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Free University of Bozen-Bolzano

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Selected work

Representative Papers

Teaching Complex Systems based on Microservices

Jun 19, 2025

Undergraduate microservices education often lacks systematicity, conceptual depth, and hands-on rigor. Method: This study designed and implemented a structured microservices pedagogical framework for senior undergraduate computer science students at the University of São Paulo, involving over 80 learners. The framework integrates authentic industrial scenarios, collaborative learning, and agile development practices, covering core technical competencies—including microservices architecture, Docker-based containerization, RESTful API design, and CI/CD automation pipelines. Unlike conventional curricula, it delivers the first undergraduate-level, lifecycle-oriented instruction spanning microservices design, development, deployment, and team collaboration. Contribution/Results: Empirical evaluation demonstrated 100% project completion rate; students exhibited significantly enhanced comprehension of distributed systems principles and engineering proficiency; industry-readiness assessments improved by an average of 37%.

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Latest Papers

Time to Reason: Scalable Neurosymbolic Learning for LTLf via Fuzzy Semantics

Aug 17, 2026

This study addresses the limitations of existing neuro-symbolic approaches for Linear Temporal Logic over finite traces (LTLf), which lack unified differentiable semantics and suffer from poor scalability due to automaton dependence. We propose DiffLTLf, a novel framework that formalizes fuzzy semantics for LTLf and exploits operator duality to establish an automaton-free differentiable neuro-symbolic learning paradigm alongside a high-complexity evaluation protocol. Experimental results demonstrate that fuzzy semantics significantly influence model performance. Furthermore, DiffLTLf matches or surpasses state-of-the-art probabilistic methods while substantially improving scalability. Consequently, this work provides an efficient and unified solution for neuro-symbolic learning with temporal logic, overcoming critical bottlenecks in current methodologies.

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