Faceted execution is a technique for dynamic information flow control that allows programmers to program in a policy-agnostic
manner. Racets uses macros to extend Racket with support for faceted execution. We present ...
We present two versions of "shatter functions" and show that they are upper-bounded by the same polynomial, known as the
Sauer-Shelah dichotomy. Then, we analyze a third setting due to Chase and Freitag [3], the ...
Reversible computing is the paradigm in which every computation is physically and easily reversible in an unambiguous way. For a
reversible machine, this would apply to programs, gates, memory, and everything in ...
Reinforcement Learning is concerned with developing machine learning approaches to answer the question: "What should I do?"
Transfer Learning attempts to use previously trained Reinforcement Learning models to ...
Authorship attribution is the process of identifying the author of a given work. This thesis surveys the history and foundations of
authorship attribution, and then analyzes multiple machine learning methods that ...
This thesis explores the many methods of Grapheme to Phoneme Conversion (G2P) including dictionary look-up, rule-based
approaches, and probabilistic approaches such as Joint Sequence Models (JSM), Recurrent Neural ...
This thesis reviews several challenges in the use and implementation of faceted execution, a programming-language mechanism for
enforcing privacy policies. I present a proof-of-concept of a module-rewriting technique ...
For my thesis, I analyzed the COMPAS recidivism prediction tool made by Equivant which aims to see how likely a defendant charged
with a crime will re-offending given a score from 1-10 where 1 indicating lowest ...
Reasoning about information flow is essential when creating a secure system, and faceted execution is one way to ensure that data is
only being accessed and modified by processes that have permission to do so. ...
Recommendation systems exist in almost every aspect of online business. Whether the system is recommending books, movies,
clothes, etc., an algorithm is running behind the scenes. These algorithms are hard to acquire ...
Machine learning has become a popular tool for decision making. As these algorithms continue to make more of our decisions, the
question arises of whether they are sound and just. Seeking the answer to this question ...
An exploration of previous work on planarity with a relatively simple and complete proof of Kuratowski's theorem and an in depth explanation of the foundations of the Left-Right Planarity algorithm.
Improving the performance of dense numerical programs is an area that has been studied for decades. There are numerous approaches that people have applied on those problems, including upgrading the algorithm, running the ...
Fairness, as it applies to algorithms,
implies that the decisions made by an algorithm are being made such that there is no
discrimination against individuals or groups in labeled data sets. In this paper, ...
The PageRank of a vector is a measure of centrality for nodes in a network. Three ways to calculate the PageRank are using power iteration, Monte Carlo methods, and iterative graph methods using Spark. The graph methods ...
Androids phones were first introduced late 2008 and has become increasingly popular today. While android phones became increasingly popular so did the Android applications market; not only are more and more people downloading ...
The parallel computing community has produced various cluster computing frameworks to process the immense amounts of data we generate in the modern age. Two in particular, Hadoop MapReduce and Spark, rose above the rest ...
This paper examines the ideas presented in
Van Horn and Might’s 2010 paper, Abstracting Abstract Machines as a recent development of
static analysis of programs. The paper consolidates background information ...
No matter how efficient or satisfactory an
algorithm or program may be, we as computer scientists are always looking to improve our
programs by means of speeding them up or making them more accurate. In ...
Currently, many societally impactful
decisions are made by machine learning algorithms. As these algorithms are given more power,
we want to ensure that they are not making discriminatory or biased decisions. ...