Titlebook: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track; European Conference, Gianmarco De Francisci Mor

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PICT: Precision-enhanced Road Intersection Recognition Using Cycling Trajectoriesward to identify the intersections of different scales correctly. Finally, extensive comparative experiments on two real-world datasets demonstrate that . significantly outperforms the state-of-the-art methods by 52.13% in the F1-score of intersection recognition.

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FDTI: Fine-Grained Deep Traffic Inference with?Roadnet-Enriched Graphate that our method achieves state-of-the-art performance and learned traffic dynamics with good properties. To the best of our knowledge, we are the first to conduct the city-level fine-grained traffic prediction.

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RulEth: Genetic Programming-Driven Derivation of?Security Rules for?Automotive Ethernets. Although the attacks examined in this work are far more complex than those considered in most other works in the automotive domain, our results show that most of the attacks examined can be well identified. By being able to evaluate each rule generated separately, the rules that are not working e

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Spatial-Temporal Graph Sandwich Transformer for?Traffic Flow Forecastingansformer as sliced meat to capture prosperous spatial-temporal interactions. We also assemble a set of such sandwich Transformers together to strengthen the correlations between spatial and temporal domains. Extensive experimental studies are performed on public traffic benchmarks. Promising result

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Predictive Maintenance, Adversarial Autoencoders and?Explainabilityur to minimize negative impacts, but also to provide explanations for the failure warnings that can aid in decision-making processes. We propose an autoencoder architecture trained with an adversarial loss, known as the Wasserstein Autoencoder with Generative Adversarial Network (WAE-GAN), designed

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