Due to climate change, man-made conflicts, and rising inflation, a growing number of people around the world are struggling to have consistent access to safe and nutritious food. This phenomenon is known as food insecurity (FI). Therefore, we take in this thesis the first steps towards developing a monitoring process for assessing FI using Human-Centered AI (HCAI). We developed this process for, and in collaboration with, the Integrated Food Security Phase Classification (IPC). The IPC is an organization that helps countries classify levels of food insecurity in their regions to inform humanitarian decision-making. During our research process, we found that any form of HCAI for the IPC would need to be informed by input from their domain experts, and we concluded that we could not start implementing HCAI until we found a way to formalize their input in a way that was robust and suited their technical capabilities. To this end, we ran an experiment with 18 IPC experts in Malawi to see whether they could quantify their assumptions by setting thresholds for food security drivers. The results are encouraging but show that there is still much to be done to bridge the gap between domain knowledge and technical expertise. We also show in this thesis that there is a lack of real-life case studies on HCAI development and share therefore our lessons learned from our real-world HCAI case study on FI monitoring. In this way, we hope to promote the development of a practice-informed methodology for HCAI.

Ripple is a relatively new payments network that aims to improve the financial system by unifying its underlying infrastructure. Given its critical function, its system must be reliable and free of bugs. Therefore it should be tested extensively. One of the test methods that has not been used on it yet is log inference, a method that has a good potential for modelling complex communication protocols. Therefore, we have developed an empirical model of the Ripple Consensus Protocol by learning a Deterministic Finite Automaton (DFA) from the log files of two servers in the Ripple network. We have also developed a theoretical DFA of the Ripple Consensus Protocol and compared this to the empirical model to verify that the two systems function comparably. There has been found one notable difference between the two models, but whether this difference has a critical impact remains to be discussed.

This report documents the DaisyRiot global illumination renderer. The renderer applies the radiosity method and uses spectral rendering in order to simulate fluorescent materials: materials can reflect energy of a given frequency at another frequency. Rendering is accelerated by NVIDIA OptiX and CUDA parallelization. In this document you will find details of the design of the renderer, its architecture, a discussion of its results and performance and a reflection on its future. All source code is released online: github.com/asylunatic/DaisyRiot.