Mohamed Farag

Ahmed Emam, Mohamed Farag, Ribana Roscher

Protected natural areas are regions that have been minimally affected by human activities such as urbanization, agriculture, and other human interventions. To better understand and map the naturalness of these areas, machine learning models can be used to analyze satellite imagery. Specifically, explainable machine learning methods show promise in uncovering patterns that contribute to the concept of naturalness within these protected environments. Additionally, addressing the uncertainty inherent in machine learning models is crucial for a comprehensive understanding of this concept. However, existing approaches have limitations. They either fail to provide explanations that are both valid and objective or struggle to offer a quantitative metric that accurately measures the contribution of specific patterns to naturalness, along with the associated confidence. In this paper, we propose a novel framework called the Confident Naturalness Explanation (CNE) framework. This framework combines explainable machine learning and uncertainty quantification to assess and explain naturalness. We introduce a new quantitative metric that describes the confident contribution of patterns to the concept of naturalness. Furthermore, we generate an uncertainty-aware segmentation mask for each input sample, highlighting areas where the model lacks knowledge. To demonstrate the effectiveness of our framework, we apply it to a study site in Fennoscandia using two open-source satellite datasets.

Mohamed Farag

For many years, developers could not figure out the mystery of OS kernels. The main source of this mystery is the interaction between operating systems and hardware while system's boot up and kernel initialization. In addition, many operating system kernels differ in their behavior toward many situations. For instance, kernels act differently in racing conditions, kernel initialization and process scheduling. For such operations, kernel debuggers were designed to help in tracing kernel behavior and solving many kernel bugs. The importance of kernel debuggers is not limited to kernel code tracing but also, they can be used in verification and performance comparisons. However, developers had to be aware of debugger commands thus introducing some difficulties to non-expert programmers. Later, several visual kernel debuggers were presented to make it easier for programmers to trace their kernel code and analyze kernel behavior. Nowadays, several kernel debuggers exist for solving this mystery but only very few support line-by-line debugging at run-time. In this paper, a generic approach for operating system source code debugging in graphical mode with line-by-line tracing support is proposed. In the context of this approach, system boot up and evaluation of two operating system schedulers from several points of views will be discussed.