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This dataset contains the data used in the paper "Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data" (FUSION 2023) and "Learning to Sense: A Principled Approach to Wi-Fi Sensing Using Variational Auto-Encoders" (submitted to IEEE Transactions on Mobile Computing). latent_space_dataset.zip contains data already processed by the variational autoencoders KERAS_models.zip contains the weights of the EDL/MLP models used in the FUSION paper VAE_models.zip contains the weights of the VAE models Version 1.0 includes the data from 5 activities used in our FUSION 2023 paper. Version 2.0 also includes the data from 12 different activities and the weights of different classifiers in models_12activities.zip
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 7 | |
| downloads | 6 |

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