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2026

Ethical Considerations in AI-Based Brain Tumour Diagnosis (2026)In Intelligent Systems and Applications: Proceedings of the 2025 Intelligent Systems Conference (IntelliSys) (pp. 60-77) (Lecture Notes in Networks and Systems (LNNS); Vol. 1660). Springer (E-pub ahead of print/First online). Rangelov, D., Miltchev, R. & Genchev, E.https://doi.org/10.1007/978-3-032-07109-5_5AI-Powered API for Brain Tumour Classification: A Deep Learning Approach to Accessible Medical Imaging (2026)In Flexible Query Answering Systems: 16th International Conference, FQAS 2025, Burgas, Bulgaria, September 11–13, 2025, Proceedings (pp. 53–65) (Lecture Notes in Computer Science; Vol. 16119). Springer. Rangelov, D., Miltchev, R. & Genchev, E.https://doi.org/10.1007/978-3-032-05607-8_7Deep Learning with Infinite-Dimensional Priors (2026)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Dummer, S.https://doi.org/10.3990/1.9789036570534FLUXSynID: A Framework for Identity-Controlled Synthetic Face Generation with Document and Live Images (2026)In 2025 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW): ICCV-W 2025, 19-20 October 2025 Honolulu, United States (pp. 3757-3767). Article 11374353 (International Conference on Computer Vision Workshops (ICCV Workshops); Vol. 2025). IEEE. Ismayilov, R., Sero, D. & Spreeuwers, L.https://doi.org/10.1109/ICCVW69036.2025.00392Artificial Intelligence in Eating Disorder Treatment: A Qualitative Analysis of Clinical Opportunities, Barriers, and Ethical Considerations From Multi‐Disciplinary Focus Groups (2026)International Journal of Eating Disorders, 59(2), 299-310. Maas, J., Franssen, S., Petkovic, M., Cardona Cano, S., Dingemans, A. E., van Oosterzee, A. M., Slof‐Op ’t Landt, M. C. T., Talavera Martínez, E., Vreeswijk, C. M. J. M. & Simeunovic‐Ostojic, M.https://doi.org/10.1002/eat.24579Trade-offs in Financial AI: Explainability in a Trilemma with Accuracy and Compliance (2026)[Working paper › Preprint]. ArXiv.org. Evite, P. M., Svetlova, E. & Bucur, D.https://doi.org/10.48550/arXiv.2602.01368Visual Grounding in 2D and 3D: A unified perspective and survey (2026)Information Fusion, 126(Part B). Article 103625. Guo, K., Huang, Y., Jia, T., Song, X., Sun, S., Wei, H., Han, X. F., Huang, S., Strisciuglio, N. & Li, S.https://doi.org/10.1016/j.inffus.2025.103625Handling missing data with meta-learning and large language models (2026)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Baysal Erez, I.https://doi.org/10.3990/1.9789036570633A Comparative Study of Machine Learning and Neural Network Models for Phishing Detection (2026)In Data Information in Online Environments: 5th International Conference, DIONE 2024, Sanya, China, November 11, 2024, Proceedings (pp. 99–113) (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering; Vol. 569). Springer. Rangelov, D., Miltchev, R. & Genchev, E.https://doi.org/10.1007/978-3-031-97352-9_8Comparative Analysis of Machine Learning Algorithms for Phishing Detection (2026)In Pattern Recognition and Artificial Intelligence: Selected papers from the 6th Mediterranean Conference on Pattern Recognition and Artificial Intelligence (MedPRAI24) (pp. 1-16) (Lecture Notes in Networks and Systems; Vol. 1393). Springer. Rangelov, D., Miltchev, R., Genchev, E. & Kirkov, P.https://doi.org/10.1007/978-3-031-90893-4_1DADO: A Depth-Attention Framework for Object Discovery (2026)In Computer Analysis of Images and Patterns: 21st International Conference, CAIP 2025, Las Palmas de Gran Canaria, Spain, September 22–25, 2025, Proceedings, Part II (pp. 281-291) ( Lecture Notes in Computer Science; Vol. 15622). Springer. Gonzalez, F., Talavera, E. & Radeva, P.https://doi.org/10.1007/978-3-032-05060-1_24Integrating Privacy with Process Mining for an Efficient Business Workflow: A Case Study (2026)In ICT for Intelligent Systems: Proceedings of ICTIS 2025 (pp. 523-532) (Smart Innovation, Systems and Technologies; Vol. 125). Springer. Sohail, S. & van Keulen, M.https://doi.org/10.1007/978-981-95-1357-4_40Sparse GAIN: Imputation Methods to Handle Missing Values with Sparse Initialization (2026)In Intelligent Data Engineering and Automated Learning – IDEAL 2025: 26th International Conference, Jaén, Spain, November 13–15, 2025, Proceedings, Part I (pp. 232-238) (Lecture Notes in Computer Science; Vol. 16238). Springer. van Oers, B. P., Baysal Erez, I. & van Keulen, M.https://doi.org/10.1007/978-3-032-10486-1_22XAI In Fraud Detection: A Causal Perspective (2026)In Explainable Artificial Intelligence: Third World Conference, xAI 2025, Istanbul, Turkey, July 9–11, 2025, Proceedings, Part IV (pp. 317-329) (Communications in Computer and Information Science; Vol. 2579). Springer. van Veen, K., Ahmed, F. & van Keulen, M.https://doi.org/10.1007/978-3-032-08330-2_15

2025

2024

2023

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2023)ACM computing surveys, 55(13s). Article 295. Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M. & Seifert, C.https://doi.org/10.1145/3583558Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI (2023)[Working paper › Preprint]. ArXiv.org. Laso, P., Cerri, S., Sorby-Adams, A., Guo, J., Mateen, F., Goebl, P., Wu, J., Liu, P., Li, H., Young, S. I., Billot, B., Puonti, O., Sze, G., Payabavash, S., DeHavenon, A., Sheth, K. N., Rosen, M. S., Kirsch, J., Strisciuglio, N., … Iglesias, J. E.https://doi.org/10.48550/arXiv.2312.05119E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation (2023)[Working paper › Preprint]. ArXiv.org. Wu, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., Mocanu, D. C., van Keulen, M. & Mocanu, E.https://doi.org/10.48550/arXiv.2312.04727Mathematical Camera Array Optimization for Face 3D Modeling Application (2023)Sensors (Switzerland), 23(24). Article 9776. Alsadik, B., Spreeuwers, L., Dadrass Javan, F. & Manterola, N.https://doi.org/10.3390/s23249776Usability Evaluation of Imikode Virtual Reality Game to Facilitate Learning of Object-Oriented Programming (2023)Technology, Knowledge and Learning, 28, 1871–1902. Sunday, K., Oyelere, S. S., Agbo, F. J., Aliyu, M. B., Balogun, O. S. & Bouali, N.https://doi.org/10.1007/s10758-022-09634-6Underlying dataset of Experts and Machines against Bullies: A Hybrid Approach to Detect Cyberbullies (2023)[Dataset Types › Dataset]. Zenodo. Dadvar, M., Trieschnigg, R. B. & de Jong, F.https://doi.org/10.5281/zenodo.10143096Fall Detection with a Nonintrusive and First-Person Vision Approach (2023)IEEE sensors journal, 23(22), 28304-28317. Wang, X., Talavera, E., Karastoyanova, D. & Azzopardi, G.https://doi.org/10.1109/JSEN.2023.3314828Worst-Case Morphs Using Wasserstein ALI and Improved MIPGAN (2023)IET biometrics, 2023(1). Article 9353816 (E-pub ahead of print/First online). Kelly, U. M., Nauta, M., Liu, L., Spreeuwers, L. J. & Veldhuis, R. N. J.https://doi.org/10.1049/2023/9353816Investigating Imputation Methods for Handling Missing Data (2023)[Contribution to conference › Poster] Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023. Maas, J., Römer, J. G. W. T., Baysal Erez, I. & van Keulen, M.Investigating Imputation Methods for Handling Missing Data (2023)[Contribution to conference › Paper] Joint International Scientific Conferences on AI and Machine Learning, BNAIC/BeNeLearn 2023. Maas, J., Römer, J. G. W. T., Baysal Erez, I. & van Keulen, M.Deep neural networks for explainable feature extraction in orchid identification (2023)Applied intelligence, 53(21), 26270-26285. Apriyanti, D. H., Spreeuwers, L. J. & Lucas, P. J. F.https://doi.org/10.1007/s10489-023-04880-2Case-level Breast Cancer Prediction for Real Hospital Settings (2023)[Working paper › Preprint]. ArXiv.org. Pathak, S., Schlötterer, J., Geerdink, J., Veltman, J., van Keulen, M., Strisciuglio, N. & Seifert, C.https://doi.org/10.48550/arXiv.2310.12677Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings (2023)[Working paper › Preprint]. ArXiv.org. Pathak, S., Schlötterer, J., Geerdink, J., Vijlbrief, O. D., Keulen, M. v. & Seifert, C.The Impact of Eyebrows Region on Deep Face Recognition (2023)In 2023 10th International Conference on Computer, Control, Informatics and its Applications: Exploring the Power of Data: Leveraging Information to Drive Digital Innovation, IC3INA 2023 (pp. 319-323). IEEE. Lestriandoko, N. H., Spreeuwers, L. & Veldhuis, R.https://doi.org/10.1109/IC3INA60834.2023.10285734Worst-Case Morphs using Wasserstein ALI and Improved MIPGAN (2023)[Working paper › Preprint]. ArXiv.org. Kelly, U. M., Nauta, M., Liu, L., Spreeuwers, L. J. & Veldhuis, R. N. J.https://doi.org/10.48550/arXiv.2310.08371DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning (2023)In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (pp. 129-138). Article 10350684 (Proceedings IEEE/CVF International Conference on Computer Vision Workshops (ICCVW); Vol. 2023). IEEE. Wang, S., Brune, C., Veldhuis, R. & Strisciuglio, N.https://doi.org/10.1109/ICCVW60793.2023.00020What do neural networks learn in image classification? A frequency shortcut perspective (2023)In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 1433-1442). Article 10378114 (Proceedings IEEE/CVF International Conference on Computer Vision (ICCV); Vol. 2023). IEEE. Wang, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.1109/ICCV51070.2023.00138Behavioural patterns discovery for lifestyle analysis from egocentric photo-streams (2023)Pervasive and Mobile Computing, 95. Article 101846. Menchón, M., Talavera, E., Massa, J. & Radeva, P.https://doi.org/10.1016/j.pmcj.2023.1018463D printed realistic finger vein phantoms (2023)[Working paper › Preprint]. ArXiv.org. Spreeuwers, L., van der Grift, R. & Normakristagaluh, P.https://doi.org/10.48550/arXiv.2309.14806Riemannian Shape Manifold Learning with Applications to Biological Data (2023)[Contribution to conference › Poster] EEMCS Research Networking Day 2023. Dummer, S., Strisciuglio, N. & Brune, C.Vulnerability of 3D Face Recognition Systems to Morphing Attacks (2023)[Working paper › Preprint]. ArXiv.org. Vardam, S. & Spreeuwers, L.https://doi.org/10.48550/arXiv.2309.12118Weighted Multivariate Mean Reversion for Online Portfolio Selection (2023)In Machine Learning and Knowledge Discovery in Databases: Research Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part V (pp. 255-270) (Lecture Notes in Computer Science; Vol. 14173). Wu, B., Lyu, B. & Gu, J.https://doi.org/10.1007/978-3-031-43424-2_16WashWall: An Interactive Smart Mirror for Motivating Handwashing Among Primary School Children (2023)In Human-Computer Interaction – INTERACT 2023: 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part II (pp. 234-253) (Lecture Notes in Computer Science; Vol. 14143). Springer. Postma, D. B. W., Ranasinghe, C. M., Constantinou, C., Diks, V. P. G., Rhee, Y., van Dijk, W. H. P., Sassanian, A. & Reidsma, D.https://doi.org/10.1007/978-3-031-42283-6_14DFM-X: Augmentation by Leveraging Prior Knowledge of Shortcut Learning (2023)[Working paper › Preprint]. ArXiv.org. Wang, S., Brune, C., Veldhuis, R. & Strisciuglio, N.https://doi.org/10.48550/arXiv.2308.06622A labeled spectral dataset with cassava disease occurrences using virus titre determination protocol (2023)Data in brief, 49. Article 109387. Owomugisha, G., Nakatumba-Nabende, J., Dhikusooka, J. J., Talavera [Taravera], E., Nuwamanya, E. & Mwebaze, E.https://doi.org/10.1016/j.dib.2023.109387AI-based Forensic Evaluation in Court: The Desirability of Explanation and the Necessity of Validation (2023)In Artificial Intelligence (AI) in Forensic Sciences. Wiley. Ypma, R. J. F., Ramos, D. & Meuwly, D.Visual Assistance in Development and Validation of Bayesian Networks for Clinical Decision Support (2023)IEEE transactions on visualization and computer graphics, 29(8), 3602-3616. Muller-Sielaff, J., Beladi, S. B., Meuschke, M., Vrede, S., Lucas, P. J. F., Pijnenborg, J. M. A. & Oeltze-Jafra, S.https://doi.org/10.1109/TVCG.2022.3166071Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space (2023)[Working paper › Preprint]. ArXiv.org. Mazilu, I., Wang, S., Dummer, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.48550/arXiv.2307.15461A Method for Bottleneck Detection, Prediction, and Recommendation Using Process Mining Techniques (2023)In E-Business and Telecommunications: 18th International Conference on E-Business and Telecommunications, ICETE 2021, Virtual Event, July 6–9, 2021, Revised Selected Papers (pp. 118-136) (Communications in Computer and Information Science (CCIS); Vol. 1795). Springer. Piest, J. P. S., Bemthuis, R. H., Cutinha, J. A., Arachchige, J. J. & Bukhsh, F. A.https://doi.org/10.1007/978-3-031-36840-0_7Interpreting and Correcting Medical Image Classification with PIP-Net (2023)[Working paper › Preprint]. ArXiv.org. Nauta, M., Hegeman, J. H., Geerdink, J., Schlötterer, J., Keulen, M. v. & Seifert, C.https://doi.org/10.48550/arXiv.2307.10404What do neural networks learn in image classification?: A frequency shortcut perspective (2023)[Working paper › Preprint]. ArXiv.org. Wang, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.48550/arXiv.2307.09829Encoding Variables, Evaluation Criteria, and Evaluation Methods for Data Physicalisations: A Review (2023)Multimodal Technologies and Interaction, 7(7). Article 73. Epa Ranasinghe, C. M. & Degbelo, A.https://doi.org/10.3390/mti7070073Improved preoperative risk stratification in endometrial carcinoma patients: external validation of the ENDORISK Bayesian network model in a large population-based case series (2023)Journal of Cancer Research and Clinical Oncology, 149, 3361–3369. Grube, M., Reijnen, C., Lucas, P. J. F., Kommoss, F., Kommoss, F. K. F., Brucker, S. Y., Walter, C. B., Oberlechner, E., Krämer, B., Andress, J., Neis, F., Staebler, A., Pijnenborg, J. M. A. & Kommoss, S.https://doi.org/10.1007/s00432-022-04218-4Parallels in the symbolism of star constellations (2023)[Working paper › Preprint]. ArXiv.org. Bucur, D.https://doi.org/10.48550/arXiv.2306.17573QuestionMark (2023)[Dataset Types › Dataset]. Zenodo. Zandbergen, N., van Keulen, M., van Dijk, T. & Flokstra, J.https://doi.org/10.5281/zenodo.8146101Visualizing landmark based face morphing traces on digital images (2023)Frontiers in Computer Science, 5. Article 981933. Batskos, I., Spreeuwers, L. & Veldhuis, R.https://doi.org/10.3389/fcomp.2023.981933PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification (2023)In CVPR 2023 (pp. 2744-2753). Nauta, M., Schlötterer, J., van Keulen, M. & Seifert, C.PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification (2023)[Contribution to conference › Abstract] 2nd Explainable AI for Computer Vision Workshop, XAI4CV 2023. Nauta, M., Schlötterer, J., van Keulen, M. & Seifert, C.Data-efficient visual place recognition with graded similarity supervision (2023)[Contribution to conference › Paper] IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.https://openaccess.thecvf.com/content/CVPR2023/papers/Leyva-Vallina_Data-Efficient_Large_Scale_Place_Recognition_With_Graded_Similarity_Supervision_CVPR_2023_paper.pdfIndustry 4.0 and healthcare: Context, applications, benefits and challenges (2023)IET software, 17(3), 195-248. Kotzias, K., Bukhsh, F. A., Arachchige, J. J., Daneva, M. & Abhishta, A.https://doi.org/10.1049/sfw2.12074Understanding Sparse Neural Networks from their Topology via Multipartite Graph Representations (2023)[Working paper › Preprint]. ArXiv.org. Cunegatti, E., Farina, M., Bucur, D. & Iacca, G.https://doi.org/10.48550/arXiv.2305.16886RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations (2023)[Working paper › Preprint]. ArXiv.org. Dummer, S., Strisciuglio, N. & Brune, C.https://doi.org/10.48550/arXiv.2305.12854A Survey on the Robustness of Computer Vision Models against Common Corruptions (2023)[Working paper › Preprint]. ArXiv.org. Wang, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.48550/arXiv.2305.06024Encoding Variables, Evaluation Criteria and Evaluation Methods for Data Physicalizations: A Review (2023)[Working paper › Preprint]. ArXiv.org. Ranasinghe, C. & Degbelo, A.A Survey on Datasets for Emotion Recognition from Vision: Limitations and In-the-Wild Applicability (2023)Applied Sciences, 13(9). Article 5697. Costa, W., Talavera, E., Oliveira, R., Figueiredo, L., Teixeira, J. M., Lima, J. P. & Teichrieb, V.https://doi.org/10.3390/app13095697Automated assessment of human engineered heart tissues using deep learning and template matching for segmentation and tracking (2023)Bioengineering and Translational Medicine, 8(3). Article e10513. Rivera-Arbeláez, J. M., Keekstra, D., Cofiño-Fabres, C., Boonen, T., Dostanic, M., ten Den, S. A., Vermeul, K., Mastrangeli, M., van den Berg, A., Segerink, L. I., Ribeiro, M. C., Strisciuglio, N. & Passier, R.https://doi.org/10.1002/btm2.10513Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning (2023)In AAMAS '23: Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (pp. 1932-1941) (Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS; Vol. 2023). ACM Press. Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.https://dl.acm.org./doi/10.5555/3545946.3598862Can Less Yield More? Insights into Truly Sparse Training (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Wu, B., Yin, L., van Keulen, M. & Pechenizkiy, M.https://drive.google.com/file/d/1kbWZ9ejU9XvtOMRtAcVYmcoRCDIWj3zy/viewDynamic Sparse Network for Time Series Classification: Learning What to “See” (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/viewDynamic Sparse Network for Time Series Classification: Learning What to “See” (2023)[Contribution to conference › Poster] ICLR 2023 Workshop on Sparsity in Neural Networks. Xiao, Q., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://drive.google.com/file/d/10pxPf2aWTdMumUba_8-7v_jEZ3-K_uV3/viewMore convnets in the 2020s: Scaling up kernels beyond 51x51 using sparsity (2023)In The Eleventh International Conference on Learning Representations (ICLR 2023). OpenReview. Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q., Wu, B., Pechenizkiy, M., Mocanu, D. C. & Wang, Z.https://arxiv.org/abs/2207.03620Facilitating free travel in the Schengen area—A position paper by the European Association for Biometrics (2023)IET biometrics, 12(2), 112-128 (E-pub ahead of print/First online). Busch, C., Deravi, F., Frings, D., Kindt, E., Lessmann, R., Nouak, A., Salomon, J., Achcar, M., Alonso-Fernandez, F., Bachenheimer, D., Bethell, D., Bigun, J., Brawley, M., Brockmann, G., Cabello, E., Campisi, P., Cepilovs, A., Clee, M., Cohen, M., … Uhl, A.https://doi.org/10.1049/bme2.12107Explainable AI and Interpretable Computer Vision: From Oversight to Insight (2023)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Nauta, M.https://doi.org/10.3990/1.9789036555753Position Paper: Physicalization of Human Body Sensing Data (2023)In CHI 2023 Workshop on Physicalization from Theory to Practice: Exploring Physicalization Design across Domains. Epa Ranasinghe, C. M. & Bults, R. G. A.http://dataphys.org/workshops/chi23/wp-content/uploads/sites/8/2023/04/Physicalization-of-Human-Body-Sensing-Data.pdfSeparation of Ambient Radio Noise and Radio Signals Received via Ionospheric Propagation (2023)Atmosphere, 14(3), 529. Article 529. Witvliet, B. A., Alsina-Pagès, R. M., Altadill, D., van Maanen, E. & Laanstra, G. J.https://doi.org/10.3390/atmos14030529Benchmarking deep networks for facial emotion recognition in the wild (2023)Multimedia tools and applications, 82(8), 11189-11220. Greco, A., Strisciuglio, N., Vento, M. & Vigilante, V.https://doi.org/10.1007/s11042-022-12790-7Covid severity prediction: Who cares about the data quality? (2023)In Covid severity prediction: Who cares about the data quality? (pp. 225-230). IEEE. Nae, T., Krabbe, J., Bukhsh, F. A., Jayasinghe Arachchige, J. & Ahmed, F.https://doi.org/10.1109/FIT57066.2022.00049Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning (2023)[Working paper › Preprint]. ArXiv.org. Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2302.06548A comparative study of source-finding techniques in HI emission line cubes using SoFiA, MTObjects, and supervised deep learning (2023)Astronomy & astrophysics, 670. Article A55. Barkai, J. A., Verheijen, M. A. W., Talavera, E. & Wilkinson, M. H. F.https://doi.org/10.1051/0004-6361/202244708Data set of a scientific experiment involving polarisation measurements in Spain of electromagnetic waves refracted in the ionosphere (NVIS CIRC4) (2023)[Dataset Types › Dataset]. 4TU.Centre for Research Data. Witvliet, B. A., Alsina-Pagès, R. M., van Maanen, E. & Laanstra, G. J.Data set of a scientific experiment involving polarisation measurements in Spain of electromagnetic waves refracted in the ionosphere (NVIS CIRC4). (2023)[Dataset Types › Dataset]. 4TU.Centre for Research Data. Witvliet, B., Alsina-Pagès, R. M., Maanen, E. v. & Laanstra, G. J.https://doi.org/10.4121/21888366Software supporting the publication: Background Electromagnetic Noise Received via Ionospheric Propagation in a Remote Rural Location in Spain (set 1) (2023)[Dataset Types › Dataset]. 4TU.Centre for Research Data. Witvliet, B., Alsina-Pagès, R. M., Maanen, E. v. & Laanstra, G. J.https://doi.org/10.4121/21890493Look back, look around: A systematic analysis of effective predictors for new outlinks in focused Web crawling (2023)Knowledge-based systems, 260, 1-16. Article 110126. Dang, T. K. N., Bucur, D., Atil, B., Pitel, G., Ruis, F., Kadkhodaei, H. & Litvak, N.https://doi.org/10.1016/j.knosys.2022.110126Exploring Face De-Identification using Latent Spaces (2023)In 2022 IEEE International Joint Conference on Biometrics, IJCB 2022 (pp. 1-7). Article 10007990 (IEEE International Joint Conference on Biometrics (IJCB); Vol. 2022). IEEE. Kelly, U. M., Spreeuwers, L. & Veldhuis, R.https://doi.org/10.1109/IJCB54206.2022.10007990A field-based recommender system for crop disease detection using machine learning (2023)Frontiers in Artificial Intelligence, 6. Article 1010804. Omara, J., Talavera, E., Otim, D., Turcza, D., Ofumbi, E. & Owomugisha, G.https://doi.org/10.3389/frai.2023.1010804A Review of Text-to-Animation Systems (2023)IEEE Access, 11, 86071 - 86087. Article 10216285. Bouali, N. & Cavalli-Sforza, V.https://doi.org/10.1109/ACCESS.2023.3304903Balancing Simplicity and Complexity in Modeling Mined Business Processes: A User Perspective (2023)In Enterprise Information Systems. ICEIS 2022 (pp. 3-21) (Lecture Notes in Business Information Processing; Vol. 487). Springer. Maneschijn, D. G. J. C., Bemthuis, R. H., Arachchige, J. J., Bukhsh, F. A. & Iacob, M. E.https://doi.org/10.1007/978-3-031-39386-0_1Causal Discovery with Missing Data in a Multicentric Clinical Study (2023)In Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings (pp. 40-44) (Lecture Notes in Computer Science; Vol. 13897). Springer. Zanga, A., Bernasconi, A., Lucas, P. J. F., Pijnenborg, H., Reijnen, C., Scutari, M. & Stella, F.https://doi.org/10.1007/978-3-031-34344-5_5Cold Case - Solved & Unsolved: Use of digital tools and data science techniques to facilitate cold case investigation (2023)European Law Enforcement Research Bulletin, 245-254 (Accepted/In press). Kuznecova, T., Rangelov, D. & Knotter, J.Data-Efficient Large Scale Place Recognition with Graded Similarity Supervision (2023)In Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 (pp. 23487-23496) (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; Vol. 2023). IEEE. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.https://doi.org/10.1109/CVPR52729.2023.02249Defocus Blur Synthesis and Deblurring via Interpolation and Extrapolation in Latent Space (2023)In Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Limassol, Cyprus, September 25–28, 2023, Proceedings (pp. 201-211) (Lecture Notes in Computer Science; Vol. 14185). Springer. Mazilu, I., Wang, S., Dummer, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://doi.org/10.1007/978-3-031-44240-7_20EmoClock: Communicating Real-Time Emotional States Through Data Physicalizations (2023)In Human-Computer Interaction – INTERACT 2023: 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part I (pp. 416-425) (Lecture Notes in Computer Science; Vol. 14142). Springer. Peeters, D., Ranasinghe, C., Degbelo, A. & Ahmad, F.https://doi.org/10.1007/978-3-031-42280-5_26Enhancing Learning in Sparse Neural Networks: A Hebbian Learning Approach (2023)In BNAIC/BENELEARN 2023. de Ranitz, A., Beldad, A. D. & Mocanu, E.Exploring the Untapped Potential of Unsupervised Representation Learning for Training Set Agnostic Finger Vein Recognition (2023)In BIOSIG 2023 - Proceedings of the 22nd International Conference of the Biometrics Special Interest Group (pp. 1-6) (Proceedings International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2023). IEEE. Arican, T., Veldhuis, R. & Spreeuwers, L.https://doi.org/10.1109/BIOSIG58226.2023.10345775Fall Detection with Event-Based Data: A Case Study (2023)In Computer Analysis of Images and Patterns: 20th International Conference, CAIP 2023, Limassol, Cyprus, September 25–28, 2023, Proceedings, Part II (pp. 33-42) (Lecture Notes in Computer Science; Vol. 14185). Springer. Wang, X., Risi, N., Talavera, E., Chicca, E., Karastoyanova, D. & Azzopardi, G.https://doi.org/10.1007/978-3-031-44240-7_4Formalising Legal Knowledge of Sri Lankan Civil Appellate High Court Domain from Ontological Perspective (2023)In Advances in Conceptual Modeling: ER 2023 Workshops, CMLS, CMOMM4FAIR, EmpER, JUSMOD, OntoCom, QUAMES, and SmartFood, Lisbon, Portugal, November 6–9, 2023, Proceedings (pp. 189-194) (Lecture Notes in Computer Science; Vol. 14319). Springer. Liyanage, C., Ilayperuma, T., Jayasinghe Arachchige, J. & Bukhsh, F. A.https://doi.org/10.1007/978-3-031-47112-4_18High-level context representation for emotion recognition in images (2023)In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 (pp. 326-334) (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2023-June). IEEE. De Lima Costa, W., Talavera Martínez, E., Figueiredo, L. S. & Teichrieb, V.https://doi.org/10.1109/CVPRW59228.2023.00038Investigating Aha Moment Through Process Mining (2023)In Proceedings of the 25th International Conference on Enterprise Information Systems (pp. 164-172) (Proceedings International Conference on Enterprise Information Systems; Vol. 2023). SCITEPRESS. Chiang, W.-H., Ahmad, U., Wang, S. & Bukhsh, F. A.https://doi.org/10.5220/0011848800003467ParaGnosis: A Tool for Parallel Knowledge Compilation (2023)In Model Checking Software: 29th International Symposium, SPIN 2023, Paris, France, April 26–27, 2023, Proceedings (pp. 22-37) (Lecture Notes in Computer Science; Vol. 13872). Springer. Dal, G. H., Laarman, A. & Lucas, P. J. F.https://doi.org/10.1007/978-3-031-32157-3_2Point cloud analysis of railway infrastructure: a systematic literature review (2023)IEEE Access, 11, 134355-134373. Dekker, B., Ton, B., Meijer, J., Bouali, N., Linssen, J. & Ahmed, F.https://doi.org/10.1109/ACCESS.2023.3337049Process Mining and Perceived Privacy Violations: A Pilot-Study (2023)In Proceedings of the 25th International Conference on Enterprise Information Systems - Volume 2, ICEIS 2023 (pp. 289-296) (International Conference on Enterprise Information Systems, ICEIS - Proceedings; Vol. 2). SCITEPRESS. Zuidema-Tempel, E., Bukhsh, F. A., Effing, R. & van Hillegersberg, J.https://doi.org/10.5220/0011745200003467Towards a Transportable Causal Network Model Based on Observational Healthcare Data (2023)In HC@AIxIA 2023: Proceedings of the 2nd AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2023) (pp. 122-129) (CEUR workshop proceedings; Vol. 3578). CEUR. Bernasconi, A., Zanga, A., Lucas, P. J. F., Scutari, M. & Stella, F.https://ceur-ws.org/Vol-3578/paper7.pdf

2022

Dynamic Sparse Network for Time Series Classification: Learning What to "see'' (2022)[Working paper › Preprint]. ArXiv.org. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://doi.org/10.48550/arXiv.2212.09840Application of NLP on student's Discord messages for automatic Belbin role identification (2022)In 2022 International Conference on Frontiers of Information Technology (FIT) (pp. 302-307). Article 10043095. IEEE. Dichev, K., Bukhsh, F. & Barrios-Fleitas, Y.https://doi.org/10.1109/FIT57066.2022.00062Presentation attack detection and biometric recognition in a challenge-response formalism (2022)Eurasip Journal on Information Security, 2022(1). Article 5. Haasnoot, E., Spreeuwers, L. J. & Veldhuis, R. N. J.https://doi.org/10.1186/s13635-022-00131-ySpatial-Temporal Transformer for Crime Recognition in Surveillance Videos (2022)In AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS 2022 - 18th IEEE International Conference on Advanced Video and Signal-Based Surveillance). IEEE. Boekhoudt, K. & Talavera, E.https://doi.org/10.1109/AVSS56176.2022.9959414Structure preserving implicit shape encoding via flow regularization (2022)[Contribution to conference › Abstract] Geometric Deep Learning in Medical Image Analysis, GeoMedIA 2022. Dummer, S., Strisciuglio, N. & Brune, C.https://openreview.net/pdf?id=YcjlgyX_Ur1Influence of discretization granularity on learning classification models (2022)[Contribution to conference › Paper] BNAIC/BeNeLearn 2022 Joint International Scientific Conferences on AI and Machine Learning. Tran, T. H. A., Wiesner, M. L. & van Keulen, M.https://bnaic2022.uantwerpen.be/BNAICBeNeLearn_2022_submission_8652Dynamic Sparse Network for Time Series Classification: Learning What to “See” (2022)[Contribution to conference › Paper] 36th Annual Conference on Neural Information Processing Systems, NeurIPS 2022. Xiao, Q., Wu, B., Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E. & Mocanu, D. C.https://openreview.net/forum?id=ZxOO5jfqSYwVisualisation Methods for Diachronic Semantic Shift (2022)In Proceedings of the Third Workshop on Scholarly Document Processing (pp. 89-94). Association for Computational Linguistics (ACL). Kazi, R., Amato, A., Wang, S. & Bucur, D.https://aclanthology.org/2022.sdp-1.10Visual response inhibition for increased robustness of convolutional networks to distribution shifts (2022)[Contribution to conference › Paper] 36th Annual Conference on Neural Information Processing Systems, NeurIPS 2022. Strisciuglio, N. & Azzopardi, G.https://openreview.net/forum?id=enByqfq18tVisualization Methods for Diachronic Semantic Shift (2022)In Proceedings of the Third Workshop on Scholarly Document Processing (pp. 89-94) (Proceedings - International Conference on Computational Linguistics, COLING). Association for Computational Linguistics (ACL). Kazi, R., Amato, A., Wang, S. & Bucur, D.https://aclanthology.org/2022.sdp-1.10Fingers Crossed: An Analysis of Cross-Device Finger Vein Recognition (2022)In 2022 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE. Arican, T., Veldhuis, R. & Spreeuwers, L.https://doi.org/10.1109/BIOSIG55365.2022.9897029Understanding and Modelling the Vascular Biometric Imaging Procedure (2022)In BIOSIG 2022: Proceedings of the 21st International Conference of the Biometrics Special Interest Group: 14–16 September 2022, Darmstadt, Germany International Conference. Article 9897048 (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2022). IEEE. van der Spek, M. & Spreeuwers, L.https://doi.org/10.1109/BIOSIG55365.2022.9897048Understanding dynamic sparse training capabilities in accommodating sparse data (2022)[Contribution to conference › Paper] European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2022. Baysal Erez, I. & van Keulen, M.Sub-byte quantization of Mobile Face Recognition Convolutional Neural Networks (2022)In 2022 International Conference of the Biometrics Special Interest Group (BIOSIG) (pp. 1-5). Article 9897025 (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2022). IEEE. Bunda, S., Spreeuwers, L. & Zeinstra, C.https://doi.org/10.1109/BIOSIG55365.2022.9897025Data-Efficient Large Scale Place Recognition With Graded Similarity Supervision (2022)[Dataset Types › Dataset]. DataverseNL. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.https://doi.org/10.34894/W4LIGPGeneralized Contrastive Optimization of Siamese Networks for Place Recognition (2022)[Dataset Types › Dataset]. DataverseNL. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.https://doi.org/10.34894/w4ligpAligning Dutch Logistics Data Spaces Initiatives to The International Data Spaces: Discussing The State of Development (2022)In Proceedings of the Workshop of I-ESA’22 (CEUR Workshop Proceedings; Vol. 3214). CEUR. Piest, J. P. S., De Alencar Silva, P. & Bukhsh, F. A.http://ceur-ws.org/Vol-3214/Performance Enhancement of Formula One Drivers with the Use of Group Driven Learning (2022)In Proceedings of the 12th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (pp. 260-270). SCITEPRESS. Moghaddar, A., Bukhsh, F. A. & Bruinsma, G. W. J.https://doi.org/10.5220/0000163300003274Large-scale multi-objective influence maximisation with network downscaling (2022)In Parallel Problem Solving from Nature – PPSN XVII: 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II (pp. 207-220) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13399 LNCS). Cunegatti, E., Iacca, G. & Bucur, D.https://doi.org/10.1007/978-3-031-14721-0_15External validation study of endometrial cancer preoperative risk stratification model (ENDORISK) (2022)Frontiers in oncology, 12. Article 939226. Vinklerová, P., Ovesná, P., Hausnerová, J., Pijnenborg, J. M. A., Lucas, P. J. F., Reijnen, C., Vrede, S. & Weinberger, V.https://doi.org/10.3389/fonc.2022.939226The contribution of different face parts to deep face recognition (2022)Frontiers in Computer Science, 4, 1-16. Article 958629. Lestriandoko, N. H., Veldhuis, R. & Spreeuwers, L.https://doi.org/10.3389/fcomp.2022.958629Automated Fault Tree Learning from Continuous-valued Sensor Data: A Case Study on Domestic Heaters (2022)International Journal of Prognostics and Health Management, 13(2), 1-12. Verkuil, B., Budde, C. E. & Bucur, D.https://doi.org/10.36001/ijphm.2022.v13i2.3160Vision-Based Module for Herding with a Sheepdog Robot (2022)Sensors (Switzerland), 22(14). Article 5321. Riego Del Castillo, V., Sánchez-González, L., Campazas-Vega, A. & Strisciuglio, N.https://doi.org/10.3390/s22145321Towards Implementing Truly Sparse Connections in Deep RL Agents (2022)[Contribution to conference › Poster] Sparsity in Neural Networks: Advancing Understanding and Practice 2022. Grooten, B. J., Sokar, G., Mocanu, E., Dohare, S., Taylor, M. E., Pechenizkiy, M. & Mocanu, D. C.More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity (2022)[Working paper › Preprint]. ArXiv.org. Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q., Wu, B., Kärkkäinen, T., Pechenizkiy, M., Mocanu, D. & Wang, Z.https://doi.org/10.48550/arXiv.2207.03620Artificial neural network for technical feasibility prediction of seismic retrofitting in existing RC structures (2022)Structures, 41, 1220-1234. Falcone, R., Ciaramella, A., Carrabs, F., Strisciuglio, N. & Martinelli, E.https://doi.org/10.1016/j.istruc.2022.05.008Dynamic Sparse Training for Deep Reinforcement Learning (2022)In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022 (pp. 3437-3443). Sokar, G. A. Z. N., Mocanu, E., Mocanu, D. C., Pechenizkiy, M. & Stone, P.https://doi.org/10.24963/ijcai.2022/477The network signature of constellation line figures (2022)PLoS ONE, 17. Article e0272270. Bucur, D.https://doi.org/10.1371/journal.pone.0272270Evaluating Clinical-Care Metadata Share and its FAIRification using the REA Ontology (2022)In VMBO 2022, Value Modelling and Business Ontologies 2022: Proceedings of the 16th International Workshop on Value Modelling and Business Ontologies (VMBO 2022), held in conjunction with the 34th International Conference on Advanced Information Systems Engineering (CAiSE 2022), June 06–10, 2022, Leuven, Belgium (CEUR Workshop Proceedings; Vol. 3155). CEUR. Sohail, S. A., Bukhsh, F. A., van Keulen, M., Krabbe, J. G. & Hruby, P.http://ceur-ws.org/Vol-3155/Biometric Testing: aligning standards and practice (2022)[Contribution to conference › Paper] 42nd WIC Symposium on Information Theory and Signal Processing in the Benelux, SITB 2022. de Wit, F. F., Spreeuwers, L. & Zeinstra, C. G.Dynamic detection of mobile malware using smartphone data and machine learning (2022)Digital Threats, 3(2). Article 9. Panman de Wit, S., Bucur, D. & van der Ham, J.https://doi.org/10.1145/3484246Exploring the GANformer for Face Generation: Investigating the segmentation and smile augmentation potential (2022)In Proceedings of the 2022 Symposium on Information Theory and Signal Processing in the Benelux (pp. 38). Ferla, R., Spreeuwers, L. & Zeinstra, C. G.From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI (2022)[Working paper › Preprint]. ArXiv.org. Nauta, M., Trienes, J., Pathak, S., Nguyen, E., Peters, M., Schmitt, Y., Schlötterer, J., van Keulen, M. & Seifert, C.https://doi.org/10.48550/arXiv.2201.08164TB-Places: A Data Set for Visual Place Recognition in Garden Environments (2022)[Dataset Types › Dataset]. DataverseNL. Leyva-Vallina, M., Strisciuglio, N., López Antequera, M., Tylecek, R., Blaich, M. & Petkov, N.https://doi.org/10.34894/vil0evSelf-supervised Learning Through Colorization for Microscopy Images (2022)In Image Analysis and Processing – ICIAP 2022: 21st International Conference, Lecce, Italy, May 23-27, 2022. Proceedings, Part II (pp. 621-632) (Lecture Notes in Computer Science; Vol. 13232). Springer. Pandey, V., Brune, C. & Strisciuglio, N.https://doi.org/10.1007/978-3-031-06430-2_52InstaIndoor and multi-modal deep learning for indoor scene recognition (2022)Neural Computing and Applications, 34, 6861-6877. Glavan, A. & Talavera, E.https://doi.org/10.1007/s00521-021-06781-2Physicalizing Sustainable Development Goals Data: An Example with SDG 7 (Affordable and Clean Energy) (2022)In CHI EA '22: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. Article 346. Association for Computing Machinery. van Loenhout, R., Ranasinghe, C., Degbelo, A. & Bouali, N.https://doi.org/10.1145/3491101.3519638Understanding and modeling finger vascular pattern imaging (2022)IET Image Processing, 16(5), 1280-1292. Normakristagaluh, P., Laanstra, G. J., Spreeuwers, L. & Veldhuis, R.https://doi.org/10.1049/ipr2.12408Facial recognition as a tool to identify Roman emperors: towards a new methodology (2022)Humanities & Social Sciences Communications, 9(1). Article 78. Ramesh, D. S., Heijnen, S., Hekster, O., Spreeuwers, L. & de Wit, F.https://doi.org/10.1057/s41599-022-01090-ySurvey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers (2022)[Working paper › Preprint]. ArXiv.org. Haller, S., Aldea, A., Seifert, C. & Strisciuglio, N.Bias in Automated Image Colorization: Metrics and Error Types (2022)[Working paper › Preprint]. Stapel, F., Weers, F. & Bucur, D.https://doi.org/10.48550/arXiv.2202.08143SixFeet: An Interactive, Corona-Safe, Multiplayer Sports Platform (2022)In TEI '22: Proceedings of the 16th International Conference on Tangible, Embedded, and Embodied Interaction (pp. 1-7). Article 3505570. ACM Publishing. Postma, D., de Ruiter, A., Reidsma, D. & Ranasinghe, C.https://doi.org/10.1145/3490149.3505570Optimal Scenario Mining for Business Strategy Decision-making through Process Mining (2022)In Proceedings - 2021 International Conference on Frontiers of Information Technology, FIT 2021 (pp. 311-316). IEEE. Van Midden, Y., Arachchige, J. J. & Bukhsh, F. A.https://doi.org/10.1109/FIT53504.2021.00064Deep Ensembling with No Overhead for either Training or Testing: The All-Round Blessings of Dynamic Sparsity (2022)In The Tenth International Conference on Learning Representations, ICLR 2022. OpenReview. Liu, S., Chen, T., Atashgahi, Z., Chen, X., Sokar, G., Mocanu, E., Pechenizkiy, M., Wang, Z. & Mocanu, D. C.https://openreview.net/forum?id=RLtqs6pzj1-&noteId=d7CKVDyMGZiMultimodal Machine Learning for 30-Days Post-Operative Mortality Prediction of Elderly Hip Fracture Patients (2022)In Proceedings - 21st IEEE International Conference on Data Mining Workshops, ICDMW 2021 (pp. 508-516) (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2021-December). IEEE. Yenidogan, B., Pathak, S., Geerdink, J., Hegeman, J. H. & Van Keulen, M.https://doi.org/10.1109/ICDMW53433.2021.00068A Methodology for Aligning Process Model Abstraction Levels and Stakeholder Needs (2022)In Proceedings of the 24th International Conference on Enterprise Information Systems (pp. 137-147) (International Conference on Enterprise Information Systems, ICEIS - Proceedings; Vol. 1). SCITEPRESS. Maneschijn, D. G. J. C., Bemthuis, R. H., Bukhsh, F. A. & Iacob, M.-E.https://doi.org/10.5220/0011029600003179A strawman with machine learning for a brain: A response to Biedermann (2022) the strange persistence of (source) “identification” claims in forensic literature (2022)Forensic Science International: Synergy, 4. Article 100230. Morrison, G. S., Ramos, D., Ypma, R. J., Basu, N., de Bie, K., Enzinger, E., Geradts, Z., Meuwly, D., van der Vloed, D., Vergeer, P. & Weber, P.https://doi.org/10.1016/j.fsisyn.2022.100230Frequency Shortcut Learning in Neural Networks (2022)In NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and Applications. Wang, S., Veldhuis, R., Brune, C. & Strisciuglio, N.https://openreview.net/forum?id=zAfUHtSGWwPractical Evaluation of Face Morphing Attack Detection Methods (2022)In Handbook of Digital Face Manipulation and Detection: From DeepFakes to Morphing Attacks (pp. 351-365) (Advances in Computer Vision and Pattern Recognition). Springer. Spreeuwers, L., Schils, M., Veldhuis, R. & Kelly, U.https://doi.org/10.1007/978-3-030-87664-7_16Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (2022)Machine Learning, 111, 377–414. Atashgahi, Z., Sokar, G., van der Lee, T., Mocanu, E., Mocanu, D. C., Veldhuis, R. & Pechenizkiy, M.https://doi.org/10.1007/s10994-021-06063-xRisk Assessment of Lymph Node Metastases in Endometrial Cancer Patients: A Causal Approach (2022)In HC@AIxIA 2022: 1st AIxIA Workshop on Artificial Intelligence For Healthcare (HC@AIxIA 2022) (pp. 1-15) (CEUR workshop proceedings; Vol. 3307). CEUR. Zanga, A., Bernasconi, A., Lucas, P. J. F., Pijnenborg, H., Reijnen, C., Scutari, M. & Stella, F.https://ceur-ws.org/Vol-3307/paper1.pdfSocial Influence Analysis (SIA) in Online Social Networks (2022)[Contribution to conference › Paper] 4th Multidisciplinary International Symposium, MISDOOM 2022 . Leszkiewicz, A., Bucur, D., Grimme, C., Michalski, R., Clever, L., Pohl, J., Rook, J., Bossek, J., Preuss, M., Squillero, G., Quer, S., Calabrese, A., Iacca, G., Kizgin, D. H. & Trautmann, H.Worst-Case Morphs: a Theoretical and a Practical Approach (2022)In 2022 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE. Kelly, U. M., Spreeuwers, L. & Veldhuis, R. N. J.https://doi.org/10.1109/BIOSIG55365.2022.9896965

2021

Worst-Case Morphs: a Theoretical and a Practical Approach (2021)[Working paper › Preprint]. ArXiv.org. Kelly, U. M., Spreeuwers, L. & Veldhuis, R.https://arxiv.org/abs/2111.15416v2Framework for assessing ethical aspects of algorithms and their encompassing socio-technical system (2021)Applied Sciences, 11(23). Article 11187. van Bruxvoort, X. & van Keulen, M.https://doi.org/10.3390/app112311187Semantic Description of Explainable Machine Learning Workflows for Improving Trust (2021)Applied Sciences, 11(22). Article 10804. Nakagawa, P. I., Pires, L. F., Rebelo Moreira, J. L., Bonino da Silva Santos, L. O. & Bukhsh, F.https://doi.org/10.3390/app112210804Multilevel privacy assurance evaluation of healthcare metadata (2021)Proceedings (MDPI), 11(22). Article 10686. Sohail, S. A., Bukhsh, F. A. & van Keulen, M.https://doi.org/10.3390/app112210686Burr detection and classification using RUSTICO and image processing (2021)Journal of computational science, 56. Article 101485. Riego, V., Sánchez-González, L., Fernández-Robles, L., Gutiérrez-Fernández, A. & Strisciuglio, N.https://doi.org/10.1016/j.jocs.2021.101485Look back, look around: a systematic analysis of effective predictors for new outlinks in focused Web crawling (2021)[Working paper › Preprint]. ArXiv.org. Dang, T. K. N., Bucur, D., Atil, B., Pitel, G., Ruis, F., Kadkhodaei, H. & Litvak, N.https://doi.org/10.48550/arXiv.2111.05062Prediction of new outlinks for focused Web crawling (2021)[Working paper › Preprint]. Dang, T. K. N., Bucur, D., Atil, B., Pitel, G., Ruis, F., Kadkhodaei, H. & Litvak, N.A compositional approach to probabilistic knowledge compilation (2021)International Journal of Approximate Reasoning, 138, 38-66. Dal, G. H., Laarman, A. W., Hommersom, A. & Lucas, P. J. F.https://doi.org/10.1016/j.ijar.2021.07.007A survey of face recognition techniques under occlusion (2021)IET biometrics, 10(6), 581-606. Zeng, D., Veldhuis, R. & Spreeuwers, L.https://doi.org/10.1049/bme2.12029Occlusion-invariant face recognition using simultaneous segmentation (2021)IET biometrics, 10(6), 679-691. Zeng, D., Veldhuis, R., Spreeuwers, L. & Arendsen, R.https://doi.org/10.1049/bme2.12036HR-Crime: Human-Related Anomaly Detection in Surveillance Videos (2021)In Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings (pp. 164-174) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13053 LNCS). Springer. Boekhoudt, K., Matei, A., Aghaei, M. & Talavera, E.https://doi.org/10.1007/978-3-030-89131-2_15MTStereo 2.0: Accurate Stereo Depth Estimation via Max-Tree Matching (2021)In Computer Analysis of Images and Patterns - 19th International Conference, CAIP 2021, Proceedings (pp. 110-119) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13052 LNCS). Springer. Brandt, R., Strisciuglio, N. & Petkov, N.https://doi.org/10.1007/978-3-030-89128-2_11Automated color detection in orchids using color labels and deep learning (2021)PLoS ONE, 16(10 October). Article e0259036. Apriyanti, D. H., Spreeuwers, L. J., Lucas, P. J. F. & Veldhuis, R. N. J.https://doi.org/10.1371/journal.pone.0259036The network signature of constellation line figures (2021)[Working paper › Preprint]. Bucur, D.Automatic 3D building model generation using deep learning methods based on cityjson and 2D floor plans (2021)In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (pp. 49-54) (International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences). Copernicus. Kippers, R., Koeva, M. N., van Keulen, M. & Oude Elberink, S. J.https://doi.org/10.5194/isprs-archives-XLVI-4-W4-2021-49-2021EgoFoodPlaces: Hierarchical Approach to Classify Food Scenes in Egocentric Photo-Streams (2021)[Dataset Types › Dataset]. 4TU.Centre for Research Data. Talavera Martínez, E., Leyva-Vallina, M., Radeva, P. & Petkov, N.https://doi.org/10.4121/16592420EgoRoutine: Topic modelling for routine discovery from egocentric photo-streams (2021)[Dataset Types › Dataset]. 4TU.Centre for Research Data. Talavera Martínez, E., Radeva, P. & Petkov, N.https://doi.org/10.4121/16577627Zero-downtime schema changes (2021)[Thesis › EngD Thesis]. University of Twente. Dijkstra, J.-J.HR-Crime: Human-Related Anomaly Detection in Surveillance Videos (2021)[Dataset Types › Dataset]. DataverseNL. Boekhoudt, K., Matei, A., Aghaei, M. & Martinez, E. T.https://doi.org/10.34894/irrdjeAn evolutionary framework for maximizing influence propagation in social networks (2021)Software Impacts, 9. Article 100107. Iacca, G., Konotopska, K., Bucur, D. & Tonda, A.https://doi.org/10.1016/j.simpa.2021.100107Dynamic detection of mobile malware using smartphone data and machine learning (2021)[Working paper › Preprint]. ArXiv.org. Panman de Wit, J. S., van der Ham, J. & Bucur, D.https://doi.org/10.48550/arXiv.2107.11167Source code for "Autoencoder-based cleaning in probabilistic databases" (2021)[Dataset Types › Dataset]. Zenodo. Mauritz, R. R., Nijweide, F. P. J., Goseling, J. & Keulen, M. v.https://doi.org/10.5281/zenodo.5136612Brain-Inspired Algorithms for Processing of Visual Data (2021)In Brain-Inspired Computing - 4th International Workshop, BrainComp 2019, Revised Selected Papers (pp. 105-115) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12339 LNCS). Springer. Strisciuglio, N. & Petkov, N.https://doi.org/10.1007/978-3-030-82427-3_8Forecasting (2021)In Local Electricity Markets. Elsevier. Mocanu, E., Mocanu, D. C., Paterakis, N. G. & Gibescu, M.https://www.sciencedirect.com/book/9780128200742/local-electricity-marketsDynamic Sparse Training for Deep Reinforcement Learning (Poster) (2021)[Contribution to conference › Poster] Sparsity in Neural Networks: Advancing Understanding and Practice 2021. Sokar, G. A. Z. N., Mocanu, E., Mocanu, D. C., Pechenizkiy, M. & Stone, P.FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity (2021)[Contribution to conference › Poster] Sparsity in Neural Networks: Advancing Understanding and Practice 2021. Liu, S., Chen, T., Atashgahi, Z., Chen, X., Sokar, G. A. Z. N., Mocanu, E., Pechenizkiy, M., Wang, Z. & Mocanu, D. C.Preventing face morphing attacks by using legacy face images (2021)IET biometrics, 10(4), 430-440. Batskos, I., de Wit, F. F., Spreeuwers, L. & Veldhuis, R. N. J.https://doi.org/10.1049/bme2.12047Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders (poster) (2021)[Contribution to conference › Poster] Sparsity in Neural Networks: Advancing Understanding and Practice 2021. Atashgahi, Z., Sokar, G. A. Z. N., van der Lee, T., Mocanu, E., Mocanu, D. C., Veldhuis, R. N. J. & Pechenizkiy, M.Autoencoder-based cleaning in probabilistic databases (2021)[Working paper › Working paper]. ArXiv.org. Mauritz, R. R., Nijweide, F. P. J., Goseling, J. & van Keulen, M.https://arxiv.org/abs/2106.09764Dynamic Sparse Training for Deep Reinforcement Learning (2021)[Working paper › Working paper]. ArXiv.org. Sokar, G., Mocanu, E., Mocanu, D. C., Pechenizkiy, M. & Stone, P.Does our social life influence our nutritional behaviour? Understanding nutritional habits from egocentric photo-streams (2021)Expert systems with applications, 171. Article 114506. Glavan, A., Matei, A., Radeva, P. & Talavera, E.https://doi.org/10.1016/j.eswa.2020.114506Independent Prototype Propagation for Zero-Shot Compositionality (2021)[Working paper › Preprint]. Ruis, F., Burghouts, G. & Bucur, D.Sparse Training Theory for Scalable and Efficient Agents (2021)In AAMAS '21: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp. 34-38). ACM Publishing. Mocanu, D. C., Mocanu, E., Pinto, T., Curci, S., Nguyen, P. H., Gibescu, M., Ernst, D. & Vale, Z.https://dl.acm.org/doi/10.5555/3463952.3463960Consensus on validation of forensic voice comparison (2021)Science & justice, 61(3), 299-309. Morrison, G. S., Enzinger, E., Hughes, V., Jessen, M., Meuwly, D., Neumann, C., Planting, S., Thompson, W. C., van der Vloed, D., Ypma, R. J. F. & Zhang, C.https://doi.org/10.1016/j.scijus.2021.02.002Evaluating the Use of the Open Trip Model for Process Mining: An Informal Conceptual Mapping Study in Logistics (2021)In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS) (pp. 290-296). SCITEPRESS. Piest, J. P. S., Cutinha, J. A., Bemthuis, R. H. & Bukhsh, F. A.https://doi.org/10.5220/0010477702900296Identifying Materialized Privacy Claims of Clinical-Care Metadata Share using Process-Mining and REA ontology (2021)[Contribution to conference › Paper] 15th International Workshop on Value Modelling and Business Ontologies, VMBO 2021. Sohail, S. A., Bukhsh, F. A., van Keulen, M. & Krabbe, J. G.http://ceur-ws.org/Vol-2835/Generalized Contrastive Optimization of Siamese Networks for Place Recognition (2021)[Working paper › Preprint]. ArXiv.org. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.Morphing Attacks on Face Recognition Systems (2021)[Contribution to conference › Other] Norwegian Biometrics Laboratory Annual Workshop, EAB/NBLAW 2021. Spreeuwers, L.Sparse Training Theory for Scalable and Efficient Agents: Blue Sky Ideas Track (2021)[Working paper › Preprint]. ArXiv.org. Mocanu, D. C., Mocanu, E., Pinto, T., Curci, S., Nguyen, P. H., Gibescu, M., Ernst, D. & Vale, Z. A.https://doi.org/10.48550/arXiv.2103.01636Understanding Event Boundaries for Egocentric Activity Recognition from Photo-Streams (2021)In Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part III (pp. 334-347) (Lecture notes in computer science; Vol. 12663). Cartas, A., Talavera, E., Radeva, P. & Dimiccoli, M.https://doi.org/10.1007/978-3-030-68796-0_24A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports (2021)In 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI) (pp. 72-81). Article 9319371. IEEE. Nguyen, E., Theodorakopoulos, D., Pathak, S., Geerdink, J., Vijlbrief, O., van Keulen, M. & Seifert, C.https://doi.org/10.1109/CogMI50398.2020.00019A Classification of Process Mining Bottleneck Analysis Techniques for Operational Support (2021)In Proceedings of the 18th International Conference on e-Business (ICE-B 2021) (pp. 127-135). SCITEPRESS. Bemthuis, R., van Slooten, N., Jayasinghe Arachchige, J., Piest, J. & Bukhsh, F. A.https://doi.org/10.5220/0010578601270135Can face morphs be detected using face recognition systems? (2021)[Contribution to conference › Paper] Intergraf Currency+Identity 2021. Spreeuwers, L.Deepfake detection using capsule networks and long short-term memory networks (2021)In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP (pp. 407-414) (VISIGRAPP). SCITEPRESS. Mehra, A., Spreeuwers, L. & Strisciuglio, N.https://doi.org/10.5220/0010289004070414Fall Detection and Recognition from Egocentric Visual Data: A Case Study (2021)In Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings (pp. 431-443) (Lecture Notes in Computer Science; Vol. 12661). Springer. Wang, X., Talavera, E., Karastoyanova, D. & Azzopardi, G.https://doi.org/10.1007/978-3-030-68763-2_33Finger Vein Verification with a Convolutional Auto-encoder (2021)In Proceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux (pp. 43-51). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). Arican, T., Veldhuis, R. N. J. & Spreeuwers, L.https://www.w-i-c.org/proceedings/proceedings_SITB2021.pdfIdentification through Finger Bone Structure Biometrics (2021)In Proceedings of the 2021 Symposium on Information Theory and Signal Processing in the Benelux (pp. 56-63). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). van der Spek, M. & Spreeuwers, L.https://www.w-i-c.org/proceedings/proceedings_SITB2021.pdfIndependent Prototype Propagation for Zero-Shot Compositionality (2021)In 35th Conference on Neural Information Processing Systems, NeurIPS 2021 (pp. 10641-10653) (Advances in Neural Information Processing Systems; Vol. 13). Neural information processing systems foundation. Ruis, F., Burghouts, G. J. & Bucur, D.Individual Action and Group Activity Recognition in Soccer Videos from a Static Panoramic Camera (2021)In ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods (pp. 594-601) (International Conference on Pattern Recognition Applications and Methods; Vol. 1). SCITEPRESS. Gerats, B., Bouma, H., Uijens, W., Englebienne, G. & Spreeuwers, L.https://doi.org/10.5220/0010303505940601Morphing Attack Detection - Database, Evaluation Platform and Benchmarking (2021)IEEE transactions on information forensics and security, 16, 4336-4351. Article 9246583. Raja, K., Ferrara, M., Franco, A., Spreeuwers, L., Batskos, I., de Wit, F., Gomez-Barrero, M., Scherhag, U., Fischer, D., Venkatesh, S., Mohan Singh, J., Ramachandra, R., Rathgeb, C., Frings, D., Seidel, U., Knopjes, F., Veldhuis, R. N. J., Maltoni, D. & Busch, C.https://doi.org/10.1109/TIFS.2020.3035252Morphing-aanvallen op gezichtsherkenningssystemen (2021)[Contribution to conference › Other] VVBI Najaarsbijeenkomst 2021 “Biometrische herkenning is nog geen identificatie”. Spreeuwers, L.Multimodal (and more physical) Interaction to Improve In-Car User Experience (2021)In Human-Computer Interaction to Support Work and Wellbeing in Mobile Environments (pp. 33-34) (Dagstuhl Reports; Vol. 11). Dagstuhl. Ranasinghe, C.https://doi.org/10.4230/DagRep.11.5.23On the Efficacy of Online Proctoring using Proctorio (2021)In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) (pp. 279-290). SCITEPRESS. Bergmans, L., Bouali, N., Luttikhuis, M. & Rensink, A.https://doi.org/10.5220/0010399602790290Playing to distraction: towards a robust training of CNN classifiers through visual explanation techniques (2021)Neural Computing and Applications, 33, 16937–16949. Morales, D., Talavera, E. & Remeseiro, B.https://doi.org/10.1007/s00521-021-06282-2Preface (2021)In Applications of Evolutionary Computation: 24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings (pp. V-vii) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12694). Castillo, P. A., Laredo, J. L. J., Iacca, G., Bucur, D., Cotta, C., Fernández, P., Santucci, V., Caraffini, F., Mesejo, P., Al-Sahaf, H., Machado, P., Banzhaf, W., Smith, S., Vallejo, M., Mora, A., Sánchez, P. G., Tonda, A. P. & Merelo Guervós, J. J.Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders (Extended Abstract) (2021)In BNAIC/BENELEARN 2021: The 33rd Benelux Conference on Artificial Intelligence and the 30th Belgian Dutch Conference on Machine Learning. Atashgahi, Z., Sokar, G. A. Z. N., van der Lee, T., Mocanu, E., Mocanu, D. C., Veldhuis, R. N. J. & Pechenizkiy, M.SMARTERCARE 2021: Towards Smarter Health Care: Can Artificial Intelligence Help? (2021)[Book/Report › Book editing] Workshop on Towards Smarter Health Care: Can Artificial Intelligence Help?, SMARTERCARE 2021. CEUR. Lucas, P. J. F. & Stella, F. A.https://ceur-ws.org/Vol-3060/Towards Eating Habits Discovery in Egocentric Photo-Streams (2021)IEEE Access, 9, 17495-17506. Article 9328814. Matei, A., Glavan, A., Radeva, P. & Talavera, E.https://doi.org/10.1109/ACCESS.2021.3053175Towards Smarter Health Care: Can Artificial Intelligence Help? (2021)In SMARTERCARE 2021: Towards Smarter Health Care: Can Artificial Intelligence Help? (CEUR workshop proceedings; Vol. 3060). CEUR. Lucas, P. J. F. & Stella, F. A.https://ceur-ws.org/Vol-3060/xpreface.pdfUser-centered Development of a Clinical Decision Support System (2021)In SMARTERCARE 2021: Towards Smarter Health Care: Can Artificial Intelligence Help? (pp. 67-78) (CEUR workshop proceedings; Vol. 3060). CEUR. Kleinau, A., Mo, A., Stella, F. A., Müller-Sielaff, J., Pijnenborg, J. M. A., Lucas, P. J. F. & Oeltze-Jafra, S.https://ceur-ws.org/Vol-3060/paper-8.pdfValue-Based Fuzzy Approach for Non-functional Requirements Prioritization (2021)In Business Modeling and Software Design: 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5–7, 2021, Proceedings (pp. 330-342) (Lecture Notes in Business Information Processing; Vol. 422). Springer. Ijaz, K. B., Inayat, I., Daneva, M. & Bukhsh, F. A.https://doi.org/10.1007/978-3-030-79976-2_21

2020

Behaviour understanding through the analysis of image sequences collected by wearable cameras (2020)Electronic Letters on Computer Vision and Image Analysis, 19(2), 1-4. Martínez, E. T.https://doi.org/10.5565/rev/elcvia.1240Improving deep-learning-based face recognition to increase robustness against morphing attacks (2020)In 9th International Conference on Signal, Image Processing and Pattern Recognition (SPPR 2020), December 19 ~ 20, 2020, Sydney, Australia (Computer Science & Information Technology; Vol. 10). Academy and Industry Research Collaboration Center (AIRCC). Kelly, U. M., Veldhuis, R. N. J. & Spreeuwers, L.https://doi.org/10.5121/csit.2020.101901Effectiveness of neural language models for word prediction of textual mammography reports (2020)In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE International Conference on Systems, Man, and Cybernetics (SMC); Vol. 2020). IEEE. Marin, M. D., Mocanu, E. & Seifert, C.https://doi.org/10.1109/SMC42975.2020.9283304Enhanced Robustness of Convolutional Networks with a Push-Pull Inhibition Layer (2020)Neural Computing and Applications, 32(24), 17957-17971. Strisciuglio, N., Lopez-Antequera, M. & Petkov, N.https://doi.org/10.1007/s00521-020-04751-8Exploring the trends of educational virtual reality games: a systematic review of empirical studies (2020)Smart learning environments, 7(1). Article 31. Oyelere, S. S., Bouali, N., Kaliisa, R., Obaido, G., Yunusa, A. A. & Jimoh, E. R.https://doi.org/10.1186/s40561-020-00142-7Predicting parking occupancy via machine learning in the web of things (2020)Internet of Things, 12, 100301. Provoost, J. C., Kamilaris, A., Wismans, L. J. J., van der Drift, S. J. & van Keulen, M.https://doi.org/10.1016/j.iot.2020.100301Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders (2020)[Working paper › Working paper]. ArXiv.org. Atashgahi, Z., Sokar, G. A. Z. N., van der Lee, T., Mocanu, E., Mocanu, D. C., Veldhuis, R. & Pechenizkiy, M.https://doi.org/10.48550/arXiv.2012.00560Top influencers can be identified universally by combining classical centralities (2020)Scientific reports, 10(1). Article 20550. Bucur, D.https://doi.org/10.1038/s41598-020-77536-7Human-in-the-loop Language-agnostic Extraction of Medication Data from Highly Unstructured Electronic Health Records (2020)In 2020 International Conference on Data Mining Workshops (ICDMW) (pp. 644-650). Article 9346382 (International Conference on Data Mining Workshops (ICDMW); Vol. 2020). IEEE. Ruis, F., Pathak, S., Geerdink, J., Hegeman, J. H., Seifert, C. & van Keulen, M.https://doi.org/10.1109/ICDMW51313.2020.00091On the detection of real and synthetic ghosts (2020)In EAB Webinars. Spreeuwers, L.Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance (2020)IEEE Transactions on Computational Biology and Bioinformatics, 17(6), 1883-1894. Article 8705380. Pathak, S., van Rossen, J., Vijlbrief, O., Geerdink, J., Seifert, C. & van Keulen, M.https://doi.org/10.1109/TCBB.2019.2914678A UML approach for designing a VR-based smart learning environment for programming education (2020)In 2020 IEEE Frontiers in Education Conference, FIE 2020 - Proceedings. Article 9273956 (Proceedings - Frontiers in Education Conference (FIE); Vol. 2020). IEEE. Agbo, F. J., Sunday Oyelere, S. & Bouali, N.https://doi.org/10.1109/FIE44824.2020.92739563D Face Recognition for Cows (2020)In BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group. Article 9211005 (Proceedings of the International Conference of the Biometrics Special Interest Group, BIOSIG; Vol. 2020). IEEE. Yeleshetty, D., Spreeuwers, L. & Li, Y.https://ieeexplore.ieee.org/document/9211005Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium (2020)In ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium - International Workshops: DOING, MADEISD, SKG, BBIGAP, SIMPDA, AIMinScience 2020 and Doctoral Consortium, Proceedings (pp. 3-20) (Communications in Computer and Information Science; Vol. 1260). Springer. Bellatreche, L., Bentayeb, F., Bieliková, M., Boussaid, O., Catania, B., Ceravolo, P., Demidova, E., Halfeld Ferrari, M., Lopez, M. T. G., Hara, C. S., Kordić, S., Luković, I., Mannocci, A., Manghi, P., Osborne, F., Papatheodorou, C., Ristić, S., Sacharidis, D., Romero, O., … Zumer, M.https://doi.org/10.1007/978-3-030-55814-7_1Automatic Process Comparison for Subpopulations: Application in Cancer Care (2020)International journal of environmental research and public health, 17(16), 1-23. Article 5707. Marazza, F., Bukhsh, F. A., Geerdink, J., Vijlbrief, O., Pathak, S., van Keulen, M. & Seifert, C.https://doi.org/10.3390/ijerph17165707Topic modelling for routine discovery from egocentric photo-streams (2020)Pattern recognition, 104. Article 107330. Talavera, E., Petkov, N. & Radeva, P.https://doi.org/10.1016/j.patcog.2020.107330U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity (2020)Neural Computing and Applications, 32(16), 12453-12468. Ramachandran, S., Strisciuglio, N., Vinekar, A., John, R. & Azzopardi, G.https://doi.org/10.1007/s00521-019-04697-6Beyond ranking nodes: Predicting epidemic outbreak sizes by network centralities (2020)PLoS Computational Biology, 16(7), e1008052. Article e1008052. Bucur, D. & Holme, P.https://doi.org/10.1371/journal.pcbi.1008052Putting Attacks in Context: A Building Automation Testbed for Impact Assessment from the Victim’s Perspective (2020)In Detection of Intrusions and Malware, and Vulnerability Assessmen: 17th International Conference, DIMVA 2020, Lisbon, Portugal, June 24–26, 2020, Proceedings (pp. 44-64) (Lecture Notes in Computer Science; Vol. 12223). Springer. Esquivel-Vargas, H., Caselli, M., Laanstra, G. J. & Peter, A.https://doi.org/10.1007/978-3-030-52683-2_3A robust contour detection operator with combined push-pull inhibition and surround suppression (2020)Information sciences, 524, 229-240. Melotti, D., Heimbach, K., Rodríguez-Sánchez, A., Strisciuglio, N. & Azzopardi, G.https://doi.org/10.1016/j.ins.2020.03.026Efficient binocular stereo correspondence matching with 1-D Max-Trees (2020)Pattern recognition letters, 135, 402-408. Brandt, R., Strisciuglio, N., Petkov, N. & Wilkinson, M. H. F.https://doi.org/10.1016/j.patrec.2020.02.019Orchid Flowers Dataset (2020)[Dataset Types › Dataset]. Harvard Dataverse. Apriyanti, D. H., Spreeuwers, L., Lucas, P. J. F., Veldhuis, R. N. J. & Apriyanti, D. H.https://doi.org/10.7910/dvn/0hnecyOrchid Flowers Dataset-v1.1.zip (2020)[Dataset Types › Dataset]. Harvard Dataverse. Apriyanti, D. H., Spreeuwers, L., Lucas, P. J. F., Veldhuis, R. & Apriyanti, D. H.https://doi.org/10.7910/dvn/0hnecy/gszichA survey of face recognition techniques under occlusion (2020)[Working paper › Preprint]. Zeng, D., Veldhuis, R. & Spreeuwers, L.Top influencers can be identified universally by combining classical centralities (2020)[Working paper › Preprint]. ArXiv.org. Bucur, D.https://doi.org/10.48550/arXiv.2006.07657Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking (2020)[Working paper › Preprint]. ArXiv.org. Raja, K., Ferrara, M., Franco, A., Spreeuwers, L., Batskos, I., de Wit, F., Scherhag, U., Fischer, D., Venkatesh, S., Mohan Singh, J., Li, G., Bergeron, L., Isadskiy, S., Ramachandra, R., Rathgeb, C., Frings, D., Seidel, U., Knopjes, F., Veldhuis, R., … Busch, C.https://doi.org/10.48550/arXiv.2006.06458Bluetooth Smartphone Apps: Are they the most private and effective solution for COVID-19 contact tracing? (2020)[Working paper › Working paper]. ArXiv.org. McLachlan, S., Lucas, P., Dube, K., Hitman, G. A., Osman, M., Kyrimi, E., Neil, M. & Fenton, N. E.https://doi.org/10.48550/arXiv.2005.06621Detecting morphed face attacks using residual noise from deep multi-scale context aggregation network (2020)In Proceedings - 2020 IEEE Winter Conference on Applications of Computer Vision, WACV 2020 (pp. 269-278). Article 9093488 (Proceedings - IEEE Winter Conference on Applications of Computer Vision (WACV); Vol. 2020). IEEE. Venkatesh, S., Ramachandra, R., Raja, K., Spreeuwers, L., Veldhuis, R. & Busch, C.https://doi.org/10.1109/WACV45572.2020.9093488Preoperative risk stratification in endometrial cancer (ENDORISK) by a Bayesian network model: A development and validation study (2020)PLoS medicine, 17(5). Article e1003111. Reijnen, C., Gogou, E., Visser, N. C. M., Engerud, H., Ramjith, J., Van Der Putten, L. J. M., Van De Vijver, K., Santacana, M., Bronsert, P., Bulten, J., Hirschfeld, M., Colas, E., Gil-Moreno, A., Reques, A., Mancebo, G., Krakstad, C., Trovik, J., Haldorsen, I. S., Huvila, J., … Pijnenborg, J. M. A.https://doi.org/10.1371/journal.pmed.1003111The fundamental limitations of COVID-19 contact tracing methods and how to resolve them with a Bayesian network approach (2020)[Working paper › Preprint]. ResearchGate. McLachlan, S., Lucas, P., Dube, K., Hitman, G. A., Osman, M., Kyrimi, E., Neil, M. & Fenton, N. E.https://doi.org/10.13140/RG.2.2.27042.66243Autonomous Vehicle-Pedestrian Interaction Across Cultures: Towards Designing Better External Human Machine Interfaces (eHMIs) (2020)In CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–8). ACM Publishing. Ranasinghe, C., Holländer, K., Schneegass, S., Currano, R., Sirkin, D., Moore, D. & Ju, W.https://doi.org/10.1145/3334480.3382957Privacy Value Modeling: A Gateway To Ethical Big Data Handling (2020)In VMBO 2020: Value Modelling and Business Ontologies (pp. 5-15) (CEUR Workshop Proceedings; Vol. 2574). CEUR. Sohail, S. A., Krabbe, J., de Alencar Silva, P. & Bukhsh, F. A.http://ceur-ws.org/Vol-2574/Multi-Objective Optimization for Asynchronous Positioning Systems Based on a Complete Characterization of Ranging Errors in 3D Complex Environments (2020)IEEE Access, 8, 43046-43056. Article 9023962. Alvarez, R., Diez-gonzalez, J., Strisciuglio, N. & Perez, H.https://doi.org/10.1109/ACCESS.2020.2978336A systematic literature review on requirement prioritization techniques and their empirical evaluation (2020)Computer Standards & Interfaces, 69. Article 103389. Bukhsh, F. A., Bukhsh, Z. A. & Daneva, M.https://doi.org/10.1016/j.csi.2019.103389Editorial - Proceedings EAFS 2018 - Lyon (2020)Forensic science international, 308. Article 110067. Meuwly, D.https://doi.org/10.1016/j.forsciint.2019.110067From Subjective To Objective Informed Consent In Healthcare: Biobanks In Focus (2020)[Contribution to conference › Poster] Alice & Eve 2020. Sohail, S. A. & Bukhsh, F. A.Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records (2020)[Working paper › Preprint]. ArXiv.org. Jan, T., Trieschnigg, D., Seifert, C. & Hiemstra, D.https://doi.org/10.48550/arXiv.2001.057143D Face Recognition for Cows (2020)In BIOSIG 2020: Proceedings of the 19th International Conference of the Biometrics Special Interest Group (pp. 163-171) (Lecture Notes in Informatics (LNI); Vol. P-306). Gesellschaft für Informatik. Yeleshetty, D., Spreeuwers, L. & Li, Y.https://dl.gi.de/handle/20.500.12116/34323A High-Quality Finger Vein Dataset Collected Using a Custom-Designed Capture Device (2020)In Handbook of Vascular Biometrics (pp. 63-75) (Advances in Computer Vision and Pattern Recognition). Springer. Veldhuis, R., Spreeuwers, L., Ton, B. & Rozendal, S.https://doi.org/10.1007/978-3-030-27731-4_2A privacy-preserving Bayesian network model for personalised COVID19 risk assessment and contact tracing (2020)[Working paper › Preprint]. MedRxiv. Fenton, N. E., McLachlan, S., Lucas, P., Dube, K., Hitman, G. A., Osman, M., Kyrimi, E. & Neil, M.https://doi.org/10.1101/2020.07.15.20154286A Process Mining Starting Guideline for Process Analysts and Process Owners: A Practical Process Analytics Guide using ProM (2020)[Book/Report › Report]. Yasmin, F. A., Bemthuis, R., Elhagaly, M., Wijnhoven, F. & Bukhsh, F. A.APPIS 2020: Proceedings of the 3rd International Conference on Applications of Intelligent Systems (2020)[Book/Report › Book editing] 3rd International Conference on Applications of Intelligent Systems, APPIS 2020. ACM Publishing. Petkov, N., Strisciuglio, N. & Travieso-González, C. M.https://doi.org/10.1145/3378184Automatic Q.A-Pair Generation for Incident Tickets Handling: An Application of NLP (2020)In Economics of Grids, Clouds, Systems, and Services: 17th International Conference, GECON 2020, Izola, Slovenia, September 15–17, 2020, Revised Selected Papers (pp. 15-27) (Lecture Notes in Computer Science; Vol. 12441). Springer. Lammers, M., Wijnhoven, F., Bukhsh, F. A. & De Alencar Silva, P.https://doi.org/10.1007/978-3-030-63058-4_2Availability Incidents in the Telecommunication Domain: A Literature Review (2020)[Book/Report › Report]. University of Twente. Bukhsh, F. A., Vriezekolk, E., Wienen, H. & Wieringa, R.Behavioural Pattern Discovery from Collections of Egocentric Photo-Streams (2020)In Computer Vision – ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings (pp. 469-484) (Lecture Notes in Computer Science; Vol. 12538=). Springer. Menchón, M., Talavera, E., Massa, J. & Radeva, P.https://doi.org/10.1007/978-3-030-66823-5_28Covid-19 Probabilistic Surveillance of a Nation’s Population: a Proposal of a Quick Project (2020)[Book/Report › Report]. University of Twente. Lucas, P. J. F.Deep Learning for Scene Recognition from Visual Data: A Survey (2020)In Hybrid Artificial Intelligent Systems: 15th International Conference, HAIS 2020, Gijón, Spain, November 11-13, 2020, Proceedings (pp. 763-773) (Lecture Notes in Computer Science; Vol. 12344). Springer. Matei, A., Glavan, A. & Talavera, E.https://doi.org/10.1007/978-3-030-61705-9_64Egocentric vision for lifestyle understanding (2020)In Wearable Sensors: Fundamentals, Implementation and Applications (pp. 415-433). Elsevier. Talavera, E., Petkov, N. & Radeva, P.https://doi.org/10.1016/B978-0-12-819246-7.00015-2Hierarchical Approach to Classify Food Scenes in Egocentric Photo-Streams (2020)IEEE journal of biomedical and health informatics, 24(3), 866-877. Talavera Martínez, E., Leyva Vallina, M., Sarker, M. M. K., Puig, D., Petkov, N. & Radeva, P.https://doi.org/10.1109/JBHI.2019.2922390Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients (2020)In KDH 2020: 5th International Workshop on Knowledge Discovery in Healthcare Data (pp. 5-11) (CEUR Workshop Proceedings; Vol. 2675). CEUR. Nauta, M., Putten, M. J. A. M. v., Tjepkema-Cloostermans, M. C., Bos, J. P., Keulen, M. v. & Seifert, C.http://ceur-ws.org/Vol-2675/Lifestyle understanding through the analysis of egocentric photo-streams (2020)[Thesis › PhD Thesis - Research external, graduation external]. University of Groningen. Talavera Martínez, E.https://doi.org/10.33612/diss.112971105Preface (2020)In Applications of Evolutionary Computation (pp. V-VI) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104). Castillo, P. A., Laredo, J. L. J., Vega, F. F. d., Iacca, G., Bucur, D., Smith, S., Vallejo, M., Mora, A., Sánchez, P. G., Tonda, A. P., Guervós, J. J. M., Cotta, C., Fernández, P., Machado, P. & Banzhaf, W.Privacy From Subjective Value To (Subjective) Value Object: An E3-Value Modeling In The Wake Of Ethical Big Data Handling In Healthcare (2020)[Contribution to conference › Poster] ICT-OPEN 2020. Sohail, S. A. & Bukhsh, F. A.Scalable Interdisciplinary Data Science Teaching at the University of Twente (2020)Berlin Journal of Data Science, 1. van Keulen, M., Seifert, C., Poel, M. & Oudshoorn, C.Temporal exceptional model mining using dynamic Bayesian networks (2020)In Advanced Analytics and Learning on Temporal Data - 5th ECML PKDD Workshop, AALTD 2020, Revised Selected Papers (pp. 97-112) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12588 LNAI). Springer. Bueno, M. L. P., Hommersom, A. & Lucas, P. J. F.https://doi.org/10.1007/978-3-030-65742-0_7The thorny problems of Covid-19 Contact Tracing Apps: The need for a holistic approach (2020)Journal of Behavioral Economics for Policy, 4(S), 57-61. Osman, M., Fenton, N. E., McLachlan, S., Lucas, P., Dube, K., Hitman, G. A., Kyrimi, E. & Neil, M.https://econpapers.repec.org/RePEc:beh:jbepv1:v:4:y:2020:i:s:p:57-61

2019

Airport restroom cleanliness prediction using real time user feedback data (2019)In Proceedings - 2019 IEEE 5th International Conference on Collaboration and Internet Computing, CIC 2019 (pp. 1-10). Article 8998481. IEEE. Ros, K., Mocanu, E. & Seifert, C.https://doi.org/10.1109/CIC48465.2019.00010Robust Inhibition-Augmented Operator for Delineation of Curvilinear Structures (2019)IEEE transactions on image processing, 28(12), 5852-5866. Strisciuglio, N., Azzopardi, G. & Petkov, N.https://doi.org/10.1109/TIP.2019.2922096Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques (2019)[Contribution to conference › Paper] 99th Transportation Research Board (TRB) Annual Meeting 2020. Provoost, J., Wismans, L., van der Drift, S., van Keulen, M. & Kamilaris, A.https://doi.org/10.48550/arXiv.1911.13178An Agent-Based Process Mining Architecture for Emergent Behavior Analysis (2019)In 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 54-64). Article 8907303 (Proceedings IEEE International Enterprise Distributed Object Computing Workshop (EDOCW); Vol. 23). IEEE. Bemthuis, R. H., Koot, M., Mes, M. R. K., Bukhsh, F. A., Iacob, M.-E. & Meratnia, N.https://doi.org/10.1109/EDOCW.2019.00022Collaborative learning for classification and prediction of building energy flexibility (2019)In 9th IEEE PES Innovative Smart Grid Technology Conference Europe. Article 8905597. IEEE. Kumar, A., Mocanu, E., Babar, M. & Nguyen, P. H.https://doi.org/10.1109/ISGTEurope.2019.8905597Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm (2019)In Dependable Software Engineering. Theories, Tools, and Applications: 5th International Symposium, SETTA 2019, Shanghai, China, November 27-29, 2019, Proceedings (pp. 19-37) (Lecture Notes in Computer Science; Vol. 11951) (Programming and Software Engineering). Springer. Linard, A., Bucur, D. & Stoelinga, M.https://doi.org/10.1007/978-3-030-35540-1_2Adapting Navigation Support to Location Information Quality: A Human Centered Approach (2019)In Adjunct Proceedings of the 15th International Conference on Location Based Services (LBS 2019), LBS 2019, 11–13 November 2019, Vienna, Austria (pp. 71-83). Vienna University of Technology. Ranasinghe, C. & Kray, C.Morphed Face Detection Based on Deep Color Residual Noise (2019)In 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019. Article 8936088 (International Conference on Image Processing Theory, Tools and Applications (IPTA); Vol. 2019). IEEE. Venkatesh, S., Ramachandra, R., Raja, K., Spreeuwers, L., Veldhuis, R. & Busch, C.https://doi.org/10.1109/IPTA.2019.8936088Evaluating user experience under location quality variations: A framework for in-the-wild studies (2019)In MobileHCI '19: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and Services. Article 45. Association for Computing Machinery. Ranasinghe, C., Heitmann, S., Pfeiffer, M. & Kray, C.https://doi.org/10.1145/3338286.3344392Detecting sounds of interest in roads with deep networks (2019)In Image Analysis and Processing – ICIAP 2019: 20th International Conference (pp. 583-592) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11752 LNCS). Springer. Foggia, P., Saggese, A., Strisciuglio, N., Vento, M. & Vigilante, V.https://doi.org/10.1007/978-3-030-30645-8_53Data underlying the paper: An agent-based process mining architecture for emergent behavior analysis (2019)[Dataset Types › Dataset]. 4TU.Centre for Research Data. Bemthuis, R., Koot, M., Mes, M. R. K., Bukhsh, F. A., Iacob, M. E. & Meratnia, N.https://doi.org/10.4121/12708839Place Recognition in Gardens by Learning Visual Representations: Data Set and Benchmark Analysis (2019)In Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Proceedings (pp. 324-335) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11678 LNCS). Springer. Leyva-Vallina, M., Strisciuglio, N. & Petkov, N.https://doi.org/10.1007/978-3-030-29888-3_26BACRank: Ranking building automation and control system components by business continuity impact (2019)In Computer Safety, Reliability, and Security: 38th International Conference, SAFECOMP 2019, Proceedings (pp. 183-199) (Lecture Notes in Computer Science; Vol. 11698). Springer. Esquivel-Vargas, H., Caselli, M., Tews, E., Bucur, D. & Peter, A.https://doi.org/10.1007/978-3-030-26601-1_13Combined training strategy for low-resolution face recognition with limited application-specific data (2019)IET Image Processing, 13(10), 1790-1796. Zeng, D., Spreeuwers, L., Veldhuis, R. & Zhao, Q.https://doi.org/10.1049/iet-ipr.2018.5732Learning representations of sound using trainable COPE feature extractors (2019)Pattern recognition, 92, 25-36. Strisciuglio, N., Vento, M. & Petkov, N.https://doi.org/10.1016/j.patcog.2019.03.016Non-functional Requirements Prioritization: A Systematic Literature Review (2019)In Proceedings - 45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019 (pp. 379-386). Article 8906708. IEEE. Ijaz, K. B., Inayat, I. & Allah Bukhsh, F.https://doi.org/10.1109/SEAA.2019.00064A probabilistic framework for predicting disease dynamics: a case study of psychotic depression (2019)Journal of biomedical informatics, 95. Article 103232. Bueno, M. L. P., Hommersom, A., Lucas, P. J. F. & Janzing, J.https://doi.org/10.1016/j.jbi.2019.103232On-line building energy optimization using deep reinforcement learning (2019)IEEE transactions on smart grid, 10(4), 3698-3708. Article 8356086. Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M. & Slootweg, J. G.https://doi.org/10.1109/TSG.2018.2834219Semiparametric Likelihood-ratio-based Biometric Score Level Fusion via Parametric Copula (2019)IET biometrics, 8(4), 277-283. Susyanto, N., Veldhuis, R. N. J., Spreeuwers, L. & Klaassen, C.https://doi.org/10.1049/iet-bmt.2018.5106Information audit for knowledge discovery: A systematic literature review (2019)In VMBO 2019: Proceedings of the 13th International Workshop on Value Modeling and Business Ontologies Stockholm, Sweden, March 4-5, 2019 (CEUR workshop proceedings; Vol. 2383). CEUR. Bukhsh, F. A. & Nurlatifah, E.http://ceur-ws.org/Vol-2383/Low-resolution face recognition and the importance of proper alignment (2019)IET biometrics, 8(4), 267-276. Article 18726042. Peng, Y., Spreeuwers, L. & Veldhuis, R. N. J.https://doi.org/10.1049/iet-bmt.2018.5008Likelihood Ratio based Loss to fine tune CNNs for Very Low Resolution Face Verification (2019)In The 12th IAPR International Conference on Biometrics (ICB 2019) (pp. 1). Zeng, D., Veldhuis, R. N. J., Spreeuwers, L. & Zhao, Q.Design and validation of probes and sensors for the characterization of magneto-ionic radio wave propagation on Near Vertical Incidence Skywave paths (2019)Sensors (Switzerland), 19(11). Article 2616. Witvliet, B. A., Alsina-Pagès, R. M., van Maanen, E. & Laanstra, G. J.https://doi.org/10.3390/s19112616Likelihood Ratio based Loss to finetune CNNs for Very Low Resolution Face Verification (2019)In 2019 International Conference on Biometrics, ICB 2019. Article 8987249 (International Conference on Biometrics, ICB; Vol. 2019). IEEE. Zeng, D., Veldhuis, R., Spreeuwers, L. & Zhao, Q.https://doi.org/10.1109/ICB45273.2019.8987249A high resolution pressure sensor for measurement of grip force (2019)[Contribution to conference › Paper] 40th WIC Symposium on Information Theory in the Benelux 2019. Spreeuwers, L. & Wang, H.Finger-vein Pattern Recognition Based on ICP on Contours (2019)In 2019 Symposium on Information Theory and Signal Processing in the Benelux (pp. 97-101). Katholieke Universiteit Leuven. Normakristagaluh, P., Spreeuwers, L. & Veldhuis, R. N. J.Multi-Resolution Face Recognition: The Behaviors of Local Binary Pattern at Different Frequency Bands (2019)In Proceedings of the 2019 Symposium on Information Theory and Signal Processing in the Benelux: May 28-29 2019, KU Leuven, Technologiecampus Gent, Belgium (pp. 63-70). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). Lestriandoko, N. H., Spreeuwers, L. & Veldhuis, R.https://www.w-i-c.org/proceedings/Applying Generic AcciMap to a DDOS Attack on a Western-European Telecom Operator (2019)In Proceedings of the 16th ISCRAM Conference 2019 (pp. 528-535). International Association for Information Systems for Crisis Response and Management (ISCRAM). Wienen, H., Bukhsh, F. A., Vriezekolk, E. & Wieringa, R. J.Gender homophily in online book networks (2019)Information sciences, 481, 229-243. Bucur, D.https://doi.org/10.1016/j.ins.2019.01.003Mind the Gap: A practical framework for classifiers in a forensic context (2019)In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018. Article 8698583 (IEEE nternational Conference on Biometrics Theory, Applications and Systems (BTAS); Vol. 2018). IEEE. Zeinstra, C., Meuwly, D., Veldhuis, R. & Spreeuwers, L.https://doi.org/10.1109/BTAS.2018.8698583Evolution of Compliance Checking in Process Mining Discipline (2019)In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). Article 8673437. IEEE. Elhagaly, M., Drvoderic, K., Kippers, R. G. & Bukhsh, F. A.https://doi.org/10.1109/ICOMET.2019.8673437Ethics evaluation of big data in Practice: Healthcare in focus (2019)[Contribution to conference › Poster] 7th ICT.OPEN 2019. Sohail, S. A. & Bukhsh, F. A.Forensic gait analysis — Morphometric assessment from surveillance footage (2019)Forensic science international, 296, 57-66. Seckiner, D., Mallett, X., Maynard, P., Meuwly, D. & Roux, C.https://doi.org/10.1016/j.forsciint.2019.01.007Face recognition at a distance: low-resolution and alignment problems (2019)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Peng, Y.https://doi.org/10.3990/1.9789036547116A Layer-Based Sequential Framework for Scene Generation with GANs (2019)[Working paper › Preprint]. ArXiv.org. Turkoglu, M. O., Thong, W., Spreeuwers, L. & Kicanaoglu, B.https://doi.org/10.48550/arXiv.1902.00671A Layer-Based Sequential Framework for Scene Generation with GANs (2019)In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 (pp. 8901-8908) (33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019). AAAI. Turkoglu, M. O., Spreeuwers, L., Thong, W. & Kicanaoglu, B.Learning skeleton representations for human action recognition (2019)Pattern recognition letters, 118, 23-31. Saggese, A., Strisciuglio, N., Vento, M. & Petkov, N.https://doi.org/10.1016/j.patrec.2018.03.005Brain-inspired robust delineation operator (2019)In Computer Vision – ECCV 2018 Workshops, Proceedings (pp. 555-565) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11131 LNCS). Springer. Strisciuglio, N., Azzopardi, G. & Petkov, N.https://doi.org/10.1007/978-3-030-11015-4_41Macnet: Multi-scale atrous convolution networks for food places classification in egocentric photo-streams (2019)In Computer Vision - ECCV 2018 Workshops: Munich, Germany, September 8-14, 2018, Proceedings, Part V (pp. 423-433) (Lecture Notes in Computer Science; Vol. 11133). Springer. Sarker, M. K., Rashwan, H. A., Talavera, E., Banu, S. F., Radeva, P. & Puig, D.https://doi.org/10.1007/978-3-030-11021-5_26From Traditional to Technologically Influenced Audit: A Compliance Perspective (2019)In 2018 International Conference on Frontiers of Information Technology (FIT) (pp. 164-169). Article 8616985. IEEE. Bukhsh, F. A., Silva, P. d. A., Bukhsh, B. A. & Syed, S.https://doi.org/10.1109/FIT.2018.00036Causal Discovery with Attention-Based Convolutional Neural Networks (2019)Machine Learning and Knowledge Extraction, 1(1), 312-340. Nauta, M., Bucur, D. & Seifert, C.https://doi.org/10.3390/make1010019A comparison between discrete and continuous time Bayesian networks in learning from clinical time series data with irregularity (2019)Artificial intelligence in medicine, 95, 104-117. Liu, M., Stella, F., Hommersom, A., Lucas, P. J. F., Boer, L. & Bischoff, E.https://doi.org/10.1016/j.artmed.2018.10.002A Data-Driven Exploration of Hypotheses on Disease Dynamics (2019)In Artificial Intelligence in Medicine: 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, June 26–29, 2019, Proceedings (pp. 170-179) (Lecture Notes in Computer Science; Vol. 11526). Springer. Bueno, M. L. P., Hommersom, A., Lucas, P. J. F. & Janzing, J.https://doi.org/10.1007/978-3-030-21642-9_23A smart mHealth tool versus a paper action plan to support self-management of COPD exacerbations: a randomised controlled trial (2019)European respiratory journal. Supplement, 54(Suppl. 63). Article PA2238. Bischoff, E., Boer, L., van der Heijden, M., Lucas, P., Akkermans, R., Vercoulen, J., Heijdra, Y., Assendelft, P. & Schermer, T.https://doi.org/10.1183/13993003.congress-2019.PA2238A Smart Mobile Health Tool Versus a Paper Action Plan to Support Self-Management of Chronic Obstructive Pulmonary Disease Exacerbations: Randomized Controlled Trial (2019)JMIR mHealth and uHealth, 7(10). Article e14408. Boer, L., Bischoff, E., van der Heijden, M., Lucas, P., Akkermans, R., Vercoulen, J., Heijdra, Y., Assendelft, W. & Schermer, T.https://doi.org/10.2196/14408Comparing Process Models for Patient Populations: Application in Breast Cancer Care (2019)[Contribution to conference › Paper] International Workshop Process-Oriented Data Science for Healthcare 2019. Marazza, F., Bukhsh, F. A., Vijlbrief, O., Geerdink, J., Pathak, S., van Keulen, M. & Seifert, C.Comparing Process Models for Patient Populations: Application in Breast Cancer Care (2019)In Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers (pp. 496-507) (Lecture Notes in Business Information Processing; Vol. 362). Springer. Marazza, F., Bukhsh, F. A., Vijlbrief, O., Geerdink, J., Pathak, S., van Keulen, M. & Seifert, C.https://doi.org/10.1007/978-3-030-37453-2_40Conceptual Modeling for Corporate Social Responsibility: A Systematic Literature Review (2019)In Economics of Grids, Clouds, Systems, and Services - 16th International Conference, GECON 2019, Proceedings (pp. 218-227) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11819 LNCS). Springer. de Sousa Santos, O., de Alencar Silva, P., Bukhsh, F. A. & Queiroz, P. G. G.https://doi.org/10.1007/978-3-030-36027-6_19Data-Driven Process Discovery and Analysis: 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Neuchatel, Switzerland, December 6-8, 2017, Revised Selected Papers (2019)[Book/Report › Book editing] 7th IFIP WG 2.6 International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2017. Springer. Ceravolo, P., van Keulen, M. & Stoffel, K.https://doi.org/10.1007/978-3-030-11638-5Development and validation of an endometrial carcinoma preoperative bayesian network using molecular and clinical biomarkers (ENDORISK): an ENITEC collaboration study (2019)International journal of gynecological cancer, 29(Suppl. 4), A6-A7. Reijnen, C., Gogou, E., van der Putten, L., Visser, N., van de Vijver, K., Santacana, M., Bulten, J., Colas, E., Gil-Moreno, A., Reques, A., Mancebo, G., Krakstad, C., Trovik, J., Haldorsen, I., Engerud, H., Huvila, J., Koskas, M., Weinberger, V., Minar, L., … Pijnenborg, J.https://doi.org/10.1136/IJGC-2019-ESGO.7Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm (2019)[Working paper › Working paper]. ArXiv.org. Linard, A., Bucur, D. & Stoelinga, M.https://arxiv.org/abs/1909.06258FFORT: A benchmark suite for fault tree analysis (2019)In ESREL 2019: Proceedings of the 29th European Safety and Reliability Conference (pp. 878-885). Research Publishing. Ruijters, E. J. J., Budde, C. E., Chenariyan Nakhaee, M., Stoelinga, M. I. A., Bucur, D., Hiemstra, D. & Schivo, S.https://doi.org/10.3850/978-981-11-2724-3_0641-cdForeword (2019)In Proceedings of APPIS 2019: 2nd International Conference on Applications of Intelligent Systems (ACM International Conference Proceeding Series). ACM Publishing. Petkov, N., Strisciuglio, N. & Travieso, C. M.Induction of Fault Trees through Bayesian Networks (2019)In Proceedings of the 29th European Safety and Reliability Conference (ESREL) (pp. 910-918). Research Publishing. Linard, A., Bueno, M. L. P., Bucur, D. & Stoelinga, M.Lifelog retrieval for memory stimulation of people with memory impairment (2019)In Multimodal Behavior Analysis in the Wild: Advances and Challenges (pp. 135-158). Elsevier. Oliveira-Barra, G., Bolanos, M., Talavera, E., Gelonch, O., Garolera, M. & Radeva, P.https://doi.org/10.1016/B978-0-12-814601-9.00016-XPlace and Object Recognition by CNN-Based COSFIRE Filters (2019)IEEE Access, 7, 66157-66166. Article 8719902. Lopez-Antequera, M., Leyva Vallina, M., Strisciuglio, N. & Petkov, N.https://doi.org/10.1109/ACCESS.2019.2918267Preface (2019)In Data-Driven Process Discovery and Analysis: 7th IFIP WG 2.6 International Symposium, SIMPDA 2017, Neuchatel, Switzerland, December 6-8, 2017, Revised Selected Papers (pp. V-VI) (Lecture notes in business information processing; Vol. 340). Springer. Ceravolo, P., Van Keulen, M. & Stoffel, K.https://link.springer.com/content/pdf/bfm%3A978-3-030-11638-5%2F1.pdfRecognizing food places in egocentric photo-streams using multi-scale atrous convolutional networks and self-attention mechanism (2019)IEEE Access, 39069 - 39082. Sarker, M. K., Rashwan, H. A., Akram, F., Talavera, E., Banu, S., Radeva, P. & Puig, D.https://doi.org/10.1109/ACCESS.2019.2902225TB-places: A data set for visual place recognition in garden environments (2019)IEEE Access, 7, 52277-52287. Article 8698240. Leyva-Vallina, M., Strisciuglio, N., Lopez Antequera, M., Tylecek, R., Blaich, M. & Petkov, N.https://doi.org/10.1109/ACCESS.2019.2910150Towards green value network modeling: A case from the agribusiness sector in Brazil (2019)In On the Move to Meaningful Internet Systems: OTM 2019 Conferences - Confederated International Conferences: CoopIS, ODBASE, C and TC 2019, Rhodes, Greece, October 21–25, 2019, Proceedings (pp. 458-475) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11877 LNCS). Springer. Gomes Avelino, J., de Alencar Silva, P. & Allah Bukhsh, F.https://doi.org/10.1007/978-3-030-33246-4_29Trainable COPE Features for Sound Event Detection (2019)In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications: 24th Iberoamerican Congress (pp. 599-609) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11896 LNCS). Springer. Strisciuglio, N. & Petkov, N.https://doi.org/10.1007/978-3-030-33904-3_56Unsupervised Routine Discovery in Egocentric Photo-Streams (2019)In Computer Analysis of Images and Patterns: 18th International Conference, CAIP 2019, Salerno, Italy, September 3–5, 2019, Proceedings (pp. 576-588) (Lecture Notes in Computer Science; Vol. 11678). Springer. Talavera, E., Petkov, N. & Radeva, P.https://doi.org/10.1007/978-3-030-29888-3_47Visualising Location Uncertainty to Support Navigation under Degraded GPS Signals: a Comparison Study (2019)In MobileHCI '19: Proceedings of the 21st International Conference on Human-Computer Interaction with Mobile Devices and ServicesOctober 2019 Article (pp. 1-11). ACM Publishing. Ranasinghe, C., Schiestel, N. & Kray, C.https://doi.org/10.1145/3338286.3340128

2018

Process enhancement in process mining: A literature review (2018)CEUR workshop proceedings, 2270, 65-72. Yasmin, F. A., Bukhsh, F. A. & De Alencar Silva, P.Towards Understanding the Effects of Practice on Behavioural Biometric Recognition Performance (2018)In 2018 26th European Signal Processing Conference (EUSIPCO) (pp. 558-562). Article 8553446 (European Signal Processing Conference). IEEE. Haasnoot, E., Barnhoorn, J. S., Spreeuwers, L. S., Veldhuis, R. N. J. & Verwey, W. B.https://doi.org/10.23919/EUSIPCO.2018.8553446Pedestrian navigation and GPS deteriorations: user behavior and adaptation strategies (2018)In OzCHI '18: Proceedings of the 30th Australian Conference on Computer-Human Interaction (pp. 266–277). ACM Publishing. Epa Ranasinghe, C. M., Heitmann, S., Hamzin, A., Pfeiffer, M. & Kray, C.https://doi.org/10.1145/3292147.3292154SIMPDA 2018: Data-driven Process Discovery and Analysis: Proceedings of the 8th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2018), Seville, Spain, December 13-14, 2018 (2018)[Book/Report › Book editing] 8th International Symposium on Data-driven Process Discovery and Analysis 2018. CEUR. Ceravolo, P., Teresa Gómez López, M. & van Keulen, M.http://ceur-ws.org/Vol-2270Towards Robust Evaluation of Face Morphing Detection (2018)In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (pp. 1027-1031). Article 8553018 (European Signal Processing Conference; Vol. 2018-September). IEEE. Spreeuwers, L., Veldhuis, R. & Schils, M.https://doi.org/10.23919/EUSIPCO.2018.8553018Automatic structuring of breast cancer radiology reports for quality assurance (2018)In Proceedings of the Workshop on Data Mining in Biomedical Informatics and Healthcare (DMBIH 2018) (pp. 732-739). Article 8637387. IEEE. Pathak, S., van Rossen, J., Vijlbrief, O., Geerdink, J., Seifert, C. & van Keulen, M.https://doi.org/10.1109/ICDMW.2018.00111Automatic face recognition for home safety using video-based side-view face images (2018)IET biometrics, 7(6), 606-614. Santemiz, P., Spreeuwers, L. J. & Veldhuis, R. N. J.https://doi.org/10.1049/iet-bmt.2017.0203Flipper 2.0: A Pragmatic Dialogue Engine for Embodied Conversational Agents (2018)In Proceedings of the 18th International Conference on Intelligent Virtual Agents (pp. 43-50). ACM Press. van Waterschoot, J. B., Bruijnes, M., Flokstra, J., Reidsma, D., Davison, D. P., Theune, M. & Heylen, D. K. J.https://doi.org/10.1145/3267851.3267882Location Information Quality: A Review (2018)Sensors (Switzerland), 18(11). Article 3999. Epa Ranasinghe, C. M. & Kray, C.https://doi.org/10.3390/s18113999FEERCI: A Package for Fast Non-Parametric Confidence Intervals for Equal Error Rates in Amortized O(m log n) (2018)In 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018. Article 8553607 (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. Haasnoot, E., Khodabakhsh, A., Zeinstra, C., Spreeuwers, L. & Veldhuis, R.https://doi.org/10.23919/BIOSIG.2018.8553607Predicted Templates: Learning-curve Based Template Projection for Keystroke Dynamics (2018)In 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018. Article 8553293 (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. Khodabakhsh, A., Haasnoot, E. & Bours, P.https://doi.org/10.23919/BIOSIG.2018.8553293Shallow CNNs for the Reliable Detection of Facial Marks (2018)In 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018. Article 8553157 (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. Zeinstra, C. & Haasnoot, E.https://doi.org/10.23919/BIOSIG.2018.8553157Camera Localization in Outdoor Garden Environments Using Artificial Landmarks (2018)In 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings. Article 8464139. IEEE. Strisciuglio, N., Vallina, M. L., Petkov, N. & Salinas, R. M.https://doi.org/10.1109/IWOBI.2018.8464139Learning from Accidents: A Systematic Review of Accident Analysis Methods and Models (2018)International journal of information systems for crisis response and management, 10(3), 42-62. Wienen, H., Bukhsh, F. A., Vriezekolk, E. & Wieringa, R. J.https://doi.org/10.4018/IJISCRAM.2018070103Prof. Bram Nauta, Prof. Luuk Spreeuwers, and Students from the University of Twente Visit Tsinghua University (2018)IEEE Solid-State Circuits Magazine, 10(4), 91-92. Song, W., Zhang, M., Nauta, B. & Spreeuwers, L.https://doi.org/10.1109/MSSC.2018.2867300One-shot learning using Mixture of Variational Autoencoders: A generalization learning approach (2018)In 17th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2018 (pp. 2016-2018). The International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). Mocanu, D. C. & Mocanu, E.http://ifaamas.org/Proceedings/aamas2018/pdfs/p2016.pdfEvaluating surrogate models for multi-objective influence maximization in social networks (2018)In GECCO 2018 Companion : Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1258-1265). Association for Computing Machinery. Bucur, D., Iacca, G., Marcelli, A., Squillero, G. & Tonda, A.https://doi.org/10.1145/3205651.3208238Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science (2018)Nature communications, 9(1). Article 2383. Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M. & Liotta, A.https://doi.org/10.1038/s41467-018-04316-3Big Data Semantics (2018)Journal on Data Semantics, 7(2), 65-85. Ceravolo, P., Azzini, A., Angelini, M., Catarci, T., Cudré-Mauroux, P., Damiani, E., Mazak, A., van Keulen, M., Jarrar, M., Santucci, G., Sattler, K.-U., Scannapieco, M., Wimmer, M., Wrembel, R. & Zaraket, F.https://doi.org/10.1007/s13740-018-0086-2A Prototype of Finger-vein Phantom (2018)In Proceedings of the 2018 Symposium on Information Theory and Signal Processing in the Benelux: May 31-1 June, 2018, University of Twente, Enschede, The Netherlands (pp. 163-166). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). Normakristagaluh, P., Spreeuwers, L. J. & Veldhuis, R. N. J.http://www.w-i-c.org/proceedings/proceedings_SITB2018.pdfThe Behavior of Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) for Face Recognition (2018)In Proceedings of the 2018 Symposium on Information Theory and Signal Processing in the Benelux: May 31-1 June, 2018, University of Twente, Enschede, The Netherlands (pp. 133-148). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). Lestriandoko, N. H., Spreeuwers, L. & Veldhuis, R.https://www.utwente.nl/en/eemcs/sitb2018/sitb2018proceedings.pdfAccident Analysis Methods and Models — a Systematic Review (2018)In Proceedings of the 15th ISCRAM Conference (pp. 398-408). International Association for Information Systems for Crisis Response and Management (ISCRAM). Wienen, H. C. A., Bukhsh, F. A., Vriezekolk, E. & Wieringa, R. J.Forensic image analysis - CCTV distortion and artefacts (2018)Forensic science international, 285, 77-85. Article 285. Seckiner, D., Mallett, X., Roux, C., Meuwly, D. & Maynard, P.https://doi.org/10.1016/j.forsciint.2018.01.024Configuring value networks based on subjective business values (2018)CEUR workshop proceedings, 2239, 158-170. Da Silva Reis, J., De Alencar Silva, P., Bukhsh, F. A. & De Castro, A. F.Visualizing Location Uncertainty on Mobile Devices: Cross-Cultural Differences in Perceptions and Preferences (2018)Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1). Article 30. Epa Ranasinghe, C. M., Krukar, J. & Kray, C.https://doi.org/10.1145/3191762Probabilistic Data Integration (2018)In Encyclopedia of Big Data Technologies (pp. 1–9). Springer. van Keulen, M.https://doi.org/10.1007/978-3-319-63962-8_18-1Data pre-processing: Case of sensor data consistency based on Bi-temporal concepts (2018)In 2017 13th International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE. Bukhsh, F. A., De Alencar Silva, P. & Wienen, H.https://doi.org/10.1109/ICET.2017.8281746Deep learning versus traditional machine learning methods for aggregated energy demand prediction (2018)In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). Article 17507113. IEEE. Paterakis, N. G., Mocanu, E., Gibescu, M., Stappers, B. & Alst, W. v.https://doi.org/10.1109/ISGTEurope.2017.8260289Supporting the Exploration of Online Cultural Heritage Collections: The Case of the Dutch Folktale Database (2018)Digital humanities quarterly, 11(4). Muiser, I., Theune, M., de Jong, R., Smink, N., Trieschnigg, D., Hiemstra, D. & Meder, T.http://www.digitalhumanities.org/dhq/vol/11/4/000327/000327.htmlAn improved diagnostic method for probabilistic consistency-based diagnosis (2018)In 28th International Workshop on Principles of Diagnosis (DX '17) (pp. 65-77) (Kalpa Publications in Computing). EasyChair. de Paula Bueno, M. L., Hommersom, A. & Lucas, P.https://doi.org/10.29007/4t6nAnalyzing excessive user feedback: A big data challenge (2018)In 2018 International Conference on Frontiers of Information Technology (FIT) (pp. 206-211) (International Conference on Frontiers of Information Technology (FIT); Vol. 2018). Bukhsh, F. A., Jayasinghe Arachchige, J. & Malik, F.https://doi.org/10.1109/FIT.2018.00043AUI story maker: Animation generation from natural language (2018)In Artificial Intelligence in Education: 19th International Conference, AIED 2018, London, UK, June 27–30, 2018, Proceedings, Part II (pp. 424–428) (Lecture Notes in Computer Science; Vol. 10948). Springer. Bouali, N. & Cavalli-Sforza, V.https://doi.org/10.1007/978-3-319-93846-2_79Automatic segmentation of indoor and outdoor scenes from visual lifelogging (2018)In Applications of Intelligent Systems - Proceedings of the 1st International APPIS Conference 2018, APPIS 2018 (pp. 194-202) (Frontiers in Artificial Intelligence and Applications; Vol. 310). IOS. Buhagiar, J., Strisciuglio, N., Petkov, N. & Azzopardi, G.https://doi.org/10.3233/978-1-61499-929-4-194Breaking Out of the Black Box in Automated Flower Recognition (2018)In Proceedings of the 2018 Symposium on Information Theory and Signal Processing in the Benelux: May 31-1 June, 2018, University of Twente, Enschede, The Netherlands (pp. 28-34). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). Apriyanti, D. H., Spreeuwers, L. J. & Veldhuis, R. N. J.https://www.utwente.nl/en/eemcs/sitb2018/sitb2018proceedings.pdfCompliance Checking of Shipment Request by Utilizing Process Mining Concepts: An Evaluation of Smart Auditing Framework (2018)In 2017 International Conference on Frontiers of Information Technology (FIT) (pp. 235-240). IEEE. Bukhsh, F. A. B. & Weigand, H. W.https://doi.org/10.1109/FIT.2017.00049Deep reinforcement learning for scheduling (2018)[Contribution to conference › Paper] IEEE Power and Energy Society General Meeting, PESGM 2018. Mocanu, E., Nguyen, P. H. & Gibescu, M.Enabling cooperative behavior for building demand response based on extended joint action learning (2018)IEEE transactions on industrial informatics, 14(1), 127-136. Article 8039194. Hurtado Muñoz, L. A., Mocanu, E., Nguyen, P. H., Gibescu, M. & Kamphuis, R. I. G.https://doi.org/10.1109/TII.2017.2753408Forensic face recognition as a means to determine strength of evidence: A survey (2018)Forensic Science Review, 30(1), 21-32. Zeinstra, C. G., Meuwly, D., Ruifrok, A. C. C., Veldhuis, R. N. J. & Spreeuwers, L. J.http://forensicsciencereview.com/content.htm#v30Grid-Based Likelihood Ratio Classifiers for the Comparison of Facial Marks (2018)IEEE transactions on information forensics and security, 13(1), 253-264. Article 8017453. Zeinstra, C., Veldhuis, R. & Spreeuwers, L.https://doi.org/10.1109/TIFS.2017.2746013Improving Multi-objective Evolutionary Influence Maximization in Social Networks (2018)In Applications of Evolutionary Computation: 21st International Conference, EvoApplications 2018, Proceedings (pp. 117-124) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10784 LNCS). Springer. Bucur, D., Iacca, G., Marcelli, A., Squillero, G. & Tonda, A.https://doi.org/10.1007/978-3-319-77538-8_9Learning audio and image representations with bio-inspired trainable feature extractors (2018)Electronic Letters on Computer Vision and Image Analysis, 16(2), 17-20. Strisciuglio, N.https://doi.org/10.5565/rev/elcvia.1128LIFT: Learning Fault Trees from Observational Data (2018)In Quantitative Evaluation of Systems: 15th International Conference, QEST 2018, Beijing, China, September 4-7, 2018, Proceedings (pp. 306-322) (Lecture Notes in Computer Science; Vol. 11024). Springer. Nauta, M., Bucur, D. & Stoelinga, M.https://doi.org/10.1007/978-3-319-99154-2_19Making continuous time Bayesian networks more flexible (2018)In Proceedings of the Ninth International Conference on Probabilistic Graphical Models: 11-14 September 2018, Prague, Czech Republic (pp. 237-248) (Proceedings of Machine Learning Research (PMLR); Vol. 72). MLResearchPress. Liu, M., Stella, F., Hommersom, A. & Lucas, P. J. F.https://proceedings.mlr.press/v72/liu18a.htmlModeling the dynamics of multiple disease occurrence by latent states (2018)In Scalable Uncertainty Management : 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings (pp. 93-107) (Lecture Notes in Computer Science; Vol. 11142). Springer. Bueno, M. L. P., Hommersom, A., Lucas, P. J. F., Lobo, M. & Rodrigues, P. P.https://doi.org/10.1007/978-3-030-00461-3_7On the Gender of Books: Author Gender Mixing in Book Communities (2018)In Complex Networks and Their Applications VI: Proceedings of Complex Networks 2017 (The Sixth International Conference on Complex Networks and Their Applications) (pp. 797-808) (Studies in Computational Intelligence; Vol. 689). Springer. Bucur, D.https://doi.org/10.1007/978-3-319-72150-7_64Parallel probabilistic inference by weighted model counting (2018)In Proceedings of the Ninth International Conference on Probabilistic Graphical Models: 11-14 September 2018, Prague, Czech Republic (pp. 97-108) (Proceedings of Machine Learning Research (PMLR); Vol. 72). MLResearchPress. Dal, G. H., Laarman, A. W. & Lucas, P. J. F.https://proceedings.mlr.press/v72/dal18a.htmlPreface (2018)In Applications of Intelligent Systems: Proceedings of the 1st International APPIS Conference 2018 (pp. V-V) (Frontiers in artificial intelligence and applications; Vol. 310). IOS. Petkov, N., Strisciuglio, N. & Travieso-González, C. M.http://ebooks.iospress.nl/volumearticle/50897Representing hypoexponential distributions in continuous time Bayesian networks (2018)In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications: 17th International Conference, IPMU 2018, Cádiz, Spain, June 11-15, 2018, Proceedings, Part III (pp. 565-577) (Communications in Computer and Information Science; Vol. 855). Springer. Liu, M., Stella, F., Hommersom, A. & Lucas, P. J. F.https://doi.org/10.1007/978-3-319-91479-4_47Rule-based conditioning of probabilistic data (2018)In Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings (pp. 290-305) (Lecture Notes in Computer Science; Vol. 11142). Springer. van Keulen, M., Kaminski, B., Matheja, C. & Katoen, J. P.https://doi.org/10.1007/978-3-030-00461-3_20Towards Egocentric Person Re-Identification and Social Pattern Analysis (2018)In Applications of Intelligent Systems: Proceedings of the 1st International APPIS Conference 2018 (pp. 203-211) (Frontiers in Artificial Intelligence and Applications; Vol. 310). IOS. Talavera Martínez, E., Cola, A., Petkov, N. & Radeva, P.https://doi.org/10.3233/978-1-61499-929-4-203Trimbot2020: An outdoor robot for automatic gardening (2018)In 50th International Symposium on Robotics, ISR 2018. VDE Verlag. Strisciuglio, N., Tylecek, R., Blaich, M., Petkov, N., Biber, P., Hemming, J., van Henten, E., Sattler, T., Pollefeys, M., Gevers, T., Brox, T. & Fisher, R. B.Validation of ACCESS: An automated tool to support self-management of COPD exacerbations (2018)International journal of chronic obstructive pulmonary disease, 13, 3255-3267. Boer, L. M., van der Heijden, M., van Kuijk, N. M. E., Lucas, P. J. F., Vercoulen, J. H., Assendelft, W. J. J., Bischoff, E. W. & Schermer, T. R.https://doi.org/10.2147/COPD.S167272

2017

Deep Physiological Arousal Detection in a Driving Simulator Using Wearable Sensors (2017)In 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 486-493). IEEE. Saeed, A., Trajanovski, S., van Keulen, M. & van Erp, J. B. F.https://doi.org/10.1109/ICDMW.2017.69Exploiting Natural Language Processing for Improving Health Processes (2017)In Proceedings of the 7th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017) (pp. 145-146) (CEUR Workshop Proceedings; Vol. 2016). CEUR. van Keulen, M., Geerdink, J., Linssen, G. C. M., Slart, R. H. J. A. & Vijlbrief, O.http://ceur-ws.org/Vol-2016/Foreword (2017)In 7th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017) (pp. II) (CEUR Workshop Proceedings; Vol. 2016). CEUR. Ceravolo, P., Van Keulen, M. & Stoffel, K.http://ceur-ws.org/Vol-2016/Proceedings of the 7th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017) (2017)[Book/Report › Book editing]. CEUR. Ceravolo, P., Van Keulen, M. & Stoffel, K.http://ceur-ws.org/Vol-2016Serious Games Application for Memory Training Using Egocentric Images: Workshop on Social Signal Processing and Beyond (2017)In New Trends in Image Analysis and Processing – ICIAP 2017: ICIAP International Workshops, WBICV, SSPandBE, 3AS, RGBD, NIVAR, IWBAAS, and MADiMa 2017, Catania, Italy, September 11-15, 2017, Revised Selected Papers (pp. 120-130) (Lecture Notes in Computer Science; Vol. 10590). Springer. Oliveira-Barra, G., Bolanos, M., Talavera, E., Gelonch, O. & Garolera, M.https://doi.org/10.1007/978-3-319-70742-6_11Deep learning for power system data analysis (2017)In Big Data Application in Power Systems (pp. 125-158). Elsevier. Mocanu, E., Nguyen, P. H. & Gibescu, M.https://doi.org/10.1016/B978-0-12-811968-6.00007-3Forensic Face Recognition: From characteristic descriptors to strength of evidence (2017)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. Zeinstra, C. G.https://doi.org/10.3990/1.9789036543750Probabilistic Data Integration (2017)[Contribution to conference › Poster] Dutch-Belgian Database Day, DBDBD 2017. van Keulen, M.http://wwwis.win.tue.nl/dbdbd2017/abstracts/dbdbd2017-pdi.pdfExploiting Experts' Knowledge for Structure Learning of Bayesian Networks (2017)IEEE transactions on pattern analysis and machine intelligence, 39(11), 2154-2170. Article 7776879. Amirkhani, H., Rahmati, M., Lucas, P. J. F. & Hommersom, A.https://doi.org/10.1109/TPAMI.2016.2636828ForenFace: A unique annotated forensic facial image dataset and toolset (2017)IET biometrics, 6(6), 487-494. Zeinstra, C. G., Veldhuis, R. N. J., Spreeuwers, L. J., Ruifrok, A. C. C. & Meuwly, D.https://doi.org/10.1049/iet-bmt.2016.0160Low-resolution face alignment and recognition using mixed-resolution classifiers (2017)IET biometrics, 6(6), 418-428. Peng, Y., Spreeuwers, L. & Veldhuis, R.https://doi.org/10.1049/iet-bmt.2016.0026Weighted positive binary decision diagrams for exact probabilistic inference (2017)International Journal of Approximate Reasoning, 90, 411-432. Dal, G. H. & Lucas, P. J. F.https://doi.org/10.1016/j.ijar.2017.08.003A real-time system for audio source localization with cheap sensor device (2017)In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2017. Article 8078461. IEEE. Saggese, A., Strisciuglio, N., Vento, M. & Petkov, N.https://doi.org/10.1109/AVSS.2017.8078461Machine learning applied to smart grids (2017)[Thesis › PhD Thesis - Research external, graduation external]. Technische Universiteit Eindhoven. Mocanu, E.MTCB: A Multi-Tenant Customizable database Benchmark (2017)In ICME 2017: Proceedings of the 9th International Conference on Information Management and Engineering (pp. 17-23). Association for Computing Machinery. van der Zijden, W., Hiemstra, D. & van Keulen, M.https://doi.org/10.1145/3149572.3149585Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting (2017)In 2017 International Conference of the Biometrics Special Interest Group, BIOSIG 2017. Article 8053499. Gesellschaft für Informatik. Scherhag, U., Nautsch, A., Rathgeb, C., Gomez-Barrero, M., Veldhuis, R. N. J., Spreeuwers, L., Schils, M., Maltoni, D., Grother, P., Marcel, S., Breithaupt, R., Ramachandra, R. & Busch, C.https://doi.org/10.23919/BIOSIG.2017.8053499De-Duplication Using Automated Face Recognition: A Mathematical Model and All Babies Are Equally Cute (2017)In 2017 International Conference of the Biometrics Special Interest Group, BIOSIG 2017. Article 8053500. Gesellschaft für Informatik. Spreeuwers, L.https://doi.org/10.23919/BIOSIG.2017.8053500How Random Is a Classifier Given Its Area under Curve? (2017)In 2017 International Conference of the Biometrics Special Interest Group (BIOSIG): BIOSIG 2017. Article 8053509. Gesellschaft für Informatik. Zeinstra, C., Veldhuis, R. & Spreeuwers, L.https://doi.org/10.23919/BIOSIG.2017.8053509Making Likelihood Ratios Digestible for Cross-Application Performance Assessment (2017)IEEE signal processing letters, 24(10), 1552-1556. Article 17176768. Nautsch, A., Meuwly, D., Ramos, D., Lindh, J. & Busch, C.https://doi.org/10.1109/LSP.2017.2748899Asymmetric hidden Markov models (2017)International Journal of Approximate Reasoning, 88, 169-191. Bueno, M. L. P., Hommersom, A., Lucas, P. J. F. & Linard, A.https://doi.org/10.1016/j.ijar.2017.05.011Improved search methods for assessing Delay-Tolerant Networks vulnerability to colluding strong heterogeneous attacks (2017)Expert systems with applications, 80, 311-322. Bucur, D. & Iacca, G.https://doi.org/10.1016/j.eswa.2017.03.035Towards accurate de novo assembly for genomes with repeats (2017)In 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Bucur, D.https://doi.org/10.1109/CIBCB.2017.8058534Delineation of line patterns in images using B-COSFIRE filters (2017)In 2017 International Work Conference on Bio-Inspired Intelligence: Intelligent Systems for Biodiversity Conservation, IWOBI 2017 - Proceedings. Article 7985538. IEEE. Strisciuglio, N. & Petkov, N.https://doi.org/10.1109/IWOBI.2017.7985538A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation (2017)Forensic science international, 276, 142-153. Meuwly, D., Ramos, D. & Haraksim, R.https://doi.org/10.1016/j.forsciint.2016.03.048Big IoT data mining for real-time energy disaggregation in buildings (extended abstract) (2017)In Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning. TU/e. Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M. & Liotta, A.Accident Analysis Methods and Models — a Systematic Literature Review (2017)[Book/Report › Report]. Centre for Telematics and Information Technology (CTIT). Wienen, H. C. A., Bukhsh, F. A., Vriezekolk, E. & Wieringa, R. J.Medium voltage DC power systems on ships: An offline parameter estimation for tuning the controllers' linearizing function (2017)IEEE Transactions on Energy Conversion, 32(2), 748-758. Article 7867753. Bosich, D., Sulligoi, G., Mocanu, E. & Gibescu, M.https://doi.org/10.1109/TEC.2017.2676618Understanding (2017)[Contribution to conference › Paper] 14th IAPR/IEEE International Summer School on Biometrics 2017. Haasnoot, E., Barnhoorn, J., Spreeuwers, L., Veldhuis, R. & Verwey, W.Manually annotated characteristic descriptors: Measurability and variability (2017)In 2017 5th International Workshop on Biometrics and Forensics (IWBF 2017) . Article 7935095. IEEE. Zeinstra, C., Veldhuis, R., Spreeuwers, L. & Ruifrok, A.https://doi.org/10.1109/IWBF.2017.7935095Performance Study of a Score-based Likelihood Ratio System for Forensic Fingermark Comparison (2017)Journal of Forensic Sciences, 62(3), 626-640. Leegwater, A. J., Meuwly, D., Sjerps, M., Vergeer, P. & Alberink, I.https://doi.org/10.1111/1556-4029.13339ISIS in the Eyes of the Dutch (2017)In Proceedings of the Workshop on Social Media for Personalization and Search (SoMePeAs 2017): co-located with 39th European Conference on Information Retrieval (ECIR 2017) (pp. 28-33). Article 4 (CEUR Workshop Proceedings; Vol. 1840). CEUR. Hendrikse, B., Habib, M. B. & van Keulen, M.De Novo DNA Assembly with a Genetic Algorithm Finds Accurate Genomes Even with Suboptimal Fitness (2017)In Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I (pp. 67-82) (Lecture Notes in Computer Science; Vol. 10199). Springer. Bucur, D.https://doi.org/10.1007/978-3-319-55849-3_5Detecting Hacked Twitter Accounts based on Behavioural Change (2017)In Proceedings of the 13th International Conference on Web Information Systems and Technologies (WEBIST 2017): 19-31, 2017, Porto, Portugal (pp. 19-31). INSTICC Institute for Systems and Technologies of Information, Control and Communication. Nauta, M., Habib, M. B. & van Keulen, M.https://doi.org/10.5220/0006213600190031Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks (2017)In Applications of Evolutionary Computation: 20th European Conference, EvoApplications 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings, Part I (pp. 221-233) (Lecture Notes in Computer Science; Vol. 10199). Springer. Bucur, D., Iacca, G., Marcelli, A., Squillero, G. & Tonda, A.https://doi.org/10.1007/978-3-319-55849-3_15Testing Sleep Consolidation in Skill Learning: A Field Study Using an Online Game (2017)Topics in Cognitive Science, 9(2), 485-496. Stafford, T. & Haasnoot, E.https://doi.org/10.1111/tops.12232Truth assessment of objective facts extracted from tweets: A case study on world cup 2014 game facts (2017)In Proceedings of the 13th International Conference on Web Information Systems and Technologies: April 25-27, 2017, in Porto, Portugal (pp. 187-195). SCITEPRESS. Janssen, B., Habib, M. & Van Keulen, M.https://doi.org/10.5220/0006185101870195From image sequence to frontal image: reconstruction of the unknown face: a forensic case (2017)[Thesis › PhD Thesis - Research UT, graduation UT]. University of Twente. van Dam, C.https://doi.org/10.3990/1.9789036543248Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions (2017)IET biometrics, 6(2), 61-69. Haraksim, R., Ramos, D. & Meuwly, D.https://doi.org/10.1049/iet-bmt.2015.0059Modeling E-Business Customization with e3value Modeling (2017)In 2016 International Conference on Frontiers of Information Technology (FIT) (pp. 187-192). Article 7866751. IEEE. Bukhsh, F. A. & Silva, P. D. A.https://doi.org/10.1109/FIT.2016.042Big IoT data mining for real-time energy disaggregation in buildings (2017)In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), 9-12 October 2016, Budapest, Hungary (pp. 003765-003769). IEEE. Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M. & Liotta, A.https://doi.org/10.1109/SMC.2016.7844820Information technology project management viewpoint: A case study from PTCL (2017)In 2016 6th International Conference on Innovative Computing Technology, INTECH 2016 (pp. 24-29). Article 7845086. IEEE. Bukhsh, F. A., Younus, I. & Jayasinghe Arachchige, J.https://doi.org/10.1109/INTECH.2016.7845086Automated Fingerprint Identification Systems: From Fingerprints to Fingermarks (2017)In Handbook of Biometrics for Forensic Science (pp. 37-61) (Advances in Computer Vision and Pattern Recognition). Springer. Maltoni, D., Cappelli, R. & Meuwly, D.https://doi.org/10.1007/978-3-319-50673-9_3Likelihood ratio data to report the validation of a forensic fingerprint evaluation method (2017)Data in brief, 10, 75-92. Ramos, D., Haraksim, R. & Meuwly, D.https://doi.org/10.1016/j.dib.2016.11.008SR-clustering: Semantic regularized clustering for egocentric photo streams segmentation (2017)Computer vision and image understanding, 155, 55-69. Dimiccoli, M., Bolanos, M., Talavera, E., Aghaei, M., Nikolov, S. G. & Radeva, P.https://doi.org/10.1016/j.cviu.2016.10.005A stochastic de novo assembly algorithm for viral-sized genomes obtains correct genomes and builds consensus (2017)Information sciences, 420, 184-199. Bucur, D.https://doi.org/10.1016/j.ins.2017.07.039Accuracy of the Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS) application to support patients’ exacerbation self-management: Preliminary results (2017)European respiratory journal, 50(S61). Boer, L., Bischoff, E., van Kuijk, N., van der Heijden, M., Lucas, P., Vercoulen, J. & Schermer, T.https://doi.org/10.1183/1393003.congress-2017.OA4870Agency monitoring patterns for value networks (2017)In Economics of Grids, Clouds, Systems, and Services: 14th International Conference, GECON 2017, Proceedings (pp. 81-93) (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10537 LNCS). Springer. de Alencar Silva, P., Allah Bukhsh, F., da Silva Reis, J. & de Castro, A. F.https://doi.org/10.1007/978-3-319-68066-8_7Constrained parameter estimation with uncertain priors for Bayesian networks (2017)Electronic Journal of Statistics, 11(2), 4000-4032. Karimnezhad, A., Lucas, P. J. F. & Parsian, A.https://doi.org/10.1214/17-EJS1350Deep Verification Learning (2017)In Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands (pp. 97-104). Delft University of Technology. Hillerström, F., Veldhuis, R. & Spreeuwers, L.Detection of curved lines with B-COSFIRE filters: A case study on crack delineation (2017)In Computer Analysis of Images and Patterns: 17th International Conference, CAIP 2017, Ystad, Sweden, August 22-24, 2017, Proceedings, Part I (pp. 108-120) (Lecture Notes in Computer Science; Vol. 10424). Springer. Strisciuglio, N., Azzopardi, G. & Petkov, N.https://doi.org/10.1007/978-3-319-64689-3_9From biometric scores to forensic likelihood ratios (2017)In Handbook of Biometrics for Forensic Science: Advances in Computer Vision and Pattern Recognition (pp. 305-327) (Advances in Computer Vision and Pattern Recognition). Springer. Ramos, D., Krish, R. P., Fierrez, J. & Meuwly, D.https://doi.org/10.1007/978-3-319-50673-9_14Hybrid time Bayesian networks (2017)International Journal of Approximate Reasoning, 80, 460-474. Liu, M., Hommersom, A., van der Heijden, M. & Lucas, P. J. F.https://doi.org/10.1016/j.ijar.2016.02.009Real-time measures of social interaction as predictors for team effectiveness (2017)[Contribution to conference › Paper] WAOP conference 2017. de Laat, S., Endedijk, M. D., Ufkes, E. G., van Keulen, M. & de Vries, R. E.Reducing the cost of probabilistic knowledge compilation (2017)In Proceedings of Machine Learning Research: Advanced Methodologies for Bayesian Networks, 20-22 September 2017 (pp. 141-152). Dal, G., Michels, S. & Lucas, P. J. F.Sentiment Recognition in Egocentric Photostreams: Iberian Conference on Pattern Recognition and Image Analysis (2017)In Pattern Recognition and Image Analysis: 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings (pp. 471-479) (Lecture Notes in Computer Science; Vol. 10255). Springer. Talavera, E., Strisciuglio, N., Petkov, N., Radeva, P., Alexandre, L. A., Salvador Sanchez, J. & Rodrigues, J. M. F.https://doi.org/10.1007/978-3-319-58838-4Sentiment recognition in egocentric photostreams (2017)In Pattern Recognition and Image Analysis: 8th Iberian Conference, IbPRIA 2017, Faro, Portugal, June 20-23, 2017, Proceedings (pp. 471-479) (Lecture Notes in Computer Science; Vol. 10255) (Image Processing, Computer Vision, Pattern Recognition, and Graphics). Springer. Talavera, E., Strisciuglio, N., Petkov, N. & Radeva, P.https://doi.org/10.1007/978-3-319-58838-4_52Towards Egocentric Sentiment Analysis: Sixteenth International Conference on Computer Aided Systems Theory (2017)In Computer Aided Systems Theory – EUROCAST 2017: 16th International Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017, Revised Selected Papers (pp. 297-305) (Lecture Notes in Computer Science; Vol. 10672). Springer. Talavera, E., Radeva, P. & Petkov, N.https://doi.org/10.1007/978-3-319-74727-9_35Towards understanding behavioural biometric classifier performance over time and practice (2017)In Proceedings of the 2017 Symposium on Information Theory and Signal Processing in the Benelux: May 11-12, 2017, Delft University of Technology, Delft, the Netherlands (pp. 79-88). Delft University of Technology. Haasnoot, E., Barnhoorn, J. S., Spreeuwers, L. J., Veldhuis, R. N. J. & Verwey, W. B.