DeepProbCEP: A neuro-symbolic approach for complex event processing in adversarial settings

Roig Vilamala, Marc, Xing, Tianwei, Taylor, Harrison, Garcia, Luis, Srivastava, Mani, Kaplan, Lance, Preece, Alun ORCID: https://orcid.org/0000-0003-0349-9057, Kimmig, Angelika and Cerutti, Federico ORCID: https://orcid.org/0000-0003-0755-0358 2023. DeepProbCEP: A neuro-symbolic approach for complex event processing in adversarial settings. Expert Systems with Applications 215 , 119376. 10.1016/j.eswa.2022.119376

Preview

PDF - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

Abstract

Detecting complex events from subsymbolic data streams (such as images, audio recordings or videos) is a challenging problem, as traditional symbolic approaches cannot be used to process subsymbolic data, and neural-only approaches usually require larger amounts of training data than available. In this paper, we present DeepProbCEP, a Complex Event Processing (CEP) approach designed with four objectives: (i) allowing the use of subsymbolic data as an input, (ii) retaining flexibility and modularity in the definition of complex event rules, (iii) limiting the cost of obtaining training data and (iv) being robust against adversarial conditions. DeepProbCEP archives this by using a neuro-symbolic approach, which combines the neural and symbolic approaches to allow training with sparse data. This is made possible through the injection of human knowledge. In this paper, we demonstrate that DeepProbCEP outperforms other state-of-the-art approaches when training using sparse data. We also show that DeepProbCEP is robust in different adversarial settings. Finally, DeepProbCEP’s flexibility is demonstrated by showing it can be used to process both images and audio as input.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Additional Information:	License information from Publisher: LICENSE 1: Title: This article is under embargo with an end date yet to be finalised.
Publisher:	Elsevier
ISSN:	0957-4174
Date of First Compliant Deposit:	6 December 2022
Date of Acceptance:	26 November 2022
Last Modified:	05 May 2023 06:37
URI:	https://orca.cardiff.ac.uk/id/eprint/154688

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item