Abstract Convolutional Neural Networks (CNNs) are state-of-the-art models for computer
vision tasks such as image classification, object detection, and segmentation. However …

We present a novel framework for generating adversarial benchmarks to evaluate the
robustness of image classification models. The RLAB framework allows users to customize …

Recent Wave Energy Converters (WEC) are equipped with multiple legs and generators to
maximize energy generation. Traditional controllers have shown limitations to capture …

The industrial multi-generator Wave Energy Converters (WEC) must handle multiple
simultaneous waves coming from different directions called spread waves. These complex …

In recent years, the increasing emphasis on sustainability and carbon footprint reduction has
required the exploration of innovative optimization techniques for data center operators. In …

A measure of robustness against naturally occurring distortions is key to the trustworthiness,
safety, and success of machine learning models on deployment. We investigate an …

The rapid growth of machine learning (ML) has led to an increased demand for
computational power, resulting in larger data centers (DCs) and higher energy consumption …

There have been growing discussions on estimating and subsequently reducing the
operational carbon footprint of enterprise data centers. The design and intelligent control for …

As machine learning workloads are significantly increasing energy consumption,
sustainable data centers with low carbon emissions are becoming a top priority for …

We present a generic Reinforcement Learning (RL) framework optimized for crafting
adversarial attacks on different model types spanning from ECG signal analysis (1D), image …