The PMDI project aims at radically improving the safety of urban mobility by extending STEP, an automotive data management and analytics platform, to support real-time and near-real-time use cases, particularly focusing on dangerous crossings at urban intersections. Such capabilities will be...
04b Atto di convegno in volume
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We study Consistent Query Answering (CQA) over knowledge bases with existential rules. Specifically, we propose a novel framework for CQA that combines previous approaches, allowing for the simultaneous presence of both open and closed predicates, i.e. predicates interpreted under open- and closed-...
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Spatio-Temporal predictive Learning is a self-supervised learning paradigm that enables models to identify spatial and temporal patterns by predicting future frames based on past frames. Traditional methods, which use recurrent neural networks to capture temporal patterns, have proven their...
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The role of anomaly detection systems in Critical Infrastructures (CIs) is critical due to the complexity of CIs and their control systems, which are usually implemented by computer-based controllers that constantly produce logs of their activities. Moreover, many CIs, located in different...
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In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data...
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In modern smart manufacturing environments, human involvement remains critical for addressing complex tasks that require adaptability and decision-making, despite the growing presence of automation and artificial intelligence. This paper introduces SAMBA - Service-Augmented Manufacturing-Based...
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The Varroa destructor mite poses a critical threat to global honey bee populations, contributing to colony collapse through parasitic infestation. Current detection methods rely on labor-intensive visual inspections, which are prone to human error and impractical for large-scale monitoring. To...
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The number of pretrained Large Language Models (LLMs) is increasing steadily, though the majority are designed predominantly for the English language. While state-of-the-art LLMs can handle other languages, due to language contamination or some degree of multilingual pretraining data, they are not...
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In modern smart manufacturing environments, human involvement remains critical for addressing complex tasks that require adaptability and decision-making, despite the growing presence of automation and artificial intelligence. This paper introduces SAMBA - Service-Augmented Manufacturing-Based...
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Binary function similarity, which often relies on learning-based algorithms to identify what functions in a pool are most similar to a given query function, is a soughtafter topic in different communities, including machine learning, software engineering, and security. Its importance stems from the...