational fear or discrimination against Islam and Muslims-has emerged as a pressing issue, especially on social media platforms. In this paper we employs a multi-class classification system, moving beyond traditional binary models. We categorize Islamophobic content into three main classes and various subclasses, covering a range from subtle biases to explicit incitement. Comparative analysis of data from Reddit and Twitter illuminates the distinct prevalence and types of Islamophobic content specific to each platform. This paper deepens our understanding of digital Islamophobia and provides insights for crafting targeted online counter strategies. …

Predictive Modeling Of Healthcare Traffic Using Machine Learning: A Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md. Rafid Hassan, Nondon Lal Dey, Md. Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Effective healthcare traffic management is critical for ensuring prompt medical services, particularly in emergencies where delays can have life-threatening consequences. This study conducts a comparative analysis of three popular machine learning models—Linear Regression, Decision Trees, and Random Forests—for predicting healthcare-related traffic volumes. Utilizing a comprehensive dataset from a metropolitan interstate traffic system, the models were evaluated based on key performance metrics, including Mean Squared Error (MSE), R² Score, and execution time. The findings demonstrate that the Random Forest model outperforms the others, offering superior predictive accuracy and efficiency. These insights are valuable for optimizing traffic management in healthcare, ultimately contributing …

Dilf: Differentiable Rendering-Based Multi-View Image-Language Fusion For Zero-Shot 3d Shape Understanding, Xin Ning, Zaiyang Yu, Lusi Li, Weijun Li, Prayag Tiwari

Computer Science Faculty Publications

Zero-shot 3D shape understanding aims to recognize “unseen” 3D categories that are not present in training data. Recently, Contrastive Language–Image Pre-training (CLIP) has shown promising open-world performance in zero-shot 3D shape understanding tasks by information fusion among language and 3D modality. It first renders 3D objects into multiple 2D image views and then learns to understand the semantic relationships between the textual descriptions and images, enabling the model to generalize to new and unseen categories. However, existing studies in zero-shot 3D shape understanding rely on predefined rendering parameters, resulting in repetitive, redundant, and low-quality views. This limitation hinders the model’s …

A Survey On Few-Shot Class-Incremental Learning, Songsong Tian, Lusi Li, Weijun Li, Hang Ran, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental …

Identifying Patterns For Neurological Disabilities By Integrating Discrete Wavelet Transform And Visualization, Soo Yeon Ji, Sampath Jayarathna, Anne M. Perrotti, Katrina Kardiasmenos, Dong Hyun Jeong

Computer Science Faculty Publications

Neurological disabilities cause diverse health and mental challenges, impacting quality of life and imposing financial burdens on both the individuals diagnosed with these conditions and their caregivers. Abnormal brain activity, stemming from malfunctions in the human nervous system, characterizes neurological disorders. Therefore, the early identification of these abnormalities is crucial for devising suitable treatments and interventions aimed at promoting and sustaining quality of life. Electroencephalogram (EEG), a non-invasive method for monitoring brain activity, is frequently employed to detect abnormal brain activity in neurological and mental disorders. This study introduces an approach that extends the understanding and identification of neurological disabilities …

Triphlapan: Predicting Hla Molecules Binding Peptides Based On Triple Coding Matrix And Transfer Learning, Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li

Computer Science Faculty Publications

Human leukocyte antigen (HLA) recognizes foreign threats and triggers immune responses by presenting peptides to T cells. Computationally modeling the binding patterns between peptide and HLA is very important for the development of tumor vaccines. However, it is still a big challenge to accurately predict HLA molecules binding peptides. In this paper, we develop a new model TripHLApan for predicting HLA molecules binding peptides by integrating triple coding matrix, BiGRU + Attention models, and transfer learning strategy. We have found the main interaction site regions between HLA molecules and peptides, as well as the correlation between HLA encoding and binding …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

Can Large Language Models Discern Evidence For Scientific Hypotheses? Case Studies In The Social Sciences, Sai Koneru, Jian Wu, Sarah Rajtmajer

Computer Science Faculty Publications

Hypothesis formulation and testing are central to empirical research. A strong hypothesis is a best guess based on existing evidence and informed by a comprehensive view of relevant literature. However, with exponential increase in the number of scientific articles published annually, manual aggregation and synthesis of evidence related to a given hypothesis is a challenge. Our work explores the ability of current large language models (LLMs) to discern evidence in support or refute of specific hypotheses based on the text of scientific abstracts. We share a novel dataset for the task of scientific hypothesis evidencing using community-driven annotations of studies …

Enhanced Skin Cancer Diagnosis Through Grid Search Algorithm-Optimized Deep Learning Models For Skin Lesion Analysis, Rudresh Pillai, Neha Sharma, Sheifali Gupta, Deepali Gupta, Sapna Juneja, Saurav Malik, Hong Qin, Mohammed S. Alqahtani, Amel Ksibi

Computer Science Faculty Publications

Skin cancer is a widespread and perilous disease that necessitates prompt and precise detection for successful treatment. This research introduces a thorough method for identifying skin lesions by utilizing sophisticated deep learning (DL) techniques. The study utilizes three convolutional neural networks (CNNs)-CNN1, CNN2, and CNN3-each assigned to a distinct categorization job. Task 1 involves binary classification to determine whether skin lesions are present or absent. Task 2 involves distinguishing between benign and malignant lesions. Task 3 involves multiclass classification of skin lesion images to identify the precise type of skin lesion from a set of seven categories. The most optimal …

The Combined Focal Loss And Dice Loss Function Improves The Segmentation Of Beta-Sheets In Medium-Resolution Cryo-Electron-Microscopy Density Maps, Yongcheng Mu, Thu Nguyen, Bryan Hawickhorst, Willy Wriggers, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Although multiple neural networks have been proposed for detecting secondary structures from medium-resolution (5–10 Å) cryo-electron microscopy (cryo-EM) maps, the loss functions used in the existing deep learning networks are primarily based on cross-entropy loss, which is known to be sensitive to class imbalances. To monitor and tune the performance of various loss functions for the secondary structure detection problem, we investigated five loss functions: cross-entropy, Focal loss, Dice loss, and two combined loss functions. Using a U-Net architecture in our DeepSSETracer method and a dataset composed of 1,355 box-cropped atomic-structure/density-map pairs, we found that a newly designed loss function …

Exacfs - A Cil Method To Mitigate Catastrophic Forgetting, S. Balasubramanian, Sai Subramaniam M., Sai Sriram Talasu, Manepalli Pranav Phanindra Sai, Yedu P. Krishna, Darshan Gera, Ravi Mukkamala

Computer Science Faculty Publications

Deep neural networks (DNNs) excel at learning from static datasets but struggle with continual learning, where data arrives sequentially. Catastrophic forgetting, the phenomenon of forgetting previously learned knowledge, is a primary challenge. This paper introduces EXponentially Averaged Class-wise Feature Significance (EXACFS) to mitigate this issue in the class incremental learning (CIL) setting. By estimating the significance of model features for each learned class using loss gradients, gradually aging the significance through the incremental tasks and preserving the significant features through a distillation loss, EXACFS effectively balances remembering old knowledge (stability) and learning new knowledge (plasticity). Extensive experiments on CIFAR-100 and …

Age Of Sensing Empowered Holographic Isac Framework For Nextg Wireless Networks: A Vae And Drl Approach, Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Monishanker Halder, Choong Seon Hong

School of Cybersecurity Faculty Publications

This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)- enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO base station. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received signal, beam-pattern gains to improve the sensing SINR of reflected echo signals and maximizing the evidence lower bound …

Reverse-Engineering Of Disinformation Campaigns During The War In Ukraine, Lora Pitman, Ava Baratz, Kelly Morgan, Marcy Alvarado

School of Cybersecurity Faculty Publications

Information operations have long been a part of warfare. Disinformation campaigns, in particular, are usually launched by states in order to mislead and confuse populations in adversarial countries, but also to obtain support for their actions from domestic audiences. These campaigns threaten human security, at the individual level, but also state- and even international security. The invasion of Ukraine by Russia came with a new wave of disinformation not only in Ukraine itself, but also in countries from various other continents. This paper studies the characteristics of the spread of disinformation from the first day of the war in February …

Robustsentembed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning, Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco, Daniel Takabi, Zhipeng Cai

School of Cybersecurity Faculty Publications

Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often exhibit poor robustness in adversarial settings. In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks. Through the generation of high-risk adversarial perturbations and their utilization in a novel objective function, RobustSentEmbed adeptly learns high-quality and robust sentence embeddings. Our experiments confirm the superiority of RobustSentEmbed over state-of-the-art representations. Specifically, …

Autonomous Strike Uavs In Support Of Homeland Security Missions: Challenges And Preliminary Solutions, Meshari Aljohani, Ravi Mukkamala, Stephan Olariu

Computer Science Faculty Publications

Unmanned Aerial Vehicles (UAVs) are becoming crucial tools in modern homeland security applications, primarily because of their cost-effectiveness, risk reduction, and ability to perform a wider range of activities. This study focuses on the use of autonomous UAVs to conduct, as part of homeland security applications, strike missions against high-value terrorist targets. Owing to developments in ledger technology, smart contracts, and machine learning, activities formerly carried out by professionals or remotely flown UAVs are now feasible. Our study provides the first in-depth analysis of the challenges and preliminary solutions for the successful implementation of an autonomous UAV mission. Specifically, we …

Machine Learning Based Three-Limb Core-Type Transformer Core Aspect Ratios Identification, Ananta Bijoy Bhadra

College of Graduate Studies: Theses & Dissertations

Power transformers are considered one of the key elements of electric grids. Transient studies include transformer transient analysis which is required for the continuous power supply. However, to perform the transient analysis, the details of the internal structure of the transformer are required which are unobtainable and considered as confidential information. Therefore, the application of topological-based transformer models is limited although the models can accurately represent the transformers. To address this concern, a novel approach utilizing Machine Learning (ML) to identify the core aspect ratios of the three-limb core-type transformer is introduced. The proposed approach, using only the voltage and …