Digital Commons Network^™

High Technology And The Developing State: The Arrival Of Supercomputers In India, Ramesh Subramanian

Journal of International Technology and Information Management

While India has made vast strides in information technology in the last few decades, its success is mainly attributed to its software, rather than its hardware sector. In fact, India’s attempts at developing computer hardware that can match international standards have largely been unsuccessful. A notable exception is its development of a series of supercomputers that match and exceed many international standards. This paper looks at an interesting period in India’s computing history – namely the 1980s and 1990s – focusing on its development of an indigenous supercomputer. During that period, supercomputers were thought to be the sole privy of …

Responsible People Analytics For Remote-Work Decisions: A Machine-Learning Benchmark For Classifying Perceived Productivity, Ruth Menjivar, Nima Molavi, Narges Mashhadi Nejad

Journal of International Technology and Information Management

This study examines whether employee-perception survey data can support responsible people-analytics decisions about remote-work productivity. Using the public New South Wales (NSW) Remote Working Survey 2021 (N=1,512), the study benchmarks statistical and machine-learning classifiers for self-reported perceived productivity classes (same, less, or more productive when working remotely relative to onsite work), not objective output, under default, class-weighted, and resampling protocols. Main evidence comes from 5×5 repeated stratified cross-validation using macro F1 and balanced accuracy with fixed model specifications. Class-balanced separability is modest. Random Forest, CatBoost, and LightGBM form a leading cluster with overlapping confidence intervals (macro F1 ≈0.51–0.52). Affective/well-being items, …

The Value Of Personal Data Ecosystems: A Flemish Media Sector Case Study, Maarten De Mildt, Melanie Verstraete, Sofie Verbrugge, Didier Colle

Journal of International Technology and Information Management

Personal Data Stores (PDSs) have been proposed as a privacy-preserving approach to data sharing that increases individual control over personal data while enabling new forms of cross-organizational collaboration. This collaboration leads to the emergence of Personal Data Ecosystems (PDEs). Despite growing interest in PDEs, limited research has examined how the organizational and economic barriers identified in prior studies manifest in practice. This paper investigates these challenges through a case study of the Flemish media sector within the Solid4Media project, which explores the use of PDSs to support data sharing and personalization across media organizations. Using a qualitative research design, data …

Slm With Swarm Intelligence For Efficient Representation Of Medical Claims, Mohamed Ahmed Abo El-Enen, Ravi S. Sharma, Mustafa Abdulrazek, Amril Nazir, Reem Muhammad, Ahmed Talat Sahlol

All Works

Healthcare industry faces significant challenges due to fraudulent medical insurance claims, which result in substantial financial losses. We propose an automated system using domain-specific Small Language Models (SLMs) with a narrower scope and smaller parameter count than general-purpose Large Language Models (LLMs), combined with optimization algorithms to improve fraud detection. Our approach integrates numerical features, such as age and claim amount, with textual descriptions, including diagnoses and procedures, into a unified textual representation for each medical activity. This representation captures complex patterns, enhancing the model’s predictive ability. SLMs fine-tuned on medical corpora transform these textual inputs into fixed-dimensional numerical embeddings, …

The Deepfake Litmus Test: A Multimedia Authenticity Mechanism, Amna Alzaabi, Hessa Alqubaisi, Fatima Alzaabi, Richard Ikuesan

All Works

Deepfake technologies have made it increasingly difficult to distinguish authentic video content from manipulated media. This paper presents a forensic detection framework, referred to as the Litmus Test, which focuses on structural analysis of MP4 container files to detect signs of tampering. Unlike conventional AI-based approaches that operate as black boxes, this method examines the atomic composition of video containers to identify anomalies. The proposed method performs atom-level inspection of MP4 file hierarchies and structural markers to uncover anomalies indicative of synthetic manipulation. Evaluations using datasets such as CelebDF, UADFV, and DeeperForensics reveal that the framework can identify inconsistencies common …

Modelling Route-Level Interzonal Travel Time Using Gps Trajectories, Muhammad Faiq Ahmed, Tom Bellemans, Fatma Outay, Muhammad Ahmed, Afzal Ahmed, Feng Liu, Muhammad Adnan

All Works

Activity-based models (ABMs) require accurate travel-time estimates for accessibility calculations, yet many implementations rely on static routing outputs that fail to capture temporal congestion dynamics due to limited high-resolution data. This paper develops route-level travel-speed prediction models using GPS trajectory data from 48 vehicles in Flanders, Belgium. GPS trajectories are integrated with OpenStreetMap and land-use data through destination-based segmentation, in which trips from fixed origins are cumulatively segmented at zone crossings. To capture behavioural differences by trip length, separate Gamma regression models are estimated for short (≤5 km) and long (>5 km) trips using temporal, network, and spatial variables. …

Smart Health Care Application For Predicting Complications Risk In Type 2 Diabetes Management Using Personalized Digital Twins: A Focus On Early Intervention And Prevention Strategies, Haifaa Alkaabi, Ahed Abugabah

All Works

The study examined the application of Personalized Digital Twins (PDTs) to prevent complications during the management of Type 2 Diabetes, especially in early intervention and prevention plans. Based on a high-quality dataset related to the CDC Behavioral Risk Factor Surveillance System (BRFSS) data, we tested multiple predictive models such as the Random Forest, Gradient Boosting machines (GBM), and Extreme Gradient Boosting (XGBoost). We developed a composite risk indicator from established clinical risk factors (hypertension, dyslipidemia, elevated BMI) to stratify complication risk. The Random Forest model achieved 99% accuracy (AUC: 0.98) at the population-level risk classification. The GBM model was optimized …

Adaptive Multi-Agent Learning For Infrastructure-Aware Its: The Imer Data-Processing Approach, Mayssa Hamdani, Nafaa Jabeur, Ansar Yasar, Fatma Outay, Li Li

All Works

The performance of Intelligent Transportation Systems (ITS) critically depends on accurate and efficient road-condition monitoring. This paper presents IMER (Inspect–Map–Eliminate–Reduce), a novel AI-driven data-processing framework that extends the traditional Map-Reduce paradigm for infrastructure maintenance. IMER integrates confidence-based validation, redundancy elimination, and severity prioritization to enhance data quality and decision efficiency. Implemented within a multi-agent architecture, IMER enables autonomous agents to inspect, classify, and fuse multi-source road data in real time, supporting predictive and adaptive maintenance planning. Simulation results using augmented pothole datasets demonstrate a 39.9 % reduction in redundant reports and 39.8 % fewer false positives. These findings highlight IMER’s …

A Design Science Research Architecture For Xr-Based Pre-Visit Cultural Heritage Learning Applications, Mousa Al-Kfairy, Omar Alfandi, Saed Alrabaee

All Works

Pre-Visit preparation plays a critical role in shaping visitors’ learning and engagement in cultural heritage sites; however, existing approaches largely rely on static and passive materials that fail to foster meaningful understanding before the physical visit. Extended Reality (XR) technologies offer new opportunities to address this gap by enabling immersive, narrative-driven pre-visit learning experiences. This paper proposes a conceptual architecture for XR-based pre-visit cultural heritage learning applications, grounded in Design Science Research (DSR). Drawing on museum pedagogy, experiential learning, and XR interaction design, the study identifies key educational and technical requirements and translates them into a layered, modular system architecture. …

Mapping Llm Misuse In Computing Education: A Survey-Based Risk Analysis Of Faculty And Student Contexts, Noura Alzaabi, Mohamed El-Attar, Sarah Kohail, Mahmood Niazi

All Works

Large Language Models (LLMs) have become deeply embedded in computing higher education, yet the misuse risks they introduce for faculty and students remain insufficiently understood from a cybersecurity and data privacy perspective. This paper presents an empirical study in which a structured survey of 105 participants at a computing college was used to identify and systematically risk-score thirteen LLM misuse cases across faculty and student contexts. Using a Likelihood × Impact scoring model, the resulting taxonomy classifies misuse cases as Critical, High, or Medium severity, with over-reliance and skill atrophy, academic integrity violations, and research integrity risks emerging as the …

Evaluating Chatgpt-5 For Misuse Case Diagram Generation: An Empirical Evaluation, Alia Alzarooni, Yasser Khan, Hassan Alsayegh, Mohamed El-Attar, Rima Grati

All Works

Misuse case diagrams are a widely adopted technique in security requirements engineering, enabling analysts to model adversarial threats and derive countermeasures early in the software development lifecycle. However, manual construction of these diagrams is prone to incompleteness and subjectivity, requiring significant security expertise. Large language models (LLMs) such as ChatGPT present a promising opportunity to automate this process, yet their effectiveness for generating structured security modeling artifacts remains largely unexplored. This paper presents an exploratory study evaluating ChatGPT-5's ability to generate misuse case diagrams directly from textual security requirements, using 12 case studies of varying complexity spanning small, medium, and …

Aoi-Aware Agentic Federated Mixture-Of-Digital-Twin Experts For 6g Vehicular Edge Intelligence, Asadullah Tariq, Mohamed Adel Serhani, Ikbal Taleb, Shayma Alkobaisi, Tariq Qayyum, Irfan Ud Din

All Works

Digital twin-enabled vehicular edge intelligence is expected to become a fundamental service paradigm for sixth-generation (6G) intelligent transportation systems. However, the performance of such systems depends not only on model accuracy, but also on the freshness of digital twin states, timeliness of inference, privacy-preserving model training, and efficient use of heterogeneous edge resources. Existing DT-assisted federated learning and edge mixture-of-experts solutions optimize digital twin synchronization, distributed learning, and sparse inference largely independently, without allowing digital twin states to actively govern expert specialization, expert refreshing, and distributed orchestration. Nevertheless, the joint problem of how digital twins should guide federated expert specialization, …

Drone Authentication System Using Radio Frequency Fingerprinting, Jamila Muhsen Alnuaimi, Shamma Ghaleb Almansoori, Noura Ahmed Alrumeithi, Richard Ikuesan

All Works

The widespread integration of unmanned aerial vehicles (UAVs) across domains such as logistics, surveillance, and emergency response has introduced critical security challenges, particularly unauthorized access, identity spoofing, and drone cloning. Traditional software-based authentication methods, including GPS tracking and encryption, have proven inadequate against advanced cyber-physical threats. This paper proposes a secure and automated drone authentication framework based on Radio Frequency (RF) fingerprinting, leveraging intrinsic hardware-level signal imperfections to generate unique and unclonable drone identities. Using Random Forest classifiers, the system captures, preprocesses, and analyses RF features to distinguish between authorized and unauthorized UAVs. Validation with real-world RF datasets demonstrates high …

Human Factors In Visual Attention: Gender Differences In Engagement With Male-Oriented Ads, Mohamed Basel Almourad, Emad Bataineh, Zelal Wattar, Mohammed Hussain

All Works

In today's competitive market, it is increasingly important to understand how visual design shapes customer behaviour. This study examines the decision drivers influencing female customers' purchase choices when buying male-oriented products as gifts, identifying the visual components of advertisements that attract them by analysing the relationship between purchase intention and visual attention. Results show that participants with higher purchase intent focused more on product imagery and branding, indicating that visual appeal, perceived quality, and brand familiarity significantly guide their decisions, with brand awareness speeding up decision-making by reducing the need for repeated visual checks. Conversely, those with low purchase intent …

Towards A Context-Aware Driving Assistance System (Ca-Das): Advancing Intelligent Vehicular Safety Through Multimodal Context Integration, Fatma Outay, Siham Farrag, Anjum Zameer, Ansar Yassar

All Works

Driving-related behavioural factors are responsible for 90% of traffic collisions. The rapid growth of urbanization and the complexity of the traffic conditions demand a smart, efficient, and flexible transportation system. The advancement of transportation through technologies such as the Internet of Things (IoT) and AI have reshaped the way that drivers interact with their vehicles and the surrounding environment. In this paper, we propose a comprehensive Context-Aware Driving Assistance System (CA-DAS) that employs sensor fusion, semantic context modelling, along with a machine-learning-based approach to provide personalised and proactive driving assistance across dynamic scenarios. The proposed CA-ADS was developed using a …

From Image To Insight: Evaluating Llm Accuracy In Understanding Uml Use Case Diagrams With Claude, Mohamed El-Attar, Yasser Khan, Mahmood Niazi, Sajjad Mahmood, Mohammad Alshayeb

All Works

UML use case diagrams are a prominent artefact of requirements engineering, capturing the functional scope of a software system in terms of actors, use cases, and their stereotyped relationships. The emergence of multimodal large language models with image understanding capabilities raises the question of whether such models can reliably extract structured construct-level information from use case diagram images. This paper reports an empirical evaluation of Claude on the task of counting 14 notational construct types from a corpus of 78 computer-generated UML use case diagrams, assessed against manually verified ground truth annotations. Results reveal a strongly differentiated accuracy profile: Claude …

Epistemic Edge: Subjective Logic Guardrails For Llm-Driven Iot Actuation, Muntaser Syed, Marius Silaghi

Electrical Engineering and Computer Science Student Publications

Deploying large language models (LLMs) as decision-making agents in safety-critical Internet of Things (IoT) systems introduces risks that purely behavioral guardrails, such as action whitelists, cannot fully address. This paper presents Epistemic Edge, a four-tier neuro-symbolic pipeline that augments LLM-driven actuation with subjective logic (SL) uncertainty quantification, temporal decay, and dual guardrails combining epistemic threshold checks with behavioral whitelists. We evaluate seven locally-deployed models: three PrismML Bonsai 1-bit models (1.7B, 4B, 8B), three 4-bit quantized models (Qwen3-8B, Llama 3.2-3B, Phi-3.5-mini), and the DeepSeek-R18B reasoning model, across eight ablation conditions and five IoT actuation scenarios (2,800 controlled trials). We further validate …

Corporations’ First Amendment Rights And Algorithmic Cocoons, Sol Murgui Orsucci

Fordham Journal of Corporate & Financial Law

This Note examines how corporate First Amendment doctrine applies to algorithmic systems that curate commercial information and public discourse across online platforms. Courts have increasingly treated ranking, recommendation, and moderation practices as exercises of protected editorial judgment. At the same time, the Court has justified protection for corporate speech in part by reference to listener-centered interests in access to information and competitive comparison. This Note argues that the interaction between these doctrinal strands and contemporary algorithmic intermediation reveals a structural tension. Data-driven optimization can generate individualized informational environments—what this Note terms “algorithmic cocoons”—in which users encounter content, offers, and prices …

Oxytocin Dosing During Trial Of Labor After Cesarean To Minimize The Risk Of Uterine Rupture: A Systematic Review And Meta-Analysis, Pierpaolo Nicolì, Moti Gulersen, Jordan Beacham, Hila Hochler, Alessandra Familiari, Anna Locatelli, Cynthia Abraham, Ettore Cicinelli, Amerigo Vitagliano, Vincenzo Berghella

Department of Obstetrics and Gynecology Faculty Papers

OBJECTIVE: Oxytocin remains the mainstay for induction and for the management of labor arrest during trial of labor after cesarean (TOLAC). Therefore, the clinical question should be not whether to use oxytocin, but how to optimize its administration. This meta-analysis aimed to assess which oxytocin protocol, in terms of initial dose, amount of increase, timing to next increase, and maximum dose, may minimize the risk of uterine rupture (UR) during TOLAC.

DATA SOURCES: PubMed, Embase and Clinicaltrials.gov were searched up to September 21, 2024.

STUDY ELIGIBILITY CRITERIA: Randomized and nonrandomized studies evaluating the association between induction and/or augmentation with oxytocin …

Impact Of Prophylactic Oral Azithromycin During Labor On Azithromycin Resistance (Amr) In Nasal Staphylococcus Aureus And Streptococcus Pneumoniae In Women And Infants In The Multi-Country Azithromycin Prevention In Labor Use Study (A-Plus)., Patricia L. Hibberd, Jean H. Kim, Marissa Trotta, Sixto M. Leal, Anna Aceituno, Doyle V. Ward, Archana Patel, Akila Subramaniam, Waldemar A. Carlo, Imran Ahmed, Sarah Saleem, Sk Masum Billah, Rashidun Haque, Manolo Mazariegos, Fabian Esamai, Manjunath S. Somannavar, Shivaprasad S. Goudar, Elwyn Chomba, Muska Mwenchanya, Adrien Lokangaka, Antoinette Tshefu, Robert L. Goldenberg, Melissa Bauserman, Nancy F. Krebs, Sherri Bucher, Richard J. Derman, William A. Petri, Marion Koso-Thomas, Denise C. Babineau, Elizabeth M. Mcclure, Alan T. N. Tita

Department of Obstetrics and Gynecology Faculty Papers

BACKGROUND: Prophylactic oral azithromycin vs. placebo reduced maternal, but not neonatal, mortality/sepsis in the A-PLUS Randomized Trial. While prophylactic intrapartum azithromycin reduces maternal mortality/sepsis, it may promote antimicrobial resistance (AMR) in commensal bacteria,.

METHODS: Randomly selected women and their infants participating in A-PLUS were enrolled in a longitudinal cross-sectional sub-study to assess the presence of azithromycin resistance in selected bacteria in nasal cultures. Staphylococcus aureus and Streptococcus pneumoniae were cultured on selective agar, then azithromycin-containing agar to select for azithromycin resistant bacteria, identified biochemically. Azithromycin susceptibility was assessed by E-test. Nasal cultures were collected from women and infants between August …

Association Of Mhc Class Ii Gene Polymorphisms With Gastroenteritis Susceptibility In Shiraz Cats, Taghrid Hamza, Basma M Hendam, Ahmed Ateya, Usama Abou-Ismail

Mansoura Veterinary Medical Journal

BACKGROUND: The aim of this study is to consider the genetic alterations in the major histocompatibility complex (MHC) gene and its effect on Gastroenteritis tolerance/susceptibility in Shiraz cats.

METHODS: Blood samples were collected into tubes containing EDTA as anticoagulant from twenty Shiraz Tom cats (10 healthy and 10 suffering from gastroenteritis) to extract DNA. Then, PCR was performed to amplify a 500-bp fragment of the MHC gene.

RESULT: Genetic analysis via PCR-DNA sequencing identified specific single-nucleotide polymorphisms (SNPs) within the MHC gene, namely A115C, G399T, and A363G. The A363G variant was linked to increased disease risk (OR=13.44), while …

The Role Of Dromedary Camels In The Pprv Epidemiology: Episystem Possibility And Evidence Supporting Global Eradication., Reham Karam, Mahmoud S. Safwat

Mansoura Veterinary Medical Journal

Background: Peste des petits ruminants (PPR) is a notifiable, highly contagious and transboundary viral disease infecting a wide range of domestic and wild ruminant species and is caused by a morbillivirus. The disease represents a major predisposing factor of poverty in many endemic regions. Although global eradication of PPR is an internationally endorsed objective, achieving this goal has become increasingly challenging due to the expanding host range of the virus. Methods: Narrative review methodology was used and SANRA criteria to access reliability and quality of the methodology. focuses on PPR infection in camels, which has been reported in some instances …

My Negotiation Chatbot Toby And Pedagogical Governance, Renee Henson

Faculty Publications

The use of artificial intelligence (AI) chatbots is exploding. Adolescents increasingly prefer interactions with AI companions over peers. Adults rely on these systems for health planning, therapy, administrative assistance, nutrition guidance, and even simulated “romantic relationships.” Individuals and institutions are adopting AI tools at a pace that far exceeds the development of governance frameworks.

I developed Toby for use in my first-year Lawyering: Problem-Solving and Dispute Resolution (“Lawyering”) course to expose students to the dynamics of negotiating against an opposing counsel with a difficult personality—a type of adversary all too common in legal practice, yet difficult to prepare for. Toby …

Scholarly Program Notes On A Graduate Viola Recital, Clayton J. Bridgeman

Research Papers

No abstract provided.

Boundaries Of Rules: Social Reactions To Legal Intervention, Francesco Parisi

Articles

This chapter examines what happens at the boundaries of rules: when social practices acquire legal recognition, when legal rules seek to reshape social expectations, and when formal and informal enforcement operate together. The conventional economic model treats the expected formal sanction as the principal source of compliance. Although this model provides a fundamental benchmark, it captures only part of the effects of legal intervention. The taxonomy developed here identifies the different ways in which legal, moral, and social incentives may interact. The kinds of public and private enforcement through which individuals express norms shape the additive, nonadditive, and countervailing effects …

Exercise Rehabilitation And Functional Capacity In An Older Adult Cancer Survivor, Ibsaa G. Benti

Research Papers

No abstract provided.

Therapeutic Landscape Of Early Symptomatic Alzheimer's Disease Translated Into Everyday Practice For Geriatric Providers., Melissa J Bailey-Taylor, Paula Morrow, James Hendrix, Katherine E. Galluzzi, Golnosh Sharafsaleh

PCOM Scholarly Works

Alzheimer's disease (AD) accounts for 60-80% of all dementia cases. Recent advances in diagnostic biomarkers of early symptomatic AD (ie, mild cognitive impairment and mild dementia due to AD) and amyloid-targeting therapies (ATTs) have the potential to improve outcomes for patients with AD. Two ATTs (donanemab and lecanemab) are currently approved and available for use in the US. Both ATTs can slow disease progression as well as cognitive and functional decline in patients with early symptomatic AD. Treatment with ATTs is associated with specific safety concerns such as amyloid-related imaging abnormalities. Therefore, the benefit versus risk profile needs to be …

Bankruptcy As Presidential Resistance, Alvin Velazquez

Fordham Urban Law Journal Online

No abstract provided.

Deep Learning Based Classification Of Retinal Oct Images Using Custom Cnn And Resnet50, Ahmed Afzal

ICT

Retinal diseases such as Choroidal Neovascularization, Diabetic Macular Edema and Drusen can affect vision if they are not detected and treated early. Optical Coherence Tomography images provide detailed cross sectional views of the retina, making them useful for identifying retinal abnormalities. This project investigates the use of deep learning for classifying retinal images from the OCT2017 dataset into four categories: CNV, DME, DRUSEN, and NORMAL. The project follows the CRISP-DM methodology, covering data understanding, data preparation, modelling, evaluation and deployment. During data preparation, duplicate images were removed, image dimensions were standardised and class imbalance was addressed using class weights during …

Pxaa: A Root-Cause Decision-Support System For Airline Passenger Complaints Using Sentiment Analysis And Complaint Classification, Adélia Acacio Da Silva, Stefani Rabelo De Campos Nunes

ICT

PXAA (Passenger Experience Analytics Assistant) is a machine learning and decision-support prototype developed to analyse airline passenger complaints using sentiment analysis and complaint classification techniques. The project follows the CRISP-DM methodology and uses airline passenger reviews collected from AirlineQuality.com through Python web scraping. Natural language processing (NLP) techniques were applied to clean and preprocess the textual data, while logistic Regression was used to classify passenger sentiment into positive, negative and neutral categories.

The project also implemented rule-based complaint classification to identify common complaint themes such as delays, baggage issues, customer service problems, comfort issues, payments/refund complaints and communication failures. These …