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Articles 31 - 60 of 3959

Full-Text Articles in Physical Sciences and Mathematics

Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis Jan 2025

Leveraging Counterfactuals For Enhanced Natural Language Inference: A Multitask Knowledge Distillation Approach, Brian J. Davis

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Natural-language inference (NLI) asks whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise. Modern transformers reach high raw accuracy on benchmarks such as SNLI, MNLI, and ANLI, yet they often rely on brittle lexical shortcuts and provide little insight into their decision process. This thesis shows that counterfactual-augmented knowledge distillation can simultaneously boost robustness and supply faithful, token-level explanations—without scaling model size. Four T5-v1_1 students (60M, 220M, 770M, 3B parameters) are trained under four curricula: (1) standard fine-tuning, (2) fine-tuning with free-text rationales, (3) multi-task distillation with naive counterfactuals, and (4) multi-task distillation with …

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Searching For Cages In Graphs And Signed Graphs, Isaac Partee Jan 2025

Searching For Cages In Graphs And Signed Graphs, Isaac Partee

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The girth of a graph G is the minimum length of a cycle in G. A (k,g)-graph is a k-regular graph of girth g. A (k,g)-cage is a (k,g)-graph with the smallest possible number of vertices. For example, K4 is the unique (3,3)-cage, K3,3 is the unique (3,4)-cage, and the Petersen Graph is the unique (3,5)-cage. The search for cages particular values of (k,g) is an ongoing area of considerable research. For values of (k,g) where the number vertices in a (k,g)-cage is not known, covering graphs have recently been used for constructing progressively smaller and smaller (k,g)-graphs. The covering …

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Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew Jan 2025

Optimizing Cloud Computing Resources For Operational Cost And Application Performance Using Machine Learning, Isaac K. Matthew

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As AI-driven workloads accelerate the growth of cloud initiatives and spending, resource waste also increases due to persistent inefficiencies in cloud compute and infrastructure management. Overprovisioned resources and suboptimal configurations often lead to operational inefficiencies and unnecessary financial overhead. These challenges arise from the difficulty of anticipating resource demands in dynamic workloads and selecting suitable virtual machines to ensure optimal performance. Our research proposes a holistic, data-driven framework for managing cloud compute resources that reduces costs without compromising application performance. We integrate a predictive, model-driven, threshold-based autoscaling solution for cloud-native applications with an optimized instance right-sizing approach to select cost-effective …

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Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel Jan 2025

Reducing Operator Training Time Through Virtual Reality: A Case Study On The Lpkf Protomat E44 Machine, Joshua C. Patel

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This thesis presents the development of an immersive virtual reality (VR) simulation that replicates the operation of the LPKF ProtoMat E44 PCB milling machine. Aimed at reducing operator training time and improving procedural understanding, the simulation offers an interactive and realistic environment where users can safely engage with machine workflows and start-up sequences. The emphasis is on accurate representation, usability, and maintaining immersion to support intuitive learning. Although formal evaluation is outside the scope of this work, the system is designed to serve as a foundation for cost-effective, scalable training in technical and manufacturing contexts, offering a modern alternative to …

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Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman Jan 2025

Scalable Real-Time Stream Clustering For Unbounded Text Streams, Nathaniel C. Crossman

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Social media, AI systems, IoT sensors, and other platforms generate vast amounts of streaming data. Given this vast volume of information, techniques that can reduce and aggregate data into meaningful topics are essential. One such technique is the two-phase stream clustering approach. In the first, online micro-clustering phase, the system forms micro-clusters from the incoming data stream, incrementally merges new items into related existing micro-clusters, and prunes or fades micro-clusters as they become inactive, producing a constantly updating yet compact set of micro-clusters representing potential topics and subtopics of the stream. In the second, offline macro-clustering phase, these micro-clusters are …

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Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes Jan 2025

Patient Subset Classification Using Encoded Embeddings And Knowledge Graph Retrieval-Augmented Generation, Benjamin A. Holmes

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The widespread adoption of electronic medical records has created a vast reservoir of clinical data that can be leveraged to better understand how interventions relate to patient outcomes. Much of this information, however, exists as unstructured free-text, posing significant challenges for traditional statistical and machine-learning methods. Solving these challenges would allow the extraction of specific patient subpopulations (clinically relevant cohorts of individuals who share overlapping symptoms, risk factors, or diagnostic criteria), which could be used in precision medicine. Despite this promise, extracting these subpopulations from unstructured medical notes is an ongoing challenge due to the variability of clinical language and …

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Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar Jan 2025

Generative Adversarial Networks (Gans) For High-Dimensional Biological Data, Harigovind Harikumar

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This thesis investigates the application of Generative AI models, mainly Generative Adversarial Network (GAN) models to high dimensional and low sample size biological datasets like Motion Sickness, Breast Cancer, Crohn, and Melanoma. We utilized and compared three generative AI frameworks: Vanilla GAN, Wasserstein GAN (WGAN), Locality-Sensitive Hashing GAN (LSH-GAN) and Omics GAN. To address the challenges associated with high-dimensionality and low sample size, which was leading to very poor outputs of biological synthetic samples, we came up with an approach to stop the model when it reaches its saturation level. That is, we printed the loss plots to see where …

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Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz Jan 2025

Pixmix Attack: Implementation And Evaluation Of A Novel Pixel Injection On Digital Video Port (Dvp) Interface In Embedded Camera Systems With Pcb Hardware Trojan, Sayed Md Tashfi Nowroz

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Image sensors are at the heart of machine vision systems in robotics, industrial automation, and surveillance systems which ideally operate with minimal human supervision and only occasional maintenance. The image sensors convert visible light into electrical signals which are locally decoded to image on the printed circuit board (PCB) by an ordinary embedded processor System on Chip (SoC). This thesis investigates a critical vulnerability in such systems, targeting the communication protocol at the signal level during runtime. Specifically, it focuses on a novel attack in the Digital Video Port (DVP) protocol, possible to exploit with PCB-based hardware Trojans, to craft …

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A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat Jan 2025

A Secure Ml-Assisted Framework For Resilient And Efficient Prediction Of Physiotherapy Sequence In Bilateral Carpal Tunnel Syndrome, Pratik Pandurang Kharat

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Bilateral idiopathic carpal tunnel syndrome (CTS) is a neuromuscular disorder characterized by compression of the median nerve at both wrists, leading to symptoms such as pain, numbness, tingling, and muscle weakness. Unlike unilateral cases, bilateral idiopathic CTS presents distinct therapeutic challenges due to the simultaneous involvement of both hands and the lack of an identifiable underlying cause. This study explores the application of machine learning techniques to predict the optimal sequence of physiotherapeutic interventions Stretching followed by Myofascial Mobilization (S/M) or the reverse (M/S) in female patients with bilateral idiopathic CTS and right hand dominance. Data were drawn from a …

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Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald Jan 2025

Learning Under Data Scarcity: Reasoning And Negative Distillation For Texts And Graphs, Calvin T. Greenewald

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Modern machine learning (ML) models rely on large amounts of high-quality labeled data to achieve optimal performance. However, in many real-world domains, such as cyber security, acquiring sufficient labeled data is often infeasible due to cost, privacy concerns, and the rapid evolution of underlying phenomena. This challenge underscores the importance of learning under data scarcity. This thesis addresses this challenge by proposing distinct, modality-specific techniques for text and graph domains, which allow models to generalize effectively with minimal data. For text classification task, we incorporate distilled rationales from large language models and adversarial perturbations into the input space to improve …

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Reduced K+ Build-Up In T-Tubules Contributes To Resistance Of The Diaphragm To Myotonia, Jessica H. Myers, Kirsten Denman, Chris Dupont, Brent D. Foy, Mark M. Rich Nov 2024

Reduced K+ Build-Up In T-Tubules Contributes To Resistance Of The Diaphragm To Myotonia, Jessica H. Myers, Kirsten Denman, Chris Dupont, Brent D. Foy, Mark M. Rich

Physics Faculty Publications

Patients with myotonia congenita suffer from slowed muscle relaxation caused by hyperexcitability. The diaphragm is only mildly affected in myotonia congenita; discovery of the mechanism underlying its resistance to myotonia could identify novel therapeutic targets. Intracellular recordings from two mouse models of myotonia congenita revealed the diaphragm had less myotonia than either the extensor digitorum longus (EDL) or the soleus muscles. A mechanism contributing to resistance of the diaphragm to myotonia was reduced depolarization of the interspike membrane potential during repetitive firing of action potentials, a process driven by build-up of K+ in small invaginations of muscle membrane known as …

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Towards A Global Food Systems Datahub: Editorial, Hande Küçük Mcginty, Cogan Shimizu, Pascal Hitzler, Ajay Sharda Oct 2024

Towards A Global Food Systems Datahub: Editorial, Hande Küçük Mcginty, Cogan Shimizu, Pascal Hitzler, Ajay Sharda

Computer Science and Engineering Faculty Publications

In the quest for agricultural sustainability, we face the challenge of feeding the global population under the constraints of finite resources and a delicate ecological balance. The intricate interplay of climate dynamics, socio-economic factors, and environmental stewardship demands an approach to agriculture that is as intelligent and adaptive as it is respectful of our planet’s capacities. Central to this endeavor is the synthesis and utilization of vast, heterogeneous datasets that span from crop genomics to market trends, and from soil health to consumer preferences. Yet, the current paradigm is fragmented, with valuable data isolated across domains, lacking the coherence and …

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Ontology Design Facilitating Wikibase Integration — And A Worked Example For Historical Data, Cogan Shimizu, Andrew Eells, Seila Gonzalez, Lu Zhou, Pascal Hitzler, Alicia Sheill, Catherine Foley, Dean Rehberger Oct 2024

Ontology Design Facilitating Wikibase Integration — And A Worked Example For Historical Data, Cogan Shimizu, Andrew Eells, Seila Gonzalez, Lu Zhou, Pascal Hitzler, Alicia Sheill, Catherine Foley, Dean Rehberger

Computer Science and Engineering Faculty Publications

Wikibase – which is the software underlying Wikidata – is a powerful platform for knowledge graph creation and management. However, it has been developed with a crowd-sourced knowledge graph creation scenario in mind, which in particular means that it has not been designed for use case scenarios in which a tightly controlled high-quality schema, in the form of an ontology, is to be imposed, and indeed, independently developed ontologies do not necessarily map seamlessly to the Wikibase approach. In this paper, we provide the key ingredients needed in order to combine traditional ontology modeling with use of the Wikibase platform, …

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Festival Of Research Abstracts, Fall 2024, College Of Science And Mathematics, Wright State University Oct 2024

Festival Of Research Abstracts, Fall 2024, College Of Science And Mathematics, Wright State University

Festival of Research

The collection of abstracts accepted for the Fall 2024 Festival of Research hosted by the Wright State University College of Science and Mathematics.

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Effects Of The Biochar Of Senna Tora On The Soil Fertility Of Misau Farmlands, Bauchi State, Northern Nigeria, Ozoilo Onyeka Calistus, Umar Faruk Hassan, Auwal Adamu Mahmoud, Haruna Baba, Hamza Badamasi, Hannatu Akanang Jun 2024

Effects Of The Biochar Of Senna Tora On The Soil Fertility Of Misau Farmlands, Bauchi State, Northern Nigeria, Ozoilo Onyeka Calistus, Umar Faruk Hassan, Auwal Adamu Mahmoud, Haruna Baba, Hamza Badamasi, Hannatu Akanang

Journal of Bioresource Management

The use of biochar as an amendment to the soil is attracting the scientific community's attention because of its cost-effectiveness, eco-friendliness, and its promising success in increasing the fertility of the soil and crop yield. In the present study, the effects of biochar from Senna tora applications on soil characteristics and the availability of micronutrients in Misau, Bauchi State, Nigeria, were explored. A completely randomized approach was adopted and duplicated three times, with reference soil (no biochar), 30-day, and 60-day biochar treatments. The biochar was removed at the end of the treatment, and soil samples from the various treatments were …

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Easy-Ai: Semantic And Composable Glyphs For Representing Ai Systems, Alexis Ellis, Brandon Dave, Hugh Salehi, Subhashini Ganapathy, Cogan Shimizu Jun 2024

Easy-Ai: Semantic And Composable Glyphs For Representing Ai Systems, Alexis Ellis, Brandon Dave, Hugh Salehi, Subhashini Ganapathy, Cogan Shimizu

Computer Science and Engineering Faculty Publications

Despite the rapid integration of artificial intelligence (AI) into various research domains and the lives of everyday people, challenges with communicating and understanding these AI systems arise. The lack of a consistent method of communication highlights the need for a transdisciplinary approach to explain the inner workings of AI systems in a cohesive and accessible manner. We thus propose an ontological visual framework using semantically-enhanced, symbols, providing a symbolic language for conveying the structure, purpose, and characteristics of AI systems. The framework encompasses a generalizable glyph set of various AI system components, ensuring both common and obscure architectures can be …

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Festival Of Research Abstracts, Spring 2024, College Of Science And Mathematics, Wright State University Apr 2024

Festival Of Research Abstracts, Spring 2024, College Of Science And Mathematics, Wright State University

Festival of Research

The collection of abstracts accepted for the Spring 2024 Festival of Research hosted by the Wright State University College of Science and Mathematics.

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Desalination As A Source Of Freshwater, Jacob Pensky Mar 2024

Desalination As A Source Of Freshwater, Jacob Pensky

Best Integrated Writing

Jacob Pensky's article deals with technology we use to make saltwater drinkable. Drought-stricken coastal communities need desalination plants, especially as Earth's climate warms, but they are expensive and energy-intensive. This article describes ways to reduce the environmental and monetary costs.

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Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu Jan 2024

Understanding Patient Profiles In Sickle Cell Disease Using Unsupervised Machine Learning, Raj Kamal Somavarapu

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Sickle Cell Disease (SCD) is one of the most prevalent genetic blood disorders affecting millions of people worldwide. It is often accompanied by acute and/or chronic pain leading to increased healthcare costs and adverse outcomes. Effective management of SCD requires an understanding of the diverse physiological profiles. This study employs unsupervised machine learning, specifically K-means clustering to categorize the patients suffering with SCD into different clusters based on their vital signs. The main aim is to identify the groups that reflect similarities in physiological and pain profiles, allowing an in-depth analysis to reveal distinctive features distinguishing patient clusters. The project …

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Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi Jan 2024

Enhancing Robustness Of Graph Neural Network Against Adversarial Attacks By Balancing Local And Global Perspectives, Bibek Raj Joshi

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Graph Neural Networks (GNNs) have increasingly gained popularity as tools for analyzing graph data in areas like biology, knowledge-graphs, social networks, biology, and recommendation systems. However, their vulnerability to adversarial attacks - small, targeted manipulations of graph structures or node features - raises serious concerns about their reliability in real-world applications. Existing defense strategies, such as adversarial training, edge filtering, low-rank approximations, and randomization-based methods, often suffer from high computational costs, scalability issues, or reduced clean-data performance. Unlike these methods, the proposed approach integrates multi-hop relationships, applies adaptive regularization, and maintains a balance between feature-based and structural embeddings, ensuring improved …

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Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram Jan 2024

Meta-Learning-Based Model Stacking Framework For Hardware Trojan Detection In Fpga Systems, Mani Rupak Gurram

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In today's technological landscape, hardware devices are integral to critical applications such as industrial automation, autonomous vehicles, and medical equipment, relying on advanced platforms like FPGAs for core functionalities. However, the multi-stage manufacturing process, often distributed across various foundries, introduces substantial security risks, notably the potential for hardware Trojan insertion. These malicious modifications compromise the reliability and safety of hardware systems. This research addresses the detection of hardware Trojans through side-channel analysis, utilizing power and electromagnetic signal data, combined with meta-learning techniques, specifically model stacking. By employing diverse base models and a meta-model to consolidate predictions, this non-invasive approach effectively …

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Adsorption Of Perfluoroalkyl Substances With Activated Carbon: Characterizing Non-Hazardous Simulants, Jennifer Hensley Jan 2024

Adsorption Of Perfluoroalkyl Substances With Activated Carbon: Characterizing Non-Hazardous Simulants, Jennifer Hensley

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Per- and polyfluoroalkyl substances (PFAS) are of great interest recently because some members of this class exhibit human health risks at extremely low levels, making them the current subject of proposed regulation and policy. Treatment of PFAS contaminated wastewater often is performed through adsorptive processes, e.g., with activated carbon. Due to complexities with PFAS laboratory analysis, assessing treatment efficacy through direct measurement of PFAS is expensive and time consuming, potentially delaying the design and implementation of site-specific treatment systems. This project identified and characterized potential suitable simulants for PFAS during wastewater treatment for which analysis can be timely and economical. …

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Evaluating The Deductive Competence Of Large Language Models, Spencer M. Seals, Valerie L. Shalin Jan 2024

Evaluating The Deductive Competence Of Large Language Models, Spencer M. Seals, Valerie L. Shalin

Psychology Faculty Publications

The development of highly fluent large language models (LLMs) has prompted increased interest in assessing their reasoning and problem-solving capabilities. We investigate whether several LLMs can solve a classic type of deductive reasoning problem from the cognitive science literature. The tested LLMs have limited abilities to solve these problems in their conventional form. We performed follow up experiments to investigate if changes to the presentation format and content improve model performance. We do find performance differences between conditions; however, they do not improve overall performance. Moreover, we find that performance interacts with presentation format and content in unexpected ways that …

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Enhancing Cross-Modal Contextual Congruence For Crowdfunding Success Using Knowledge-Infused Learning, Trilok Padhi, Ugur Kursuncu, Yaman Kumar, Valerie L. Shalin, Lane Peterson Fronczek Jan 2024

Enhancing Cross-Modal Contextual Congruence For Crowdfunding Success Using Knowledge-Infused Learning, Trilok Padhi, Ugur Kursuncu, Yaman Kumar, Valerie L. Shalin, Lane Peterson Fronczek

Psychology Faculty Publications

The digital landscape continually evolves with multimodality, enriching the online experience for users. Creators and marketers aim to weave subtle contextual cues from various modalities into congruent content to engage users with a harmonious message. This interplay of multimodal cues is often a crucial factor in attracting users' attention. However, this richness of multimodality presents a challenge to computational modeling, as the semantic contextual cues spanning across modalities need to be unified to capture the true holistic meaning of the multimodal content. This contextual meaning is critical in attracting user engagement as it conveys the intended message of the brand …

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The Easy-Ai Symbology, Alexis Ellis, Cogan Shimizu Jan 2024

The Easy-Ai Symbology, Alexis Ellis, Cogan Shimizu

Computer Science and Engineering Faculty Publications

As artificial intelligence (AI) surges into the forefront of research and the lives of everyday people, challenges in understanding and communicating how these systems operate are becoming more prevalent. The need for a common language for AI systems that allows for multidisciplinary understanding and communication is a prevalent topic within the field. In this work, we take the visual framework EASY-AI and create a symbolic system that overlays the framework’s ontology to facilitate such communication and understanding. Poster submission.

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An Ontology Design Pattern For Role-Dependent Names, Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler Jan 2024

An Ontology Design Pattern For Role-Dependent Names, Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler

Computer Science and Engineering Faculty Publications

We present an ontology design pattern for modeling Names as part of Roles, to capture scenarios where an Agent performs different Roles using different Names associated with the different Roles. Examples of an Agent performing a Role using different Names are rather ubiquitous, e.g., authors who write under different pseudonyms, or different legal names for citizens of more than one country. The proposed pattern is a modified merger of a standard Agent Role and a standard Name pattern stub.

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The Hip Ontology: A Formal Framework To Support Disaster Risk Reduction And Management, Shirly Stephen, Mark Schildhauer, Krzysztof Janowicz, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Colby K. Fisher, Dean Rehberger Jan 2024

The Hip Ontology: A Formal Framework To Support Disaster Risk Reduction And Management, Shirly Stephen, Mark Schildhauer, Krzysztof Janowicz, Kitty Currier, Pascal Hitzler, Cogan Shimizu, Colby K. Fisher, Dean Rehberger

Computer Science and Engineering Faculty Publications

Open data initiatives and knowledge graphs, in synergy, have contributed to an increasing volume of disaster-related data in the Semantic Web. Synthesizing and enriching these data is critical to support all aspects of data-driven disaster risk reduction and management. A standard template that coherently defines, maps, and classifies the wide range of hazards to which communities are exposed is a key input for this task. The UNDRR-ISC Hazard Information Profiles (HIPs) provide evidence-informed standardization of hazard nomenclature and definitions and a “science-backed” classification. Unfortunately, they are not in a machine-readable format. This paper develops the HIP Ontology as its FAIR …

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An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma Jan 2024

An Efficient And Trusted Deep Learning Framework For Real-Time Ppe Detection In Secure Iomt Environment, Anusha Verma

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Occupationally-acquired infections impact thousands of healthcare workers (HCWs) in the U.S., with many cases preventable through proper use of personal protective equipment (PPE). This study seeks to develop a robust system to enhance PPE compliance and reduce infection risks among HCWs. The objectives of this thesis are twofold: (1) to create a hybrid machine learning model that combines object detection and keypoint detection to ensure correct donning and doffing of PPE, and (2) to design a real-time feedback system using LED indicators and a display interface to offer actionable guidance to HCWs during PPE usage. The goal is to optimize …

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An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda Jan 2024

An Enhanced Real-Time Object Detection Of Helmets And License Plates Using A Lightweight Yolov8 Deep Learning Model, Mounika Thatikonda

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Traffic surveillance and enforcement heavily depend on the real-time detection of helmets and license plates, particularly in high-density urban environments. This study presents a dynamic and optimized lightweight model, the proposed G-YOLOv8n, designed for resource constrained edge devices like the Raspberry Pi. By integrating the GhostNet module into the YOLOv8n architecture, this research achieves a nearly 50% reduction in model size and computational load, while maintaining comparable detection accuracy to the original YOLOv8n. These enhancements enable real-time processing capabilities crucial for traffic monitoring operations. The growing demand for real-time, low-power solutions in intelligent transportation systems necessitates lightweight, efficient detection models. …

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A Systematic Study Of Freezing Behavior In Earthworms In Response To Auditory And Vibratory Stimuli, Navjot Singh, A. Burton, Dragana Ivkovich Claflin Jan 2024

A Systematic Study Of Freezing Behavior In Earthworms In Response To Auditory And Vibratory Stimuli, Navjot Singh, A. Burton, Dragana Ivkovich Claflin

Celebration of Undergraduate & Graduate Research, Scholarship, and Creative Activities Materials

This study examined the parameters needed to reliably induce a freezing fear response to a predator-like auditory stimulus in the earthworm species Eisenia Fetida. Previous work from our lab found that a grunting noise was more reliable in causing freezing compared to a mole sound, i.e. artificial versus natural predators of E. Fetida (Worthen et al., 2024). In the present study, 8 amplitude levels of grunting sound were presented in either serial or random order. The speaker location was varied so that it either did or did not touch the apparatus, thus producing a mechanical vibration in addition to the …

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