Healthcare is an integral component in peoples lives, especially for the rising elderly population, and must be affordable. The United States Medicare program is vital in serving the needs of the elderly. The growing number of people enrolled in the Medicare program, along with the enormous volume of money involved, increases the appeal for, and risk of, fraudulent activities. For many real-world applications, including Medicare fraud, the interesting observations tend to be less frequent than the normative observations. This difference between the normal observations and those observations of interest can create highly imbalanced datasets. The problem of class imbalance, to include the classification of rare cases indicating extreme class imbalance, is an important and well-studied area in machine learning. The effects of class imbalance with big data in the real-world Medicare fraud application domain, however, is limited. In particular, the impact of detecting fraud in Medicare claims is critical in lessening the financial and personal impacts of these transgressions. Fortunately, the healthcare domain is one such area where the successful detection of fraud can garner meaningful positive results. The application of machine learning techniques, plus methods to mitigate the adverse effects of class imbalance and rarity, can be used to detect fraud and lessen the impacts for all Medicare beneficiaries. This dissertation presents the application of machine learning approaches to detect Medicare provider claims fraud in the United States. We discuss novel techniques to process three big Medicare datasets and create a new, combined dataset, which includes mapping fraud labels associated with known excluded providers. We investigate the ability of machine learning techniques, unsupervised and supervised, to detect Medicare claims fraud and leverage data sampling methods to lessen the impact of class imbalance and increase fraud detection performance. Additionally, we extend the study of class imbalance to assess the impacts of rare cases in big data for Medicare fraud detection. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Modern applications are increasingly backed by large-scale data centres. Systems software in these data centre environments, however, faces substantial challenges: the lack of uniform resource abstractions makes sharing and resource management inefficient, infrastructure software lacks end-to-end access control mechanisms, and work placement ignores the effects of hardware heterogeneity and workload interference. In this dissertation, I argue that uniform, clean-slate operating system (OS) abstractions designed to support distributed systems can make data centres more efficient and secure. I present a novel distributed operating system for data centres, focusing on two OS components: the abstractions for resource naming, management and protection, and the scheduling of work to compute resources. First, I introduce a reference model for a decentralised, distributed data centre OS, based on pervasive distributed objects and inspired by concepts in classic 1980s distributed OSes. Translucent abstractions free users from having to understand implementation details, but enable introspection for performance optimisation. Fine-grained access control is supported by combining storable, communicable identifier capabilities, and context-dependent, ephemeral handle capabilities. Finally, multi-phase I/O requests implement optimistically concurrent access to objects while supporting diverse application-level consistency policies. Second, I present the DIOS operating system, an implementation of my model as an extension to Linux. The DIOS system call API is centred around distributed objects, globally resolvable names, and translucent references that carry context-sensitive object meta-data. I illustrate how these concepts support distributed applications, and evaluate the performance of DIOS in microbenchmarks and a data-intensive MapReduce application. I find that it offers improved, finegrained isolation of resources, while permitting flexible sharing. Third, I present the Firmament cluster scheduler, which generalises prior work on scheduling via minimum-cost flow optimisation. Firmament can flexibly express many scheduling policies using pluggable cost models; it makes high-quality placement decisions based on fine-grained information about tasks and resources; and it scales the flow-based scheduling approach to very large clusters. In two case studies, I show that Firmament supports policies that reduce colocation interference between tasks and that it successfully exploits flexibility in the workload to improve the energy efficiency of a heterogeneous cluster. Moreover, my evaluation shows that Firmament scales the minimum-cost flow optimisation to clusters of tens of thousands of machines while still making sub-second placement decisions.

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 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. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

The primary goal of this research is to help any organization, which is planning to transform to the big data analytics era, by providing a systematic and comprehensive model that this organization can use to better understand what factors influence big data projects. Also, the organization's current status against those factors. Finally, what enhancements are needed in the organization's current capabilities for optimal management of factors influencing an upcoming big data project. However, big data applications are vast and cover many sectors, and while most of the factors influencing big data projects are common across sectors, there are some factors that are related to the specific circumstances of each sector. Therefore, this research will focus on one sector only, which is the smart city sector, and its generalizability to other sectors is discussed at the end of the research. In this research, literature review and experts feedback were used to identify the most critical factors influencing big data projects, with focus on smart city. Then, the HDM methodology was used to elicit experts' judgment to identify the relative importance of those factors. In addition, experts' feedback was used to identify possible statuses an organization might have regarding each factor. Finally, a case study of four projects related to the City of Portland, Oregon, was conducted to demonstrate the practicality and value of the research model. The research findings indicated that there are complicated internal and external, sometimes competing, factors affecting big data projects. The research identified 18 factors as being among the most important factors affecting smart-city-related big data projects. Those factors are grouped into four perspectives: people, technology, legal, and organization. Furthermore, the case study demonstrated how the model could pinpoint shortcomings in a city's capabilities before the project start, and how to address those shortcomings to increase chances of a successful big data project.

The primary objective of this study was to explore the potential of an airborne infrared scanner for the census of mule deer (Odocoileus hemionus) and elk (Cervus canadensis) in the Intermountain West. Flight altitude was varied in hopes of achieving species separation, and ground studies were conducted, using a hand-held radiometer and captive deer, to find the optimum time of morning to census. The problems and potentialities of infrared imagery scanning systems for censusing big game are discussed and compared to visual aerial census methods.

<p>Studiens syfte var att se vilka personlighetsegenskaper som anses vara nödvändiga inom polisyrket samt om dessa sammanfaller med Big Five dimensionerna.</p><p>     Tidigare studier har utförts och påvisat samband mellan polislämplighet och personlighet. Det har påvisats en negativ samband mellan neuroticism och prestation inom polisyrket samt ett positivt samband mellan extraversion och prestation inom polisyrket.</p><p>     För att belysa arbets- och personkrav för poliser intervjuades sex personer av både manligt och kvinnligt kön. Urvalet bestod av poliser från Södra Sverige. Intervjupersonerna fick besvara frågor utifrån en utformad intervjuguide som bestod av tre olika delar: vilka krav poliser ställs inför i sitt arbete, vilka egenskaper poliser har samt om dessa två delar sammanfaller med Big Five dimensionerna.</p><p>     Resultatet visade på att intervjupersonerna tog upp åtta olika nödvändiga egenskaper inom polisyrket: tolerans, ärlighet, analysförmåga, kommunikation, samvetsgrannhet, neuroticism, trevlighet, öppenhet och extraversion.</p><p>     I diskussionen framgick det att tidigare studier och den utförda studiens resultat hade mycket gemensamt. De egenskaper som ansågs vara nödvändiga inom polisyrket enligt de sex intervjupersonerna hade relativt god överensstämmelse med tidigare forskning inom området samt polisens aktuella kravprofiler som används vid rekrytering.</p> / <p>The purpose of this study was to find possible connections between the police job and necessary personality traits and if the traits that are found co-occur with the Big Five traits.</p><p>     Other studies have shown a negative correlation between neuroticism and police job performance and a positive correlation between extroversion and police job performance.</p><p>     In purpose to illustrate police job- and personality needs the study had both male and female subjects who was interviewed. There were six police officers from the south of Sweden. The subjects got to answer questions from an already formed interview guide which contained three parts: general demands on a police officers in their job, necessary police personality traits and if these two parts co-occor with the Big Five traits.</p><p>     The present results showed that there are eight different characteristics needed as a police man: tolerance, honesty, ability to analyze, communication, conscientiousness, neuroticism, agreeableness, openness and extroversion.</p><p>     The discussion showed that the earlier studies and the present study had a lot in common. The qualities considered necessary within the police force according to the six subjects agreed with earlier research in this field as well as the police current demand profile used when recruiting.<em></em></p>

<p>The study tries to increase the understanding of the phenomenon known as the Big bath, on the question whether the market accepts Big bath accounting principle or not. Big bath is an accounting theory meaning that a company is likely to increase its impairment in a specific year. This could be as a reaction on a change in the leadership, a depreciation of the result or maybe an external decrease in demand. Through a quantitative survey of the market it is examining whether the companies themselves are inclined to use the procedure and if the stock market accepts it. The study concludes that Big bath similar procedures are a fairly common accounting practice and that the stock market does not seem to mind.</p><p> </p>

Den svenska välfärden finner stora problem p g a den höga andelen arbetsoförmögna och forskningen inom området är ansenlig. Fokus i dem traditionella, kvantitativa studierna ligger oftast på faktorer i arbetet som orsakar ohälsa utan att ta hänsyn till personlighetsrelaterade faktorer. I kontrast till det syftar föreliggande studie att undersöka personlighetens betydelse för individens copingstrategi samt upplevelse av psykosociala hälsoriskfaktorer på arbetsplatsen. Kvalitativa intervjuer genomfördes med 23 individer som samtliga arbetar inom tjänstemannasektorn, och både induktiv och deduktiv analys användes. Personlighet analyserades efter Big Five och förhållningssätten enligt problem- respektive emotionsfokuserad coping. Personlighet visade sig ha betydelse för copingstrategi, även om mönstret visar en aning oenighet mellan personlighetsdimensionerna, vilket skulle kunna bero på otillräcklig data. Betydelsen av personligheten för upplevda hälsoriskfaktorer var däremot svårare att se, dock verifierar studien tidigare kvantitativa resultat inom arbetsmiljöforskning och ger krav, kontroll och socialt stöd en djupare och kvalitativ innebörd.

The study tries to increase the understanding of the phenomenon known as the Big bath, on the question whether the market accepts Big bath accounting principle or not. Big bath is an accounting theory meaning that a company is likely to increase its impairment in a specific year. This could be as a reaction on a change in the leadership, a depreciation of the result or maybe an external decrease in demand. Through a quantitative survey of the market it is examining whether the companies themselves are inclined to use the procedure and if the stock market accepts it. The study concludes that Big bath similar procedures are a fairly common accounting practice and that the stock market does not seem to mind.

I denna utredning har vi undersökt hur beroendeproblematiken påverkat regler och professionalism inom revisorsyrket på de större revisionsbyråerna. Vi har genom teori och intervjuer sökt klargöra yrkesattribut, samband, risker och förebyggande åtgärder mot den enskilde revisorn, samt dennes revisionsbyrås beroende gentemot klient. Vi har från respondenterna stött på överensstämmande uppgifter gällande hot såsom ekonomiskt beroende, alltför informella kundrelationer, tilltagande konkurrens och tung arbetsbörda, vilken riskerar att gå ut över revisionsarbetets oberoende. De förebyggande åtgärderna som revisionsbyråerna använder sig av för att stävja beroendehot är lagarbete, utbildning, avgränsade arbetsuppgifter, kontrollfunktioner. Vi har kommit fram till att den analytiska förmågan hos revisorn är viktig i sammanhanget och kan komma att få utökad betydelse i förlängningen. Vi har sedan ställt frågan hur de stora revisionsbyråernas utökade konsultverksamhet har påverkat branschens fokus på lönsamhet. Vi ser en fortsatt expansion av konsulttjänsternas lönsamhet som en andel av branschens aggregerade intäkter. Teoretiker, yrkesmän och lagstiftare arbetar konsekvent med att utforma ett praktiskt regelverk, vilket bland annat omfattar den oberoendeproblematik som vi har behandlat. Ett stort problem är att behålla jämvikt mellan lönsamhet och oberoende i en bransch som är så pass föränderlig.