Comprehension of the Unified Modeling Language is essential for learners in the context of software modeling. However, current UML learning tools provide minimal guidance to novice modelers as they are insufficient in analyzing modeling behaviour adopted during the diagram creation process. In order to address this gap, we present an enhanced version of UML Miner, a plugin for Visual Paradigm, that systematically records and analyzes UML modeling activities through the use of Process Mining techniques. UML Miner tracks all modeling events, resulting in event logs that warrant conformance checking against expert modeling practices. This tool establishes flexible, yet structured learning pathways through Declarative Process Mining, supporting trace-based and event-based filtering, customized violation reports, and integration with external process mining tools. This work emphasizes the potential of process mining in computing education, demonstrating how conformance checking can strengthen UML modeling proficiency.

Currently and in the near future, Smart Cities are vital to enhance urban living, address resource challenges, optimize infrastructure, and harness technology for sustainability, efficiency, and improved quality of life in rapidly urbanizing environments. Owing to the high usage of networks, sensors, and connected devices, Smart Cities generate a massive amount of data. Therefore, Smart City security concerns encompass data privacy, Internet-of-Things (IoT) vulnerabilities, cyber threats, and urban infrastructure risks, requiring robust solutions to safeguard digital assets, citizens, and critical services. Some solutions include robust cybersecurity measures, data encryption, Artificial Intelligence (AI)-driven threat detection, public–private partnerships, standardized security protocols, and community engagement to foster a resilient and secure smart city ecosystem. For example, Security Information and Event Management (SIEM) helps in real-time monitoring, threat detection, and incident response by aggregating and analyzing security data. To this end, no integrated systems are operating in this context. In this paper, we propose a Hybrid Quantum-Classical Architecture for bolstering Smart City security that exploits Quantum Machine Learning (QML) and SIEM to provide security based on Quantum Artificial Intelligence and patterns/rules. The validity of the hybrid quantum-classical architecture was proven by conducting experiments and a comparison of the QML algorithms with state-of-the-art AI algorithms. We also provide a proof of concept dashboard for the proposed architecture.