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Found 4 papers, 2 papers with code
Boost Distribution System Restoration with Emergency Communication Vehicles Considering Cyber-Physical Interdependence
Enhancing restoration capabilities of distribution systems is one of the main strategies for resilient power systems to cope with extreme events.
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
We present a spatial-temporal federated learning framework for graph neural networks, namely STFL.
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
The rapid development of artificial intelligence, especially deep learning technology, has advanced autonomous driving systems (ADSs) by providing precise control decisions to counterpart almost any driving event, spanning from anti-fatigue safe driving to intelligent route planning.
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
We derive several implications for system and middleware builders: (1) when adding a defense component against adversarial attacks, it is important to deploy multiple defense methods in tandem to achieve a good coverage of various attacks, (2) a blackbox attack is much less effective compared with a white-box attack, implying that it is important to keep model details (e. g., model architecture, hyperparameters) confidential via model obfuscation, and (3) driving models with a complex architecture are preferred if computing resources permit as they are more resilient to adversarial attacks than simple models.
