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

We propose a Reinforcement Learning (RL) based adversarial Black-box attack (RLAB) that
aims at adding minimum distortion to the input iteratively to deceive image classification …

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

Bayesian Optimization (BO), guided by Gaussian process (GP) surrogates, has proven to be
an invaluable technique for efficient, high-dimensional, black-box optimization, a critical …

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 …

Machine learning has driven an exponential increase in computational demand, leading to
massive data centers that consume significant amounts of energy and contribute to climate …

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 …

We propose a self-correction mechanism for Large Language Models (LLMs) to mitigate
issues such as toxicity and fact hallucination. This method involves refining model outputs …