Searched for: collection%253Air
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Averta, Giuseppe (author), Arapi, Visar (author), Bicchi, Antonio (author), Della Santina, C. (author), Bianchi, Matteo (author)
The need for users’ safety and technology acceptability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions and interactions. In this manuscript, we present a...
book chapter 2021
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Liem, C.C.S. (author), Langer, Markus (author), Demetriou, A.M. (author), Hiemstra, Annemarie M.F. (author), Achmadnoer Sukma Wicaksana, Sukma (author), Born, Marise Ph. (author), König, Cornelis J. (author)
In a rapidly digitizing world, machine learning algorithms are increasingly employed in scenarios that directly impact humans. This also is seen in job candidate screening. Data-driven candidate assessment is gaining interest, due to high scalability and more systematic assessment mechanisms. However, it will only be truly accepted and trusted...
book chapter 2018
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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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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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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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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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Degeler, V. (author), Heydenrijk-Ottens, L.J.C. (author), Luo, D. (author), van Oort, N. (author)
We perform analysis of public transport data from March 2015 from The<br/>Hague, the Netherlands, combined from three sources: static network information, automatic vehicles location (AVL) and automated fare collection (AFC) data. We highlight the effect of bunching swings, and show that this phenomenon can be extracted using unsupervised...
conference paper 2018
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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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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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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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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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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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Feirstein (student), D.S. (author), Koryakovskiy, I. (author), Kober, J. (author), Vallery, H. (author)
Reinforcement learning is a powerful tool to derive controllers for systems where no models are available. Particularly policy search algorithms are suitable for complex systems, to keep learning time manageable and account for continuous state and action spaces. However, these algorithms demand more insight into the system to choose a...
conference paper 2016
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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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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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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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Panichella, A. (author), Alexandru, Carol V. (author), Panichella, Sebastiano (author), Bacchelli, A. (author), Gall, Harald C. (author)
Research has yielded approaches to predict future defects in software artifacts based on historical information, thus assisting companies in effectively allocating limited development resources and developers in reviewing each others' code changes. Developers are unlikely to devote the same effort to inspect each software artifact predicted...
conference paper 2016
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Liu Cheng, Alexander (author), Bier, H.H. (author), Mostafavi, Sina (author)
This paper presents a new instance in a series of discrete proof-of-concept implementations of comprehensively intelligent built-environments based on Design-to-Robotic-Production and -Operation (D2RP&amp;O) principles developed at Delft University of Technology (TUD). With respect to D2RP, the featured implementation presents a customized...
conference paper 2017
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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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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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