Digital Commons Network^™

Research On Emerging Technology Topic Identification Based On Bertopic, Dakun Wang, Bolin Hua

Journal of Scientific Information Research

[Purpose/significance]Identifying and foreseeing emerging technologies, bring technological first-mover advantages to enterprises and governments, and grasp technological development trends in a timely manner. [Method/process]This study uses BERTopic's topic modeling method to obtain domain topic distribution, and merges paper and patent topics based on the cosine similarity of topic vectors to identify emerging topics. [Result/conclusion]Using the BERTopic topic modeling method combined with index evaluation can effectively identify emerging topics and emerging terms.Taking the field of new energy vehicles as an example to carry out empirical research, using two methods: divided verification period and data verification method, 12 of the 16 identified topics …

Navigating Copyright In Ai-Enhanced Game Design: Legal Challenges In Multimodal And Dynamic Content Creation, Andrew Begemann, James Hutson

Faculty Scholarship

The integration of artificial intelligence (AI) in video game design has transformed traditional workflows, allowing for the generation of text, images, music, videos, and code at unprecedented scales. However, this advancement presents complex challenges for copyright law, traditionally rooted in human originality and authorship. This article examines recent case law that underscores the evolving legal landscape, exploring landmark cases such as Zarya of the Dawn and Andersen v. Stability AI. These cases reveal the tensions between AI-generated outputs and copyright eligibility, especially in the dynamic, multimodal compositions inherent to video games. The review analyzes how various AI tools are employed …

Message From Workshop Chairs, Sushil K. Prasad, Srishti Srivastava, Satish Puri, David Bunde, Shubbhi Taneja, Buddhi Ashan Mallika Kankanamalage

Computer Science Faculty Research & Creative Works

No abstract provided.

Alertble: Alert Workzone Hazards Using Hybrid Filtering And Machine-Learning-Enabled Ble, Samuel Akinyede, Sejun Song

Computer Science Faculty Research & Creative Works

Collision hazard detection in industrial work zones faces challenges from signal instability, mobility-induced fluctuations, and nonline-of-sight (NLOS) conditions. While Bluetooth low energy (BLE) offers cost-effective proximity sensing, its received signal strength indicator (RSSI) variability - fluctuating by ±10 dBm even at fixed distances - limits reliability in safety-critical applications. This article presents AlertBLE, a hybrid BLE-based hazard detection system that combines extended Kalman filter (EKF) and adaptive moving average (AMA) algorithms to achieve up to 94% RSSI variance reduction in static NLOS conditions. The system introduces speed-aware safety thresholds based on reaction time and braking distance models, dynamically expanding hazard …

Deep Learning-Based Ensemble Two-Step Classification Of Medical Images Using Cnn Architectures And Ensemble Methods, Noreliz Alorico

Master's Theses or Doctor of Nursing Practice

Breast cancer remains one of the most common cancers amongst women globally. Early detection is crucial for improving survival rates. While mammography is widely used and an effective imaging technique, it can sometimes yield false positive or false negatives. Mammogram interpretation is highly operator-dependent, introducing variability and the potential for diagnostic errors. Additionally, mammographic images have limitations, such as low contrast in breast tissue and overlapping structures that can obscure lesions or mimic abnormalities. These limitations can lead to unnecessary biopsies or delayed diagnosis. These challenges highlight the needs for advanced and data driven diagnostic tools to support and enhance …

Towards Smart Farming: Image-Based Crop Health Assessment And Disease Diagnosis Using Deep Learning Techniques, Kristina Botova

Master's Theses or Doctor of Nursing Practice

Accurate crop monitoring is essential for optimizing agricultural productivity and ensuring food security. This study presents a comprehensive deep learning framework for image crop type recognition, health status prediction, and disease detection using multiple Convolutional Neural Network (CNN) models. The proposed approach uses open-source datasets consisting of five crop types (apple, corn, grape, potato, tomato), varying health conditions, and common diseases. By deploying specialized CNN architecture focused on each task, the system achieves a high accuracy of 99.25% in classifying crop types, identifying health status, and detecting specific diseases. Compared to a single CNN model, the use of the proposed …

The Knowwheregraph Ontology, Cogan Shimizu, Shirly Stephen, Adrita Barua, Ling Cai, Antrea Christou, Kitty Currier, Abhilekha Dalal, Colby K. Fisher, Pascal Hitzler, Krzysztof Janowicz, Wenwen Li, Zilong Liu, Mohammad Saeid Mahdavinejad, Gengchen Mai, Dean Rehberger, Mark Schildhauer, Meilin Shi, Sanaz Saki Norouzi, Yuanyuan Tian, Sizhe Wang, Zhangyu Wang, Joseph Zalewski, Lu Zhou, Rui Zhu

Computer Science and Engineering Faculty Publications

KnowWhereGraph is one of the largest fully publicly available geospatial knowledge graphs. It includes data from 30 layers on natural hazards (e.g., hurricanes, wildfires), climate variables (e.g., air temperature, precipitation), soil properties, crop and land-cover types, demographics, and human health, various place and region identifiers, among other themes. These have been leveraged through the graph by a variety of applications to address challenges in food security and agricultural supply chains; sustainability related to soil conservation practices and farm labor; and delivery of emergency humanitarian aid following a disaster. In this paper, we introduce the ontology that acts as the schema …

A Community-Driven Vision For A New Knowledge Resource For Ai, Vinay K. Chaudhri, Chaitan Baru, Brandon Bennett, Mehul Bhatt, Darion Cassel, Anthony G. Cohn, Rina Dechter, Esra Erdem, Dave Ferrucci, Ken Forbus, Gregory Gelfond, Michael Genesereth, Andrew S. Gordon, Benjamin Grosof, Gopal Gupta, Jim Hendler, Sharat Israni, Tyler R. Josephson, Patrick Kyllonen, Yuliya Lierler, Vladimir Lifschitz, Clifton Mcfate, Hande Küçük Mcginty, Leora Morgenstern, Alessandro Oltramari, Praveen Paritosh, Dan Roth, Blake Shepard, Cogan Shimizu, Denny Vrandečić, Mark Whiting, Michael Witbrock

Computer Science and Engineering Faculty Publications

The long-standing goal of creating a comprehensive, multi-purpose knowledge resource, reminiscent of the 1984 Cyc project, still persists in AI. Despite the success of knowledge resources like WordNet, ConceptNet, Wolfram|Alpha and other commercial knowledge graphs, verifiable, general-purpose, widely available sources of knowledge remain a critical deficiency in AI infrastructure. Large language models struggle due to knowledge gaps; robotic planning lacks necessary world knowledge; and the detection of factually false information relies heavily on human expertise. What kind of knowledge resource is most needed in AI today? How can modern technology shape its development and evaluation? A recent AAAI workshop gathered …

A Study Of User Experiences Of Pediatric Physicians With Electronic Health Record Systems: Encounters With Task Complexity And Efficiency Of User Task Flows, Roseanne Alhindi

CCAC Theses and Dissertations

way patient information is stored, managed, and accessed. This transition to Electronic Health Record (EHR) systems has enhanced the efficiency and accuracy of healthcare delivery by enabling quick access to patient records, reduction of errors, and facilitation of coordination among healthcare providers. In the EHR system, diverse tasks are performed for clinical processes and patient care. These tasks can be considered simple or complex, ranging from documenting patient visits and updating medical histories to ordering tests and managing prescriptions. Although EHR systems have become more prevalent in their use, there are noted challenges associated with the design of the system …

Leveraging Distributed Semantics From Deep Learning Architectures For Literature-Based Discovery, Clint A. Cuffy

Theses and Dissertations

Literature-based discovery (LBD) is a scientific process that introduces methods to automatically identify novel insights between non-interacting sets of literature. To date, numerous statistical and machine learning-based methods have been applied in the biomedical domain to find treatments for diseases such as Raynaud's disease, Parkinson's disease, and Multiple Sclerosis. However, the lack of standardized practices and creation of bespoke methodologies produces a scenario where the adoption of LBD remains challenging in real-world systems. Our work addresses these concerns through the improvement of five critical areas: 1) error propagation within LBD's a priori dependent tasks, 2) exploring the integration of modern …

Ms-Yolo: Infrared Object Detection For Edge Deployment Via Mobilenetv4 And Slideloss, Jiali Zhang, Thomas S. White, Haoliang Zhang, Wenqing Hu, Donald C. Wunsch, Jian Liu

Mathematics and Statistics Faculty Research & Creative Works

Infrared imaging has emerged as a robust solution for urban object detection under low-light and adverse weather conditions, offering significant advantages over traditional visible-light cameras. However, challenges such as class imbalance, thermal noise, and computational constraints can significantly hinder model performance in practical settings. To address these issues, we evaluate multiple YOLO variants on the FLIR ADAS V2 dataset, ultimately selecting YOLOv8 as our baseline due to its balanced accuracy and efficiency. Building on this foundation, we present MS-YOLO (MobileNetv4 and SlideLoss based on YOLO), which replaces YOLOv8's CSPDarknet backbone with the more efficient MobileNetV4, reducing computational overhead by 1.5% …

Extending A Pretrained Language Model (Bert) Using An Ontological Perspective To Classify Students' Scientific Expertise Level From Written Responses, Heqiao Wang, Kevin C. Haudek, Amanda D. Manzanares, Chelsie L. Romulo, Emily A. Royse, Caterina B. Azzarello

Human Movement Studies & Special Education Faculty Publications

The complex and interdisciplinary nature of scientific concepts presents formidable challenges for students in developing their knowledge-in-use skills. The utilization of computerized analysis for evaluating students' contextualized constructed responses offers a potential avenue for educators to develop personalized and scalable interventions, thus supporting the current teaching and learning of science. While prior research in artificial intelligence has demonstrated the effectiveness of algorithms, including Bidirectional Encoder Representations from Transformers (BERT), in tasks like automated classifications of constructed responses, these efforts have predominantly leaned towards text-level features, often overlooking the exploration of conceptual ideas embedded in students' responses from a cognitive perspective. …

The Role Of Mineral Raw Material Imports In Driving The Energy Transition, Mahelet G. Fikru, Nurcan Kilinc-Ata

Economics Faculty Research & Creative Works

This study contributes to the mineral-energy nexus by examining the role of importing mineral raw materials (ores and concentrates) on subsequent progress in the energy transition among 33 countries from 1992 to 2015. We focus on net imports of ores and concentrates for five energy transition minerals (copper, cobalt aluminum, nickel, and manganese) and present an economic production framework to link the mineral raw materials with renewable electricity generation shares. The distinction between mineral raw materials and processed/refined inputs is important because processing capabilities vary among nations, influencing their import-export dynamics and energy transition strategies. Our empirical analysis based on …

Intergenerational Classification Of Reddit Comments Based On Slang And Emoji Usage, James T. Dracup

West Chester University Master’s Theses

The rapid evolution of language, driven by technological advancements, has created notable cultural gaps between generations, particularly in how they communicate. This gap is most apparent in the growing use of slang and emojis among younger generations. This study aims to explore whether Reddit comments can be classified by generation based on the usage of slang and emojis, the frequency of their use across generations, and how such features (slang and emojis) might influence the meaning of traditional language. Using Reddit’s API, we collected comments from four generational subreddits and applied various machine learning models, Naïve Bayes, Neural Networks, and …

Fares On Fairness: Using A Total Error Framework To Examine The Role Of Measurement And Representation In Training Data On Model Fairness And Bias, Patrick Oliver Schenk, Christoph Kern, Trent D. Buskirk

Information Technology & Decision Sciences Faculty Publications

Data-driven decisions, often based on predictions from machine learning (ML) models are becoming ubiquitous. For these decisions to be just, the underlying ML models must be fair, i.e., work equally well for all parts of the population such as groups defined by gender or age. What are the logical next steps if, however, a trained model is accurate but not fair? How can we guide the whole data pipeline such that we avoid training unfair models based on inadequate data, recognizing possible sources of unfairness early on? How can the concepts of data-based sources of unfairness that exist in the …

Robust Palm Print For Mobile Authentication System, Son Nguyen

Chulalongkorn University Theses and Dissertations (Chula ETD)

Smartphones are gateways to financial, health, and personal data; consequently, mobile authentication must be accurate, fast, privacy-preserving, and scalable. This thesis presents an end-to-end palmprint authentication framework that addresses a practical trilemma: label-efficient learning, on-device efficiency, and cloud-scale identification. We pretrain a ResNet-18 encoder with self-supervised contrastive learning on unlabeled palm images, distill its representation to a lightweight MobileNetV3 student for real-time inference on phones, and support both 1:1 on-device verification and 1:N cloud identification using FAISS/HNSW. On public datasets, the system attains 99.2% accuracy, a 0.15% equal-error rate (EER), and ~87 ms end-to-end latency on iPhone-class hardware. FAISS scales …

Artificial Intelligence And Environmental Sustainability: Review And Research Directions, Troy Strader, Yu-Hsiang (John) Huang, Yu-Ju Tu

Journal of International Technology and Information Management

Environmental sustainability is one of the most important and complex issues currently facing our global society. One solution to some aspects of this problem could come from artificially intelligent systems and data analytics methods. The objective for this study is to identify the range of recently published research that addresses issues involving the convergence of artificial intelligence (AI) and environmental sustainability. A systematic literature review produced a sample of 62 journal articles from 2018-2024 that were each categorized into one of six research themes that included studies of AI and the ways in which it impacted natural resources, energy and …

Human Perception Of Ai Capabilities At Classifying Perturbed Roadway Signs, Katherine R. Garcia, Jing Chen, Yanru Xiao, Scott Mishler, Cong Wang, Bin Hu

Psychology Faculty Publications

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated …

Tedvil: Leveraging Transformer-Based Embeddings For Vulnerability Detection In Lifted Code, Gary Mccully, John Hastings, Shengjie Xu

Research & Publications

Ransomware and other malware inflict devastating financial and operational damage on organizations worldwide by exploiting deeply embedded, hard-to-detect vulnerabilities in their systems. Detecting these vulnerabilities in compiled code before malicious actors exploit them remains a critical challenge in cybersecurity. This research introduces TEDVIL (Transformer-based Embeddings for Discovering Vulnerabilities in Lifted Code), a novel framework which uses transformer-based embeddings to train neural networks to detect vulnerabilities in lifted code. The framework was implemented using bidirectional (BERT and RoBERTa) and unidirectional (GPT-1 and GPT-2) transformer-based models to generate embeddings for training Long Short-Term Memory (LSTM) neural networks to detect stack-based buffer overflows …

Solar Flare Forecasting Multiple Ml And Curation Technique Study Hour-By-Hour Sharp Parameter Data Archive, Timothy S. Newman

Open Data

Hour-by-hour AR parameter data as a series of .csv files in a zip archive. Contains the "filtered" data described in the "Solar Flare Forecasting using Machine Learning (ML) and SDO/HMI Data: Multiple ML Model and Data Curation Technique Comparison Study" paper of Newman, Hall, Farris, Singh, Pogorelov, Benson, Raza, and Trital paper, 2025 submission date, for ApJS. Each file in the zip has data for one class of flares at a timepoint a certain number of hours in advance of flare onset. The first letter of such file names indicates flare class and the number before "hrs" in the title …

Optimizing Decision-Making In A Cerebral Palsy Model Using Reinforcement Learning, Richard Ampah

Pitzer Senior Theses

This study presents an original interdisciplinary investigation into how reinforcement learning (RL) can model motor and cognitive defects and potentially improve motor and cognitive functions in individuals with cerebral palsy (CP), a non-progressive neurological disorder that impairs movement and adaptability. Integrating computational neuroscience and machine learning, the research applies policy gradient methods and Markov Decision Processes (MDPs) to simulate adaptive learning in agents with and without CP-related constraints.

The central aim is to compare the cumulative rewards of optimal policies, derived from value iteration, and human-like learning policies using the REINFORCE algorithm, both with and without the Bellman baseline. The …

Predicting Human Steering From Optic Flow Using Deep Convolutional Neural Networks, Katie E. Bernard

Honors Theses

Human driving is a complex visuomotor task and the specific visual clues that guide it remain under investigation. While prior research has emphasized gaze-based strategies such as the Tangent Point and Future Path hypotheses, recent evidence highlights the potential role of optic flow, the visual motion pattern perceived during self-movement, as critical to steering ability. This thesis explores whether raw optic flow alone can support accurate predictions of human steering behavior. We trained a convolutional neural network to map optic flow vector fields to steering angles in a virtual reality driving simulation. The dataset, collected by Giguere et al., included …

Optimizing Radial Interfaces For Eye-Movement Authentication On Smartphones, Trey V. Tuscai

Honors Theses

Radial authentication interfaces offer privacy-preserving, calibration-free eye-movement authentication. While their effectiveness has been demonstrated on large displays, their performance on smartphones remains underexplored. This study investigates seven radial interface configurations on the iPhone 13, varying the number of radial indicators and password lengths to examine trade-offs between accuracy, security, and entry time. Through a controlled eye-tracking experiment with 27 participants, we evaluate each configuration’s performance and collect user prioritizations of the three factors. Our findings reveal that shorter passwords with fewer indicators improve speed and accuracy but reduce security, while longer configurations enhance security at the cost of usability. Based …

The Future Of Code Style: Learning With Gamified Online Tools, Jacob C. Tjaden

Honors Theses

High-quality code is universally pursued by software developers, and one of the most effective indicators of code quality is code style. However, code style is difficult to teach, particularly to introductory students and programmers who benefit most. In this project, we aim to investigate how online tools can improve and teach Python code style, as well as identify the role of gamification in the process. We build an online platform called Fishy that combines code style appraisal tools and utilizes gamification concepts. Our platform incorporates educational metrics such as a code analysis score and targeted quizzes to assess user performance. …

Revealing Spatiotemporal Neural Activation Patterns In Electrocorticography Recordings Of Human Speech Production By Mutual Information, Julio Kovacs, Dean Krusienski, Minu Maninder, Willy Wriggers

Mechanical & Aerospace Engineering Faculty Publications

Background

Spatiotemporal mapping of neural activity during continuous speech production has been traditionally approached using correlation coefficient (CC) analysis between cortical signals and speech recordings. A prior study employed this approach using electrocorticography (ECoG) data from participants who underwent invasive intracranial monitoring for epilepsy. However, CC cannot detect nonlinear relationships and is dominated by the correspondence between periods of silence and of non-silence.

New Method

We introduce the mutual information (MI) measure, which can capture both linear and nonlinear dependencies. We validated CC and MI on the sub-second spatiotemporal brain activity recorded during continuous speech tasks. To refine the results, …

Mass-Adaptive Admittance Control For Robotic Manipulators, Hossein Gholampour, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Handling objects with unknown or changing masses is a common challenge in robotics, often leading to errors or instability if the control system cannot adapt in realtime. In this paper, we present a novel approach that enables a six-degrees-of-freedom robotic manipulator to reliably follow waypoints while automatically estimating and compensating for unknown payload weight. Our method integrates an admittance control framework with a mass estimator, allowing the robot to dynamically update an excitation force to compensate for the payload mass. This strategy mitigates end-effector sagging and preserves stability when handling objects of unknown weights. We experimentally validated our approach in …

Optimal Manipulation Motion Action Planner Enabled By Physics Informed Neural Networks, Jonathon E. Slightam, Logan E. Beaver

Mechanical & Aerospace Engineering Faculty Publications

Autonomous robotic manipulation in unstructured environments faces many challenges and is hindered by capabilities that bridge the gap between perception and acting on the world. Action plans that are centric to object motion rather than end-of-arm tooling behavior may aid this. This paper presents an autonomous action planner for a feedback linearizeable system comprised of three base motions that can be leveraged on their own or in combination to give custom motion plans. The optimization routine for the three different types of motion are presented, which are integrated into physics informed neural networks. A component of this is the autonomy …

From Walking To Parkour: A Structured Survey Of Rl For Dynamic Skills In Legged Robots, Christopher Allred, Chandler Justice, Rosario Scalise, Yan Gu, Jonathan Clark, Mario Harper, Jason Pusey

Computer Science Student Research

This survey reviews recent advances in applying reinforcement learning (RL) to enable dynamic and ballistic motions in legged robots, including running, jumping, stair climbing, and parkour. Focusing on high-agility behaviors that challenge traditional control frameworks, we categorize foundational locomotion tasks and highlight the RL methods, such as Proximal Policy Optimization, curriculum learning, and hybrid model-based strategies that have proven effective. We discuss the key challenges in transferring learned policies to real-world robots, managing uncertainty, and integrating perception and proprioception. Drawing from over 150 recent works, we provide a structured taxonomy of objectives, algorithms, and platforms, and identify trends in simulation …

Insights In Cybersecurity Of A Smart Campus - A Review, Mircea Ţălu

Journal of Cybersecurity Education, Research and Practice

The profound impact of the Internet of Things (IoT) on various fronts, is driven by technological advancements, the ubiquitous spread of information, and the emergence of transformative events. IoT presents a diverse array of possibilities within university environments, fostering a more connected and enhanced educational experience. This research undertakes a comprehensive review of existing literature to provide context to the IoT and underscore its crucial significance in the realm of smart campuses. Additionally, the paper explores the intricate connections between IoT and key concepts such as cybersecurity and wireless sensor networks to present a holistic perspective. It delves into the …

Applying Machine Learning Methods To Generate Understandings Of Differential Item Functioning In A Flu Knowledge Assessment, William L. Romine, Tanvi Banerjee, Derrick Cox

Computer Science and Engineering Faculty Publications

Current influenza trends, including the severity of the 2025 flu season and the prevalence of H5 bird flu in livestock, necessitate efforts to better understand how to educate students about its transmission. Although validated assessments of influenza knowledge exist, these have not been evaluated for affective and demographic biases. We explore differential item functioning (DIF) effects in four items focused on specific aspects of flu transmission derived from a validated influenza knowledge assessment. In doing so, we introduce and utilize a machine learning framework for exploration of DIF which offers greater flexibility than traditional statistical approaches in terms of studying …