Publikationer

Søg publikationer fra Institut for Datalogi

Liste over publikationer af videnskabelige medarbejdere fra Institut for Datalogi, Aarhus Universitet.

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Van Mechelen, M., Smith, R. C., Schaper, M.-M. A., Tamashiro, M. A., Bilstrup, K.-E. K., Lunding, M. S., Petersen, M. G. & Iversen, O. S. (2022). Emerging Technologies in K-12 Education: A Future HCI Research Agenda. ACM Transactions on Computer-Human Interaction, 30(3), Artikel 47. https://doi.org/10.1145/3569897

Vanegas, H., Cabarcas, D. & Aranha, D. F. (2023). Privacy-Preserving Edit Distance Computation Using Secret-Sharing Two-Party Computation. I A. Aly & M. Tibouchi (red.), Progress in Cryptology – LATINCRYPT 2023: 8th International Conference on Cryptology and Information Security in Latin America, LATINCRYPT 2023, Quito, Ecuador, October 3–6, 2023, Proceedings (s. 67-86). Springer. https://doi.org/10.1007/978-3-031-44469-2_4

Van Der Berg, F. I. & Van De Pol, J. (2019). Concurrent chaining hash maps for software model checking. I C. Barrett & J. Yang (red.), Proceedings of the 19th Conference on Formal Methods in Computer-Aided Design, FMCAD 2019 (s. 46-54). Artikel 8894279 IEEE. https://doi.org/10.23919/FMCAD.2019.8894279

van de Pol, J. (2019). Concurrent algorithms and data structures for model checking. I W. Fokkink & R. van Glabbeek (red.), 30th International Conference on Concurrency Theory, CONCUR 2019 Artikel 4 Dagstuhl Publishing. https://doi.org/10.4230/LIPIcs.CONCUR.2019.4

van de Pol, J. (2022). Exploring a Parallel SCC Algorithm: Using TLA ⁺ and the TLC Model Checker. I T. Margaria & B. Steffen (red.), Leveraging Applications of Formal Methods, Verification and Validation. Verification Principles - 11th International Symposium, ISoLA 2022, Proceedings (s. 535-555). Springer. https://doi.org/10.1007/978-3-031-19849-6_30

van de Pol, J. & Petrucci, L. (2021). On Completeness of Liveness Synthesis for Parametric Timed Automata (Extended Abstract). I M. Roggenbach (red.), Recent Trends in Algebraic Development Techniques - 25th International Workshop, WADT 2020, Revised Selected Papers (s. 3-10). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-73785-6_1

Van Bergerem, S., Grohe, M. & Ritzert, M. (2022). On the Parameterized Complexity of Learning First-Order Logic. I PODS 2022 - Proceedings of the 41st ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (s. 337-346). Association for Computing Machinery. https://doi.org/10.1145/3517804.3524151

Valsted, F. M., Nielsen, C. V. H., Jensen, J. Q., Sonne, T. & Jensen, M. M. (2017). Strive: Exploring Assistive Haptic Feedback on the Run. I M. Brereton, D. Vyas, A. Soro, B. Ploderer, J. Waycott & A. Morrison (red.), Proceedings of the 29th Australian Computer-Human Interaction Conference: Human-Nature, OzCHI 2017 (s. 275-284). Association for Computing Machinery. https://doi.org/10.1145/3152771.3152801

Urazimbetova, S. (2012). A Case Study - On Patient Empowerment and Integration of Telemedicine to National Healthcare Services. Afhandling præsenteret på International Conference on Health Informatics, Vilamoura, Algarve, Portugal. https://doi.org/10.5220/0003870902630269

Urazimbetova, S. (2011). Patient and Care Network can Improve Interoperability of Telemedicine. D A I M I P B, (594), 51-54.

Urazimbetova, S. & Kyng, M. (2012). Bridging the Patient Empowerment Gap of National Healthcare Services with Quick Response Code. Global Telemedicine and eHealth Updates : Knowledge Resources, 5, 444-448. http://www.medetel.lu/download/2012/Med-e-Tel_2012_Global_Telemedicine_and_eHealth_Updates_Volume_5_Contents.pdf

Urazimbetova, S. & Kyng, M. (2013). From Many to One: Analysis of Information Models for Patient Centric Telemedicine. Global Telemedicine and eHealth Updates : Knowledge Resources, 6, 7-12. http://www.medetel.eu/download/2013/parallel_sessions/presentation/day3/From_Many_to_One.pdf

Uhrmacher, A. M., Schulz, H.-J., Schumann, H., Schwabe, L. & Timmermann, D. (2009). Regenerative Systems - Challenges and Opportunities for Modeling, Simulation, and Visualization. I G. Stea, J. Mairesse & J. Mendes (red.), Proceedings of the 4th International Conference on Performance Evaluation Methodologies and Tools Artikel 45. https://doi.org/10.4108/ICST.VALUETOOLS2009.7907

Tzoumas, K., Deshpande, A. & Jensen, C. S. (2011). Lightweight Graphical Models for Selectivity Estimation Without Independence Assumptions. Proceedings of the VLDB Endowment, 4(11), 852-863. http://www.vldb.org/pvldb/vol4/p852-tzoumas.pdf

Tzoumas, K., Deshpande, A. & Jensen, C. S. (2013). Efficiently adapting graphical models for selectivity estimation. V L D B Journal, 22(1), 3-27. https://doi.org/10.1007/s00778-012-0293-7

Tziavelis, N., Giannakopoulos, I., Doka, K., Koziris, N. & Karras, P. (2019). Equitable Stable Matchings in Quadratic Time. I H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox & R. Garnett (red.), Advances in Neural Information Processing Systems 32 (NIPS 2019): Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019 (Bind 32, s. 455-465). Curran Associates, Inc.. https://papers.nips.cc/paper/8337-equitable-stable-matchings-in-quadratic-time

Tzavelis, N., Giannakopoulos, I., Johansen, R. Q., Doka, K., Koziris, N. & Karras, P. (2020). Fair Procedures for Fair Stable Marriage Outcomes. I AAAI 2020 - 34th AAAI Conference on Artificial Intelligence (Bind 34, no. 5, s. 7269-7276). AAAI Press. https://doi.org/10.1609/aaai.v34i05.6218

Turon, A., Dreyer , D. & Birkedal, L. (2013). Unifying refinement and hoare-style reasoning in a logic for higher-order concurrency. I G. Morrisett & T. Uustalu (red.), Proceedings of the 18th ACM SIGPLAN International Conference on Functional Programming, ICFP '13 (s. 377-390 ). Association for Computing Machinery. https://doi.org/10.1145/2500365.2500600

Türkmen, R., Pfeuffer, K., Barrera MacHuca, M. D., Batmaz, A. U. & Gellersen, H. (2022). Exploring Discrete Drawing Guides to Assist Users in Accurate Mid-air Sketching in VR. I CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems Artikel 276 Association for Computing Machinery. https://doi.org/10.1145/3491101.3519737

Turkmen, R., Gelmez, Z. E., Batmaz, A. U., Stuerzlinger, W., Asente, P., Sarac, M., Pfeuffer, K. & Machuca, M. D. B. (2024). EyeGuide & EyeConGuide: Gaze-based Visual Guides to Improve 3D Sketching Systems. I CHI 2024 - Proceedings of the 2024 CHI Conference on Human Factors in Computing Sytems Artikel 178 Association for Computing Machinery. https://doi.org/10.1145/3613904.3641947

Tunç, H. C., Mathur, U., Pavlogiannis, A. & Viswanathan, M. (2023). Sound Dynamic Deadlock Prediction in Linear Time. Proceedings of the ACM on Programming Languages , 7, 1733-1758. https://doi.org/10.1145/3591291

Tunç, H. C., Abdulla, P. A., Chakraborty, S., Krishna, S., Mathur, U. & Pavlogiannis, A. (2023). Optimal Reads-From Consistency Checking for C11-Style Memory Models. Proceedings of the ACM on Programming Languages , 7(PLDI), 761–785. Artikel 137. https://doi.org/10.1145/3591251

Tunç, H. C., Deshmukh, A. P., Çirisci, B., Enea, C. & Pavlogiannis, A. (2024). CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution Analysis. I ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (Bind 3, s. 223-238). Association for Computing Machinery. https://doi.org/10.1145/3620666.3651358

Tunç, H. C. (2024). Efficient Data Structures and Algorithms for Dynamic Analysis of Concurrent Programs.

Tu, S., Aslay, C. & Gionis, A. (2020). Co-exposure maximization in online social networks. I H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan & H. Lin (red.), Advances in Neural Information Processing Systems: NeurIPS 2020 (Bind 33). Neural Information Processing Systems Foundation. https://proceedings.neurips.cc/paper/2020/hash/212ab20dbdf4191cbcdcf015511783f4-Abstract.html

Tsourakakis, C. E., Mitzenmacher, M., Larsen, K. G., Blasiok, J., Lawson, B., Nakkiran, P. & Nakos, V. (2018). Predicting Positive and Negative Links with Noisy Queries: Theory Practice. arxiv.org. http://arxiv.org/abs/1709.07308

Tsitsulin, A., Mottin, D., Karras, P., Bronstein, A. & Müller, E. (2018). NetLSD: Hearing the Shape of a Graph. I Y. Guo & F. Farooq (red.), KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (s. 2347-2356). Association for Computing Machinery. https://doi.org/10.1145/3219819.3219991

Tsitsulin, A., Mottin, D., Karras, P. & Müller, E. (2018). VERSE: Versatile Graph Embeddings from Similarity Measures. I P.-A. Champin, F. Gandon & L. Médini (red.), The Web Conference 2018 - Proceedings of the World Wide Web Conference, WWW 2018: WWW '18 (s. 539-548). Association for Computing Machinery. https://doi.org/10.1145/3178876.3186120

Tsitsulin, A., Mottin, D., Karras, P., Bronstein, A. & Müller, E. (2019). Spectral graph complexity. I L. Liu & R. White (red.), The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019 (s. 308-309). Association for Computing Machinery. https://doi.org/10.1145/3308560.3316589

Tsitsulin, A., Munkhoeva, M., Mottin, D., Karras, P., Bronstein, A., Oseledets, I. & Müller, E. (2020). The Shape of Data: Intrinsic Distance for Data Distributions. Afhandling præsenteret på The International Conference on Learning Representations (ICLR), Ababa, Etiopien.

Tsitsulin, A., Munkhoeva, M., Mottin, D., Karras, P., Oseledets, I. & Mueller, E. (2021). FREDE: Anytime Graph Embeddings. Proceedings of the VLDB Endowment, 14(6), 1102-1110. https://doi.org/10.14778/3447689.3447713

Tsirogiannis, K. & de Berg, M. (2011). Exact and approximate computations of watersheds on triangulated terrains. I Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (s. 74-83). Association for Computing Machinery. https://doi.org/10.1145/2093973.2093985

Tsirogiannis, K. & Haverkort, H. (2011). Flow on noisy terrains: an experimental evaluation. I Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (s. 84-91). Association for Computing Machinery. https://doi.org/10.1145/2093973.2093986

Tsirogiannis, C., Sandel, B. S. & Cheliotis, D. (2012). Efficient Computation of Popular Phylogenetic Tree Measures. Lecture Notes in Computer Science, 7534, 30-43. https://doi.org/10.1007/978-3-642-33122-0_3

Tsirogiannis, C. & Sandel, B. S. (2013). Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time. I A. Darling & J. Stoye (red.), Algorithms in Bioinformatics: 13th International Workshop, WABI 2013, Sophia Antipolis, France, September 2-4, 2013. Proceedings (s. 170-184). Springer VS. https://doi.org/10.1007/978-3-642-40453-5_14

Tsirogiannis, C., Sandel, B. S. & Kalvisa, A. (2014). New Algorithms for Computing Phylogenetic Biodiversity. I D. Brown & B. Morgenstern (red.), Algorithms in Bioinformatics: 14th International Workshop, WABI 2014, Wroclaw, Poland, September 8-10, 2014. Proceedings (s. 187-203). Springer VS. https://doi.org/10.1007/978-3-662-44753-6_15

Tsirogiannis, C. & Sandel, B. S. (2014). Computing the Skewness of the Phylogenetic Mean Pairwise Distance in Linear Time. Algorithms for Molecular Biology, 9(1), Artikel 15. https://doi.org/10.1186/1748-7188-9-15

Tsirogiannis, C. & Tsirogiannis, C. (2016). Uncovering the Hidden Routes: Algorithms for Identifying Paths and Missing Links in Trade Networks . I T. Brughmans, A. Collar & F. Coward (red.), The Connected Past: Challenges to Network Studies in Archaeology and History Oxford University Press.

Tsirogiannis, C. & Sandel, B. S. (2016). PhyloMeasures: A package for computing phylogenetic biodiversity measures and their statistical moments. Ecography, 39(7), 709-714. https://doi.org/10.1111/ecog.01814

Tsirogiannis, C., Staals, F. & Pellissier, V. (2018). Computing the expected value and variance of geometric measures. ACM Journal of Experimental Algorithmics, 23(1), 2.4:1-2.4:32. Artikel 2.4. https://doi.org/10.1145/3228331

Tsimbalistaia, U., Berger, C. P., Gellersen, H. & Manakhov, P. (Accepteret/In press). On-body Icons: Designing a 3D Interface for Launching Apps in Augmented Reality. I CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Sytems Association for Computing Machinery. https://doi.org/10.1145/3706598.3713954

Tsigaridas, E. (2011). Improved bounds for continued fractions variants for real root isolation. Afhandling præsenteret på 4th Internatinal Conference on Mathematical Aspects of Computer Information, Beijing, Kina.

Tshagharyan, G. & Schulz, H.-J. (2013). A Graph-based Overview Visualization for Data Landscapes. Computer Science and Information Technology, 1(3), 225-232. http://www.hrpub.org/journals/article_info.php?aid=672

Truong, T. T., Airao, J., Hojati, F., Ilvig, C. F., Azarhoushang, B., Karras, P. & Aghababaei, R. (2024). Data-driven prediction of tool wear using Bayesian regularized artificial neural networks. Measurement: Journal of the International Measurement Confederation, 238, Artikel 115303. https://doi.org/10.1016/j.measurement.2024.115303

Truong, T. T., Airao, J., Fattahi, S., Azarhoushang, B., Karras, P. & Aghababaei, R. (2025). Image-based machine learning model for tool wear estimation in milling Inconel 718. Wear, Artikel 205865. Advance online publication. https://doi.org/10.1016/j.wear.2025.205865

Truică, C. O., Apostol, E. S., Darmont, J. & Assent, I. (2021). TextBenDS: a Generic Textual Data Benchmark for Distributed Systems. Information Systems Frontiers, 23(1), 81-100. https://doi.org/10.1007/s10796-020-09999-y

Truica, C.-O., Apostol, E. S., Stefu, T. & Karras, P. (2021). A Deep Learning Architecture for Audience Interest Prediction of News Topic on Social Media. I Y. Velegrakis, D. Zeinalipour-Yazti, P. K. Chrysanthis & F. Guerra (red.), Advances in Database Technology - EDBT 2021: 24th International Conference on Extending Database Technology, Proceedings (s. 588-599). openproceedings.org. https://doi.org/10.5441/002/edbt.2021.69

Truelsen, J. (2015). Space Efficient Data Structures and External Terrain Algorithms. Department of Computer Science, Aarhus University.

Troullinou, G., Kondylakis, H., Lissandrini, M. & Mottin, D. (2021). SOFOS: Demonstrating the Challenges of Materialized View Selection on Knowledge Graph. I Proceedings of the 2021 International Conference on Management of Data (SIGMOD/PODS '21) (s. 2789–2793). Association for Computing Machinery. https://doi.org/10.1145/3448016.3452765

Trittenbach, H., Böhm, K. & Assent, I. (2020). Active Learning of SVDD Hyperparameter Values. I G. Webb, Z. Zhang, V. S. Tseng, G. Williams, M. Vlachos & L. Cao (red.), Proceedings - 2020 IEEE 7th International Conference on Data Science and Advanced Analytics, DSAA 2020 (s. 109-117). Artikel 9260103 IEEE. https://doi.org/10.1109/DSAA49011.2020.00023

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