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Human-centered XAI: Developing design patterns for explanations of clinical decision support systems

Schoonderwoerd, T.A.J. (author), Jorritsma, W. (author), Neerincx, M.A. (author), van den Bosch, K. (author)

Much of the research on eXplainable Artificial Intelligence (XAI) has centered on providing transparency of machine learning models. More recently, the focus on human-centered approaches to XAI has increased. Yet, there is a lack of practical methods and examples on the integration of human factors into the development processes of AI-generated...

article 2021

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Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent

Veugen, P.J.M. (author), Kamphorst, B (author), van de L'Isle, N. (author), van Egmond, M.B. (author)

We show how multiple data-owning parties can collabora tively train several machine learning algorithms without jeopardizing the privacy of their sensitive data. In particular, we assume that every party knows specific features of an overlapping set of people. Using a secure implementation of an advanced hidden set intersection protocol and a...

conference paper 2021

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Classification Using a Two-Qubit Quantum Chip

Neumann, N.M.P. (author)

conference paper 2021

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Machine learning-based slice management in 5G networks for emergency scenarios

Arora, A. (author), Dimitrovski, T. (author), Litjens, R. (author), Zhang, H. (author)

conference paper 2021

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Detecting Message Modification Attacks on the CAN Bus with Temporal Convolutional Networks

Chiscop, I. (author), Gazdag, A. (author), Bosman, J. (author), Biczók, G. (author)

Multiple attacks have shown that in-vehicle networks have vulnerabilities which can be exploited. Securing the Controller Area Network (CAN) for modern vehicles has become a necessary task for car manufacturers. Some attacks inject potentially large amount of fake messages into the CAN network; however, such attacks are relatively easy to detect...

conference paper 2021

document

Improving the analytical framework for quantifying technological progress in energy technologies

Santhakumar, S. (author), Meerman, J.C. (author), Faaij, A.P.C. (author)

This article reviews experience curve applications in energy technology studies to illustrate best practices in analyzing technological learning. Findings are then applied to evaluate future performance projections of three emerging offshore energy technologies, namely, offshore wind, wave & tidal, and biofuel production from seaweed. Key...

article 2021

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Quantum Computing for Military Applications

Neumann, N.M.P. (author), van Heesch, M.P.P. (author), Phillipson, F. (author), Smallegange, A.A.P. (author)

Quantum computers have the potential to outshine classical alternatives in solving specific problems, under the assumption of mature enough hardware. A specific subset of these problems relate to military applications. In this paper we consider the state-of-the-art of quantum technologies and different applications of this technology....

conference paper 2021

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The FATE System: FAir, Transparent and Explainable Decision making

de Greeff, J. (author), de Boer, M.H.T. (author), Hillerström, F.H.J. (author), Bomhoff, F.W. (author), Jorritsma, W. (author), Neerinx, M. (author)

AI tools are becoming more commonly used in a variety of application domains. In this paper, we describe a system named FATE that combines state of the art AI tools. The goal of the FATE system is decision support with use of ongoing human-AI co-learning, explainable AI and fair, bias-free and secure usage of data. These topics are societally...

conference paper 2021

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Hybrid AI: The Way Forward in AI by Developing Four Dimensions

Huizing, A. (author), Veenman, C.J. (author), Neerincx, M. (author), Dijk, J. (author)

In recent years, AI based on deep learning has achieved tremendous success in specialized tasks such as speech recognition, machine translation, and the detection of tumors in medical images. Despite these successes there are also some clear signs of the limitations of the current state-of-the-art in AI. For example, biases in AI-enabled face...

conference paper 2021

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Using scaffolding to formalize digital coach support for low-literate learners

Schouten, D.G.M. (author), Massink, P. (author), Donker, S.F. (author), Neerincx, M.A. (author), Cremers, A.H.M. (author)

In this study, we attempt to specify the cognitive support behavior of a previously designed embodied conversational agent coach that provides learning support to low-literates. Three knowledge gaps are identified in the existing work: an incomplete specification of the behaviors that make up ‘support,’ an incomplete specification of how this...

article 2021

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Deep convolutional neural networks for surface coal mines determination from sentinel-2 images

Madhuanand, L. (author), Sadavarte, P. (author), Visschedijk, A.J.H. (author), Denier van der Gon, H.A.C. (author), Aben, I. (author), Osei, F.B. (author)

article 2021

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Exploring the complex origins of energy poverty in The Netherlands with machine learning

Dalla Longa, F. (author), Sweerts, B. (author), van der Zwaan, B.C.C. (author)

Energy poverty is receiving increased attention in developed countries like the Netherlands. Although it only affects a relatively small share of the population, it constitutes a stern challenge that is hard to quantify and monitor, hence difficult to effectively tackle through adequate policy measures. In this paper we introduce a framework to...

article 2021

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Early detection of exposure to toxic chemicals using continuously recorded multi-sensor physiology

van Baardewijk, J.U. (author), Agarwal, S. (author), Cornelissen, A.S. (author), Joosen, M.J.A. (author), Kentrop, J. (author), Varon, C. (author), Brouwer, A.M. (author)

Early detection of exposure to a toxic chemical, e.g., in a military context, can be life-saving. We propose to use machine learning techniques and multiple continuously measured physiological signals to detect exposure, and to identify the chemical agent. Such detection and identification could be used to alert individuals to take appropriate...

article 2021

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Indoor–Outdoor Detection in Mobile Networks Using Quantum Machine Learning Approaches

Phillipson, F. (author), Wezeman, R.S. (author), Chiscop, I. (author)

Communication networks are managed more and more by using artificial intelligence. Anomaly detection, network monitoring and user behaviour are areas where machine learning offers advantages over more traditional methods. However, computer power is increasingly becoming a limiting factor in machine learning tasks. The rise of quantum computers...

article 2021

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Thickness evaluation of AlOx barrier layers for encapsulation of flexible PV modules in industrial environments by normal reflectance and machine learning

Grau-Luque, E. (author), Guc, M. (author), Becerril-Romero, I. (author), Izqueierdo-Roca, V. (author), Perez-Rodriguez, A. (author), Bolt, P. (author), van den Bruele, F.J. (author), Ruhle, U. (author)

Flexible photovoltaic (PV) devices, such as those based on Cu (In,Ga)Se2 (CIGS) and perovskites, use polymeric front sheets for encapsulation that do not provide sufficient protection against the environment. The addition of nanometric AlxO layers by spatial atomic layer deposition (S-ALD) to these polymeric materials can highly improve...

article 2021

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A Machine Learning Approach for Identifying Children at Risk of Suboptimal Adherence to Growth Hormone Therapy

Spataru, A. (author), van Dommelen, P. (author), Arnaud, L. (author), Le Masne, Q. (author), Quarteroni, S. (author), Koledova, E.B. (author)

Background: Suboptimal adherence to recombinant human growth hormone (r-hGH) treatment can lead to suboptimal clinical outcomes. Being able to identify children who are at risk of suboptimal adherence in the near future, and take adequate measures to support adherence, may maximize clinical outcomes. Our aim was to develop a model based on data...

article 2021

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Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning

van Zoelen, E.M. (author), van den Bosch, K. (author), Neerincx, M. (author)

Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication...

article 2021

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Technologische doorbraak eindelijk een privacyvriendelijke manier om data te benutten [Technological breakthrough finally, a privacy-friendly way to harness data]

Attema, T. (author), Worm, D. (author)

Data sharing and analysis are essential when it comes to achieving economic growth and solving societal challenges. However, data sharing is yet to really get off the ground due to commercial and/or legal barriers, including the fundamental right to privacy. Innovative technologies such as Federated Learning and Multi-Party Computation offer a...

report 2021

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Preface

van Gulijk, C. (author), Zaitseva, E. (author)

[No abstract available]

bookPart 2021

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Natural language processing for cognitive therapy: Extracting schemas from thought records

Burger, F. (author), Neerincx, M.A. (author), Brinkman, W.P. (author)

The cognitive approach to psychotherapy aims to change patients' maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far,...

article 2021