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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
document
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&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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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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Zhang, Y. (author), Olenick, Jeffrey (author), Chang, Chu-Hsiang (author), Kozlowski, Steve W.J. (author), Hung, H.S. (author)
Social interaction plays a key role in assessing teamwork and collaboration. It becomes particularly critical in team performance when coupled with isolated, confined, and extreme conditions such as undersea missions. This work investigates how social interactions of individual members in a small team evolve during the course of a long...
conference paper 2018
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Zhu, Jichen (author), Liapis, Antonios (author), Risi, Sebastian (author), Bidarra, Rafael (author), Michael Youngblood, G. (author)
Growing interest in eXplainable Artificial Intelligence (XAI) aims to make AI and machine learning more understandable to human users. However, most existing work focuses on new algorithms, and not on usability, practical interpretability and efficacy on real users. In this vision paper, we propose a new research area of eXplainable AI for...
conference paper 2018
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Virgolin, M. (author), Alderliesten, Tanja (author), Bel, Arjan (author), Witteveen, C. (author), Bosman, P.A.N. (author)
The recently introduced Gene-pool Optimal Mixing Evolutionary Algorithm for Genetic Programming (GP-GOMEA) has been shown to find much smaller solutions of equally high quality compared to other state-of-the-art GP approaches. This is an interesting aspect as small solutions better enable human interpretation. In this paper, an adaptation of...
conference paper 2018
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van Rooijen, S. J. (author), Ellerbroek, Joost (author), Borst, C. (author), van Kampen, E. (author)
Lack of trust has been identified as an obstacle in the introduction of workload-alleviating automation in air traffic control. The work presented in this paper describes a concept to generate individual-sensitive resolution advisories for air traffic conflicts, with the aim of increasing acceptance by adapting advisories to different...
conference paper 2019
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Malsattar, N.P. (author), Kihara, Tomo (author), Giaccardi, Elisa (author)
This paper describes ObjectResponder - a tool that allows designers to use Artificial Intelligence (AI) to rapidly prototype concepts for context-aware intelligent interaction in the wild. To our knowledge, there are currently no available tools for designing and prototyping with AI within the actual context of use. Our application uses...
conference paper 2019
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Virgolin, M. (author), Wang, Ziyuan (author), Alderliesten, T. (author), Bosman, P.A.N. (author)
The advent of Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To capture the effects of radiation treatment, treatment plans are typically simulated...
conference paper 2020
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Lai, Marco (author), Skyrman, Simon (author), Shan, Caifeng (author), Paulussen, Elvira (author), Manni, Francesca (author), Swamy, A. (author), Babic, Drazenko (author), Edstrom, Erik (author), Persson, Oscar (author), Burstrom, Gustav (author), Elmi-Terander, Adrian (author), Hendriks, B.H.W. (author), De With, Peter H.N. (author)
In neurosurgery, technical solutions for visualizing the border between healthy brain and tumor tissue is of great value, since they enable the surgeon to achieve gross total resection while minimizing the risk of damage to eloquent areas. By using real-time non-ionizing imaging techniques, such as hyperspectral imaging (HSI), the spectral...
conference paper 2020
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Hillege, Roderic H.L. (author), Lo, J.C. (author), Janssen, Christian P. (author), Romeijn, Nico (author)
This paper investigates whether mental workload can be classified in an operator setting using unobtrusive psychophysiological measures. Having reliable predictions of workload using unobtrusive sensors can be useful for adaptive instructional systems, as knowledge of a trainee’s workload can then be used to provide appropriate training level...
conference paper 2020
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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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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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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
document
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
document
Amanatidis, Petros (author), Iosifidis, G. (author), Karampatzakis, Dimitris (author)
Computer science and engineering have evolved rapidly over the last decade offering innovative Machine Learning frameworks and high-performance hardware devices. Executing data analytics at the edge promises to transform the mobile computing paradigm by bringing intelligence next to the end user. However, it remains an open question to...
conference paper 2021
document
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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