Social and Behavioral Sciences Commons^™

Sensitivity Analysis Of An Agent-Based Simulation Model Using Reconstructability Analysis, Andey M. Nunes, Martin Zwick, Wayne Wakeland

Systems Science Faculty Publications and Presentations

Reconstructability analysis, a methodology based on information theory and graph theory, was used to perform a sensitivity analysis of an agent-based model. The NetLogo BehaviorSpace tool was employed to do a full 2^k factorial parameter sweep on Uri Wilensky’s Wealth Distribution NetLogo model, to which a Gini-coefficient convergence condition was added. The analysis identified the most influential predictors (parameters and their interactions) of the Gini coefficient wealth inequality outcome. Implications of this type of analysis for building and testing agent-based simulation models are discussed.

The Machines Aren’T Taking Over (Yet): An Empirical Comparison Of Traditional, Profiling, And Machine Learning Approaches To Criterion-Related Validation, Kristin S. Allen, Mathijs Affourtit, Craig M. Reddock

Personnel Assessment and Decisions

Criterion-related validation (CRV) studies are used to demonstrate the effectiveness of selection procedures. However, traditional CRV studies require significant investment of time and resources, as well as large sample sizes, which often create practical challenges. New techniques, which use machine learning to develop classification models from limited amounts of data, have emerged as a more efficient alternative. This study empirically investigates the effectiveness of traditional CRV with a variety of profiling approaches and machine learning techniques using repeated cross-validation. Results show that the traditional approach generally performs best both in terms of predicting performance and larger group differences between candidates …

Technology Criticism And Data Literacy: The Case For An Augmented Understanding Of Media Literacy, Thomas Knaus

Journal of Media Literacy Education

Reviewing the history of media literacy education might help us to identify how creating media as an approach can contribute to fostering knowledge, understanding technical issues, and to establishing a critical attitude towards technology and data. In a society where digital devices and services are omnipresent and decisions are increasingly based on data, critical analysis must penetrate beyond the “outer shell” of machines – their interfaces – through the technology itself, and the data, and algorithms, which make these devices and services function. Because technology and data constitute the basis of all communication and collaboration, media literate individuals …

An Assessment Of The Hydrological Trends Using Synergistic Approaches Of Remote Sensing And Model Evaluations Over Global Arid And Semi-Arid Regions, Wenzhao Li, Hesham El-Askary, Rejoice Thomas, Surya Prakash Tiwari, Karuppasamy Manikandan, Thomas Piechota, Daniele Struppa

Mathematics, Physics, and Computer Science Faculty Articles and Research

Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010–2019) using an integrative approach of …

Enhanced Traffic Incident Analysis With Advanced Machine Learning Algorithms, Zhenyu Wang

Computational Modeling & Simulation Engineering Theses & Dissertations

Traffic incident analysis is a crucial task in traffic management centers (TMCs) that typically manage many highways with limited staff and resources. An effective automatic incident analysis approach that can report abnormal events timely and accurately will benefit TMCs in optimizing the use of limited incident response and management resources. During the past decades, significant efforts have been made by researchers towards the development of data-driven approaches for incident analysis. Nevertheless, many developed approaches have shown limited success in the field. This is largely attributed to the long detection time (i.e., waiting for overwhelmed upstream detection stations; meanwhile, downstream stations …

Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak

USF Tampa Graduate Theses and Dissertations

The commercial platforms that use recommender systems can collect relevant information to produce useful recommendations to the platform users. However, these sources usually contain missing values, imbalanced and heterogeneous data, and noisy observations. Such characteristics render the process of exploiting the information nontrivial, as one should carefully address them during the data fusion process. In addition to the degenerative characteristics, some entries can be fake, i.e., they can be the outcomes of malicious intents to manipulate the system. These entries should be eliminated before incorporation to any recommendation task. Detecting such malicious attacks quickly and accurately and then mitigating them …

Using Data Analytics To Predict Students Score, Nang Laik Ma, Gim Hong Chua

Research Collection School Of Computing and Information Systems

Education is very important to Singapore, and the government has continued to invest heavily in our education system to become one of the world-class systems today. A strong foundation of Science, Technology, Engineering, and Mathematics (STEM) was what underpinned Singapore's development over the past 50 years. PISA is a triennial international survey that evaluates education systems worldwide by testing the skills and knowledge of 15-year-old students who are nearing the end of compulsory education. In this paper, the authors used the PISA data from 2012 and 2015 and developed machine learning techniques to predictive the students' scores and understand the …

Developing Employment Environments Where Individuals With Asd Thrive: Using Machine Learning To Explore Employer Policies And Practices, Amy Jane Griffiths, Amy E. Hurley Hanson, Cristina M. Giannantonio, Sneha Kohli Mathur, Kayleigh Hyde, Erik Linstead

Education Faculty Articles and Research

An online survey instrument was developed to assess employers’ perspectives on hiring job candidates with Autism Spectrum Disorder (ASD). The investigators used K-means clustering to categorize companies in clusters based on their hiring practices related to individuals with ASD. This methodology allowed the investigators to assess and compare the various factors of businesses that successfully hire employees with ASD versus those that do not. The cluster analysis indicated that company structures, policies and practices, and perceptions, as well as the needs of employers and employees, were important in determining who would successfully hire individuals with ASD. Key areas that require …

Automatic Traffic Monitoring And Management For Pedestrian And Cyclist Safety Using Deep Learning And Artificial Intelligence, Mohammad Pourhomayoun

Mineta Transportation Institute Publications

In this project, we have designed and developed an effective end-to-end system based on advanced Artificial Intelligence (AI), machine learning, and computer vision to automatically monitor, detect, track, and count pedestrians and bicyclists. The main objective of this project is to improve the safety of pedestrians and bicyclists, by applying self-sensed and AI-powered systems to monitor and control the flow of pedestrians/bicyclists. The developed system includes algorithms for detecting the pedestrians and bicyclists, as well as algorithms for tracking and counting the pedestrians. We evaluated the developed system on real videos captured by actual traffic cameras in the city of …

Does The Word "Chien" Bark? Representation Learning In Neural Machine Translation Encoders, Emily Campbell

Dissertations, Theses, and Capstone Projects

This thesis presents experiments with using representation learning to explore how neural networks learn. Neural networks which take text as input create internal representations of the text during their training. Recent work has found that these representations can be used to perform other downstream linguistic tasks, such as part-of-speech (POS) tagging. This demonstrates that the neural networks are learning linguistic information and storing this information in the representations. We focus on the representations created by neural machine translation (NMT) models and whether they can be used in POS tagging. We train 5 NMT models including an auto-encoder. We extract the …

Brave New World: Ethical Considerations And Risks, Rhonda S. Binda

Open Educational Resources

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more …

Brave New World: Privacy, Data Sharing And Evidence Based Policy Making, Rhonda S. Binda

Open Educational Resources

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more …

Brave New World - Emerging Threats In Cybersecurity, Rhonda S. Binda

Open Educational Resources

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities in particular are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, governments are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more …

Brave New World: Artificial Intelligence And Machine Learning, Rhonda S. Binda

Open Educational Resources

The trifecta of globalization, urbanization and digitization have created new opportunities and challenges across our nation, cities, boroughs and urban centers. Cities are in a unique position at the center of commerce and technology becoming hubs for innovation and practical application of emerging technology. In this rapidly changing 24/7 digitized world, city governments worldwide are leveraging innovation and technology to become more effective, efficient, transparent and to be able to better plan for and anticipate the needs of its citizens, businesses and community organizations. This class will provide the framework for how cities and communities can become smarter and more …

Gaining Computational Insight Into Psychological Data: Applications Of Machine Learning With Eating Disorders And Autism Spectrum Disorder, Natalia Rosenfield

Computational and Data Sciences (PhD) Dissertations

Over the past 100 years, assessment tools have been developed that allow us to explore mental and behavioral processes that could not be measured before. However, conventional statistical models used for psychological data are lacking in thoroughness and predictability. This provides a perfect opportunity to use machine learning to study the data in a novel way. In this paper, we present examples of using machine learning techniques with data in three areas: eating disorders, body satisfaction, and Autism Spectrum Disorder (ASD). We explore clustering algorithms as well as virtual reality (VR).

Our first study employs the k-means clustering algorithm to …

Archaeology Or Crime Scene? Teeth Micro And Macro Structure Analysis As Dating Variable, Jessica A. Vincenty

Student Theses

Simple methods to aid in the determination of forensic or archaeologic relevancy of skeletonized remains have been researched since the 1950s. With advances in microscopic imaging techniques and machine learning computer data analysis methods the relevancy of decontextualized, comingled remains has room for improvement. This thesis is a study done to pioneer a new approach to analyzing dental skeletal remains to determine forensic relevancy.

Archaeological dental samples collected from the ancient city of Ur in modern day southern Iraq in addition to modern dental extractions were processed for scanning electron microscopy imaging. Archaeological and modern samples displayed different surface and …

Identifying Smokestacks In Remotely Sensed Imagery Via Deep Learning Algorithms, Kenneth Moss

Masters Theses

Locating smokestacks in remote sensing imagery is a crucial first step to calculating smokestack heights, which allows for the accurate modeling of dioxin pollution spread and the study of resulting health impacts. In the interest of automating this process, this thesis examines deep learning networks and how changes in input datasets and network architecture affect image detection accuracy. This initial image detection serves as the first step in automated object recognition and height calculation. While this is applicable to general land use classification, this study specifically addresses detecting smokestack images. Different dataset scenarios are generated from the massive Functional Map …

Critical Media, Information, And Digital Literacy: Increasing Understanding Of Machine Learning Through An Interdisciplinary Undergraduate Course, Barbara R. Burke, Elena Machkasova

Irish Communication Review

Widespread use of Artificial Intelligence in all areas of today’s society creates a unique problem: algorithms used in decision-making are generally not understandable to those without a background in data science. Thus, those who use out-of-the-box Machine Learning (ML) approaches in their work and those affected by these approaches are often not in a position to analyze their outcomes and applicability.

Our paper describes and evaluates our undergraduate course at the University of Minnesota Morris, which fosters understanding of the main ideas behind ML. With Communication, Media & Rhetoric and Computer Science faculty expertise, students from a variety of majors, …

Southwest Pacific Tropical Cyclone Frequency And Intensity Related To Observed And Modeled Geophysical And Aerosol Variables, Rupsa Bhowmick

LSU Doctoral Dissertations

The dissertation focuses on western region of Southwest Pacific Ocean (SWPO)

basin (135E - 180, and 5S - 35S) tropical cyclone (TC) climatology using observed

and modeled data. The classification-based machine learning approach

identifies the synoptic geophysical and aerosol environment favorable or unfavorable

for TC intensification and intensity change prior to landfall incorporating

observational and satellite data. A multiple poisson regression model with varying

temporal monthly lags was used to build a relationship between the number of

monthly TC days with basin wide average dust aerosol optical depth (AOD), sea

surface temperature (SST), and upper ocean temperature (UOT). This idea …

Literature Review: How U.S. Government Documents Are Addressing The Increasing National Security Implications Of Artificial Intelligence, Bert Chapman

Libraries Faculty and Staff Scholarship and Research

This article emphasizes the increasing importance of artificial intelligence (AI) in military and national security policy making. It seeks to inform interested individuals about the proliferation of publicly accessible U.S. government and military literature on this multifaceted topic. An additional objective of this endeavor is encouraging greater public awareness of and participation in emerging public policy debate on AI's moral and national security implications..

Conference Roundup: Smart Cataloging - Beginning The Move From Batch Processing To Automated Classification, Rachel S. Evans

Articles, Chapters and Online Publications

This article reviewed the Amigos Online Conference titled “Work Smarter, Not Harder: Innovating Technical Services Workflows” keynote session delivered by Dr. Terry Reese on February 13, 2020. Excerpt:

"As the developer of MarcEdit, a popular metadata suite used widely across the library community, Reese’s current work is focused on the ways in which libraries might leverage semantic web techniques in order to transform legacy library metadata into something new. So many sessions related to using new technologies in libraries or academia, although exciting, are not practical enough to put into everyday use by most librarians. Reese’s keynote, titled Smart Cataloging: …

Neurobiological Markers For Remission And Persistence Of Childhood Attention-Deficit/Hyperactivity Disorder, Yuyang Luo

Dissertations

Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorders in children. Symptoms of childhood ADHD persist into adulthood in around 65% of patients, which elevates the risk for a number of adverse outcomes, resulting in substantial individual and societal burden. A neurodevelopmental double dissociation model is proposed based on existing studies in which the early onset of childhood ADHD is suggested to associate with dysfunctional subcortical structures that remain static throughout the lifetime; while diminution of symptoms over development could link to optimal development of prefrontal cortex. Current existing studies only assess basic measures including regional brain activation …

Deploying Machine Learning For A Sustainable Future, Cary Coglianese

All Faculty Scholarship

To meet the environmental challenges of a warming planet and an increasingly complex, high tech economy, government must become smarter about how it makes policies and deploys its limited resources. It specifically needs to build a robust capacity to analyze large volumes of environmental and economic data by using machine-learning algorithms to improve regulatory oversight, monitoring, and decision-making. Three challenges can be expected to drive the need for algorithmic environmental governance: more problems, less funding, and growing public demands. This paper explains why algorithmic governance will prove pivotal in meeting these challenges, but it also presents four likely obstacles that …

Alexa, Ask My Library: How Do I Build A Custom Skill To Extend Reference Services?, Christopher M. Jimenez

Works of the FIU Libraries

The Reference Technology team at Florida International University recently published an Alexa Skill that incorporates the LibAnswers API into the device’s answer bank. We have several Echo Show devices at our public service desks to meet the demands of extended hours while also enhancing public service presence beyond the reference desk.

The Green Library at FIU’s Modesto Maidique Campus now operates on a 24/5 schedule, allowing students to access library facilities at any time during the week. In addition, both the Hubert Library and the Engineering Library Service Center stay open past times when personal reference assistance is available. This …

Bibliometric Study Of Bibliometric Papers About Clustering, Preeti Mulay, Rahul Raghvendra Joshi, Archana Chaudhari

Library Philosophy and Practice (e-journal)

Bibliometric survey or bibliometric review papers generally analyses the work done previously by eminent personalities, authors, countries and various institutions which was published in giant databases like Scopus, Web of Science, Google Scholar, Research Gate and others. Bibliometric papers provide amalgamation of wide range of research papers from journals, conferences, reviews and other papers, which are working papers, papers with results, proposals and few of them are survey papers etc. Bibliometric papers are One-Stop-Solution for the readers and upcoming researchers to get acquainted entirely about the specific topic / domain. Bibliometric papers also help in smartly locating research-gaps for the …

Old Dogs, New Tricks: Authoritarian Regime Persistence Through Learning, Nicholas Ryan Davis

Theses and Dissertations

How does diffusion lead to authoritarian regime persistence? Political decisions, regardless of what the actors involved might believe or espouse, do not happen in isolation. Policy changes, institutional alterations, regime transitions-- these political phenomena are all in some part a product of diffusion processes as much as they are derived from internal determinants. As such, political regimes do not exist in a vacuum, nor do they ignore the outside world. When making decisions about policy and practice, we should expect competent political actors to take a look at the wider external world. This dissertation project presents a theory of regime …

Determinants Of Safety Outcomes In Organizations: Exploring O*Net Data To Predict Occupational Accident Rates, Lavanya Shravan Kumar

Theses and Dissertations

Workplace safety is of utmost importance given the regular occurrence of both fatal and nonfatal occupational injuries all around the world. Although research in this area is hugely prevalent, it is focused mainly on safety climate and lacks an integrated approach when examining predictors of safety outcomes. The development of an occupational risk factor that predicts safety outcomes will aid in understanding the relative importance of different factors that contribute to safety and help organizations target their safety programs and interventions efficiently. The present study is an exploratory analysis utilizing publicly available O*NET data (work activities, work context features, and …

Three Essays On Financial Economics, Jiangyuan Li

Dissertations and Theses Collection (Open Access)

Disagreement measures are known to predict cross-sectional stock returns but fail to predict market returns. This paper proposes a partial least squares disagreement index by aggregating information across individual disagreement measures and shows that this index significantly predicts market returns both in- and out-ofsample. Consistent with the theory in Atmaz and Basak (2018), the disagreement index asymmetrically predicts market returns with greater power in high sentiment periods, is positively associated with investor expectations of market returns, predicts market returns through a cash flow channel, and can explain the positive volume-volatility relationship.

Forecast Combinations In Machine Learning, Yue Qiu, Tian Xie, Jun Yu

Research Collection School Of Economics

This paper introduces novel methods to combine forecasts made by machine learning techniques. Machine learning methods have found many successful applications in predicting the response variable. However, they ignore model uncertainty when the relationship between the response variable and the predictors is nonlinear. To further improve the forecasting performance, we propose a general framework to combine multiple forecasts from machine learning techniques. Simulation studies show that the proposed machine-learning-based forecast combinations work well. In empirical applications to forecast key macroeconomic and financial variables, we find that the proposed methods can produce more accurate forecasts than individual machine learning techniques and …

Meaning In The Noise: Neural Signal Variability In Major Depressive Disorder, Sally M. Pessin

Theses

Clinical research has revealed aberrant activity and connectivity in default mode (DMN), frontoparietal (FPN), and salience (SN) network regions in major depressive disorder (MDD). Recent functional magnetic resonance imaging (fMRI) studies suggest that variability in brain activity, or blood oxygen level-dependent (BOLD) signal variability, may be an important novel predictor of psychopathology. However, to our knowledge, no studies have yet determined the relationship between resting-state BOLD signal variability and MDD nor applied BOLD signal variability features to the classification of MDD history using machine learning (ML). Thus, the current study had three aims: (i) to investigate the differences …