Review Research 2021

Computer Science

DMB



Jump to: 2020 | 2019 | 2018 | 2017 | 2016 | 2015

2020

Article (contribution to journal)



Brandt, R. , Strisciuglio, N., Petkov, N., & Wilkinson, M. H. F. (2020). Efficient binocular stereo correspondence matching with 1-D Max-Trees. Pattern recognition letters, 135, 402-408. https://doi.org/10.1016/j.patrec.2020.02.019



Cui, X., Zheng, S., Heuvelmans, M. A., Du, Y., Sidorenkov, G., Dorrius, M. D. , Veldhuis, R. N. J., Oudkerk, M., de Bock, G. H., van Ooijen, P. M. A., Vliegenthart, R., & Ye, Z. (2020). Validation of a deep learning-based computer-aided system for lung nodule detection in a Chinese lung cancer screening program. European respiratory journal, 56(64), [4168]. https://doi.org/10.1183/13993003.congress-2020.4168

Marazza, F. , Bukhsh, F. A., Geerdink, J., Vijlbrief, O. , Pathak, S. , van Keulen, M. , & Seifert, C. (2020). Automatic Process Comparison for Subpopulations: Application in Cancer Care. International journal of environmental research and public health, 17(16), 1-23. [5707]. https://doi.org/10.3390/ijerph17165707

Melotti, D., Heimbach, K., Rodríguez-Sánchez, A. , Strisciuglio, N., & Azzopardi, G. (2020). A robust contour detection operator with combined push-pull inhibition and surround suppression. Information sciences, 524, 229-240. https://doi.org/10.1016/j.ins.2020.03.026

Pathak, S., van Rossen, J., Vijlbrief, O., Geerdink, J. , Seifert, C. , & van Keulen, M. (2020). Post-Structuring Radiology Reports of Breast Cancer Patients for Clinical Quality Assurance. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(6), 1883-1894. https://doi.org/10.1109/TCBB.2019.2914678


Raja, K., Ferrara, M., Franco, A. , Spreeuwers, L. , Batskos, I. , de Wit, F. 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. (2020). Morphing Attack Detection - Database, Evaluation Platform and Benchmarking. IEEE transactions on information forensics and security. https://doi.org/10.1109/TIFS.2020.3035252

Ramachandran, S. , Strisciuglio, N., Vinekar, A., John, R., & Azzopardi, G. (2020). U-COSFIRE filters for vessel tortuosity quantification with application to automated diagnosis of retinopathy of prematurity. Neural Computing and Applications, 32(16), 12453-12468. https://doi.org/10.1007/s00521-019-04697-6

Schlötterer, J. , Seifert, C., Satchell, C., & Granitzer, M. (2020). QueryCrumbs Search History Visualization: Usability, Transparency and Long-term Usage. Journal of Computer Languages, 57, [100941]. https://doi.org/10.1016/j.cola.2020.100941

Strisciuglio, N., Lopez-Antequera, M., & Petkov, N. (2020). Enhanced Robustness of Convolutional Networks with a Push-Pull Inhibition Layer. Neural Computing and Applications, 32(24), 17957-17971. https://doi.org/10.1007/s00521-020-04751-8

Yaman, A., Iacca, G. , Mocanu, D. C., Coler, M., Fletcher, G., & Pechenizkiy, M. (2020). Evolving plasticity for autonomous learning under changing environmental conditions. Evolutionary Computation. https://arxiv.org/abs/1904.01709

Zheng, S., Cui, X., Vonder, M. , Veldhuis, R. N. J., Dorrius, M. D., Ye, Z., Vliegenthart, R., Oudkerk, M., & van Ooijen, P. M. A. (2020). Effect of slab thickness on pulmonary nodule detection using maximum intensity projection in a deep learning-based computer-aided detection system. European respiratory journal, 56(64), [4169]. https://doi.org/10.1183/13993003.congress-2020.4169

Zheng, S., Guo, J., Cui, X. , Veldhuis, R. N. J., Oudkerk, M., & Van Ooijen, P. M. A. (2020). Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection. IEEE transactions on medical imaging, 39(3), 797-805. [8801875]. https://doi.org/10.1109/TMI.2019.2935553


van den Oever, L. B., Cornelissen, L., Vonder, M., Xia, C., van Bolhuis, J. N., Vliegenthart, R. , Veldhuis, R. N. J., de Bock, G. H., Oudkerk, M., & van Ooijen, P. M. A. (2020). Deep learning for automated exclusion of cardiac CT examinations negative for coronary artery calcium. European journal of radiology, 129, [109114]. https://doi.org/10.1016/j.ejrad.2020.109114

Article (contribution to specialist publication)



Book


Uhl, A., Busch, C., Marcel, S. , & Veldhuis, R. N. J. (Eds.) (2020). Handbook of Vascular Biometrics. (Advances in Computer Vision and Pattern Recognition; Vol. 79, No. 4205). Springer. https://doi.org/10.1007/978-3-030-27731-4

Book editing



Chapter


Veldhuis, R. , Spreeuwers, L., Ton, B., & Rozendal, S. (2020). A High-Quality Finger Vein Dataset Collected Using a Custom-Designed Capture Device. In A. Uhl, C. Busch, S. Marcel, & R. Veldhuis (Eds.), Handbook of Vascular Biometrics (pp. 63-75). (Advances in Computer Vision and Pattern Recognition). Springer. https://doi.org/10.1007/978-3-030-27731-4_2

Conference article


Botteghi, N. , Sirmacek, B. , Poel, M. , & Brune, C. (2020). Reinforcement learning helps slam: Learning to build maps. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 43(B4), 329-336. https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-329-2020

Conference contribution


Bossek, J., Grimme, C. , & Trautmann, H. (2020). Dynamic Bi-Objective Routing of Multiple Vehicles. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '20) (pp. 166-174). ACM SIGCOMM.

Bueno, M. L. P., Hommersom, A. , & Lucas, P. J. F. (2020). Temporal exceptional model mining using dynamic Bayesian networks. In V. Lemaire, S. Malinowski, A. Bagnall, T. Guyet, R. Tavenard, & G. Ifrim (Eds.), 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. https://doi.org/10.1007/978-3-030-65742-0_7

Coutinho, J. C., Moreira, J. , & Rebelo de Sá, C. (2020). UnFOOT - Unsupervised Football Analytics Tool. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III (pp. 786-789). (Lecture notes in computer science; Vol. 11908). Springer. https://doi.org/10.1007/978-3-030-46133-1_52

Esquivel Vargas, H. T., Caselli, M. , Laanstra, G. J. , & Peter, A. (2020). Putting Attacks in Context: A Building Automation Testbed for Impact Assessment from the Victim’s Perspective. 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). https://doi.org/10.1007/978-3-030-52683-2_3

Garcia, K. D., Carvalho, T., Mendes-Moreira, J., Cardoso, J. M. P., & de Carvalho, A. C. P. L. F. (2020). A Study on Hyperparameter Configuration for Human Activity Recognition. In H. Quintián, J. A. Sáez Muñoz, E. Corchado, F. Martínez Álvarez, & A. Troncoso Lora (Eds.), 14th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2019, Proceedings (pp. 47-56). (Advances in Intelligent Systems and Computing; Vol. 950). Springer Verlag. https://doi.org/10.1007/978-3-030-20055-8_5

Kelly, U. M. , Veldhuis, R. N. J. , & Spreeuwers, L. (2020). Improving deep-learning-based face recognition to increase robustness against morphing attacks. In D. C. Wyld, & J. Zizka (Eds.), 9th International Conference on Signal, Image Processing and Pattern Recognition (SPPR 2020), December 19 ~ 20, 2020, Sydney, Australia (Computer Science & Information Technology; Vol. 10, No. 19). Academy and Industry Research Collaboration Center (AIRCC). https://doi.org/10.5121/csit.2020.101901

Lammers, M. , Wijnhoven, F. , Bukhsh, F. A., & Silva, P. D. A. (2020). Automatic Q.A-Pair Generation for Incident Tickets Handling: An Application of NLP. In K. Djemame, J. Á. Bañares, V. Stankovski, J. Altmann, O. A. Ben-Yehuda, & B. Tuffin (Eds.), 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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12441 LNCS). Springer. https://doi.org/10.1007/978-3-030-63058-4_2

Marin, M. D. , Mocanu, E. , & Seifert, C. (2020). Effectiveness of neural language models for word prediction of textual mammography reports. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) IEEE SMC. https://doi.org/10.1109/SMC42975.2020.9283304

Muñoz Sánchez, M., Silvas, E., Pogosov, D. , & Mocanu, D. C. (2020). A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction. In 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 (pp. 444-450). [9282933] IEEE SMC. https://doi.org/10.1109/SMC42975.2020.9282933

Nauta, M. , Putten, M. J. A. M. V. , Tjepkema-Cloostermans, M. C., Bos, J. P. , Keulen, M. V. , & Seifert, C. (2020). Interactive Explanations of Internal Representations of Neural Network Layers: An Exploratory Study on Outcome Prediction of Comatose Patients. In K. Bach, R. Bunescu, C. Marling, & N. Wiratunga (Eds.), KDH 2020: 5th International Workshop on Knowledge Discovery in Healthcare Data (Vol. 2675, pp. 5-11). (CEUR Workshop Proceedings; Vol. 2675). CEUR. http://ceur-ws.org/Vol-2675/paper1.pdf

Oude Nijeweme - d'Hollosy, W. , van Velsen, L. , Poel, M. , Groothuis-Oudshoorn, C., Soer, R., Stegeman, P. , & Hermens, H. (2020). Applying machine learning on patient-reported data to model the selection of appropriate treatments for low back pain: A Pilot Study. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) (Vol. 5: HEALTHINF, pp. 117-124). SCITEPRESS. https://doi.org/10.5220/0008962101170124

Ruis, F. , Pathak, S., Geerdink, J. , Hegeman, J. H. , Seifert, C. , & van Keulen, M. (2020). Human-in-the-loop Language-agnostic Extraction of Medication Data from Highly Unstructured Electronic Health Records. In 20th International Conference on Data Mining Workshops 2020 IEEE EDS.

Sohail, S. A., Krabbe, J., de Alencar Silva, P. , & Bukhsh, F. A. (2020). Privacy Value Modeling: A Gateway To Ethical Big Data Handling. In B. Roelens, W. Laurier, G. Poels, & H. Weigand (Eds.), VMBO 2020: Value Modelling and Business Ontologies (pp. 5-15). (CEUR Workshop Proceedings; Vol. 2574). CEUR. http://ceur-ws.org/Vol-2574/paper1.pdf

Sokar, G. A. Z. N. , Mocanu, D. C., & Pechenizkiy, M. (Accepted/In press). Learning Invariant Representation for Continual Learning. In Meta-Learning for Computer Vision (MeL4CV) workshop at AAAI Conference on Artificial Intelligence (AAAI-21)

Thammasan, N., Brouwer, A-M. , Poel, M. , & van Erp, J. (2020). Interpersonal EEG synchrony while listening to a story recorded using consumer-grade EEG devices. In F. D. Davis, R. Riedl, J. vom Brocke, P-M. Léger, A. Randolph, & T. Fischer (Eds.), Information Systems and Neuroscience: NeuroIS Retreat 2019 (pp. 253-259). (Lecture Notes in Information Systems and Organisation; Vol. 32). Springer. https://doi.org/10.1007/978-3-030-28144-1_28

Theodorus, A. , Nauta, M. , & Seifert, C. (2020). Evaluating CNN interpretability on sketch classification. In W. Osten, D. Nikolaev, & J. Zhou (Eds.), 12th International Conference on Machine Vision, ICMV 2019 [114331Q] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 11433). SPIE Press. https://doi.org/10.1117/12.2559536

Yeleshetty, D. , Spreeuwers, L., & Li, Y. (2020). 3D Face Recognition for Cows. In A. Bromme, A. Dantcheva, C. Rathgeb, C. Busch, K. Raja, & A. Uhl (Eds.), BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group [9211005] (Proceedings of the International Conference of the Biometrics Special Interest Group, BIOSIG; Vol. 2020). IEEE. https://ieeexplore.ieee.org/document/9211005

Foreword/postscript


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. (2020). Preface. 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).

Uhl, A., Busch, C., Marcel, S. , & Veldhuis, R. (2020). Preface. In A. Uhl, C. Busch, S. Marcel, & R. Veldhuis (Eds.), Handbook of Vascular Biometrics (pp. ix-xiv). (Advances in Computer Vision and Pattern Recognition). Springer. https://doi.org/10.7591/9780801458286-001

Paper



Jan, T., Trienschnigg, D. , Seifert, C. , & Hiemstra, D. (2020). Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records. Paper presented at ACM Health Search and Data Mining Workshop, HSDM 2020, Houston, United States.

Legoy, V., Caselli, M. , Seifert, C. , & Peter, A. (2020). Automated Retrieval of ATT&CK Tactics and Techniques for Cyber Threat Reports. Paper presented at 1st Cyber Threat Intelligence Symposium, CTI 2020, . https://arxiv.org/abs/2004.14322

Liu, S., van der Lee, T., Yaman, A. , Atashgahi, Z., Ferraro, D., Sokar, G. A. Z. N., Pechenizkiy, M. , & Mocanu, D. C. (2020). Topological Insights into Sparse Neural Networks. Paper presented at European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2020, .

Provoost, J. C. , Wismans, L. J. J., van der Drift, S. J. , van Keulen, M. , & Kamilaris, A. (Accepted/In press). Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques. Paper presented at 99th Transportation Research Board (TRB) Annual Meeting 2020, Washington, United States. https://annualmeeting.mytrb.org/OnlineProgramArchive/Details/13689

Sokar, G. A. Z. N. , Mocanu, D. C., & Pechenizkiy, M. (Accepted/In press). Self-Attention Meta-Learner for Continual Learning. Paper presented at 20th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2021, Virtual Event, United Kingdom.

Poster


Sohail, S. A. , & Bukhsh, F. A. (2020). From Subjective To Objective Informed Consent In Healthcare: Biobanks In Focus. Poster session presented at Alice & Eve 2020, Enschede, Netherlands.


Report



Pinho Rebelo de Sá, C. F. (2020). KidsFirst report on Social Media Analysis. (DSI technical report series). University of Twente.


Review article



Working paper





2019

Article (contribution to journal)


Boer, L., Bischoff, E., van der Heijden, M. , Lucas, P. , Akkermans, R., Vercoulen, J., Heijdra, Y., Assendelft, W., & Schermer, T. (2019). A Smart Mobile Health Tool Versus a Paper Action Plan to Support Self-Management of Chronic Obstructive Pulmonary Disease Exacerbations: Randomized Controlled Trial. JMIR mHealth and uHealth, 7(10), [e14408]. https://doi.org/10.2196/14408


Bueno, M., Hommersom, A. , Lucas, P. J. F., & Janzing, J. (2019). A probabilistic framework for predicting disease dynamics: a case study of psychotic depression. Journal of biomedical informatics, 95, [103232]. https://doi.org/10.1016/j.jbi.2019.103232

Cauteruccio, F., Fortino, G., Guerrieri, A., Liotta, A. , Mocanu, D. C., Perra, C., Terracina, G., & Torres Vega, M. (2019). Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance. Information Fusion, 52, 13-30. https://doi.org/10.1016/j.inffus.2018.11.010









Book editing



Chapter


Sillitti, A., Anakabe, J. F., Basurko, J., Dam, P., Ferreira, H., Ferreiro, S., Gijsbers, J., He, S., Hegedűs, C., Holenderski, M., Hooghoudt, J-O., Lecuona, I., Leturiondo, U., Marcelis, Q., Moldován, I., Okafor, E. , Rebelo de Sá, C., Romero, R., Sarr, B., ... Zurutuza, U. (2019). Providing Proactiveness: Data Analysis Techniques Portfolios. In M. Albano, E. Jantunen, G. Papa, & U. Zurutuza (Eds.), The MANTIS Book: Cyber Physical System Based Proactive Collaborative Maintenance (River Publishers Series in Automation, Control and Robotics). River Publishers. https://doi.org/10.13052/rp-9788793609846

Conference article


Bischoff, E., Boer, L., van der Heijden, M. , Lucas, P. , Akkermans, R., Vercoulen, J., Heijdra, Y., Assendelft, P., & Schermer, T. (2019). A smart mHealth tool versus a paper action plan to support self-management of COPD exacerbations: a randomised controlled trial. European respiratory journal. Supplement, 54(Suppl. 63), [PA2238]. https://doi.org/10.1183/13993003.congress-2019.PA2238


Mustafa, K. A. A. , Botteghi, N. , Sirmacek, B. , Poel, M. , & Stramigioli, S. (2019). Towards continuous control for mobile robot navigation: A reinforcement learning and slam based approach. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(2/W13), 857-863. https://doi.org/10.5194/isprs-archives-XLII-2-W13-857-2019

Conference contribution


Bemthuis, R. H. , Koot, M. , Mes, M. R. K. , Bukhsh, F. A. , Iacob, M-E. , & Meratnia, N. (2019). An Agent-Based Process Mining Architecture for Emergent Behavior Analysis. In 2019 IEEE 23rd International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 54-64). [8907303] (Proceedings IEEE International Enterprise Distributed Object Computing Workshop (EDOCW); Vol. 23). IEEE. https://doi.org/10.1109/EDOCW.2019.00022

Bukhsh, F. A., Silva, P. D. A., Bukhsh, B. A., & Syed, S. (2019). From Traditional to Technologically Influenced Audit : A Compliance Perspective. In 2018 International Conference on Frontiers of Information Technology (FIT) (pp. 164-169). IEEE. https://doi.org/10.1109/FIT.2018.00036


Coutinho, J. C., Moreira, J. M. , & de Sá, C. R. (2019). Mining Frequent Distributions in Time Series. In H. Yin, R. Allmendinger, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings (Vol. II, pp. 271-279). (Lecture Notes in Computer Science; Vol. 11872), (Information Systems and Applications, incl. Internet/Web, and HCI). Springer. https://doi.org/10.1007/978-3-030-33617-2_28

Elhagaly, M., Drvoderic, K., Kippers, R. G. , & Bukhsh, F. A. (2019). Evolution of Compliance Checking in Process Mining Discipline. In 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) IEEE. https://doi.org/10.1109/ICOMET.2019.8673437

Esquivel-Vargas, H., Caselli, M. , Tews, E. , Bucur, D. , & Peter, A. (2019). BACRank: Ranking building automation and control system components by business continuity impact. In A. Romanovsky, E. Troubitsyna, & F. Bitsch (Eds.), Computer Safety, Reliability, and Security: 38th International Conference, SAFECOMP 2019, Proceedings (pp. 183-199). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11698 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-26601-1_13

Ferreira, P. J. S., Magalhães, R. M. C. , Dearo Garcia, K., Cardoso, J. M. P., & Mendes-Moreira, J. (2019). An Efficient Scheme for Prototyping kNN in the Context of Real-Time Human Activity Recognition. In H. Yin, D. Camacho, P. Tino, A. J. Tallón-Ballesteros, & R. Menezes (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2019: 20th International Conference, Manchester, UK, November 14–16, 2019, Proceedings, Part I (pp. 486-493). (Lecture Notes in Computer Science; Vol. 11871), (Information Systems and Applications, incl. Internet/Web, and HCI). Springer. https://doi.org/10.1007/978-3-030-33607-3_52

Garcia, K. D. , Poel, M. , Kok, J. N., & de Carvalho, A. C. P. L. F. (2019). Online Clustering for Novelty Detection and Concept Drift in Data Streams. In P. Moura Oliveira, P. Novais, & L. P. Reis (Eds.), Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Vila Real, Portugal, September 3–6, 2019, Proceedings (pp. 448-459). (Lecture Notes in Computer Science; Vol. 11805), (Lecture Notes in Artificial Intelligence), (Lecture Notes in Bioinformatics). Springer. https://doi.org/10.1007/978-3-030-30244-3_37

Garcia, K. D., de Faria, E. R. , de Sá, C. R., Mendes-Moreira, J., Aggarwal, C. C., de Carvalho, A. C. P. L. F. , & Kok, J. N. (2019). Ensemble Clustering for Novelty Detection in Data Streams. In P. Kralj Novak, S. Džeroski, & T. Šmuc (Eds.), Discovery Science : 22nd International Conference, DS 2019, Splitm, Croatia, October 28-30, 2019. Proceedings (pp. 460-470). (Lecture Notes in Artificial Intelligence; subseries of Lecture Notes in Computer Science; Vol. 11828 LNAI). Springer. https://doi.org/10.1007/978-3-030-33778-0_34

Gomes Avelino, J. , de Alencar Silva, P. , & Allah Bukhsh, F. (2019). Towards green value network modeling: A case from the agribusiness sector in Brazil. In H. Panetto, C. Debruyne, D. Lewis, M. Hepp, C. A. Ardagna, & R. Meersman (Eds.), 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 Singapore. https://doi.org/10.1007/978-3-030-33246-4_29

Ijaz, K. B., Inayat, I. , & Allah Bukhsh, F. (2019). Non-functional Requirements Prioritization: A Systematic Literature Review. In M. Staron, R. Capilla, & A. Skavhaug (Eds.), Proceedings - 45th Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2019 (pp. 379-386). [8906708] IEEE. https://doi.org/10.1109/SEAA.2019.00064

Jorge, C. C., Atzmueller, M., Heravi, B. M., Gibson, J. L. , de Sá, C. R., & Rossetti, R. J. F. (2019). Mining Exceptional Social Behaviour. In P. Moura Oliveira, P. Novais, & L. P. Reis (Eds.), Progress in Artificial Intelligence: 19th EPIA Conference on Artificial Intelligence, EPIA 2019, Proceedings (Vol. Part II, pp. 460-472). (Lecture Notes in Computer Science; Vol. 11805), (Lecture notes in Artificial Intelligence). Springer. https://doi.org/10.1007/978-3-030-30244-3_38

Kumar, A. , Mocanu, E., Babar, M., & Nguyen, P. H. (2019). Collaborative learning for classification and prediction of building energy flexibility. In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) [8905597] IEEE. https://doi.org/10.1109/ISGTEurope.2019.8905597

Kumar, A. , Mocanu, E., Babar, M., & Nguyen, P. H. (Accepted/In press). Collaborative learning for classification and prediction of building energy flexibility. In 9th IEEE PES Innovative Smart Grid Technology Conference Europe IEEE.

Lestriandoko, N. H. , Spreeuwers, L. , & Veldhuis, R. (2019). Multi-Resolution Face Recognition: The Behaviors of Local Binary Pattern at Different Frequency Bands. In G. Callebaut, K. Verniers, & B. Cox (Eds.), 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). http://www.w-i-c.org/proceedings/proceedings_SITB2019.pdf

Linard, A. , Bucur, D. , & Stoelinga, M. (2019). Fault Trees from Data: Efficient Learning with an Evolutionary Algorithm. In N. Guan, J-P. Katoen, & J. Sun (Eds.), 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. https://doi.org/10.1007/978-3-030-35540-1_2

Linard, A., Bueno, M. , Bucur, D. , & Stoelinga, M. I. A. (2019). Induction of Fault Trees through Bayesian Networks. In M. Beer, & E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference (ESREL) (pp. 910-918). Research Publishing. http://itekcmsonline.com/rps2prod/esrel2019/e-proceedings/pdf/0596.pdf

Marazza, F. , Bukhsh, F. A., Vijlbrief, O. , Geerdink, J. , Pathak, S. , van Keulen, M. , & Seifert, C. (2019). Comparing Process Models for Patient Populations: Application in Breast Cancer Care. In C. Di Francescomarino, R. Dijkman, & U. Zdun (Eds.), Business Process Management Workshops - BPM 2019 International Workshops, Revised Selected Papers (pp. 496-507). (Lecture Notes in Business Information Processing; Vol. 362). Springer. https://doi.org/10.1007/978-3-030-37453-2_40

Nakhaee, M. C. , Hiemstra, D. , Stoelinga, M., & Noort, M. V. (2019). The Recent Applications of Machine Learning in Rail Track Maintenance: A Survey. In S. Collart-Dutilleul, T. Lecomte, & A. B. Romanovsky (Eds.), Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification: Third International Conference, RSSRail 2019, Lille, France, June 4-6, 2019, Proceedings (pp. 91-105). (Lecture Notes in Computer Science; Vol. 11495). Springer. https://doi.org/10.1007/978-3-030-18744-6_6

Normakristagaluh, P. , Spreeuwers, L. , & Veldhuis, R. N. J. (2019). Finger-vein Pattern Recognition Based on ICP on Contours. In G. Callebaut, K. Verniers, & B. Cox (Eds.), 2019 Symposium on Information Theory and Signal Processing in the Benelux (pp. 97-101). Katholieke Universiteit Leuven.

Okai, J., Paraschiakos, S., Beekman, M., Knobbe, A. , & Pinho Rebelo de Sá, C. F. (2019). Building robust models for Human Activity Recognition from raw accelerometers data using Gated Recurrent Units and Long Short Term Memory Neural Networks. In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); Vol. 2019, No. 41). IEEE. https://doi.org/10.1109/EMBC.2019.8857288

Rebelo de Sá, C., Shekar, A. K., Ferreira, H., & Soares, C. (2019). Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks. In H. Quintián, J. A. Sáez Muñoz, E. Corchado, F. Martínez Álvarez, & A. Troncoso Lora (Eds.), 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019) (pp. 142-153). (Advances in Intelligent Systems and Computing; Vol. 950). Springer. https://doi.org/10.1007/978-3-030-20055-8_14

Rivolli, A., Amaral, C., Guardão, L. , de Sá, C. R., & Soares, C. (2019). KnowBots: Discovering Relevant Patterns in Chatbot Dialogues. In P. Kralj Novak, S. Džeroski, & T. Šmuc (Eds.), Discovery Science : 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019. Proceedings (pp. 481-492). (Lecture Notes in Artificial Intelligence ; subseries of Lecture Notes in Computer Science; Vol. 11828 LNAI). Springer. https://doi.org/10.1007/978-3-030-33778-0_36

Ruijters, E. J. J. , Budde, C. E. , Chenariyan Nakhaee, M. , Stoelinga, M. I. A. , Bucur, D. , Hiemstra, D. , & Schivo, S. (2019). FFORT: A benchmark suite for fault tree analysis. In M. Beer, & E. Zio (Eds.), ESREL 2019: Proceedings of the 29th European Safety and Reliability Conference (pp. 878-885). Research Publishing. https://doi.org/10.3850/978-981-11-2724-3_0641-cd

Scherzinger, S. , Seifert, C., & Wiese, L. (Accepted/In press). The Best of both Worlds: Challenges in Linking Provenance and Explainability in Distributed Machine Learning. In Proceedings of the 39th International Conference on Distributed Computing Systems IEEE Computer Society.

Thammasan, N. , Stuldreher, I., Wismeijer, D. , Poel, M. , van Erp, J., & Brouwer, A-M. (2019). A novel, simple and objective method to detect movement artefacts in electrodermal activity. In 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019 (pp. 371-377). [8925512] (International Conference on Affective Computing and Intelligent Interaction (ACII); Vol. 2019). IEEE. https://doi.org/10.1109/ACII.2019.8925512

Trienes, J., & Balog, K. (2019). Identifying unclear questions in community question answering websites. In B. Stein, P. Mayr, L. Azzopardi, D. Hiemstra, N. Fuhr, & C. Hauff (Eds.), Advances in Information Retrieval - 41st European Conference on IR Research, ECIR 2019, Proceedings (pp. 276-289). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11437 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-15712-8_18

Venkatesh, S., Ramachandra, R., Raja, K. , Spreeuwers, L. , Veldhuis, R., & Busch, C. (2019). Morphed Face Detection Based on Deep Color Residual Noise. In 2019 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019 [8936088] (International Conference on Image Processing Theory, Tools and Applications (IPTA); Vol. 2019). IEEE. https://doi.org/10.1109/IPTA.2019.8936088

Zeinstra, C. , Meuwly, D. , Veldhuis, R. , & Spreeuwers, L. (2019). Mind the Gap: A practical framework for classifiers in a forensic context. In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems, BTAS 2018 [8698583] (IEEE nternational Conference on Biometrics Theory, Applications and Systems (BTAS); Vol. 2018). IEEE. https://doi.org/10.1109/BTAS.2018.8698583

Zeng, D. , Veldhuis, R. N. J. , Spreeuwers, L., & Zhao, Q. (2019). Likelihood Ratio based Loss to fine tune CNNs for Very Low Resolution Face Verification. In M. Nixon, & P. J. Flynn (Eds.), The 12th IAPR International Conference on Biometrics (ICB 2019) (pp. 1)

Zeng, D. , Veldhuis, R. , Spreeuwers, L., & Zhao, Q. (2019). Likelihood Ratio based Loss to finetune CNNs for Very Low Resolution Face Verification. In 2019 International Conference on Biometrics, ICB 2019 [8987249] (International Conference on Biometrics, ICB; Vol. 2019). IEEE. https://doi.org/10.1109/ICB45273.2019.8987249

de Sousa Santos, O. , de Alencar Silva, P. , Bukhsh, F. A., & Queiroz, P. G. G. (2019). Conceptual Modeling for Corporate Social Responsibility: A Systematic Literature Review. In K. Djemame, J. Altmann, J. Á. Bañares, O. Agmon Ben-Yehuda, & M. Naldi (Eds.), 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 Singapore. https://doi.org/10.1007/978-3-030-36027-6_19

de Sá, C. R. (2019). Variance-Based Feature Importance in Neural Networks. In P. Kralj Novak, S. Džeroski, & T. Šmuc (Eds.), Discovery Science : 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019. Proceedings (pp. 306-315). (Lecture Notes in Artificial Intelligence; subseries of Lecture Notes in Computer Science; Vol. 11828 LNAI). Springer. https://doi.org/10.1007/978-3-030-33778-0_24

Foreword/postscript


Ceravolo, P. , Van Keulen, M., & Stoffel, K. (2019). Preface. In P. Ceravolo, M. van Keulen, & K. Stoffel (Eds.), 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. https://link.springer.com/content/pdf/bfm%3A978-3-030-11638-5%2F1.pdf

Paper


Enendu, S., Scholtes, J., Smeets, J. , Hiemstra, D. , & Theune, M. (2019). Predicting semantic labels of text regions in heterogeneous document images. 203-11. Paper presented at 15th Conference on Natural Language Processing, KONVENS 2019, Erlangen, Germany. https://corpora.linguistik.uni-erlangen.de/data/konvens/proceedings/papers/KONVENS2019_paper_54.pdf


Marazza, F. , Bukhsh, F. A., Vijlbrief, O., Geerdink, J. , Pathak, S. , van Keulen, M. , & Seifert, C. (2019). Comparing Process Models for Patient Populations: Application in Breast Cancer Care. Paper presented at International Workshop Process-Oriented Data Science for Healthcare 2019, Vienna, Austria.

Papenmeier, A. , Englebienne, G. , & Seifert, C. (2019). How model accuracy and explanation fidelity influence user trust in AI. Paper presented at IJCAI Workshop on Explainable Artificial Intelligence (XAI) 2019, Macau, China.

Peters, M., Kempen, L. , Nauta, M. , & Seifert, C. (2019). Visualising the Training Process of Convolutional Neural Networks for Non-Experts. Paper presented at 31st Benelux Conference on Artificial Intelligence, BNAIC 2019, Brussels, Belgium.

Ros, K. , Mocanu, E. , & Seifert, C. (2019). Airport Restroom Cleanliness Prediction Using Real Time User Feedback Data. Paper presented at 5th International Conference on Collaboration and Internet Computing , Los Angeles, United States.

Seifert, C., Scherzinger, S., & Wiese, L. (2019). Towards Generating Consumer Labels for Machine Learning Models. Paper presented at 1st IEEE International Conference on Cognitive Machine Intelligence, Los Angeles, United States.

Spreeuwers, L., & Wang, H. (2019). A high resolution pressure sensor for measurement of grip force. Paper presented at 40th WIC Symposium on Information Theory in the Benelux 2019, Leuven, Belgium.

Turkoglu, M. O. , Spreeuwers, L., Thong, W., & Kicanaoglu, B. (2019). A Layer-Based Sequential Framework for Scene Generation with GANs. Paper presented at 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, Honolulu, United States.

PhD Thesis - Research UT, graduation UT




Poster


Sohail, S. A. , & Bukhsh, F. A. (2019). Ethics evaluation of big data in Practice: Healthcare in focus. Poster session presented at 7th ICT.OPEN 2019, Hilversum, Netherlands.

Working paper




2018

Article (contribution to journal)


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. (2018). Big Data Semantics. Journal on Data Semantics, 7(2), 65-85. https://doi.org/10.1007/s13740-018-0086-2






Book editing


Ceravolo, P., Teresa Gómez López, M. , & Van Keulen, M. (Eds.) (2018). Proceedings of the 8th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2018). (CEUR Workshop Proceedings; Vol. 2270). CEUR. http://ceur-ws.org/Vol-2270

Conference article


Da Silva Reis, J., De Alencar Silva, P. , Bukhsh, F. A., & De Castro, A. F. (2018). Configuring value networks based on subjective business values. CEUR workshop proceedings, 2239, 158-170.


Yasmin, F. A. , Bukhsh, F. A., & De Alencar Silva, P. (2018). Process enhancement in process mining: A literature review. CEUR workshop proceedings, 2270, 65-72.

Conference contribution


Abate, A. , Budde, C. E., Cauchi, N. , van Harmelen, A., Hoque, K. A. , & Stoelinga, M. I. A. (2018). Modelling Smart Buildings Using Fault Maintenance Trees. In R. Bakhshi, P. Ballarini, B. Barbot, H. Castel-Taleb, & A. Remke (Eds.), Computer Performance Engineering: 15th European Workshop, EPEW 2018, Paris, France, October 29-30, 2018, Proceedings (pp. 110-125). (Lecture Notes in Computer Science; Vol. 11178). Springer. https://doi.org/10.1007/978-3-030-02227-3_8

Apriyanti, D. H. , Spreeuwers, L. J. , & Veldhuis, R. N. J. (2018). Breaking Out of the Black Box in Automated Flower Recognition. In L. Spreeuwers, & J. Goseling (Eds.), 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). https://www.utwente.nl/en/eemcs/sitb2018/sitb2018proceedings.pdf

Barbosa, P. , Dearo Garcia, K., & Mendes-Moreira, J. (2018). Unsupervised Domain Adaptation for Human Activity Recognition. In H. Yin, P. Novais, D. Camacho, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings: IDEAL 2018 (pp. 623-630). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11314 LNCS). Springer. https://doi.org/10.1007/978-3-030-03493-1_65

Bucur, D. (2018). On the Gender of Books: Author Gender Mixing in Book Communities. In C. Cherifi, H. Cherifi, M. Karsai, & M. Musolesi (Eds.), 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. https://doi.org/10.1007/978-3-319-72150-7_64

Bucur, D., Iacca, G., Marcelli, A., Squillero, G., & Tonda, A. (2018). Evaluating surrogate models for multi-objective influence maximization in social networks. In GECCO 2018 Companion : Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion (pp. 1258-1265). Association for Computing Machinery (ACM). https://doi.org/10.1145/3205651.3208238

Bucur, D., Iacca, G., Marcelli, A., Squillero, G., & Tonda, A. (2018). Improving Multi-objective Evolutionary Influence Maximization in Social Networks. In K. Sim, & P. Kaufmann (Eds.), 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. https://doi.org/10.1007/978-3-319-77538-8_9

Bukhsh, F. A. B., & Weigand, H. W. (2018). Compliance Checking of Shipment Request by Utilizing Process Mining Concepts: An Evaluation of Smart Auditing Framework. In 2017 International Conference on Frontiers of Information Technology (FIT) (pp. 235-240). IEEE. https://doi.org/10.1109/FIT.2017.00049

Bukhsh, F. A., Arachchige, J. J., & Malik, F. (2018). Analyzing excessive user feedback: A big data challenge. In 2018 International Conference on Frontiers of Information Technology (FIT) (pp. 206-211) https://doi.org/10.1109/FIT.2018.00043

Bukhsh, F. A., De Alencar Silva, P. , & Wienen, H. (2018). Data pre-processing: Case of sensor data consistency based on Bi-temporal concepts. In 2017 13th International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE. https://doi.org/10.1109/ICET.2017.8281746

Dearo Garcia, K., de Carvalho, A. C. P. L. F., & Mendes-Moreira, J. (2018). A cluster based prototype reduction for online classification. In H. Yin, D. Camacho, P. Novais, & A. J. Tallón-Ballesteros (Eds.), Intelligent Data Engineering and Automated Learning - IDEAL 2018: 19th International Conference, Madrid, Spain, November 21–23, 2018, Proceedings, Part I (pp. 603-610). (Lecture Notes in Computer Science; Vol. 11314). Springer. https://doi.org/10.1007/978-3-030-03493-1_63


Haasnoot, E., Khodabakhsh, A. , Zeinstra, C. , Spreeuwers, L. , & Veldhuis, R. (2018). FEERCI: A Package for Fast Non-Parametric Confidence Intervals for Equal Error Rates in Amortized O(m log n). In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 [8553607] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553607

Khodabakhsh, A. , Haasnoot, E., & Bours, P. (2018). Predicted Templates: Learning-curve Based Template Projection for Keystroke Dynamics. In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 [8553293] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553293

Lestriandoko, N. H. , Spreeuwers, L. , & Veldhuis, R. (2018). The Behavior of Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) for Face Recognition. In L. Spreeuwers, & J. Goseling (Eds.), 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). https://www.utwente.nl/en/eemcs/sitb2018/sitb2018proceedings.pdf

Milz, T. , & Seifert, C. (2018). Analysing Author Self-Citations in Computer Science Publications. In M. Elloumi, M. Granitzer, A. Hameurlain, C. Seifert, B. Stein, A. Min Tjoa, & R. Wagner (Eds.), Database and Expert Systems Applications: DEXA 2018 International Workshops, BDMICS, BIOKDD, and TIR, Regensburg, Germany, September 3–6, 2018, Proceedings (pp. 289-300). (Communications in Computer and Information Science; Vol. 903). Springer. https://doi.org/10.1007/978-3-319-99133-7_24

Milz, T. , & Seifert, C. (2018). Who cites what in Computer Science? - Analysing Citation Patterns across Conference Rank and Gender. In E. Mendez, C. Ribeiro, G. David, J. C. Lopes, & F. Crestani (Eds.), Digital Libraries for Open Knowledge - 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, Proceedings (pp. 321-325). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11057 LNCS). https://doi.org/10.1007/978-3-030-00066-0_32

Nauta, M. , Bucur, D. , & Stoelinga, M. (2018). LIFT: Learning Fault Trees from Observational Data. In A. McIver, & A. Horvath (Eds.), 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. https://doi.org/10.1007/978-3-319-99154-2_19

Normakristagaluh, P. , Spreeuwers, L. J. , & Veldhuis, R. N. J. (2018). A Prototype of Finger-vein Phantom. In L. Spreeuwers, & J. Goseling (Eds.), 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). http://www.w-i-c.org/proceedings/proceedings_SITB2018.pdf

Pathak, S., van Rossen, J., Vijlbrief, O., Geerdink, J. , Seifert, C. , & van Keulen, M. (2018). Automatic structuring of breast cancer radiology reports for quality assurance. In J. Yu, Z. Li, H. Tong, & F. Zhu (Eds.), Proceedings of the Workshop on Data Mining in Biomedical Informatics and Healthcare (DMBIH 2018) (Vol. 2018-November, pp. 732-739). [8637387] IEEE Computer Society. https://doi.org/10.1109/ICDMW.2018.00111

Schlötterer, J. , Seifert, C., & Granitzer, M. (2018). QueryCrumbs for Experts: A Compact Visual Query Support System to Facilitate Insights into Search Engine Internals. In J. M. Pires, N. M. S. Datia, G. Polese, M. Temperini, F. Sciarrone, M. Risi, G. Venturini, T. Di Mascio, R. Zaccagnino, V. Deufemia, D. Malandrino, P. Diaz, A. F. Anta, E. Banissi, T. G. Wyeld, M. Sarfraz, F. Bouali, M. W. M. Bannatyne, F. Papadopoulo, U. Erra, V. Rossano, A. Ursyn, A. Cuzzocrea, ... R. Francese (Eds.), Proceedings of the 22nd International Conference on Information Visualization (IV) (pp. 78-84) https://doi.org/10.1109/iV.2018.00024

Spreeuwers, L. , Veldhuis, R., & Schils, M. (2018). Towards Robust Evaluation of Face Morphing Detection. In 2018 26th European Signal Processing Conference, EUSIPCO 2018 (pp. 1027-1031). [8553018] (European Signal Processing Conference; Vol. 2018-September). IEEE. https://doi.org/10.23919/EUSIPCO.2018.8553018

Toepfer, M. , & Seifert, C. (2018). Content-Based Quality Estimation for Automatic Subject Indexing of Short Texts under Precision and Recall Constraints. In E. Mendez, C. Ribeiro, G. David, J. C. Lopes, & F. Crestani (Eds.), Digital Libraries for Open Knowledge - 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, Proceedings (pp. 3-15). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11057 LNCS). https://doi.org/10.1007/978-3-030-00066-0_1

Veldhuis, R., Raja, K., & Ramachandra, R. (2018). A Likelihood Ratio Classifier for Histogram Features. In 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS) (IEEE International Conference on Biometrics Theory, Applications and Systems (BTAS); Vol. 2018). IEEE. https://doi.org/10.1109/BTAS.2018.8698573

Witt, N., Granitzer, M. , & Seifert, C. (2018). Most Important First – Keyphrase Scoring for Improved Ranking in Settings With Limited Keyphrases. In L. Soldatova, J. Vanschoren, G. Papadopoulos, & M. Ceci (Eds.), Discovery Science: 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings (pp. 373-385). (Lecture notes in computer science; Vol. 11198). Springer. https://doi.org/10.1007/978-3-030-01771-2_24

Zeinstra, C. , & Haasnoot, E. (2018). Shallow CNNs for the Reliable Detection of Facial Marks. In A. Bromme, A. Uhl, C. Busch, C. Rathgeb, & A. Dantcheva (Eds.), 2018 International Conference of the Biometrics Special Interest Group, BIOSIG 2018 [8553157] (International Conference of the Biometrics Special Interest Group (BIOSIG); Vol. 2018). IEEE. https://doi.org/10.23919/BIOSIG.2018.8553157

van Keulen, M., Kaminski, B., Matheja, C. , & Katoen, J. P. (2018). Rule-based conditioning of probabilistic data. In D. Ciucci, G. Pasi, & B. Vantaggi (Eds.), Scalable Uncertainty Management: 12th International Conference, SUM 2018, Milan, Italy, October 3-5, 2018, Proceedings (pp. 290-305). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11142 LNAI). Springer. https://doi.org/10.1007/978-3-030-00461-3_20


Entry for encyclopedia/dictionary


van Keulen, M. (2018). Probabilistic Data Integration. In S. Sakr, & A. Zomaya (Eds.), Encyclopedia of Big Data Technologies Springer. https://doi.org/10.1007/978-3-319-63962-8_18-1

Paper


Oude Nijeweme - d'Hollosy, W. , Poel, M. , van Velsen, L. S. , Oudshoorn, C. , Hermens, H. J., Stegeman, P., Wolff, A. P., Reneman, M. F., & Soer, R. (2018). Personalized advice on chronic low back pain interventions: a machine learning approach. 25. Paper presented at SBPR 2018, Groningen, Netherlands.


Poster



Review article



2017

Article (contribution to journal)


Hiemstra, D., Harman, D. (Ed.), Allan, J., Kelly, D. (Ed.), Belkin, N. J., Bennet, P., Callan, J., Clarke, C., Diaz, F., Dumais, S., Ferro, N., Harman, D., Ruthven, I., Sakai, T., Smucker, M. D., & Zobel, J. (2017). Overview of Special Issue. SIGIR forum, 51(2), 1-25. https://doi.org/10.1145/3130348.3130350

Nautsch, A. , Meuwly, D., Ramos, D., Lindh, J., & Busch, C. (2017). Making Likelihood Ratios Digestible for Cross-Application Performance Assessment. IEEE signal processing letters, 24(10), 1552-1556. [17176768]. https://doi.org/10.1109/LSP.2017.2748899



Book editing



Chapter


Maltoni, D., Cappelli, R. , & Meuwly, D. (2017). Automated Fingerprint Identification Systems: From Fingerprints to Fingermarks. In M. Tistarelli, & C. Champod (Eds.), Handbook of Biometrics for Forensic Science (pp. 37-61). (Advances in Computer Vision and Pattern Recognition). Springer. https://doi.org/10.1007/978-3-319-50673-9_3

Conference contribution


Bortolameotti, R. , van Ede, T. S., Caselli, M. , Everts, M. H. , Hartel, P. H., Hofstede, R. , Jonker, W. , & Peter, A. (2017). DECANTeR: DEteCtion of Anomalous outbouNd HTTP TRaffic by Passive Application Fingerprinting. In ACSAC 2017, Proceedings of the 33rd Annual Computer Security Applications Conference (pp. 373-386) https://doi.org/10.1145/3134600.3134605

Bucur, D. (2017). Towards accurate de novo assembly for genomes with repeats. In 2017 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) https://doi.org/10.1109/CIBCB.2017.8058534

Bukhsh, F. A., & Silva, P. D. A. (2017). Modeling E-Business Customization with e3value Modeling. In 2016 International Conference on Frontiers of Information Technology (FIT) (pp. 187-192). [7866751] IEEE. https://doi.org/10.1109/FIT.2016.042

Bukhsh, F. A., Younus, I., & Arachchige, J. J. (2017). Information technology project management viewpoint: A case study from PTCL. In 2016 6th International Conference on Innovative Computing Technology, INTECH 2016 (pp. 24-29). [7845086] IEEE. https://doi.org/10.1109/INTECH.2016.7845086

Hiemstra, D., Hauff, C., & Azzopardi, L. (2017). Exploring the Query Halo Effect in Site Search: Leading People to Longer Queries. In N. Kando, T. Sakay, H. Joho, H. Li, A. P. de Vries, & R. W. White (Eds.), SIGIR'17. Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 981 - 984). Association for Computing Machinery (ACM). https://doi.org/10.1145/3077136.3080696

Mocanu, D. C. , Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2017). Big IoT data mining for real-time energy disaggregation in buildings (extended abstract). In W. Duivesteijn, M. Pechenizkiy, G. Fletcher, V. Menkovski, E. Postma, J. Vanschoren, & P. van der Putten (Eds.), Benelearn 2017: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning TU/e.

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. (2017). Biometric Systems under Morphing Attacks: Assessment of Morphing Techniques and Vulnerability Reporting. In 2017 International Conference of the Biometrics Special Interest Group, BIOSIG 2017 [8053499] Gesellschaft für Informatik. https://doi.org/10.23919/BIOSIG.2017.8053499

Spreeuwers, L. (2017). De-Duplication Using Automated Face Recognition: A Mathematical Model and All Babies Are Equally Cute. In 2017 International Conference of the Biometrics Special Interest Group, BIOSIG 2017 [8053500] Gesellschaft für Informatik. https://doi.org/10.23919/BIOSIG.2017.8053500

Van Keulen, M., Geerdink, J., Linssen, G. C. M. , Slart, R. H. J. A., & Vijlbrief, O. (2017). Exploiting Natural Language Processing for Improving Health Processes. In P. Ceravolo, M. van Keulen, & K. Stoffel (Eds.), Proceedings of the 7th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017) (pp. 145-146). (CEUR Workshop Proceedings; Vol. 2016). CEUR. http://ceur-ws.org/Vol-2016/paper11.pdf

de Alencar Silva, P. , Allah Bukhsh, F., da Silva Reis, J., & de Castro, A. F. (2017). Agency monitoring patterns for value networks. In J. A. Banares, C. Pham, & J. Altmann (Eds.), 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. https://doi.org/10.1007/978-3-319-68066-8_7

Foreword/postscript


Ceravolo, P. , Van Keulen, M., & Stoffel, K. (2017). Foreword. In P. Ceravolo, M. van Keulen, & K. Stoffel (Eds.), 7th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2017) (pp. II). (CEUR Workshop Proceedings; Vol. 2016). CEUR. http://ceur-ws.org/Vol-2016/preface.pdf

Paper


van de Kamp, T. R. , Peter, A. , Everts, M. H. , & Jonker, W. (2017). Multi-client Predicate-only Encryption for Conjunctive Equality Tests. Paper presented at 16th International Conference on Cryptology And Network Security 2017, Hong Kong, China.

2016

Chapter


Zwicklbauer, S. , Seifert, C., & Granitzer, M. (2016). Robust and Collective Entity Disambiguation through Semantic Embeddings. In SIGIR'16. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16 (pp. 425-434). ACM Press. https://doi.org/10.1145/2911451.2911535

Conference contribution


Mocanu, E., Nguyen, P. H., & Gibescu, M. (2016). Energy disaggregation for real-time building flexibility detection. In 2016 IEEE Power and Energy Society General Meeting, PESGM 2016: 17-21 July 2016, Boston, Massachusetts [7741966] (IEEE Power and Energy Society General Meeting; Vol. 2016). IEEE Computer Society. https://doi.org/10.1109/PESGM.2016.7741966

Poster


Mocanu, D. C., Exarchakos, G., & Liotta, A. (2016). The double link between network science and artificial intelligence: a key to scalable learning solutions?. Poster session presented at European Data Forum, EDF 2016, Eindhoven, Netherlands.

2015

Conference contribution


Mocanu, D. C., Turkmen, F., & Liotta, A. (2015). Towards ABAC Policy Mining from Logs with Deep Learning. In V. A. Fomichov, & O. S. Fomichova (Eds.), Proceedings of the 18th International Multiconference - Intelligent Systems, IS 2015 Jožef Stefan Institute. https://research.tue.nl/files/9876041/ABACPolicyMining_author_version.pdf

Poster


Mocanu, D. C., Ammar, H. B., Lowet, D., Driessens, K., Liotta, A., Weiss, G., & Tuyls, K. (2015). Factored four-way conditional restricted Boltzmann machines (FFW-CRBMs) for activity recognition. Poster session presented at SNN Symposium Intelligent Machines 2015, Nijmegen, Netherlands.

This publication list has been generated from UT Research Information.