Digital Commons Network^™

An Open-Source Evaluation Framework For Risc-V Co-Design-Based Decimal Arithmetic, Riaz Ul Haque Mian, Michiko Inoue

Research outputs 2022 to 2026

Hardware–software co-design is a balanced strategy for computationally intensive algorithms such as decimal computing. It can provide several Pareto points for the development of embedded systems in terms of hardware cost and performance. In this study, we propose an efficient and accurate evaluation framework for decimal computing. The framework was designed and developed for hardware–software co-design decimal arithmetic using the RISC-V ecosystem. New binary and decimal-oriented instructions supported by an accelerator were developed. The framework can perform cycle-accurate analysis for performance and assess hardware overhead for co-design-based decimal arithmetic. Unlike previous studies that focused primarily on implementing and evaluating individual …

Continuous Authentication For Industrial System Access: Evaluating Bluetooth Low Energy Direction Finding And Channel Sounding For Tailgating And Relay Attacks, Mitchell Mennelle

LSU New Orleans Theses and Dissertations

In physical access control, authentication is often viewed as a one-time event, where, once an authorized user crosses a protected boundary, downstream systems assume the user remains physically present. Tailgating and relay attacks violate this assumption. In this thesis we propose a continuous authentication layer based on two Bluetooth Low Energy spatial signals. Angle of Arrival direction finding follows the trail of a worn credential to determine when an operator exits a work zone. Bluetooth Channel Sounding measures a physical property of the radio path and verifies distance during stationary periods. Limiting Relay Attacks with Event-Driven Distance Verification. A stream …

Artificial Intelligence (Ai) In Forensic Psychology: An Umbrella Review Of Potentials And Pitfalls, Ysabel Thereze Ang Guevarra, Nur Eva Alisha Binte Mohamed Hisham, Andree Hartanto

Research Collection School of Social Sciences

Artificial intelligence (AI) is becoming increasingly embedded within forensic psychological practice, shaping how criminal risk, legal responsibility and public safety are assessed. AI tools are now used in recidivism prediction, behavioural analysis, deception detection and investigative support, high-stakes domains where errors can have profound consequences. Despite this rapid adoption, the existing literature remains fragmented, with most reviews confined to narrow subdomains and offering limited integrated synthesis of AI′s broader role in forensic psychology. Thus, this umbrella review addresses this gap by synthesising findings from 43 reviews obtained from five major databases, namely EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus and Web …

Enhancing Stem Education With Modeling, Simulation, And Ai Technologies: From Virtual Laboratories To Intelligent Teaching Assistants, Yiyang Li

Electrical & Computer Engineering Theses & Dissertations

Rapid advancements in modeling and simulation (M&S) and artificial intelligence (AI) present new opportunities to enhance various aspects of STEM education, from virtual laboratories that simulate physical lab environments in software to intelligent teaching assistants that provide on-demand, curriculum-aligned instructional support. Virtual laboratories offer a potential solution to the access and scalability challenges of laboratory courses by allowing students to conduct experiments without physical equipment or geographical constraints. AI-powered teaching assistants, particularly those grounded in course-specific materials, can help mitigate the instructional support gap that arises when students work independently in digital learning environments. This dissertation presents three-phase research into …

Modeling And Generating Crash Avoidance Behaviors In Safety-Critical Vehicle–Pedestrian Interactions Using Deep Reinforcement Learning, Qingwen Pu

Civil & Environmental Engineering Theses & Dissertations

Traffic crashes between vehicles and pedestrians arise from complex, split-second interactions in which both parties make rapid evasive decisions. Four fundamental gaps persist in existing research. Surrogate safety measures assume linear trajectories, failing to capture the curved movements of turning vehicles and crossing pedestrians at intersections. Single-agent modeling treats one party as a fixed obstacle, ignoring the joint decision-making that governs near-miss outcomes. The effect of vehicle type on pedestrian avoidance behavior—whether pedestrians respond differently to automated vehicles (AVs) than to human-driven vehicles (HDVs)—remains poorly understood. Finally, automated driving system development is hampered by a severe scarcity of large-scale, behaviorally …

Physics-Guided Deep Learning For Predictive Modeling Of Spatiotemporal Dynamical Systems, Niharika Deshpande

Engineering Management & Systems Engineering Theses & Dissertations

Many physical and networked systems evolve under continuously changing spatial and temporal conditions. Transportation networks respond to fluctuating demand, atmospheric fields reorganize as storms intensify, and coastal response depends on localized forcing pathways. Modeling such systems requires learning formulations that adapt to evolving structure, operate on irregular geometries, and provide interpretable measures of predictive uncertainty. This dissertation develops a physics-guided spatiotemporal learning framework designed for structured dynamical systems whose governing interactions are neither static nor Euclidean. The central premise is that spatial relationships in these systems are dynamic and geometry-dependent. To represent this behavior, system states are modeled on time-varying …

Efficient Test-Time Retrieval Augmented Generation, Hailong Yin, Bin Zhu, Jingjing Chen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Although Large Language Models (LLMs) demonstrate significant capabilities, their reliance on parametric knowledge often leads to inaccuracies. Retrieval Augmented Generation (RAG) mitigates this by incorporating external knowledge, but these methods may introduce irrelevant retrieved documents, leading to inaccurate responses. While the integration methods filter out incorrect answers from multiple responses, but lack external knowledge like RAG methods, and their high costs require balancing overhead with performance gains. To address these issues, we propose an Efficient Test-Time Retrieval-Augmented Generation Framework named ET2RAG to improve the performance of LLMs while maintaining efficiency. Specifically, ET2RAG is a training-free method, that first retrieves the …

Lessons From The Club Homeschool Capstone: Testing, Data Discipline, And The Computer Science Curriculum, Shane Brown

University Honors Theses

This thesis looks at the CLUB Homeschool Capstone project to argue that Portland State University's Computer Science curriculum should introduce testing and data quality discipline earlier and more intentionally than it does now. As team lead of a seven-person team, I coordinated sprint planning, communicated with the sponsor, and developed custom Discourse plugins that enhanced an existing forum platform instead of creating a separate application database, as requested by the sponsor. The project's requirements document called for a formal testing plan, but our team lacked the practical experience to implement one. This gap became evident through my internships as a …

Evaluating Machine Learning Models On Classification Of Novel Cyber Attacks In The Healthcare Domain, Promise Ehimen

Dissertations, Theses, and Projects

The increasing adoption of the Internet of Medical Things (IoMT) has improved healthcare delivery through connected medical devices while simultaneously expanding the cybersecurity risks facing healthcare organizations. Although machine learning based intrusion detection systems have demonstrated high detection accuracy, their ability to respond reliably to previously unseen cyberattacks remains uncertain. This study investigated how a Neural Network model and a Logistic Regression model classified novel cyberattacks within the IoMT environment. The Neural Network and Logistic Regression models were both trained and tested using a subset of the CICIoMT2024 benchmark dataset. The Neural Network achieved 99.82% test accuracy and a 0.94 …

Evaluating Generative Ai-Based User Interfaces Using An Integrated Neutrosophic Multi-Criteria Decision-Making Framework, Nada Mohamed, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

User Interface (UI) design can be seen as an essential aspect of human-computer interaction (HCI) and makes communication easier between people and technology. In today's digital economy, interface quality has become one of the most important business concerns, since it has a direct impact on customer satisfaction and retention while affecting revenue. Although creating user-centered and accessible interfaces is crucial, doing so is a difficult and time-consuming process, which leads to burnout for many usability professionals. Although conventional artificial intelligence (AI) was utilized for design assessment and automation, the arrival of generative AI technology has created new possibilities for automated …

A Visitation Grid For Complete Coverage Foraging In Robot Swarms, Arturo Gonzalez, Yifeng Gao, Li Zhang, Qi Lu

Computer Science Faculty Publications

The complete collection of sparse resources in large, unknown environments remains a challenging problem for autonomous robot swarms. Previous studies have shown that a substantial portion of total mission time is consumed during the final stage of collection, where only a small fraction of randomly scattered resources remain. Consequently, many existing swarm foraging algorithms (search and collection) focus on collecting most resources within a limited time window, rather than improving end-stage efficiency for collecting all resources. We propose a grid-based stochastic foraging strategy that explicitly reduces redundant visits and accelerates late-stage collection. The unknown search area is partitioned into a …

A Mathematical Decision-Making Framework For Athlete Development In A Collegiate Taekwondo Community: Prioritizing Coaching Interventions Using Statistical Analysis And The Analytic Hierarchy Process, King Harold A. Recto, Hazel Jade L. Antonio, Jhyrald Anthony P. Dalida

Electronics, Computer, and Communications Engineering Faculty Publications

Athlete development within collegiate sports communities requires informed decisions regarding the prioritization of coaching interventions and allocation of developmental resources. However, such decisions are frequently guided by experience and intuition, limiting opportunities for systematic and evidence-based decision-making. This study develops a mathematical decision-making framework for athlete development by integrating statistical analysis and the Analytic Hierarchy Process (AHP) within a collegiate taekwondo community. Data were collected from 25 collegiate taekwondo athletes who satisfied established eligibility criteria, including participation in University Athletic Association of the Philippines (UAAP) competitions during the previous three seasons. Athletes evaluated coaching practices across five dimensions: Training and …

Ai-Powered Resume Screening, Sang Suh, Numery Zaber

Faculty Publications

Traditional resume screening is manual, slow, and susceptible to bias, and it struggles to keep pace with today’s application volumes. This paper presents a dual-engine, AI-powered resume screening system designed for transparency and reproducibility. The primary (classical) pipeline encodes resumes and job descriptions using Sentence-BERT (SBERT), computes a resume–job match score via cosine similarity, classifies candidates into 25 job categories using XGBoost, and provides model interpretability through SHAP. In parallel, a prompted large language model (LLM) baseline (GPT-4o/4o-mini) outputs a match score and predicted category for comparative analysis. A Streamlit-based interface integrates both engines to support recruiter workflows and human-in-the-loop …

Surveyception: An Exploration Of Deceptive Survey Forms, Muhammad Danish

Computer Science ETDs

Survey platforms such as Google Forms and Microsoft Forms are widely used for feedback, data collection, and engagement, but scammers increasingly exploit them to distribute phishing and deceptive attacks. This thesis presents a large-scale study of survey-form abuse across ten major providers. We collected 140,000 forms from three sources: public posts on X, search-engine results, and web pages from the top 10 million DomCop-ranked domains. Using automated filtering and manual qualitative review, we identified 2,645 forms requesting sensitive information and classified 566 as scams. These forms used techniques including phishing, private-secret theft, account and personal-data harvesting, financial deception, and psychological …

A Hybrid Deep Learning Model Combining Cnn And Extreme Learning Machine For Cyberattack Classification, Israa S. Kamil

Journal of Intelligent Informatics, Networking, and Cybersecurity

As ransomware attacks and zero-day exploits grow sophisticated, the need for intelligent, accurate systems to detect such threats becomes clearer. In this paper, a hybrid learning model based on Convolutional Neural Networks (CNNs) and Extreme Learning Machine (ELM) is presented to improve multiclass classification performance for cybersecurity applications. The framework combines CNNs' hierarchical feature learning with ELMs' fast classification. An attention mechanism that assigns weights to each feature based on importance is included in the final model. The hybrid model performed well on the metrics: precision = 0.97, recall = 0.98, and F1- score = 0.97, and, as expected from …

Towards Intelligent Iot-Ndn Security: Ai-Driven Pit Attack Detection And Cache Attack Analysis, Sura Haidar Ali, Alaa Shawqi Jaber

Journal of Intelligent Informatics, Networking, and Cybersecurity

Beginning with Named Data Networking (NDN), an early form of information-centric networks, the paradigm of how data is transmitted over a network was changed through the use of ``content-based'' communication instead of ``host-based'', while creating native caching at intermediate points along the path to each destination, and improving upon the security of all previous paradigms. NDN contains many inherent benefits such as caching, security, etc., but like any other paradigm, NDN creates new types of vulnerabilities, particularly within some of the key elements of this paradigm; namely the Content Store (CS), Pending Interest Table (PIT), and the Forwarding Information Base …

Stop Blaming My Users: Illumination Of The Technocentric Mythos Bias, Ervin H. Frenzel, Richard Lightcap

Journal of Cybersecurity Education, Research and Practice

 Abstract -This conceptual essay addresses the need for systemic and systematic transdisciplinary analytical techniques within cybersecurity and technical security. This conceptual essay is contingent upon recognition that cybersecurity is not simply technical in nature, it does not need an adversary, and more importantly it is based upon systems engineering and systems thinking.  The essay contributes a socio-technical attribution chain and field-specific ontology/taxonomy which distinguish user-triggered events from root causes, latent conditions, technical debt, validation failures, governance failures, and attribution bias before assigning responsibility to end users. It systematically defines an ontology inclusive of developer technical debt, organizational debt arising from …

Match Made In Ml: Developing Compatibility Relationships In Evidential Reasoning Approaches With Machine Learning, Ella Jolie Thomas

Master's Theses

The presented expectation maximization informed evidential reasoning model extends the ability of the evidential reasoning calculus to support decision making by integrating an adaptive model learning capability. Compatibility relationships in Evidential Reasoning models are traditionally built by human domain experts. This process is labor-intensive, especially for large and complex models. Additionally, when new data becomes available, compatibility relationships must be reconstructed. Using machine learning and the expectation maximization algorithm, it is demonstrated that compatibility relationships can be constructed that learn relationships between domain knowledge that is used to make decisions. Using drug development as a domain of application, a traditional …

Can Machines Testify? Llms And The Boundaries Of Testimonial Epistemology, Michael J. Cummins

Philosophy Summer Fellows

As Large Language Models and AI chatbots become increasingly prevalent, pressing questions are raised about whether beliefs formed through LLM interactions carry the same epistemic weight as beliefs formed through human testimony. How we answer this question depends on whether LLMs can function as testifiers, a role which is typically assumed to require a human or human-like agent. This assumption has gone largely unexamined, yet its consequences are significant: if LLM outputs cannot constitute testimony, then the justificatory tools of testimonial epistemology are unavailable to any beliefs formed through LLM interaction. This paper challenges that assumption. It first argues that …

Stylometric And Formal Patterns In The Scholarly Impact Of Scientific Literature, Joshua Ange, Eric Godat, Rajani Sudan

SMU Journal of Undergraduate Research

Scientific communication is typically tied to promoting public engagement and interest in science, increasing scientific literacy, and playing an essential role in policymaking. The success of public communication of scientific findings is largely associated with secondary characteristics of research (e.g. the style of writing and presentation), rather than the primary content or research quality. But it is unclear to what extent the success of scientific literature intended for working scientists is influenced by those same secondary characteristics. Does the writing style of scientific articles impact their success in academic spheres? In this study, we explore the stylometric and formal characteristics …

Lossless Medical Image Compression Using Integer Discrete Wavelet Transform With Adaptive Subband Differencing And Context-Adaptive Entropy Coding, Rasha F. Nadhim, Ibrahim Adel Ibrahim, Ashwaq T. Hashim

Journal of Intelligent Informatics, Networking, and Cybersecurity

From transform-domain decorrelation and adaptive entropy coding, we propose a method for efficient lossless compression of medical images in this work. We implement the Integer Discrete Wavelet Transform (IDWT) to decompose the input image into four subbands of LL, LH, HL, HH, encompassing approximation and directional detail elements, in the initial implementation. It also removes spatial redundancy in image information and decomposes image information into a less correlated and more sparsely distributed set of coefficients. To decrease redundancy further, it proposes a subband-dependent differencing scheme, which decorrelates neighbouring wavelet coefficients with directional prediction. So horizontal differencing is done on LH, …

A Blockchain-Integrated Federated Learning Model And Autoencoder-Based Feature Reduction For Improving Iot Intrusion Detection, Tahseen A. Wotaifi

Journal of Intelligent Informatics, Networking, and Cybersecurity

The rapid growth of Internet of Things (IoT) environments has brought forth a wealth of security challenges in detecting network intrusions in diverse and resource-restricted systems. Privacy, scalability, and single point of failure issues plague traditional centralized intrusion detection solutions. To address these challenges, the study proposes a secure and adaptive intrusion detection model using Federated Learning (FL) and Blockchain, augmented with autoencoder-based feature reduction. The ToN-IoT dataset is pre-processed, and then an unsupervised autoencoder is used to build informative low-dimensional feature representations. The processed data is deployed to various clients to mimic a real federated situation. Every client will …

Mathematically Driven Enhancement In Information Security, Suhaib Badran, Asmaa Alqassab

Karbala International Journal of Modern Science

A new mathematically based cryptographic method has been proposed to improve information security; it involves transforming data into a matrix and performing bit-level operations. This decryption then follows the basic mechanism of a deterministic, fully invertible algorithm, with this invertible algorithm having the same principle as the existing ones: the process of decryption is symmetrical, where the process is reversed based on the input of the ciphertext. The plaintext is transformed into a square matrix, which includes matrix rotation, permutation of rows, circular bit shifting, and finally an affine linear transformation followed by an additional Base62-like encoding layer to further …

Coordinating Meaning With Ai System Cards: A Thematic Analysis, Jennifer Rene French Cyrek

Doctoral Dissertations and Projects

As the meaning of AI risk remains unsettled across sociotechnical and public discourse, AI system cards are an emergent, yet understudied, genre of technical documentation through which AI technology developers publicly frame new AI system capabilities including risks. This thematic content analysis study examines how AI technology developers coordinate meaning regarding risk and responsible development in stewardship of AI. Guided by a constitutive view of communication and systems theory, second-order cybernetics, and the cybernetic tradition, this study analyzes a purposive corpus of AI system cards collected from 2023-2025 using thematic content analysis and the hierarchy of meaning heuristic from coordinated …

Building Ai-Native Innovation System To Drive Transformation And Innovation In Research Organization And Management Models, Hong Xuehai

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is profoundly reshaping research paradigms. This study aims to analyze the intrinsic mechanisms through which AI empowers scientific research and its impact on the organizational management models of research. By summarizing what AI can and cannot do in empowering research, it reveals the current effectiveness and capability boundaries of AI in this domain. Based on the extraction of common core conditions for AI-empowered research and the deconstruction of typical cases of AI-enabled research organizational models, this study analyzes the differences between the organizational management model of AI-empowered research and traditional research organizational models. Furthermore, it proposes three …

Understanding And Foresight On Construction Of Foundation Model Industry Innovation Ecosystem, Liu Liu, Chunhui Jia, Yangfan Han, Qiqi Zhang, Jialin Li, Dayuan Li, Junpu Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Foundation models are a key vehicle driving artificial intelligence toward general intelligence, and their industrialization urgently requires support from a systematic and collaborative innovation ecosystem. This study focuses on the construction of the foundation model industry innovation ecosystem. It first reviews the frontier progress and identifies its essence as a complex innovation network featuring the three-dimensional synergy of technological, organizational, and industrial architectures, and then analyzes the architecture along the upstream, midstream, and downstream of the industrial chain: the upstream supports computing power and data, the midstream undertakes algorithmic innovation and platform services, and the downstream realizes multi-scenario value transformation. …

Ai-Powered Knowledge Engines As Research Infrastructure For Systematic Knowledge Discovery, Gary Welz

Publications and Research

This paper proposes knowledge engines as a framework for understanding how intelligent systems — both human and artificial — systematically discover, integrate, and generate knowledge. We argue that history’s greatest scientific minds functioned as knowledge engines, processing information through iterative cycles of ingestion, analysis, synthesis, and communication, guided by curiosity and willingness to challenge established beliefs.

We propose a taxonomy of nine integrated capabilities — ingestion, digestion, analysis, calculation, comparison, connection, association, analogy, and multimodal communication — that any serious knowledge engine must combine systematically. The argument is deliberately integrative: achieving ambitious research goals requires orchestrating all nine capabilities within …

Reproduction Beyond Benchmarks: Constbert And Colbert-V2 Across Backends And Query Distributions, Utshab Kumar Ghosh, Ashish David, Shubham Chatterjee

Computer Science Faculty Research & Creative Works

Reproducibility must validate architectural robustness, not just numerical accuracy. We evaluate ColBERT-v2 and ConstBERT across five dimensions, finding that while ConstBERT reproduces within 0.05% MRR@10 on MS-MARCO, both models show a drop of 86-97% on long, narrative queries (TREC ToT 2025). Ablations prove this failure is architectural: performance plateaus at 20 words because the MaxSim operator's uniform token weighting cannot distinguish signal from filler noise. Furthermore, undocumented backend parameters create an 8-point gap due to ConstBERT's sparse centroid coverage, and fine-tuning with 3x more data actually degrades performance by up to 29%. We conclude that architectural constraints in multi-vector retrieval …

Together//Apart Explorations In Choreorobotic Performance Ontologies, Kate Sicchio, Patrick J. Martin

Computer Science Faculty Publications

This chapter presents a practice-as-research approach to developing an improvisational choreorobotic performance. Our performance process motivated the creation of new human–robot interaction and live choreography technologies. These technologies were tested during the performance and evaluated by the audience through feedback on their perceptions about the coexistence of humans and machines in a shared space. Examining these results through both autonomous robotics and performance studies ontologies, we formulated a new analytical process in which choreographic practice informs design and robots inform performance.

Depro: Understanding The Role Of Llms In Debugging Competitive Programming Code, Nabiha Parvez, Md Tanvin Sarkar Pallab, Mia Mohammad Imran, Tarannum Shaila Zaman

Computer Science Faculty Research & Creative Works

Debugging consumes a substantial portion of the software development lifecycle, yet researchers do not yet understand well the effectiveness of Large Language Models (LLMs) in this task. Competitive programming offers a rich benchmark for such evaluation, given its diverse problem domains and strict efficiency requirements. We present an empirical study of LLM-based debugging on competitive programming problems and introduce DePro, a test-case-driven approach that assists programmers by correcting existing code rather than generating new solutions. DePro combines brute-force reference generation, stress testing, and iterative LLM-guided refinement to efficiently identify and resolve errors. Experiments on 13 faulty user submissions from Codeforces …