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Title: Belongings.
Creator: McLean, Samantha, Hart, Sharon, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of Visual Arts and Art History
Abstract/Description: Belongings hybridizes photography, sculpture, and printmaking through new laser technology. The exhibited work communicates a lingering sense of homesickness and maps a path through the objects discovered in my father’s wallet shortly after his passing.
Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004870
Subject Headings: McLean, Sammi--Personal narratives., Symbolism in art., Time and art., Fathers and daughters--Personal narratives., Photography, Artistic., Digital media--Social aspects., Discourse analysis, Narrative.
Format: Document (PDF)

Title: An evaluation of machine learning algorithms for tweet sentiment analysis.
Creator: Prusa, Joseph D., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Sentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we...
Show moreSentiment analysis of tweets is an application of mining Twitter, and is growing in popularity as a means of determining public opinion. Machine learning algorithms are used to perform sentiment analysis; however, data quality issues such as high dimensionality, class imbalance or noise may negatively impact classifier performance. Machine learning techniques exist for targeting these problems, but have not been applied to this domain, or have not been studied in detail. In this thesis we discuss research that has been conducted on tweet sentiment classification, its accompanying data concerns, and methods of addressing these concerns. We test the impact of feature selection, data sampling and ensemble techniques in an effort to improve classifier performance. We also evaluate the combination of feature selection and ensemble techniques and examine the effects of high dimensionality when combining multiple types of features. Additionally, we provide strategies and insights for potential avenues of future work.
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Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004460, http://purl.flvc.org/fau/fd/FA00004460
Subject Headings: Social media., Natural language processing (Computer science), Machine learning., Algorithms., Fuzzy expert systems., Artificial intelligence.
Format: Document (PDF)

Title: An evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program.
Creator: Da Rosa, Raquel C., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which...
Show moreThe population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which indicates reasonable fraud detection results. We employ two unsupervised machine learning algorithms, Isolation Forest and Unsupervised Random Forest, which have not been previously used for the detection of fraud and abuse on Medicare data. Additionally, we implement three other machine learning methods previously applied on Medicare data which include: Local Outlier Factor, Autoencoder, and k-Nearest Neighbor. For our dataset, we combine the 2012 to 2015 Medicare provider utilization and payment data and add fraud labels from the List of Excluded Individuals/Entities (LEIE) database. Results show that Local Outlier Factor is the best model to use for Medicare fraud detection.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013042
Subject Headings: Machine learning, Medicare fraud, Algorithms
Format: Document (PDF)

Title: An Evaluation of Deep Learning with Class Imbalanced Big Data.
Creator: Johnson, Justin Matthew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Effective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g. anomaly detection. Modeling such skewed data distributions is often very difficult, and non-standard methods are sometimes required to combat these negative effects. These challenges have been studied thoroughly using traditional machine learning algorithms, but very little empirical work exists in the area of deep learning with...
Show moreEffective classification with imbalanced data is an important area of research, as high class imbalance is naturally inherent in many real-world applications, e.g. anomaly detection. Modeling such skewed data distributions is often very difficult, and non-standard methods are sometimes required to combat these negative effects. These challenges have been studied thoroughly using traditional machine learning algorithms, but very little empirical work exists in the area of deep learning with class imbalanced big data. Following an in-depth survey of deep learning methods for addressing class imbalance, we evaluate various methods for addressing imbalance on the task of detecting Medicare fraud, a big data problem characterized by extreme class imbalance. Case studies herein demonstrate the impact of class imbalance on neural networks, evaluate the efficacy of data-level and algorithm-level methods, and achieve state-of-the-art results on the given Medicare data set. Results indicate that combining under-sampling and over-sampling maximizes both performance and efficiency.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013221
Subject Headings: Deep learning, Big data, Medicare fraud--Prevention
Format: Document (PDF)

Title: An Exploration into Synthetic Data and Generative Aversarial Networks.
Creator: Shorten, Connor M., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: This Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as Generative Adversarial Networks (GANs). A survey on GANs was conducted to understood recent developments and the problems related to training them. Following this survey, four experiments were proposed to test the application of GANs for data augmentation and to contribute to the quality improvement in GAN-generated data....
Show moreThis Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as Generative Adversarial Networks (GANs). A survey on GANs was conducted to understood recent developments and the problems related to training them. Following this survey, four experiments were proposed to test the application of GANs for data augmentation and to contribute to the quality improvement in GAN-generated data. Experimental results demonstrate the effectiveness of GAN-generated data as a pre-training metric. The other experiments discuss important characteristics of GAN models such as the refining of prior information, transferring generative models from large datasets to small data, and automating the design of Deep Neural Networks within the context of the GAN framework. This Thesis will provide readers with a complete introduction to Data Augmentation and Generative Adversarial Networks, as well as insights into the future of these techniques.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013263
Subject Headings: Neural networks (Computer science), Computer vision, Images, Generative adversarial networks, Data sets
Format: Document (PDF)

Title: And yet: studio sulla traduzione di alcuni “appunti” epigrammatici di sandro penna.
Creator: Scalzo, Zachary J., Ruthenberg, Myriam Swennen, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of Languages, Lingustics and Comparative Literature
Abstract/Description: Sandro Penna, an understudied Italian poet whose literary corpus is produced during the end period and eventual fall of Italian fascism, writes Appunti, the second volume of his major poetic corpus, from 1938-49. In it, he explicates a poetic of an unapologetic, open homoeroticism that allows one to examine the obstacles a translator faces in considering how one can remain faithful to the original poems and the identity the poet creates. Keeping in mind theoretical influences informing the...
Show moreSandro Penna, an understudied Italian poet whose literary corpus is produced during the end period and eventual fall of Italian fascism, writes Appunti, the second volume of his major poetic corpus, from 1938-49. In it, he explicates a poetic of an unapologetic, open homoeroticism that allows one to examine the obstacles a translator faces in considering how one can remain faithful to the original poems and the identity the poet creates. Keeping in mind theoretical influences informing the creation and translation of poetry and the political choices inherent therein, my translations of these poems mediate the content and form in the target text to maintain the importance of the context in which the originals are written. This thesis and these translations aim to reexamine the importance of Penna as a poet, address the importance of translation in the establishment of foreign poets, and develop a new perspective in Translation Studies that considers the interdisciplinary applications of Gender and Sexuality Studies.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004158, http://purl.flvc.org/fau/fd/FA00004158
Subject Headings: Homosexuality in literature, Intimacy (Psychology) in literature, Penna, Sandro -- Appunti -- Criticism and interpretation, Poetry, Italian -- 20th century -- Translations into English
Format: Document (PDF)

Title: An empathetic approach to information design.
Creator: McCawley, Frederick J., Landes, Eric, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of Visual Arts and Art History
Abstract/Description: This thesis will explore the vital importance of empathy on the part of graphic designers when creating information graphics. Today’s over-mediated public expects a rich user experience that is emotionally engaging, and multi-sensory by nature. To meet the public’s need, graphic designers must accept the cognitive responsibility to be empathetic to the viewers’ relationship to the information, and not just the surface issues of form, media, and content.
Date Issued: 2015
PURL: http://purl.flvc.org/fau/fd/FA00004451
Subject Headings: Communication -- Graphic methods, Graphic arts -- Social aspects, Graphic design (Typography), Information visualization, Sustainable design, Visual communication
Format: Document (PDF)

Title: An Analysis of Discourse Present in Sex Education Literature from Palm Beach County Middle Schools: Are Kids Really Learning?.
Creator: De Avila, Elizabeth, Durnell-Uwechue, Nannetta Y., Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, School of Communication and Multimedia Studies
Abstract/Description: Issues of sexual assault have become pervasive across all social strata in American society. Citizens need to start having conversations regarding these issues. To combat the issue of sexual assault, children need to be educated regarding the multifaceted aspects of sex through sex education in order to understand consent and resources they have available to them. Utilizing grounded theory methodology, this thesis analyzes sex education literature provided to Palm Beach County Middle School...
Show moreIssues of sexual assault have become pervasive across all social strata in American society. Citizens need to start having conversations regarding these issues. To combat the issue of sexual assault, children need to be educated regarding the multifaceted aspects of sex through sex education in order to understand consent and resources they have available to them. Utilizing grounded theory methodology, this thesis analyzes sex education literature provided to Palm Beach County Middle School students. Using Burke’s theory of terministic screens and Foucauldian theories of power and control; an understanding of the ideological underpinnings of this literature and discourse were acquired. After analysis, suggestions for disclosure and sex education programs are provided.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00004842, http://purl.flvc.org/fau/fd/FA00004842
Subject Headings: Sex instruction for youth--Florida--Palm Beach County., Middle school education--Florida--Palm Beach County., Middle school teaching--Florida--Palm Beach County--Evaluation., Middle school students--Attitudes., Sex differences in education.
Format: Document (PDF)

Title: Analysis of machine learning algorithms on bioinformatics data of varying quality.
Creator: Shanab, Ahmad Abu, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: One of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One...
Show moreOne of the main applications of machine learning in bioinformatics is the construction of classification models which can accurately classify new instances using information gained from previous instances. With the help of machine learning algorithms (such as supervised classification and gene selection) new meaningful knowledge can be extracted from bioinformatics datasets that can help in disease diagnosis and prognosis as well as in prescribing the right treatment for a disease. One particular challenge encountered when analyzing bioinformatics datasets is data noise, which refers to incorrect or missing values in datasets. Noise can be introduced as a result of experimental errors (e.g. faulty microarray chips, insufficient resolution, image corruption, and incorrect laboratory procedures), as well as other errors (errors during data processing, transfer, and/or mining). A special type of data noise called class noise, which occurs when an instance/example is mislabeled. Previous research showed that class noise has a detrimental impact on machine learning algorithms (e.g. worsened classification performance and unstable feature selection). In addition to data noise, gene expression datasets can suffer from the problems of high dimensionality (a very large feature space) and class imbalance (unequal distribution of instances between classes). As a result of these inherent problems, constructing accurate classification models becomes more challenging.
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Date Issued: 2015
PURL: http://purl.flvc.org./fau/fd/FA00004425, http://purl.flvc.org/fau/fd/FA00004425
Subject Headings: Artificial intelligence, Bioinformatics, Machine learning, System design, Theory of computation
Format: Document (PDF)

Title: A Network Telescope Approach for Inferring and Characterizing IoT Exploitations.
Creator: Neshenko, Nataliia, Bou-Harb, Elias, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: While the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements on many aspects and in diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. The aim of this thesis is to generate cyber threat intelligence related to Internet-scale inference and evaluation of malicious activities...
Show moreWhile the seamless interconnection of IoT devices with the physical realm is envisioned to bring a plethora of critical improvements on many aspects and in diverse domains, it will undoubtedly pave the way for attackers that will target and exploit such devices, threatening the integrity of their data and the reliability of critical infrastructure. The aim of this thesis is to generate cyber threat intelligence related to Internet-scale inference and evaluation of malicious activities generated by compromised IoT devices to facilitate prompt detection, mitigation and prevention of IoT exploitation. In this context, we initially provide a unique taxonomy, which sheds the light on IoT vulnerabilities from five di↵erent perspectives. Subsequently, we address the task of inference and characterization of IoT maliciousness by leveraging active and passive measurements. To support large-scale empirical data analytics in the context of IoT, we made available corresponding raw data through an authenticated platform.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013089
Subject Headings: Internet of things., Internet of things--Security measures., Cyber intelligence (Computer security)
Format: Document (PDF)

Title: A REFERENCE ARCHITECTURE FOR NETWORK FUNCTION VIRTUALIZATION.
Creator: Alwakeel, Ahmed M., Fernandez, Eduardo B., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Cloud computing has provided many services to potential consumers, one of these services being the provision of network functions using virtualization. Network Function Virtualization is a new technology that aims to improve the way we consume network services. Legacy networking solutions are different because consumers must buy and install various hardware equipment. In NFV, networks are provided to users as a software as a service (SaaS). Implementing NFV comes with many benefits, including...
Show moreCloud computing has provided many services to potential consumers, one of these services being the provision of network functions using virtualization. Network Function Virtualization is a new technology that aims to improve the way we consume network services. Legacy networking solutions are different because consumers must buy and install various hardware equipment. In NFV, networks are provided to users as a software as a service (SaaS). Implementing NFV comes with many benefits, including faster module development for network functions, more rapid deployment, enhancement of the network on cloud infrastructures, and lowering the overall cost of having a network system. All these benefits can be achieved in NFV by turning physical network functions into Virtual Network Functions (VNFs). However, since this technology is still a new network paradigm, integrating this virtual environment into a legacy environment or even moving all together into NFV reflects on the complexity of adopting the NFV system. Also, a network service could be composed of several components that are provided by different service providers; this also increases the complexity and heterogeneity of the system. We apply abstract architectural modeling to describe and analyze the NFV architecture. We use architectural patterns to build a flexible NFV architecture to build a Reference Architecture (RA) for NFV that describe the system and how it works. RAs are proven to be a powerful solution to abstract complex systems that lacks semantics. Having an RA for NFV helps us understand the system and how it functions. It also helps us to expose the possible vulnerabilities that may lead to threats toward the system. In the future, this RA could be enhanced into SRA by adding misuse and security patterns for it to cover potential threats and vulnerabilities in the system. Our audiences are system designers, system architects, and security professionals who are interested in building a secure NFV system.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013434
Subject Headings: Virtual computer systems, Cloud computing, Computer network architectures, Computer networks
Format: Document (PDF)

Title: COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION.
Creator: Andrews, Whitney Angelica Johanna, Furht, Borko, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description: Gliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre...
Show moreGliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre-trained networks to reduce computational costs. However, with various MRI modalities, MRI machines, and poor image scan quality cause different network structures to have different performance metrics. Each pre-trained network is designed with a different structure that allows robust results given specific problem conditions. This thesis aims to cover the gap in the literature to compare the performance of popular pre-trained networks on a controlled dataset that is different than the network trained domain.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013450
Subject Headings: Gliomas, Neural networks (Computer science), Deep Learning, Convolutional neural networks
Format: Document (PDF)

Title: Bodily knowledge in dance transferred to the creation of sculpture.
Creator: Feliciano, Nazare, McConnell, Brian E., Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Department of Visual Arts and Art History
Abstract/Description: The main focus of this dissertation is a discussion of how an artist uses her dance bodily knowledge to develop in a static art form a more bodily sense of movement. For this purpose this dissertation examines four clay sculptures by contemporary artist Mary Frank. The analysis suggests that the uncharacteristic sense of movement displayed in these works derives from her experiential knowledge of dance. This sense of movement is achieved through the considered assemblage and inextricable...
Show moreThe main focus of this dissertation is a discussion of how an artist uses her dance bodily knowledge to develop in a static art form a more bodily sense of movement. For this purpose this dissertation examines four clay sculptures by contemporary artist Mary Frank. The analysis suggests that the uncharacteristic sense of movement displayed in these works derives from her experiential knowledge of dance. This sense of movement is achieved through the considered assemblage and inextricable relationship between Frank’s dance bodily knowledge (body knowledge a dancer acquires through years of dance practice) and the manipulation of clay, the plastic medium she uses to create these forms. The study reveals that Frank’s ceramic assemblages of organic shapes resembling a figure could be related to somatic awareness of arms, legs, torso, hips, and head that dancers experience while dancing. Similarly, the fluid quality of her ceramic assemblages and their seamless coexistence with the environment can be correlated to the proprioceptic sensibilities (the reception of stimuli produced within the organism by movement or tension) that a dancer’s body senses as it navigates through the air and across the ground managing the pull of gravity. These findings are developed through a discussion of the philosophic theories on bodily knowledge (knowing in and through the body) by Maurice Merleau-Ponty, Michael Polanyi, Edward Casey, Pierre Bourdieu, and Richard Shusterman, as well as the philosophic theories on dance bodily knowledge (my own term) developed by Barbara Mettler, Maxine Sheets-Johnstone, and Jaana Parviainen. In addition, Mary’s sculptures are compared to traditionally built sculptures to illustrate the bodily sensory quality of the sense of movement of her structures. Although the scope of this study is limited to the application of dance bodily knowledge onto sculpture, perceived through the clay sculptures of Mary Frank, this research adds to the debate on the interrelationships between dance education and the arts, the body and institutions of learning, and the body and society. It suggests that dance practice and introspection of one’s body movement affects how one perceives the world around us and therefore how one reacts and expresses oneself on to the world.
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Date Issued: 2014
PURL: http://purl.flvc.org/fau/fd/FA00004106, http://purl.flvc.org/fau/fd/FA00004106
Subject Headings: Aesthetics -- Psychological aspects, Dance -- Philosophy, Human body (Philosophy), Phenomenology, Sculpture -- Philosophy
Format: Document (PDF)

Title: Big Data Analytics and Engineering for Medicare Fraud Detection.
Creator: Herland, Matthew Andrew, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: The United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the...
Show moreThe United States (U.S.) healthcare system produces an enormous volume of data with a vast number of financial transactions generated by physicians administering healthcare services. This makes healthcare fraud difficult to detect, especially when there are considerably less fraudulent transactions than non-fraudulent. Fraud is an extremely important issue for healthcare, as fraudulent activities within the U.S. healthcare system contribute to significant financial losses. In the U.S., the elderly population continues to rise, increasing the need for programs, such as Medicare, to help with associated medical expenses. Unfortunately, due to healthcare fraud, these programs are being adversely affected, draining resources and reducing the quality and accessibility of necessary healthcare services. In response, advanced data analytics have recently been explored to detect possible fraudulent activities. The Centers for Medicare and Medicaid Services (CMS) released several ‘Big Data’ Medicare claims datasets for different parts of their Medicare program to help facilitate this effort. In this dissertation, we employ three CMS Medicare Big Data datasets to evaluate the fraud detection performance available using advanced data analytics techniques, specifically machine learning. We use two distinct approaches, designated as anomaly detection and traditional fraud detection, where each have very distinct data processing and feature engineering. Anomaly detection experiments classify by provider specialty, determining whether outlier physicians within the same specialty signal fraudulent behavior. Traditional fraud detection refers to the experiments directly classifying physicians as fraudulent or non-fraudulent, leveraging machine learning algorithms to discriminate between classes. We present our novel data engineering approaches for both anomaly detection and traditional fraud detection including data processing, fraud mapping, and the creation of a combined dataset consisting of all three Medicare parts. We incorporate the List of Excluded Individuals and Entities database to identify real world fraudulent physicians for model evaluation. Regarding features, the final datasets for anomaly detection contain only claim counts for every procedure a physician submits while traditional fraud detection incorporates aggregated counts and payment information, specialty, and gender. Additionally, we compare cross-validation to the real world application of building a model on a training dataset and evaluating on a separate test dataset for severe class imbalance and rarity.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013215
Subject Headings: Big data, Medicare fraud, Data analytics, Machine learning
Format: Document (PDF)

Title: Care at Work: A Feminist Analysis of the Long-Term Care Industry in the United States.
Creator: Tunick, Rachel, Beoku-Betts, Josephine, Lange, Bernadette, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Center for Women, Gender and Sexuality Studies
Abstract/Description: This research provides a feminist perspective on the lowest paid sector of the United States long-term care industry, Certified Nursing Assistants. This research adds to current feminist scholarship on the modern professional caregiving industry by focusing on the perspective of the workers. As the population of older adults requiring care is expected to increase over the coming decades, the demand for paid caregivers will increase as well. Historically, care work was an expected duty done...
Show moreThis research provides a feminist perspective on the lowest paid sector of the United States long-term care industry, Certified Nursing Assistants. This research adds to current feminist scholarship on the modern professional caregiving industry by focusing on the perspective of the workers. As the population of older adults requiring care is expected to increase over the coming decades, the demand for paid caregivers will increase as well. Historically, care work was an expected duty done freely by the women of the family, but today much of the vital intimate caring labor is relegated to paid caregivers. I examine how alternative social, political and economic frameworks can transform United States society’s attitude towards the increasingly relevant issue of caring labor. I argue that incorporating a feminist perspective will be helpful in developing a sustainable model for caring labor that acknowledges the dignity of both patients and their caregivers.
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Date Issued: 2016
PURL: http://purl.flvc.org/fau/fd/FA00004801, http://purl.flvc.org/fau/fd/FA00004801
Subject Headings: Nurses' aides., Medical personnel-caregiver relationships., Nursing homes--Employees--Attitudes., Feminist theory., Caring--Moral and ethical aspects., Feminism--Political aspects., Long-term care facilities--Administration., Nursing home patients--Care., Older people--Nursing home care.
Format: Document (PDF)

Title: DU FANTASTIQUE FRANÇAIS AU RÉEL MERVEILLEUX HAÏTIEN : L’INCONTOURNABLE VA-ET-VIENT LITTÉRAIRE.
Creator: Noel, Lochard, Esquilín, Mary Ann Gosser, Florida Atlantic University, Department of Languages, Linguistics and Comparative Literature, Dorothy F. Schmidt College of Arts and Letters
Abstract/Description: French literature has undoubtedly exerted a marked influence over Haitian letters. Since the Middle Ages, notable elements of the fantastic, such as loups-garous and talking animals in lais and fables, all the way to the unheimlich narratives of the nineteenth century, are also present in Haitian works with strong overtones of the oral traditions of slave narratives. However, Haitian literature, given its syncretic nature, offers not just an array of talking animals and “magic realist”...
Show moreFrench literature has undoubtedly exerted a marked influence over Haitian letters. Since the Middle Ages, notable elements of the fantastic, such as loups-garous and talking animals in lais and fables, all the way to the unheimlich narratives of the nineteenth century, are also present in Haitian works with strong overtones of the oral traditions of slave narratives. However, Haitian literature, given its syncretic nature, offers not just an array of talking animals and “magic realist” episodes, but a unique “fantastic being,” the zombie. In turn, these figures have made their way not just into the Haitian folkloric tradition, but infused with political undertones, have become pivotal metaphors for contemporary Haitian writers on the island, as well as for those who write in the diaspora, to explore the nation’s oppressive governments. This dissertation traces the origins of such figures and their creative reincarnations today.
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Date Issued: 2020
PURL: http://purl.flvc.org/fau/fd/FA00013598
Subject Headings: Haitian literature, Comparative literature, French literature, Fantastic literature
Format: Document (PDF)

Title: DECODING DEXTER: AN ANALYSIS OF AMERICA’S FAVORITE SERIAL KILLER.
Creator: Burns-Davies, Erin, Caputi, Jane, Florida Atlantic University, Dorothy F. Schmidt College of Arts and Letters, Center for Women, Gender and Sexuality Studies
Abstract/Description: In an intersectional feminist analysis of Dexter in both the novels by Jeff Lindsay as well as the Showtime television series, this dissertation will explore the challenging but compelling nature of the serial killer as a pop culture icon, and address themes of gender and sexuality as well as class, ethnicity and regions as they are portrayed in the series. Dexter Morgan, on the Showtime series and in the novels, both exposes popular culture’s problematic identification with the serial killer...
Show moreIn an intersectional feminist analysis of Dexter in both the novels by Jeff Lindsay as well as the Showtime television series, this dissertation will explore the challenging but compelling nature of the serial killer as a pop culture icon, and address themes of gender and sexuality as well as class, ethnicity and regions as they are portrayed in the series. Dexter Morgan, on the Showtime series and in the novels, both exposes popular culture’s problematic identification with the serial killer and solidifies it by being a socially palatable anti-hero.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013288
Subject Headings: Dexter, Crime in popular culture, Antiheroes, Serial murderers--Drama, Serial murderers--Fiction
Format: Document (PDF)

Title: Design of a Test Framework for the Evaluation of Transfer Learning Algorithms.
Creator: Weiss, Karl Robert, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: A traditional machine learning environment is characterized by the training and testing data being drawn from the same domain, therefore, having similar distribution characteristics. In contrast, a transfer learning environment is characterized by the training data having di erent distribution characteristics from the testing data. Previous research on transfer learning has focused on the development and evaluation of transfer learning algorithms using real-world datasets. Testing with real...
Show moreA traditional machine learning environment is characterized by the training and testing data being drawn from the same domain, therefore, having similar distribution characteristics. In contrast, a transfer learning environment is characterized by the training data having di erent distribution characteristics from the testing data. Previous research on transfer learning has focused on the development and evaluation of transfer learning algorithms using real-world datasets. Testing with real-world datasets exposes an algorithm to a limited number of data distribution di erences and does not exercise an algorithm's full capability and boundary limitations. In this research, we de ne, implement, and deploy a transfer learning test framework to test machine learning algorithms. The transfer learning test framework is designed to create a wide-range of distribution di erences that are typically encountered in a transfer learning environment. By testing with many di erent distribution di erences, an algorithm's strong and weak points can be discovered and evaluated against other algorithms. This research additionally performs case studies that use the transfer learning test framework. The rst case study focuses on measuring the impact of exposing algorithms to the Domain Class Imbalance distortion pro le. The next case study uses the entire transfer learning test framework to evaluate both transfer learning and traditional machine learning algorithms. The nal case study uses the transfer learning test framework in conjunction with real-world datasets to measure the impact of the base traditional learner on the performance of transfer learning algorithms. Two additional experiments are performed that are focused on using unique realworld datasets. The rst experiment uses transfer learning techniques to predict fraudulent Medicare claims. The second experiment uses a heterogeneous transfer learning method to predict phishing webgages. These case studies will be of interest to researchers who develop and improve transfer learning algorithms. This research will also be of bene t to machine learning practitioners in the selection of high-performing transfer learning algorithms.
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Date Issued: 2017
PURL: http://purl.flvc.org/fau/fd/FA00005925
Subject Headings: Dissertations, Academic -- Florida Atlantic University, Machine learning., Algorithms., Machine learning Development.
Format: Document (PDF)

Title: Development of A Portable Impedance Based Flow Cytometer for Diagnosis of Sickle Cell Disease.
Creator: Dieujuste, Darryl, Zhuang, Hanqi, Du, Sarah, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description: Sickle cell disease is an inherited blood cell disorder that affects about 100,000 people in the US and results in high cost of medical care exceeding $1.1 billion annually. Sickle cell patients suffer from unpredictable, painful vaso-occlusive crises. Portable, costeffective approaches for diagnosis and monitoring sickle blood activities are important for a better management of the disease and reducing the medical cost. In this research, a mobile application controlled, impedance-based flow...
Show moreSickle cell disease is an inherited blood cell disorder that affects about 100,000 people in the US and results in high cost of medical care exceeding $1.1 billion annually. Sickle cell patients suffer from unpredictable, painful vaso-occlusive crises. Portable, costeffective approaches for diagnosis and monitoring sickle blood activities are important for a better management of the disease and reducing the medical cost. In this research, a mobile application controlled, impedance-based flow cytometer is developed for the diagnosis of sickle cell disease. Calibration of the portable device is performed using a component of known impedance value. The preliminary test results are then compared to those obtained by a commercial benchtop impedance analyzer for further validation. With the developed portable flow cytometer, experiments are performed on two sickle cell samples and a healthy cell sample. The acquired results are subsequently analyzed with MATLAB scripts to extract single-cell level impedance information as well as statistics of different cell conditions. Significant differences in cell impedance signals are observed between sickle cells and normal cells, as well as between sickle cells under hypoxia and normoxia conditions.
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Date Issued: 2018
PURL: http://purl.flvc.org/fau/fd/FA00013145
Subject Headings: Sickle cell disease, Sickle cell anemia--Diagnosis, Flow cytometry--Diagnostic use, Mobile Applications
Format: Document (PDF)

Title: EL SENO ESCONDIDO: NODRIZAS Y NANAS COMO AGENTES MARAVILLOSOS EN LA NOVELA LATINOAMERICANA DE LA SEGUNDA MITAD DEL SIGLO VEINTE.
Creator: Casanova, Betsaida L., Gosser Esquilín, Mary Ann, Florida Atlantic University, Department of Languages, Linguistics and Comparative Literature, Dorothy F. Schmidt College of Arts and Letters
Abstract/Description: In Latin America, wet nurses and nannies have played a relevant role in the transmission of legends, myths, medicinal knowledge, popular beliefs, and the religious practices of marginalized groups. This historical reality also ties them closely to the vitality of the marvelous real in Latin American culture and history as theorized by Alejo Carpentier. This dissertation focuses on examining the characters of wet nurses and nannies, especially in connection with the expression of the marvelous...
Show moreIn Latin America, wet nurses and nannies have played a relevant role in the transmission of legends, myths, medicinal knowledge, popular beliefs, and the religious practices of marginalized groups. This historical reality also ties them closely to the vitality of the marvelous real in Latin American culture and history as theorized by Alejo Carpentier. This dissertation focuses on examining the characters of wet nurses and nannies, especially in connection with the expression of the marvelous real in Latin American novels published in the second half of the twentieth century. Employing primarily Alex Woloch’s theory of characterization, this dissertation explores the character space and position within the character system of la Vieja in El acoso (1956) by Alejo Carpentier, Peta Ponce in El obsceno pájaro de la noche (1970) by José Donoso, and Petra Avilés in La casa de la laguna (1996) by Rosario Ferré. They serve as marvelous agents introducing elements of the marvelous real in the narrative. These characters are at the center of an extensive network of cultural codes that signify different sources of the marvelous real in Latin American culture. The marvelous network they establish functions as a vindicating mechanism that leads to the penalization of the families that hire their services, who represent a decadent and oppressive social system, whereas the wet nurses or nannies embody the oppressed groups in society. This is a literary strategy to impart, at a symbolic level, the justice that traditionally has been denied, both textually and socially, to these women.
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Date Issued: 2019
PURL: http://purl.flvc.org/fau/fd/FA00013361
Subject Headings: Wet nurses in literature, Carpentier, Alejo, 1904-1980 Acoso, Donoso, José, 1924-1996 Obsceno pájaro de la noche English, Ferré, Rosario Casa de la laguna, Characters and characteristics in literature, Nannies--Fiction
Format: Document (PDF)

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