activity
20182025
most citedAccurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data

9 citations · 26 across the 9 of their papers we have counts for

collaborators

14 papers

cs.LG20251 cited

Toward Foundation Models for Online Complex Event Detection in CPS-IoT: A Case Study

Liying Han, Gaofeng Dong, Xiaomin Ouyang +3

Complex events (CEs) play a crucial role in CPS-IoT applications, enabling high-level decision-making in domains such as smart monitoring and autonomous systems. However, most exis…

cs.LG2025

Scaling Online Complex Event Detection with Synthetic Supervision and Mamba-Based Neural Algorithmic Reasoning

Liying Han, Gaofeng Dong, Xiaomin Ouyang +4

Modern machine learning models excel at detecting individual actions, sounds, or scene attributes from short, localized observations. However, many real-world tasks, such as in sma…

eess.SP20249 cited

Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data

Marco Cominelli, Francesco Gringoli, Lance M. Kaplan +2

Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a pr…

cs.LG20231 cited

Knowledge from Uncertainty in Evidential Deep Learning

Cai Davies, Marc Roig Vilamala, Alun D. Preece +3

This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL). EDL is one example of a class of uncertainty-aware deep learning a…

cs.SD20212 cited

Using DeepProbLog to perform Complex Event Processing on an Audio Stream

Marc Roig Vilamala, Tianwei Xing, Harrison Taylor +6

In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymboli…

cs.AI2021

Handling Epistemic and Aleatory Uncertainties in Probabilistic Circuits

Federico Cerutti, Lance M. Kaplan, Angelika Kimmig +1

When collaborating with an AI system, we need to assess when to trust its recommendations. If we mistakenly trust it in regions where it is likely to err, catastrophic failures may…