15 research outputs found
Impact of Local Congruences in Attribute Reduction
Local congruences are equivalence relations whose equivalence classes are convex sublattices of the original lattice. In this paper,
we present a study that relates local congruences to attribute reduction
in FCA. Specifically, we will analyze the impact in the context of the use
of local congruences, when they are used for complementing an attribute
reduction
Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service
We present two datasets for Machine Learning (ML)-based Predictive Quality of Service (PQoS) comprising Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) radio channel measurements. As V2V and V2I are both indispensable elements for providing connectivity in Intelligent Transport Systems (ITS), we argue that a combination of the two datasets enables the study of Vehicle-to-Everything (V2X) connectivity in its entire complexity. We describe in detail our methodologies for performing V2V and V2I measurement campaigns, and we provide illustrative examples on the use of the collected data. Specifically, we showcase the application of approximate Bayesian Methods using the two presented datasets to portray illustrative use cases of uncertainty-aware Quality of Service and Channel State Information forecasting. Finally, we discuss novel exploratory research direction building upon our work. The V2I and V2V datasets are available on IEEE Dataport, and the code utilized in our numerical experiments is publicly accessible via CodeOcean
Vehicle-to-Everything (V2X) datasets for Machine Learning-based Predictive Quality of Service
Abstract
We present two datasets for Machine Learning (ML)-based Predictive Quality of Service (PQoS) comprising Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) radio channel measurements. As V2V and V2I are both indispensable elements for providing connectivity in Intelligent Transport Systems (ITS), we argue that a combination of the two datasets enables the study of Vehicle-to-Everything (V2X) connectivity in its entire complexity. We describe in detail our methodologies for performing V2V and V2I measurement campaigns, and we provide illustrative examples on the use of the collected data. Specifically, we showcase the application of approximate Bayesian Methods using the two presented datasets to portray illustrative use cases of uncertainty-aware Quality of Service and Channel State Information forecasting. Finally, we discuss novel exploratory research direction building upon our work. The V2I and V2V datasets are available on IEEE Dataport, and the code utilized in our numerical experiments is publicly accessible via CodeOcean