1.1k citations · 2.7k across the 12 of their papers we have counts for
14 papers
Defending Against Poisoning Attacks in Federated Learning with Blockchain
Nanqing Dong, Zhipeng Wang, Jiahao Sun +3
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learnin…
Time-series Transformer Generative Adversarial Networks
Padmanaba Srinivasan, William J. Knottenbelt
Many real-world tasks are plagued by limitations on data: in some instances very little data is available and in others, data is protected by privacy enforcing regulations (e.g. GD…
On the Dynamics of Solid, Liquid and Digital Gold Futures
Toshiko Matsui, Ali Al-Ali, William J. Knottenbelt
This paper examines the determinants of the volatility of futures prices and basis for three commodities: gold, oil and bitcoin -- often dubbed solid, liquid and digital gold -- by…
A Scalable Inference Method For Large Dynamic Economic Systems
Pratha Khandelwal, Philip Nadler, Rossella Arcucci +2
The nature of available economic data has changed fundamentally in the last decade due to the economy's digitisation. With the prevalence of often black box data-driven machine lea…
SoK: Decentralized Finance (DeFi)
Sam M. Werner, Daniel Perez, Lewis Gudgeon +3
Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. Two years ago the total value locked in DeFi systems was approximately 700m USD, n…
DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency
Lewis Gudgeon, Sam M. Werner, Daniel Perez +1
We coin the term *Protocols for Loanable Funds (PLFs)* to refer to protocols which establish distributed ledger-based markets for loanable funds. PLFs are emerging as one of the ma…
Unstable Throughput: When the Difficulty Algorithm Breaks
Dragos I. Ilie, Sam M. Werner, Iain Stewart +1
In Proof-of-Work blockchains, difficulty algorithms serve the crucial purpose of maintaining a stable transaction throughput by dynamically adjusting the block difficulty in respon…
Towards Safer Smart Contracts: A Survey of Languages and Verification Methods
Dominik Harz, William Knottenbelt
With a market capitalisation of over USD 205 billion in just under ten years, public distributed ledgers have experienced significant adoption. Apart from novel consensus mechanism…
Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature
Jack Kelly, William Knottenbelt
We examine 12 studies on the efficacy of disaggregated energy feedback. The average electricity reduction across these studies is 4.5%. However, 4.5% may be a positively-biased est…
A Novel Offloading Partitioning Algorithm in Mobile Cloud Computing
Huaming Wu, Daniel Seidenstücker, Yi Sun +3
This paper has been withdrawn by the author
Neural NILM: Deep Neural Networks Applied to Energy Disaggregation
Jack Kelly, William Knottenbelt
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand. Recently, deep neural networks…
NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring
Nipun Batra, Jack Kelly, Oliver Parson +5
Non-intrusive load monitoring, or energy disaggregation, aims to separate household energy consumption data collected from a single point of measurement into appliance-level consum…
The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
Jack Kelly, William Knottenbelt
Many countries are rolling out smart electricity meters. These measure a home's total power demand. However, research into consumer behaviour suggests that consumers are best able…
Metadata for Energy Disaggregation
Jack Kelly, William Knottenbelt
Energy disaggregation is the process of estimating the energy consumed by individual electrical appliances given only a time series of the whole-home power demand. Energy disaggreg…