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Edwards, Alec (author), Peruffo, A. (author), Abate, Alessandro (author)This paper presents Fossil 2.0, a new major release of a software tool for the synthesis of certificates (e.g., Lyapunov and barrier functions) for dynamical systems modelled as ordinary differential and difference equations. Fossil 2.0 is much improved from its original release, including new interfaces, a significantly expanded certificate...conference paper 2024
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Deist, Timo M. (author), Grewal, M. (author), Dankers, Frank J.W.M. (author), Alderliesten, T. (author), Bosman, P.A.N. (author)Real-world problems are often multi-objective, with decision-makers unable to specify a priori which trade-off between the conflicting objectives is preferable. Intuitively, building machine learning solutions in such cases would entail providing multiple predictions that span and uniformly cover the Pareto front of all optimal trade-off...conference paper 2023
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Lee, Y. (author), Chen, H. (author), Zhao, Guoying (author), Specht, M.M. (author)Human attention is critical yet challenging cognitive process to measure due to its diverse definitions and non-standardized evaluation. In this work, we focus on the attention self-regulation of learners, which commonly occurs as an effort to regain focus, contrary to attention loss. We focus on easy-to-observe behavioral signs in the real...conference paper 2022
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Hafner, Frank M. (author), Zeller, Matthias (author), Schutera, Mark (author), Abhau, Jochen (author), Kooij, J.F.P. (author)Customization of a convolutional neural network (CNN) to a specific compute platform involves finding an optimal pareto state between computational complexity of the CNN and resulting throughput in operations per second on the compute platform. However, existing inference performance benchmarks compare complete backbones that entail many...conference paper 2022
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Miccini, Riccardo (author), Spagnol, S. (author)We present a hybrid approach to individualized head-related transfer function (HRTF) modeling which requires only 3 anthropometric measurements and an image of the pinna. A prediction algorithm based on variational autoencoders synthesizes a pinna-related response from the image, which is used to filter a measured head-andtorso response. The...conference paper 2021
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Alves, Flavia (author), Gairing, Martin (author), Oliehoek, F.A. (author), Do, Thanh-Toan (author)The field of Human Activity Recognition (HAR) focuses on obtaining and analysing data captured from monitoring devices (e.g. sensors). There is a wide range of applications within the field; for instance, assisted living, security surveillance, and intelligent transportation. In HAR, the development of Activity Recognition models is dependent...conference paper 2020
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Reggiani, G. (author), Dabiri, A. (author), Daamen, W. (author), Hoogendoorn, S.P. (author)A tool for travel time estimation of cyclists approaching a traffic light can monitor level of service of intersections in bike crowded cities. This work represents a first step in developing such a tool. Neural Network models are evaluated on how they perform in estimating individual travel time of cyclists approaching a signalized...conference paper 2020
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Castellini, Jacopo (author), Oliehoek, F.A. (author), Savani, Rahul (author), Whiteson, Shimon (author)Recent years have seen the application of deep reinforcement learning techniques to cooperative multi-agent systems, with great empirical success. In this work, we empirically investigate the representational power of various network architectures on a series of one-shot games. Despite their simplicity, these games capture many of the crucial...conference paper 2019
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Alipour, Babak (author), Tonetto, Leonardo (author), Ketabi, Roozbeh (author), Ding, Aaron Yi (author), Ott, Jörg (author), Helmy, Ahmed (author)Understanding and predicting mobility are essential for the design and evaluation of future mobile edge caching and networking. Consequently, research on human mobility prediction has drawn significant attention in the last decade. Employing information-theoretic concepts and machine learning methods, earlier research has shown evidence that...conference paper 2019
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de Bruin, T.D. (author), Kober, J. (author), Tuyls, K.P. (author), Babuska, R. (author)Recent years have seen a growing interest in the use of deep neural networks as function approximators in reinforcement learning. In this paper, an experience replay method is proposed that ensures that the distribution of the experiences used for training is between that of the policy and a uniform distribution. Through experiments on a...conference paper 2016
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Faghih Roohi, S. (author), Hajizadeh, S. (author), Nunez, Alfredo (author), Babuska, R. (author), De Schutter, B.H.K. (author)In this paper, we propose a deep convolutional neural network solution to the analysis of image data for the detection of rail surface defects. The images are obtained from many hours of automated video recordings. This huge amount of data makes it impossible to manually inspect the images and detect rail surface defects. Therefore, automated...conference paper 2016