activity
20202022
most citedAn Experimentation Platform for Explainable Coalition Situational Understanding

2 citations · 6 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CR2022

Let's Talk Through Physics! Covert Cyber-Physical Data Exfiltration on Air-Gapped Edge Devices

Matthew Chan, Nathaniel Snyder, Marcus Lucas +7

Although organizations are continuously making concerted efforts to harden their systems against network attacks by air-gapping critical systems, attackers continuously adapt and u…

eess.SP20222 cited

PhysioGAN: Training High Fidelity Generative Model for Physiological Sensor Readings

Moustafa Alzantot, Luis Garcia, Mani Srivastava

Generative models such as the variational autoencoder (VAE) and the generative adversarial networks (GAN) have proven to be incredibly powerful for the generation of synthetic data…

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.AI20202 cited

An Experimentation Platform for Explainable Coalition Situational Understanding

Katie Barrett-Powell, Jack Furby, Liam Hiley +8

We present an experimentation platform for coalition situational understanding research that highlights capabilities in explainable artificial intelligence/machine learning (AI/ML)…

cs.AI2020

A Hybrid Neuro-Symbolic Approach for Complex Event Processing

Marc Roig Vilamala, Harrison Taylor, Tianwei Xing +6

Training a model to detect patterns of interrelated events that form situations of interest can be a complex problem: such situations tend to be uncommon, and only sparse data is a…