Computer Sciences Commons^™

Lightweight Driver Face Object Detection Algorithm Based On Yolov8-Df, Mingyu Li, Jiaquan Lin

Journal of System Simulation

Abstract: The YOLOv8n detection algorithm has a large amount of computation and parameters in the driving environment. To address this issue, a lightweight driver facial object detection algorithm YOLOv8-DF was proposed. A lightweight multi-scale convolution module (LMCM) was proposed to replace the Conv module in the network, and the dual-channel design could reduce the computation and parameter quantity of the algorithm; the multi-scale design could enrich the feature information inside the network. The lightweight convolutional GhostConv, Fasterblock module, and C2f module were fused, and a dual-channel lightweight convolution module (DLCM) was fused with the SPPF module. The experimental results show …

Optimal Scheduling Of An Integrated Energy System Considering Demand Response And Two-Stage P2g, Xinhui Duan, Zelong Cheng, Dongchao Zhang, Xiaochong Duan

Journal of System Simulation

Abstract: In the context of carbon peaking and carbon neutrality goals, this study aims to improve the energy utilization rate and further explore the role of user-side flexible loads and P2G equipment in energy saving and emission reduction. An optimal scheduling model for integrated energy systems considering demand response and two-stage P2G was proposed. A regional integrated energy system coupled with electricity, heating, cooling, gas, storage, and hydrogen was taken as the research object. Models for system equipment and two-stage P2G were established. Based on load characteristics, a multi-load demand response model for electricity, heating, and cooling was constructed using …

Detection Of Small Apple Targets Based On Improved Yolov5 In Natural Environments, Zilong Liu, Lei Zhang

Journal of System Simulation

Abstract: The distribution of apples usually features occlusion and small and dense targets. To address these issues, a target detection algorithm was proposed based on an improved YOLOv5 model. Specifically, this paper added the coordinate attention (CA) mechanism, receptive field block (RFB), and adaptively spatial feature fusion (ASFF) modules to the YOLOv5, enhancing the ability to detect small targets. Additionally, the proposed algorithm replaced the CIoU in YOLOv5 with SIoU to improve the target detection box's prediction accuracy. Finally, some normal convolutions were replaced with depthwise separable convolutions (DSC), effectively reducing the calculation burden. Experiment results show that the comprehensive …

Computer-Automated Systems And Methods For Using Language Models To Generate Text Based On Reading Errors, Scott Sosso, Siyu Chen, Ciara Figliuolo, Jack Mostow, Marlies Goes

AFIT Patents

A computer-implemented system and method generate personalized text based on statistics derived from input received from a user representing the user's attempts to decode graphemes into phonemes. Such statistics may be measured and recorded at the grapheme-phoneme level, and may include substitutions, insertions, deletions, and correct utterances of phonemes by the user when reading text. A language model may be trained based on characteristics of the user, such as the user's age and/or reading grade level, and the personalized text may be generated after such training of the language model. Generating the personalized text may include generating a text creation …

Introduction: Symposium ‒ Ai Disrupting Law, Edward Lee

Chicago-Kent Law Review

No abstract provided.

Anatomy Of An Ai Arms Race: U.S. And China Technological Dispute For Ai Leadership, Denisse I. Rojas Maldonado

Dartmouth College Master’s Theses

The history of societies and the emergence of powerful states have been marked by cycles of conflict and war, followed by periods of cooperation that foster international stability. Similarly, the Cold War era saw a significant rise in military and economic capabilities, which highlighted a security dilemma as the former USSR and the United States sought to protect their national interests. Currently, artificial intelligence has expanded the scope of invisible warfare beyond the atomic bomb. Some scholars like Paul Scharre advocate that there is no arms race in place, and others support the idea of a healthy competition and collaboration …

Investigating Information Extraction And Language Models In Medical Domain Text Processing, Pouyan Nahed

UNLV Theses, Dissertations, Professional Papers, and Capstones

This dissertation demonstrates that carefully adapted language-model pipelines can transform unstructured clinical-trial and pharmacological prose into reliable, low-latency structured data. Four interconnected studies support this claim.Tri-AL platform. An open-source dashboard ingests all 440 k+ ClinicalTrials.gov records—including every historical revision—into a normalized schema and parses the 20 GB XML archive over 10x faster than a BeautifulSoup baseline, while exposing hooks for demographic analytics and supporting integration of user-defined modules. Clinical trial summarization. An encoder–decoder model is trained on 57k description–summary pairs to condense clinical trials into a few sentences. ROUGE evaluation shows a 20% improvement over the baseline, while graph-based evaluation …

Advanced Robotic Multimodal Imaging With Real-Time Motion Compensation For Dynamic Structural Health Monitoring, George Papaioannou, Christos Mitrogiannis, Mark Schweitzer, Maria Pappa, Pegah Khosravi, Apostolos Karantanas, Chris Ruberg, Shawn Owens, Nikolaos Michailidis

Publications and Research

Modern composite materials promise superior performance and load-bearing capabilities, yet evaluating their structural integrity remains challenging. Current testing methods, such as visual, thermographic, ultrasonic, optical, electromagnetic, terahertz, shearography, X-ray, and neutron imaging, are hampered by long scan durations, limited field of view, suboptimal accuracy, and high costs, particularly when applied to large structures.

This paper addresses these issues by introducing a novel robotic multimodal imaging system that overcomes the limitations of traditional methods. This system dynamically captures both static and dynamic properties of materials using advanced motion compensation techniques. By integrating multiple radiographic modalities into a coordinated robotic platform, it …

Jazz Scale Patterns With Abjad And Lilypond, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

Background

Jazz method books often advise students to “learn it in all keys,” yet most present examples in only one or two keys (if that, as they start from C, the easiest key, and usually stop there). For many "classically-trained" players—especially those who check fingerings, enharmonics, and voice-leading by reading—the absence of complete, notated materials is a barrier. While most scales can be internalized as Whole (W) / Half (H) step patterns, important exceptions (e.g., harmonic and melodic minor, octatonic, whole tone, and blues) are aided by having notated patterns in front of us.

Aims

To generate clear, consistent notation-first …

Bring Back The Blue-Book Exam: In An Age Of Ai, We Need To Return To Handwritten Assignments., Katie Day Good

University Faculty Publications and Creative Works

When ChatGPT was released three years ago, its ability to mimic human writing unsettled me. I’m a professor of communication; what did it mean that my students now had access to a machine that could communicate for them? My initial unease led to a half-joke with my colleagues. Universities could survive this threat, I ventured, but only if we reverted back to 19th-century teaching methods like Socratic dialogue, oral defenses, and lengthy essay exams. This once-laughable scenario is now a serious consideration for many faculty, including me.

Like many professors, I’ve recently abandoned take-home essays in favor of blue-book exams. …

A Deep Learning Framework With Explainable Ai For Atmospheric Blocking Detection And Interpretation, Devansh Khandelwal

Discovery Undergraduate Interdisciplinary Research Internship

Atmospheric blocking is a large-scale quasi-stationary phenomenon in mid-latitude circulation, characterized by persistent high-pressure systems that disrupt the typical west-to-east flow of the jet stream. These systems can cause extreme weather events—such as heatwaves, cold spells, or droughts—that persist for days or even weeks. This study proposes a deep learning framework to predict and interpret the occurrence of atmospheric blocking by integrating geophysical precursors such as geopotential height (Z500), stream function (SF200), and potential vorticity. These features, which are dynamically linked to blocking onset and persistence, serve as inputs to a Convolutional Neural Network model trained on the CESM Large …

Development And Validation Of Venous Thromboembolism-Bidirectional Encoder Representations From Transformers (Vte-Bert) Natural Language Processing Model, Omid Jafari, Shengling Ma, Barbara D Lam, Jun Y Jiang, Emily Zhou, Mrinal Ranjan, Justine Ryu, Raka Bandyo, Arash Maghsoudi, Bo Peng, Christopher I Amos, Abiodun Oluyomi, Nathanael R Fillmore, Jennifer La, Ang Li

Faculty, Staff and Students Publications

Background: Accurate and rapid phenotyping of venous thromboembolism (VTE) in longitudinal studies is important. A natural language processing (NLP) tool externally validated in representative patients is lacking.

Objectives: To train and validate an efficient NLP model to detect incident VTE event.

Methods: We designed a novel NLP platform, NLPMed, to assist thrombosis researchers with data preprocessing, phenotype annotation, language model finetuning, and NLP application. Using clinical notes, discharge summaries, and radiology reports from patients with cancer at 2 healthcare institutions, we finetuned Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) to develop VTE-BERT. The new model was trained to detect acute …

Faithfulrag: Fact-Level Conflict Modeling For Context-Faithful Retrieval-Augmented Generation, Qinggang Zhang, Zhishang Xiang, Yilin Xiao, Le Wang, Junhui Li, Xinrun Wang, Jinsong Su

Research Collection School Of Computing and Information Systems

Large language models (LLMs) augmented with retrieval systems have demonstrated significant potential in handling knowledge-intensive tasks. However, these models often struggle with unfaithfulness issues, generating outputs that either ignore the retrieved context or inconsistently blend it with the LLM’s parametric knowledge. This issue is particularly severe in cases of knowledge conflict, where the retrieved context conflicts with the model’s parametric knowledge. While existing faithful RAG approaches enforce strict context adherence through well-designed prompts or modified decoding strategies, our analysis reveals a critical limitation: they achieve faithfulness by forcibly suppressing the model’s parametric knowledge, which undermines the model’s internal knowledge structure …

Sifar: A Simple Faster Accelerated Variance‑Reduced Gradient Method, Zhize Li

Research Collection School Of Computing and Information Systems

In this paper, we propose a simple faster accelerated gradient method called SIFAR for solving the finite-sum optimization problems. Concretely, we consider both general convex and strongly convex settings: i) For general convex finite-sum problems, SIFAR improves previous state-of-the-art result given by Varag. In particular, for large-scale problems or the convergence error is not very small, SIFAR obtains the first optimal result O(n), matching the lower bound. ii) For strongly convex finite-sum problems, we also show that SIFAR can achieve the optimal convergence rate matching the lower bound. Besides, SIFAR enjoys a simpler loopless algorithmic structure while previous algorithms use …

Role Of Social Media Mindfulness In Combatting Fake News Propagation, Gaurav Bansal, Fiona Fui-Hoon Nah, Jason B. Thatcher

Research Collection School Of Computing and Information Systems

The dissemination of fake news by social media users is a key factor in the escalation of misinformation. Research suggests that social media networks are becoming increasingly homophilic, which leads to an overreliance on social media friends that contributes to the spread of fake news. However, little is known about how social media mindfulness can reduce the sharing of fake news. To investigate this research question, we conceptualized a social media mindfulness construct and developed the social media mindfulness scale. We also hypothesize that social media mindfulness lowers overreliance on friends’ knowledge, which increases skepticism about social media news that …

Zero-Shot Generalist Graph Anomaly Detection With Unified Neighborhood Prompts, Chaoxi Niu, Hezhe Qiao, Changlu Chen, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD), which aims to identify nodes in a graph that significantly deviate from normal patterns, plays a crucial role in broad application domains. However, existing GAD methods are one-model-for-one-dataset approaches, i.e., training a separate model for each graph dataset. This largely limits their applicability in real-world scenarios. To overcome this limitation, we propose a novel zero-shot generalist GAD approach UNPrompt that trains a one-for-all detection model, requiring the training of one GAD model on a single graph dataset and then effectively generalizing to detect anomalies in other graph datasets without any retraining or fine-tuning. The key insight …

Input Structure Based Optimization For Privacy Preserving Ai Systems, Feng Yizhou

Electrical & Computer Engineering Theses & Dissertations

As Artificial Intelligence (AI) systems become increasingly integrated into critical domains, ensuring privacy-preserving model design and system deployment has become a pressing priority. Safeguarding both sensitive user data and proprietary model parameters is critical throughout the AI model and system, from data acquisition and pre-processing to model inference and deployment. However, existing privacy-preserving frameworks face several limitations, including fragmented data ownership, incomplete protection across system stages, substantial computational overhead, and poor scalability to modern architectures such as large language models. This dissertation explores a unifying optimization strategy centered on input structure design to address these challenges. The core idea is …

Bibliography For "Ai 2.0: Is Ai A Tool, A Threat, Or A Teammate?", Annikah Carpio, Sally Park, Melody Madrigal

Library Displays and Bibliographies

A bibliography created to support a display about AI 2.0 in August 2025 at the Leatherby Libraries at Chapman University.

Human-Ai-Collaboration-For-Coding, Siddhardha Ravi

Theses, Dissertations and Culminating Projects

AI-generated code, while rapidly producing functional solutions, often falls short in aspects like comprehensive error handling, robust documentation, and optimal architectural design, areas where human expertise excels. Conversely, humans can greatly benefit from AI's rapid code generation capabilities. This project proposes and evaluates "A Framework to Improve Code Quality by Utilizing Generative AI Coding Along With Human-Written Code", designed to create a synergy between AI and human intelligence for enhanced software development. Conducted over four weeks, the research leverages BigCodeBench as its core dataset to rigorously investigate how human intervention can improve AI-generated code quality, identify the most effective human-AI …

Effective Transformer Networks For Undersampled Magnetic Resonance Image Reconstruction, Tahsin Rahman

Open Access Theses & Dissertations

The proliferation of data-driven tools for solving problems in every possible domain, coupled with rapid advances in computing technology, has led to an arms race of AI development and application research in industry and academia. One field of research that stands to gain immeasurably from this revolution is medical imaging. It is a critical part of modern diagnostics, and advancements in this area can directly benefit the average person by making healthcare more accessible, accurate, and affordable. Breakthroughs in mainstream image processing and computer vision have long fueled development in medical imaging, and it is now common to see cutting …

Unsupervised Deep Learning For Video Restoration, Mary Damilola Aiyetigbo

All Dissertations

In today's digital era, visual data is vital across several domains such as medical diagnostics, scientific imaging, surveillance, and entertainment. However, video data often suffers from degradations like noise, blur, compression artifacts, and low resolution, which degrade quality and downstream usability. Video restoration aims to recover clean, high-fidelity video from such corrupted inputs. Unlike static images, video restoration must maintain temporal consistency across frames, making it a significantly more complex problem. While supervised deep learning methods have achieved state-of-the-art results, they typically require large datasets of paired noisy-clean video datasets that are scarce or impractical to obtain in real-world settings …

Robustness Investigation, Detection, And Defense Of Deep Learning Models Against False Data Injection, Amirhossein Nazeri

All Dissertations

This dissertation addresses the critical challenge of adversarial robustness in deep learning systems, focusing on two fundamental domains: time-series prediction and object detection. As these AI systems become increasingly deployed in safety-critical applications from power grid management to autonomous vehicles their vulnerability to adversarial attacks poses significant risks to infrastructure and human safety.

The first contribution introduces a novel stealthy black-box False Data Injection (FDI) attack specifically designed for quasi-periodic time-series data. Unlike existing attacks that produce easily detectable anomalies, our method generates adversarial perturbations that preserve the underlying periodicity and statistical properties of the data, effectively bypassing traditional anomaly …

Evaluating Immersion And Agency In Ai-Assisted Live Murder Mystery Games, Seraphina C. Courtney

LSU New Orleans Theses and Dissertations

This thesis explores the impact of AI-assisted narrative generation on player immersion and agency in a live-action roleplaying (LARP) experience. A live-action murder mystery game was designed and run in two formats: a static version with GPT-4 generated characters, dialogue, and stage directions, and a dynamic version where players created their own characters and improvised freely, guided by AI-generated narrative beats, a story element that moves the plot forward [21], that provided a narrative scaffolding for the players. The dynamic version employed a distributed computer vision system that tracks the movement of key items in the play-space so their relevance …

Optimal Transport Alignment Of User Preferences From Ratings And Texts, Nhu Thuat Tran, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Modeling hidden factors driving user preferences is crucial for recommendation yet challenging due to sparse rating data. While aligning preference factors from ratings and texts, as a solution, shows improvements, existing methods impose restrictive one-to-one factor correspondences and underutilize cross-modal interest signals. We propose an optimal transport (OT) approach to address these gaps. By modeling rating- and text-based preference factors as distributions, we compute an OT plan that captures their probabilistic relationships. This plan serves dual roles: 1) to regularize cross-modal preference factors without rigid correspondence assumptions, and 2) to blend preference signals across modalities through barycentric mapping. Experiments on …

R2dqg: A Quality Meets Diversity Framework For Question Generation Over Knowledge Bases, Yimeng Ren, Yanhua Yu, Lizi Liao, Yuhu Shang, Kangkang Lu, Mingliang Yan

Research Collection School Of Computing and Information Systems

The task of Knowledge-Based Question Generation (KBQG) involves generating natural language questions from structured knowledge sources, posing unique challenges in balancing linguistic diversity and semantic relevance. Existing models often focus on maximizing surface-level similarity to ground-truth questions, neglecting the need for diverse syntactic forms and leading to semantic drift during generation. To overcome these challenges, we propose Refine-Reinforced Diverse Question Generation (R2DQG), a two-phase framework leveraging a generation-then-refinement paradigm. The Generator first constructs a diverse set of expressive templates using dependency parse tree similarity, capturing a wide range of syntactic patterns and styles. These templates guide the creation of question …

Study Of Ai Applications In Biomedical Data Acquisition, Communication, And Analysis: Cest Mri Acceleration And Ecg Transmissions, Adarsha Bhattarai

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

This dissertation investigates the application of artificial intelligence in biomedical data acquisition, communication, and analysis to advance neurological research and to enable the early detection of cardiovascular conditions. Despite significant advances in imaging and physiological modalities, challenges persist. Imaging modalities, such as the chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) technique are challenged by a prolonged data acquisition time and high operational costs. In addition, physiological modalities such as electrocardiogram (ECG) sensors face constraints in providing uninterrupted signal monitoring which is crucial for the timely detection of premature cardiac abnormalities. The primary goal of this work is to …

A Digital Engineering Framework For Ai-Driven Trade-Off Evaluation And Predictive Component Classification, Alejandro Silva Au

Open Access Theses & Dissertations

This thesis introduces a digital engineering tool designed to help engineers make smarter decisions when choosing actuators. At its core, the system brings together machine learning (specifically XGBoost) and a decision-making method called Multi-Utility Attribute Theory (MUAT). The goal is to support engineers in picking components based on what really matters for their designs, whether that's speed, cost, durability, or any other performance factor. What makes this tool stand out is its user-friendly interface that lets people interact with the system directly. It takes a set of actuator performance data, classifies each one into a relevant use category, and then …

Human Activity Recognition And Identification Driven Automated Deep Learning For Time-Series Classification, Justin Alan Gamble

Engineering Management & Systems Engineering Theses & Dissertations

The growing emphasis on Digital Engineering (DE) within the U.S. Department of Defense (DoD) demands advanced methods for leveraging vast time-series data generated by sensor-rich environments. Deep learning models offer promising solutions for complex timeseries classification tasks, however their design and optimization remain highly resource intensive, requiring specialized expertise. This dissertation addresses this challenge by developing and evaluating an Automated Machine Learning (AutoML) framework specifically tailored for the time-series classification task of Human Activity Recognition and Identification (HARI).

A systematic investigation was conducted using the Design Science Research Methodology (DSRM) comparing traditional search strategies of grid search and random search …

Advancing Fishery Dependent And Independent Habitat Assessments Using Automated Image Analysis: A Fisheries Management Agency Case Study, Scott Evans, Bronson Philippa, Carlo Mattone, Nick Konzewitsch, Renae Hovey, Marcus Sheaves, Gary A. Kendrick, Lynda M. Bellchambers

Fisheries Research Articles

Advances in artificial intelligence and machine learning have revolutionised data analysis, including in the field of marine and fisheries sciences. However, many fisheries agencies manage sensitive or proprietary data that cannot be shared externally, which can limit the adoption of externally hosted artificial intelligence platforms. In this study, we develop and evaluate two residual network-based automatic image annotation models to process fishery specific habitat data to support ecosystem-based fisheries management in the Exmouth Gulf Prawn Managed Fishery in Western Australia. Using an extensive dataset of 13,128 manually annotated benthic habitat images, we train a grid-based annotation model and an image-level …

Solving Two-Stage Stochastic Integer Programs Via Representation Learning, Yaoxin Wu, Zhiguang Cao, Wen Song, Yingqian Zhang

Research Collection School Of Computing and Information Systems

Solving stochastic integer programs (SIPs) is extremely intractable due to the high computational complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) for scenario representation learning. A graph convolutional network (GCN) based VAE embeds scenarios into a low-dimensional latent space, conditioned on the deterministic context of each instance. With the latent representations of stochastic scenarios, we perform two auxiliary tasks: objective prediction and scenario contrast, which predict scenario objective values and the similarities between them, respectively. These tasks further integrate objective information into the representations through gradient backpropagation. Experiments show that the learned scenario representations can …