<p> Digital currency comes in many forms however Bitcoin stands out as the most popular. Bitcoin, released to the public in 2009, remains in the infancy stage of the technology lifecycle. Bitcoin has no regulatory body, central bank or government backing creating doubt as to the digital currency&rsquo;s legitimacy. Despite Bitcoin&rsquo;s lack of &ldquo;official&rdquo; recognition, the digital currency&rsquo;s popularity continues to grow as the number of merchants and vendors accepting the currency expands globally. </p><p> Focusing solely on the U.S. economy and monetary system, the lack of regulation and government recognition leaves legal disputes involving users of Bitcoin in the hands of a U.S. judicial system lacking previous case law as guidance. This research paper provides an in-depth analysis of the technical, economic and regulatory challenges facing the U.S. Federal Court system involving Bitcoin. A qualitative content analysis was employed in the exploratory review of 50 federal judicial cases involving Bitcoin. Key findings include discrepancies between the U.S. Judicial System and the U.S. Internal Revenue System on what and how to categorize Bitcoin, the value of bitcoin mining equipment, and the types of federal cases coming before the U.S. Judicial Courts involving Bitcoin.</p><p>

<p> 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 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.</p><p>

Thesis (MSc (Mathematical Sciences. Computer Science))--University of Stellenbosch, 2008. / Consider a path-oriented telecommunications network where calls arrive to each route in a Poisson process. Each call brings on average a fixed number of packets that are offered to route. The packet inter-arrival times and the packet lengths are exponentially distributed. Each route can queue a finite number of packets while one packet is being transmitted. Each accepted packet/call generates an amount of revenue for the route manager. At specified time instants a route manager can acquire additional capacity (“interface capacity”) in order to carry more calls and/or the manager can acquire additional buffer space in order to carry more packets, in which cases the manager earns more revenue; alternatively a route manager can earn additional revenue by selling surplus interface capacity and/or by selling surplus buffer space to other route managers that (possibly temporarily) value it more highly. We present a method for efficiently computing the buying and the selling prices of buffer space. Moreover, we propose a bandwidth reallocation scheme capable of improving the network overall rate of earning revenue at both the call level and the packet level. Our reallocation scheme combines the Erlang price [4] and our proposed buffer space price (M/M/1/K prices) to reallocate interface capacity and buffer space among routes. The proposed scheme uses local rules and decides whether or not to adjust the interface capacity and/or the buffer space. Simulation results show that the reallocation scheme achieves good performance when applied to a fictitious network of 30-nodes and 46-links based on the geography of Europe.