Fund for the Arts is one of the oldest arts fund in the country. Since its formation in 1949, the organization has raised over 200 million for the community, which includes Kentucky and Southern Indiana. This Master’s project will focus on one of the organization’s programs entitled 5x5. The goal of 5x5 is to expose elementary school students to five art experiences before they finish the fifth grade. Several years’ worth of data has been compiled, including school names, performance names, performance type, number of students served, and total cost, among others. Using a combination of these parameters, maps will be auto-generated using CSV templates. The auto-generated maps will show a variety of data, including the amount of art funding per zip code, per program type, per grade, per art group, per school, and per student. The maps will serve as visual evidence of the program’s progress and will be shared with Fund for the Arts Board of Directors and CEO, internal staff, as well as other community stakeholders such as community liaisons, participating schools, current and potential donors and the Louisville Metro Council. Fund for the Arts is a nonprofit that does not have access to ESRI products. This Master’s project combines cartography and scripting to create a functioning deliverable using open-source GIS software that enables the organization to auto-generate maps at will and forego the need to request maps from the local university once a year.

State Climate Offices (SCO’s) in the United States are critical conduits for improving weather and climate data in local communities. Two states do not have a state-recognized SCO: Tennessee and Massachusetts. Efforts are underway at East Tennessee State University to develop the Tennessee Climate Office (TCO). Currently, climate services and products are severely lacking across Tennessee. This thesis provides an improved methodology for an existing TCO product and outlines the development of a new product using Python scripting. Daily storm reports within the monthly climate report are automated and a Weather Forecasts Hazard Index (WFHI) web application is developed. Both products utilize data from the National Oceanic and Atmospheric Administration (NOAA), with the automated daily storm reports providing substantial time savings and the WFHI providing a high resolution web application for emergency managers and others to interpret potentially hazardous forecasts for extreme temperatures, high winds, snowfall/ice accumulation, and tornado/hail events.

The aim of this thesis is to study problems of deep convolutional neural networks and the connected classification of images and to experiment with the architecture of particular network with the aim to get the most accurate results on the selected dataset. The thesis is divided into two parts, the first part theoretically outlines the properties and structure of neural networks and briefly introduces selected networks. The second part deals with experiments with this network, such as the impact of data augmentation, batch size and the impact of dropout layers on the accuracy of the network. Subsequently, all results are compared and discussed with the best result achieved an accuracy of 86, 44% on test data.

The thesis deals with possibilities for more precise determination of geographical position of given IP address by combination of estimates of data obtained from geolocation databases. The first part introduces the issue on theoretical level. Possibilities of network device identification, its geographical location and principles of geolocation databases are described here. After analyzing the theoretical part of this issue, the current state of application for geolocation databases is described. The next section introduces a new application which uses original software ip2geotools as a library. Using the three implemented methods for combining results (based on average, median, and cluster analysis by K-Means algorithm), the new program allows to estimate the physical location of IP address. The app is complemented with console interface and geographic data visualization on the map. In the last part, the accuracy of computational methods is validated and a detailed statistical analysis of data obtained by performing a calculation over a set of IP addresses with known geographical location of the RIPE Atlas service is performed.

Stress detection based on non-EEG physiological data can be useful for monitoring drivers, pilots, and also for monitoring of people in ordinary situation, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature recorded for 3 type of stress alternated with relax state. Two final models were created in this thesis. First model for Binary classification stress/relax, second for classification of 4 different type of psychical state. Best results were reached using model created by decision tree algorithm with 8 features for binary classification and with 8 features for classification of 4 psychical state. Accuracy of final models is aproximately 95 % for binary model and 99 % for classification of 4 psychical state. All algorithms were implemented in Python.

The master's thesis is focused on the design and implementation of a tool for generating configuration named Ansible. The result of using this tool is generated configuration, which contains random values chosen according to specified parameters and it was deployed on a virtual testing infrastructure. The theoretical part describes approaches of network automation in the process of deploying and configuration of network devices called Infrastructure as code. It also describes programme Ansible, which will be using the output of the implemented tool. The practical part of the thesis is focused on designing the functionality and internal structure of the tool, implementation of the tool and testing implemented tool as well as generated configuration.

This master's thesis deals with eye movement tracking using the Raspberry Pi platform. The theoretical part describes eye anatomy, eye detection and eyetracking. A system in Python programming language was designed in the practical part. This algorithm is able to perform the eye tracking function using the Raspberry Pi platform and the RPi Camera module. The OpenCV library is used for loading and preprocessing images from the camera. A method that detects and evaluates the direction of view after a calibration is available. The accuracy of the program is tested on three vector methods and two target methods for four screen resolutions.

This thesis deals with the removal of noise in speech recordings that have been recorded in an MRI environment. For this purpose, the Nvidia RTX Voice technology, the VST plug-in module Noisereduce and a self-designed method of subtractive de-noising of recordings are used. A program with a simple graphical interface in Python is implemented within the work to retrieve the recordings and then de-noise them using the proposed methods. The work includes measurements in a magnetic resonance environment with two microphones. The quality of the processed recordings is tested within the program using the STOI (Short-Time Objective Intelligibility Measure) method as well as the subjective analysis method within listening tests.

Programming languages like Python, JavaScript, and Ruby are becoming increasingly popular due to their dynamic capabilities. These languages are often much easier to learn than other, statically type checked, languages such as C++ or Rust. Unfortunately, these dynamic languages come at the cost of losing compile-time optimizations. Python is arguably the most popular language for data scientists and researchers in the artificial intelligence and machine learning communities. As this research becomes increasingly popular, and the problems these researchers face become increasingly computationally expensive, questions are being raised about the performance of languages like Python. Language features found in Python, more specifically dynamic typing and run-time modification of object attributes, preclude common static analysis optimizations that often yield improved performance. This thesis attempts to quantify the cost of dynamic features in Python. Namely, the run-time modification of objects and scope as well as the dynamic type system. We introduce Cannoli, a Python 3.6.5 compiler that enforces restrictions on the language to enable opportunities for optimization. The Python code is compiled into an intermediate representation, Rust, which is further compiled and optimized by the Rust pipeline. We show that the analyzed features cause a significant reduction in performance and we quantify the cost of these features for language designers to consider.

Testing code is important, but writing test cases can be time consuming, particularly for beginning programmers who are already struggling to write an implementation. We present TestBuilder, a system for test case generation which uses an SMT solver to generate inputs to reach specified lines in a function, and asks the user what the expected outputs would be for those inputs. The resulting test cases check the correctness of the output, rather than merely ensuring the code does not crash. Further, by querying the user for expectations, TestBuilder encourages the programmer to think about what their code ought to do, rather than assuming that whatever it does is correct. We demonstrate, using mutation testing of student projects, that tests generated by TestBuilder perform better than merely compiling the code using Python’s built-in compile function, although they underperform the tests students write when required to achieve 100% test coverage.