Computer Sciences Commons^™

Method Entity And Relation Extraction Based On Automatically Generated Syntactic Templates: A Case Study Of Csdn Artificial Intelligence Blog, Kuiliang Li, Bolin Huang

Journal of Scientific Information Research

[Purpose/significance]There are many relationships between method entities and application scenarios, problems,organizations and other entities. Extracting these entity relationships helps to capture the development trend of technology and promote the improvement of innovation ability.[Method/process]This paper discusses a method for extracting method entities and relations based on automatically generated syntactic templates. By designing a new adaptive template, the method improves flexibility and adaptability, reducing dependence on large-scale labeled data. Using a small number of seed triples, the method iteratively generates syntactic templates and extracts method entities and relations for the CSDN artificial intelligence topic blog. It also improves the extraction quality using …

A Proposed Ehrenfeucht-Fraïssé Game Model For Natural Language Processing Generative Adversarial Networks, Don Li

Anthós

Large Language Models (LLM’s) (e.g., ChatGPT) constitute both a significant research area and commercial application of AI. Current major LLM’s are built on Generative Pre-Trained Transformer (GPT) neural network architecture to perform natural language processing (NLP) tasks. Generative Adversarial Network (GAN) is another popular neural network architecture, which leverages a zero-sum game between constituent neural networks within the architecture to train the GAN, and is widely used for visual data applications. This article proposes a new GAN architecture for NLP: an EF-GAN whose underlying algorithm uses Ehrenfeucht–Fraïssé (EF) games, a game-theoretic approach from model theory to determine elementary equivalence of …

A Happy Medium?: Using Image Generators To Explore Solution-Focused Art Therapy’S Miracle Question, Daniel A. Hernried

Art Therapy | Master's Theses

This mixed methods, randomized, single-session study tested whether integrating text-to-image generations into Solution-Focused Brief Art Therapy alters therapeutic rapport and short-term outcomes relative to traditional artmaking materials. Participants were assigned by coin flip to create using either a text-to-image generator or convention media (23 per group), completing immediate and three-day follow-ups. Alliance was measured using DREAM (Dimensions of Regard, Empathy, and Authenticity Metric), and problems were rated pre/post; groups did not differ significantly on DREAM total or facets, and both modalities produced reliable pre-to-post reductions in problem severity. At the same time, process differences were pronounced: the AI condition showed …

Generative Ai And Finding The Law, Paul D. Callister

Faculty Works

Legal information science requires, among other things, principles and theories. The article states six principles or considerations that any discussion of generative AI large language models and their role in finding the law must include. The article concludes that law librarianship will increasingly become legal information science and require new paradigms. In addition to the six principles, the article applies ecological holistic media theory to understand the relationship of the legal community’s cognitive authority, institutions, techné (technology, medium and method), geopolitical factors, and the past and future to understand the changes in this information milieu. The article also explains generative …

Evaluating The Evaluators: The Role Of Benchmarks In Legal Ai, Jonathan A. Franklin, Sean Harrington, Christine Hye Won Park

Other Faculty Publications

No abstract provided.

Clinician Perspectives On Virtual Reality Use In Physical Therapy Practice In The United States, Danielle T. Felsberg, Jared T. Mcguirt, Scott E. Ross, Louisa D. Raisbeck, Charlend K. Howard, Christopher K. Rhea

Rehabilitation Sciences Faculty Publications

The primary goal of physical rehabilitation is to assess movement impairments and restore function to improve overall quality of life. Virtual reality (VR) may provide the optimal environment to promote these goals due to its motivating and modifiable nature which can be difficult to accomplish through traditional real-world therapeutic methods. Current research of VR for rehabilitation has demonstrated that VR interventions can produce clinically meaningful change in motor outcomes. Despite this, adoption and usage of VR by physical therapy professionals is unclear due to the limited research in this area. Thus, the purpose of this study was to identify the …

Long-Term Traffic Prediction Using Deep Learning Long Short-Term Memory, Ange-Lionel Toba, Sameer Kulkarni, Wael Khallouli, Timothy Pennington

School of Cybersecurity Faculty Publications

Traffic conditions are a key factor in our society, contributing to quality of life and the economy, as well as access to professional, educational, and health resources. This emphasizes the need for a reliable road network to facilitate traffic fluidity across the nation and improve mobility. Reaching these characteristics demands good traffic volume prediction methods, not only in the short term but also in the long term, which helps design transportation strategies and road planning. However, most of the research has focused on short-term prediction, applied mostly to short-trip distances, while effective long-term forecasting, which has become a challenging issue …

Security Enhancement In Uav Swarms: A Case Study Using Federated Learning And Shap Analysis, Sushmitha Halli Sudhakara, Lida Haghnegahdar

School of Cybersecurity Faculty Publications

As cyber-physical systems (CPSs) increasingly integrate physical and digital realms, securing critical infrastructure, such as the Port of Virginia, becomes paramount. Among CPSs, Unmanned Aerial Vehicles (UAVs) are vital for monitoring, communication, and supporting the command and control through remote reconnaissance and surveillance missions. These UAV applications often require coordination, planning, and runtime reconfiguration, traditionally managed by human decision-makers. However, this approach has limitations, as extensively documented in the literature. Artificial Intelligence (AI) has emerged as a pivotal tool to address these limitations, enhancing risk mitigation and informed decision-making. This research proposes a machine learning (ML) based security mechanism, leveraging …

An Integrated Machine Learning Approach For Identifying Emergency Rescue Messages On Social Media During Natural Disasters, Wael Khallouli, Jiang Li, Jingwei Huang, Ghaith Rabadi, Samuel Kovacic

School of Cybersecurity Faculty Publications

During large-scale disasters, emergency call centers are often overwhelmed by the large volume of rescue requests and calls for help. Consequently, people are turning to social media platforms to seek assistance. Rescue information posted on these platforms is extremely valuable for first responders to make informed rescue decisions. Therefore, the automatic identification of these requests from the vast amount of data posted on social media during crises is critical yet challenging. This work presents our ongoing research on applying deep learning techniques to extract actionable rescue information from social media during crises. We proposed a novel deep learning model that …

In-Season Nitrogen Mmanagement: Leveraging Data Visualization For Precision Agriculture, Chathurika Harshani Narayana

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Effective nitrogen management is vital for sustainable agriculture, impacting both crop yield and environmental health. Traditional methods often use fixed application rates set before planting, which do not adapt to changing crop needs during the season. This can lead to over- or under-application, reducing efficiency and sustainability. While modern tools like sensors, satellites, and UAVs provide valuable real-time data on crop and field conditions, integrating and using this data to guide timely nitrogen decisions remains a major challenge. In-season nitrogen management offers a solution by allowing for dynamic adjustments to nitrogen applications, addressing crop needs as they arise. This approach …

Leveraging Data Science For Resilience: Improving Trauma-Informed Care Practice For Adverse Childhood Experience With Ai & Data Science Application, Mohmmad Arif Shaik

Master's Theses

Adverse Childhood Experiences (ACEs) have long-lasting effects on physical health, mental well-being, education, and socioeconomic outcomes. Resilient Georgia (RG), a statewide initiative, seeks to address ACEs through trauma-informed care and data-driven strategies. However, challenges in data collection, analysis, and tracking set back the effectiveness of these efforts. This study explores the role of data science and interactive visualization tools in improving outcomes for individuals and communities affected by ACEs. A key focus of this research is the development of a data science management application designed to enhance data collection and improve real-time decision-making. The application features interactive dashboards that allow …

From Cyclones To Cybersecurity: A Call For Convergence In Risk And Crisis Communications Research, Ann Marie Reinhold, Ross J. Gore, Barry Ezell, Clemente I. Izurieta, Elizabeth A. Shanahan

VMASC Publications

Effective risk and crisis communication can improve health and safety and reduce harmful effects of hazards and disasters. A robust body of literature investigates mechanisms for improving risk and crisis communication. While effective risk and crisis communication strategies are equally desired across different hazard types (e.g., natural hazards, cyber security), the extent to which risk and crisis communication experts utilize the “lessons learned” from scientific domains outside their own is suspect. Therefore, we hypothesized that risk and crisis communication research is siloed according to academic disciplines at the detriment to the advancement of the field of risk communications research writ …

Applying Transfer Learning For Street-Scale Nuisance Flood Forecasting In Coastal-Urban Cities, Binata Roy, Jonathan L. Goodall, Diana Mcspadden, Chetan Kumar, Steven Goldenberg, Yidi Wang, Malachi Schram

VMASC Publications

An important challenge with Machine Learning (ML) is its transferability; i.e., whether a ML model trained on one set of data can be applied to a second set of data without requiring full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained for one …

A New Deepfake Detection Method With No-Reference Image Quality Assessment To Resist Image Degradation, Jiajun Jiang, Wen-Chao Yang, Chung-Hao Chen, Timothy Young

Electrical & Computer Engineering Faculty Publications

Deepfake technology, which utilizes advanced AI models such as Generative Adversarial Networks (GANs), has led to the proliferation of highly convincing manipulated media, posing significant challenges for detection. Existing detection methods often struggle with the low-quality or compressed press, which is prevalent on social media platforms. This paper proposes a novel Deepfake detection framework that leverages No-Reference Image Quality Assessment (NRIQA) techniques, specifically, BRISQUE, NIQE, and PIQUE, to extract quality-related features from facial images. These features are then classified using a Support Vector Machine (SVM) with various kernel functions. We evaluate our method under both intra-dataset and cross-dataset settings. For …

A Bibliometric Analysis Of Ai-Driven Healthcare Literature Containing Kos Keywords: Trends, Themes, And Gaps, Julaine Clunis, Eric Asare

STEMPS Faculty Publications

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. …

Comprehensive Benchmarking Of Several Machine Learning And Bayesian Models For Early-Stage Diabetes Risk Prediction: A Large-Scale Comparative Study, Md. Iqbal Hossain, Najila Alam Porno

Mathematics & Statistics Faculty Publications

Diabetes remains a critical global health challenge, with early detection is crucial for effective management. This study presents a comprehensive benchmarking analysis of 14 diverse machine learning and Bayesian models for early-stage diabetes risk prediction using clinical data [2] from Sylhet, Bangladesh. This research evaluated traditional methods (Logistic Regression, Decision Trees), ensemble techniques (Random Forest, XGBoost, LightGBM), Bayesian approaches (BART, Bayesian Logistic Regression), and advanced neural architectures (Deep Belief Networks) using both 70-30 train-test splits and 10-fold cross-validation. The results demonstrate that ensemble methods consistently outperformed other approaches, with Random Forest(RF) achieving the highest cross-validated AUC (0.9951) and accuracy (0.9699). …

Leveraging Transformer-Based Ocr Model With Generative Data Augmentation For Engineering Document Recognition, Wael Khallouli, Mohammad Shahab Uddin, Andres Sousa-Poza, Jiang Li, Samuel Kovacic

Engineering Management & Systems Engineering Faculty Publications

The long-standing practice of document-based engineering has resulted in the accumulation of a large number of engineering documents across various industries. Engineering documents, such as 2D drawings, continue to play a significant role in exchanging information and sharing knowledge across multiple engineering processes. However, these documents are often stored in non-digitized formats, such as paper and portable document format (PDF) files, making automation difficult. As digital engineering transforms processes in many industries, digitizing engineering documents presents a crucial challenge that requires advanced methods. This research addresses the problem of automatically extracting textual content from non-digitized legacy engineering documents. We introduced …

Governing Intelligence: Singapore’S Evolving Ai Governance Framework, Jason G. Allen, Jane Loo, Jose Luna

Research Collection Yong Pung How School Of Law

This paper provides an outline analysis of the evolving governance framework for Artificial Intelligence (AI) in Singapore. Across the Singapore government, AI solutions are being adopted in line with Singapore’s “Smart Nation Initiative” to leverage technology to make impactful changes to the nation and the economy. In tandem, Singaporean authorities have been assiduous to release a growing number of governance documents, which we analyse together to chart the city-state’s approach to AI governance in international comparison. Characteristics of Singapore’s AI governance approach include an emphasis on consensusbuilding between stakeholders (particularly government and industry but also citizens) andvoluntary or “quasi” regulation, …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Hunting And Fishing Ceos: Environmental Plunderers Or Saviors?, Thomas Covington, Steve Widler, Keven Yost

Finance Faculty Works

CEOs who participate in hunting and fishing benefit by appreciating natural environments and permanently consuming natural resources. We examine whether CEOs who hunt and fish make different environmental decisions and find that firms led by CEOs who obtain the most hunting and fishing licenses have lower environmental performance as measured by MSCI-KLD. This effect is strongest in the environmental category of climate change but also extends to pollution, waste, and the protection of natural capital. Furthermore, firms led by CEOs with the most hunting and fishing licenses are significantly more likely to pay a regulatory settlement for an environmental regulatory …

Transforming Urban Dynamics: Harnessing Large Language Models For Smarter Mobility, Hao Xue, Ming Jin, Shirui Pan, Flora Salim, Guansong Pang

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI) has the potential to analyze mobility data and make mobility systems smarter by leveraging diverse data sources such as geospatial data, transportation logs, and real-time sensor data to optimize traffic flow, enhance public transportation systems, and support the development of autonomous vehicles. With the newly emerged generative AI paradigm, exemplified by large language models (LLMs), there is great potential to transform the current AI applications in mobility, transportation, and urban domains. This article provides an overview of recent efforts and aims to shed light on the challenges and future opportunities to facilitate the adaptation of LLMs for …

Cyberattacks On Port Infrastructures: A Decade Of Trends, Incidents, And Mitigation Strategies (2011-2024), Minodora Badea, Olga Bucovetchi, Adrian V. Gheorghe, Gabriel Raicu

Engineering Management & Systems Engineering Faculty Publications

Port infrastructures are critical to global trade, handling over 80% of the world's cargo by volume. However, their increasing reliance on digital technologies has exposed them to a wide range of cyber threats. This paper provides a comprehensive analysis of cyberattacks targeting port infrastructures from 2011 to the present. We examine the types of attacks, geographical distribution, notable incidents, and underlying vulnerabilities. Additionally, we discuss mitigation strategies and future directions for enhancing cybersecurity in the maritime sector. Our findings highlight the urgent need for robust regulatory frameworks, advanced technological solutions, and collaborative efforts to safeguard critical port operations.

Enhancing Adhd Diagnosis In College Students Using Multimodal Integration Of Nicats And Iva-2 Tools, Rushmila Shabneen

College of Graduate Studies: Theses & Dissertations

This study explores enhanced methods for accurately identifying Attention Deficit Hyperactivity Disorder (ADHD) indicators in college students. ADHD, a neurodevelopmental disorder, impacts attention, impulse control, and emotional regulation, often leading to academic and social difficulties. Many students remain undiagnosed due to symptom overlap with stress and other factors. Traditional tools like the IVA-2 assess behavioral responses but may not fully capture ADHD complexity. This research integrates IVA-2 data with multimodal metrics from the Non-Intrusive Classroom Attention Tracking System (NiCATS), which monitors facial expressions, eye movements, and computer interactions. Preliminary results show that combining these tools improves ADHD detection, reduces false …

Analyzing Political Sentiment On Micro-Blogging Data: A Lexicon And Machine Learning Approach To The 2024 U.S. Presidential Election, Ava Grey

CMC Senior Theses

This paper explores the trends in sentiment towards U.S. presidential candidates Kamala Harris and Donald Trump through micro-blogging social media text during the five months leading up to the election. Two datasets of varying sizes and origins were used to contextualize and validate analysis findings. The analyses include both a lexicon-based approach and a machine learning predictive method. Common sentiment analysis techniques like term frequency, term frequency inverse, various lexicons, and n-grams were utilized during the lexicon approach. During the modeling, a random forest was utilized in addition to the methods used during the lexicon approach. Results showed that overall …

Consequences Of Artificial Intelligence In Health Insurance: Lawsuits, Policy, And Ethics, Alyssa N. Roberts

Honors Undergraduate Theses

In recent years, the healthcare system has been burdened by a multitude of obstacles that hinder the ability to provide effective, affordable, and timely care. Among these, one of the most significant challenges is the role that health insurance plays in shaping the quality of care. Health insurance companies are designed to decrease financial strain on patients, but they have introduced inefficiencies through delayed coverage approvals, increased denials, and administrative costs. Artificial intelligence (AI) has started to play an integral role in resolving these issues for the health insurance industry. Through its quick automated claim processing, fraud screening, and reduced …

Relational Database Schema To Support Research Profiling Studies, Natural Language Processing, And Bibliometric Analysis, Darnelle Melvin

Library Faculty Research

In this paper, a relational database schema is introduced that supports rapid prototyping, data preprocessing, and warehousing tasks associated with research profiling studies, natural language processing, and bibliometric analysis. Python scripts are leveraged for the seamless retrieval and processing of data from Semantic Scholar. This schema is tailored to efficiently analyze entities such as authors, their scientific papers, referenced papers, and cited papers. Adhering to the relational model, this schema offers a standardized approach to data storage and detailed information retrieval for scientific papers. Enhancing knowledge discovery in scientific databases, this schema provides researchers with a powerful platform for robust …

Cyber Warfare And The Future Of Conflict, Victor A. Mercado

Graduate Theses/Dissertations

This thesis examines whether cyber warfare now poses a more immediate threat to U.S. national security than weapons of mass destruction (WMD). Cyber operations have become a dominant instrument of contemporary conflict, with malicious actors operating in a persistent “grey zone” that blurs traditional boundaries between war and peace. These operations target military and civilian entities, both directly and often as collateral damage due to the uncontrollable nature of cyber threats. WMDs, despite their catastrophic destructive potential, remain largely constrained by established deterrence frameworks.

The evolving nature of cyber warfare warrants comparison to WMD effects and impact, as cyber capabilities …

Fostering Critically Conscious Lesson Planning In A Generative Artificial Intelligence Era, Derek Riddle, Paula Cristina Azevedo, Catharyn Shelton, Jaime Colwell, Jori Beck

Teaching & Learning Faculty Publications

Teacher candidates (TCs) use digital resources and social media to plan and develop learning material, and with publically accessible generative artificial intelligence (GAI), TCs are able to generate lesson plans within seconds rather than hours or days. While there is research on how to support TCs' evaluation of reliable digital media, there is little known on how to prepare TCs for GAI content. Using the complementary frameworks of Freire’s (1970) critical consciousness and Jonnasen’s (1991) theory of constructivism, this in-progress design based research seeks to develop an adaptable framework that addresses the evolving nature of technology, specifically GAI, and the …

Risk Spillover Effect Of China-Asean Supply Chains: Insights Of Industrial Transfer, Zeyang Bian, Yuning Zhang, Keng Siau, Yaqian Zhang, Jianjia He

Research Collection School Of Computing and Information Systems

As labour costs in China increase, labour-intensive industries are migrating to ASEAN countries, attracted by lower labour costs and market potential. This shift not only affects the economies of China and ASEAN but also reshapes the global manufacturing landscape. This paper investigates the correlation and spillover of supply chain risks using production exposure indicators derived from inter-country input-output data and the R-Vine Copula model. We assess the risk spillover of each country within the global supply chain. Our findings indicate that industrial relocation can significantly alter supply chain structures, thereby affecting the concentration and direction of risks. While China's role …

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …