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Quality assurance (QA) plays a crucial role in manufacturing to ensure that products meet their specifications. However, manual QA processes are costly and time-consuming, thereby making artificial intelligence (AI) an attractive solution for automation and expert support. In particular, convolutional neural networks (CNNs) have gained a lot of interest in visual inspection. Next to AI methods, the explainable artificial intelligence (XAI) systems, which achieve transparency and interpretability by providing insights into the decision-making process of the AI, are interesting methods for achieveing quality inspections in manufacturing processes. In this study, we conducted a systematic literature review (SLR) to explore AI and XAI approaches for visual QA (VQA) in manufacturing. Our objective was to assess the current state of the art and identify research gaps in this context. Our findings revealed that AI-based systems predominantly focused on visual quality control (VQC) for defect detection. Research addressing VQA practices, like process optimization, predictive maintenance, or root cause analysis, are more rare. Least often cited are papers that utilize XAI methods. In conclusion, this survey emphasizes the importance and potential of AI and XAI in VQA across various industries. By integrating XAI, organizations can enhance model transparency, interpretability, and trust in AI systems. Overall, leveraging AI and XAI improves VQA practices and decision-making in industries.
Cloud computing infrastructures availability rely on many components, like software, hardware, cloud man- agement system (CMS), security, environmental, and human operation, etc. If something goes wrong the root cause analysis (RCA) is often complex. This paper explores the integration of Machine Learning (ML) with Fault Tree Analysis (FTA) to enhance explainable failure detection in cloud computing systems. We introduce a framework employing ML for FT selection and generation, and for predicting Basic Events (BEs) to enhance the explainability of failure analysis. Our experimental validation focuses on predicting BEs and using these predictions to calculate the Top Event (TE) probability. The results demonstrate improved diagnostic accuracy and reliability, highlighting the potential of combining ML predictions with traditional FTA to identify root causes of failures in cloud computing environments and make the failure diagnostic more explainable.
Digital, interactive content can support active learning and provide both motivation and an automated feedback to students. Unfortunately authoring interactive content remains a difficult task for teachers. There are few interoperable standards and e-learning platforms often restrict what is technically possible. Even if some teachers create amazing interactive content it is challenging to publish and share this content with colleagues. This paper proposes a way to leverage Web Components, a rather new technology supported by modern browsers, to allow teachers to quickly author interactive exercises involving mathematical expressions and visualizations. As Web Components are standardized they can be shared and embedded in any website. The proposed Web Components for mathematics allow for: 1. A convenient visual input method for expressions and formulas, 2. A sophisticated validation system for these expressions in order to give immediate feedback to students on their solutions.