Digital Commons Network^™

Dblp: Noise Bridge Consistency Distillation For Efficient And Reliable Adversarial Purification, Chihan Huang, Belal Alsinglawi, Islam Al-Qudah

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Recent advances in deep neural networks (DNNs) have led to remarkable success across a wide range of tasks. However, their susceptibility to adversarial perturbations remains a critical vulnerability. Existing diffusion-based adversarial purification methods often require intensive iterative denoising, severely limiting their practical deployment. In this paper, we propose Diffusion Bridge Distillation for Purification (DBLP), a novel and efficient diffusion-based framework for adversarial purification. Central to our approach is a new objective, noise bridge distillation, which constructs a principled alignment between the adversarial noise distribution and the clean data distribution within a latent consistency model (LCM). To further enhance semantic fidelity, …

Artificial Intelligence (Ai) For Social Innovation In Health Education: Promoting Health Literacy Through Personalized Ai-Driven Learning Tools – A Systematic Review, Dina Mansour Tbaishat, Maha Waleed Elfadel

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Background: Artificial Intelligence (AI) is transforming health education by enabling personalized, adaptive, and scalable approaches that may enhance aspects of health literacy. Despite rapid adoption, comprehensive synthesis of AI tools’ impact on health literacy as social innovation is limited. Understanding these effects guides educators, developers, and policymakers in designing potentially effective, inclusive, and ethical AI interventions. This review examines generative AI models, chatbots, and adaptive learning systems in supporting health literacy globally. Methods: A systematic review was conducted following PRISMA guidelines. Literature was identified primarily through PubMed/Medline, Scopus, and ScienceDirect. Connectedpapers.com was used exclusively as a citation chasing tool, performing …

Radio Frequency Tagging–Enabled Patient Monitoring: Integrating Mobility Tracking With Early Warning Systems For Enhanced Safety, Ahed Abugabah, Prashant Kumar Shukla, Piyush Kumar Shukla, Abhishek Dwivedi

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Ensuring patient safety in healthcare environments requires continuous monitoring systems capable of identifying early warning signs of clinical risk. Traditional surveillance methods often fail to capture meaningful patterns in patient movement, limiting their ability to prevent incidents such as falls, prolonged immobility, or unnoticed health deterioration. Radio Frequency Tagging technology has been increasingly adopted for real-time patient tracking; however, existing systems are generally limited to location detection and lack predictive insights into patient behaviour. To overcome these limitations, this study presents a Radio Frequency Tagging-based patient monitoring framework that integrates mobility tracking with an early warning mechanism to enable proactive …

Understanding Chatbot-Assisted Collaborative Learning Among Female Undergraduate Students, Mohammad Amin Kuhail, Ahmed Shuhaiber, Sinan Salman, Nazik Alturki

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Computer programming can be daunting for beginners due to complex concepts and syntax. Traditional teaching methods, while engaging through gamification and active learning, often lack personalized approaches. Recent advancements in artificial intelligence (AI), particularly large language models (LLMs), present new possibilities for personalized and interactive learning environments. This study introduces a chatbot-assisted collaborative learning environment (CCLE) that leverages an LLM (GPT-4) to enhance collaborative programming education. The CCLE enables real-time guidance and collaboration through natural language interactions, allowing students to work together on programming tasks, edit code collaboratively, and engage with both peers and the educational chatbot. We conducted an …

An Advanced Healthcare System With An Automated Vit Model For Dermoscopic Skin Cancer Identification, Ahed Abugabah, Prashant Kumar Shukla, Suchi Mishra, Abhishek Dwivedi

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Early and reliable diagnosis of skin cancer from dermoscopic images remains challenging due to class imbalance, subtle inter-class variations, lesion boundary ambiguity, and illumination inconsistency, which can degrade the robustness of conventional convolutional neural networks (CNNs). To address these limitations, this study proposes an automated smart healthcare framework for dermoscopic skin cancer diagnosis using an Enhanced Vision Transformer (E-ViT) that improves global-context modeling through self-attention while strengthening fine-grained lesion representation learning. Unlike standard ViT configurations, the proposed architecture integrates multi-scale patch embedding and attention refinement to better capture border irregularities and color–texture heterogeneity that are critical for melanoma discrimination. Furthermore, …

Large Language Models In Nlp: Evolution, Architectural Trends, And Open Challenges, Haseeb Javed, Babar Shah, Farman Ali, Daehan Kwak

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The rise of Large Language Models (LLMs) has transformed how Natural Language Processing (NLP) and its subdomains are approached. Recent technological advancements have driven this transformation. This study offers researchers a detailed overview of LLMs, comparing them with traditional rule-based systems, statistical techniques, machine learning, neural networks, and the rise of transformer-based architectures. From a wider perspective, language models such as GPT, BERT, T5, PaLM, and LLaMA have facilitated the transformation of entire sectors, including healthcare and business, due to their highly scalable nature. Despite their wide range of applications, LLMs face numerous challenges, such as output biases, limited interpretability, …

The Role Of Corporate Sustainability Goals In Shaping Organizational Intentions And Adoption Of Green Technologies In Small- And Medium-Sized Enterprises, Syed Zamberi Ahmad, Abdul Rahim Abu Bakar, Imane Belyamani, Manar Fawzi Bani Mfarrej

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This study investigates green technology adoption (GTA) among small and medium-sized enterprises (SMEs) in the United Arab Emirates (UAE), focusing on the influence of corporate sustainability goals (CSG) and sustainability motivation (SM). Utilizing institutional theory, the theory of planned behavior (TPB), and resource-based view (RBV), the research highlights how SMEs integrate environmental, social, governance (ESG) and economic considerations into their CSG to enhance GTA. Addressing a gap in prior research that has largely emphasized external drivers of adoption while underexploring internal organizational mechanisms, the study conceptualizes CSG as strategic intent and models SM as a second-order construct . Based on …

Image And Metadata-Driven Personality Inference For Career Recommendation: A Social Media-Based Ai Framework For Adolescents, Heba Ismail, Maryam Alhefeiti, Ashraf Khalil

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This study presents a novel AI-based framework that leverages Instagram image and metadata analysis to infer Big Five personality traits and deliver personalized career recommendations for high school students in the UAE. Addressing the limitations of traditional recommender systems that rely on self-reported questionnaires or text, the proposed approach uses multimodal visual features—including profile metrics, HSV color patterns, semantic image labels, and texture analysis—to enable a non-intrusive, scalable personalization method. A pilot study involving data from 30 student accounts served as a proof of concept. Correlation analysis identified profile and HSV features as the most predictive, and four machine learning …

Fig-Gan: Fundus Image Generation Via Deep Learning Based Generative Adversarial Network For Amd Disease Diagnosis, Kailasa Thrishul, Ahed Abugabah, Amina Salhi, Manel Ayadi, Mohamed M. Sithik, D. Jayaprakash, A. Ahilan

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Globally, age-related macular degeneration (AMD) remains a main cause of irreversible vision loss. Recently, deep learning models have primarily focused on classifying fundus images for early detection of AMD progression. However, existing models rarely address the generation of future progression-aware fundus images, particularly when complete real longitudinal follow-up scans are unavailable. This limitation makes it difficult to track retinal changes over time and highlights the need for generative models capable of producing realistic drusen-level structural variations. To address these issues, a novel deep learning-based FIG-GAN model is to generate synthetic future fundus images from baseline inputs. Multi-Attention U-Net (MAU-Net) is …

A Robust Approach For Olive Leaf Disease Detection In Uncontrolled Environments, Rima Grati, Khouloud Boukadi, Emna Ben Abdallah, Ahmed Seffah

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Detecting diseases in olive leaves is crucial for maintaining tree health and ensuring stable olive production. Early signs of infection often appear on the leaves, making them a key indicator for timely disease detection and intervention. Traditionally, farmers rely on visual inspection or laboratory tests to diagnose plant diseases. However, recent advancements in deep learning (DL) have significantly improved the accuracy and efficiency of olive leaf disease diagnosis. Numerous studies in the literature have explored this task using CNN-based architectures and, more recently, Vision Transformers. While these models have shown promising performance on benchmark datasets, they are often trained and …

Marbs & Carbs, Kevin M. Mcghee

Game Design

Marbs & Carbs is a 3D rolling game set in an oversized kitchen. Guide Marb across countertops, past stove burners, and through sink-side shortcuts while collecting pasta and donuts that make the marble grow. Larger sizes open new routes, including a breakable sink drain. Collect at least 25 carbs to enter the mouse hole and finish the level. Special pasta can turn Marb into a tumbling cube or pyramid, changing how it moves and adding a score bonus. A results screen tallies your pickups and time before the next run.

This game is inspired by the classic Atari arcade game …

Data From: Physics-Data-Hybrid Modeling And Control For Vtol Aircraft, Samantha Dawn Burton

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The skeleton code corresponding to many of the results from the dissertation "Physics-Data-Hybrid Modeling and Control for VTOL Aircraft" By Samantha Dawn Burton.

Factors Influencing Non-Perennial Stream Initiation In The Mt. Hood National Forest, Allison Bast

Dissertations and Theses

Historically, non-perennial headwater streams have been overlooked due to their small size and infrequent flow regimes, often leading to them not being recognized as legitimate streams, despite scientific evidence that has shown these streams significantly affect the quality of downstream waters. To combat this issue, environmental managers need to be able to accurately identify and delineate the origins of these streams; however, despite decades of research, an accurate, universal method for predicting how and where these streams commence has yet to be developed. Early studies conducted in steep, humid environments, identified a strong inverse linear relationship between the size of …

Calibration And Performance Of Germanium High Voltage Detectors For Supercdms Snolab, M. F. Albakry, I. Alkhatib, D. Alonso-González, J. Anczarski, T. Aralis, T. Aramaki, A. Ashtari Esfahani, I. Ataee Langroudy, R. Bhattacharyya, A. J. Biffl, P. L. Brink, M. Buchanan, R. Bunker, B. Cabrera, R. Calkins, R. A. Cameron, P. Camus, C. Cartaro, D. G. Cerdeño, Y. Y. Chang, M. Chaudhuri, J. H. Chen, R. Chen, J. Cooley, J. Corbett, P. Cushman, R. Cyna, S. Das

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As SuperCDMS SNOLAB is getting ready to search for low mass dark matter particles, using cryogenic Ge and Si detectors, a set of six of the new SuperCDMS High Voltage (HV) detectors (four Ge and two Si) were tested in the Cryogenic Underground TEst facility (CUTE) at SNOLAB. This provided the first opportunity to gain experience with this new detector type and assess their performance thoroughly under low background conditions. Here we describe the SuperCDMS HV detector concept and discuss some of the newly developed analysis methods and approaches. Focusing on the Ge detectors, we investigate the detector performance under …

Preliminary Data: Old Trails Fire--Field Reconnaissance (Rpd1195), Rapid, University Of Washington

Spokane Wildfire Academic Consortium Repository

The .ZIP file contains preliminary field reconnaissance findings from the Old Trails Fire in Spokane, Washington, including geospatial datasets and the source code used to support the project's interactive web map. The data and observations reflect work completed through September 4 and should be considered preliminary. As additional analysis and information become available, updates will be published as new records in the Wildfire Consortium Repository.

Access the interactive map: https://designsafe-ci.github.io/rapid-fire-field-reconnaissance/

Access the DesignSafe-CI rapid-fire-field-reconnaissance GitHub repository: https://github.com/DesignSafe-CI/rapid-fire-field-reconnaissance/

Toward Equitable Energy Futures: The Influence Of Digital Government And Financial Development On Clean Energy Justice, Martin Spraggon, Muhammad Hafeez, Sana Ullah, Chanyanan Somthawinpongsai, Zurul Aisya Osman, Sidra Sohail

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Fossil fuel energy is the largest source of carbon emissions and the main driver of climate change and global warming. Therefore, the world is switching towards clean and green energy sources as an alternative to fossil fuel-based energy sources. Nevertheless, the equitable distribution of clean energy resources in urban and rural areas among every segment of society has become a concern for policymakers. Thus, urban-rural clean energy justice is an investigation topic that needs our attention. In this analysis, our main focus is on investigating the influence of financial development and digital government on clean energy justice using an advanced …

Ecu-Malnett V2, Matthew G. Gaber, Mohiuddin Ahmed, Michael N. Johnstone

Research Datasets

ECU-MALNETT (ECU MALware NETwork Traffic) is a real world, reproducible dataset of labeled benign and malicious network flows built from the Peekaboo execution corpus. Peekaboo runs evasive malware with dynamic binary instrumentation and records raw host-level PCAPs while granting full Internet access, yielding noisy, real-world captures with background OS activity and concurrent processes. To derive trustworthy labels from these traces, we apply Construct, a baseline aware, zero-trust labeling framework. Construct first ingests a baseline capture to establish reference sets (DNS qnames, HTTP hosts, TLS SNIs, and socket endpoints) and grows a conservative benign IP pool only via whitelisted DNS resolutions. …

The Application Of Machine Learning And Deep Learning On Demand Forecasting Across Time-Critical Industries: A Systematic Review, Asmaa Seyam, Sujith Samuel Mathew, May El Barachi, Cheng Zhang, Jun Shen

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The applications of machine learning and deep learning in demand forecasting have attracted increasing attention, as they offer remarkable predictive capabilities that help automate forecasting processes and achieve higher accuracy. While numerous review studies have examined solutions within specific industries, there is a lack of comprehensive literature review investigating these solutions across different sectors. Therefore, this study overviews machine learning and deep learning applications in demand forecasting across time-critical industries, including power, tourism, water, transportation, and food. A two-tier classification framework is proposed to categorize demand forecasting studies by both application industry and methodological architecture. In addition, the most popular …

Light-Wdef: Lightweight Deepfake Evaluation Framework For Efficient And Reliable Visual Detection, Muhammad Yousaf, Sarwar Shah Khan, Babar Shah, Muhammad Shehzad Khan, Muhammad Awais Bacha, Mian Saeed Akbar, Ijaz Ali, Fernando Moreira

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Deepfake technology synthesizes and manipulates digital content to produce artificial videos in which an individual’s identity, facial appearance, or actions are convincingly altered. These forgeries can be highly realistic, posing serious risks to trust in digital media and undermining personal reputation, dignity, and the integrity of public discourse. This paper presents the Lightweight Deepfake Evaluation Framework (Light-wDEF)– a resource-efficient, reproducible framework tailored for large-scale evaluation of visual deepfake detectors on modest hardware. Light-wDEF combines deterministic frame sampling, batched MTCNN face extraction, streaming append-only storage with lazy offset indexing, online Albumentations augmentations, stratified splits with class-aware sampling, and robust training (AdamW, …

Droplet Clustering Signatures Of Entrainment In Clouds, Suryadev Pratap Singh, Michael Larsen, Jesse Anderson, Hamed F. Sadi, Jae Min Yeom, Will Cantrell, Raymond A. Shaw

Michigan Tech Research Data

The spatial distribution of cloud droplets is relevant for various microphysical processes, such as condensational growth, collision-coalescence, riming and aggregation, and radiative transfer. Cloud droplets and other hydrometeors exhibit clustering at dissipation scales and above due to turbulence-induced preferential concentration. Here, we investigate the role of entrainment in modulating the scale-dependent clustering of cloud droplets and explore how this mechanism differs from inertial clustering. We have performed experiments in the Pi Convection-Cloud Chamber, and imaged 2-D droplet fields in the cloud-top region where dry-air entrained through an open cylindrical flange leads to an entrainment-mixing process. Captured images are processed to …

Ground To Roof Snow Load Ratio (Gr) Data Release, Brennan Bean, Cooper Nelson, Jesse Wheeler, Scout Jarman, Salam Adil Al-Rubaye, Marc Maguire

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This data release provides historical ground-to-roof snow load ratio (GR) datasets used for snow load research and model development. The release includes original referenced datasets, cleaned country specific datasets, and a master dataset that combines Canadian and United States datasets into a standardized format for research and engineering applications.

Data Repository For "The Influence Of Hydrologic And Salinity Conditions On Drought-Tolerant Great Salt Lake Wetland Plant Species" By Samantha Kurkowski And Karin Kettenring, Samantha Kurkowski

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This greenhouse experiment investigated the effects of salinity (3 levels) and hydrologic condition (5 levels),  on the cover and biomass of a single mix of 11 native drought- and salt-tolerant species.

Aqqd: Annotated Quranic Qira’At Dataset, Linda Smail, Mohammed Lataifeh, Md Sohazur Islam Sozib, Arthur Diniz De Souza

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AQQD (Annotated Quranic Qira'at Dataset) is an open audio dataset of Quranic recitations annotated across canonical Qira'at styles. The dataset is designed to support research in machine learning, speech and audio processing, computational linguistics, and Quranic studies. The current release contains 24,183 WAV audio files from 309 reciters and covers 70 selected Quranic Surahs segmented into representative verses and phonetic variation points. Of these, 23,111 recordings were collected from publicly available sources, including official reciter websites, the Midad repository, MP3Quran, and verified YouTube channels, while an additional controlled subset of 1,072 recordings was obtained from a single reciter recorded as …

P-Value Visualizer, Manish Rami

Software

An interactive tool demonstrating what a user set p-value indicates with regards to probability.

Effect Size & Distributional Overlap Visualizer, Manish Rami

Software

An interactive tool comparing a control group and a treatment group, both with standard scores (M = 100, SD = 15). Cohen's d is expressed in standard deviation units. The three shaded regions show what each group's distribution looks like and how much they overlap. You can either use the preset buttons for effect sizes (SLP benchmarks) or the slider to change the values of Cohen's d to examine the distributions.

Correlation Visualizer, Manish Rami

Software

An interactive tool to understand correlations. The tool uses an example relationship between phonological awareness (CTOPP-2) and reading fluency. Both measures use standard scores (M = 100, SD = 15). Viewers can adjust r to explore how the strength and direction of correlation affects the scatter pattern and shared variance. Use the slider for r to see corresponding changes in the plot and the values of r and effect size.

Interactive Distribution Visualizer, Manish Rami

Software

An interactive tool visualizing types of distribution. Viewers can morph between a bell curve, box plot, and cumulative frequency curve.

Reliability Vs. Validity Visualizer, Manish Rami

Software

A visualization tool to demonstrate the concepts of reliability and validity.

Each target represents repeated measurements of the same person or construct. The crosshair (✛) marks the true score. Reliability = how tightly shots cluster together. Validity = whether shots center on the true score. Click any panel for a clinical SLP example. Also see notes below the plots.

R Code And Supporting Data For "Fast And Slow Water Handling Strategies Explain Pinyon Pine Decline And Juniper Expansion", Andrew Kulmatiski, Muhammad Faraz Rehman

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This repository contains the datasets, R scripts, HYDRUS-1D model files, weather data, and supporting materials used for the M.S. thesis, "Fast and Slow Water Handling Strategies Explain Pinyon Pine Decline and Juniper Expansion." The study investigated root water uptake and water-use strategies of pinyon pine (Pinus edulis) and Utah juniper (Juniperus osteosperma) using stable isotope tracer experiments, soil water flow modeling, leaf water potential measurements, stomatal conductance measurements, and environmental data collected across an aridity gradient in southern Utah, USA.

Synthetic-Chicken-Fillets, Chirantan Sen Mukherjee, Seung-Chul Yoon, William J. Beksi

Agriculture

This Synthetic-Chicken-Fillets dataset contains 1,000 synthetic 3D meshes designed to capture the natural variance and size diversity of real broiler fillets. The collection was developed to test automated woody breast detection algorithms within a physics-based simulation environment. We utilized a seed dataset of 2D depth maps derived from 40 real-world RGBD point cloud scans. These real depth maps were fed into a few-shot transfer learning pipeline using a generative adversarial network architecture. The resulting generated depth maps were reconstructed back into 3D meshes. The length and thickness of each mesh were randomly scaled based on physical measurements of real broiler …