Service stations equipped with multiple delivery points for plug-in electric vehicles fast charging are expected to gradually replace petrol-based service stations. Operators interested in managing the new stations are going to face key challenges in terms of sizing the station's point of...
04b Atto di convegno in volume
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Each year, car accidents impact billions of people, resulting in numerous casualties. Consequently, road safety remains a top priority for nations worldwide. This project aims to enhance driver safety through a feedback system that relies solely on a monocular camera mounted atop the vehicle. The...
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We present a new approach for real-time retrieval and classification of solar images using a proposed sector-based image hashing technique. To this end, we generate intermediate hand-crafted features from automatically detected active regions in the form of layer-sector-based descriptors....
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Speech and Language Impairments (SLI) affect a large and heterogeneous group of people. With our work, we propose a novel, easy, and immediate detection tool to help diagnose people who suffer from SLI using speech audio signals, along with a new dataset containing English speakers affected by SLI...
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Eye-tracking technology has long been a valuable tool across various domains, and recent advancements in neural networks have significantly expanded its versatility and potential. However, real-world applications continue to face challenges such as accommodating users’ natural movements, variations...
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This paper deals with offline (or batch) Reinforcement Learning (RL) in episodic Regular Decision Processes (RDPs). RDPs are the subclass of Non-Markov Decision Processes where the dependency on the history of past events can be captured by a finite-state automaton. We consider a setting where the...
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Every automaton can be decomposed into a cascade of basic prime automata. This is the Prime Decomposition Theorem by Krohn and Rhodes. Guided by this theory, we propose automata cascades as a structured, modular, way to describe automata as complex systems made of many components, each implementing...
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Non-markovian Reinforcement Learning (RL) tasks are very hard to solve, because agents must consider the entire history of state-action pairs to act rationally in the environment. Most works use symbolic formalisms (as Linear Temporal Logic or automata) to specify the temporally-extended task....
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Despite significant advancements in deep learning for sequence forecasting, neural models are typically trained only on data, and the incorporation of high-level prior logical knowledge in their training is still an hard challenge. This limitation hinders the exploitation of background knowledge,...
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In this work, we introduce DeepDFA, a novel approach to identifying Deterministic Finite Automata (DFAs) from traces, harnessing a differentiable yet discrete model. Inspired by both the probabilistic relaxation of DFAs and Recurrent Neural Networks (RNNs), our model offers interpretability post-...