Artificial Intelligence and Robotics Commons^™

Ai And The Creative Process: Part Three, James Hutson

Faculty Scholarship

Article discussing the effects of artificial intelligence on the creative process in the art world.

Artst: Arabic Text And Speech Transformer, Hawau Olamide Toyin, Amirbek Djanibekov, Ajinkya Kulkarni, Hanan Al Darmaki

Natural Language Processing Faculty Publications

We present ArTST, a pre-trained Arabic text and speech transformer for supporting open-source speech technologies for the Arabic language. The model architecture follows the unified-modal framework, SpeechT5, that was recently released for English, and is focused on Modern Standard Arabic (MSA), with plans to extend the model for dialectal and code-switched Arabic in future editions. We pre-trained the model from scratch on MSA speech and text data, and fine-tuned it for the following tasks: Automatic Speech Recognition (ASR), Text-To-Speech synthesis (TTS), and spoken dialect identification. In our experiments comparing ArTST with SpeechT5, as well as with previously reported results in …

Statistical And Machine Learning Approaches To Describe Factors Affecting Preweaning Mortality Of Piglets, Md Towfiqur Rahman, Tami M. Brown-Brandl, Gary A. Rohrer, Sudhendu R. Sharma, Vamsi Manthena, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

High preweaning mortality (PWM) rates for piglets are a significant concern for the worldwide pork industries, causing economic loss and well-being issues. This study focused on identifying the factors affecting PWM, overlays, and predicting PWM using historical production data with statistical and machine learning models. Data were collected from 1,982 litters from the United States Meat Animal Research Center, Nebraska, over the years 2016 to 2021. Sows were housed in a farrowing building with three rooms, each with 20 farrowing crates, and taken care of by well-trained animal caretakers. A generalized linear model was used to analyze the various sow, …

A Psychometric Analysis Of Natural Language Inference Using Transformer Language Models, Antonio Laverghetta Jr.

USF Tampa Graduate Theses and Dissertations

Large language models (LLMs) are poised to transform both academia and industry. But the excitement around these generative AIs has also been met with concern for the true extent of their capabilities. This dissertation helps to address these questions by examining the capabilities of LLMs using the tools of psychometrics. We focus on analyzing the capabilities of LLMs on the task of natural language inference (NLI), a foundational benchmark often used to evaluate new models. We demonstrate that LLMs can reliably predict the psychometric properties of NLI items were those items administered to humans. Through a series of experiments, we …

Text Augmentation For Semantic Frame Induction And Parsing, Saba Anwar, Artem Shelmanov, Nikolay Arefyev, Alexander Panchenko, Chris Biemann

Natural Language Processing Faculty Publications

Semantic frames are formal structures describing situations, actions or events, e.g., Commerce buy, Kidnapping, or Exchange. Each frame provides a set of frame elements or semantic roles corresponding to participants of the situation and lexical units (LUs)—words and phrases that can evoke this particular frame in texts. For example, for the frame Kidnapping, two key roles are Perpetrator and the Victim, and this frame can be evoked with lexical units abduct, kidnap, or snatcher. While formally sound, the scarce availability of semantic frame resources and their limited lexical coverage hinders the wider adoption of frame semantics across languages and domains. …

Yet Another Model For Arabic Dialect Identification, Ajinkya Kulkarni, Hanan Al Darmaki

Natural Language Processing Faculty Publications

In this paper, we describe a spoken Arabic dialect identification (ADI) model for Arabic that consistently outperforms previously published results on two benchmark datasets: ADI-5 and ADI-17. We explore two architectural variations: ResNet and ECAPA-TDNN, coupled with two types of acoustic features: MFCCs and features exratected from the pre-trained self-supervised model UniSpeech-SAT Large, as well as a fusion of all four variants. We find that individually, ECAPA-TDNN network outperforms ResNet, and models with UniSpeech-SAT features outperform models with MFCCs by a large margin. Furthermore, a fusion of all four variants consistently outperforms individual models. Our best models outperform previously reported …

Decentralized Science (Desci): A New Paradigm For Diverse And Sustainable Scientific Development, Feiyue Wang, Wenwen Ding

Bulletin of Chinese Academy of Sciences (Chinese Version)

The rise of artificial intelligence for science (AI4S) has made it particularly important and urgent to ensure the openness, fairness, impartiality, diversity, and sustainability of scientific systems. This is significant to the discourse power and leadership of countries in global innovation and industrial revolution, and also affects the security, stability, and sustainable development of a community with a shared future for mankind. To address these challenges, AI4S needs to adopt new scientific organizational and operational methods. Decentralized science (DeSci) has emerged to vitalize AI4S and provide strong support, effectively addressing issues such as information silos, biases, unfair distribution, and monopolies …

Metaverse Key Requirements And Platforms Survey, Akbobek Abilkaiyrkyzy, Ahmed Elhagry, Fedwa Laamarti, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

The growing interest in the metaverse has led to an abundance of platforms, each with its own unique features and limitations. This paper's objective is two-fold. First, we aim at providing an objective analysis of requirements that need to be fulfilled by metaverse platforms. We survey a broad set of criteria including interoperability, immersiveness, persistence, multimodal and social interaction, scalability, level of openness, configurability, market access, security, and blockchain integration, among others. Second, we review a wide range of existing metaverse platforms, and we critically evaluate their ability to meet the requirements listed. We identify their limitations, which must be …

Lrtransformer: Learn-Region Transformer For Object-Agnostic Point Cloud Segmentation, Dipesh Gyawali

LSU Master's Theses

3D point cloud segmentation segments the 3D point cloud data into different regions/instances depending on their features that have numerous applications in robotics, autonomous driving, digital twinning, augmented reality, etc. The majority of the existing point cloud segmentation methods depend on class labels to identify 3D objects in the surroundings. Our work focuses on segmenting point clouds into different regions/instances in an object-agnostic manner for any number of objects in the environment. Given the point cloud, our method can segment the entire scene into multiple instances without depending on object shape and size. We leverage the power of the self-attention …

Improving Human-Automation Collaboration In Motion Planning, Torin J. Adamson

Computer Science ETDs

Human-automation collaboration is becoming a part of everyday life as AI helps us drive, make decisions, and solve a variety of other tasks. However, safe and effective collaboration systems depend on factors in trust, communication, and more. Existing studies to explore these are typically carried out in laboratory settings, providing robust data under tight environmental control. However, human behavior evolves over time, driven by external factors that cannot be fully captured in single participation sessions. These factors form the "human context", contextualizing the behavioral data for a more complete understanding. In this thesis, video game adaptations upon conventional subject studies …

Ai As A License Review Assistant, Nat Gustafson-Sundell

Library Services Publications

I will present the steps we have taken to develop a prototype AI assistant for license review. I’ll explain our criteria for the selection of an AI tool for this project. We reviewed ChatGPT, Claude 2, Bard, and PDF readers. My goal was to develop an initial prototype in a Jupyter Notebook environment so I could easily re-load context information, including a license checklist, but I’ll explain why I revised this goal, instead to linger over license review interactions with ChatBots. I’ll discuss early results, demonstrate example license review interactions, and outline my next steps.

Artificial Intelligence History, And Libraries: History And Legacy Of Library Contributions To Machine Learning, Wilhelmina Randtke

University Libraries: Faculty Presentations

Machine learning seems to be newly everywhere. It's not new, so much as faster processing makes it newly useful. Imagine an automated cataloging program that takes 300 years to run, versus one that takes a week to run. Increased processing speed is a substantive change. This presentation overviews the history of libraries and artificial intelligence. First, teasing out past applications of machine learning in libraries. High quality results and concrete applications of artificial intelligence in libraries have been explored and published for decades. Over time, faster processing allows use at scale. Second, how library and metadata work contributes to machine …

Teacher Candidates’ Conceptions And Practices Of Computational Thinking For Equity, Heather F. Clark, Symone A. Gyles, Imelda Nava-Landeros

Journal of Computer Science Integration

This study documents novice science and math teachers’ developing pedagogical approaches to integrating computational thinking (CT) and data into their courses to support educational equity and social justice. The 10 novice teacher candidates (TCs) studied were part of an urban teacher residency program that empowered them with an asset-based pedagogy we describe as “CT for Equity.” Drawing on coursework and interviews as data, we asked three questions: What are teachers’ conceptions of CT? What are their CT instructional practices? And how did their students respond to those practices? To explore conceptions of CT, we used Kafai et al.’s (2020) articulation …

Artificial Intelligence And Human Hope, Michael Paulus

SPU Works

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Investigating Continual Learning Strategies In Neural Networks, Christopher Tam, Luiz Fernando Capretz

Electrical and Computer Engineering Publications

This paper explores the role of continual learning strategies when neural networks are confronted with learning tasks sequentially. We analyze the stability-plasticity dilemma with three factors in mind: the type of network architecture used, the continual learning scenario defined and the continual learning strategy implemented. Our results show that complementary learning systems and neural volume significantly contribute towards memory retrieval and consolidation in neural networks. Finally, we demonstrate how regularization strategies such as elastic weight consolidation are more well-suited for larger neural networks whereas rehearsal strategies such as gradient episodic memory are better suited for smaller neural networks.

Evocative And Provocative Image-Making In The Age Of Generative Ai, Julian Kilker

Tradition Innovations in Arts, Design, and Media Higher Education

Editorial for inaugural AI-focused special issue of Tradition-Innovations in Arts, Design, and Media Higher Education, published under the auspices of the Alliance for the Arts in Research Universities (a2ru). Discusses three articles by five authors in this issue: (1) Choreographing Shadows: Interdisciplinary collaboration to orchestrate ethical image-making by Mark Burchick and Diana Pasulka; (2) Giving Up Control: Hybrid AI-augmented workflows for image-making by Joshua Vermillion; and (3) Hands are Hard: Unlearning how we talk about machine learning in the arts by Adam Hyland and Oscar Keyes.

Editing this special issue explored several key questions: What does “innovation” mean when …

Ai In The College Classroom: From Communication To Animation, Stefanie Powers, Erika Grodzki, Gary Carlin, Hung Chum Kao

Faculty and Staff Publications & Presentations

No abstract provided.

Dtitd: An Intelligent Insider Threat Detection Framework Based On Digital Twin And Self-Attention Based Deep Learning Models, Zhi Qiang Wang, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

Recent statistics and studies show that the loss generated by insider threats is much higher than that generated by external attacks. More and more organizations are investing in or purchasing insider threat detection systems to prevent insider risks. However, the accurate and timely detection of insider threats faces significant challenges. In this study, we proposed an intelligent insider threat detection framework based on Digital Twins and self-attentions based deep learning models. First, this paper introduces insider threats and the challenges in detecting them. Then this paper presents recent related works on solving insider threat detection problems and their limitations. Next, …

Adapting The Adapters For Code-Switching In Multilingual Asr, Atharva Kulkarni, Ajinkya Kulkarni, Miguel Couceiro, Hanan Al Darmaki

Natural Language Processing Faculty Publications

Recently, large pre-trained multilingual speech models have shown potential in scaling Automatic Speech Recognition (ASR) to many low-resource languages. Some of these models employ language adapters in their formulation, which helps to improve monolingual performance and avoids some of the drawbacks of multi-lingual modeling on resource-rich languages. However, this formulation restricts the usability of these models on code-switched speech, where two languages are mixed together in the same utterance. In this work, we propose ways to effectively fine-tune such models on code-switched speech, by assimilating information from both language adapters at each language adaptation point in the network. We also …

Editorial For "Automated Breast Density Assessment In Mri Using Deep Learning And Radiomics: Strategies For Reducing Inter-Observer Variability"., Pegah Khosravi

Publications and Research

No abstract provided.