Computer Engineering Commons^™

A Hands-On Lab On Agentic Scientific Discovery For Time Series With The Model Context Protocol, Dhaval Patel, Miaomiao Feng, Chathurangi Shyalika, Shuxin Lin, Hasnat Md. Abdullah

Publications

Agentic AI offers new opportunities to accelerate scientific discovery for time series by enabling autonomous systems to inspect data, formulate hypotheses, compose experiments, and refine models based on empirical evidence. However, building agents that make sound modeling decisions requires more than connecting a language model to forecasting tools. Key challenges include exposing large model catalogues without overwhelming the agent, grounding parameter choices in data characteristics, supporting iterative experimentation, and evaluating the quality of the reasoning behind each decision.

This hands-on lab introduces participants to agentic scientific discovery for time series using the Model Context Protocol (MCP). Through five interactive sprints, …

Factors Influencing Enterprise Adoption Of Ai-Enabled Computers: An Expert Judgment Quantification Approach Across Multiple Sectors, Yu Shan Su, Tugrul Daim, Chia-Hao Hung, Leong Chan, Dana Bakry

Engineering and Technology Management Faculty Publications and Presentations

This study examines the key factors influencing the adoption of Artificial Intelligence Personal Computers (AIPCs) by enterprises, exploring both the benefits and challenges of their business applications. As enterprises increasingly require real-time computing, autonomous decision-making, and improved cybersecurity, AIPC—combining artificial intelligence and edge computing—has become a strategic technology for boosting competitiveness. Particularly in scenarios with less reliance on cloud services, businesses are more likely to adopt devices with local processing and standalone AI capabilities to meet the dual needs of operational efficiency and data privacy. Through an extensive review of the literature, this study identifies four main dimensions and sixteen …

A Unified Pipeline For Network-Based Gene Pattern Discovery, Siddhartha Reddy Pullannagari, Sharlee Climer

Undergraduate Research Symposium

Large-scale genetic datasets from diverse diseases, including cancer and neurodegenerative disorders such as Alzheimer’s disease, provide unprecedented opportunities to study disease pathogenesis and generate hypotheses for drug discovery. However, identifying meaningful genetic interaction patterns within these datasets remains challenging due to combinatorial relationships amongst genes and heterogeneous biological underpinnings across subtypes. This project systematically analyzes a diverse collection of omics datasets (e.g., GSE44076, GSE97810, GSE127711, GSE112679) obtained from the NCBI Gene Expression Omnibus (GEO), spanning multiple biological conditions and data types. We use a unified computational pipeline designed to handle variation in dataset size, structure, and context. The steps in …

Context Matters: Evaluating Llm-Generated Knowledge Graph Schemas, Ritvik Garimella, Riju Marwah, Atishay Jain, Khusham Bansal, Amit Sheth

Publications

Knowledge graph (KG) schema engineering is labor-intensive and resists automation at scale. We investigate whether LLMs can generate domain-specific KG schemas of sufficient quality for downstream symbolic reasoning. We propose a tiered contextual framework that varies domain context richness across four levels: zero context, domain scope, task requirements, and data distribution. Generated schemas are evaluated intrinsically on BioRED (600 PubMed abstracts, multi-type entities and relations), where automated tiered schemas match an established KG construction baseline at 79.9% EC, with edge conformance rising from 47.5% at L1 to a stable 78–80% from L2 onward. Extrinsic evaluation on a 50-record MedHop controlled …

Doing Less With More: A First-Principles Exploration Of The Suitability Of Agentic Computing Over Alternative Architectural Choices, Ritvik Garimella, Biplav Srivastava, Amit Sheth

Publications

There is growing interest in automating business activities with Agentic Artificial Intelligence (AI) due to latter's seeming ease of use. Never has it been easier, or costlier, to do less with more. However, little is known about when agents are preferable to established alternatives such as local computation, Representational State Transfer (REST), the Simple Object Access Protocol (SOAP), and the Model Context Protocol (MCP), particularly when development speed, performance, and operational cost are considered. We investigate this question using a controlled mathematical task that compares seven methods on a benchmark of 1,000 arithmetic expressions where semantics of operator precedence has …

Harmonicthreads: A Formative Evaluation Of A Fabric-Based Digital Musical Instrument Toward Inclusive Music-Making, Ellie Nguyen, Franceli L. Cibrian

Engineering Faculty Articles and Research

Background:

Inclusive music-making requires instruments that support varied bodies, abilities, musical backgrounds, and forms of participation. Digital musical instruments provide diverse approaches to sound creation, and fabric-based interfaces offer an alternative interaction modality that may support participation for some users and contexts. Their tactile and deformable properties enable forms of interaction that differ from conventional rigid or screen-based controllers and may offer inclusive possibilities in particular settings.

Objective:

This paper presents HarmonicThreads as a formative interaction-design case of a fabric-based digital musical instrument. The prototype explores how tactile cues, fabric deformation, projected visual feedback, and assisted accompaniment can support low-barrier …

One Size Does Not Fit All: Revisiting World Models And Neurosymbolic Ai, Amit P. Sheth, Madhur Thareja, Anushka Pawar, Niyati Rawal

Publications

World models are being built twice, from opposite ends, without a shared theory of how the two halves should meet. One lineage grounds the world model in perception: a self-supervised, latent-predictive encoder – exemplified by Joint Embedding Predictive Architectures (JEPA) – that learns the structure of sensory experi-ence. A second, older lineage grounds the world model in cognition: an explicit, inspectable structure of entities, rules, and constraints, ranging from knowledge graphs to formal logic to physical law. Neither lineage alone has produced a world model that is simultane-ously adaptive and auditable. We argue this is not solved by picking a …

The Implications Of Autonomous Vehicle Adoption For Parking Dynamics And Traffic Flow, Hamid Bagha

Staff Scholarship - Australia & Dubai

Parking dynamics directly influence land use and transportation efficiency. While conventional vehicles remain stationary for roughly 95% of their operational lifetime, private autonomous vehicles (AVs) can actively reposition themselves using strategies such as empty cruising, returning home, or seeking cheaper peripheral parking. This shifts space occupation from parking stalls to the active road network.

Using a high-fidelity microscopic simulation dataset for the City of Melbourne, this thesis examines the spatiotemporal trade-offs between parking dynamics and traffic flow across heterogeneous Traffic Analysis Zones (TAZs). Experimental results show that at a 30% AV adoption rate, alternative parking strategies decrease network-wide parking search …

The Self-Aware Room: A Framework For Operational Self-Awareness In Evolvable Blended Environments, David B. Smith

Publications and Research

The Self-Aware Room (SAR) is a room-scale research environment developed within the larger Balanced Blended Space and Blended Reality Performance System research trajectory. Rather than treating the room as a conventional “smart” environment composed of fixed automation technologies, SAR approaches it as an evolvable blended environment made from physical, virtual, conceptual, sensory, computational, and performative relationships. Its defining feature is not any particular sensor, model, or output device, but the set of transformations through which physical activity becomes structured observation, bounded representation, interpreted state, governed decision, and mediated response.

This paper develops the conceptual and methodological foundations of SAR as …

Engineering For Compromise, Ron Ross

Other Faculty Materials

Modern systems cannot credibly depend on every component, account, service, person, supplier, and supporting dependency remaining trustworthy secure throughout the system life cycle. Prevention-centered cybersecurity remains essential, but for mission-critical systems it is not a sufficient engineering strategy. This paper introduces the concept of Engineering for Compromise (EfC), a systems security engineering strategy that deliberately assumes selected system elements may fail, become malicious, lose integrity, or fall under adversary control and then asks whether the system can continue to prevent unacceptable loss. EfC separates component compromise, system compromise, and mission loss as distinct events and uses architecture to place barriers …

Ai Cyber First Responders: Bottleneck Analysis Of Heterogeneous Cpu–Gpu Pipelines For Security Operations Center Triage, Christine Pierce

Harrisburg University Other Works

Abstract — Modern Security Operations Centers (SOCs) must continuously process massive volumes of heterogeneous security telemetry while meeting stringent throughput, latency, and operational continuity requirements. Although transformer-based artificial intelligence has significantly improved threat detection and alert prioritization, most cybersecurity research evaluates model accuracy rather than the end-to-end behavior of AI-enabled operational pipelines. Consequently, relatively little is known about how heterogeneous CPU–GPU coordination, scheduling overhead, memory movement, and synchronization collectively influence operational SOC performance. This paper presents the AI Cyber First Responder, a heterogeneous SOC triage architecture that integrates GPU-accelerated transformer inference with CPU-based doctrine-driven reasoning to investigate end-to-end pipeline behavior …

What Drives Blockchain Technology Adoption? A Meta-Analysis Across Tam, Tpb, And Utaut Frameworks, Amir Rahmani, Roohollah Ahmadi, Tugrul Unsal Daim, Mehdi Zamani, Dilek Ozdemir

Engineering and Technology Management Faculty Publications and Presentations

Research on blockchain adoption has expanded rapidly, with most studies conceptualizing adoption as behavioural intention. Technology adoption models, such as the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT), are commonly applied to explore blockchain technology adoption (BTA). However, empirical results from the 12 hypotheses associated with these models remain fragmented and, at times, inconsistent, with limited comprehensive quantitative integration. To address this gap, the present study performs a meta-analysis to evaluate the degree and direction of these hypotheses systematically. In accordance with PRISMA guidelines, 149 quantitative …

Optimizing Few-Shot Learning In Pruned Large Language Models With Task-Specific Prompts, Danyal Aftab

Dissertations

Few-shot learning enables large language models to efficiently perform tasks given only a limited number of labeled examples. However, training these models entirely from scratch requires substantial computational resources, making it challenging for many organizations to fully leverage their potential. This thesis explores how structured pruning, task-specific prompting, and parameter-efficient fine-tuning can be combined to preserve few-shot learning capabilities in compressed LLMs, while also extending their utility to real-world recommendation systems.

In this research, we propose the Tailored LLM framework, which first reduces model size through structured pruning and then enhances few-shot learning performance using carefully designed prompts. We experiment …

Genwriter 2.0: A Hybrid Case-Based And Llm Rewriting Approach For Mitigating Implicit Gender Cues In Text, Shweta Soundararajan, Sarah Jane Delany

Conference papers

Gendered language is the use of words or phrases that indicate an individual's gender. Although useful in some contexts, gendered language can reinforce gender stereotypes and introduce bias, particularly in machine learning models for tasks involving people such as recruitment or occupation classification. When textual content about individuals, such as biographies, contain gender cues, models can learn spurious associations between gender and other characteristics of individuals, such as profession, potentially resulting in unfair outcomes such as reduced hiring opportunities for women.

To address this challenge, we propose GenWriter 2.0, a hybrid approach that integrates Case-Based Reasoning (CBR) with Large …

Tinyml-Based Embedded Vision System For Ic Detection In Microcontroller Manufacturing, Mark M. Pallones, King Harold A. Recto, Rynne Daven A. Barrios

Electronics, Computer, and Communications Engineering Faculty Publications

Mixing of microcontroller unit (MCU) integrated circuits (ICs) during the final testing stage of semiconductor manufacturing can lead to material waste, production delays, and customer dissatisfaction. This issue often occurs when standard JEDEC Matrix Trays (JMTs) are reused without confirming that all ICs have been removed after testing, a process typically performed through manual inspection and therefore susceptible to human error due to high test volumes, small IC package sizes, and visual similarity between IC packages and tray surfaces. This study develops an automated IC Detection Test System using embedded vision to determine whether JMT trays are empty prior to …

Analyzing Energy Use In 2d & 3d Imaging Systems And Workflows, Michael J. Bennett

Published Works

This study examines energy consumption in cultural heritage imaging systems and workflows, addressing a gap in sustainability research that has to date focused primarily on data storage infrastructure estimations. Using Home Assistant edge computing and Z-Wave smart plugs, seven distinct imaging systems were monitored over 203 hours, capturing 55,211 images, and rendering 2,448 objects. Results show an average energy requirement of 11.1 Wh per object, with an annual total of 747 kWh for digitization activities. Findings highlight opportunities to reduce energy demand and improve efficiency, such as automating continuous light shutoff and optimizing postprocessing routines that support institutional sustainability goals …

Case Study: Feasibility Of Creating A Simulated Mobile Data Center For Cross-Disciplinary Academic Programs, Stanley Mierzwa, Christoper J. Schultz, Iassen Christov, Michael Fagioli, Thomas Ikeda, Reinaldo Jaramillo, Giolian Sanagustin

Center for Cybersecurity

This case study examines the potential to envision, create, and deploy a simulated mobile micro data center solution that can be easily replicated and transported between locations and educational settings. The coined term for this solution is the Mobile AI-Centered Data Center (Mobile ACDC), which provides students with a platform to construct, in a hands-on fashion, such a solution and navigate the product to gain greater competencies and understanding of the components found in a data center. Instructor and student feedback assessments from the pilot classroom modules and laboratory experiential learning activities indicate that such a solution helps to improve …

Anomaly Management In Unmanned Aerial Vehicles: A Systematic Literature Review, Ivan Tan Wei Han, Christopher M. Poskitt, Lingxiao Jiang, Lwin Khin Shar

Research Collection School Of Computing and Information Systems

Unmanned Aerial Vehicles (UAVs) are increasingly deployed in safety-critical applications such as logistics, surveillance, disaster response, and urban air mobility. While their autonomy enables powerful capabilities, it also introduces vulnerabilities due to hardware faults, software defects, communication failures, and adversarial interference. This survey presents a comprehensive review of research studies closely related to UAV anomalies published between 2015 and 2025, covering 111 papers from academic and industrial sources. We introduce a unified five-pillar taxonomy—anomaly generation, prevention, detection, recovery, and analysis—that organizes existing work across the full anomaly management lifecycle. In contrast to prior surveys that focus primarily on detection algorithms, …

Summer Proceedings 2026, Computational Modeling Serving Portland, The Altreu Program, Portland State University, Teuscher Lab, Portland State University

altREU Projects

The 2026 Summer Proceedings bring together research conducted by undergraduate students participating in three complementary programs at Portland State University: the National Science Foundation Research Experiences for Undergraduates (NSF REU) site, Computational Modeling Serving Portland; the alternative Research Experiences for Undergraduates (altREU) program, Designing, Programming, and Using Computers to Benefit Society; and the Teuscher Lab internship program.

The NSF REU is a fully virtual, 10-week program centered on computational modeling that serves Portland and the greater region. Students begin with two weeks of intensive training in computational modeling, followed by eightweeks of mentored research with faculty, research collaborators, and community …

Dataset To Analyzing Energy Use In 2 & 3d Imaging Systems And Workflows, Michael J. Bennett

Published Works

Analyzing Energy Use in 2D & 3D Imaging Systems and Workflows Dataset

CONTENTS: Z-WaveReportingProfiles; SessionInput; DroneFlights; SessionsComputed; Types; ByType; GrossSummaryUnweighted; WeightingSummary; Raw Sampling History Data

Hsv-1 Us3 Hijacks Conserved Actin Regulatory Complexes To Drive F-Actin Remodeling, Md Imran Hossain, Md Arifuzzaman, Md Mehedi Hasan, Seung Jong Park, Leila Rahimian, Ojasvi Dutta, Vladimir Chouljenko, Harikrishnan Mohan, Reza Ghavimi, Konstantin G. Kousoulas

Computer Science Faculty Research & Creative Works

The herpes simplex virus 1 (HSV-1) US3 is a multifunctional serine/threonine kinase that promotes HSV-1 replication and spread. But its role and the mechanisms by which US3 regulates actin cytoskeletal remodeling remain poorly defined. We combined flow cytometry, confocal microscopy, immunoprecipitation-mass spectrometry (IP-MS), protein complex mapping, and machine learning to characterize US3-mediated F-actin dynamics. Flow cytometry and confocal microscopy showed that wild-type HSV-1 induces significant F-actin remodeling, while the ΔUS3 mutant displays F-actin levels comparable to uninfected cells, identifying US3 as a key regulator. IP-MS identified 47 high-confidence US3 interactors enriched in conserved actin regulatory complexes, including Arp2/3 nucleation machinery, …

Monitoring And Short-Term Forecasting Of Atmospheric Air Pollutants Using Deep Neural Networks, Prasanjit Dey

Dissertations

Accurate forecasting of near-surface atmospheric air pollutants such as PM2.5, NO2, SO2, CO, and O3 remains a critical scientific and societal challenge. This difficulty arises from several factors, including nonlinear pollutant dynamics, sparse ground monitoring net works, heterogeneous satellite observations, and strong cross-pollutant interdependencies. Substantial advances have been achieved in temporal deep learning, probabilistic modeling, satellite-based estimation, physics-informed methods, and foundation models. However, these paradigms have largely evolved in isolation. As a result, existing systems are often station-dependent or pollutant-specific and optimized for a single forecasting task. This limits their robustness and generalizability across regions and heterogeneous data regimes.

This …

Design And Evaluation Of A Distributed Ticket Booking System Using Cqrs And Hybrid Consistency Models, Tai Nguyen Vo, Sangwhan Cha

Harrisburg University Other Works

This paper presents the design and evaluation of a Distributed Ticket Booking System (DTBS) built to handle extreme concurrency in high-traffic digital environments. Motivated by real-world failures such as the 2022 Taylor Swift Eras Tour sale—where 14 million simultaneous requests overwhelmed Ticketmaster—the system addresses the fundamental conflict between strong consistency and high availability under massive demand. The proposed solution adopts the Command Query Responsibility Segregation (CQRS) pattern to decouple read and write operations, and applies a hybrid consistency model grounded in the CAP theorem: a CP-oriented booking subsystem with Redis-based distributed locking, and an AP-oriented search subsystem using Elasticsearch for …

Towards Fast Detection Of Suspicious Bluetooth Trackers Using Anomaly Detection, Orobosa Ekhator, Dylan Conklin, Primal Pappachan, Roberto Yus

Computer Science Faculty Publications and Presentations

Detecting malicious Bluetooth Low Energy (BLE) trackers remains challenging because existing approaches rely on fixed time and distance thresholds that are brittle across environments. These heuristics produce false positives in dense environments and require long observation windows before flagging a device. To address these limitations, we present BL(u)E CRAB, a cross-platform (iOS/Android) mobile system that represents nearby devices using three risk factors derived from BLE scan data. Our detection model adapts Clustering-Based Local Outlier Factor (CBLOF) to BLE tracker detection and adds a gap-thresholding mechanism to separate high-scoring outliers from the benign majority. Across micro-benchmarks and end-to-end case studies, CBLOF …

Exploring Gene Regulatory Neural Network Biocomputing Of Bacteria, Adrian Merle Ratwatte

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Artificial Intelligence (AI) has evolved from brain-inspired algorithms into a discipline that increasingly integrates with biological systems. While silicon-based platforms have advanced machine learning, they remain limited in energy efficiency and operation in environments beyond silicon. This motivates biological computing as an alternative, enabling efficient, resource-aware, and reconfigurable computing within living systems. This dissertation addresses these limitations by introducing a bacterial computing framework that models Gene Regulatory Networks (GRNs) as Gene Regulatory Neural Networks (GRNNs). The GRNN mirrors the structure and function of Artificial Neural Networks (ANNs) through gene-gene interactions across trans-omic layers, enabling natural, self-regulating information processing within living …

Trends In Non-Profit Cybersecurity: Analyzing Three Years Of Incident Data From The Npcir, Stanley Mierzwa, Joanna Paliszkiewicz, Edyta Skarzyńska

Center for Cybersecurity

This study analyzes cyberattack trends targeting non-profit organizations using longitudinal data collected over a three-year period within the Non-Profit Cybersecurity Incident Repository (NPCIR). Developed through a National Security Agency Center of Academic Excellence in Cyber Defense (NSA CAE-CD) designated center, the NPCIR applies an open-source intelligence (OSINT) methodology to systematically document cybersecurity incidents affecting the global non-profit sector. This study examines attack types, threat actor characteristics, sectoral distribution, and cybersecurity impacts using the Confidentiality–Integrity–Availability (CIA) triad framework. The results indicate that availability-related incidents, particularly ransomware and distributed denial-of-service (DDoS) attacks, constitute the most prevalent threats, while confidentiality breaches remain highly …

Deficient Executive Control In Transformer Attention, Suketu Chandrakant Patel, Hongbin Wang, Jin Fan

Publications and Research

Although transformers in large language models (LLMs) effectively implement a self-attention mechanism that has revolutionized natural language processing, they lack an explicit architecture for the executive control of attention found in humans, which is essential for resolving conflicts and selecting relevant information in the presence of competing computations and is critical for adaptive behavior. To investigate the impact of this limitation in LLMs, we employed the classic color Stroop task, widely regarded as the gold standard, to test the executive control of attention in these models. Our results revealed a typical conflict effect of underperformance in terms of accuracy in …

Trust And Pre-Employment Background Checks When Onboarding And Maintaining Information Security And Cybersecurity Staff, Stanley Mierzwa

Center for Cybersecurity

The realm of trust is broad and can include many facets that are difficult to capture and catalog. In relation to the work roles of information security and cybersecurity, the intersection of trust in human resource management is critical and an evolving area within most modern organizations, in almost any sector, and of any size. A foundational element of trust is fundamental to effective mission and work roles in information security and cybersecurity, as well as to every employee tasked with contributing to the security of an organization’s assets. This chapter will include sections on the role trust can and …

Toward Data-Centric Ai: A Comprehensive Survey Of Traditional, Reinforcement, And Generative Approaches For Tabular Data Transformation, Dongjie Wang, Yanyong Huang, Wangyang Ying, Haoyue Bai, Nanxu Gong, Xinyuan Wang, Sixun Dong, Tao Zhe, Kunpeng Liu, Meng Xiao, Multiple Additional Authors

Computer Science Faculty Publications and Presentations

Tabular data is one of the most widely used formats across industries, driving critical applications in areas such as finance, healthcare, and marketing. In the era of data-centric AI, improving data quality and representation has become essential for enhancing model performance, particularly in applications centered around tabular data. This survey examines the key aspects of tabular data-centric AI, emphasizing feature selection and feature generation as essential techniques for data space refinement. We provide a systematic review of feature selection methods, which identify and retain the most relevant data attributes, and feature generation approaches, which create new features to simplify the …

Stock Market Analysis And Volatility Behavior During The Covid-19 Pandemic, Manoj Venkatachalaiah, Soon Leong Yeap, Salman Ahmed Lnu, Sangwhan Cha

Harrisburg University Other Works

This report outlines the structural design, cloud implementation, and analytical findings of a scalable Big Data architecture deployed on Google Cloud Platform (GCP). The primary objective is to investigate the macroeconomic and microeconomic disruption caused by the COVID-19 pandemic on global equities, focusing on two dominant digital business models: online retail/cloud computing (Amazon, Inc. - AMZN) and subscription-based digital streaming entertainment (Netflix, Inc. - NFLX). Through a serverless orchestration pipeline leveraging GCP Cloud Run, automated workflows fetched and blended high-velocity epidemiological metrics alongside daily financial asset layers. Data transformations and parallel analytical calculations were executed utilizing Apache Beam pipelines inside …