Shen Wang and Professor Feng Hao from the Systems and Security theme at the Department of Computer Science and Ehsan Toreini from Durham University, have had the paper ‘Anti-Counterfeiting for Polymer Banknotes Based on Polymer Substrate Fingerprinting’, published in the journal IEEE Transactions on Information Forensics and Security, in which they propose a novel technique called Polymer Substrate Fingerprinting, which can identify each banknote’s own unique, unclonable fingerprint.
The researchers have found that every polymer banknote has a unique "fingerprint", which is caused by the inevitable imperfection in the physical manufacturing process, whereby the opacity coating, a critical step during the production of polymer notes, leaves an uneven coating layer with a random dispersion of impurities in the ink. This imperfection results in random translucent patterns when a polymer banknote is back-lit by a light source.
Prof. Graham Cormode of the Department of Computer Science has been named among the 2020 Association for Computing Machinery (ACM) Fellows, for contributions to computer science. The ACM is the world's leading learned society for computer science. Prof. Cormode is recognised for his contributions to data summarisation and privacy enabling data management and analysis. His work on data streams and sketching has been widely implemented in many high tech companies and organisations.
EPSRC funding awarded to Prof. Yulan He and Prof. Rob Procter on developing an AI solution for tackling “infodemic”
Prof. Yulan He and Prof. Rob Procter have been awarded funding from the EPSRC under the UKRI’s COVID-19 call. During the COVID-19 pandemic, national and international organisations are using social media and online platforms to communicate information about the virus to the public. However, propagation of misinformation has also become prevalent. This can strongly influence human behaviour and negatively impact public health interventions, so it is vital to detect misinformation in a timely manner. This project aims to develop machine learning algorithms for automatic collection of external evidence relating to COVID-19 and assessment of veracity of claims.
WM5G funding awarded to Prof. Hakan Ferhatosmanoglu on machine learning based spatio-temporal forecasting
Warwick's Department of Computer Science has been awarded a new research grant to develop a machine learning solution for dynamic forecasting of available capacity on road networks. The developed software is planned to be integrated within the TfWM's Regional Transport Coordination Centre for adaptive route planning and traffic management mitigation against disruptions, incidents and roadworks.
The “5G Enabled Dynamic Network Capacity Manager” project is in collaboration with commercial partners, Blacc, Immense, one.network, and O2. The team has won the WM5G’s transport competition to leverage 5G networks for near real-time AI based modelling.
Prof. Hakan Ferhatosmanoglu is leading the development of the scalable ML solution to forecast residual capacities in a dynamic spatio-temporal graph. The solution is designed to benefit from high-granular and low-latency data feeds from 5G cellular and sensor data enabling congestion to be accurately monitored, modelled, and predicted.
Suzanne Candanedo, who recently graduated from Computer Systems Engineering at the University of Warwick, has won the UKESF and UltraSoC Automotive Electronics Competition 2020.
The competition requires entrants to produce a 'think piece' about the future of cyber security for connected and autonomous vehicles, written along the lines of a blog post in style rather than a formal essay. You can read Suzy's winning entry here.
Florin Ciucu has been successful with a 491K EPSRC grant application ‘Practical Analysis of Parallel and Networked Queueing Systems’. The project will run for 4 years and will address some fundamental queueing problems at the core of modern computing and communication systems with parallel or network structures. The technical objective is to develop novel martingale-based models and techniques circumventing the historical Poisson assumption on the systems’ input, which has been convincingly shown to be highly misleading for practical purposes. The proposal was supported by IBM Research, Microsoft Research, and VMware.
Prof. Yulan He from the department's Data Science and Human-Centred Computing research themes has been awarded a 3-year EPSRC grant to develop a new framework to study model/data uncertainty and model interpretability of AI systems. The interdisciplinary project will assist system stakeholders and developers to understand and reason about the (business, personal, social, etc.) impact of intelligent systems on the world in which they operate, and to understand how and why decisions are taken. It will run in collaboration with Dr. Ritabrata Dutta from the Statistics Department, and Dr. Nelly Bencomo and Prof. Pete Sawyer from Aston University.