Digital Commons Network^™

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 …

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 …

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 …

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 …

A Unified Dnn Weight Compression Framework Using Reweighted Optimization Methods, Mengchen Fan, Tianyun Zhang, Xiaolong Ma, Jiacheng Guo, Zheng Zhan, Et. Al.

Computer Science Faculty Publications

To address the large model sizes and intensive computation requirements of deep neural networks (DNNs), weight pruning techniques have been proposed and generally fall into two categories: static regularization-based pruning and dynamic regularization-based pruning. However, the static method often leads to either complex operations or reduced accuracy, while the dynamic method requires extensive time to adjust parameters to maintain accuracy while achieving effective pruning. In this paper, we propose a unified robustness-aware framework for DNN weight pruning that dynamically updates regularization terms bounded by the designated constraint. This framework can generate both non-structured sparsity and different kinds of structured sparsity, …

Assessing The Effectiveness Of Crawlers And Large Language Models In Detecting Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the influence of these attacks on data collection by web crawlers, which famously elude conventional detection techniques using large language models (LLMs). Empirically, we find some vulnerabilities in the current crawler mechanisms and large language model detection, especially in code inspection, and propose enhancements that will help mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, …

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 …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

The Density Distribution Of The Material Around Betelgeuse, Iman Behbehani

Dissertations, Theses, and Capstone Projects

Betelgeuse is a red supergiant star visible in the constellation Orion. Its windy and highly convective surface results in a complicated mass loss pattern difficult to understand and replicate in simulations. The ejected mass can form a shell around the star we consider the circumstellar material (CSM). In this study, we use ALMA interferometric observations to find the structure of Betelgeuse's CSM, and connect to the mass loss mechanisms that could form it. We measure a bipolar circumstellar structure with a position angle of 42.3$\pm 7.0^\circ$. We observe asymmetries in the form of hot spots in the north east of …

Exact And Approximate Conformal Inference For Multi-Output Regression, Chancellor Johnstone, Eugene Ndiaye

Faculty Publications

It is common in machine learning to estimate a response y given covariate information x . However, these predictions alone do not quantify any uncertainty associated with said predictions. One way to overcome this deficiency is with conformal inference methods, which construct a set containing the unobserved response with a prescribed probability. Unfortunately, even with a one-dimensional response, conformal inference is computationally expensive despite recent encouraging advances. In this paper, we explore multi-output regression, delivering exact derivations of conformal inference p-values when the predictive model can be described as a linear function of y . Additionally, we introduce a multivariate …

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.

Script-Based Inferences In An Image Schema Story Understander, Jamie C. Macbeth, Boming Zhang, Sharmin Badhan

Computer Science: Faculty Publications

Recent studies of large language models (LLMs) have revealed that they lack human-like cognitive models of reasoning and understanding. An important thread of research merges image schemas into symbolic artificial intelligence systems where their use as conceptual building blocks and primitives shows promise for the study of human cognition through intelligent systems that perform neurosymbolically. The work presented in this paper demonstrates image schema primitives being used in structures of a representation system called conceptual dependency (CD) and in broader commonsense knowledge structures called scripts. We present a story under- standing system that uses image schemas as primitives in scripts …

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.

Reinforcement Learning Based Resource Management In Edge Systems, Motahare Mounesan

Dissertations, Theses, and Capstone Projects

Modern end-user applications that are highly compute- and data-intensive, while being extremely latency- and accuracy-sensitive, are increasingly reliant on distributed computing. This paradigm spans a range of architectures, from cloud computing to in-device processing. Cloud computing, though scalable, often incurs high latency and cost, constraints that are particularly problematic for time-sensitive applications. In contrast, in-device computing on end or IoT devices is limited by resource constraints, making it inadequate for many complex workloads. Edge computing presents a compelling alternative by bringing computation closer to data sources, thereby reducing end-to-end latency and improving responsiveness. However, the inherent decentralized and dynamic nature …

Towards Automated Evolution Of Imperative Deep Learning Programs, Tatiana Castro-Vélez

Dissertations, Theses, and Capstone Projects

Software engineering (SE) is increasingly intersecting with data-centric domains such as machine learning (ML) and deep learning (DL). Similar to bugs in traditional software systems, defects can emerge in ML and DL systems. ML, including DL, systems are now widespread and rely on dynamic models defined by input data. Developers face the challenge of building dependable systems while addressing the demand for scalable software.

Efficiency is essential to support responsiveness with respect to ever-growing datasets. Traditional DL frameworks achieve scalability through deferred execution, enabling symbolic, graph-based deep neural network (DNN) computation. While efficient, this approach is error-prone, cumbersome, and difficult …

Multi-Modal Depth Estimation Using Camera And Mmwave Sensors, Alston D. Devero-Belfon

Student Theses

For accurately estimating the depth of environments with varying lighting conditions, reliable methods are limited. By utilizing wireless sensor technology in conjunction with cameras, a wide range of environments can be visualized, and objects within these environments can be tracked and monitored. Such methods offer cost-effective alternatives and provide a more secure, data-at-rest option for individuals with low vision, while also enhancing machine perception. In this work, we develop such a prototype that utilizes wireless sensors and cameras, which act in sync, enabling us to estimate the depth of objects within varying lighting environments to a level that is recognizable …

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 …

The “Founder’S Gaze”: How The Fourth Amendment Is A Surveillance Technology That Enables Ai To Scale Control Over The Subaltern, Diego H. Alcalá Laboy

Michigan Journal of Race and Law

Much has been written about the rise of artificial intelligence and machine learning applications and how the current Fourth Amendment law has been unable to mitigate the privacy harm that these tools produce. This article explores how the development and usage of AI and machine learning models is dependent on the originalism principles of Fourth Amendment Law. Utilizing Critical Surveillance Studies and Anticolonial Theory, I posit that the Fourth Amendment is a surveillance technology that categorizes conduct, persons, and places to impose the material conditions for the subjugation of historically minoritized communities within the United States. Furthermore, this article explores …

Boosting Symbolic Execution For Vulnerability Detection, Haoxin Tu

Dissertations and Theses Collection (Open Access)

Software systems written by humans tend to be unreliable and insecure, hence, bugs or vulnerabilities in them are inevitable. Symbolic execution has shown considerable potential in detecting diverse types of software bugs and also vulnerabilities that have severe security implications. However, existing symbolic execution engines still suffer from at least three fundamental limitations in memory modeling, path exploration, and structured input generation, which significantly impede existing engines from efficiently and effectively detecting software bugs and vulnerabilities.

The objective of this dissertation is to boost existing symbolic execution engines by designing a new memory model, two new path exploration strategies, and …

Memory-Efficient Graph Processing On Gpus: Reducing Intermediate Data Structure Overhead, Chang Ye

Dissertations and Theses Collection (Open Access)

The increasing scale of real-world graphs in domains such as fraud detection, community detection, and biological analysis demands high-throughput, memory-efficient graph processing solutions. GPUs offer massive parallelism for accelerating such workloads, and numerous frameworks have been developed to leverage their computational power. These frameworks primarily focus on optimizing scheduling to better align graph processing with GPU architectures. It performs well for algorithms with low memory demands, such as BFS, SSSP, and PageRank. However, for algorithms that require substantial memory, such as label propagation, and subgraph counting, the limited memory capacity of GPUs often becomes a significant bottleneck.

This dissertation addresses …

Optimal Abort Policy For Mission-Critical Systems Under Imperfect Condition Monitoring, Qiuzhuang Sun, Jiawen Hu, Zhi-Sheng Ye

Research Collection College of Integrative Studies

Although most on-demand mission-critical systems are engineered to be reliable to support critical tasks, occasional failures may still occur during missions. To increase system survivability, a common practice is to abort the mission before an imminent failure. We consider optimal mission abort for a system whose deterioration follows a general three-state (normal, defective, failed) semi-Markov chain. The failure is assumed self-revealed, whereas the healthy and defective states have to be inferred from imperfect condition-monitoring data. Because of the non-Markovian process dynamics, optimal mission abort for this partially observable system is an intractable stopping problem. For a tractable solution, we introduce …

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. …

Scientific Multimodal Summarization : Integrating Knowledge Across Textual, Visual And Auditory Content, Zusheng Tan

Lingnan Theses (MPhil & PhD)

As scientific publications increasingly incorporate multimodal content, ranging from textual descriptions to figures, tables, presentation videos, and audio, there is a growing need for summarization systems that can effectively process and integrate information across these diverse modalities.

This work presents a comprehensive exploration of Scientific Multimodal Summarization, introducing a series of novel architectures and datasets aimed at advancing this emerging field. 1): We begin by introducing CMT-Sum, which integrates multimodal scientific source content (i.e., primarily paper text and figures) to generate high-quality textual summaries and identify representative graphical abstracts. We refer to this task as Scientific Multimodal Summarization with …

Machine Learning-Based Electric Vehicle Charging Demand Forecasting: A Systematized Literature Review, Maher Alaraj, Mohammed Radi, Elaf Alsisi, Munir Majdalawieh, Mohamed Darwish

All Works

The transport sector significantly contributes to global greenhouse gas emissions, making electromobility crucial in the race toward the United Nations Sustainable Development Goals. In recent years, the increasing competition among manufacturers, the development of cheaper batteries, the ongoing policy support, and people’s greater environmental awareness have consistently increased electric vehicles (EVs) adoption. Nevertheless, EVs charging needs—highly influenced by EV drivers’ behavior uncertainty—challenge their integration into the power grid on a massive scale, leading to potential issues, such as overloading and grid instability. Smart charging strategies can mitigate these adverse effects by using information and communication technologies to optimize EV charging …

Advancing U.S. Competitiveness In Agentic Gen Ai: A Strategic Framework For Interoperability And Governance, Satyadhar Joshi

Harrisburg University Other Works

Abstract : The rapid evolution of artificial intelligence has given rise to agentic AI systems—autonomous entities capable of perceiving their environment, making decisions, and executing actions with minimal human intervention. This work provides a systematic analysis of agentic AI frameworks, governance models, and implementation strategies. Drawing on a comprehensive review of the literature, we examine the current state of agentic AI technologies, highlight key challenges in governance, security, and ethical oversight, and compare architectural frameworks for responsible deployment. Our results, illustrated through detailed framework comparisons and governance analyses, demonstrate that while agentic AI holds transformative potential across multiple sectors, notable …

A Dynamic Hierarchical Attention Framework For Multimodal Malware Detection, Tamanna Nazmin

Graduate Theses and Dissertations

The increasing use of Android in the worldwide mobile ecosystem has come along with a significant increase in advanced malware, highlighting the critical necessity for efficient, scalable, and adaptable detection systems. Despite recent advancements in machine learning improving malware detection, the majority of current solutions are limited to one, two, or three data modalities, hence neglecting the comprehensive behavioral spectrum of contemporary multi-vector threats. This thesis presents the first comprehensive multimodal framework for Android malware detection, which combines textual, time-series (temporal), graph-based (structural), and visual information using an innovative hierarchical attention mechanism and Dynamic Fusion Controller(DFC). Our methodology consistently classifies …

Deep Learning For Land Use Classification: A Systematic Review Of Hs-Lidar Imagery, Muhammad Zia Ur Rehman, Syed Mohammed Shamsul Islam, David Blake, Anwaar Ulhaq, Naeem Janjua

Research outputs 2022 to 2026

Remote sensing (RS) technologies have significantly advanced Earth observation capabilities, enhancing the characterization and identification of surface materials through both spaceborne and airborne systems. These advancements are crucial for improving environmental monitoring and urban planning. As RS datasets have become more accessible, their increased complexity has necessitated a shift from traditional machine learning techniques to more robust deep learning approaches, particularly convolutional neural networks (CNNs) and transformer-based models known for their superior feature extraction capabilities. This systematic review focuses on the application of these deep learning techniques in land use classification, emphasizing the fusion of hyperspectral (HS) and LiDAR data. …

A Study Of The Privacy Paradox Amongst Young Adults In The United Arab Emirates, Lena Yuryna Connolly, Michael Lang, Justin Giboney

All Works

The rapid digitalisation of society has significantly increased the collection and processing of personal data, raising concerns about individuals’ privacy. The privacy paradox, where individuals express privacy concerns yet continue to disclose personal information, has been widely studied in Western and Asian contexts, but remains underexplored in the Arab world. This study investigates privacy attitudes and behaviors in the United Arab Emirates (UAE), a region at the crossroads of traditional Islamic values and Western influences. Using survey data from 216 Emirati university students, we tested a model that incorporates five constructs: peer interaction and influence, desire for privacy, privacy concerns, …

A Difficult Act To Maintain, J.G. Allen

Research Collection Yong Pung How School Of Law

In "Where is Singapore's AI regulation headed?" (Issues, Summer 2025), Manoj Harjani starts by recounting its delicate balancing act in governing artificial intelligence. The city-state is able to maintain credibility on the global stage while remaining pragmatically grounded in technical experimentation, maintaining policy realism without overcommitting to rigid legal frameworks. The choice to adopt a regulatory “light touch” is often framed as a way to maintain flexibility, avoid overregulation, and enable innovation. But this obscures the extent to which this posture is itself a political and economic settlement—one that reinforces Singapore’s position in the global digital economy by facilitating capital …

Bridging The Great Wall: China’S Evolving Cross-Border Data Flow Policies And Implications For Global Data Governance, Sheng Zhang, Henry S. Gao

Research Collection Yong Pung How School Of Law

Despite the rapid expansion of the digital economy, the global regulatory framework for data flows remains fragmented, with countries adopting divergent approaches shaped by their own regulatory priorities. As a key player in the Internet economy, China’s approach to cross-border data flows (CBDF) not only defines its domestic digital landscape but also influences emerging global norms. This paper takes a comprehensive view of the evolution of China’s CBDF regime, examining its development through both domestic and international lenses. Domestically, China’s regulation of CBDF has evolved from a security-first approach to one that seeks to balance security with economic development. This …