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Rybinski, M., Kusa, W., Karimi, S., & Hanbury, A. (2024). Learning to match patients to clinical trials using large language models. Journal of Biomedical Informatics, 159, Article 104734. https://doi.org/10.1016/j.jbi.2024.104734 ( reposiTUm)

Heiler, G., Hanbury, A., & Filzmoser, P. (2023). The Impact of COVID-19 on Relative Changes in Aggregated Mobility Using Mobile-phone Data. Austrian Journal of Statistics, 52(4), 163–179. https://doi.org/10.17713/ajs.v52i4.1510 ( reposiTUm)

Kusa, W., Mendoza, Ó. E., Knoth, P., Pasi, G., & Hanbury, A. (2023). Effective matching of patients to clinical trials using entity extraction and neural re-ranking. Journal of Biomedical Informatics, 144, Article 104444. https://doi.org/10.1016/j.jbi.2023.104444 ( reposiTUm)

Kusa, W., Lipani, A., Knoth, P., & Hanbury, A. (2023). An analysis of work saved over sampling in the evaluation of automated citation screening in systematic literature reviews. Intelligent Systems with Applications, 18, Article 200193. https://doi.org/10.1016/j.iswa.2023.200193 ( reposiTUm)

Kovacs, P., Tran, F., Hanbury, A., & Madsen, G. K. H. (2022). Similarity Clustering for Representative Sets of Inorganic Solids for Density Functional Testing. Journal of Chemical Theory and Computation, 18(1), 441–447. https://doi.org/10.1021/acs.jctc.1c00536 ( reposiTUm)

Brassey, J., Price, C., Edwards, J., Zlabinger, M., Bampoulidis, A., & Hanbury, A. (2021). Developing a fully automated evidence synthesis tool for identifying, assessing and collating the evidence. BMJ Evidence-Based Medicine, 26(1), 24–27. https://doi.org/10.1136/bmjebm-2018-111126 ( reposiTUm)

Kromp, F., Fischer, L., Bozsaky, E., Ambros, I., Dörr, W., Beiske, K., Ambros, P., Hanbury, A., & Taschner-Mandl, S. (2021). Evaluation of Deep Learning architectures for complex immunofluorescence nuclear image segmentation. IEEE Transactions on Medical Imaging, 40(7), 1934–1949. https://doi.org/10.1109/tmi.2021.3069558 ( reposiTUm)

Reisch, T., Heiler, G., Hurt, J., Klimek, P., Hanbury, A., & Thurner, S. (2021). Behavioral gender differences are reinforced during the COVID-19 crisis. Scientific Reports, 11(19241). https://doi.org/10.1038/s41598-021-97394-1 ( reposiTUm)

Fink, T., Andersson, L., & Hanbury, A. (2021). Detecting Multi Word Terms in patents the same way as entities. World Patent Information, 67, Article 102078. https://doi.org/10.1016/j.wpi.2021.102078 ( reposiTUm)

Kromp, F., Bozsaky, E., Rifatbegovic, F., Fischer, L., Ambros, M., Berneder, M., Weiss, T., Lazic, D., Dörr, W., Hanbury, A., Beiske, K., Ambros, P. F., Ambros, I. M., & Tashner-Mandl, S. (2020). An annotated fluorescence image dataset for training nuclear image segmentation methods. Scientific Data, 7(262). https://doi.org/10.1038/s41597-020-00608-w ( reposiTUm)

Maier-Hein, L., Reinke, A., Kozubek, M., Martel, A. L., Arbel, T., Eisenmann, M., Hanbury, A., Jannin, P., Müller, H., Onogur, S., Saez-Rodriguez, J., van Ginneken, B., Kopp-Schneider, A., & Landman, B. A. (2020). BIAS: Transparent reporting of biomedical image analysis challenges. Medical Image Analysis, 66(101796), 101796. https://doi.org/10.1016/j.media.2020.101796 ( reposiTUm)

Palotti, J., Zuccon, G., & Hanbury, A. (2019). Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms. Journal of Medical Internet Research, 21(1), Article e10986. https://doi.org/10.2196/10986 ( reposiTUm)

Palotti, J., Zuccon, G., & Hanbury, A. (2019). Consumer Health Search on the Web: Study of Web Page Understandability and Its Integration in Ranking Algorithms. Journal of Medical Internet Research, 21(1), 1–28. https://doi.org/10.2196/10986 ( reposiTUm)

Bhatti, N., Hanbury, A., & Stottinger, J. (2018). Contextual local primitives for binary patent image retrieval. Multimedia Tools and Applications, 77(7), 9111–9151. https://doi.org/10.1007/s11042-017-4808-5 ( reposiTUm)

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Lipani, A., Roelleke, T., Lupu, M., & Hanbury, A. (2018). A systematic approach to normalization in probabilistic models. Information Retrieval, 21(6), 565–596. https://doi.org/10.1007/s10791-018-9334-1 ( reposiTUm)

Hopfgartner, F., Hanbury, A., & Müller, H. (2018). Evaluation-as-a-Service for the Computational Sciences: Overview and Outlook. ACM Journal of Data and Information Quality, 10(4), 1–32. https://doi.org/10.1145/3239570 ( reposiTUm)

Auffray, C., Balling, R., Barroso, I., Bencze, L., Benson, M., Bergeron, J., Bernal-Delgado, E., Blomberg, N., Bock, C., Conesa, A., Del Signore, S., DELOGNE, C., Devilee, P., Di Meglio, A., Eijkemans, M., Flicek, P., Graf, N., Grimm, V., Hanbury, A., & Zanetti, G. (2016). Making sense of big data in health research: Towards an EU action plan. Genome Medicine, 8, Article 71. https://doi.org/10.1186/s13073-016-0323-y ( reposiTUm)

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Taha, A. A., & Hanbury, A. (2015). An Efficient Algorithm for Calculating the Exact Hausdorff Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(11), 2153–2163. https://doi.org/10.1109/tpami.2015.2408351 ( reposiTUm)

Alsallakh, B., Hanbury, A., Hauser, H., Miksch, S., & Rauber, A. (2014). Visual Methods for Analyzing Probabilistic Classification Data. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1703–1712. https://doi.org/10.1109/tvcg.2014.2346660 ( reposiTUm)

Khan, R., Hanbury, A., Sablatnig, R., Stöttinger, J., Khan, F. A., & Khan, F. A. (2014). Systematic skin segmentation: merging spatial and non-spatial data. Multimedia Tools and Applications, 69(3), 717–741. https://doi.org/10.1007/s11042-012-1124-y ( reposiTUm)

Stöttinger, J., Hanbury, A., Sebe, N., & Gevers, T. (2012). Sparse Color Interest Points for Image Retrieval and Object Categorization. IEEE Transactions on Image Processing, 21(5), 2681–2692. https://doi.org/10.1109/tip.2012.2186143 ( reposiTUm)

Ferro, N., Berendsen, R., Hanbury, A., Lupu, M., Petras, V., de Rijke, M., & Silvello, G. (2012). PROMISE retreat report prospects and opportunities for information access evaluation. ACM SIGIR Forum, 46(2), 60–84. https://doi.org/10.1145/2422256.2422265 ( reposiTUm)

Khan, R., Hanbury, A., Sablatnig, R., Stöttinger, J., Khan, F. A., & Khan, F. A. (2012). Systematic skin segmentation: merging spatial and non-spatial data. Multimedia Tools and Applications, 69(3), 717–741. https://doi.org/10.1007/s11042-012-1124-y ( reposiTUm)

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Ferro, N., Hanbury, A., Müller, H., & Santucci, G. (2011). Harnessing the Scientific Data Produced by the Experimental Evaluation Search Engines and Information Access Systems. Procedia Computer Science, 4, 740–749. https://doi.org/10.1016/j.procs.2011.04.078 ( reposiTUm)

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Ningtyas, A. M., El-Ebshihy, A., Piroi, F., & Hanbury, A. (2024). Improving Laypeople Familiarity with Medical Terms by Informal Medical Entity Linking. In Experimental IR Meets Multilinguality, Multimodality, and Interaction : 15th International Conference of the CLEF Association, CLEF 2024, Grenoble, France, September 9–12, 2024, Proceedings, Part I (pp. 113–126). https://doi.org/10.1007/978-3-031-71736-9_6 ( reposiTUm)

Staudinger, M., Kusa, W., Piroi, F., & Hanbury, A. (2024). An Analysis of Tasks and Datasets in Peer Reviewing. In Proceedings of the Fourth Workshop on Scholarly Document Processing (SDP 2024) (pp. 257–268). Association for Computational Linguistics. ( reposiTUm)

Staudinger, M., Kern, B. M. J., Miksa, T., Arnhold, L., Knees, P., Rauber, A., & Hanbury, A. (2024). Mission Reproducibility: An Investigation on Reproducibility Issues in Machine Learning and Information Retrieval Research. In Proceedings 2024 IEEE 20th International Conference on e-Science (e-Science). IEEE eScience 2024, Osaka, Japan. IEEE. https://doi.org/10.1109/e-Science62913.2024.10678657 ( reposiTUm)

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Pachinger, P., Goldzycher, J., Planitzer, A. M., Kusa, W., Hanbury, A., & Neidhardt, J. (2024). AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection. In The 62nd Annual Meeting of the Association for Computational Linguistics : Findings of the Association for Computational Linguistics: ACL 2024 (pp. 11990–12001). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.findings-acl.713 ( reposiTUm)

Arzt, V., & Hanbury, A. (2024). Beyond the Numbers: Transparency in Relation Extraction Benchmark Creation and Leaderboards. In D. Hupkes, V. Dankers, K. Batsuren, A. Kazemnejad, C. Christodoulopoulos, M. Giulianelli, & R. Cotterel (Eds.), Proceedings of the 2nd GenBench Workshop on Generalisation (Benchmarking) in NLP (pp. 120–130). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.genbench-1.8 ( reposiTUm)

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Styll, P., Kusa, W., & Hanbury, A. (2024). Enhancing Clinical Data Capture: Developing a Natural Language Processing Pipeline for Converting Free Text Admission Notes to Structured EHR Data. In NL4AI 2024: Eight Workshop on Natural Language for Artificial Intelligence. NL4AI 2024: Eight Workshop on Natural Language for Artificial Intelligence, Bolzano, Italy. http://hdl.handle.net/20.500.12708/210208 ( reposiTUm)

Styll, P., Campillos-Llanos, L., Kusa, W., & Hanbury, A. (2024). Cross-Linguistic Disease and Drug Detection in Cardiology Clinical Texts: Methods and Outcomes. In Working Notes of the Conference and Labs of the Evaluation Forum (CLEF 2024) (pp. 223–244). http://hdl.handle.net/20.500.12708/210253 ( reposiTUm)

Banyasz, D., Hofstätter, S., & Hanbury, A. (2023). Search in Archival Facsimile Documents for Digital History. In 2023 IEEE 19th International Conference on e-Science (e-Science). IEEE 19th International Conference on eScience 2023, Limassol, Cyprus. IEEE. https://doi.org/10.1109/e-Science58273.2023.10254826 ( reposiTUm)

Dhrangadhariya, A., Kusa, W., Müller, H., & Hanbury, A. (2023). HEVS-TUW at SemEval-2023 Task 8: Ensemble of Language Models and Rule-based Classifiers for Claims Identification and PICO Extraction. In The 17th International Workshop on Semantic Evaluation (SemEval-2023). Proceedings of the Workshop (pp. 1776–1782). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.246 ( reposiTUm)

Kusa, W., Zuccon, G., Knoth, P., & Hanbury, A. (2023). Outcome-based evaluation of systematic review automation. In ICTIR ’23: Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval (pp. 125–133). Association for Computing Machinery. https://doi.org/10.1145/3578337.3605135 ( reposiTUm)

Kusa, W., Knoth, P., & Hanbury, A. (2023). CRUISE-Screening: Living Literature Reviews Toolbox. In CIKM ’23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (pp. 5071–5075). Association for Computing Machinery. https://doi.org/10.1145/3583780.3614736 ( reposiTUm)

Darmanovic, F., Hanbury, A., & Zlabinger, M. (2023). SCI-3000: A Dataset for Figure, Table and Caption Extraction from Scientific PDFs. In G. Fink, R. Jain, K. Kise, & R. Zanibbi (Eds.), Document Analysis and Recognition - ICDAR 2023 : 17th International Conference, San José, CA, USA, August 21–26, 2023, Proceedings, Part I (pp. 234–251). Springer Cham. https://doi.org/10.1007/978-3-031-41676-7_14 ( reposiTUm)

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Ghafourian, Y., Hanbury, A., & Knoth, P. (2023). Readability Measures as Predictors of Understandability and Engagement in Searching to Learn. In O. Alonso, H. Cousijn, G. Silvello, M. Marrero, C. T. Lopes, & S. MARCHESIN (Eds.), Linking Theory and Practice of Digital Libraries: 27th International Conference on Theory and Practice of Digital Libraries, TPDL 2023, Zadar, Croatia, September 26–29, 2023, Proceedings (pp. 173–181). Springer. https://doi.org/10.1007/978-3-031-43849-3_15 ( reposiTUm)

Pachinger, P., Hanbury, A., Neidhardt, J., & Planitzer, A. M. (2023). Toward Disambiguating the Definitions of Abusive, Offensive, Toxic, and Uncivil Comments. In Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP) (pp. 107–113). https://doi.org/10.18653/v1/2023.c3nlp-1.11 ( reposiTUm)

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Kusa, W., Styll, P., Seeliger, M., Espitia Mendoza, Ó., & Hanbury, A. (2023). DoSSIER at TREC 2023 Clinical Trials Track. In I. Soboroff (Ed.), The Thirty-Second Text REtrieval Conference (TREC 2023) Conference Proceedings. NIST. https://doi.org/10.34726/7159 ( reposiTUm)

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Ghafourian, Y., Hanbury, A., & Knoth, P. (2023). Ranking for Learning: Studying Users’ Perceptions of Relevance, Understandability, and Engagement. In O. Alonso, H. Cousijn, G. Silvello, M. Marrero, C. T. Lopes, & S. Marchesin (Eds.), Linking Theory and Practice of Digital Libraries: 27th International Conference on Theory and Practice of Digital Libraries, TPDL 2023, Zadar, Croatia, September 26–29, 2023, Proceedings (pp. 284–291). Springer. https://doi.org/10.1007/978-3-031-43849-3_25 ( reposiTUm)

Althammer, S., Hofstätter, S., Verberne, S., & Hanbury, A. (2022). TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval. In CIKM ’22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 3801–3805). https://doi.org/10.1145/3511808.3557714 ( reposiTUm)

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Ningtyas, A. M., El-Ebshihy, A., Herwanto, G. B., Piroi, F., & Hanbury, A. (2022). Leveraging Wikipedia Knowledge for Distant Supervision in Medical Concept Normalization. In Experimental IR Meets Multilinguality, Multimodality, and Interaction (pp. 33–47). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-13643-6_3 ( reposiTUm)

Mendoza, O., Kusa, W., El-Ebshihy, A. M., Wu, R., Pride, D., Knoth, P., Herrmannova, D., Piroi, F., Pasi, G., & Hanbury, A. (2022). Benchmark for Research Theme Classification of Scholarly Documents. In Proceedings of the Workshop. Third Workshop on Scholarly Document Processing (pp. 253–262). Association for Computational Linguistics. https://doi.org/10.34726/4521 ( reposiTUm)

Kusa, W., Peikos, G., Espitia Mendoza, Ó., Hanbury, A., & Pasi, G. (2022). DoSSIER at MedVidQA 2022: Text-based Approaches to Medical Video Answer Localization Problem. In Proceedings of the 21st Workshop on Biomedical Language Processing (pp. 432–440). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.bionlp-1.43 ( reposiTUm)

Hofstätter, S., Khattab, O., Althammer, S., Sertkan, M., & Hanbury, A. (2022). Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction. In CIKM ’22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 737–747). Association for Computing Machinery (ACM). https://doi.org/10.1145/3511808.3557367 ( reposiTUm)

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Rekabsaz, N., West, R., Henderson, J., & Hanbury, A. (2021). Measuring Societal Biases from Text Corpora with Smoothed First-Order Co-occurrence. In Proceedings of the International AAAI Conference on Web and Social Media (pp. 549–560). AAAI Press. http://hdl.handle.net/20.500.12708/55690 ( reposiTUm)

Bogensperger, J., Schlarb, S., Hanbury, A., & Recski, G. (2021). DreamDrug - A crowdsourced NER dataset for detecting drugs in darknet markets. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021). The Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.wnut-1.17 ( reposiTUm)

Recski, G., Lellmann, B., Kovacs, A., & Hanbury, A. (2021). Explainable Rule Extraction via Semantic Graphs. In Proceedings of the Fifth Workshop on Automated Semantic Analysis of Information in Legal Text (ASAIL 2021) (pp. 24–35). CEUR-WS.org. http://hdl.handle.net/20.500.12708/58465 ( reposiTUm)

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Hanbury, A., Bhatti, N., Lupu, M., & Mörzinger, R. (2011). Patent image retrieval. In Proceedings of the 4th workshop on Patent information retrieval - PaIR ’11. ACM. https://doi.org/10.1145/2064975.2064979 ( reposiTUm)

Lupu, M., Hanbury, A., & Rauber, A. (2011). 4th international workshop on patent information retrieval (PaIR’11). In Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM ’11. ACM. https://doi.org/10.1145/2063576.2064044 ( reposiTUm)

Stöttinger, J., Goras, B. T., Pönitz, T., Sebe, N., Hanbury, A., & Gevers, T. (2011). Systematic Evaluation of Spatio-Temporal Features on Comparative Video Challenges. In R. Koch & F. Huang (Eds.), Computer Vision – ACCV 2010 Workshops (pp. 349–358). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-22822-3_35 ( reposiTUm)

Mörzinger, R., Horti, A., Thallinger, G., Bhatti, N., & Hanbury, A. (2011). Classifying Patent Images. In CLEF (Notebook Papers/Labs/Workshop). Conference on Multilingual and Multimodal Information Access Evaluation (CLEF 2011), Amsterdam, Niederlande, EU. http://hdl.handle.net/20.500.12708/53721 ( reposiTUm)

Khan, R., Hanbury, A., & Stöttinger, J. (2010). Weighted Skin Color Segmentation and Detection Using Graph Cuts. In L. Spaček & V. Franc (Eds.), Proceedings of the Computer Vision Winter Workshop 2010 (pp. 107–114). Czech Society for Cybernetics and Informatics. http://hdl.handle.net/20.500.12708/53489 ( reposiTUm)

Stöttinger, J., Goras, B. T., Sebe, N., & Hanbury, A. (2010). Behavior and properties of spatio-temporal local features under visual transformations. In Proceedings of the international conference on Multimedia - MM ’10. ACM Multimedia 2010 International Conference, Florenz, Italien, EU. Association for Computing Machinery (ACM). https://doi.org/10.1145/1873951.1874174 ( reposiTUm)

Stöttinger, J., Zambanini, S., Khan, R., & Hanbury, A. (2010). FeEval A Dataset for Evaluation of Spatio-temporal Local Features. In M. Cetin, K. Boyer, & S.-W. Lee (Eds.), 2010 20th International Conference on Pattern Recognition. IEEE. https://doi.org/10.1109/icpr.2010.128 ( reposiTUm)

Bhatti, N., & Hanbury, A. (2010). Co-occurrence Bag of Words for Object Recognition. In L. Spaček & V. Franc (Eds.), Proceedings of the Computer Vision Winter Workshop 2010 (pp. 21–28). Czech Society for Cybernetics and Informatics. http://hdl.handle.net/20.500.12708/53488 ( reposiTUm)

Khan, R., Hanbury, A., & Stöttinger, J. (2010). Universal Seed Skin Segmentation. In G. Bebis, R. Boyle, B. Parvin, D. Koracin, R. Chung, R. Hammound, M. Hussain, T. Kar-Han, R. Crawfis, D. Thalmann, D. Kao, & L. Avila (Eds.), Advances in Visual Computing (pp. 75–84). Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-17274-8_8 ( reposiTUm)

Khan, R., Hanbury, A., & Stöttinger, J. (2010). Augmentation of Skin Segmentation. In H. Arabnia, L. Deligiannidis, G. Schaefer, & A. Solo (Eds.), Proceedings of the 2010 International Conference on Image Processing, Computer Vision, & Pattern Recognition, IPCV 2010, July 12-15, 2010, Las Vegas, Nevada, USA, 2 Volumes (pp. 473–479). CSREA Press. http://hdl.handle.net/20.500.12708/53501 ( reposiTUm)

Pönitz, T., Stöttinger, J., Donner, R., & Hanbury, A. (2010). Efficient and Distinct Large Scale Bags of Words. In P. Blauensteiner, M. Lettner, & J. Stöttinger (Eds.), Computer Vision in a Global Society - 34th Annual Workshop of the Austrian Association for Pattern Recognition (AAPR) and the WG Visual Computing of the Austrian Computer Society (pp. 139–146). Österreichische Computer Gesellschaft. http://hdl.handle.net/20.500.12708/53480 ( reposiTUm)

Machajdik, J., & Hanbury, A. (2010). Affective image classification using features inspired by psychology and art theory. In Proceedings of the international conference on Multimedia - MM ’10. ACM Multimedia 2010 International Conference, Florenz, Italien, EU. Association for Computing Machinery (ACM). https://doi.org/10.1145/1873951.1873965 ( reposiTUm)

Khan, R., Hanbury, A., & Stoettinger, J. (2010). Skin detection: A random forest approach. In 2010 IEEE International Conference on Image Processing. International Conference on Image Processing 2010, Hong Kong, Non-EU. IEEE. https://doi.org/10.1109/icip.2010.5651638 ( reposiTUm)

Stöttinger, J., Banova, J., Ponitz, T., Sebe, N., & Hanbury, A. (2009). Translating Journalists’ Requirements into Features for Image Search. In 2009 15th International Conference on Virtual Systems and Multimedia. 15th International Conference on Virtual Systems and Multimedia ({VSMM} 2009), Wien, Austria. IEEE. https://doi.org/10.1109/vsmm.2009.28 ( reposiTUm)

Stöttinger, J., Hanbury, A., Liensberger, C., & Khan, R. (2009). Skin Paths for Contextual Flagging Adult Videos. In Advances in Visual Computing (pp. 303–314). Springer. http://hdl.handle.net/20.500.12708/52945 ( reposiTUm)

Stöttinger, J., Hanbury, A., Gevers, T., & Sebe, N. (2009). Lonely but Attractive: Sparse Color Salient Points for Object Retrieval and Categorization. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Workshop on Feature Detectors and Descriptors: The State Of The Art and Beyond (pp. 1–8). http://hdl.handle.net/20.500.12708/52943 ( reposiTUm)

Hanbury, A. (2008). How Do Superpixels Affect Image Segmentation? In Progress in Pattern Recognition, Image Anlysis and Applications (pp. 178–186). Springer. http://hdl.handle.net/20.500.12708/52472 ( reposiTUm)

Deselaers, T., Hanbury, A., Viitaniemi, V., Benczur, A., Brendel, M., Daroczy, B., Escalante Balderas, H. J., Gevers, T., Hernandez Gracidas, C. A., Laaksonen, J., Li, M., Marın Castro, H. M., Ney, H., Rui, X., Sebe, N., & Stöttinger, J. (2008). Overview of the ImageCLEF 2007 Object Retrieval Task. In Advances in Multilingual and Multimodal Information Retrieval (pp. 445–471). Springer. http://hdl.handle.net/20.500.12708/52470 ( reposiTUm)

Grubinger, M., Clough, P., Hanbury, A., & Müller, H. (2008). Overview of the ImageCLEFphoto 2007 Photographic Retrieval Task. In Advances in Multilingual and Multimodal Information Retrieval (pp. 433–444). Springer. http://hdl.handle.net/20.500.12708/52471 ( reposiTUm)

Hanbury, A., & Stöttinger, J. (2008). On Segmentation Evaluation Metrics and Region Counts. In Proceedings of the the 19th International Conference on Image Processing (ICPR09). The 19th International Conference on Image Processing (ICPR08), Tampa, Florida, Non-EU. IEEE Computer Society. http://hdl.handle.net/20.500.12708/52629 ( reposiTUm)

Stöttinger, J., Donner, R., Szumilas, L., & Hanbury, A. (2008). Evaluation of Gradient Vector Flow for Interest Point Detection. In Advances in Visual Computing, Part I (pp. 338–348). Springer, LNCS. http://hdl.handle.net/20.500.12708/52626 ( reposiTUm)

Hanbury, A. (2007). Morphological Distinguished Regions. In Proc. 12th Iberoamerican Congress on Pattern Recognition (CIARP). 12th Iboamerican Congress on Pattern Recognition, CIAPR2007, Vina del Mar-Valparaiso, Chile, Non-EU. Springer Lecture Notes in Computer Science. http://hdl.handle.net/20.500.12708/51983 ( reposiTUm)

Hanbury, A., & Marcotegui, B. (2007). Colour Adjacency Histograms for Image Matching. In Proceedings of the Computer Analysis of Images and Patterns Conference (CAIP). 12th International Conference CAIP 2007, Wien, Austria. Springer Lecture Notes in Computer Science. http://hdl.handle.net/20.500.12708/51982 ( reposiTUm)

Szumilas, L., Wildenauer, H., Hanbury, A., & Donner, R. (2007). Radial Edge Configuration for Semi-local Image Structure Description. In Advances in Visual Computing (pp. 633–643). Springer Lecture Notes in Computer Science. http://hdl.handle.net/20.500.12708/52045 ( reposiTUm)

Stöttinger, J., Hanbury, A., Sebe, N., & Gevers, T. (2007). Do Colour Interest Points Improve Image Retrieval? In Proc. International Conference on Image Processing (ICIP) (pp. 169–172). http://hdl.handle.net/20.500.12708/51981 ( reposiTUm)

Hanbury, A. (2007). A Study of Vocabularies for Image Annotation. In Proceedings of the second international conference on Semantics And digital Media Technologies (SAMT) (pp. 284–287). Springer Lecture Notes in Computer Science. http://hdl.handle.net/20.500.12708/51985 ( reposiTUm)

Szumilas, L., Donner, R., Langs, G., & Hanbury, A. (2007). Local Structure Detection with Orientation-invariant Radial Configuration. In Computer Vision and Pattern Recognition (p. 7). http://hdl.handle.net/20.500.12708/52043 ( reposiTUm)

Zaharieva, M., Kampel, M., & Zambanini, S. (2007). Image based recognition of ancient coins. In W. Kropatsch, M. Kampel, & A. Hanbury (Eds.), Computer Analysis of Images and Patterns (pp. 547–554). Springer Lecture Notes in Computer Science. http://hdl.handle.net/20.500.12708/51975 ( reposiTUm)

Stöttinger, J., Sebe, N., Gevers, T., & Hanbury, A. (2007). Colour Interest Points for Image Retrieval. In Proceedings of the 12th Computer Vision Winter Workshop (pp. 83–90). http://hdl.handle.net/20.500.12708/51841 ( reposiTUm)

Szumilas, L., & Hanbury, A. (2006). Color Pair Clustering for Texture Detection. In Advances in Visual Computing (pp. 255–264). Springer. http://hdl.handle.net/20.500.12708/51583 ( reposiTUm)

Wildenauer, H., Blauensteiner, P., Hanbury, A., & Kampel, M. (2006). Motion Detection Using an Improved Colour model. In Advances in Visual Computing (pp. 607–616). Springer Berlin-Heidelberg. http://hdl.handle.net/20.500.12708/51626 ( reposiTUm)

Micusik, B., & Hanbury, A. (2006). Automatic Image Segmentation by Positioning a Seed. In Computer Vision - ECCV2006 (pp. 468–480). Springer. http://hdl.handle.net/20.500.12708/51511 ( reposiTUm)

Micusik, B., & Hanbury, A. (2006). Template patch driven image segmentation. In British Machine Vision Conference 2006, Volume Two (pp. 819–829). http://hdl.handle.net/20.500.12708/51510 ( reposiTUm)

Seitner, F., & Hanbury, A. (2006). Fast pedestrian tracking based on spatial features and colour. In Proceedings of the 11th Computer Vision Winter Workshop (pp. 105–110). http://hdl.handle.net/20.500.12708/51540 ( reposiTUm)

Szumilas, L., Micusik, B., & Hanbury, A. (2006). Texture segmentation through salient texture patches. In Proceedings of the 11th Computer Vision Winter Workshop (pp. 111–116). http://hdl.handle.net/20.500.12708/51542 ( reposiTUm)

Kuthan, S., & Hanbury, A. (2006). Extraction of Attributes, Nature and Context of Images. In Proceedings of the 11th Computer Vision Winter Workshop (pp. 28–33). http://hdl.handle.net/20.500.12708/51541 ( reposiTUm)

Blauensteiner, P., Wildenauer, H., & Hanbury, A. (2006). On Colour Spaces for Change Detection and Shadow Suppression. In Proceedings of the 11th Computer Vision Winter Workshop (pp. 117–122). http://hdl.handle.net/20.500.12708/51543 ( reposiTUm)

Hanbury, A., & Marcotegui, B. (2006). Waterfall Segmentation of Complex Scenes. In Computer Vision - ACCV 2006 (pp. 888–897). Springer. http://hdl.handle.net/20.500.12708/51544 ( reposiTUm)

Clough, P., Grubinger, M., Deselaers, T., Hanbury, A., & Müller, H. (2006). Overview of the ImageCLEF 2006 Photographic Retrieval and Object Annotation Tasks. In Proceedings of the CLEF 2006 Workshop (pp. 579–594). Springer Lecture Notes in Computer Science. http://hdl.handle.net/20.500.12708/51984 ( reposiTUm)

Blauensteiner, P., Wildenauer, H., Hanbury, A., & Kampel, M. (2006). Motion and Shadow Detection with an Improved Colour Model. In First International Conference on Signal and Image Processing (pp. 627–632). IEEE. http://hdl.handle.net/20.500.12708/51653 ( reposiTUm)

Lettner, M., & Sablatnig, R. (2005). Texture Based Drawing Tool Classification in Infrared Reflectograms. In A. Hanbury & H. Bischof (Eds.), Proc. of the 10th Computer Vision Winter Workshop (pp. 63–72). Eigenverlag. http://hdl.handle.net/20.500.12708/51288 ( reposiTUm)

Aguilera Antequera, J., Kampel, M., & Blauensteiner, P. (2005). Robust Detection and Performance Evaluation of Individuals and Vehicles on an Airport’s Apron. In A. Hanbury & H. Bischof (Eds.), In Proc. of the Computer Vision Winter Workshop (pp. 145–154). Eigenverlag. http://hdl.handle.net/20.500.12708/51298 ( reposiTUm)

Zambanini, S., Langs, G., & Bischof, H. (2005). Segmentation and Surveying of Cutaneous Hemangiomas. In A. Hanbury & H. Bischof (Eds.), Proceedings of Computer Vision Winter Workshop CVWW (pp. 103–112). Eigenverlag. http://hdl.handle.net/20.500.12708/51297 ( reposiTUm)

Micusik, B., & Hanbury, A. (2005). Supervised Texture Detection in Images. In A. Gagalowicz & W. Philips (Eds.), Computer Analysis of Images and Patterns (CAI (pp. 441–448). Springer, LNCS. http://hdl.handle.net/20.500.12708/51314 ( reposiTUm)

Micusik, B., & Hanbury, A. (2005). Semi-automatic Segmentation of Textured Images. In A. Hanbury & H. Bischof (Eds.), Proceedings of the 10th Computer Vision Winter Workshop (CVWW 2005) (pp. 53–62). Eigenverlag. http://hdl.handle.net/20.500.12708/51315 ( reposiTUm)

Belbachir, A. N., & Göbel, P. (2005). A Sparse Image Representation Using Contourlets. In A. Hanbury & H. Bischof (Eds.), Proc. of 10th Computer Vision Winter Workshop (pp. 165–174). Eigenverlag. http://hdl.handle.net/20.500.12708/51318 ( reposiTUm)

Micusik, B., & Hanbury, A. (2005). Steerable Semi-automatic Segmentation of Textured Images. In H. Kalviainen, J. Parkkinen, & A. Kaarna (Eds.), 14th Scandinavian Conference on Image Analysis (SCIA) (pp. 35–44). Springer, LNCS. http://hdl.handle.net/20.500.12708/51316 ( reposiTUm)

Asinger, C., Kammerer, P., & Zolda, E. (2005). Classification of Color Pigments in Hyperspectral Images. In A. Hanbury & H. Bischof (Eds.), Proc. of the 10th Computer Vision Winter Workshop (pp. 205–214). Eigenverlag. http://hdl.handle.net/20.500.12708/51332 ( reposiTUm)

Langs, G., Peloschek, P. L., & Bischof, H. (2005). MDL-based Splitting of PCA Models. In A. Hanbury & H. Bischof (Eds.), Proceedings of Computer Vision Winter Workshop CVWW (pp. 13–22). Eigenverlag. http://hdl.handle.net/20.500.12708/51296 ( reposiTUm)

Göbel, P., & Belbachir, A. N. (2005). Blind Background Substraction in Dental Panoramic X-ray Images: An Application Approach. In W. Kropatsch, R. Sablatnig, & A. Hanbury (Eds.), Proc. of the 27th DAGM Symposium (pp. 434–441). Springer, LNCS. http://hdl.handle.net/20.500.12708/51320 ( reposiTUm)

Belbachir, A. N., & Göbel, P. (2005). Color Image Compression: Early Vision and the Multiresolution Representations. In W. Kropatsch, R. Sablatnig, & A. Hanbury (Eds.), Proc. of the 27th DAGM Symposium (pp. 25–32). Springer, LNCS. http://hdl.handle.net/20.500.12708/51319 ( reposiTUm)

Zollner, H., & Sablatnig, R. (2005). A Method for Determining Geometrical Distortion of Off-The-Shelf Wide-Angle Cameras. In W. Kropatsch, R. Sablatnig, & A. Hanbury (Eds.), Proc. of the 27th DAGM Symposium (pp. 224–229). Springer, LNCS. http://hdl.handle.net/20.500.12708/51328 ( reposiTUm)

Donner, R., Langs, G., Reiter, M., & Bischof, H. (2005). CCA-based Active Appearance Model Search. In A. Hanbury & H. Bischof (Eds.), Computer Vision Winter Workshop 2005 (pp. 73–82). Eigenverlag. http://hdl.handle.net/20.500.12708/51329 ( reposiTUm)

Ion, A., Haxhimusa, Y., Kropatsch, W., & Brun, L. (2005). Hierarchical Image Partitioning using Combinatorial Maps. In A. Hanbury & H. Bischof (Eds.), 10th Computer Vision Winter Workshop - CVWW 2005 (pp. 43–52). Eigenverlag. http://hdl.handle.net/20.500.12708/51281 ( reposiTUm)

Hanbury, A., Kandaswamy, U., & Adjeroh, D. (2005). Illumination-invariant morphological texture classification. In C. Ronse, L. Najman, & E. Decenciere (Eds.), Proceedings of the International Symposium on Mathematical Morphology (pp. 377–386). Springer. http://hdl.handle.net/20.500.12708/51313 ( reposiTUm)

Müller, H., & Hanbury, A. (2019). EaaS: Evaluation-as-a-Service and Experiences from the VISCERAL Project. In Information Retrieval Evaluation in a Changing World (pp. 161–173). Springer. https://doi.org/10.1007/978-3-030-22948-1_6 ( reposiTUm)

Piroi, F., & Hanbury, A. (2019). Multilingual Patent Text Retrieval Evaluation: CLEF–IP. In N. Ferro & C. Peters (Eds.), Information Retrieval Evaluation in a Changing World (Vol. 41, pp. 365–387). https://doi.org/10.1007/978-3-030-22948-1_15 ( reposiTUm)

Andersson, L., Hanbury, A., & Rauber, A. (2017). The Portability of Three Types of Text Mining Techniques into the Patent Text Genre. In M. Lupu, K. Mayer, N. Kando, & A. Trippe (Eds.), Current Challenges in Patent Information Retrieval (pp. 241–280). Springer. http://hdl.handle.net/20.500.12708/24332 ( reposiTUm)

Zlabinger, M., Andersson, L., Brassey, J., & Hanbury, A. (2017). Extracting the Population, Intervention, Comparison and Sentiment from Randomized Controlled Trials. In A. Ugon, D. Karlsson, G. O. Klein, & A. Moen (Eds.), Building Continents of Knowledge in Oceans of Data: The Future of Co-Created eHealth – Proceedings of MIE2018 (Vol. 247, pp. 146–150). IOS Press. https://doi.org/10.3233/978-1-61499-852-5-146 ( reposiTUm)

Rekabsaz, N., Bierig, R., Lupu, M., & Hanbury, A. (2017). Toward Optimized Multimodal Concept Indexing. In A. M. Pinto & J. Cardoso (Eds.), Transactions on Computational Collective Intelligence XXVI (Vol. 10190, pp. 144–161). Springer. https://doi.org/10.1007/978-3-319-59268-8_7 ( reposiTUm)

Lupu, M., Hanbury, A., & Salampasis, M. (2014). Domain Specific Search. In G. Paltoglou, F. Loizides, & P. Hansen (Eds.), Professional Search in the Modern World (pp. 96–117). Springer LNCS. https://doi.org/10.1007/978-3-319-12511-4_6 ( reposiTUm)

Hanbury, A. (2003). Mathematical Morphology Applied to Circular Data. In Advances in Imaging and Electron Physics (pp. 123–204). ACM Press. http://hdl.handle.net/20.500.12708/25343 ( reposiTUm)

Hanbury, A., Müller, H., & Langs, G. (Eds.). (2017). Cloud-Based Benchmarking of Medical Image Analysis. Springer International Publishing. http://hdl.handle.net/20.500.12708/24397 ( reposiTUm)

Information Access Evaluation. Multilinguality, Multimodality, and Interaction. (2014). In E. Kanoulas, M. Lupu, P. Clough, M. Sanderson, M. Hall, A. Hanbury, & E. Toms (Eds.), Lecture Notes in Computer Science. Springer. https://doi.org/10.1007/978-3-319-11382-1 ( reposiTUm)

Patent Retrieval. (2013). In M. Lupu & A. Hanbury (Eds.), Foundations and Trends® in Information Retrieval (p. 97). Now Publishers. https://doi.org/10.1561/1500000027 ( reposiTUm)

Krestel, R., Aras, H., Andersson, L., Piroi, F., Hanbury, A., & Alderucci, D. (Eds.). (2022). Proceedings of the 3rd Workshop on Patent Text Mining and Semantic Technologies. https://doi.org/10.34726/3550 ( reposiTUm)

Andersson, L., Aras, H., Piroi, F., & Hanbury, A. (Eds.). (2019). Proceedings of the 1st Workshop on on Patent Text Mining and Semantic Technologies : PatentSemTech2019. https://doi.org/10.34726/pst2019 ( reposiTUm)

Kropatsch, W., Kampel, M., & Hanbury, A. (Eds.). (2007). Computer Analysis of Images and Patterns, 12th International Conference, CAIP 2007. Springer LNCS. http://hdl.handle.net/20.500.12708/22351 ( reposiTUm)

Hanbury, A., & Bischof, H. (Eds.). (2005). Proceedings of the 10th Computer Vision Winter Workshop CVWW 2005. Eigenverlag. http://hdl.handle.net/20.500.12708/22302 ( reposiTUm)

Kropatsch, W., Sablatnig, R., & Hanbury, A. (Eds.). (2005). Pattern Recognition: 27th DAGM Symposium. Springer, LNCS. http://hdl.handle.net/20.500.12708/22301 ( reposiTUm)

Staudinger, M., Kusa, W., Piroi, F., Lipani, A., & Hanbury, A. (2024, November 9). Beyond ChatGPT: A Reproducibility and Generalizability Study of Large Language Models for Query Generation [Poster Presentation]. ML in PL Conference 2024, Warsaw, Poland. ( reposiTUm)

Hanbury, A. (2024, December 13). Evaluating Search for Systematic Review Creation [Keynote Presentation]. Forum for Information Retrieval Evaluation, Gandhinagar, India. ( reposiTUm)

Kusa, W., Scells, H., Staudinger, M., & Hanbury, A. (2024, March 28). Leveraging Cochrane Systematic Literature Reviews for Prospective Evaluation of Large Language Models [Conference Presentation]. ALTARS 2024: 3rd Workshop on Augmented Intelligence for Technology-Assisted Reviews Systems: Evaluation Metrics and Protocols for eDiscovery and Systematic Review Systems, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/200670 ( reposiTUm)

Kusa, W., Staudinger, M., Harry Scells, & Hanbury, A. (2024, August 16). Using Cochrane Systematic Literature Reviews to Reduce Contamination in the Evaluation of Large Language Models [Poster Presentation]. The 1st Workshop on Data Contamination (CONDA), Bangkok, Thailand. http://hdl.handle.net/20.500.12708/208243 ( reposiTUm)

Pachinger, P., Goldzycher, J., Planitzer, A. M., Kusa, W., Hanbury, A., & Neidhardt, J. (2024, June 20). A Dataset for Span-Based Austrian German and English Offensive Language Detection [Poster Presentation]. Workshop on Online Abuse and Harms 2024, Mexico City, Mexico. http://hdl.handle.net/20.500.12708/210352 ( reposiTUm)

Hanbury, A. (2022, April 21). Automated analysis of legal text for building regulation: the BRISE project [Presentation]. HPC for digital history and public administration, Luxembourg. http://hdl.handle.net/20.500.12708/153859 ( reposiTUm)

Hanbury, A. (2022, July 11). AI and Data Landscape in Austria [Presentation]. Horizon Europe – AI, Data & Robotics Consortia Building Event, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/153256 ( reposiTUm)

Hanbury, A. (2022, November 2). Professional Search in Context [Presentation]. University of Queensland, Brisbane, Australia. http://hdl.handle.net/20.500.12708/153988 ( reposiTUm)

Hanbury, A. (2022, April 10). The DoSSIER project: Domain Specific Systems for Information Extraction and Retrieval [Keynote Presentation]. 1st Workshop on Augmented Intelligence for Technology-Assisted Reviews Systems: Evaluation Metrics and Protocols for eDiscovery and Systematic Review Systems (with the ECIR), Stavanger, Norway. http://hdl.handle.net/20.500.12708/153992 ( reposiTUm)

Hanbury, A., & Langs, G. (2021). Daten für neue Technologien und Forschung in der Gesundheit - eine Arbeitsgruppe der DIO. Imagine 21 - Create your Future - Mission Impossible?, Wien, Austria. http://hdl.handle.net/20.500.12708/87307 ( reposiTUm)

Hanbury, A. (2021). Open Science with Closed Data: Data Spaces and Data Circles. D4Dairy project meeting, Linz, Austria. http://hdl.handle.net/20.500.12708/87308 ( reposiTUm)

Hanbury, A. (2021). Durch Daten zu neuen Erkenntnissen. Forum Digitalisierung, an internal event of the Administration of the Province of Lower Austria, St. Pölten, Austria, Austria. http://hdl.handle.net/20.500.12708/87309 ( reposiTUm)

Hanbury, A. (2021). Open Science with Closed Data. FIRE 2021 - Forum for Information Retrieval Evaluation, Virtual Event, Information Retrieval Society of India, India. http://hdl.handle.net/20.500.12708/87310 ( reposiTUm)

Hanbury, A., & Heiler, G. (2021). Wie die Partnerschaft von T-Labs und TU Wien Big Data Insights nutzt, um zum Kampf gegen COVID-19 beizutragen. Internal Deutsche Telekom LEX webinar, Online, Germany. http://hdl.handle.net/20.500.12708/87302 ( reposiTUm)

Hanbury, A., & Heiler, G. (2021). How the T-Labs and TU Wien Partnership uses Big Data insights to contribute to the fight against COVID-19. Internal Deutsche Telekom LEX webinar, Online, Germany. http://hdl.handle.net/20.500.12708/87301 ( reposiTUm)

Hanbury, A. (2021). Effective and Efficient Neural Re-Ranking. Huawei Amsterdam Research Centre “Professors @ Huawei” lecture series, Amsterdam, Netherlands (the). http://hdl.handle.net/20.500.12708/87303 ( reposiTUm)

Hanbury, A., & Popper, N. (2021). Synthese von Krankheitsausbreitungs- und Netzwerksdaten für die Covid-19-Simulation. WWTF (Vienna Science and Technology Fund) Online Lecture Series “Vienna Researches Corona,” Wien, Austria. http://hdl.handle.net/20.500.12708/87304 ( reposiTUm)

Hanbury, A. (2021). Was ist KI und welchen Einfluss hat sie auf die Bauwirtschaft? Kolloquium: Zukunftsfragen des Baubetriebs: Komplexe Baubetriebssysteme, Institut für Interdisziplinäres Bauprozessmanagement der TU Wien, Austria. http://hdl.handle.net/20.500.12708/87306 ( reposiTUm)

Hanbury, A. (2021). Effective and Efficient Neural Re-Ranking in Information Retrieval. The USM (University of Southern Maine) Data Science Ensemble seminar series, United States of America (the). http://hdl.handle.net/20.500.12708/87305 ( reposiTUm)

Hanbury, A. (2020). Medizinische Daten und künstliche Intelligenz. Webinar “Medizinische Datenintelligenz”, organised by the Research and Transfer Support office of the TU Wien, Wien (online), Austria. http://hdl.handle.net/20.500.12708/87134 ( reposiTUm)

Hanbury, A. (2020). Supporting Systematic Reviews in Medicine. WOSP 2020 - 8th International Workshop on Mining Scientific Publications, Wuhan, China. http://hdl.handle.net/20.500.12708/87135 ( reposiTUm)

Hanbury, A. (2020). Von der KI Forschung zum Produkt - Herausforderungen und Erfolge. Workshop: Künstliche Intelligenz in der Gesundheit, Regensburg (online), Germany. http://hdl.handle.net/20.500.12708/87136 ( reposiTUm)

Hanbury, A. (2020). Die Datenwirtschaft und die Data Intelligence Offensive. ADV Tagung 2020: Trends in der Digitalisierung, Wien, Austria. http://hdl.handle.net/20.500.12708/87137 ( reposiTUm)

Hanbury, A. (2020). Corona und die Wissenschaft. Panel Diskussion. 27. TU Forum: Corona und die Wissenschaft, Wien (online), Austria. http://hdl.handle.net/20.500.12708/87138 ( reposiTUm)

Hanbury, A. (2020). Explainable AI in the medical and legal domains. International Digital Security Forum Vienna, Wien (online), Austria. http://hdl.handle.net/20.500.12708/87139 ( reposiTUm)

Hanbury, A. (2020). Open Science with Closed Data. Online conference “Openness and Commercialisation: How the two can go together”, TU Delft, Delft (online), Netherlands (the). http://hdl.handle.net/20.500.12708/87140 ( reposiTUm)

Hanbury, A. (2019). Datengetriebene Innovation zwischen Forschung & Wirtschaft: Kooperative Projekte, Spin-offs, Crowd-Solving. Second Austrian Data Governance Conference, Vienna, Austria. http://hdl.handle.net/20.500.12708/87142 ( reposiTUm)

Hanbury, A. (2019). Alexa, wir müssen reden! Panel Diskussion: Alexa, wir müssen reden! International Forum for Business Communication., Wien, Austria. http://hdl.handle.net/20.500.12708/87141 ( reposiTUm)

Hanbury, A. (2019). Challenges in developing a search system to support radiologists. Advances in Information Retrieval. 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14-18, 2019, Cologne, Germany. http://hdl.handle.net/20.500.12708/87143 ( reposiTUm)

Hanbury, A. (2019). Some Biases in Data Science. Austrian Computer Science Day, Wien, Austria. http://hdl.handle.net/20.500.12708/87144 ( reposiTUm)

Hanbury, A. (2019). Searching and Mining Medical Documents. First Vienna Symposium Meeting on Machine Learning in Medicine & Biology, Wien, Austria. http://hdl.handle.net/20.500.12708/87145 ( reposiTUm)

Hanbury, A. (2019). Data Market Austria: Anforderungsanalyse und Entwicklungsherausforderungen. Data Market Austria Closing Conference, Wien, Austria. http://hdl.handle.net/20.500.12708/87146 ( reposiTUm)

Hanbury, A. (2019). AI Strategy in Austria. European Big Data Value Forum. Session: “Challenges & Strategies of Member States as Foundations for a European AI Strategy,” Helsinki, Finland. http://hdl.handle.net/20.500.12708/87147 ( reposiTUm)

Hanbury, A. (2019). Data Market Austria / Data Intelligence Offensive. German Ministry of Economics and Energy (Member of the Austrian Delegation), Berlin, Germany. http://hdl.handle.net/20.500.12708/87149 ( reposiTUm)

Hanbury, A. (2019). Supporting Manual Annotation of Chatbot Information. Eötvös Loránd University (ELTE), Budapest, Hungary. http://hdl.handle.net/20.500.12708/87148 ( reposiTUm)

Hanbury, A. (2019). Search to Support Radiologists: from a research prototype to a product. Search Solutions (BCS IRSG), London, United Kingdom of Great Britain and Northern Ireland (the). http://hdl.handle.net/20.500.12708/87150 ( reposiTUm)

Hanbury, A. (2018). Maschinen als Begleiter: Wie digitale Assistenten zum Alltag werden. Austrian Press Agency Digital Business Trends event, Wien, Austria. http://hdl.handle.net/20.500.12708/87156 ( reposiTUm)

Hanbury, A. (2018). Data Intelligence und Recht. Working group “Perspektiven der Rechtsetzung” of the Directorate of the Austrian Parliament, Wien, Austria. http://hdl.handle.net/20.500.12708/87157 ( reposiTUm)

Hanbury, A. (2018). Lexical and Statistical Semantics in Professional Search. Semantics 2018 conference, Wien, Austria. http://hdl.handle.net/20.500.12708/87155 ( reposiTUm)

Hanbury, A. (2018). Credibility of sources, content, and algorithms. Fake News and other AI Challenges for the News Media in the 21st Century, panel member for the discussion on “Solving Ethical & Technical Challenges of Fake News,” Wien, Austria. http://hdl.handle.net/20.500.12708/87158 ( reposiTUm)

Hanbury, A. (2018). Word Relatedness from Word Embedding in Information Retrieval. Forum for Information Retrieval Evaluation (FIRE), Virtual Conference, Unknown. http://hdl.handle.net/20.500.12708/87159 ( reposiTUm)

Hanbury, A. (2018). Unstructured Information in Data Science: Word Relatedness from Word Embedding. Data Science Society Meet-Up, Vienna, Austria. http://hdl.handle.net/20.500.12708/87152 ( reposiTUm)

Hanbury, A. (2018). Word Embedding and Applications. U Wien Inter-Faculty Research Centre on Computational Complex Systems Meeting: Drawing Insights from Complex Data, Wien, Austria. http://hdl.handle.net/20.500.12708/87153 ( reposiTUm)

Hanbury, A. (2018). Sentiment Analysis in Finance. VISS 2018 - Vienna International Summer School on Machine Learning Methods and Data Analytics in Risk and Insurance, Wien, Austria. http://hdl.handle.net/20.500.12708/87154 ( reposiTUm)

Hanbury, A. (2018). Word relatedness from Word Embedding in text analysis and information retrieval. TU Berlin, Berlin, Germany. http://hdl.handle.net/20.500.12708/87151 ( reposiTUm)

Rekabsaz, N., Lupu, M., & Hanbury, A. (2016). Uncertainty in Neural Network Word Embedding Exploration of Threshold for Similarity. Neu-IR: The SIGIR 2016 Workshop on Neural Information Retrieval, Pisa, Italy. http://hdl.handle.net/20.500.12708/86458 ( reposiTUm)

Andersson, L., Lupu, M., & Hanbury, A. (2013). Domain Adaptation of General Natural Language Processing Tools for a Patent Claim Visualization System. In Proceedings of Multidisciplinary Information Retrieval, Berlin, Germany. http://hdl.handle.net/20.500.12708/86542 ( reposiTUm)

Andersson, L., Mahdabi, P., Hanbury, A., & Rauber, A. (2013). Exploring patent passage retrieval using nouns phrases. Proceeding of the 35th European conference on Advances in Information Retrieval (ECIR’13), Berlin, Germany. http://hdl.handle.net/20.500.12708/86543 ( reposiTUm)

Andersson, L., Mahdabi, P., Rauber, A., & Hanbury, A. (2012). Report on the CLEF-IP 2012 Experiments: Exploring Passage Retrieval with the PIPExtractor. In Proceeding of the of the Conference and Labs of the Evaluation Forum (CLEF2012), Pisa, EU. http://hdl.handle.net/20.500.12708/86544 ( reposiTUm)

Mahdabi, P., Andersson, L., Hanbury, A., & Crestani, F. (2011). Report on the CLEF-IP 2011 Experiments: Exploring Patent Summarisation. In Proceeding of the of the Conference and Labs of the Evaluation Forum (CLEF2011), Pisa, EU. http://hdl.handle.net/20.500.12708/86545 ( reposiTUm)

Samwald, M., Kritz, M., Gschwandtner, M., Stefanov, V., & Hanbury, A. (2011). An open, trustworthy and multilingual search engine for medical practitioners. 23rd International Conference of the European Federation for Medical Informatics (MIE2011), Oslo, Norwegen, Non-EU. http://hdl.handle.net/20.500.12708/85232 ( reposiTUm)

Machajdik, J., & Hanbury, A. (2010). Affective Image Classification. Computer Vision Winter Workshop 2010, Nove Hrady, EU. http://hdl.handle.net/20.500.12708/85619 ( reposiTUm)

Hanbury, A. (2006). The MUSCLE / ImageCLEF Image Retrieval Evaluation Campaigns. PASCAL Visual Object Classes Challenge Workshop, Graz, Austria. http://hdl.handle.net/20.500.12708/84670 ( reposiTUm)

Hanbury, A. (2006). Results of the MUSCLE CIS Coin Competition 2006. MUSCLE CIS Coin Competition Workshop, Berlin, EU. http://hdl.handle.net/20.500.12708/84672 ( reposiTUm)

Hanbury, A. (2006). Analysis of Keywords used in Image Understanding Tasks. OntoImage International Workshop, Genova, Italy, EU. http://hdl.handle.net/20.500.12708/84671 ( reposiTUm)

Hanbury, A. (2006). A Dataset of Annotated Animals. Second MUSCLE/ImageCLEF Workshop on Image and Video Retrieval Evaluation, Alicante, Spain, EU. http://hdl.handle.net/20.500.12708/84673 ( reposiTUm)

Taha Abdel, A., Hanbury, A., & Jimenez del Toro, O. A. (2014). Test Data and Results of the Automatic Metric Selection Method. http://hdl.handle.net/20.500.12708/38055 ( reposiTUm)

Pottmann, H., Steiner, T., Hofer, M., Haider, C., & Hanbury, A. (2003). The isophotic metric and its application to feature sensitive morphology on surfaces. http://hdl.handle.net/20.500.12708/31621 ( reposiTUm)

Hofstätter, S., Althammer, S., Schröder, M., Sertkan, M., & Hanbury, A. (2020). Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation (p. 8). arXiv. http://hdl.handle.net/20.500.12708/141680 ( reposiTUm)

Heiler, G., Reisch, T., Hurt, J., Forghani, M., Omani, A., Hanbury, A., & Karimipour, F. (2020). Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic (p. 9). arXiv. https://doi.org/10.48550/arXiv.2008.10064 ( reposiTUm)

Heiler, G., Hanbury, A., & Filzmoser, P. (2020). The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data (p. 14). arXiv. https://doi.org/10.48550/arXiv.2009.03798 ( reposiTUm)

Reisch, T., Heiler, G., Hurt, J., Klimek, P., Hanbury, A., & Thurner, S. (2020). Behavioural gender differences are increased by lock-down measures. (p. 26). arXiv. http://hdl.handle.net/20.500.12708/141682 ( reposiTUm)

Hanbury, A. (2007). Morphological processing and segmentation of colour images [Professorial Dissertation, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/183592 ( reposiTUm)