1. 2019
  2. Spenader, J., & Roest, C. (2019). Facilitating Quantifier Acquisition: Training Can Eliminate Children's Spreading Errors. In BUCLD 43: Proceedings of the 43rd annual Boston University Conference on Language Development edited by Megan M. Brown and Brady Dailey (Vol. 2, pp. 653-666). Boston, USA: Cascadilla Press.
  3. Ayoobi, H., Cao, M., Verbrugge, L., & Verheij, B. (Accepted/In press). Handling Unforeseen Failures Using Argumentation-Based Learning. In International Conference on Automation Science and Engineering (CASE) 2019 (pp. 1-8)
  4. Wolf, B., & van Netten, S. (2019). Training submerged source detection for a 2D fluid flow sensor array with Extreme Learning Machines. In Eleventh International Conference on Machine Vision (ICMV 2018) (Vol. 11041, pp. 1104126). SPIE.Digital Library. https://doi.org/10.1117/12.2522667
  5. van Vugt, M. K., Moye, A., Pollock, J., Johnson, B., Bonn-Miller, M. O., Gyatso, K., ... Fresco, D. M. (2019). Tibetan Buddhist monastic debate: Psychological and neuroscientific analysis of a reasoning-based analytical meditation practice. In Imagining the Brain: Episodes in the History of Brain Research (Progress in brain research). Elsevier. https://doi.org/10.1016/bs.pbr.2018.10.018
  6. Sillitti, A., Schomaker, L., Anakabe, J. F., Basurko, J., Dam, P., Ferreira, H., ... Zurutuza, U. (2019). Providing Proactiveness: Data Analysis Techniques Portfolios. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 145-238). Gistrup (DK): River Publishers.
  7. Bosnic, A., & Spenader, J. (2019). Acquisition Path of Distributive Markers in Serbian and Dutch: Evidence from an Act-Out Task. In M. M. Brown, & B. Dailey (Eds.), Proceedings of the 43rd Boston University Conference on Language Development (pp. 94-108). Somerville, MA: Cascadilla Press.
  8. Dijkstra, K., van de Loosdrecht, J., Schomaker, L. R. B., & Wiering, M. A. (2019). CentroidNet: A Deep Neural Network for Joint Object Localization and Counting. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, M. Berlingerio, ... N. Hurly (Eds.), ECML PKDD 2018: Machine Learning and Knowledge Discovery in Databases (pp. 585-601). ( Lecture Notes in Computer Science; Vol. 11053). Cham: Springer. https://doi.org/10.1007/978-3-030-10997-4_36
  9. Okafor, E. (2019). Deep learning for animal recognition. [Groningen]: University of Groningen.
  10. Keshavarzi Zafarghandi, A., Verheij, B., & Verbrugge, L. (Accepted/In press). Embedding Probabilities, Utilities and Decisions in a Generalization of Abstract Dialectical Frameworks. In International Symposium on Imprecise Probability: Theories and Applications (ISIPTA)
  11. Vugt, M. V. (2019). Mindfulness as a Potential Tool for Productivity. In C. Sadowski, & T. Zimmermann (Eds.), Rethinking Productivity in Software Engineering (pp. 293-302). (Rethinking Productivity in Software Engineering). Apress. https://doi.org/10.1007/978-1-4842-4221-6_25
  12. Schomaker, L., Albano, M., Jantunen, E., & Ferreira, L. L. (2019). The future of Maintenance. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book : Cyber Physical System Based Proactive Collaborative Maintenance (pp. 555). Gistrup (DK): River Publishers.
  13. Vugt, M. V. (2019). Using Biometric Sensors to Measure Productivity. In C. Sadowski , & T. Zimmermann (Eds.), Rethinking Productivity in Software Engineering (pp. 159-167). (Rethinking Productivity in Software Engineering). Berkeley, CA: Apress. https://doi.org/10.1007/978-1-4842-4221-6_14
  14. 2018
  15. Lieto, A., Kennedy, W. G., Lebiere, C., Romero, O. J., Taatgen, N., & West, R. L. (2018). Higher-level Knowledge, Rational and Social Levels Constraints of the Common Model of the Mind. In A. V. Samsonovich, & C. J. Lebiere (Eds.), Postproceedings of the 9th Annual International Conference on Biologically Inspired Cognitive Architectures, BICA 2018 (Ninth Annual Meeting of the BICA Society), held August 22-24, 2018 in Prague, Czech Republic (Vol. 145, pp. 757-764). (Procedia Computer Science). Elsevier. https://doi.org/10.1016/j.procs.2018.11.033
  16. Chanda, S., Baas, J., Haitink, D., Hamel, S., Stutzmann, D., & Schomaker, L. (2018). Zero-shot learning based approach for medieval word recognition using deep-learned features. In Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR (pp. 345-350). (Proceedings of International Conference on Frontiers in Handwriting Recognition, ICFHR; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFHR-2018.2018.00067
  17. Gattinger, M., & Wagemaker, J. (2018). Towards an Analysis of Dynamic Gossip in Netkat. In J. Desharnais, W. Guttmann, & S. Joosten (Eds.), Relational and Algebraic Methods in Computer Science: 17th International Conference, RAMiCS 2018, Groningen, The Netherlands, October 29 – November 1, 2018, Proceedings (pp. 280-297). (Relational and Algebraic Methods in Computer Science; Vol. 11194). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-02149-8_17
  18. Okafor, E., Berendsen, G., Schomaker, L., & Wiering, M. (2018). Detection and Recognition of Badgers Using Deep Learning. In V. Kurkova, Y. Manolopoulos, B. Hammer, L. Iliadis, & I. Maglogiannis (Eds.), International Conference on Artificial Neural Networks (pp. 554-563). (Lecture Notes in Computer Science book series; Vol. 11141). Springer International Publishing, Cham, Switzerland. https://doi.org/10.1007/978-3-030-01424-7_54
  19. Sprenger, S., & van Rij, J. (2018). The development of idiom knowledge across the lifespan. Poster session presented at Annual Conference on Architectures and Mechanisms for Language Processing, Berlin, Germany.
  20. Anderson, J. R., Borst, J. P., Fincham, J. M., Ghuman, A. S., Tenison, C., & Zhang, Q. (2018). The Common Time Course of Memory Processes Revealed. Psychological Science, 29(9), 1463-1474. https://doi.org/10.1177/0956797618774526
  21. van der Velde, M., van Vugt, M., & Taatgen, N. (2018). Modelling the Effect of Depression on Working Memory. In I. Juvina, J. Houpt, & C. Myers (Eds.), Proceedings of the 16th International Conference on Cognitive Modeling (pp. 200). Madison, WI: University of Wisconsin.
  22. Chanda, S., Okafor, E., Hamel, S., Stutzmann, D., & Schomaker, L. (2018). Deep Learning for Classification and as Tapped-Feature Generator in Medieval Word-Image Recognition. In 13th IAPR International Workshop on Document Analysis Systems (DAS) (pp. 217-222). IEEE. https://doi.org/10.1109/DAS.2018.82
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