Deep Neural Networks (DNNs) are known as black box algorithmsthat lack transparency and interpretability for humans. eXplainableArtificial Intelligence (XAI) is introduced to tackle this problem. MostXAI methods are utilized post-training, providing explanations of themodel to clarify its predictions and inner workings for human understanding. However, there is a shortage of methods that utilize XAIduring training to not only observe the model’s behavior but alsoexploit this information for the benefit of the model.In our approach, we propose a novel method that leverages XAIduring the training process itself. Incorporating feedback from XAIcan give us insights into important features of input data that impact model decisions. This work explores focusing more on specificfeatures during training, which could potentially improve model performance introspectively throughout the training phase. We analyzethe stability of feature explanations during training and find thatthe model’s attention to specific features is consistent in the MNISTdataset. However, unimportant features lack stability. The OCTMNIST dataset, on the other hand, has stable explanations for important features but less consistent explanations for less significant features. Based on this observation, two types of masks, namely fixedand dynamic, are applied to the model’s structure using XAI’s feedback with minimal human intervention. These masks identify themore important features from the less important ones and set the pixels associated with less significant features to zero. The fixed mask isgenerated based on XAI feedback after the model is fully trained, andthen it is applied to the output of the first convolutional layer of a newmodel (with the same architecture), which is trained from scratch. Onthe other hand, the dynamic mask is generated based on XAI feedback during training, and it is applied to the model while the modelis still training. As a result, these masks are changing during different epochs. Examining these two methods on both deep and shallowmodels, we find that both masking methods, particularly the fixedone, reduce the focus of all models on the least important parts of theinput data. This results in improved accuracy and loss in all models.As a result, this approach enhances the model’s interpretability andperformance by incorporating XAI into the training process.

Personal computer desktops, and other standardized computer architectures are optimized to provide the best performance for frequently occurring conditions. Real-time systems designed using worst-case analysis for such architectures under-utilize the hardware. This shortcoming provides the motivation for scheduling algorithms that can improve overall utilization by accounting for inherent uncertainty in task execution duration. A real-time task dispatcher must perform its function with constant scheduling overhead. Given the NP-hard nature of the problem of scheduling non-preemptible tasks, dispatch decisions for such systems cannot be made in real-time. This argues for a hybrid architecture that includes an offline policy generator, and an online dispatcher. This dissertation proposes, and demonstrates a hybrid operating system architecture that enables cost-optimal task dispatch on Commercial-Off-The-Shelf (COTS) systems. This is achieved by explicitly accounting for the stochastic nature of each task?s execution time, and dynamically learning the system behavior. Decision Theoretic Scheduling (DTS) provides the framework for scheduling under uncertainty. The real-time scheduling problem is cast as a Markov Decision Process (MDP). An offline policy generator discovers an epsilon-optimal policy using value iteration with model learning. For the selected representation of state, action, model, and rewards, the policydiscovered using value iteration is proved to have a probability of failure that is less than any arbitrarily small user-specified value. The PromisQoS operating system architecture demonstrates a practical implementation of the proposed approach. PromisQoS is a Linux based platform that supports concurrent execution of time-based (preemptible and non-preemptible) real-time tasks, and best-effort processes on an interactive workstation. Several examples demonstrate that model learning, and scheduling under uncertainty enables PromisQoS to achieve better CPU utilization than other scheduling methods. Real-time task sets that solve practical problems, such as a Laplace solver, matrix multiplication, and transpose, demonstrate the robustness and correctness of PromisQoS design and implementation. This pioneering application demonstrates the feasibility of MDP based scheduling for real-time tasks in practical systems. It also opens avenues for further research into the use of such DTS techniques in real-time system design.

The master´s thesis is focused on the performance evaluation of the company S&K PUBLIC, Ltd. The theoretical part explains the term performance and describes the most used approaches of company performance measurement. The analytical part deals with the characteristic of the company and analysis of its current situation. The last part contains the performance evaluation of the company using the START Model, which is based on the principles of The EFQM Excellence Model. In conclusion, there are suggested measures to improve the company performance based on the results of the evaluation.