Here are some papers I have written in the past few years for my doctoral program. I can’t guarantee that they are perfect—I kind of burn at all hours of the day and night. Sometimes being in a doctoral program makes me wonder if my brain is turning to mush. However, I like doing what I do. These are some papers I really liked writing.
I wrote this paper for a statistics midterm. I had to make a paper on inferential statistics. Inferential statistics is when the researcher has to discover a pattern or property from samples of a population. Cryptography and discerning cyphertext was the first thing that popped into my brain. When you want to encrypt a file on your computer, an algorithm is used. The algorithm or cipher encodes the plain text, which becomes cyphertext. The cyphertext needs to be decrypted to view the plain text again. Here is the Wikipedia page about it.
Cyphertext existed long before modern computers. It was an integral part of World War II. The Germans had complex cipher machines called Enigma Machines. In the United Kingdom, Alan Turing, a computer science pioneer and brilliant mathematician, used inferential statistics and graph theory to crack the codes. This paper breaks down how Turing worked to crack the codes. Math is truly beautiful in its complexities. I wish I had the time to go back and get a four year degree in math sometimes just because it’s pretty and I like a good puzzle.
Read this statistics paperWhen it comes to security, specifically cybersecurity experts and scholars alike often make a variation of the statement that the people within the organization can be both the biggest asset and the biggest liability. Such a statement is a reflection of the challenges a cybersecurity expert faces at work. They have to convey to so many individuals the concept of risk and what constitutes risky behaviors. Successful governance means that they have effectively conveyed these concepts. This work examines risk governance and management and proposes original research asking cybersecurity experts about their practices.
Read this governance and management paperThis work encourages healthcare organizations to make a goal to improve LGBTQ+ health in their community. LGBTQ+ individuals have experienced and continue to experience significant barriers to healthcare. These barriers contribute to serious health discrepancies. Training stakeholders like doctors and nurses about LGBTQ+ inclusive and competent is important, but it is not enough. This work presents a broad strategy to improve LGBTQ+ health. While training and communication are a vital part of the strategy, IT policy must also be encompassed. Many LGBTQ+ stakeholders hold concerns about confidentiality, and creating secure IT policies could address those concerns. IT policies also sets rules for analytics which could potentially change and improve practice. The strategy involves assessing security, research, and making alterations to the IT policies important to LGBTQ+ health or LGBTQ+ patient concerns. The strategy also encourages making rules for data. Analyzing LGBTQ+ health records could lead to improving preventative care. Ultimately training, collecting analytics, and building a security-mined culture through IT policies are ways to minimize healthcare barriers and improve LGBTQ+ health outcomes.
Read this IT Strategy paperThis work aims to represent LGBTQ+ identities in Artificial Intelligence (AI). The work presents a document to train or code a BiBot, a bisexual chatbot, with resources for Bisexuals living in the United States. Bisexuals are the largest non-heterosexual orientation. Despite being the orientation with the largest share of people, bisexuals are either overlooked or lumped together with gay and lesbian populations. While there is some overlap with the gay and lesbian experience, bisexuals have their unique minority stressors. This work utilized both qualitative and quantitative research to pinpoint those issues to make the training or coding document. Many bisexuals feel like they are facing the unique minority stressors on their own, which is problematic and isolating for many. There are two goals of this AI. The first is to promote bivisibility, which is being bi and out, open, and proud. The second is to fight bi-erasure, which is when bisexuality is when people treat bisexuality like it is not a real and legitimate identity or deny that bisexuality even exists. The result of this research and the creations of BiBot is a way for bisexuals not to feel alone, invisible, and know that they have a valid identity. I didn’t put the training document with this paper because I coded it here.
Read this AI paper