Physical Sciences and Mathematics Commons^™

Can Ai Become An Information Literacy Ally? A Survey Of Library Instructor Perspectives On Chatgpt, Melissa S. Del Castillo, Hope Y. Kelly

Works of the FIU Libraries

Libraries can play a role in navigating the AI era by integrating these tools into information literacy (IL) programs. To implement generative AI tools like ChatGPT effectively, it is important to understand the attitudes of library professionals involved in IL instruction toward this tool and their intention to use it for instruction. This study explored perceptions of ChatGPT using survey data that included acceptance factors and potential uses derived from the emerging literature. While some librarians saw potential, others found it too unreliable to be useful; yet the vast majority imagined utilizing the tool in the future.

Chatgpt Is A Liar And Other Lessons Learned From Information Literacy Instructors, Melissa S. Del Castillo, Hope Y. Kelly

Works of the FIU Libraries

Wondering where generative artificial intelligence (AI) fits in information literacy instruction? This session will share findings from a recent survey of library professionals on how they are already teaching about and using AI powered ChatGPT in information literacy instruction and where they see potential opportunities and areas of concern. Survey analysis will include information about attitudes, current and anticipated use, and descriptions of teaching methods that leverage the technology. As we navigate the survey results, attendees will have the opportunity to share their own perspectives on the same questions via live polling. We will then turn to attendees to share …

Towards Understanding The Geospatial Skills Of Chatgpt: Taking A Geographic Information Systems (Gis) Exam, Peter Mooney, Wencong Cui, Boyuan Guan, Levente Juhasz

GIS Center

This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their capabilities and limitations in specialized subject areas such as GIS. Human learning of spatial concepts significantly differs from LLM training methodologies. Therefore, this study aims to assess ChatGPT's performance and ability to grasp geospatial concepts by challenging it with a real GIS exam. By analyzing ChatGPT's responses and evaluating its understanding of GIS principles, we gain insights into the potential applications and challenges …

Statistical And Machine Learning Analysis Of The Human Brain Functional Network In A Multi-Site Resting-State Functional Mri Database Framework, Oswaldo Artiles, Fahad Saeed, Ed.

School of Computing and Information Sciences

The human brain has a complex network structure that is non-random and multiscale. It consists of subsystems coupled by a nonlinear dynamic, enabling it to produce complex responses to various external inputs and self-organize. To understand the physical structure and specific brain functions, it is essential to comprehend the connectivity of the hundreds of billions of neurons in the human brain. Functional connectivity (FC) in modern neuroscience is the statistical temporal dependencies between neuronal activation events occurring in spatially separated brain regions. Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive imaging technique widely used in neuroscience to understand the …

High Performance Computing Algorithms For Accelerating Peptide Identification From Mass-Spectrometry Data Using Heterogeneous Supercomputers, Muhammad Haseeb, Fahad Saeed, Ed.

School of Computing and Information Sciences

Fast and accurate identification of peptides and proteins from the mass spectrometry (MS) data is a critical problem in modern systems biology. Database peptide search is the most commonly used computational method to identify peptide sequences from the MS data. In this method, giga-bytes of experimentally generated MS data are compared against tera-byte sized databases of theoretically simulated MS data resulting in a compute- and data-intensive problem requiring days or weeks of computational times on desktop machines. Existing serial and high performance computing (HPC) algorithms strive to accelerate and improve the computational efficiency of the search, but exhibit sub-optimal performances …

“I Think I Discovered A Military Base In The Middle Of The Ocean”—Null Island, The Most Real Of Fictional Places, Levente Juhasz, Peter Mooney

GIS Center

This paper explores Null Island, a fictional place located at 0° latitude and 0° longitude in the WGS84 (World Geodetic System 1984) geographic coordinate system. Null Island is erroneously associated with large amounts of geographic data in a wide variety of location-based services, place databases, social media and web-based maps. Whereas it was originally considered a joke within the geospatial community, this article will demonstrate implications of its existence, both technological and social in nature, promoting Null Island as a fundamental issue of geographic information that requires more widespread awareness. The article summarizes error sources that lead to data being …

An Evolutionary Optimization Algorithm For Automated Classical Machine Learning, Leila Zahedi

FIU Electronic Theses and Dissertations

Machine learning is an evolving branch of computational algorithms that allow computers to learn from experiences, make predictions, and solve different problems without being explicitly programmed. However, building a useful machine learning model is a challenging process, requiring human expertise to perform various proper tasks and ensure that the machine learning's primary objective --determining the best and most predictive model-- is achieved. These tasks include pre-processing, feature selection, and model selection. Many machine learning models developed by experts are designed manually and by trial and error. In other words, even experts need the time and resources to create good predictive …

Anomaly Detection In Sequential Data: A Deep Learning-Based Approach, Jayesh Soni

FIU Electronic Theses and Dissertations

Anomaly Detection has been researched in various domains with several applications in intrusion detection, fraud detection, system health management, and bio-informatics. Conventional anomaly detection methods analyze each data instance independently (univariate or multivariate) and ignore the sequential characteristics of the data. Anomalies in the data can be detected by grouping the individual data instances into sequential data and hence conventional way of analyzing independent data instances cannot detect anomalies. Currently: (1) Deep learning-based algorithms are widely used for anomaly detection purposes. However, significant computational overhead time is incurred during the training process due to static constant batch size and learning …

Integrating Cultural Knowledge Into Artificially Intelligent Systems: Human Experiments And Computational Implementations, Anurag Acharya

FIU Electronic Theses and Dissertations

With the advancement of Artificial Intelligence, it seems as if every aspect of our lives is impacted by AI in one way or the other. As AI is used for everything from driving vehicles to criminal justice, it becomes crucial that it overcome any biases that might hinder its fair application. We are constantly trying to make AI be more like humans. But most AI systems so far fail to address one of the main aspects of humanity: our culture and the differences between cultures. We cannot truly consider AI to have understood human reasoning without understanding culture. So it …

Distributed Machine Learning Algorithms For Resource-Constrained Heterogeneous Internet-Of-Things Environments, Ahmed Imteaj

FIU Electronic Theses and Dissertations

With the improvement of network infrastructures and advancement of IoT technologies, now it is desirable to perform computation at the edges, rather than sharing data with a central fusion center, which is privacy-intrusive. Both conventional (centralized) and distributed machine learning (ML) algorithms fail to address underlying challenges related to users’ privacy or capturing global knowledge of the whole network. To properly handle such challenges, a recently invented distributed ML technique, called Federated Learning was invented that shows us a pathway to construct a global model without exposing any user’s private data through on-device model training utilizing edge resources. However, FL …

Intelligent Data Analytics Using Deep Learning For Data Science, Maria E. Presa Reyes

FIU Electronic Theses and Dissertations

Nowadays, data science stimulates the interest of academics and practitioners because it can assist in the extraction of significant insights from massive amounts of data. From the years 2018 through 2025, the Global Datasphere is expected to rise from 33 Zettabytes to 175 Zettabytes, according to the International Data Corporation. This dissertation proposes an intelligent data analytics framework that uses deep learning to tackle several difficulties when implementing a data science application. These difficulties include dealing with high inter-class similarity, the availability and quality of hand-labeled data, and designing a feasible approach for modeling significant correlations in features gathered from …

Osm Science - The Academic Study Of The Openstreetmap Project, Data, Contributors, Community, And Applications, A. Yair Grinberger, Marco Minghini, Levente Juhasz, Godwin Yeboah, Peter Mooney

GIS Center

This paper is an Editorial for the Special Issue titled “OpenStreetMap as a multidisciplinary nexus: perspectives, practices and procedures”. The Special Issue is largely based on the talks presented in the 2019 and 2020 editions of the Academic Track at the State of the Map conferences. As such, it represents the most pressing and relevant issues and topics considered by the academic community in relation to OpenStreetMap (OSM)—a global project and community aimed to create and maintain a free and editable database and map of the world. In this Editorial, we survey the papers included in the Special Issue, grouping …

Communicating With Culture: How Humans And Machines Detect Narrative Elements, Wolfgang Victor H. Yarlott

FIU Electronic Theses and Dissertations

To understand how people communicate, we must understand how they leverage shared stories and all the knowledge, information, and associations contained within those stories. I examine three classes of narrative elements that convey a wealth of cultural knowledge: Propp's morphology, motifs, and discourse structure. Propp's morphology communicates how roles and actions drive a narrative forward; motifs fill those roles and actions with specific, remarkable events; discourse groups these into a coherent structure to convey a point.

My thesis has three aims: first, to demonstrate that people can reliably detect and identify all three of these narrative elements; second, to develop …

Detecting The Emotions Of Animate Beings In Narrative, Samira Zad

FIU Electronic Theses and Dissertations

Identifying emotions as expressed in text (a.k.a. text emotion recognition) has received a lot of attention over the past decade. Narratives often involve a great deal of emotional expression, and so emotion recognition on narrative text is of great interest to computational approaches to narrative understanding. The meaning and impact of narratives is strongly bound up with the emotions expressed therein. Emotions may be experienced by characters in a story (which may include the narrator), by a story-external narrator, or by the reader. There has been so far two separate streams of work relevant to this observation: (1) emotion detection, …

Building Capacity For Data-Driven Scholarship, Jamie Rogers

Works of the FIU Libraries

This talk provides an overview of "dLOC as Data: A Thematic Approach to Caribbean Newspapers," an initiative developed to increase access to digitized Caribbean newspaper text for bulk download, facilitating computational analysis. Capacity building for future research in Caribbean Studies being a crucial aspect of this initiative, a thematic toolkit was developed to facilitate use of the project data as well as provide replicable processes. The toolkit includes sample text analysis projects, as well as tutorials and detailed project documentation. While the toolkit focuses on the history of hurricanes and tropical cyclones of the region, the methodologies and tools used …

A Machine Learning Framework For Identifying Molecular Biomarkers From Transcriptomic Cancer Data, Md Abdullah Al Mamun

FIU Electronic Theses and Dissertations

Cancer is a complex molecular process due to abnormal changes in the genome, such as mutation and copy number variation, and epigenetic aberrations such as dysregulations of long non-coding RNA (lncRNA). These abnormal changes are reflected in transcriptome by turning oncogenes on and tumor suppressor genes off, which are considered cancer biomarkers.

However, transcriptomic data is high dimensional, and finding the best subset of genes (features) related to causing cancer is computationally challenging and expensive. Thus, developing a feature selection framework to discover molecular biomarkers for cancer is critical.

Traditional approaches for biomarker discovery calculate the fold change for each …

Bridges And Barriers: An Exploration Of Engagements Of The Research Community With The Openstreetmap Community, A. Yair Grinberger, Marco Minghini, Godwin Yeboah, Levente Juhasz, Peter Mooney

GIS Center

The academic community frequently engages with OpenStreetMap (OSM) as a data source and research subject, acknowledging its complex and contextual nature. However, existing literature rarely considers the position of academic research in relation to the OSM community. In this paper we explore the extent and nature of engagement between the academic research community and the larger communities in OSM. An analysis of OSM-related publications from 2016 to 2019 and seven interviews conducted with members of one research group engaged in OSM-related research are described. The literature analysis seeks to uncover general engagement patterns while the interviews are used to identify …

Deep Learning For Multiclass Classification, Predictive Modeling And Segmentation Of Disease Prone Regions In Alzheimer’S Disease, Maryamossadat Aghili

FIU Electronic Theses and Dissertations

One of the challenges facing accurate diagnosis and prognosis of Alzheimer’s Disease (AD) is identifying the subtle changes that define the early onset of the disease. This dissertation investigates three of the main challenges confronted when such subtle changes are to be identified in the most meaningful way. These are (1) the missing data challenge, (2) longitudinal modeling of disease progression, and (3) the segmentation and volumetric calculation of disease-prone brain areas in medical images. The scarcity of sufficient data compounded by the missing data challenge in many longitudinal samples exacerbates the problem as we seek statistical meaningfulness in multiclass …

An Exploration Of Controlling The Content Learned By Deep Neural Networks, Liqun Yang

FIU Electronic Theses and Dissertations

With the great success of the Deep Neural Network (DNN), how to get a trustworthy model attracts more and more attention. Generally, people intend to provide the raw data to the DNN directly in training. However, the entire training process is in a black box, in which the knowledge learned by the DNN is out of control. There are many risks inside. The most common one is overfitting. With the deepening of research on neural networks, additional and probably greater risks were discovered recently. The related research shows that unknown clues can hide in the training data because of the …

Towards Understanding The Temporal Accuracy Of Openstreetmap: A Quantitative Experiment, Levente Juhasz

GIS Center

No abstract provided.

A Study Of Sparse Representation Of Boolean Functions, Yekun Xu

FIU Electronic Theses and Dissertations

Boolean function is one of the most fundamental computation models in theoretical computer science. The two most common representations of Boolean functions are Fourier transform and real polynomial form. Applying analytic tools under these representations to the study Boolean functions has led to fruitful research in many areas such as complexity theory, learning theory, inapproximability, pseudorandomness, metric embedding, property testing, threshold phenomena, social choice, etc. In this thesis, we focus on \emph{sparse representations} of Boolean function in both Fourier transform and polynomial form, and obtain the following new results. A classical result of Rothschild and van Lint asserts that if …

Interpretability Of Ai In Computer Systems And Public Policy, Farzana Beente Yusuf

FIU Electronic Theses and Dissertations

Advances in Artificial Intelligence (AI) have led to spectacular innovations and sophisticated systems for tasks that were thought to be capable only by humans. Examples include playing chess and Go, face and voice recognition, driving vehicles, and more. In recent years, the impact of AI has moved beyond offering mere predictive models into building interpretable models that appeal to human logic and intuition because they ensure transparency and simplicity and can be used to make meaningful decisions in real-world applications. A second trend in AI is characterized by important advancements in the realm of causal reasoning. Identifying causal relationships is …

Inducing Stereotypical Character Roles From Plot Structure, Labiba Jahan

FIU Electronic Theses and Dissertations

If we are to understand stories, we must understand characters: characters are central to every narrative and drive the action forward. Critically, many stories (especially cultural ones) employ stereotypical character roles in their stories for different purposes, including efficient communication among bundles of default characteristics and associations, ease understanding of those characters' role in the overall narrative, and many more. These roles include ideas such as hero, villain, or victim, as well as culturally-specific roles such as, for example, the donor (in Russian tales) or the trickster (in Native American tales). My thesis aims to learn these roles automatically, inducing …

Facing Truths: Facial Recognition Software In Digital Archives, Rebecca Bakker, Kelley Flannery Rowan

Works of the FIU Libraries

This presentation discusses research conducted on various facial recognition software and was funded by a LYRASIS Catalyst Fund grant. The goal of the research was to determine whether facial recognition software could be adapted to work with older, often faded or discolored historical photos and still accurately identify faces in photographs. Such software capabilities would be highly beneficial for librarians and archivists in creating quality metadata by identifying unknown people in photos. It would also assist archivists in finding the photos patrons and partners are seeking. The research brought to light the many ethical controversies associated with facial recognition technology, …

Reimagining The Archive For Computational Analysis At Scale, Jamie Rogers

Works of the FIU Libraries

This presentation was part of a three-segment panel discussion sponsored by IS&T, the Society for Imaging Science and Technology, titled "OCR and Text Recognition: Workflows, Trends, and New Applications." This segment covers ways in which we have re-conceptualized archive materials as computationally useful data as well as the value of utilizing data at scale to impact research possibilities. We have been able to accomplish this through an ongoing project "dLOC as Data: A Thematic Approach to Caribbean Newspapers," a collaborative initiative between the Digital Library of the Caribbean, University of Florida, and Florida International University.

Technical Interviews: Another Barrier To Broadening Participation In Computing?, Stephanie Jill Lunn

FIU Electronic Theses and Dissertations

What does it take to obtain a computing position in the industry? Although anecdotal reports state that ``hiring is broken,'' empirical evidence is necessary to identify the flaws in the existing system. The goal of this dissertation was to understand what expectations companies have for job seekers in computing, and to explore students' experiences with technical interviews and their pathways to job attainment. In particular, this work considered how hiring practices may impact populations already underrepresented in computing such as women, Black/African American students, and Hispanic/Latinx students. It also sought to understand how minoritized populations leverage their own inherent capital …

Collections As Data At Florida International University, Jamie Rogers

Works of the FIU Libraries

This presentation provides an overview of the concept of collections as data; shares information about our "dLOC as Data" grant initiative, a collaboration between the Digital Library of the Caribbean (dLOC), the Florida International University (FIU) Libraries Digital Collections Center, and the University of Florida Libraries, funded by the Mellon sub-award program, "Collections as Data: Part to Whole" ; as well as provides an opportunity to talk about how we can share more collections as data resources and undertake new and exciting projects at FIU.

Although the concept of collections as data isn't new, it is becoming more mainstream. As …

Development Of A Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector, Harold Martin

FIU Electronic Theses and Dissertations

The central aim of this research is the development and deployment of a novel multilayer machine learning design with unique application for the diagnosis of myocardial infarctions (MIs) from individual heartbeats of single-lead electrocardiograms (EKGs) irrespective of their sampling frequencies over a given range. To the best of our knowledge, this design is the first to attempt inter-patient myocardial infarction detection from individual heartbeats of single-lead (lead II) electrocardiograms that achieves high accuracy and near real-time diagnosis. The processing time of 300 milliseconds to a diagnosis is just at the time range in between extremely fast heartbeats of around 300 …

Evaluation Of Parametric And Nonparametric Statistical Models In Wrong-Way Driving Crash Severity Prediction, Sajidur Rahman Nafis

FIU Electronic Theses and Dissertations

Wrong-way driving (WWD) crashes result in more fatalities per crash, involve more vehicles, and cause extended road closures compared to other types of crashes. Although crashes involving wrong-way drivers are relatively few, they often lead to fatalities and serious injuries. Researchers have been using parametric statistical models to identify factors that affect WWD crash severity. However, these parametric models are generally based on several assumptions, and the results could generate numerous errors and become questionable when these assumptions are violated. On the other hand, nonparametric methods such as data mining or machine learning techniques do not use a predetermined functional …

Correlating Water Quality And Profile Data In The Florida Keys Using Machine Learning Methods, Alejandro M. Torres Castellanos

FIU Electronic Theses and Dissertations

Water quality is a very active subject of research in the water science field, where its importance includes maintaining the environment, managing wastewater, and securing fresh water. However, the increase of human development has led to problems that are affecting the ecosystem. Motivated by these problems, this research aims to find a solution for understanding the coastal water of the Florida Keys. The research used machine learning methods to find a correlation between water quality dataset and profile measurements dataset. To achieve this objective, the research first went through cleaning, rescuing, and structuring a readable dataset of the profile measurements …