Retrosynthetic Life Cycle Assessment: A Short Perspective on the Sustainability of Integrating Thermoplastics and Artificial Intelligence Into Composite Systems

Over the past 30 years, the polymer composite industry has flourished, producing advanced structural materials for the aviation, energy, and transportation sectors. However, the use of crosslinked thermoset matrices has been linked to significant end-of-life challenges, presenting a critical issue for the industry. Moreover, the industry is...

Expensive Optimization with Model-Based Evolutionary Algorithms Applied to Medical Image Segmentation Using Deep Learning

Recently great achievements have been obtained with Artificial Intelligence (AI) methods including human-level performance in such challenging areas as image processing, natural language processing, computational biology, and game playing. Arguably, one of the most societally important application fields of such methods is healthcare. <br/>AI is...

Architectural Innovations for Efficient Denoising and Classification: A Manual vs. Neural Architecture Search Comparison

In this paper, we combine image denoising and classification, aiming to enhance human perception of noisy images captured by edge devices, like security cameras. Since edge devices have little computational power, we also optimize for efficiency by proposing a novel architecture that integrates the two tasks. Additionally, we alter a Neural...

Improve the optical neural network structure by using neural architecture search for visual classification tasks

This research explores the integration of Neural Architecture Search with Optical Neural Networks to optimize the efficiency and performance in traditional visual image classification tasks. The study introduces a new approach that applies Neural Architecture Search, a technique traditionally used to optimize the performance of Artificial Neural...

NASCTY: Neuroevolution to Attack Side-Channel Leakages Yielding Convolutional Neural Networks

Side-channel analysis (SCA) is a class of attacks on the physical implementation of a cipher, which enables the extraction of confidential key information by exploiting unintended leaks generated by a device. In recent years, researchers have observed that neural networks (NNs) can be utilized to perform highly effective SCA profiling, even...

Pruning Latent Neurons in Autoencoders using Early-Bird Tickets

Autoencoders seek to encode their input into a bottleneck of latent neurons, and then decode it to reconstruct the input. However, if the input data has an intrinsic dimension (ID) smaller than the number of latent neurons in the bottleneck, this encoding becomes redundant. <br/>In this paper, we study using the Early-Bird (EB) technique, a...

Bayesian deep learning for system identification

Applying deep neural networks (DNNs) for system identification (SYSID) has attracted more andmore attention in recent years. The DNNs, which have universal approximation capabilities for any measurable function, have been successfully implemented in SYSID tasks with typical network structures, e.g., feed-forward neural networks and recurrent...

Neuroevolution Applied to Profiled Side-Channel Attacks

Side-channel attacks (SCA) can obtain secret information related to the private key used during encryption executed on some device by exploiting leakage in power traces produced by the device. In recent years, researchers found that a neural network (NN) can be employed to execute a powerful profiled SCA, even on targets protected with...

Heed the noise in performance evaluations in neural architecture search

Neural Architecture Search (NAS) has recently become a topic of great interest. However, there is a potentially impactful issue within NAS that remains largely unrecognized: noise. Due to stochastic factors in neural network initialization, training, and the chosen train/validation dataset split, the performance evaluation of a neural network...

Evolutionary neural cascade search across supernetworks

To achieve excellent performance with modern neural networks, having the right network architecture is important. Neural Architecture Search (NAS) concerns the automatic discovery of task-specific network architectures. Modern NAS approaches leverage super-networks whose subnetworks encode candidate neural network architectures. These...

Reinforcement Learning for Profiled Side-Channel Analysis: Applications of Q-Learning in the SCA Domain

Side-channel attacks (SCA), which use unintended leakage to retrieve a secret cryptographic key, have become more sophisticated over time. With the recent successes of machine learning (ML) and especially deep learning (DL) techniques against cryptographic implementations even in the presence of dedicated countermeasures, various methods have...

Do More Elaborate Search Strategies Lead to Better Neural Architecture Search Performance?

Computer vision tasks, like supervised image classification, are effectively tackled by convolutional neural networks, provided that the architecture, which defines the structure of the network, is set correctly. Neural Architecture Search (NAS) is a relatively young and increasingly popular field that is concerned with automatically optimizing...

NASB: Neural Architecture Search for Binary Convolutional Neural Networks

Binary Convolutional Neural Networks (CNNs) have significantly reduced the number of arithmetic operations and the size of memory storage needed for CNNs, which makes their deployment on mobile and embedded systems more feasible. However, after binarization, the CNN architecture has to be redesigned and refined significantly due to two reasons:...