From this Saturday (1 Oct to 5 Oct 2022), local residents of New Town, Kolkata, India will be able to use their mobile phones to vote for their favourite puja (a festival decoration for worshipping) as part of the annual Durga Puja festival celebration. New Town is a modern satellite city of Kolkata with about one million population. Durga Puja is one of the most important festivals in India, especially in Kolkata.
The online voting system implements a cryptographic protocol called DRE-ip, which was proposed by Dr Siamak Shahandashti and Professor Feng Hao in 2016 in an ERC-funded project. This protocol ensures that the e-voting system is end-to-end verifiable, hence giving every voter a chance to verify the tallying integrity of an election. The DRE-ip protocol was previously used in a Gateshead e-voting trial for polling station voting in 2019. This time it will be trialled for online voting, supported by a Royal Society international collaboration grant in collaboration with Professor Bimal Roy of the Indian Statistical Institute and the New Town Kolkata Development Authority (NKDA), West Bengel, India.
The online voting system has been developed by a group of 2021/2022 Master of Engineering (MEng) students (Horia Druliac, Matthew Bardsley, Chris Riches and Chris Dunn) in the Computer Science department as part of their MEng group project. The same group of students won the 2022 Innovation award.
Over the recent year, Neural Architecture Search (NAS) has attracted a lot of attention. While being able to automate the discovery of better performing neural architectures than hand-crafted ones, it comes at a great price, requiring thousands of GPU hours to perform the search. The Zero Cost NAS competition challenges the participants to design efficient proxies for NAS, using negligible computational resources to evaluate neural architectures.
In collaboration with the AutoCAML team at Samsung AI Cambridge (led by Dr. Hongkai Wen), our research students, Lichuan Xiang and Youyang Sha, proposed new zero-cost NAS metrics that exploit the compressibility of neural networks. Our metrics are extremely efficient to run (reducing search cost from weeks/days to minutes), and achieves impressive results across multiple search spaces and datasets. In the competition, our teams won both the 1st and 2nd places (using different scoring functions), and the performance gap with the 3rd winning team is almost 2x. Checkout our poster here.
Oral Evidence to the House of Lords on Telephone Frauds and Countermeasures
On 23rd June 2022, Professor Feng Hao of the Systems and Security research theme was invited as one of the two expert witnesses to give oral evidence to the Fraud Act 2006 and Digital Fraud Committee appointed by the House of Lords at Parliament on trends of telephone frauds and the landscape of counter-fraud technologies. This is related to an ongoing EPSRC project, led by Professor Feng Hao (PI) from the Department of Computer Science and Dr Adrian von Mühlenen (co-I) from the Department of Psychology, the University of Warwick. In this project, the research team have been investigating a cost-effective solution to combat caller ID spoofing, a technique commonly used by fraudsters and scammers to pretend to call from trusted sources (e.g., banks, HMRC) as part of social engineering attacks. A transcript of the oral evidence session is published on the Parliament website.
HPC Research Accepted for Publication at the ICS 2022 Conference
Two papers by researchers at the Department of Computer Science have been accepted to the 36th ACM International Conference on Supercomputing ICS 2022 to be held on the 28-30th of June this year. ICS is one of the most prominent and revered conferences in High Performance Computing, highly regarded by the HPC community for publishing leading-edge research in this area. The two papers accepted are:
- High Throughput Multidimensional Tridiagonal System Solvers on FPGAs (Preprint) by Kamalavasan Kamalakkannan and Gihan Mudalige at Warwick, together with Istvan Reguly (PPCU) and Suhaib Fahmy (KAUST).
- Clairvoyant: A Log-Based Transformer-Decoder for Failure Prediction in Large-Scale Systems by Khalid Alharthi and Arshad Jhumka at Warwick, together with Sheng Di, Franck Cappello at Argonne National Laboratory. Preprint. The ACM ICS2022 full program can be found here.
Winner of the Faculty of Science, Engineering and Medicine Post-Doctoral Research Prize 2022
Gunduz Vehbi Demirci has been awarded with the Faculty of Science, Engineering and Medicine Post-Doctoral Research Prize 2022 for his paper jointly with Prof. Hakan Ferhatosmanoglu, "Partitioning sparse deep neural networks for scalable training and inference", published in the Proceedings of the ACM International Conference on Supercomputing (ICS '21) (DOI: https://doi.org/10.1145/3447818.3460372).
Training large-scale deep learning models is notoriously difficult. Gunduz develops a highly parallel solution to scale training of sparse deep learning models, which is combined with a novel combinatorial optimisation built on a hypergraph partitioning model, reducing parallelisation overheads and achieving computational balance among processors. An end-to-end software solution is released, enabling competing with big tech companies that have access to large infrastructures and datasets.
The work is summarised in a paper accepted by the 2021 ACM International Conference on Supercomputing, which is a premier conference in high-end systems. The research output will have a great potential to bring significant practical impact in long term as developing such comprehensive solutions takes time and is typically achieved only within large groups.
Members of the High-Performance and Scientific Computing Group (HPSC) at the department of Computer Science has won a best paper award at the 28th IEEE International Conference on High-Performance Computing, Data and Analytics held on the 17th-18th of December. The winning paper titled Predictive Analysis of Large-Scale Coupled CFD Simulations with the CPX Mini-App, develops a novel representative (mini-)application, specifically designed to model coupled execution of multi-physics numerical simulation codes from the CFD domain. The mini-coupler, CPX, is the first of its kind, combining multiple CFD mini-app instances to predict the run-time and scaling behaviour of large scale coupled CFD simulations, on modern multi-core and many-core clusters such as used for production turbomachinery design at Rolls-Royce plc. The work was carried out by PhD candidate, Archie Powell, in collaboration with Kabir Choudry, Arun Prabhakar, and Gihan Mudalige at the Department of CS Warwick, Dario Amirante (University of Surrey), Istvan Reguly (PPCU) and Stephen Jarvis (University of Birmingham).
The work was funded by the EPSRC Prosperity Partnership in Computational Science for Advanced Simulation and Modelling of Engineering Systems (AsiMoV) and Rolls-Royce plc.