2017, Article / Letter to editor (NTZ. Nederlands Tijdschrift voor de zorg aan mensen met verstandelijke beperkingen, iss. nr. 2, (2017), pp. 108-124)Dit artikel behandelt de problematiek van transities en continuïteit van ondersteuning bij mensen met een verstandelijke beperking. Het onderzoek kijkt naar de vraag welke wensen en behoeften mensen met een lichte en matige verstandelijke beperking (of gezinnen met één of meerdere gezinsleden met een verstandelijke beperking) hebben ten aanzien van flexibele levensloopondersteuning. Deze wensen en behoeften zijn in samenwerking met de doelgroep vertaald in een prototype van een applicatie voor het signaleren van transities en het bevorderen van ondersteuning.
2020, Article / Letter to editor (Transportation Research Record : Journal of the Transportation Research Board, vol. 2674, iss. 7, (2020), pp. 585-595)
2013, Article in monograph or in proceedings (Proceedings van het NIOC congres 2013, pp. 277-286)In deze bijdrage aan het NIOC 2013 bespraken we kort het triangulatie raamwerk dat de basis vormt voor de onderzoeksleerlijn van de Informatica en Communicatie academie(ICA) van de Hogeschool van Arnhem & Nijmegen (HAN). We denken dat de multidisciplinaire onderzoekspraktijk van ICT- en mediaonderwijs vraagt om een benadering die de diversiteit van onderzoek viert en in perspectief plaats. Het gepresenteerde triangulatie raamwerk maakt het mogelijk om de samenhang te zien in een diverse set van onderzoeksmethoden die voor praktijkonderzoek gebruikt worden. Door dit raamwerk als basis voor de onderzoeksleerlijn te gebruiken, verwachten we dat studenten beter door de opleiding heen komen (waarin ze geconfronteerd worden met diverse vormen van onderzoek) en beter leren samenwerken met collega’s die andere vormen van onderzoek hebben aangeleerd.
2013, Article in monograph or in proceedings (Proceedings of the 27th International BCS Human Computer Interaction Conference)The Development Oriented Triangulation (DOT) framework in this paper can spark and focus the debate about mixed-method approaches in HCI. The framework can be used to classify HCI methods, create mixed-method designs, and to align research activities in multidisciplinary projects. The framework is generic enough to capture the diversity of research within the HCI community, while being specific enough to foster constructive debate about combinatorial opportunities and difficulties in mixed-method research in HCI. An analysis of 10 previously published academic HCI research papers showed the utility of the framework for describing a wide range of HCI papers and for raising methodological questions about mixed method approaches in HCI.
2019, Article in monograph or in proceedings (Christophe Debruyne, Hervé Panetto, Wided Guédria, Peter Bollen, Ioana Ciuciu, and Robert Meersman (ed.), On the Move to Meaningful Internet Systems: OTM 2018 Workshops Confederated International Workshops: EI2N, FBM, ICSP, and Meta4eS 2018. Valletta, Malta)
2012, Part of book or chapter of book ()Onbekend.Economies around the globe have evolved into being largely service-oriented economies. Consumers no longer just want a printer or a car, they rather ask for a printing service or a mobility service. In addition, service-oriented organizations increasingly exploit new devices, technologies and infrastructures. Agility is the ability to deal with such changing requirements and environments. Agile ways of working embrace change as a positive force and harness it to the organizations competitive advantage. The approach described in this book focuses on the notion of a service as a piece of functionality that offers value to its customers. Instead of solely looking at agility in the context of system or software development, agility is approached in a broader context. The authors illustrate three kinds of agility that can be found in an agile enterprise: business, process and system agility. These three types of agility reinforce each other and establish the foundation for the agile enterprise. Architecture, patterns, models, and all of the best practices in system development contribute to agile service development and building agile applications. This book addresses two audiences. On the one hand, it aims at agile and architecture practitioners who are looking for more agile ways of working in designing and building business services or who are interested in extending and improving their agile methods by using models and model-based architectures. On the other hand, it addresses students of (enterprise) architecture and software development or service science courses, both in computer science and in business administration.
2018, Article in monograph or in proceedings (The 17th dutch-belgian information retrieval workshop)In this demonstration paper we describe the SMART Radio app
1
forBNRNieuwsradio. TheSMARTRadioappisanextensionto
the current BNR app, which offers users a more personalized news radio experience. It does so by automatically fragmenting shows to offer our users more targeted and focused fragments of audio, not full shows. We employ audio segmentation and audio topic- tagging techniques to achieve this, which we describe in this paper. In its present form, users can subscribe to tags to get appropriate suggestions of relevant radio fragments. In the future we would like to improve the app’s personalization, by using information of the user’s interaction with the app.
2018, Article in monograph or in proceedings (The algorithmic personalization and news (apen18) workshop at icwsm '18)FD Mediagroep (FDMG1 ) is the leading information provider in the financial economic domain in the Netherlands. FDMG operates “Het Financieele Dagblad” (FD) a daily finan- cial newspaper, similar to the Financial Times. In addition, FDMG operates the daily all-news radio station “Business News Radio” (BNR). As we have a wide variety of users with various backgrounds and interests, we believe that digital me- dia (both news articles and radio) should be personalized to match the interests of a particular customer. We are therefore working on personalization of FDMG’s digital media:
• Personalized news: Recommendations and personalized summaries of news articles that match the reading pref- erences and interests of our readers
• Personalized radio: A non-linear radio experience with ra- dio snippets that match the listener’s interests
In both personalized news and personalized radio we are looking not only at introducing recommender systems but also at personalized ways to present the information using automated summarization (news) and audio segmentation (ra- dio) methods
2018, Article in monograph or in proceedings (a Case Study, pp. 383-393)In this practice paper, we report the outcomes of a case study in a new Dutch hospital, where enterprise architects are working toward a 'lean' and 'simplified' EA model to align existing IT systems to new requirements. The objective of the case study was to examine if the developed EA model could support architects in selecting components of an existing IT infrastructure for re-use, with regard to radically new requirements. We have developed an EA model in close collaboration with enterprise architects. This study reflects on the use of this model in the hospital. The approach combines analysis of the content in the model, a study of documents in the organization, and communication with the architects. We signal that the existence of an integrated suite for an Electronic Health Record system largely determined how the model was used. Reflection disclosed that a lack of information on requirements and applications, as well as low adaptability of existing systems, negatively affected the flexibility of IT in the organization.
2018, Article in monograph or in proceedings (Buchmann, R.; Karagiannis, D.; Kirikova, M. (ed.), PoEM 2018. Lecture Notes in Business Information Processing)
2019, Article in monograph or in proceedings (Shakshuki, Elhadi (ed.), The 10th International Conference on Ambient Systems, Networks and Technologies (ANT 2019))This research is an explorative study to look for the potential to predict traffic density from driver behaviour using signals collected from the Controller Area Network (CAN) bus. The hypothesis is that driver behaviour is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behaviour. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. The data for this study is collected from five passenger cars and nineteen trucks driving on the A28 highway in Utrecht region in the Netherlands. This study is restricted to straight roads in order to isolate the steering behaviour attributable to the traffic state influences rather than following the curve in the road. The results are encouraging that the correlation between driver behaviour and traffic density can be established. An overall accuracy of over 95% is achieved with a precision of 92%. The recall rate however is low most likely caused by over-fitting due to the unbalanced data set. The results still look promising and more training data should improve the results. This research is part of the broader project VIA NOVA which aims to investigate the use of car-sensor data for traffic and road asset management.
2019, Article in monograph or in proceedings (Transportation Research Procedia)This research is an explorative study to look for the potential to predict traffic density from driver behavior using signals collected from the Controller Area Network (CAN) bus. The hypothesis is that driver behavior is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behavior. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. The data for this study is collected from five passenger cars and nineteen trucks driving on the A28 highway in Utrecht region in the Netherlands. This study is restricted to straight roads in order to isolate the steering behavior attributable to the traffic state influences rather than following the curve in the road. The results are encouraging that the correlation between driver behavior and traffic density can be established. An overall accuracy of over 95% is achieved with a precision of 92%. The recall rate however is low most likely caused by over-fitting due to the unbalanced dataset. The results still look promising and more training data should improve the results.
2019, Article in monograph or in proceedings (Proceedings: 15th World Conference on Transport Research)This research aims to predict traffic density using driver behaviour
as collected from the CAN bus. The hypothesis is that driver
behavior is influenced by traffic density in such a way that an
approximation of the traffic density can be determined from changes
in the driver behavior. Machine learning will be employed to
correlate a selection of commonly available sensors on cars to the
traffic density. Challenges in the processing of the data for this
purpose will be outlined. The data for this study is collected from
five passenger cars and nineteen trucks driving the A28 highway in
Utrecht region in the Netherlands. The results show that traffic
density can be detected using driver behaviour. An overall accuracy
of over 95\% is achieved with a precision of 92%. The recall rate
however is low most likely caused by overfitting due to the unbalanced
data set. The results still look promising and more training data
should improve the results.
2019, Article in monograph or in proceedings (Paper number ITS-1974, 13th ITS European Congress)This research is an explorative study to look for the potential to predict traffic density from driver behavior using signals collected from the Controller Area Network (CAN) bus. The hypothesis is that driver behavior is influenced by traffic density in such a way that an approximation of the traffic density can be determined from changes in the driver behavior. Machine learning will be employed to correlate a selection of commonly available sensors on cars to the traffic density. Challenges in the processing of the data for this purpose will be outlined. This study is restricted to straight roads in order to isolate the steering behavior attributable to the traffic state influences rather than following the curve in the road. The results are encouraging that the correlation between driver behavior and traffic density can be established. An overall accuracy of over 95% is achieved with a precision of 92%. The recall rate however is low most likely caused by over-fitting due to the unbalanced dataset.
2018, Article in monograph or in proceedings (Proceedings of S-BPM ONE 2018 ; 10th International Conference on Subject-Oriented Business Process Management)In this paper, we describe the development of a collaborative approach to elicit and analyse service process experience as part of a project commissioned by the Dutch Ministry of Infrastructure and Environment. We designed and deployed a model-based instrument for measuring the experiences of both the general public and the civil servants, involved in information sharing, delivery and use and execution of environmental permit application services. In addition, information on the case-specific process structure that is underlying the service delivery was to be gathered with the instrument. We combined a collaborative, stakeholder-oriented process modelling technique with workshops, inspired by the CoMPArE method, with detailed service experience-oriented probing questions focusing on interactions, roles and process ‘bottlenecks’. We carried out a first, baseline measurement on the information, processes and experiences around environmental permit services through 6 identical six-hour workshop sessions with 67 civil servants. Our experiences in executing the baseline measurement are reported, as well as some main results, and lessons learned in developing and applying the workshop approach.
2018, Article in monograph or in proceedings (The 2nd fatrec workshop on responsible recommendation)Personalization of media services is gaining more and more traction, e.g., through the rise of personalization driven by recommender systems across media outlets. At the same time, we see a general rise in distrust and skepticism around the collection and processing of personal data, spurred by policy changes such as the introduc- tion of the GDPR, data breach incidents, and the rise of privacy concerns in general. We feel it is of central importance to be trans- parent about the information we collect, and the ways we use it. In this position paper we motivate the importance of enabling transparency through explaining our recommender system. More specifically, we aim to explain the inferred user profiles that are cen- tral to content-based recommender systems. We describe how user profile explanations can contribute to, or enable different aspects of our recommender system; transparency to help users better under- stand the inner workings of the recommender system, scrutability to allow users to provide explicit feedback on the internally con- structed user profiles, and self-actualization to support users in understanding and exploring their personal preferences. Finally, we believe that user profile explanations can find novel and interesting explanations as an end in itself.
2017, Article in monograph or in proceedings (Proceedings of the 13th International Conference on Semantic Systems)In this short paper, we address the interpretability of hidden layer representations in deep text mining: deep neural networks applied to text mining tasks. Following earlier work predating deep learning methods, we exploit the internal neural network activation (latent) space as a source for performing k-nearest neighbor search, looking for representative, explanatory training data examples with similar neural layer activations as test inputs. We deploy an additional semantic document similarity metric for establishing document similarity between the textual representations of these nearest neighbors and the test inputs. We argue that the statistical analysis of the output of this measure provides insight to engineers training the networks, and that nearest neighbor search in latent space combined with semantic document similarity measures offers a mechanism for presenting explanatory, intelligible examples to users.
2017, Article in monograph or in proceedings (Metzger, A.; Person, A. (ed.), Advanced Information Systems Engineering Workshops CAiSE 2017, pp. 110-132)
2019, Article / Letter to editor (Transportation Research Record : Journal of the Transportation Research Board, vol. 2673, iss. 2, (2019), pp. 60-70)Extreme winter weather conditions severely affect the transportation sector. Technologies such as Road Weather Information Systems provide live data on the road surface conditions to assist the road authorities in providing safe mobility. The main problem is, however, the limited number of such systems that have been deployed, resulting in fragmented informa- tion about road conditions. This paper addresses the problems associated with the limited quantity of information concerning slippery winter road conditions by presenting a proof-of-concept for a system that not only detects slippery winter road con- ditions, but also predicts the type of slippery surface (ice, snow and slush) via vehicle-based systems. The concept demon- strated in this paper makes use of commonly available variables, which are, longitudinal slip ratios, longitudinal acceleration and the ambient temperature to identify such situations. The developed system employs a Fuzzy Inference System that is not only capable of identifying slippery conditions but is also capable of classifying surfaces based on the extent of slipperiness. This provides the road authorities with several moving sensors (vehicles traveling on a particular road) compared with the few fixed sensors currently available. This could deliver a pool of information to assist the road authorities to efficiently han- dle their staff and equipment so that appropriate equipment reaches the right place at the right time.
2010, Part of book or chapter of book (Aalst, Will; Mylopoulos, John; Sadeh, Norman M. (ed.), Enterprise, business-process and information systems modeling, pp. 301-313)
2018, Article in monograph or in proceedings (Book of Abstracts StochMod18)Highway congestion is an increasingly pressing societal problem, both in terms of cost (manyproductive hours lost) and safety (highway congestion increases the risk of accidents). While thereis a plethora of research on detecting and predicting traffic flow state from floating car data fromdata generators such as in-car navigation systems, little research has been done on how more de-tailed vehicle-generated data such as available on the vehicle CAN-bus (breaking, steering, etc.)could be translated into earlier or better quantification of the traffic flow state. The hypothesisunderlying my research is that the the data generated by participating cars can be modelled asa complex system of which the spatio-temporal complexity can be quantified; that a rise in thespatio-temporal complexity could be an early indicator of perturbed traffic flow; and that theaggregate patterns of multiple participating cars on a given road segment could in turn be mod-elled as such a system where the rise in spatio-temporal complexity is a good measure of thecongestion-proneness of the traffic condition. This information could be fused with historical dataon congestion probabilities to provide better congestion prediction with applications towards co-operative (cooperative adaptive speed control) or externally managed (variable message signs)traffic management strategies.
2022, Article / Letter to editor (BMC Musculoskeletal Disorders, (2022))Background
While low back pain occurs in nearly everybody and is the leading cause of disability worldwide, we lack instruments to accurately predict persistence of acute low back pain. We aimed to develop and internally validate a machine learning model predicting non-recovery in acute low back pain and to compare this with current practice and ‘traditional’ prediction modeling.
Methods
Prognostic cohort-study in primary care physiotherapy. Patients (n = 247) with acute low back pain (≤ one month) consulting physiotherapists were included. Candidate predictors were assessed by questionnaire at baseline and (to capture early recovery) after one and two weeks. Primary outcome was non-recovery after three months, defined as at least mild pain (Numeric Rating Scale > 2/10). Machine learning models to predict non-recovery were developed and internally validated, and compared with two current practices in physiotherapy (STarT Back tool and physiotherapists’ expectation) and ‘traditional’ logistic regression analysis.
Results
Forty-seven percent of the participants did not recover at three months. The best performing machine learning model showed acceptable predictive performance (area under the curve: 0.66). Although this was no better than a’traditional’ logistic regression model, it outperformed current practice.
Conclusions
We developed two prognostic models containing partially different predictors, with acceptable performance for predicting (non-)recovery in patients with acute LBP, which was better than current practice. Our prognostic models have the potential of integration in a clinical decision support system to facilitate data-driven, personalized treatment of acute low back pain, but needs external validation first.
2012, Article in monograph or in proceedings (Emerging Topics in the Practice of Enterprise Modeling)In this paper, we describe our study on the relation between formation of abstractions and aspects of executive control in the context of process modeling. We have observed and recorded three business process
modeling projects in different companies. We report on the findings resulting from the analysis of the first project. We find evidence that certain traits related to high-quality abstraction formation contribute to more structured modeling performance. Through our analysis we gain more insight in the cognitive mechanisms involved in modeling, which provides us with another step towards design of effective modeling support.
2014, Article in monograph or in proceedings (NordiCHI'14)In this paper we discuss mixed-method research in HCI. We report on an empirical literature study of the NordiCHI 2012 proceedings which aimed to uncover and describe common mixed-method approaches, and to identify good practices for mixed-methods research in HCI. We present our results as mixed-method research design patterns, which can be used to design, discuss and evaluate mixed-method research. Three dominant patterns are identified and fully described and three additional pattern candidates are proposed. With our pattern descriptions we aim to lay a foundation for a more thoughtful application of, and a stronger discourse about, mixed-method approaches in HCI.
2018, Article in monograph or in proceedings (Joint Proceedings of REFSQ-2018 Workshops, Doctoral Symposium, Live Studies Track, and Poster Trackco-located with the 23rd International Conference on Requirements Engineering: Foundation for Software Quality (REFSQ 2018))
2013, Article in monograph or in proceedings (Conceptual Modeling - 32th International Conference, ER 2013, Hong-Kong, China, November 11-13, 2013, Proceedings)