Digital Commons Network^™

Ai Exposure And The Future Of Work: Linking Task-Based Measures To U.S. Occupational Employment Projections, Erik Vasilauskas, Michael Horrigan

Reports

No abstract provided.

Memoir On A General Property Of A Very Extensive Class Of Transcendental Functions, Niels Henrik Abel 1802--1829, John Little

Mathematics and Computer Science Department Faculty Scholarship

We present this new commentary and translation anticipating the 200th anniversary of the work, commonly known as Abel's ``Paris memoir.'' This is recognized today as one of Abel's most original and influential works. It is significant mostly because it marked the first appearance of a form of a result in the theory of algebraic curves and Riemann surfaces that has come to be known as ``Abel's theorem.'' However, Abel's original understanding of the meaning and context of his result was quite different from the typical modern formulation and the development of the modern understanding has been a long and tortuous …

Green Synthesis Of Copper And Silver Nanostructured Particles From Eremurus Plant Extract And Comparison Of Their Optical Properties And Antibacterial Activities, Doaa Ayad Kamil, Ali F. Al-Rawaf, Mohammed Yarub Hani, H.H. Obeed, Tabarek Falah Deindee, Mohammed Ridha Shaeed

Karbala International Journal of Modern Science

In the present investigation, silver (Ag) and copper (Cu) nanostructured particles were synthesized from Eremurus plant by means of an environmentally benign methodology in which plant extracts served as both reducing and stabilizing agents. The resultant nanostructured particles were subjected to characterization techniques, including X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), field-emission scanning electron microscopy (FESEM), and energy-dispersive X-ray spectroscopy (EDXS). XRD analysis confirmed development of Ag nanostructured particles with face-centered cubic (fcc) structure and also Cu nanostructured particles with cubic form. Moreover, the average crystallite dimensions obtained for Ag and Cu were 18.3 nm and 65.7 nm, …

Fabrication And Characterization Of Epoxy Resin Co-Doped With 2,5-Diphenyloxazole And Cerium Fluoride Nanoparticles For Radiation Detection, Akapong Phunpueok, Jaruwan Seangrit, Sarawut Jaiyen, Krittiya Sreebunpeng, Wuttichai Chaiphaksa, Kewalee Nilgumhang, Voranuch Thongpool

Karbala International Journal of Modern Science

This paper presents the fabrication and characterization of a plastic scintillator prepared from epoxy resin doped with 2,5-diphenyloxazole (PPO) and cerium fluoride nanoparticles (CeF₃ NPs) for radiation detection. The CeF₃ NPs were prepared by a chemical process and examined by X-ray diffraction (XRD) and scanning electron microscopy (SEM); it was found that the prepared particles were true CeF₃ NPs with an average particle size of approximately 17 nm. The CeF₃ NPs were co-doped with PPO into epoxy resin and formed into a plastic scintillator of 3 cm in diameter and 2 cm in length. Analysis of …

Overcoming Variable Illumination In Photovoltaic Snow Monitoring: A Real-Time Robust Drone-Based Deep Learning Approach, Amna Mazen, Ashraf Saleem, Kamyab Yazdipaz, Ana Dyreson

Michigan Tech Publications

Snow accumulation on photovoltaic (PV) panels can cause significant energy losses in cold climates. While drone-based monitoring offers a scalable solution, real-world challenges like varying illumination can hinder accurate snow detection. We previously developed a YOLO-based drone system for snow coverage detection using a Fixed Thresholding segmentation method to discriminate snow from the solar panel; however, it struggled in challenging lighting conditions. This work addresses those limitations by presenting a reliable drone-based system to accurately estimate the Snow Coverage Percentage (SCP) over PV panels. The system combines a lightweight YOLOv11n-seg deep learning model for panel detection with an adaptive image …

Retracted: Idea Density And Grammatical Complexity As Neurocognitive Markers, Diego Iacono, Gloria Feltis

Department of Neurology Faculty Papers

Language, a uniquely human cognitive faculty, is fundamentally characterized by its capacity for complex thoughts and structured expressions. This review examines two critical measures of linguistic performance: idea density (ID) and grammatical complexity (GC). ID quantifies the richness of information conveyed per unit of language, reflecting semantic efficiency and conceptual processing. GC, conversely, measures the structural sophistication of syntax, indicative of hierarchical organization and rule-based operations. We explore the neurobiological underpinnings of these measures, identifying key brain regions and white matter pathways involved in their generation and comprehension. This includes linking ID to a distributed network of semantic hubs, like …

Exploring Students' Perceptions Of Genai Tools In Higher Education: A Case Study, Dina Mansour Tbaishat, Maha Waleed Elfadel

All Works

As artificial intelligence transforms the educational landscape, generative artificial intelligence (GenAI) tools have become influential in enhancing learning experiences. Despite their growing presence in higher education, limited research explores how university students, especially learners in non-Western contexts, perceive these tools. This study investigates students' perceptions at a university in the UAE, focusing on five dimensions: perceived benefits, institutional support, technological self-efficacy, ethical considerations and user satisfaction. The findings reveal that students generally expressed strong agreement regarding the benefits of GenAI, demonstrated a clear sense of ethical awareness and felt confident in their technological abilities. While satisfaction levels were generally high, …

Enhancing Fault Tolerance In Distributed Systems Using Shared Checkpoint Replica Mechanisms, Ameer A. Mousa, Mahdi S. Almhanna

Journal of Intelligent Informatics, Networking, and Cybersecurity

Fault tolerance is a critical requirement in distributed systems, as node and network failures can cause significant data loss, service disruption, and performance degradation. Traditional fault-tolerance methods often provide partial solutions, relying on separate recovery models for errors and delayed responses, while also incurring high resource costs. This paper aims to design and implement a more robust and efficient fault-tolerant architecture based on the concept of a shared checkpoint replica. In the proposed system, a master server and multiple slave servers collaboratively process client requests, share checkpoint replicas, and ensure seamless recovery in case of failures. To evaluate the approach, …

Anthem 2.0: Automated Reasoning For Answer Set Programming, Jorge Fandinno, Zachary Hansen, Yuliya Lierler, Christoph Glinzer, Jan Heuer, Torsten Schaub, Tobias Stolzmaan, Vladimire Lifschitz

Computer Science Faculty Publications

ANTHEM 2.0 is a tool to aid in the verification of logic programs written in an expressive fragment of CLINGO ’s input language named MINI-GRINGO, which includes arithmetic operations and simple choice rules but not aggregates. It can translate logic programs into formula representations in the logic of here-and-there and analyze properties of logic programs such as tightness. Most importantly, ANTHEM 2.0 can support program verification by invoking first-order theorem provers to confirm that a program adheres to a first-order specification or to establish strong and external equivalence of programs. This paper serves as an overview of the system’s capabilities. …

Benefit Distribution Optimization Model And Simulation For Multi-Mode Operation Of Industrial Software Platforms, Rongyu Guo, Xiaobin Li, Pei Jiang, Chuanjiang Li, Shanhui Liu, Jun Ma

Journal of System Simulation

Abstract: Industrial software service platforms, characterized by low-cost investment, customized services, and rapid application deployment, have been widely adopted in small and medium-sized industrial clusters. The benefit distribution mechanism under multi-mode operation is crucial to the sustainable development of such platforms. To address the current challenges of single-operation models and the difficulty in adapting to diverse service scenarios, this study focuses on two core stakeholders that users and software developers to analyze the core service components and cooperation mechanisms of industrial software service platforms in a multi-mode operational environment. By integrating the function point method, a multi-mode user demand quantification …

Factors Influencing The Use Of Gis-Enabled Public E-Participation For Municipal Solid Waste Management, Irene Arinaitwe, Agnes Nakakawa, Gilbert Maiga

The African Journal of Information Systems

Due to rapid global population growth and urbanization, approximately two billion metric tons of waste are generated annually. Municipal solid waste management has become a critical function for urban authorities. However, many urban authorities in low- and middle-income economies cannot provide efficient municipal solid waste management services because of suboptimal stakeholder participation in governance processes and inadequate information exchange. Therefore, this study sought to determine factors that influence the implementation of GIS-enabled public e-participation using Enhanced Adaptive Structuration Theory. A descriptive field study was conducted among staff of municipal authorities and residents in Uganda’s Kampala Metropolitan Area. Data were analyzed …

Microarchitectural Malware Detection Via Translation Lookaside Buffer (Tlb) Events, Cristian Agredo, Daniel F. Koranek, Christine M. Schubert Kabban, Jose R. Gutierrez Del Arroyo, Scott R. Graham

Faculty Publications

Prior work has shown that Translation Lookaside Buffer (TLB) data contains valuable behavioral information. Many existing methodologies rely on timing features or focus solely on workload classification. In this study, we propose a novel approach to malware classification using only TLB-related Hardware Performance Counters (HPCs), explicitly excluding any dependence on timing features such as task execution duration or memory access timing. Our methodology evaluates whether TLB data alone, without any timing information, can effectively distinguish between malicious and benign programs. We test this across three classification scenarios: (1) A binary classification problem involving distinguishing malicious from benign tasks, (2) a …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

School of Medicine Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Spectral–Spatial Transformer With Multiscale Convolutional Attention For Hyperspectral Image Classification, Junde Chen, Wenzhao Li, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Hyperspectral image (HSI) classification plays a vital role in remote sensing by leveraging rich spectral and spatial information for accurate material recognition. However, existing methods, particularly Transformer-based approaches, still face challenges in effectively modeling multiscale spatial–spectral features, preserving local details, and maintaining robustness to noise. To mitigate these limitations, we propose TMCANet, a spectral–spatial Transformer with multiscale convolutional attention, designed to effectively leverage both local and global contextual dependencies for HSI classification. Our design is guided by three core strategies: first, a convolutional feature extraction module, consisting of four convolutional layers, to learn hierarchical spectral multiscale representations and enhance local …

A Deep Learning Framework For Early Autism Detection Using Eeg Signals, Maha M. Hamzeh, Ali Y. Al-Sultan, Salah Al-Obaidi

Journal of Intelligent Informatics, Networking, and Cybersecurity

Autism spectrum disorder (ASD) is a complicated neurodevelopmental illness, affecting social interaction, communication, and cognitive function. To lower healthcare costs and facilitate prompt intervention, early and accurate detection is crucial. However, behavioral assessments—which are inherently subjective and can lead to delayed diagnoses—are a significant component of traditional diagnostic procedures. This paper presents a convolutional neural network (CNN) and time-frequency analysis-based early ASD screening using EEG signals. EEG data undergoes a preprocessing step to remove noise and power interference. After that, the signals were divided into 5-, 10-, and 20-second time frames. Each EEG time frame segment is represented in the …

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

Experiential Learning: Innovative Approaches To Post-Secondary Cybersecurity Education, Brendan Bertone, Paul Wagner, Joshua Pauli

Journal of Cybersecurity Education, Research and Practice

The cybersecurity profession continues to face a significant shortfall of qualified professionals despite steady growth in degree programs. Employers consistently cite experience as the main barrier for entry-level cybersecurity hires. This paper argues that clinic-based experiential learning offers a scalable solution to that preparation gap. A systematic literature review spanning academic and professional literature was conducted to examine: (1) barriers to entry for aspiring cybersecurity professionals; (2) the effectiveness of experiential learning compared to traditional instruction; and (3) the viability and scalability of cybersecurity clinics. Screening emphasized workforce development, experiential pedagogy, and alignment with the NICE Cybersecurity Workforce Framework. Findings …

Complexity Study Of Knowledge And Public Observation, Avijeet Ghosh

Doctoral Theses

Automated planning has been a steady branch of research in the field of Artificial Intelligence. A very interesting branch of such planning studies is epistemic planning. Epistemic plans are such plans where the attained goal revolves around knowledge of some intelligent agents. One of the more popular modeling techniques and underlying language to handle knowledge of intelligent agents is provided by dynamic epistemic logic (DEL). It uses Kripke models that have possible states of truth and relations to model knowledge. It also uses a similar technique to model actions or events that update the knowledge state. Since DEL deals with …

Characterizing Problematic Images In Retracted Scientific Articles, João Phillipe Cardenuto, Daniel Moreira, Anderson Rocha

Computer Science: Faculty Publications and Other Works

This cross-sectional study analyzed retracted articles flagged for problematic image manipulation (e.g., image duplication) in the Retraction Watch Database (56,716 entries as of October 4, 2024). We focused on entries containing the term image in the retraction reason (8002 entries) and further refined the dataset to those discussed on PubPeer (2078 after duplicate removal) to gain more detailed insights into the image problems. Data extracted included figure types (eg, microscopy, gel blot), the context of image misuse (eg, within-article, between-article), and the type of manipulation (e.g., duplication, splicing). The study highlights the prevalence of gel blot images and between-article image …

Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues

School of Medicine Faculty Publications

The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RAS gene functions and elucidate their roles in biological pathways. A total of 39,463 RAS-related publications from PubMed and PMC were processed into text format. Feature matrices were generated using TF-IDF and token processing, followed by dimensionality reduction via Principal Component Analysis (PCA). A Multi-Layer Perceptron (MLP) was applied for multi-label classification, with performance evaluated using Precision, F1-Score, Ranking Loss, and ROC-AUC metrics. The model outperformed …

Information Security Awareness And Behavior Of Smartphone Users In The Ibadan Metropolis, Nigeria, Funmilola Olubunmi Omotayo

Journal of Cybersecurity Education, Research and Practice

Today, there is a rapid increase in the number of people using the Internet via smartphones and relying on them for most of their daily activities. Consequently, smartphones are becoming the target of criminals for atrocious purposes. This study investigated the information security awareness and behavior of smartphone users in the Ibadan metropolis, Nigeria. The study adopted a descriptive survey design. Data was collected with a questionnaire from 400 respondents who were conveniently selected. Findings revealed that most smartphone users knew about the smartphone security features available on their phones. However, most also engaged in behaviors that threatened their information …

Utilizing Generative Ai To Counter Learner Groupthink By Introducing Controversy In Collaborative Problem-Based Learning Settings, Andrew Wiss, Mary Showstark, Kyle Dobbeck, Jennifer Pattershall-Geide, Elke Zschaebitz, Dawn Joosten-Hagye, Kirsten Potter, Erin Embry

Montclair State University Scholarship & Creative Works

This article highlights the foundational challenge of rapid interprofessional student team formation and the potential challenges that groupthink poses for newly-formed teams participating in collaborative problem-based learning activities. This article describes a mixed-methods study that addresses groupthink by introducing a generative artificial intelligence-based agent (genAI agent) into the small group processes of student teams engaging in a session of a well-established virtual interprofessional education methodology. The integration of this novel genAI tool into each student team was an intentional pedagogical technique, introduced in response to the challenges that newly-formed student teams may encounter as they rapidly come together and potentially …

Lighttransfer: Your Long-Context Llm Is Secretly A Hybrid Model With Effortless Adaptation, Xuan Zhang, Fengzhuo Zhang, Cunxiao Du, Chao Du, Tianyu Pang, Wei Gao, Min Lin

Research Collection School Of Computing and Information Systems

Scaling language models to handle longer contexts introduces substantial memory challenges due to the growing cost of key-value (KV) caches. Motivated by the efficiency gains of hybrid models and the broad availability of pretrained large transformer backbones, we explore transitioning transformer models into hybrid architectures for a more efficient generation. In this work, we propose LightTransfer, a lightweight method that transforms models such as LLaMA into hybrid variants. Our approach identifies lazy layers -- those focusing on recent or initial tokens -- and replaces their full attention with streaming attention. This transformation can be performed without any training for long-context …

The God Prompt And Deus Ex Machina: Techno-Theological Tropes And Operational Metaphors In Generative Media, James Hutson

Faculty Scholarship

This study reframes two durable tropes—the ―God Prompt‖ and the deus ex machina—as analytic lenses for understanding how contemporary generative systems stage beginnings and endings of cultural production. The ―God Prompt‖ denotes command-driven synthesis in which minimal textual instructions instantiate content on demand, crystallizing a production loop of input, model execution, and post hoc evaluation that orients anticipation toward instantaneous yield and controllable variation. The deus ex machina names an externally imposed resolution that interrupts causal development—historically a crane-borne god, functionally an algorithmic override—thereby concentrating attention on closure mechanics rather than world-building continuity. Read together, the pair offers a compact …

Building A Novel Question-Answering System Using Retrieval-Augmented Generation For The California Fair Political Practices Commission, Saanvi Dua

Master's Theses

The California Fair Political Practices Commission (FPPC) receives a high volume of inquiries via email from public officials, the general public, and other agencies, which currently requires staff to manually search through informational documents and manuals to provide timely responses. This process is both labor- and time-intensive.

To address this challenge, we design a question-answering (QA) system that drafts responses to emailed questions by retrieving relevant information from the FPPC’s manuals using a retrieval-augmented generation (RAG) framework. Although the current implementation focuses on a single manual, the system is designed to be adaptable to the broader set of FPPC documents. …

Futurescape Libraries Ai Toolkit, Keith Webster

Copyright, Fair Use, Scholarly Communication, etc.

A toolkit developed to explore scenario-specific strategies and activities that research libraries can undertake to prepare for various possible AI-influenced futures. The toolkit integrates the ARL/CNI AI Scenarios published in spring 2024 along with priorities trialed and refined by strategic thinkers working directly in, or adjacent to, the research library field during a Strategic Implications forum held December 7–8, 2024, in Washington, DC.

Learning Orientation Field For Osm-Guided Autonomous Navigation, Yuming Huang, Wei Gao, Zhiyuan Zhang, Maani Ghaffari, Dezhen Song, Cheng-Zhong Xu, Hui Kong

Research Collection School Of Computing and Information Systems

OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, existing methods often struggle with noisy OSM data and incomplete sensor observations, leading to inaccuracies in trajectory planning. These challenges are particularly evident in complex driving scenarios, such as at intersections or facing occlusions. To address these challenges, we propose a robust and explainable two-stage framework to learn an Orientation Field (OrField) for robot navigation by integrating LiDAR scans and OSM routes. In the first stage, we introduce a novel representation, OrField, which can provide orientations for each …

Preference-Based Deep Reinforcement Learning For Historical Route Estimation, Boshen Pan, Yaoxin Wu, Zhiguang Cao, Yaqing Hou, Guangyu Zou, Qiang Zhang

Research Collection School Of Computing and Information Systems

Recent Deep Reinforcement Learning (DRL) techniques have advanced solutions to Vehicle Routing Problems (VRPs). However, many of these methods focus exclusively on optimizing distance-oriented objectives (i.e., minimizing route length), often overlooking the implicit drivers' preferences for routes. These preferences, which are crucial in practice, are challenging to model using traditional DRL approaches. To address this gap, we propose a preference-based DRL method characterized by its reward design and optimization objective, which is specialized to learn historical route preferences. Our experiments demonstrate that the method aligns generated solutions more closely with human preferences. Moreover, it exhibits strong generalization performance across a …

Ibi-Dt: A Novel Approach Combining Individualized Bayesian Inference And Decision Tree For Identifying Cancer Drivers And Their Interactions, Md Asad Rahman, Gregory F. Cooper, Jinying Zhao, Xinghua Lu, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Cancer is mainly caused by a relatively small portion of somatic genome alterations (SGAs), called cancer drivers. Despite success in identifying a good number of cancer drivers, many more remain to be discovered to explain various cancers. Moreover, limited tools are available to identify potential interactions among cancer drivers for a better understanding of oncogenesis. To tackle these challenges, we have developed a novel approach called individualized Bayesian inference using a decision tree (IBI-DT). IBI-DT recognizes the genetic heterogeneity among cancer patients, where different individuals or patient subgroups of distinct genomic makeup may have different drivers. IBI-DT works by constructing …

Applying Artificial Intelligence And Chatbots To Enhance Collection Development In Health Sciences Libraries, Ivan Portillo, David Carson

Library Presentations, Posters, and Audiovisual Materials

The continuous advancement of artificial intelligence (AI) and large language models (LLMs) has presented several opportunities for librarians to reduce their workload and become more efficient. This session will explore the potential of generative AI chatbots in assisting health sciences librarians with collection development. Two methods that will be discussed include the potential of AI to help discover new titles and how AI can evaluate your library collection for any potential gaps based on a college program’s curriculum.