Data Science

Publications

2023

Ballhausen T., Gindl S. & Tauber M. (2023): Curatorial Companionship: A New Framework for Managing High-quality Digital Cultural Content and Data. In: ERCIM News No. 133.

Adamakis, E., Boch, M., Bampoulidis, A., Margetis, G., Gindl, S., Stephanidis, C. (2023). Visualizing the risks of de-anonymization in high-dimensional data. 6th International Conference on Information Technology & Systems (ICITS’23).

Boch, M., Adamakis, E., Gindl, S., Margetis, G., Stephanidis, C. (2023). Anonymisation Methods for High-Dimensional and Complex Data based on Privacy Models for the Prevention of De-Anonymization Attacks. 11st World Conference on Information Systems and Technologies (WorldCIST’23).

Duh, D., Goschlberger, B., Boch, M., Graser, G., Gross, M., Pitzschke, A., & Sengschmid, E. (2023). Design and Development of a Social Micro-Learning Platform in the context of Tactile Learning Materials for Students with Visual Impairments. In The 15th International Conference on Education Technology and Computers (pp. 189-194).

2022

Boch, M.; Gindl, S.; Barnett, A.; Margetis, G.; Mireles, V.; Adamakis, E. and Knoth, P. (2022). A Systematic Review of Data Management Platforms. In: WorldCIST’22, 12-14 Apr 2022, Budva, Montenegro.

Ghafourian, Y. (2022). Relevance Models Based on the Knowledge Gap. In Advances in Information Retrieval: 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II (pp. 488-495).

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: Conference and Labs of the Evaluation Forum (CLEF) 2022. Lecture Notes in Computer Science, vol 13390. Springer, Cham.

2021

Aydin, O. U., Taha, A. A., Hilbert, A., Khalil, A. A., Gali-novic, I., Fiebach, J. B., Frey, D., & Madai, V. I. (2021). On the usage ofaverage hausdorff distance for segmentation performance assessment: Hiddenbias when used for ranking.European Radiology Experimental, 5.

Ghafourian, Y.; Knoth, P. & Hanbury, A. (2021). Information retrieval evaluation in knowledge acquisition tasks. In: WEPIR 2021: The 3rd Workshop on Evaluation of Personalisation in Information Retrieval at CHIIR 2021, 19 Mar 2021, [Online], S. 88–95.

Taha, A. A., Papariello, L., Bampoulidis, A., Knoth, P., & Lupu, M. (2021). Formal Analysis and Estimation of Chance in Datasets Based on Their Properties. In: IEEE Transactions on Knowledge and Data Engineering.

Aydin, O. U., Taha, A. A., Hilbert, A., Khalil, A.A., Galinovic, I., Fiebach, J., Frey, D., & Madai, V. I. (2021). An evaluation of performance measures for arterial brain vessel segmentation. In: BMC Med Imaging, 21, S. 113.

2020

Taha, A. A., Papariello, L., Bampoulidis, A., Knoth, P., and Lupu, M. (2020). Formal analysis and estimation of chance in datasets based on their properties. IEEE Transactions on Knowledge and Data Engineering,xx(x):xx–xx.

Hilbert, A., Madai, V. I., Akay, E. M., Aydin, O. U.,Behland, J., Sobesky, J., Galinovic, I., Khalil, A. A., Taha, A. A., Wuerfel,J., Dusek, P., Niendorf, T., Fiebach, J. B., Frey, D., and Livne, M. (2020).Brave-net: Fully automated arterial brain vessel segmentation in patientswith cerebrovascular disease.Frontiers in Artificial Intelligence, 3:78.

Helminger, L., Kales, D., Rechberger, C., Walch, R., Bampoulidis, A., and Bruni, A. Privately Connecting Mobility to Infectious Diseases via Applied Cryptography. In IEEE Symposium on Security and Privacy

Papariello, L., Bampoulidis, A., and Lupu, M. (2020). On the Replicability of Combining Word Embeddings and Retrieval Models. In European Conference on Information Retrieval (pp. 50-57). Springer, Cham.

Bampoulidis, A., Bruni, A., Markopoulos, I., and Lupu, M. (2020). Practice and Challenges of (De-) Anonymisation for Data Sharing. In International Conference on Research Challenges in Information Science (pp. 515-521). Springer, Cham.

Lin, R., Molignini, P., Papariello, L., Tsatsos, M. C., Leveque, C., Weiner, S. E., Fasshauer, E., Chitra, R., and Lode, A. U. J. (2020). MCTDH-X: The multiconfigurational time-dependent Hartree method for indistinguishable particles software. Quantum Science and Technology 5 024004.

Lode, A. U. J., Lin, R., Büttner, M., Papariello, L., Leveque, C., Chitra, R., Tsatsos, M. C., Jaksch, D., and Molignini, P. (2020). Optimized Observable Readout from Single-shot Images of Ultracold Atoms via Machine Learning. arXiv:2010.14510.

Lode, A. U. J., Alon, O. E., Bastarrachea-Magnani, M. A., Bhowmik, A., Buchleitner, A., Cederbaum, L. S., Chitra, R., Fasshauer, E., de Forges de Parny, L., Haldar, S. K., Leveque, C., Lin, R., Madsen, L. B., Molignini, P., Papariello, L., Schäfer, F., Strelstov, A. I., Tsatsos, M. C., and S. E. Weiner (2020). MCTDH-X: The multiconfigurational time-dependent Hartree method for indistinguishable particles high-performance computation project. In High Performance Computing in Science and Engineering. Springer, Cham.