Computer Sciences Commons^™

Extended Event Calculus Using Neutrosophic Logic: Method, Implementation, Analysis, Recent Progress And Future Directions, Antonios Paraskevas

Neutrosophic Systems with Applications

Brains do not reason as digital computers do. Computers reason in clear steps with statements that are either true or false, while humans reason with vague terms of common sense. Neutrosophy is a new branch of philosophy and machine intelligence that deals with neutralities, specifically the idea of indeterminacy that is evident and experienced in our everyday lives. Indeterminacy is interpreted as everything that falls between a concept, idea, statement, declaration, etc. and its opposite. The fundamental thesis of neutrosophy is to employ neutrosophic logic, an extension of fuzzy logic, to incorporate fuzzy truth into complex schemes of formal reasoning. …

Extended Event Calculus Using Neutrosophic Logic: Method, Implementation, Analysis, Recent Progress And Future Directions, Antonios Paraskevas

Neutrosophic Systems with Applications

Brains do not reason as digital computers do. Computers reason in clear steps with statements that are either true or false, while humans reason with vague terms of common sense. Neutrosophy is a new branch of philosophy and machine intelligence that deals with neutralities, specifically the idea of indeterminacy that is evident and experienced in our everyday lives. Indeterminacy is interpreted as everything that falls between a concept, idea, statement, declaration, etc. and its opposite. The fundamental thesis of neutrosophy is to employ neutrosophic logic, an extension of fuzzy logic, to incorporate fuzzy truth into complex schemes of formal reasoning. …

Novel Techniques In Imaging Congenital Heart Disease: Jacc Scientific Statement, Ritu Sachdeva, Aimee K Armstrong, Rima Arnaout, Lars Grosse-Wortmann, B Kelly Han, Luc Mertens, Ryan A Moore, Laura J Olivieri, Anitha Parthiban, Andrew J Powell

Faculty, Staff and Students Publications

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow …

Towards A Transparency-Based, Value-Sensitive Design Solution For Bias In Self-Driving Cars: An Ethical Violation Assessment And Risk Analysis Framework On Consumer-Held Values, Nada Ahmad Madkour

Master's Theses and Doctoral Dissertations

Background: The rapid growth of automated systems and artificial intelligence (AI), particularly, self-driving cars (SDCs), has attracted significant investments and can potentially contribute to humanity’s flourishing. However, before widespread adoption, it is important to address ethical violations such as bias in AI, highlighted by many real-world cases of bias in AI leading to unfair outcomes in tools like facial recognition, hiring software, and pedestrian detection. Bias in AI can lead to potentially fatal outcomes in SDCs, emphasizing the need for a thorough examination of bias in SDCs.

Purpose: To enhance AI ethics by providing tools to support transparency and value- …

Survey Of Memory Consolidation Techniques For Video Question Answering, Matthew Couts, Pha Nguyen, Khoa Luu

Inquiry: The University of Arkansas Undergraduate Research Journal

Video Question Answering (VideoQA) is a field of research focused on developing models that can engage in natural conversations with humans about the content of videos. Currently, the most successful approaches involve analyzing videos frame-by-frame, which is computationally and memory-intensive. To imitate human memory, the Atkinson-Shiffrin memory model can formulate the machine’s video understanding capability through Vision-Language Models. Reducing the number of frames processed by the model is a crucial operation in this approach category and can be handled by a memory consolidation algorithm. The memory consolidation algorithm should be able to determine the keyframes to transfer from short-term to …

Using Ai For Qualitative Labeling: Consistency And Comparisons, James Mcintyre

Honors Program Theses

This paper details a research study evaluating AI's ability to perform qualitative deductive coding. Multiple AI models were utilized and compared against three human coders and one expert coder. A series of 107 statements were sourced from a group discussion for a qualitative impact assessment of an organization. The AI models were provided these statements and directed to code them using the Community Capitals Framework. Two generations of AI models were evaluated. Overall, the AI achieved a fair level of agreement with the human annotators, but the alignment was far from perfect. Newer AI models did not increase agreement with …

Using Ai For Qualitative Labeling: Consistency And Comparisons, James Temple

Honors Program Theses

This paper continues research that evaluates the capacity of artificial intelligence (AI) to perform qualitative coding tasks. The previous study found that AI models lacked consistency with themselves and did not agree with human coded data. Since that study, AI’s general level of intelligence has increased. Hence, this study re-evaluates how well the newest set of AI models (Claude 3 and Gemini) can perform qualitative coding tasks. When tested, the new AI models perform about the same or better than previous models depending on the metric tested. While Gemini and Claude 3 do not agree with human output any more …

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 …

The Specter Of Representation: Computational Images And Algorithmic Capitalism, Samine Joudat

CGU Theses & Dissertations

The processes of computation and automation that produce digitized objects have displaced the concept of an image once conceived through optical devices such as a photographic plate or a camera mirror that were invented to accommodate the human eye. Computational images exist as information within networks mediated by machines. They are increasingly less about what art history understands as representation or photography considers indexing and more an operational product of data processing.

Through genealogical, theoretical, and practice-based investigation, this dissertation project traces a lineage of computation through images from early cybernetics to contemporary machine learning under algorithmic capitalist conditions of …

Ai-Analyst: An Ai-Assisted Sdlc Analysis Framework For Business Cost Optimization, Nuruzzaman Faruqui, Priyabrata Thatoi, Rohit Choudhary, Ivana Roncevic, Hamed Alqahtani, Iqbal H. Sarker, Shapla Khanam

Research outputs 2022 to 2026

Managing the System Development Lifecycle (SDLC) is a complex task because of its involvement in coordinating diverse activities, stakeholders, and resources while ensuring project goals are met efficiently. The complex nature of the SDLC process leaves plenty of scope for human error, which impacts the overall business cost. This paper introduces AI-Analyst, an AI-assisted framework developed using the transformer-based model with more than 150 million parameters to assist with SDLC management. It minimizes manual effort errors, optimizes resource allocation, and improves decision-making processes, resulting in substantial cost savings. The statistical analysis shows that it saves around 53.33% of costs in …

Genai In Rule-Based Systems For Iomt Security: Testing And Evaluation, Kulsoom S. Bughio, David M. Cook, Syed Afaq A. Shah

Research outputs 2022 to 2026

Generative AI (GenAI) represents a significant advancement in artificial intelligence research, offering numerous benefits and opening new avenues for innovation across various domains. In healthcare, Generative AI has shown promise in applications such as drug discovery, personalized medicine, and medical imaging. This paper examines the role of Generative AI in rule-based systems, where vulnerabilities are detected with the help of formal logic. In this context, the ruleset is generated and tested to evaluate the performance of rule-based systems with the aid of GenAI. The effectiveness of the GenAI tool was evaluated using a publicly available case study from a laboratory …

Responsible Natural Language Processing To Aid Employee Performance Reviews., Grace Rubinger

ICT

This research explores the use of Natural Language Processing (NLP) techniques in assessing evaluators' written appraisals during Employee Performance Reviews (EPRs), aiming to address biases inherent in traditional methods. By integrating Responsible Artificial Intelligence (AI) and foundational Large Language Models (LLMs), the study seeks to enhance the objectivity, fairness, and ethical transparency of performance evaluations. It highlights the potential of AI systems to ensure comprehensive assessments while promoting trust, ethical standards, and employee retention.

The research also aims to advance the field of AI Ethics in practical Human Resources Management (HRM) applications, particularly through NLP-driven tools. These tools are designed …

Artificial Intelligence Enabled Machinery Fault Detection And Diagnosis Using Vibro-Acoustic Signals, Srinivasa Rao Ippili

Theses and Dissertations--Mechanical and Aerospace Engineering

In various industries, the early detection of faults in rotating machinery is crucial to prevent system failures and ensure customer satisfaction. Typically, vibration measurement and diagnosis are employed for fault detection, but this process faces challenges in automation due to the complexity of installing and maintaining accelerometers, particularly in end-of-line quality control or pre-installed machinery health assessments. Acoustic signals, as a form of mechanical wave, offer an alternative for monitoring machinery while in operation. Unlike accelerometers, acoustic transducers are non-contact and easy to set up, enabling real-time data collection without interrupting equipment operation. However, utilizing acoustic signals in manufacturing poses …

Agentes Artificiales En Las Juntas Corporativas, Sergio Alberto Gramitto Ricci, David Cordero-Heredia, Carlos A. Carillo-Jaramillo

Faculty Works

Miles de afios atras, empresarios romanos gestionaban negocios conjuntos a traves de esclavos altamente inteligentes de propiedad comun. Los esclavos romanos no tenfan plena capacidad legal y eran considerados propiedad de sus duefios comunes. Ahora, las corporaciones buscan delegar la toma de decisiones a maquinas superinteligentes mediante el uso de inteligencia artificial en las juntas corporativas. La inteligencia artificial podrfa asistir, integrar e incluso reemplazar a los directores humanos. Sin embargo, el concepto de usar inteligencia artificial en las juntas directivas esta, en gran medida, inexplorado y plantea varios problemas. Este artfculo arroja luz sobre los desaffos legales y de …

Hack24f: Alzcare Ai Assist: Empowering Alzheimer's Care, Yao Zhang, Chengjie Zheng, Oliver Francois, Lingling Zhang

Paul English Applied Artificial Intelligence (AI) Institute Publications

AlzCare AI Assist is a groundbreaking solution that leverages the power of artificial intelligence to revolutionize Alzheimer's care. By delivering personalized assessments, psychological support, and caregiver assistance, we aim to transform the lives of those affected by this debilitating condition.

Hack24f: Painsync, Zihan Li, Zhen Lu, Ping Chen

Paul English Applied Artificial Intelligence (AI) Institute Publications

Pain is one of the most disruptive human experiences, influencing not only physical well-being but also emotional and mental health. The PainSync project proposes a technology-assisted framework for pain recognition, monitoring, and management, using AI-driven tools to bridge the gap between patient experiences and professional care. PainSync begins by recognizing an individual’s discomfort and offering immediate support through a chatbot that collects symptom information and provides preliminary guidance. The system then tracks vital signs and daily activities, generating data that is subsequently analyzed by custom-built AI models to detect patterns, assess severity, and identify potential causes of pain. This analysis …

Hack24f: Ai Conversations In Healthcare, Patrick Finger, Hannah Neale, Anthony Ferreira, Ayaz Mohammed

Paul English Applied Artificial Intelligence (AI) Institute Publications

Nursing students often complete clinical hours under the supervision of instructors in traditional hospital settings. However, obtaining individualized, consistent feedback from patients about their interactions with nursing students is often not feasible. This limits students' ability to fully understand how their communication skills are perceived and how they can improve. Currently, there are no models that represent realistic real life conversations with patients. Most virtual simulation models used for nursing students provide scripted responses that do not feel genuine.

Hack24f: Ai Audio Extractor, David Wu, Tiffany Nham

Paul English Applied Artificial Intelligence (AI) Institute Publications

I want to make a next.js website locally and then be able to hopefully deploy on Vercel. Within the website I want to be able to use AI to separate the instruments (vocals, piano, guitar, drums, bass, etc.) and also identify which notes are being played. I was thinking that we might be able to use an AI stem splitter to separate the audio tracks and use another AI model for note detection.

A Survey On Artificial Intelligence In Cybersecurity For Smart Agriculture: State-Of-The-Art, Cyber Threats, Artificial Intelligence Applications, And Ethical Concerns, Guma Ali, Maad M. Mijwil, Bosco Apparatus Buruga, Mostafa Abotaleb, Ioannis Adamopoulos

Mesopotamian Journal of Computer Science

Wireless sensor networks and Internet of Things devices are revolutionizing the smart agriculture industry by increasing production, sustainability, and profitability as connectivity becomes increasingly ubiquitous. However, the industry has become a popular target for cyberattacks. This survey investigates the role of artificial intelligence (AI) in improving cybersecurity in smart agriculture (SA). The relevant literature for the study was gathered from Nature, Wiley Online Library, MDPI, ScienceDirect, Frontiers, IEEE Xplore Digital Library, IGI Global, Springer, Taylor & Francis, and Google Scholar. Of the 320 publications that fit the search criteria, 180 research papers were ultimately chosen for this investigation. The review …

Breaking The Cycle: Countering Popularity Bias For Diverse Content Discovery, Brandon J. Weaver

Master's Projects

The ways most people consume the media have become very much driven by some pre-set algorithms. It is increasingly important to examine the outcome of these artificial intelligence (AI) models and ensure that any potentially dangerous long-term effects are addressed before they have a significant negative impact in our society. Popularity bias is one of these potentially harmful impacts, which stemmed from the shift from human intelligence to AI, or machine intelligence/machine learning (ML), when one explores the media and receives recommendations (often without requesting). In ML, three key steps usually occur; i.e, pre-processing, in-processing, and post- processing steps. The …

An Ml-Assisted Golden-Free Hardware Trojan Localization And Detection Approach For Trusted Microelectronics, Ashutosh Ghimire

Browse all Theses and Dissertations

Hardware Trojans are malicious circuits, hidden in integrated circuits (ICs) which pose a significant threat to security. Detection of hardware Trojans is important to build trust, verify, and make the semiconductor ICs process secure. The existing hardware Trojan detection methods are generally destructive, require intricate comparisons, or require a long time for reverse engineering. In the initial phase of this study, the substitution of supervised hardware Trojan detection methods in ASICs chips is explored with unsupervised approaches, thereby eliminating the dependence on golden references. The Trojan detection uses a ring oscillator (RO) based on NAND as the power monitor. Frequency …

Multi-Semantic-Stage Neural Networks For Robust And Interpretable Deep Learning, Christopher J. Menart

Browse all Theses and Dissertations

Deep neural networks have great representational power. However, most deep neural nets today optimize directly for performance on a single task defined only by labeled training data. This excludes potential sources of knowledge and ways of learning which could improve their performance, and address challenges, such as explainability, which are pressing to the field. We propose a framework for neural network architecture which generalizes it to a graph of many semantically-meaningful variables. We call it the Multi-Semantic-Stage Neural Network (MSSNN). An MSSNN models its domain as a web of conditional probabilities, i.e. a collection of inter-related tasks which can learn …

Graph-Centric Bot Detection: Addressing Extreme Data Imbalances, Heterophily, And Scarcity, Bradley T. Ashmore

Browse all Theses and Dissertations

The digital landscape is ever-evolving. In recent years the amount of bot traffic, traffic generated by autonomous applications over the internet has increased significantly. Many bots perform useful and needed functions, however, malicious bots are known sources of both common and emerging security threats. Denial-of-Services (DoS), information theft, and credential stuffing have all been conducted by malicious software running on unknowingly infected machines. The dichotomy of useful bots operating in the same networks as malicious bots combined with novel bot attacks and an ever-increasing number of personal devices connecting to the Internet drives the need for continued advancement of malicious …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Road Extraction On Remote Sensing Imagery: Historical Mapping Of The Brazilian Amazon, Jonas Paiva Botelho Jr

Graduate Theses/Dissertations

This work proposes an artificial intelligence model based on U-Net architecture to map road networks in the Brazilian Amazon. Over the years, the Amazon region has been heavily exploited, leading to increased deforestation rates, contributing to CO2 emissions, amplifying global warming, and causing a disturbance in local fauna and flora. The expansion into the forest by illegal miners, loggers, and land grabbers can be tracked down by the construction of roads, which we can refer to as the arteries of deforestation. Previous works on the matter proposed algorithms that use high-resolution imagery to map roads precisely. However, this work approach …

Deepwhalenet: A Climate Change-Aware Fft-Based Neural Network For Underwater Passive Acoustic Monitoring, Nicholas Ryan Rasmussen

Dissertations and Theses

In the face of escalating climate threats, the conservation of whale species has become increasingly critical. Traditional acoustic monitoring methods, burdened by extensive pre-processing and post-processing, need more adaptability and efficiency for effective marine mammal surveillance. This study introduces DeepWhaleNet, a novel deep-learning framework tailored for Underwater Passive Acoustic Monitoring (UPAM). DeepWhaleNet is designed to streamline whale detection by directly analyzing raw log-power spectrograms, thus extracting essential acoustic features to conserve these endangered species. The framework employs an extensive short-time Fourier transform (STFT) for input processing and a customized ResNet-18 architecture for classification, distinguishing whale vocalizations from ambient noise and …

Mivt: Medical-Informed Vision Transformer For Early Epilepsy Diagnosis, Md Masum Rana

Dissertations and Theses

Epilepsy is a neurological disorder characterized by recurrent, unprovoked seizures, and early diagnosis is crucial for effective management and treatment. However, the diagnosis of epilepsy, particularly in its early stages, remains challenging due to the subtle nature of seizures and the complexity of brain activity patterns. In this research, we introduce the Medical-Informed Vision Transformer (MIVT), a deep learning architecture specifically designed to improve early epilepsy diagnosis from multimodal neuroimaging data. Our model integrates insights from both medical knowledge and state-of-the-art Vision Transformers (ViTs) to enhance the accuracy and interpretability of seizure detection and localization. The MIVT leverages the rich …

Hack24f: Ai Attacking Ai, Emry Hankins, Sofia Escobar, Yinxin Wan

Paul English Applied Artificial Intelligence (AI) Institute Publications

As MLaaS gains popularity, it also attracts new threats, in particular, model extraction attacks. These attacks involve unauthorized attempts to access and replicate AI models by querying and analyzing the response. Not only do these attacks pose a threat to security and safety but also compromises valuable intellectual property. Because businesses are increasing accessibility to their models that contain sensitive data, it is crucial that they are able to keep them safe and secure.

Hack24f: Careplus: Ai Chatbot, Arianna Fernandez, Sophia Cherkaoui

Paul English Applied Artificial Intelligence (AI) Institute Publications

90% of patients Google symptoms before they talk to their doctor. What are we developing? An app that asks for user symptoms and personal information, users AI to determine medical conditions and provides a plan of action, including recommended medications and suggested doctors to visit.

Hack24f: Llms And World Knowledge Problems, Chengtaon Lin, Joseph Lobo, Huuthanhvy Nguyen, Brian Santos, Ting Xu

Paul English Applied Artificial Intelligence (AI) Institute Publications

While AI “hallucinations” have been widely publicized by the media as shortcomings of Large Language Models (LLMs), there is much less awareness about shortcomings that derive from the potential lack of understanding of the world, from a potential lack of commonsense knowledge. We want to raise awareness among our student body about the potential fallibility of LLMs when they the problems that they need to solve require a basic understanding of the world. We hope that this awareness will prompt everyone to question more carefully the outputs generated by LLMs.