Digital Commons Network^™

Discourse- And Lesion-Based Aphasia Quotient Estimation Using Machine Learning, Nicholas Riccardi, Satvik Nelakuditi, Dirk B. Den Ouden, Chris Rorden, Julius Fridriksson, Rutvik H. Desai

Communication Sciences and Disorders Faculty Articles and Research

Discourse is a fundamentally important aspect of communication, and discourse production provides a wealth of information about linguistic ability. Aphasia commonly affects, in multiple ways, the ability to produce discourse. Comprehensive aphasia assessments such as the Western Aphasia Battery-Revised (WAB-R) are time- and resource-intensive. We examined whether discourse measures can be used to estimate WAB-R Aphasia Quotient (AQ), and whether this can serve as an ecologically valid, less resource-intensive measure. We used features extracted from discourse tasks using three AphasiaBank prompts involving expositional (picture description), story narrative, and procedural discourse. These features were used to train a machine learning model …

Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama

Neutrosophic Systems with Applications

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, …

Choosing Optimal Supply Radius Of Transformer Substations (Tss) In Iraq’S Cities Using Geometric Programming With Neutrosophic Coefficients, Ahmed K. Essa, Montifort Blessings Andrew Mitungwi, Tuweh Prince Gadama, Ahmed A. Salama

Neutrosophic Systems with Applications

Numerous uncertainties exist in various electricity power system problems due to the size, complexity, geographical distribution, and influence of unforeseen events in these systems, making it difficult for traditional mathematics tools based on crisp set theory to have an impact on and solve many power system problems. As a new branch of mathematical uncertainty techniques, the neutrosophic expert systems approach has therefore emerged with the development of electric power systems and has proven successful when correctly linked. The expert typically uses ambiguous or neutrosophic language to describe their empirical knowledge, such as "very likely," "quite likely," "if x is large, …

Improving Implicit Communication In Remote Collaboration Through Augmented Reality And Digital Twins, Nicholas Levergne

LSU Master's Theses

Large scale digital twinning projects are beginning to emerge across the tech industry. Within these projects is a desire to integrate augmented reality capabilities into industrial workflows. However, research on augmented reality technology for remote collaboration lacks ecologically valid studies of real world scenarios. Additionally, prior remote collaboration literature is focused on white-collar applications instead of blue-collar field work. Prior AR collaboration software is similarly limited, with most software allowing mixed camera views and annotation that requires participants to be stationary. This thesis introduces SpectAR, an augmented reality and desktop remote collaboration software suite developed in Unreal Engine 5.1.1. SpectAR …

Advancing Text Summarization And Classification: Deep Insights From Transformer-Based Statistical Learning, Kun Bu

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) is a part of human's daily life nowadays. Machine Learning (ML) as one aspect from AI has been rapidly developing during the past two decades, especially from the statistical learning approaches, which emphasized the use of probability and statistics to model data, such as Support Vector Machines (SVMs) for classification and regression tasks to the ensemble learning techniques, such as Random Forest, Gradient Boosting Machine (GBM), and stacking. Ensemble learning has evolved into a pivotal concept in contemporary machine learning, empowering practitioners to amalgamate multiple models to enhance generalization, accuracy, and robustness. As the field of machine …

Nowcasting Heavy Rainfall With Convolutional Long Short-Term Memory Networks: A Pixelwise Modeling Approach, Yi Victor Wang, Seung Hee Kim, Geunsu Lyu, Choeng-Lyong Lee, Soorok Ryu, Gyuwon Lee, Ki-Hong Min, Menas C. Kafatos

Institute for ECHO Articles and Research

The recent decades have seen an increasing academic interest in leveraging machine learning approaches to nowcast, or forecast in a highly short-term manner, precipitation at a high resolution, given the limitations of the traditional numerical weather prediction models on this task. To capture the spatiotemporal associations of data on input variables, a deep learning (DL) architecture with the combination of a convolutional neural network and a recurrent neural network can be an ideal design for nowcasting rainfall. In this study, a long short-term memory (LSTM) modeling structure is proposed with convolutional operations on input variables. To resolve the issue of …

Rethinking Plagiarism In The Era Of Generative Ai, James Hutson

Faculty Scholarship

The emergence of generative artificial intelligence (AI) technologies, such as large language models (LLMs) like ChatGPT, has precipitated a paradigm shift in the realms of academic writing, plagiarism, and intellectual property. This article explores the evolving landscape of English composition courses, traditionally designed to develop critical thinking through writing. As AI becomes increasingly integrated into the academic sphere, it necessitates a reevaluation of originality in writing, the purpose of learning research and writing, and the frameworks governing intellectual property (IP) and plagiarism. The paper commences with a statistical analysis contrasting the actual use of LLMs in academic dishonesty with educator …

Mcfadden's Discrete Choice And Softmax Under Interval (And Other) Uncertainty: Revisited, Bartlomiej Jacek Kubica, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Studies of how people actually make decisions have led to an empirical formula that predicts the probability of different decisions based on the utilities of different alternatives. This formula is known as McFadden's formula, after a Nobel prize winning economist who discovered it. A similar formula -- known as softmax -- describes the probability that the classification predicted by a deep neural network is correct, based on the neural network's degrees of confidence in the object belonging to each class. In practice, we usually do not know the exact values of the utilities -- or of the degrees of confidence. …

Enhancing Rotating Machinery Fault Diagnosis: A Dual-Head Attention Mechanism In Deep Learning Neural Networks, Qing Snyder

Dissertations

Rotating machinery is crucial to production efficiency and safety in manufacturing industries for an extended time. Ensuring machinery reliability necessitates effective diagnostic systems, particularly for rotating bearings, the key components of such equipment. Fault diagnosis in rotating machinery is essential to prevent failures and minimize downtime, thereby playing an important role in industrial operations. The application of advanced neural network techniques in industry has risen recently. Among these, attention-based neural networks, especially the Transformer models, are originally noteworthy for their sequential data handling capability. This research delves into attention-based algorithms for rotating machinery fault diagnosis, signifying a substantial advancement in …

Visualizing Routes With Ai-Discovered Street-View Patterns, Tsung Heng Wu, Md Amiruzzaman, Ye Zhao, Deepshikha Bhati, Jing Yang

Computer Science Faculty Publications

Street-level visual appearances play an important role in studying social systems, such as understanding the built environment, driving routes, and associated social and economic factors. It has not been integrated into a typical geographical visualization interface (e.g., map services) for planning driving routes. In this article, we study this new visualization task with several new contributions. First, we experiment with a set of AI techniques and propose a solution of using semantic latent vectors for quantifying visual appearance features. Second, we calculate image similarities among a large set of street-view images and then discover spatial imagery patterns. Third, we integrate …

Increased Perceived Confidence In Professional Role Skills Among Undergraduate Dietetic Students Following Simulation-Based Learning Experiences, Makenzie Barr-Porter, Elizabeth Combs, Lauren Batey, Dawn Brewer, Aaron Kyle Schwartz, Tammy Stephenson

UK CARES Faculty Publications

Simulation-based learning experiences (SBLEs) are effective for teaching healthcare students clinical and communication skills. The current study assessed self-perceived clinical and communication confidence among dietetics students completing a series of four SBLEs (3 group, 1 individual) across nine months. Dietetics students were recruited in February 2023 prior to their first SBLE. Simultaneously through the academic year, students completed clinical and communication courses. Students were invited to complete an online, anonymous self-reported survey regarding confidence with nutrition care and communication prior to their first SBLE (Time 1), prior to their third SBLE (Time 2), and following their final SBLE (Time 3). …

Lyraquist: Language Learning Via Music App, Vivian D'Souza, Siri Avula, Mahi Patel, Tanvi Singh, Ashley Bickham

Senior Theses

Lyraquist is a new language learning mobile app that encourages the practice of a foreign language through music. Language learners can connect their Spotify Premium account to Lyraquist to listen to music in their target languages and utilize tools such as translation and vocabulary lists to facilitate language practice. Through integration with Spotify, users can import and create Spotify playlists and search the service’s entire catalog. By combining daily listening habits with several tasks associated with language learning in one place, Lyraquist hopes to be a useful language learning tool.

Finding Identities: Identities In Video Games From A Gender, Race, And Identity Representation, Osayame Erinmwingbovo

ART 108: Introduction to Games Studies

In this paper I will bring light to the exploration of gender, race, and identity in video games. While also having a focus on how the representation crosses with social and cultural contexts. I will be researching different games from many different genres, which will show light to the way video games reflect and shape societal attitudes towards gender, race, and identity. When using close textual analysis and theoretical framework from topics that include critical race theory, media, and feminist theory. This research will help to seek to explore the nuances and complexities of representation in gaming which implicates video …

Artificial General Intelligence And The Mind-Body Problem: Exploring The Computability Of Simulated Human Intelligence In Light Of The Immaterial Mind, Caleb Parks

Senior Honors Theses

In this thesis I explore whether achieving artificial general intelligence (AGI) through simulating the human brain is theoretically possible. Because of the scientific community’s predominantly physicalist outlook on the mind-body problem, AGI research may be limited by erroneous foundational presuppositions. Arguments from linguistics and mathematics demonstrate that the human intellect is partially immaterial, opening the door for novel analysis of the mind’s simulability. I categorize mind-body problem philosophies in a manner relevant to computer science based upon state transitions, and determine their ramifications on mind-simulation. Finally, I demonstrate how classical architectures cannot resolve so-called Gödel statements, discuss why this inability …

Modeling, Characterization, And Machine Learning Algorithm For Rectangular Choke Horn Antennas, Ibrahim N. Alquaydheb, Saleh A. Alfawaz, Amirreza Ghadimi Avval, Sara Ghayouraneh, Samir M. El-Ghazaly

Electrical Engineering and Computer Science Faculty Publications and Presentations

In this work, we present the design and modeling of a new type of choke horn antenna. It incorporates a rectangular waveguide and a rectangular choke acting as a parasitic element. The four-sided geometry of the antenna is applicable to systems that utilize rectangular waveguides. Also, it can overcome the need for rectangular-to-circular transition of transmission line or mode conversion. The main objective of this paper is to develop a model that calculates the far field radiation characteristics of the proposed antenna (analytical part) and to incorporate a finite element method (FEM) solver that adds to the theoretical solution (empirical …

On Adaptivity And Randomness For Streaming Algorithms, Manuel Stoeckl

Dartmouth College Ph.D Dissertations

A streaming algorithm has a limited amount of memory and reads a long sequence (data stream) of input elements, one by one, and computes an output depending on the input. Such algorithms may be used in an online fashion, producing a sequence of intermediate outputs corresponding to the prefixes of the data stream. Adversarially robust streaming algorithms are required to give correct outputs with a desired probability even when the data stream is adaptively generated by an adversary that can see all intermediate outputs of the algorithm. This thesis binds together research on a variety of problems related to the …

Advanced Machine Learning Approaches For Predicting Mental Health Disorders Following Long Covid Diagnosis, Manoj Purohit

Master's Theses (2009 -)

After the global spread of COVID-19, the enduring effects of Long COVID and its health implications have emerged as a significant global issue, affecting people worldwide. The lingering symptoms post a COVID-19 infection can significantly affect individuals who had previously contracted the virus, exerting considerable influence over their mental well-being. Prolonged recuperation associated with Long COVID has been connected with the emergence of symptoms such as depression and anxiety, all of which can have adverse effects on emotional health. This project delves into an in-depth analysis of healthcare data pertaining to Long COVID from the Froedtert Health (FH) Medical System …

Parallelized Quadtrees For Image Compression In Cuda And Mpi, Aidan Jones

Senior Honors Theses

Quadtrees are a data structure that lend themselves well to image compression due to their ability to recursively decompose 2-dimensional space. Image compression algorithms that use quadtrees should be simple to parallelize; however, current image compression algorithms that use quadtrees rarely use parallel algorithms. An existing program to compress images using quadtrees was upgraded to use GPU acceleration with CUDA but experienced an average slowdown by a factor of 18 to 42. Another parallelization attempt utilized MPI to process contiguous chunks of an image in parallel and experienced an average speedup by a factor of 1.5 to 3.7 compared to …

Shutting Out Noise And Understanding Artificial Intelligence, Lauren J. Yu

Michigan Law Review

A review of Noise: A Flaw in Human Judgment. By Daniel Kahneman, Olivier Sibony and Cass R. Sunstein, and You Look Like a Thing and I Love You: How Artificial Intelligence Works and Why It’s Making the World a Weirder Place. By Janelle Shane.

Curiosity-Driven Testing For Sequential Decision-Making Process, Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo

Research Collection School Of Computing and Information Systems

Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios remains an open challenge. To address this, we propose CureFuzz, a novel curiosity-driven black-box fuzz testing approach for SDMs. CureFuzz proposes a curiosity mechanism that allows a fuzzer to effectively explore novel and diverse scenarios, …

Stealthy Backdoor Attack For Code Models, Zhou Yang, Bowen Xu, Jie M. Zhang, Hong Jin Kang, Jieke Shi, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Code models, such as CodeBERT and CodeT5, offer general-purpose representations of code and play a vital role in supporting downstream automated software engineering tasks. Most recently, code models were revealed to be vulnerable to backdoor attacks. A code model that is backdoor-attacked can behave normally on clean examples but will produce pre-defined malicious outputs on examples injected with that activate the backdoors. Existing backdoor attacks on code models use unstealthy and easy-to-detect triggers. This paper aims to investigate the vulnerability of code models with backdoor attacks. To this end, we propose A (dversarial eature as daptive Back). A achieves stealthiness …

Performing Information Extraction For Mission Engineering Applications, Samuel R. Koski

Engineering Management & Systems Engineering Theses & Dissertations

The process of extracting structured data from unstructured and semi-structured text is manual, time consuming and error prone. Current natural language processing approaches for automating this process are difficult to verify for non-trivial and context-sensitive corpora. Large Language Models (LLMs) like ChatGPT have become a subject of considerable interest, opening a promising avenue of exploration. However, there is limited evidence on the performance of LLMs for information extraction.

In this dissertation, an approach is proposed to evaluate the accuracy of Stanford OpenIE and OpenAI's ChatGPT for this purpose. This includes comparing Resource Description Framework (RDF) triples extracted by each of …

Study Of Deep Learning Models To Classify Nasa’S Kepler Light Curves, Heena Minnich

Computer Science Theses & Dissertations

The search for exoplanets has been an ongoing effort since the first discoveries of planets beyond our solar system in the 1990s. Finding a potentially habitable planet outside our solar system could provide key insights on life elsewhere in the universe. NASA Missions such as the Kepler, launched in 2009 and completed in 2018, have provided a massive amount of data in this goal by using the transit method to discover repetitive and periodic dips in visible light around a star. The transit method has been used to measure flux, the brightness of a star over time. These flux time …

A Trustworthy Self-Sovereign Data And Identity Management Framework, Efat Fathalla

Electrical & Computer Engineering Theses & Dissertations

Data is a fundamental building block in the digital world, providing a basis for decision making and growth across numerous applications. In our modern world, we have become accustomed to collecting data on everything, including devices, machines, and people. The increased value of such data has led to aggressive harvesting mechanisms that prioritize data collection, storage, and pervasiveness while often disregarding security, privacy concerns, and compliance with regulations and standards. Such a pervasive attitude towards data has resulted in a loss of control, prompting concerns among individuals and mobilizing the scientific community towards advocating for data self-sovereignty.

Self-Sovereign Identity (SSI) …

Toward Cyber-Attack Detection For Industrial Internet Of Things Systems Using Machine Learning, Cayden Cather

Honors Theses

In recent years, the way we interact with daily life and industrial environments has changed dramatically due to the rise of the Internet of Things (IoT), from smart homes to industrial control systems. In particular, industry devices, sensors, and actuators can be connected to form various Industrial Internet of Things (IIoT) networks and controlled using operational technologies, such as ICS and SCADA. However, the vulnerabilities and limited security features pose significant challenges in preserving IIoT systems' security as cyber-attacks increase and become more frequent, dynamic, and complex. Additionally, the lack of IIoT testbeds has made it more difficult to find …

Digital Frontiers In Aesthetics: Applying Dewey's Insights To Generative Ai, Malcolm F. Lathrop-Allen

Student Publications

The last few years have seen the emergence of ‘artificially intelligent’ systems en masse, which perform tasks which had previously only been possible by human intelligence. Arguably, the impact of ‘AI 2.0’ has been felt most prominently in the art world — artists have panicked as DALL-E, Midjourney, and other image generation algorithms manufacture pieces which previously required weeks of painstaking labor to create. This project seeks to develop a more critical framework for this novel mode of artistic creation and propose better ways of thinking about, using, and “becoming with” artificial intelligence in the domain of artistry. The first …

Combine Shapelets, Zeng Qingwen

LMU Theses and Dissertations

Sensor-based human activity recognition has become an important research field within pervasive and ubiquitous computing. Techniques for recognizing atomic activities such as gestures or actions are mature for now, but complex activity recognition still remains a challenging issue. I was a candidate in an activity classification thesis. It collected 4 activities, which included walking on the sidewalk for a set distance, walking up and down a set of stairs, walking on the treadmill at 2.5 mph for 2 minutes, and jogging on the treadmill at 5.5 mph for 1 minute. It took 30 minutes to collect one candidate data. If …

Intelligent Tutoring System Ontology, Wael Mohamed Hassan

Theses

The integration of pedagogical rules into Intelligent Tutoring Systems (ITS) using semantic web technologies, particularly the Web Ontology Language (OWL), holds great promise for enhancing the capabilities of these systems. However, a significant challenge arises from the labor-intensive process of manually constructing ontologies, which can consume valuable time and resources. While ontologies offer numerous advantages, including robust knowledge inference and scalability, the limitations of manual ontology creation are evident in terms of time and flexibility. Therefore, the primary objective of this research is to develop an efficient and automated solution that harnesses the benefits of ontologies while reducing the time …

Unveiling The Origins Of Source Code Through Authorship Attribution: A Comparative Study Of Ai And Human Coding Patterns, Shamma Humaid Alalawi

Theses

In recent years, Artificial Intelligence (AI) techniques have been used for source code authorship attribution, which is the process of identifying the original author of a given piece of code. With the advancement of AI technologies like ChatGPT, which can generate code, there is a need to accurately identify whether a piece of code is written by a human or generated by a machine. This is crucial for intellectual property protection, cybersecurity, and software forensics. The main objective of this thesis is to review existing research on source code authorship attribution and conduct several experiments to determine the best AI …

An Intelligent Framework Towards Fully Autonomous Driving Fueled By Smart Roads, Muhammad Jalal Khan

Dissertations

Autonomous Vehicles (AVs) are transforming next-generation autonomous mobility. These vehicles promise to increase road safety, improve traffic efficiency, reduce vehicle emissions, and enhance overall mobility. They achieve higher levels of Autonomous Driving (AD) by integrating sensors, communication technologies, computation, and Artificial Intelligence (AI). Apart from these advancements, significant gaps remain in integrating heterogeneous technologies and disciplines essential for optimizing AD. Therefore, the current solution approaches lack the capability to exploit intelligent road infrastructures and effectively orchestrate perceptual services for complex driving scenarios. The main objective of this dissertation is to address these challenges by proposing a novel end-to-end intelligent framework …