15 citations · 30 across the 7 of their papers we have counts for
7 papers
Neuro-Symbolic Learning of Answer Set Programs from Raw Data
Daniel Cunnington, Mark Law, Jorge Lobo +1
One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims…
FF-NSL: Feed-Forward Neural-Symbolic Learner
Daniel Cunnington, Mark Law, Alessandra Russo +1
Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To ad…
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Daniel Cunnington, Alessandra Russo, Mark Law +2
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems re…
FLAP -- A Federated Learning Framework for Attribute-based Access Control Policies
Amani Abu Jabal, Elisa Bertino, Jorge Lobo +3
Technology advances in areas such as sensors, IoT, and robotics, enable new collaborative applications (e.g., autonomous devices). A primary requirement for such collaborations is…
A Comprehensive Study on Load Balancers for VNF chains Horizontal Scaling
Jiefei Ma, Windhya Rankothge, Christian Makaya +3
We present an architectural design and a reference implementation for horizontal scaling of virtual network function chains. Our solution does not require any changes to network fu…
Data Modelling for the Evaluation of Virtualized Network Functions Resource Allocation Algorithms
Windhya Rankothge, Frank Le, Alessandra Russo +1
To conduct a more realistic evaluation on Virtualized Network Functions resource allocation algorithms, researches needed data on: (1) potential NFs chains (policies), (2) traffic…