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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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Wu, L. (author), Perin, G. (author), Picek, S. (author)
In the last decade, machine learning-based side-channel attacks have become a standard option when investigating profiling side-channel attacks. At the same time, the previous state-of-the-art technique, template attack, started losing its importance and was more considered a baseline to compare against. As such, most of the results reported...
journal article 2022
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Viana, Paula (author), Andrade, Maria Teresa (author), Carvalho, Pedro (author), Vilaça, Luis (author), Teixeira, Inês N. (author), Costa, Tiago (author), Jonker, P.P. (author)
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses...
journal article 2022
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Ganguly, Amlan (author), Abadal, Sergi (author), Thakkar, Ishan (author), Enright Jerger, Natalie (author), Riedel, Marc (author), Babaie, M. (author), Balasubramonian, Rajeev (author), Sebastian, Abu (author), Pasricha, Sudeep (author), Taskin, Baris (author)
The computing world is witnessing a proverbial Cambrian explosion of emerging paradigms propelled by applications, such as artificial intelligence, big data, and cybersecurity. The recent advances in technology to store digital data inside a deoxyribonucleic acid (DNA) strand, manipulate quantum bits (qubits), perform logical operations with...
journal article 2022
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Pérez-Dattari, Rodrigo (author), Ferreira de Brito, B.F. (author), de Groot, O.M. (author), Kober, J. (author), Alonso-Mora, J. (author)
The successful integration of autonomous robots in real-world environments strongly depends on their ability to reason from context and take socially acceptable actions. Current autonomous navigation systems mainly rely on geometric information and hard-coded rules to induce safe and socially compliant behaviors. Yet, in unstructured urban...
journal article 2022
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Perin, G. (author), Wu, L. (author), Picek, S. (author)
One of the main promoted advantages of deep learning in profiling side-channel analysis is the possibility of skipping the feature engineering process. Despite that, most recent publications consider feature selection as the attacked interval from the side-channel measurements is pre-selected. This is similar to the worst-case security...
journal article 2022
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Rijsdijk, Jorai (author), Wu, L. (author), Perin, G. (author)
Deep learning-based side-channel attacks are capable of breaking targets protected with countermeasures. The constant progress in the last few years makes the attacks more powerful, requiring fewer traces to break a target. Unfortunately, to protect against such attacks, we still rely solely on methods developed to protect against generic...
conference paper 2022
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Kerkhof, Maikel (author), Wu, L. (author), Perin, G. (author), Picek, S. (author)
The deep learning-based side-channel analysis represents one of the most powerful side-channel attack approaches. Thanks to its capability in dealing with raw features and countermeasures, it becomes the de facto standard approach for the SCA community. The recent works significantly improved the deep learning-based attacks from various...
conference paper 2022
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Mody, Prerak (author), Chaves-de-Plaza, Nicolas F. (author), Hildebrandt, K.A. (author), Staring, M. (author)
Bayesian Neural Nets (BNN) are increasingly used for robust organ auto-contouring. Uncertainty heatmaps extracted from BNNs have been shown to correspond to inaccurate regions. To help speed up the mandatory quality assessment (QA) of contours in radiotherapy, these heatmaps could be used as stimuli to direct visual attention of clinicians to...
conference paper 2022
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Andringa, S.P.E. (author), Yorke-Smith, N. (author)
Simulation–optimization is often used in enterprise decision-making processes, both operational and tactical. This paper shows how an intuitive mapping from descriptive problem to optimization model can be realized with Constraint Programming (CP). It shows how a CP model can be constructed given a simulation model and a set of business goals...
conference paper 2022
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Xiao, C. (author), Lin, H.X. (author), Leeuwenburgh, O. (author), Heemink, A.W. (author)
History matching can play a key role in improving geological characterization and reducing the uncertainty of reservoir model predictions. Application of reservoir history matching is restricted by the huge computational cost by amongst others the many runs of the full model. Surrogate models with a reduced complexity are therefore used to...
journal article 2022
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Rezaeezade, A. (author), Perin, G. (author), Picek, S. (author)
Profiling side-channel analysis allows evaluators to estimate the worst-case security of a target. When security evaluations relax the assumptions about the adversary’s knowledge, profiling models may easily be sub-optimal due to the inability to extract the most informative points of interest from the side-channel measurements. When used for...
conference paper 2022
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Danaei, Deniz (author)
<br/>The fight against the illegal hunting of African wildlife is a never-ending process. In order to preserve animal habitats and save them from extinction, many national parks utilize surveilling solutions to prevent, detect and locate intruders. One strategy to detect and locate the illegal hunters or so-called \textit{poachers} is to detect...
master thesis 2021
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Zijta, Marcella (author)
Hydrocephalus is a disease where an excess of cerebrospinal fluid (CSF) is built up in the brain. It affects approximately 180.000 infants per year in sub-Saharan Africa. Magnetic resonance imaging (MRI) is an advantageous imaging method to diagnose hydrocephalus and examine the amount of fluid in the brain for treatment. Unfortunately, in sub...
master thesis 2021
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Baas, Berend (author)
Existing work in shape editing applications using deep learning has primarily focused on shape interpolation.<br/>We propose a pair of techniques that utilize the latent space of deformation networks to provide control schemes for semantic shape editing applications.<br/><br/>The first technique presented utilizes linear directions as...
master thesis 2021
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Suryanarayanan, Surya Narayanan (author)
Inverse design with topology optimization has followed the same computational<br/>graph for decades. The unknown material density is distributed within a domain,<br/>a computational analysis predicts the response of that design and its derivative<br/>with respect to the unknown, and this information is used by a chosen gradient­<br/>based...
master thesis 2021
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Arunmoli, Karthik Arvind (author)
Learning from demonstration is a technique where the robot learns directly from humans. It can be beneficial to learn from humans directly because humans can easily demonstrate complex behaviors without being experts in demonstrating required tasks. However, it can be challenging to gather large amounts of data from humans because humans often...
master thesis 2021
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Wiersma, Mark (author)
Automated bin-picking is a difficult task that requires solving multiple robotic vision problems including object detection and grasp proposal generation. Current methods use deep learning to approach each of the vision problems of bin-picking separately with the main focus on generating the grasp proposals. For grasp proposal generation, neural...
master thesis 2021
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Patil, Sandeep (author)
Lane detection represents a fundamental task for automated/autonomous vehicles. Current lane detection methods do not provide the versatility of real-time performance, robustness,and accuracy required for real-world scenarios. The reasons include lack of computing power while being portable and inability to observe the continuity and structure...
master thesis 2021
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Corredor Mora, Diego (author)
It is vital for adequate management, and operation of water distribution systems (WDS) to have reliable short-term water demand forecasts. Conventional time-series models present limitations when dealing with non-linear changes in water demand. Thus, it is proposed to employ deep learning algorithms to offer a more reliable forecast. Three...
master thesis 2021
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