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A Fast Multi-objective Evolutionary Approach for Designing Large-Scale Optical Mode Sorter

Panichella, A. (author), Di Domenico, Giuseppe (author)

Spatial mode division de-multiplexing of optical signals has many real-world applications, such as quantum computing and both classical and quantum optical communication. In this context, it is crucial to develop devices able to efficiently sort optical signals according to the optical mode they belong to and route them on different paths....

conference paper 2023

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SoK: Explainable Machine Learning for Computer Security Applications

Nadeem, A. (author), Vos, D.A. (author), Cao, C.S. (author), Pajola, Luca (author), Dieck, S. (author), Baumgartner, R. (author), Verwer, S.E. (author)

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks. We identify 3 cybersecurity stakeholders, i.e., model users, designers,...

conference paper 2023

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Improving Adaptive Learning Models Using Prosodic Speech Features

Wilschut, Thomas (author), Sense, Florian (author), Scharenborg, O.E. (author), van Rijn, Hedderik (author)

Cognitive models of memory retrieval aim to describe human learning and forgetting over time. Such models have been successfully applied in digital systems that aid in memorizing information by adapting to the needs of individual learners. The memory models used in these systems typically measure the accuracy and latency of typed retrieval...

conference paper 2023

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Detecting moments of distraction during meditation practice based on changes in the EEG signal

Pandey, Pankaj (author), Rodriguez-Larios, Julio (author), Miyapuram, Krishna Prasad (author), Lomas, J.D. (author)

Electroencephalography (EEG) enables online monitoring brain activity, which can be used for neurofeedback. One of the growing applications of EEG neurofeedback is to facilitate meditation practice. Specifically, EEG neurofeedback can be used to alert participants whenever they get distracted during meditation practice based on changes in their...

conference paper 2023

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Acoustic Emission-Based Detection in Restricted-Access Areas Using Multiple PZT Disc Sensors

Cheng, L. (author), Nokhbatolfoghahai, A. (author), Groves, R.M. (author), Veljkovic, M. (author)

The performance of the Acoustic Emission (AE) technique is significantly dependent on the sensors attached to the structural surface. Although conventional commercially AE sensors, like R15a and WSa sensors, have been extensively employed in monitoring many different structures, they are unavailable in restricted-assess areas. In contrast,...

conference paper 2023

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Data-Driven Support Vector Machine to Predict Thin-Walled Tube Energy Absorbers Behavior

Ghasemi, Mostafa (author), Silani, Mohammad (author), Yaghoubi Nasrabadi, V. (author), Concli, Franco (author)

To design a more efficient energy absorber, it is critical to evaluate how changing the design parameters affects its performance, and also determine each one’s order of significance. In this paper, using a new approach, the behavior and response of straight, double-tapered, and triple-tapered thin-walled tubes with rectangular cross sections...

conference paper 2023

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Label-Only Membership Inference Attack against \\Node-Level Graph Neural Networks

Conti, M. (author), Li, Jiaxin (author), Picek, S. (author), Xu, J. (author)

Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representations of nodes for node classification, graph classification, and link prediction. Previous studies have indicated that node-level GNNs are vulnerable to Membership...

conference paper 2022

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Generation of design allowables on the open hole tensile strength of composite laminates using sensitivity analysis and uncertainty quantification framework

Sasikumar, Aravind (author), Ninyerola, Joan (author), Ruiz, Ivan (author), Bessa, M.A. (author), Turon Travesa, Albert (author)

Aeronautical industries are concerned about the cost effective generation of design allowables for composite laminates. Design allowables take into account the variabilities arising from different sources (material, manufacturing, defects etc.,) which are determined using expensive and time consuming experimental campaigns....

conference paper 2022

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Interpretable neural network with limited weights for constructing simple and explainable HI using SHM data

Moradi, M. (author), Komninos, P. (author), Benedictus, R. (author), Zarouchas, D. (author)

Recently, companies all over the world have been focusing on the improvement of autonomous health management systems in order to enhance performance and reduce downtime costs. To achieve this, the remaining useful life predictions have been given remarkable attention. These predictions depend on the proper designing process and the quality of...

conference paper 2022

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Classifying EEG Signals of Mind-Wandering Across Different Styles of Meditation

Chaudhary, Shivam (author), Pandey, Pankaj (author), Miyapuram, Krishna Prasad (author), Lomas, J.D. (author)

In the modern world, it is easy to get lost in thought, partly because of the vast knowledge available at our fingertips via smartphones that divide our cognitive resources and partly because of our intrinsic thoughts. In this work, we aim to find the differences in the neural signatures of mind-wandering and meditation that are common across...

conference paper 2022

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Performance Comparison of Deep RL Algorithms for Energy Systems Optimal Scheduling

Shengren, H. (author), Salazar, Edgar Mauricio (author), Vergara Barrios, P.P. (author), Palensky, P. (author)

Taking advantage of their data-driven and model-free features, Deep Reinforcement Learning (DRL) algorithms have the potential to deal with the increasing level of uncertainty due to the introduction of renewable-based generation. To deal simultaneously with the energy systems’ operational cost and technical constraints (e.g, generation-demand...

conference paper 2022

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MLSmellHound: A Context-Aware Code Analysis Tool

Kannan, Jai (author), Barnett, Scott (author), Cruz, Luis (author), Simmons, Anj (author), Agarwal, Akash (author)

Meeting the rise of industry demand to incorporate machine learning (ML) components into software systems requires interdisciplinary teams contributing to a shared code base. To maintain consistency, reduce defects and ensure maintainability, developers use code analysis tools to aid them in identifying defects and maintaining standards. With...

conference paper 2022

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MLTK01: A Prototyping Toolkit for Tangible Learning Things

Lupetti, M.L. (author), Romagnoli, L. (author)

This work illustrates and reflects on the design process of MLTK01, an open-source toolkit for fast prototyping tangible learning things, built on top of Arduino and ml5js. The toolkit was developed as a response to the current lack of fast and easy to use tools for tangible experiments with machine learning. Learning from insights gained...

conference paper 2022

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When Machine Learning Models Leak: An Exploration of Synthetic Training Data

Slokom, M. (author), de Wolf, Peter Paul (author), Larson, M.A. (author)

We investigate an attack on a machine learning classifier that predicts the propensity of a person or household to move (i.e., relocate) in the next two years. The attack assumes that the classifier has been made publically available and that the attacker has access to information about a certain number of target individuals. That attacker...

conference paper 2022

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Fusion of Radar Data Domains for Human Activity Recognition in Assisted Living

Le Kernec, Julien (author), Fioranelli, F. (author), Romain, Olivier (author), Bordat, Alexandre (author)

Radar has long been considered an important technology for indoor monitoring and assisted living. As ageing has become a worldwide problem, it causes a huge burden on the government’s healthcare expenses and infrastructure. Radar-based human activity recognition (HAR) is foreseen to become a widespread sensing modality for health monitoring...

conference paper 2022

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AI supported noise analyses for structure design requirements definition

Domazetovska, Simona (author), Gavriloski, Viktor (author), Jovanova, J. (author)

The artificial intelligence (AI) field has encountered a turning point mainly due to advancements in machine learning, which allows systems to learn, improve, and perform a specific task through data without being explicitly programmed. Machine learning can be utilized with machining processes to improve product quality levels and...

conference paper 2021

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Using Machine Learning to Quantify the Robustness of Network Controllability

Dhiman, Ashish (author), Sun, P. (author), Kooij, Robert (author)

This paper presents machine learning based approximations for the minimum number of driver nodes needed for structural controllability of networks under link-based random and targeted attacks. We compare our approximations with existing analytical approximations and show that our machine learning based approximations significantly outperform...

conference paper 2021

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ReproducedPapers.org: Openly Teaching and Structuring Machine Learning Reproducibility

Yildiz, B. (author), Hung, H.S. (author), Krijthe, J.H. (author), Liem, C.C.S. (author), Loog, M. (author), Migut, M.A. (author), Oliehoek, F.A. (author), Panichella, A. (author), Pawełczak, Przemysław (author), Picek, S. (author), de Weerdt, M.M. (author), van Gemert, J.C. (author)

We present ReproducedPapers.org : an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students and the added value of an online reproduction repository among AI researchers. We use anonymous self-assessment surveys and obtained 144 responses. Results suggest...

conference paper 2021

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Deterministic and Statistical Strategies to Protect ANNs against Fault Injection Attacks

Köylü, T.C. (author), Reinbrecht, Cezar (author), Hamdioui, S. (author), Taouil, M. (author)

Artificial neural networks are currently used for many tasks, including safety critical ones such as automated driving. Hence, it is very important to protect them against faults and fault attacks. In this work, we propose two fault injection attack detection mechanisms: one based on using output labels for a reference input, and the other on...

conference paper 2021

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Demonstrating a Bayesian Online Learning for Energy-Aware Resource Orchestration in vRANs

Ayala-Romero, Jose A. (author), Garcia-Saavedra, Andres (author), Costa-Perez, Xavier (author), Iosifidis, G. (author)

Radio Access Network Virtualization (vRAN) will spearhead the quest towards supple radio stacks that adapt to heterogeneous infrastructure: from energy-constrained platforms deploying cells-on-wheels (e.g., drones) or battery-powered cells to green edge clouds. We demonstrate a novel machine learning approach to solve resource orchestration...

conference paper 2021