1. 2019
  2. van der Ven, J. (2019). Preserving and reusing architectural design decisions. [Groningen]: University of Groningen.
  3. Vries, de, F., & Perez, J. A. (2019). Reversible Session-Based Concurrency in Haskell. In M. Palka, & M. Myreen (Eds.), Trends in Functional Programming (pp. 20-45). ( Lecture Notes in Computer Science; Vol. 11457). Cham: Springer. https://doi.org/10.1007/978-3-030-18506-0_2
  4. 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.
  5. Taatgen, N. (2019). The representation of task knowledge at multiple levels of abstraction. In K. Gluck, & J. Laird (Eds.), Interactive task learning: Humans, Robots, and Agents Acquiring New Tasks through Natural Interactions (pp. 75-87). (Strüngmann Forum Reports; Vol. 26). Cambtidge: The MIT Press.
  6. 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
  7. 2018
  8. Bhole, A., Biehl, M., & Azzopardi, G. (2018). Automatic identification of Holstein cattle using non-invasive computer vision approach. Abstract from FAIR Data Science for Green Life Sciences, Wageningen, Netherlands.
  9. 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
  10. 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
  11. Alsahaf, A., Azzopardi, G., Ducro, B., Veerkamp, R., & Petkov, N. (2018). Predicting slaughter age in pigs using random forest regression. In N. Petkov, N. Strisciuglio, & C. M. Travieso-Gonzalez (Eds.), Applications of Intelligent Systems IOS Press. https://doi.org/10.1093/jas/sky359
  12. Kim, Y., Telea, A., & Trager, S. (2018). High-Dimensional Astronomical Data Using Dimension Reduction. Poster session presented at XXX Canary Islands Winter School of Astrophysics, Tenerife, Spain.
  13. You, J., Wilkinson, M. H. F., & Trager, S. (2018). Scalable max-tree and alpha-tree algorithm for high resolution, multispectral, and extreme dynamic range images. Poster session presented at XXX Canary Islands Winter School of Astrophysics, Tenerife, Spain.
  14. Azzopardi, G., Foggia, P., Greco, A., Saggese, A., & Vento, M. (2018). Gender recognition from face images using trainable shape and colour features. In International Conference of Pattern Recognition (pp. 1983-1988). IEEE. https://doi.org/10.1109/ICPR.2018.8545771
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