Fully Homomorphically Encrypted Deep Learning as a Service

Funding: This research was funded by UKRI-EPSRC grant “The Internet of Food Things” grant number EP/R045127/1.Peer reviewedPublisher PD

Premonition Net, a multi-timeline transformer network architecture towards strawberry tabletop yield forecasting

This research was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship, United Kingdom [grant numbers 2155898, BB/S507453/1]Peer reviewedPublisher PD

EDLaaS : Fully Homomorphic Encryption Over Neural Network Graphs

The authors would like to thank the British Biotechnology and Biological Sciences Research Council (BBSRC) in collaboration with Berry Gardens Growers and the University of Lincoln for funding and support.Preprin

The Augmented Agronomist Pipeline and Time Series Forecasting

Peer reviewedPostprin

Premonition Net, A Multi-Timeline Transformer Network Architecture Towards Strawberry Tabletop Yield Forecasting

Funding statement: This research was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship [grant numbers 2155898, BB/S507453/1]. The authors would also like to thank the Lincoln Centre for Autonomous Systems (L-CAS) for their help and support with the autonomous robotic Thorvald systems, and the availability of tools and resources such as the Riseholme strawberry tabletop itseof.Preprin

EDLaaS : Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting

This research was supported in part by the Biotechnology and Biological Sciences Research Council (BBSRC) studentship 2155898 grant: BB/S507453/1.Peer reviewedPublisher PD

Nemesyst: A Hybrid Parallelism Deep Learning-Based Framework Applied for Internet of Things Enabled Food Retailing Refrigeration Systems

Deep Learning has attracted considerable attention across multiple application domains, including computer vision, signal processing and natural language processing. Although quite a few single node deep learning frameworks exist, such as tensorflow, pytorch and keras, we still lack a complete process- ing structure that can accommodate large scale data processing, version control, and deployment, all while staying agnostic of any specific single node framework. To bridge this gap, this paper proposes a new, higher level framework, i.e. Nemesyst, which uses databases along with model sequentialisation to allow processes to be fed unique and transformed data at the point of need. This facilitates near real-time application and makes models available for further training or use at any node that has access to the database simultaneously. Nemesyst is well suited as an application framework for internet of things aggregated control systems, deploying deep learning techniques to optimise individual machines in massive networks. To demonstrate this framework, we adopted a case study in a novel domain; deploying deep learning to optimise the high speed control of electrical power consumed by a massive internet of things network of retail refrigeration systems in proportion to load available on the UK Na- tional Grid (a demand side response). The case study demonstrated for the first time in such a setting how deep learning models, such as Recurrent Neural Networks (vanilla and Long-Short-Term Memory) and Generative Adversarial Networks paired with Nemesyst, achieve compelling performance, whilst still being malleable to future adjustments as both the data and requirements inevitably change over time

Decarbonising our food systems: contextualising digitalisation for net zero

The food system is undergoing a digital transformation that connects local and global supply chains to address economic, environmental, and societal drivers. Digitalisation enables firms to meet sustainable development goals (SDGs), address climate change and the wider negative externalities of food production such as biodiversity loss, and diffuse pollution. Digitalising at the business and supply chain level through public–private mechanisms for data exchange affords the opportunity for greater collaboration, visualising, and measuring activities and their socio-environmental impact, demonstrating compliance with regulatory and market requirements and providing opportunity to capture current practice and future opportunities for process and product improvement. Herein we consider digitalisation as a tool to drive innovation and transition to a decarbonised food system. We consider that deep decarbonisation of the food system can only occur when trusted emissions data are exchanged across supply chains. This requires fusion of standardised emissions measurements within a supply chain data sharing framework. This framework, likely operating as a corporate entity, would provide the foci for measurement standards, data exchange, trusted, and certified data and as a multi-stakeholder body, including regulators, that would build trust and collaboration across supply chains. This approach provides a methodology for accurate and trusted emissions data to inform consumer choice and industrial response of individual firms within a supply chain

A computational investigation on the heat transfer loss for the Geothermal District Heating in the Baltic region

Geothermal energy District Heating (DH) is mainly available in the northern EU, exhibiting the advantage of lower carbon emissions and cost. However, DH does require a larger area (plant area) and an insulated pipe network with a large diameter. This network is subject to heat losses to the ground. This study aims to investigate computationally the effect of this heat transfer loss from the piping network in the Baltic region with parametric analysis and the effect of different depths

Economic and Environmental comparison of residential Geothermal Energy Systems from the greater Baltic region to the southern EU

Ground Source Heat Pumps are used to reach EU’s goal of nearly Zero Energy Buildings. Compared to Air Source Heat Pumps, they have higher performance but also higher cost. Here it is aimed to demonstrate the difference in environmental and economic aspects, between greater Baltic region and southern EU. Cases from across the EU were used with the same technical characteristics for comparison. Life Cycle Analysis and Simple Payback Period are used for the environmental and economic evaluation