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With a prison population that has grown to 1.4 million, an imprisonment rate of 419 per 100,000 U.S. residents, and a recidivism rate of 52.2% for males and 36.4% for females, the United States is facing a crisis. Currently, no sufficient measures have been taken by the United States to reduce recidivism. Attempts have been made, but they ultimately failed. Recently, however, there has been an increase in experimentation with the concept of teaching inmates basic computer skills to reduce recidivism. As labor becomes increasingly digitized, it becomes more difficult for inmates who spent a certain period away from technology to adapt and find employment. At the bare minimum, anybody entering the workforce must know how to use a computer and other technological appliances, even in the lowest-paid positions. By incorporating basic computer skills and coding educational programs within prisons, this issue can be addressed, since inmates would be better equipped to take on a more technologically advanced labor market.<br/>Additionally, thoroughly preparing inmates for employment is a necessity because it has been proven to reduce recidivism. Prisons typically have some work programs; however, these programs are typically outdated and prepare inmates for fields that may represent a difficult employment market moving forward. On the other hand, preparing inmates for tech-related fields of work is proving to be successful in the early stages of experimentation. A reason for this success is the growing demand. According to the U.S. Bureau of Labor Statistics, employment in computer and information technology occupations is projected to grow 11 percent between 2019 and 2029. This is noteworthy considering the national average for growth of all other jobs is only 4 percent. It also warrants the exploration of educating coders because software developers, in particular, have an expected growth rate of 22 percent between 2019 and 2029. <br/>Despite the security risks of giving inmates access to computers, the implementation of basic computer skills and coding in prisons should be explored further. Programs that give inmates access to a computing education already exist. The only issue with these programs is their scarcity. However, this is to no fault of their own, considering the complex nature and costs of running such a program. Accordingly, this leaves the opportunity for public universities to get involved. Public universities serve as perfect hosts because they are fully capable of leveraging the resources already available to them. Arizona State University, in particular, is a more than ideal candidate to spearhead such a program and serve as a model for other public universities to follow. Arizona State University (ASU) is already educating inmates in local Arizona prisons on subjects such as math and English through their PEP (Prison Education Programming) program.<br/>This thesis will focus on Arizona specifically and why this would benefit the state. It will also explain why Arizona State University is the perfect candidate to spearhead this kind of program. Additionally, it will also discuss why recidivism is detrimental and the reasons why formerly incarcerated individuals re-offend. Furthermore, it will also explore the current measures being taken in Arizona and their limitations. Finally, it will provide evidence for why programs like these tend to succeed and serve as a proposal to Arizona State University to create its own program using the provided framework in this thesis.
I have challenged myself to learn Python. I did this because I wanted to improve myself and my mindset around coding. My view on coding has changed immensely. I was intimidated by the social stigmas around coding, but I have become more comfortable with it. There were times when I thought that I would never understand something, but it became familiar. Through constant exposure, such as completing modules in DataCamp and Kaggle, I better understood the basics and uses of different models. The concepts I had learned before became clearer by completing a project I was genuinely interested in. I could search for a solution or ask my thesis director if I had an error. I enjoyed working with my thesis professor and failing many times. I have learned that I do not have to be a master within the year but must remain consistent with my practice. I will continue to practice and learn more about coding now with more confidence.
Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.
TradeUp is primarily developed to cater to K-12 institutions. This means that a school would purchase a future commercial version of TradeUp. Once this is done a local database would be created in the school’s network and hosted on a server. This would allow for students to access the application by downloading it from the school’s website and would create a local network for the program to exclusively function in. This would allow for students in a school to trade textbooks amongst each other.
TradeUp is currently not available for purchase or for official use. The application is fully functional, and a version of the program can be downloaded in its totality from GitHub through the following link:
https://github.com/mgutie36/TradeUp
It is important to note that for the application to function on your laptop you must be utilizing a Windows machine. Furthermore, you must also utilize the create SQL statements found in “Create.txt” file located in the Bin/Debug folder of the solution in order to create a local database on your machine using Microsoft SQL Server Management Studio. Once that is completed you must replace the connection string in the solution with the connection string that was just created on your machine.
