Our paper, resulting from work of Master student Gustavo Claudio Karl Couto, entitled
"Hierarchical Generative Adversarial Imitation Learning with Mid-level Input Generation for Autonomous Driving on Urban Environments" has shown very interesting results:
Both the intermediate mid-level input BEV representation and the control policy are learned as the agent navigates in an urban town (click for more videos from playlist).
State-of-the-art Artificial Intelligence method for detecting that you is really you and not some intruder entering the code on your mobile phone.
In the context of energy distribution networks, frauds are non-technical losses (NTL) that may account for up to 40% of the total distributed energy in some developing countries. The fraudster alters the eletricity meter in order to pay less than the right amount. In this context, the discovery or detection of frauds is necessary in order to decrease the non-technical losses of the energy distribution networks, consequently enhancing the stability and reliability of the network.
This project proposes the use of Recurrent Neural Networks (RNNs) for projecting a times series into a spatial dimension such that it can be used as a universal temporal feature for fraud detection predictive models. The particular problem tackled here jointly with the partner company is to predict whether a given time series of monthly energy consumption data is likely to indicate a fraud (NTL) or not.
Two main approaches are planned to be used with RNNs: supervised learning with bias correction techniques, and self-organized models for unsupervised learning of new fraud (anomaly) patterns.Finally, a last step is to integrate both of the previously developed models into an unified architecture that learns the responsibilities of each model in an online way by feedback from the environment using the results of the inspections of the fraudsters - the ground truth for some of the predictions.
This project has potential not only for generating significant technological and commercial value for the industrial partner, but also outstanding scientific output, being applicable in the long-term to other fields such as monitoring, prognosis/diagnostics in robotics, medical systems and security applications.
Funding: AFR-PPP / FNR, Luxembourg.
