Digital Commons Network^™

Ai As A License Review Assistant, Nat Gustafson-Sundell

Library Services Publications

I will present the steps we have taken to develop a prototype AI assistant for license review. I’ll explain our criteria for the selection of an AI tool for this project. We reviewed ChatGPT, Claude 2, Bard, and PDF readers. My goal was to develop an initial prototype in a Jupyter Notebook environment so I could easily re-load context information, including a license checklist, but I’ll explain why I revised this goal, instead to linger over license review interactions with ChatBots. I’ll discuss early results, demonstrate example license review interactions, and outline my next steps.

Dtitd: An Intelligent Insider Threat Detection Framework Based On Digital Twin And Self-Attention Based Deep Learning Models, Zhi Qiang Wang, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

Recent statistics and studies show that the loss generated by insider threats is much higher than that generated by external attacks. More and more organizations are investing in or purchasing insider threat detection systems to prevent insider risks. However, the accurate and timely detection of insider threats faces significant challenges. In this study, we proposed an intelligent insider threat detection framework based on Digital Twins and self-attentions based deep learning models. First, this paper introduces insider threats and the challenges in detecting them. Then this paper presents recent related works on solving insider threat detection problems and their limitations. Next, …

What You See Is Not What You Know: Studying Deception In Deepfake Video Manipulation, Cathryn Allen, Bryson R. Payne, Tamirat Abegaz, Chuck Robertson

Journal of Cybersecurity Education, Research and Practice

Research indicates that deceitful videos tend to spread rapidly online and influence people’s opinions and ideas. Because of this, video misinformation via deepfake video manipulation poses a significant online threat. This study aims to discover what factors can influence viewers’ capability to distinguish deepfake videos from genuine video footage. This work focuses on exploring deepfake videos’ potential use for deception and misinformation by exploring people’s ability to determine whether videos are deepfakes in a survey consisting of deepfake videos and original unedited videos. The participants viewed a set of four videos and were asked to judge whether the videos shown …

Malfe—Malware Feature Engineering Generation Platform, Avinash Singh, Richard Adeyemi Ikuesan, Hein Venter

All Works

The growing sophistication of malware has resulted in diverse challenges, especially among security researchers who are expected to develop mechanisms to thwart these malicious attacks. While security researchers have turned to machine learning to combat this surge in malware attacks and enhance detection and prevention methods, they often encounter limitations when it comes to sourcing malware binaries. This limitation places the burden on malware researchers to create context-specific datasets and detection mechanisms, a time-consuming and intricate process that involves a series of experiments. The lack of accessible analysis reports and a centralized platform for sharing and verifying findings has resulted …

Medical Imaging Applications Of Federated Learning, Sukhveer Singh Sandhu, Hamed Taheri Gorji, Pantea Tavakolian, Kouhyar Tavakolian, Alireza Akhbardeh

Faculty, Staff and Student Publications

Since its introduction in 2016, researchers have applied the idea of Federated Learning (FL) to several domains ranging from edge computing to banking. The technique's inherent security benefits, privacy-preserving capabilities, ease of scalability, and ability to transcend data biases have motivated researchers to use this tool on healthcare datasets. While several reviews exist detailing FL and its applications, this review focuses solely on the different applications of FL to medical imaging datasets, grouping applications by diseases, modality, and/or part of the body. This Systematic Literature review was conducted by querying and consolidating results from ArXiv, IEEE Xplorer, and PubMed. Furthermore, …

Synthesizing Sentience: Integrating Large Language Models And Autonomous Agents For Emulating Human Cognitive Complexity, Jay Ratican, James Hutson, Daniel Plate

Faculty Scholarship

The paper aims to present a novel methodology for emulating the intricacies of human cognitive complexity by ingeniously integrating large language models with autonomous agents. Grounded in the theoretical framework of the modular mind theory-originally espoused by Fodor and later refined by scholars such as Joanna Bryson—the study seeks to venture into the untapped potential of large language models and autonomous agents in mirroring human cognition. Recent advancements in artificial intelligence, exemplified by the inception of autonomous agents like Age in GPT, auto GPT, and baby AGI, underscore the transformative capacities of these technologies in diverse applications. Moreover, empirical studies …

Columnas: The Honors Program Newsletter At Bentley University, Hailey Jennato, Samson Shen, Clara Williams

Honors Program

Page 1: HOW AI IS IMPACTING THE BENTLEY CLASSROOM AND EDUCATION OVERALL ~ by Nayeli Franco ’24

Page 2: A BEAUTY OF DIVERSITY ~ by Yun Song ’26

Page 3: RESURRECTING THE DEAD THROUGH COMPUTER TECHNOLOGY: HONORING THEIR MEMORY OR EXPLOITING THEIR LEGACY? ~ by Hailey Jennato ’24

Page 4: THE IMPORTANCE OF DEVELOPING EMOTIONAL INTELLIGENCE ~ by Isa Ramirez Perdomo ’26

Page 5: FROM STRUGGLE TO STRENGTH: THRIVING AS AN INTERNATIONAL STUDENT ~ by Ledion Hoti ’25

Page 6: CHASING BUTTERFLIES ~ by Alyssa Galin ’27

Object Recognition With Deep Neural Networks In Low-End Systems, Lillian Davis

Mahurin Honors College Capstone Experience/Thesis Projects

Object recognition is an important area in computer vision. Object recognition has been advanced significantly by deep learning that unifies feature extraction and classification. In general, deep neural networks, such as Convolution Neural Networks (CNNs), are trained in high-performance systems. Aiming to extend the reach of deep learning to personal computing, I propose a study of deep learning-based object recognition in low-end systems, such as laptops. This research includes how differing layer configurations and hyperparameter values used in CNNs can either create or resolve the issue of overfitting and affect final accuracy levels of object recognition systems. The main contribution …

Erosion Resistant Rock Shoulder, Chung R. Song, Richard L. Wood, Basil Abualshar, Mark O'Brien, Bashar Al-Nimri, Mitra Nasimi

Nebraska Department of Transportation: Research Reports

Highway shoulder rocks are exposed to continuous erosion force due to unexpected floodings caused by climate change. The evaluation methods of the erosion resistance of highway shoulder rocks are not currently well-developed. This study developed a new large-scale testing device, the University of Nebraska-Lincoln Erosion Testing Bed (UNLETB), which has the capability of testing the shoulder gravels. Different gradations were tested using UNLETB, and the results distinguished a high-resistant group and a low-resistant group. The low resistant group was treated with different binding agents: Ammonium Lignosulfonate (LIGNO10), Soybean Soap Stock, and DirtGlue. The treated samples showed better erosion resistance based …

Indicium Ex Machina: Unstructured Sentencing And Disparate Outcomes In Canada, Damilola Awotula

LLM Theses

Recent advancements in artificial intelligence have caused a wave of technological normality. As expected, the criminal justice system, and more increasingly, criminal sentencing is seeing a trend of “technosolutionism” due to real concerns about unjustified disparity. Truly, artificial intelligence has the prospect of making the sentencing process more effective, value-driven, consistent, and predictable. However, relying on the assumption that using such a system may require being confined to the normative sentencing traditions of each country, this thesis argues that there are crucial questions to be addressed about how this technological normality fits within the normative pillars of extant legal principles, …

Ursinus Magazine, Fall 2023, Robyn E. Hannigan, Ed Moorhouse, Jennifer Meininger Wolfe, Ben Seal, William Mongan

Ursinus Magazine, 1995–Present

On the brain • Solving the problem • Brave in Season • Energy impact • Supporting recovery • Leading the conversation • Legacies of slavery • Bears on the Hill • At the Berman • Bear bites • Office space • The new view: How an Ursinus lab is threading machine learning and the human element • Lean on me: A system of student-led support • Pitch perfect • Welcome, new Bears! • Live from New York • Scenes on stage • On the fast track • Class notes • Marriages • In memoriam • Burning question: How can we …

A Gentle Introduction To Chatgpt, Steven W. Holloway

Libraries

A guest lecture on the state of commercial generative transformer technology, mid-2023, to a general audience at Staunton Public Library.

Introduction To 'Artificial Intelligence In Failure Analysis Of Transportation Infrastructure And Materials', Yue Hou, Qiao Dong, Dawei Wang, Jenny Liu

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyze the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyze the engineering failures in transportation infrastructure and materials. AI has the advantages of …

Perceptions And Barriers To Adopting Artificial Intelligence In K-12 Education: A Survey Of Educators In Fifty States, Karen Woodruff, James Hutson, Kathryn Arnone

Faculty Scholarship

Artificial Intelligence (AI) is making significant strides in the field of education, offering new opportunities for personalized learning and access to education for a more diverse population. Despite this potential, the adoption of AI in K-12 education is limited, and educators’ express hesitancy towards its integration due to perceived technological barriers and misconceptions. The purpose of this study is to examine the perceptions of K-12 educators in all 50 states of the USA towards AI, policies, training, and resources related to technology and AI, their comfort with technology, willingness to adopt new technologies for classroom instruction, and needs assessment for …

What Is It Like To Be A Brain Organoid? Phenomenal Consciousness In A Biological Neural Network, Ivanna Montoya, Daniel Montoya

College of Humanities and Social Sciences

It has been shown that three-dimensional self-assembled multicellular structures derived from human pluripotent stem cells show electrical activity similar to EEG. More recently, neurons were successfully embedded in digital game worlds. The biologically inspired neural network (BNN), expressing human cortical cells, was able to show internal modification and learn the task at hand (predicting the trajectory of a digital ball while moving a digital paddle). In other words, the system allowed to read motor information and write sensory data into cell cultures. In this article, we discuss Neural Correlates of Consciousness (NCC) theories, and their capacity to predict or even …

Ai-Supported Academic Advising: Exploring Chatgpt’S Current State And Future Potential Toward Student Empowerment, Daisuke Akiba, Michelle C. Fraboni

Publications and Research

Artificial intelligence (AI), once a phenomenon primarily in the world of science fiction, has evolved rapidly in recent years, steadily infiltrating into our daily lives. ChatGPT, a freely accessible AI-powered large language model designed to generate human-like text responses to users, has been utilized in several areas, such as the healthcare industry, to facilitate interactive dissemination of information and decision-making. Academic advising has been essential in promoting success among university students, particularly those from disadvantaged backgrounds. Unfortunately, however, student advising has been marred with problems, with the availability and accessibility of adequate advising being among the hurdles. The current study …

Artificial Intelligence In Cardiology: An Australian Perspective, Biyanka Jaltotage, Abdul R. Ihdayhid, Nick S. R. Lan, Faraz Pathan, Sanjay Patel, Clare Arnott, Gemma Figtree, Leonard Kritharides, Syed M. S. Islam, Clara K. Chow, James M. Rankin, Stephen J. Nicholls, Girish Dwivedi

Research outputs 2022 to 2026

Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, …

Catalyst Journal Of The Amateur Yacht Research Society, J Perry, C Mccowen, I Ward, R Fish, M Howard, J Graham-Jones

School of Engineering, Computing and Mathematics

CATALYST is a journal of yacht research, design and technology published by the Amateur Yacht Research Society, BCM AYRS, London, WC1N 3XX, UK 3. Innov-Sail – 29 to 31 May 2023 by John Perry 8 Regional NWLG Windemere meeting - 6th June 2023ncrease of subscription from September 2023 10 Balanced Foils, Part 2, by Ian Ward 29 Artificial Intelligence by Richard Fish 35 Howard Fund revisited : The Winners and the Losers By Mike Howard

Framing And Intentionality In Artificial Intelligence Poetry, Alyson Elaine Marie Nichols

Student Theses and Dissertations

As natural language generation expands, increasing attention has been paid to neural networks and other machine learning techniques. This study offers a cheaper, simpler approach in the field of poetry generation. The Empath system uses a random-grammar model to constrain creativity with syntactical rules in order to produce novel and valuable poetry after the style of Emily Dickinson. It pays specific attention to using related words to create a sense of coherence and implements a framing system to prove that its creative choices are intentional. A survey of 118 students and teachers at Arkansas State University shows that Empath’s products …

Machine Learning For Low Signal-To-Noise Ratio Detection, Fred Lacy, Angel Ruiz-Reyes, Anthony Brescia

Faculty Publications

Sensor networks collect data that is often contaminated by noise. Therefore, it is often necessary to analyze sensor data to determine if a signal is present. This research project utilizes a machine learning algorithm that is able to detect a signal in the presence of noise. The algorithm incorporates the long short-term memory (LSTM) method to determine the presence or absence of a signal in the midst of white Gaussian noise. This machine learning approach was tested with computer generated data and has an accuracy of at least 98% for signal-to-noise levels greater than -12 dB. Furthermore, this algorithm can …

Vision Language Navigation With Knowledge-Driven Environmental Dreamer, Fengda Zhu, Vincent C.S. Lee, Xiaojun Chang, Xiaodan Liang

Computer Vision Faculty Publications

Vision-language navigation (VLN) requires an agent to perceive visual observation in a house scene and navigate step-by-step following natural language instruction. Due to the high cost of data annotation and data collection, current VLN datasets provide limited instruction-trajectory data samples. Learning vision-language alignment for VLN from limited data is challenging since visual observation and language instruction are both complex and diverse. Previous works only generate augmented data based on original scenes while failing to generate data samples from unseen scenes, which limits the generalization ability of the navigation agent. In this paper, we introduce the Knowledge-driven Environmental Dreamer (KED), a …

Mitigation Of Ground Control Issues Using Artificial Intelligence Techniques, Temitope Adebanjo

Engineering

Ground hazards are a major safety concern in underground mines, causing deaths, injuries, and lost work time to miners. Although ground hazards such as roof fall accidents have steadily and significantly declined over the last decade, the safety hazards associated with ground hazards still account for a significant portion of the total number of accidents in the underground mining industry. One of the main reasons behind ground hazards in underground mines is abrupt changes in geological conditions that may decrease the competence of the rocks surrounding an excavation, resulting in ground failure. The changes in geological can be detected in …

Controllable Language Generation Using Deep Learning, Rohola Zandie

Electronic Theses and Dissertations

The advent of deep neural networks has sparked a revolution in Artificial Intelligence (AI), notably with the creation of Transformer models like GPT-X and ChatGPT. These models have surpassed previous methods in various Natural Language Processing (NLP) tasks. As the NLP field evolves, there is a need to further understand and question the capabilities of these models. Text generation, a crucial part of NLP, remains an area where our comprehension is limited while being critical in research.

This dissertation focuses on the challenging problem of controlling the general behaviors of language models such as sentiment, topical focus, and logical reasoning. …

Toward A Trustworthy And Responsible Artificial Intelligence, Saleh Afroogh

Legacy Theses & Dissertations (2009 - 2024)

Being trustworthy and responsible—particularly in terms of decisions and actions—in artificial agents is as crucial as it is in human agents. However, the dominant theories surrounding pertinent concepts such as trust, trustworthiness, moral responsibility, legal rights, etc., are primarily proposed, developed, and applied to human agents, not artificial ones. The absence of well-developed conceptualization and characterization of relevant concepts concerning critical agents continues to exacerbate the challenges and importance of these concepts in the realm of artificial intelligence. This void necessitates novel perspectives and approaches toward the metrics needed to foster genuinely trustworthy and responsive artificial intelligence systems. In this …

Digital Twins And Artificial Intelligence For Applications In Electric Power Distribution Systems, Deborah George

All Theses

As modern electric power distribution systems (MEPDS) continue to grow in complexity, largely due to the ever-increasing penetration of Distributed Energy Resources (DERs), particularly solar photovoltaics (PVs) at the distribution level, there is a need to facilitate advanced operational and management tasks in the system driven by this complexity, especially in systems with high renewable penetration dependent on complex weather phenomena.

Digital twins (DTs), or virtual replicas of the system and its assets, enhanced with AI paradigms can add enormous value to tasks performed by regulators, distribution system operators and energy market analysts, thereby providing cognition to the system. DTs …

Applications Of Artificial Intelligence To Improve Coastal Ocean Modeling, Dongliang Shen

Electronic Theses and Dissertations

Numerical Modeling (NM) is widely used to simulate and predict hydrodynamic processes and marine particle movements in coastal oceans, particularly during extreme weather events and emergencies. NM offers the capability to realistically simulate multiple state variables and fill gaps caused by scarce observations. However, inherent uncertainties exist in all NMs, primarily arising from the following three factors: 1) insufficient observations leading to uncertain model initial and boundary conditions, 2) inevitable truncation errors due to coarse model resolution, and 3) imperfect physics parameterization schemes for sub-grid processes, especially those related to waves. The consequences of these uncertainties are that 1) even …

The Development Of Artificial Intelligence-Based To Ols For Expert Peer Review Of Radiotherapy Treatment Plans, Mary Gronberg

Dissertations and Theses (Open Access)

Creating a patient-specific radiation treatment plan is a time-consuming and operator-dependent manual process. The treatment planner adjusts the planning parameters in a trial-and-error fashion in an effort to balance the competing clinical objectives of tumor coverage and normal tissue sparing. Often, a plan is selected because it meets basic organ at risk dose thresholds for severe toxicity; however, it is evident that a plan with a decreased risk of normal tissue complication probability could be achieved. This discrepancy between “acceptable” and “best possible” plan is magnified if either the physician or treatment planner lacks focal expertise in the disease site. …

Generative Pre-Trained Transformers (Gpt) And Space Health: A Potential Frontier In Astronaut Health During Exploration Missions, Ethan Waisberg, Joshua Ong, Mouayad Masalkhi, Nasif Zaman, Sharif Amit Kamran, Prithul Sarker, Andrew G Lee, Alireza Tavakkoli

Faculty, Staff and Student Publications

In anticipation of space exploration where astronauts are traveling away from Earth, and for longer durations with an increasing communication lag, artificial intelligence (AI) frameworks such as large language learning models (LLMs) that can be trained on Earth can provide real-time answers. This emerging technology may be helpful for acute medical emergencies, particularly in austere and distant space environments. In this manuscript, we provide an overview of generative pre-trained transformer (GPT) technology, a rapidly emerging AI technology, and implications, considerations, and limitations of such technology for space health.

Model-Free Sliding Mode Control In The Lateral And Direction Dynamics Of An Aircraft, Nicholas Hutson

Theses

Traditional control methods require extensive tuning or a derivation of a system model making them increasingly antiquated for use on new, more complex systems. Sliding Mode Control has emerged as a more effective alternative as a control method that can directly handle nonlinear systems with increased robustness while guaranteeing stability. However, it is still limited by the need for a system model for the derivation of the controller form. This work proposes a new model-free control method based on Sliding Mode Control referred to as Model-Free Sliding Mode Control where the form of the controller is only dependent on system …

Artificial Intelligence Frameworks To Detect And Investigate The Pathophysiology Of Spaceflight Associated Neuro-Ocular Syndrome (Sans), Joshua Ong, Ethan Waisberg, Mouayad Masalkhi, Sharif Amit Kamran, Kemper Lowry, Prithul Sarker, Nasif Zaman, Phani Paladugu, Alireza Tavakkoli, Andrew G Lee

Student Papers, Posters & Projects

Spaceflight associated neuro-ocular syndrome (SANS) is a unique phenomenon that has been observed in astronauts who have undergone long-duration spaceflight (LDSF). The syndrome is characterized by distinct imaging and clinical findings including optic disc edema, hyperopic refractive shift, posterior globe flattening, and choroidal folds. SANS serves a large barrier to planetary spaceflight such as a mission to Mars and has been noted by the National Aeronautics and Space Administration (NASA) as a high risk based on its likelihood to occur and its severity to human health and mission performance. While it is a large barrier to future spaceflight, the underlying …