Computer Sciences Commons^™

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …

Am I As Effective At Identifying Emotions As Artificial Intelligence? A Comparative Study Of Emotional Recognition, Traci R. Grove, Alexandra T. Lucas, Maryann Martin, Cathleen M. Deckers, Lulu Sherif Mahmood, Nicole Danaher-Garcia, Mark W. Scerbo, Suzan Kardong-Edgren, Janice C. Palaganas

Psychology Faculty Publications

Background

Learning conversations, or dialogues aimed at deepening understanding and reflection, are deeply influenced by emotions. Effective communication is influenced by emotional intelligence - the ability to recognize, understand, and manage both one’s own and others’ emotions. While advances in artificial intelligence (AI) offer new tools for emotion recognition, these technologies still struggle with accurately interpreting subtle and culturally diverse emotional expressions, sparking debate about their reliability and effectiveness. This article provides a comparative analysis of human versus AI recognition of emotions during an end-of-course reflective learning conversation.

Methods

Emotions during a structured post-conference debriefing were analyzed and coded by …

Wormhole Attack Mitigation In Wireless Network Using Propagation Delay, Harry May, Travis Atkison

Journal of Cybersecurity Education, Research and Practice

This paper presents a novel approach for mitigating wormhole attacks on wireless networks using propagation delay timing. The wormhole attack is a persistent security threat that threatens the integrity of network communications, potentially leading to data theft or other malicious activities. While various methods exist for combating wormhole attacks, our approach offers advantages that set it apart. Our approach involves a combination of proactive and reactive measures, harnessing box plot analysis and weighting factor techniques to identify and isolate outlier node links effectively. Unlike traditional methods, our solution not only detects outlier links but also defines dynamic weighting factors, providing …

Leveraging Propagation Delay For Wormhole Detection In Wireless Networks, Harry May, Travis Atkison

Journal of Cybersecurity Education, Research and Practice

Detecting and mitigating wormhole attacks in wireless networks remains a critical challenge due to their deceptive nature and potential to compromise network integrity. This paper proposes a novel approach to wormhole detection by leveraging propagation delay analysis between network nodes. Unlike traditional methods that rely on signature-based detection or specialized hardware, our method focuses on analyzing propagation delay timings to identify anomalous behavior indicative of wormhole attacks. The proposed methodology involves collecting propagation delay data in both normal network scenarios and scenarios with inserted malicious wormhole nodes. By comparing these delay timings, our approach aims to differentiate between legitimate network …

Intrinsic Universality In Tile Automata And Related Results, Elise C. Grizzell

Theses and Dissertations

The Tile Automata (TA) model describes self-assembly systems in which monomers can build structures and transition with an adjacent monomer to change their states. This paper shows that seeded TA is a non-committal intrinsically universal model of self-assembly. We present a single universal Tile Automata system containing approximately 4600 states that can simulate (a) the output assemblies created by any other Tile Automata system Γ, (b) the dynamics involved in building Γ’s assemblies, and (c) Γ’s internal state transitions. It does so in a non-committal way: it preserves the full non-deterministic dynamics of a tile’s potential attachment or transition by …

Using Cache Files To Improve The Efficiency Of The Ucgretina Simulation Code, Blake Mcnulty

Physics and Astronomy Summer Fellows

UCGretina is a program that is used to simulate the Gretina gamma-ray tracking array used in experiments at the Facility for Rare Isotope Beams (FRIB) to collect gamma rays produced in reactions such as proton scattering. This is done by generating a simulation of the beam particles used and sending them into a simulated recreation that mimics the experimental environment. My job this summer was to tackle a major issue that the program had which is the speed or efficiency at which it ran. The main issue was that it would take days for certain simulation sets to run which …

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Missing Wedge Completion Via Unsupervised Learning With Coordinate Networks, Dave Van Veen, Jesús G Galaz-Montoya, Liyue Shen, Philip Baldwin, Akshay S Chaudhari, Dmitry Lyumkis, Michael F Schmid, Wah Chiu, John Pauly

Faculty, Staff and Students Publications

Cryogenic electron tomography (cryoET) is a powerful tool in structural biology, enabling detailed 3D imaging of biological specimens at a resolution of nanometers. Despite its potential, cryoET faces challenges such as the missing wedge problem, which limits reconstruction quality due to incomplete data collection angles. Recently, supervised deep learning methods leveraging convolutional neural networks (CNNs) have considerably addressed this issue; however, their pretraining requirements render them susceptible to inaccuracies and artifacts, particularly when representative training data is scarce. To overcome these limitations, we introduce a proof-of-concept unsupervised learning approach using coordinate networks (CNs) that optimizes network weights directly against input …

Proof-Of-Concept For Converging Beam Small Animal Irradiator, Benjamin Insley

Dissertations and Theses (Open Access)

The Monte Carlo particle simulator TOPAS, the multiphysics solver COMSOL., and

several analytical radiation transport methods were employed to perform an in-depth proof-ofconcept

for a high dose rate, high precision converging beam small animal irradiation platform.

In the first aim of this work, a novel carbon nanotube-based compact X-ray tube optimized for

high output and high directionality was designed and characterized. In the second aim, an

optimization algorithm was developed to customize a collimator geometry for this unique Xray

source to simultaneously maximize the irradiator’s intensity and precision. Then, a full

converging beam irradiator apparatus was fit with a multitude …

Advancing Game Development And Ai Integration: An Extensible Game Engine With Integrated Ai Support For Real-World Deployment And Efficient Model Development, Ryan Anderson

All Graduate Theses and Dissertations, Fall 2023 to Present

This thesis introduces Acacia, a game engine with built-in artificial intelligence (AI) capabilities. Acacia allows game developers to effortlessly incorporate Reinforcement Learning (RL) algorithms into their creations. By tagging game elements to convey information about the game state or rewards, developers gain precise control over how RL algorithms interact with their games, mirroring real player behavior or providing full knowledge of the game world.

To showcase Acacia’s versatility, the thesis presents three games across different genres, each demonstrating the engine’s AI plugin. The goal is to establish Acacia as a preferred resource for creating 2D games with RL support without …

Adaptive Content-Aware Influence Maximization Via Online Learning To Rank, Konstantinos Theocharidis, Panagiotis Karras, Manolis Terrovitis, Spiros Skiadopoulos, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a …

Strategy Optimization Method Of Multi-Dimension Projection Based On Deep Reinforcement Learning, Jing An, Guangya Si, Lei Zhang

Journal of System Simulation

Abstract: Based on the perfect performance of deep reinforcement learning (DRL) in strategy optimization, this paper proposes a strategy optimization method of action taking the multi-dimension projection action as the main research object. The method combines the simulation experiment method with the DRL method. After analyzing the current situation of strategy optimization research, the deep learning framework is selected according to the research problems, and a DRL multi-dimension projection strategy model based on the asynchronous advantage actor-critic (A3C) algorithm is constructed. Through simulation experiments, the interactive learning between the DRL model and the simulation of "out of the loop" is …

Data Driven Trade-Off Analysis For Cybersecurity, Goskel Kucukkaya, Murat Ozer, Murat Balci, Emrah Ugurlu

Engineering Management & Systems Engineering Faculty Publications

Trade-off analysis, a specialization of systems engineering, addresses design criteria like security, cost, performance, and compliance. Monte Carlo simulations are commonly employed to generate impact scenarios for trade-off analysis combined with solution alternatives that accommodate industry-specific considerations and uncertainties. In the cyber domain, this paper proposes a methodology for data-driven trade-off analysis in cybersecurity, leveraging industry reports as primary data sources using confidentiality, integrity, and availability as trade-off analysis objectives. Distribution functions are derived to manage and model uncertainties for various industries. The approach given in this study aims to facilitate informed choices and to enhance cybersecurity decision making and …

A Virtual Time Driven Simulator For Devs Models, Ronald R. Stempien

Dissertations, Master's Theses and Master's Reports

When simulating a system that includes some software component, simulation authors are faced with the problem of how to appropriately model the software within the simulation. While many formal methods for modeling software exist, in some contexts these may not be appropriate or viable for a given simulation. Instead, simulation authors may model a computer within the simulation, and run the software in question “as is” on the modeled machine. In this work, we introduce a theoretical framework to allow for the use of hardware virtualization technologies as a hardware accelerator for CPU models in Discrete Event System Specification (DEVS) …

Cloud-Edge Collaborative Service Architecture For Lvc Training System, Peng Yong, Miao Zhang, Yue Hu

Journal of System Simulation

Abstract: LVC training, an important means of military training, has received great attention from military and M&S experts. As the virtual and physical elements become more abundant and deeply integrated, LVC training systems become increasingly complex. Aiming at physical-virtual connection, information interaction, simulation computation, run-time control, etc., this paper designs a cloud-edge collaborative service architecture for LVC training systems (CESA-LVC) by reference to cyber-physical systems and cloud-edge computing architectures. CESA-LVC standardizes the structures of LVC training systems from several aspects of intelligent real-time interconnection, joint simulation computation, training auxiliary service, training cognitive decision, and dynamic configuration optimization. It provides a …

Verifying Empirical Predictive Modeling Of Societal Vulnerability To Hazardous Events: A Monte Carlo Experimental Approach, Yi Victor Wang, Seung Hee Kim, Menas C. Kafatos

Institute for ECHO Articles and Research

With the emergence of large amounts of historical records on adverse impacts of hazardous events, empirical predictive modeling has been revived as a foundational paradigm for quantifying disaster vulnerability of societal systems. This paradigm models societal vulnerability to hazardous events as a vulnerability curve indicating an expected loss rate of a societal system with respect to a possible spectrum of intensity measure (IM) of an event. Although the empirical predictive models (EPMs) of societal vulnerability are calibrated on historical data, they should not be experimentally tested with data derived from field experiments on any societal system. Alternatively, in this paper, …

Simulation And Analysis Of Self-Assembling Slat-Based Dna Ribbons, Lukas Vaughan, Hunter J. Fleming

Computer Science and Computer Engineering Undergraduate Honors Theses

Though still in its infancy, the design of DNA crisscross slats presents great potential in the algorithmic self-assembly of DNA. The provision for higher levels of cooperativity allows for fewer errors through the natural proofreading of slat placement, leading to more robust assembly. Highly accurate simulations of self-assembling DNA squares have been achieved by following the kinetic Tile Assembly Model. Building on that foundation, this study seeks to calibrate the system parameters of a kinetic simulator for self-assembling DNA slats to match experimental results and to use those ranges of parameters to perform exploratory simulations of systems not yet tested …

Numerical Design And Optimization Of Near-Infrared Band- Pass Filter, Hafiza Syeeda Faiza, Ghazi Aman Nowsherwan, Basem A. Abu Izneid, Muhammad Azhar, Saira Riaz, Syed Sajjad Hussain, Saira Ikram, Mohsin Khan, Shahzad Naseem, Mohammad Kanan, Ibrahim M. Mansour

Applied Mathematics & Information Sciences

Band-pass filters functioning in the near-infrared (IR) range are desired for laser technology, multi-photon fluorescence, and IR imaging applications. In this study, we have designed four band-pass filters in the near Infrared spectrum (900-1200 nm) by vertically stacking different high and low-index materials. The band-pass filters are modelled by Essential Macleod software with different thicknesses. The layer’s thicknesses were optimized in such a way to provide the negligible reflectance and maximum transmission on the front side. All the simulated band-pass filters exhibit high transmittance, but TiO2/Al2O3 and Ta2O5/Al2O3 outperforms other modelled structure in terms of performance due to the better …

Analyzing And Computing Complete Solution For Dots And Boxes Game, Carl Mcaninch

Graduate Dissertations and Theses

This thesis improves a process that analyzes all the states of a game of Dots and Boxes. We use retrograde analysis and simulations to create a solution that provides significant performance improvements over our previous best solution. Expanding upon a previous 4x4 solution using rotations, reflections, better optimization, and cloud computing to limit the processing time and gather more data efficiently. We compute a file and the number of states associated with each file and process every state starting with a completely filled board. We optimized the data for cloud computing by running simulations to find the most efficient number …

Uconn Baseball Batting Order Optimization, Gavin Rublewski, Gavin Rublewski

Honors Scholar Theses

Challenging conventional wisdom is at the very core of baseball analytics. Using data and statistical analysis, the sets of rules by which coaches make decisions can be justified, or possibly refuted. One of those sets of rules relates to the construction of a batting order. Through data collection, data adjustment, the construction of a baseball simulator, and the use of a Monte Carlo Simulation, I have assessed thousands of possible batting orders to determine the roster-specific strategies that lead to optimal run production for the 2023 UConn baseball team. This paper details a repeatable process in which basic player statistics …

Procedural City Generation With Combined Architectures For Real-Time Visualization, Griffin Poyck

All Theses

The work and research of this paper sought to build upon traditional city generation and simulation in creating a tool that both realistically simulates cities and their prominent features and also creates aesthetic and artistically rich cities using assets that combine several contemporary or near contemporary architectural styles. The major city features simulated are the surrounding terrain, road networks, individual buildings, and building placement. The tools used to both create and integrate these features were created in Houdini with Unreal Engine 5 as the intended final destination. This research was influenced by the city, town, and road networking of Ghost …

Modeling, Simulation And Control Of Microrobots For The Microfactory., Zhong Yang

Electronic Theses and Dissertations

Future assembly technologies will involve higher levels of automation in order to satisfy increased microscale or nanoscale precision requirements. Traditionally, assembly using a top-down robotic approach has been well-studied and applied to the microelectronics and MEMS industries, but less so in nanotechnology. With the boom of nanotechnology since the 1990s, newly designed products with new materials, coatings, and nanoparticles are gradually entering everyone’s lives, while the industry has grown into a billion-dollar volume worldwide. Traditionally, nanotechnology products are assembled using bottom-up methods, such as self-assembly, rather than top-down robotic assembly. This is due to considerations of volume handling of large …

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

All Dissertations

In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Analyzing And Computing Complete Solution For Dots And Boxes Game, Carl Mcaninch

Undergraduate Theses and Capstone Projects

This thesis improves a process that analyzes all the states of a game of Dots and Boxes. We use retrograde analysis and simulations to create a solution that provides significant performance improvements over our previous best solution. Expanding upon a previous 4x4 solution using rotations, reflections, better optimization, and cloud computing to limit the processing time and gather more data efficiently. We compute a file and the number of states associated with each file and process every state starting with a completely filled board. We optimized the data for cloud computing by running simulations to find the most efficient number …

Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis

Modeling, Simulation and Visualization Student Capstone Conference

This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.

Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang

Modeling, Simulation and Visualization Student Capstone Conference

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance …

V2v And V2i Based Safety And Platooning Algorithms For Connected And Autonomous Vehicles, Omkar Dokur

USF Tampa Graduate Theses and Dissertations

Connected Vehicles (CVs) make transportation safe by communicating with vehicles and the infrastructure in their neighborhood. CVs are embedded with onboard units (OBUs) which transmit basic safety messages (BSMs) containing the location, heading, and velocity information of the vehicle using either Dedicated Short-Range Communications (DSRC) or Cellular Vehicle-to-Everything (C-V2X) technology. These BSMs can be used to warn drivers using various vehicle-to-vehicle (V2V) or vehicle-to-infrastructure (V2I) applications. Along with these applications, CV technology also gives rise to cooperative vehicular driving applications such as platooning. A group of vehicles can negotiate and drive jointly close to each other in a cooperative manner …

Adaptation Of Network Flow Problems For Course Of Action Generation, Alexander N. Stephens

Theses and Dissertations

This thesis introduces two methods to generate Courses of Action (COA) in distributed warfare scenarios: the Wargaming Commodity Course of Action Automated Method Under Uncertainty (WCCAAM-U2) and Dynamic Transshipment Problem (DTP)-generated COAs. Previous work by Deberry et al. used a Multi-Commodity Flow Problem (MCFP) to generate COAs for single-period wargame scenarios with known enemy force amounts. In WCCAAM-U2, we adapt an MCFP to work in situations where only intelligence estimates of enemy forces are known. Compared to two other COA-generation methods, the WCCAAAM-U2 COA outperforms the next highest-performing COA by 307% when compared by a ratio of objective success rate …

Integration Of Omnet++ Into A Networking Course In An Electrical Engineering Technology Program, Murat Kuzlu, Brian Emmanuel Tamayo, Salih Sarp, Otilia Popescu, Vukica M. Jovanovic

Engineering Technology Faculty Publications

Networking courses are an integral part of electrical engineering technology programs as the majority of electronics in the modern day are required to communicate with each other. They are also getting more attention in manufacturing engineering technology programs because of the development of emerging technologies in Industry 4.0 arena. From laptops, computers, cellphones, modern day vehicles and smart refrigerators, these devices require a certain level of networking in order to communicate with other devices, whether it be locally, or even across the other side of the world. The objective of networking courses in an electrical engineering program is to demonstrate …

A Structured Narrative Prompt For Prompting Narratives From Large Language Models: Sentiment Assessment Of Chatgpt-Generated Narratives And Real Tweets, Christopher J. Lynch, Erik J. Jensen, Virginia Zamponi, Kevin O'Brien, Erika Frydenlund, Ross Gore

VMASC Publications

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents' perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt …