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Our Publications

Our lecturers constantly work on papers and frequently published in highly-regarded scholarly journals. This is an overview of the latest output of our faculty members:

Muhammad Waseem, Teerath Das, Aakash Ahmad, Mahdi Fehmideh, Peng Liang, Tommi Mikkonen  (2024) ChatGPT as a Software Development Bot: A Project-based Study. In: 19th International Conference on Evaluation of Novel Approaches to Software Engineering  (ENASE’2024) (accepted)

Muhammad Waseem, Teerath Das, Aakash Ahmad, Peng Liang, Tommi Mikkonen 2024) Issues and Their Causes in WebAssembly Applications: An Empirical Study. In: International Conference on Evaluation and Assessment in Software Engineering (EASE)  (accepted)

Moussa, A. M., Abdou, S., Elsayed, K. M., Rashwan, M., Asif, A., Khatoon, S., Alshamari, M. A. (2024). Enhanced Arabic disaster data classification using domain adaptation. PLoS one, 19(4), e0301255.

Krishnendu Chatterjee, Amir Kafshdar Goharshady, Tobias Meggendorfer, and Đorđe Žikelić (2024) Quantitative Bounds on Resource Usage of Probabilistic Programs (OOPSLA)

Roman Andriushchenko, Alexander Bork, Carlos E. Budde, Milan Češka, Ernst Moritz Hahn, Arnd Hartmanns, Bryant Israelsen, Nils Jansen, Joshua Jeppson, Sebastian Junges, Maximilian A. Köhl, Bettina Könighofer, Jan Křetínský, Tobias Meggendorfer, David Parker, Stefan Pranger, Tim Quatmann, Enno Ruijters, Landon Taylor, Matthias Volk, Maximilian Weininger, and Zhen Zhang  (2023) Tools at the Frontiers of Quantitative Verification: QComp 2023 Competition Report. In: TOOLympics

David Georg Reichelt, Lubomir Bulej, Reiner Jung and André van Hoorn (2024) Overhead Comparison of Instrumentation Frameworks. In: Companion of the International Conference on Performance Engineering

Lerch, P., Scheller, F., Reichelt, D. G., Menzel, K., & Bruckner, T. (2024). Electricity cost and CO2 savings potential for chlor-alkali electrolysis plants: Benefits of electricity price dependent demand response. Applied Energy, 355, 122263.

David Georg Reichelt, Reiner Jung, André van Hoorn  (2023) More is Less in Kieker? The Paradox of No Logging Being Slower Than Logging. Symposium on Software Performance

Hu-Bolz, J., Reed, M., Zhang, K., Liu, Z., & Hu, J. (2024). Federated data acquisition market: Architecture and a mean-field based data pricing strategy. High-Confidence Computing, 100232.

Kukushkin, M., Bogdan, M., Schmid, T. (2024) BiMAE – A Bimodal Masked Autoencoder Architecture for Single-Label Hyperspectral Image Classification. In: Perception Beyond the Visible Spectrum workshop series (IEEE PBVS) (accepted)

Boer, M.H.T., Smit, Q.T.S., Meyer-Vitali, A., Bekkum, M.A., Schmid, T. (2024) Modular Design Patterns for Generative Neuro-Symbolic Systems. In: Generative Neuro-Symbolic AI Workshop, ESWC 2024 (accepted)

Schuering, B., Schmid, T. (2024) What Can Computers Do Now? Dreyfus Revisited for the 3rd Wave of Artificial intelligence. In: AAAI 2024 Spring Symposium on Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge (AAAI-MAKE 2024), Stanford University, Palo Alto, California, USA, 2024. (in press)

Erichsmeier, F., Kukushkin, M., Fiedler, J., Enders, M., Goertz, S., Bogdan, M., Schmid, T., & Kaschuba, R. (2024) Automating the Purity Analysis of Oilseed Rape Through Usage of Hyperspectral Imaging. Proceedings of SPIE Photonics West 2024, San Francisco, USA

Kukushkin, M., Bogdan, M., Schmid, T. (2023) On Optimizing Morphological Neural Networks for Hyperspectral Image Classification. Proceedings of the 17th International Conference on Machine Vision (ICMV), Yerevan, Armenia

Hildesheim, H., Holoyad, T., Schmid, T. (2023) Machine Learning in AI Factories – Five Theses for Developing, Managing and Maintaining Data-driven Artificial Intelligence at Large Scale. Information Technology 65(4-5):218–227

Zhang, B., Liang, P., Zhou, X., Ahmad, A., Waseem, M. (2023) Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot. International Journal of Software Engineering and Knowledge Engineering (2023):1-20

Aljedaani, B., Ahmad, A., Zahedi, M., Babar, M. A. (2023). An empirical study on secure usage of mobile health apps: The attack simulation approach. Information and Software Technology, 163, 107285.

Khan, A. A., Ahmad, A., Waseem, M., Liang, P., Fahmideh, M., Mikkonen, T., Abrahamsson, P. (2023). Software architecture for quantum computing systems—A systematic review. Journal of Systems and Software, 201, 111682.

Khan, A. A., Akbar, M. A., Fahmideh, M., Liang, P., Waseem, M., Ahmad, A., Niazi, M., Abrahamsson, P. (2023). AI ethics: an empirical study on the views of practitioners and lawmakers. IEEE Transactions on Computational Social Systems.

Ahmad, A., Waseem, M., Liang, P., Fahmideh, M., Aktar, M. S., Mikkonen, T. (2023). Towards human-bot collaborative software architecting with ChatGPT. 27th International Conference on Evaluation and Assessment in Software Engineering, pp. 279-285

Khan, A. A., Ahmad, A., Waseem, M., Liang, P., Fahmideh, M., Mikkonen, T., Abrahamsson, P. (2023). Software architecture for quantum computing systems—A systematic review. Journal of Systems and Software, 201, 111682.

Aljedaani, B., Ahmad, A., Zahedi, M., Babar, M. A. (2023). End-users’ knowledge and perception about security of clinical mobile health apps: A case study with two Saudi Arabian mHealth providers. Journal of Systems and Software, 195, 111519.

Khan, A. A., Akbar, M. A., Ahmad, A., Fahmideh, M., Shameem, M., Lahtinen, V., Waseem, M., Mikkonen, T. (2023). Agile Practices for Quantum Software Development: Practitioners’ Perspectives. 2023 IEEE International Conference on Quantum Software (QSW) (pp. 9-20).

Fahmideh, M., Grundy, J., Ahmad, A., Shen, J., Yan, J., Mougouei, D., Wang, P., Ghose, A. Gunawardana, A., Aickelin, U., Abedin. B. (2022). Engineering Blockchain-based Software Systems: Foundations, Survey, and Future Directions. ACM Comput. Surv. 55(6).

Khan, A. A., Akbar, M. A., Fahmideh, M., Liang, P., Waseem, M., Ahmad, A., Niazi, M., Abrahamsson, P. (2023). AI ethics: an empirical study on the views of practitioners and lawmakers. IEEE Transactions on Computational Social Systems.

Anjum, N., Alibakhshikenari, M., Rashid, J., Jabeen, F., Asif, A., Mohamed, E. M., & Falcone, F. (2022). IoT-Based COVID-19 Diagnosing and Monitoring Systems: A Survey. IEEE Access, 10, 87168-87181.

Alghareeb, M., Albesher, A. S., Asif, A. (2023). Studying users’ perceptions of COVID-19 mobile applications in Saudi Arabia. Sustainability, 15(2), 956.

Caminati, M. B., Bowles, J. K. F. (2023). Representation Theorems Obtained by Miningacross Web Sources for Hints. Proceedings of the 6th International Conference on Information and Computer Technologies (ICICT), Raleigh, NC, USA, pp. 203-210

Caminati, M. B. (2023). Isabelle Formalisation of Original Representation Theorems. In: Dubois, C. & Kerber M. (Eds.) Intelligent Computer Mathematics, Springer Nature, Switzerland, ISBN 978-3-031-42753-4, pp. 98-112

Al-Naday, M., Thomos, N., Hu, J., Volckaert, B., de Turck, F., Reed, M. J. (2023). Service-Based, Multi-Provider, Fog Ecosystem With Joint Optimization of Request Mapping and Response Routing, IEEE Transactions on Services Computing, 16(3):2203-2214

Nalon, C., Hustadt, U., Papacchini, F., Dixon, C. (2023). Buy One Get 14 Free: Evaluating Local Reductions for Modal Logic. In: Pientka, B., Tinelli, C. (eds) Automated Deduction – CADE 29. CADE 2023. Lecture Notes in Computer Science, vol 14132. Springer, Cham.

Reichelt, D. G., Kühne, S., Hasselbring, W. (2023). Towards Solving the Challenge of Minimal Overhead Monitoring. In Companion of the 2023 ACM/SPEC International Conference on Performance Engineering, pp. 381-388.

Reichelt, D. G., Kühne, S., Hasselbring, W. (2022) Automated Identification of Performance Changes at Code Level. Proceedings of 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), Guangzhou, China, pp. 916-925

Reichelt, D. G., Krauß, H., Kühne, S., Hasselbring, W. (2022) Generic Performance Measurement in CI: The GeoMap Case Study. Proceedings of 13th Symposium on Software Performance, Stuttgart, Germany

Mendikowski, M., Schindler, B., Schmid, T., Möller, R., & Hartwig, M. (2023). Improved Techniques for Training Tabular GANs Using Cramer’s V Statistics. Proceedings of the Canadian Conference on Artificial Intelligence.

Horokh, O., Böhm, M., Schmid, T. (2023) A Parallelized Genitor II Implementation For Training Semiring Neural Networks. Proceedings of the Companion Conference on Genetic and Evolutionary Computation, pp. 287-290.

Schmid, T. (2023). A Systematic and Efficient Approach to the Design of Modular Hybrid AI Systems. Proceedings of the AAAI 2023 Spring Symposium on Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE 2023).

Schindler, B., Günzel, D., Schmid, T. (2022) Neural Noise Module: Automated Error Modeling using Adversarial Neural Networks. Intern. Conference on Bioelectromagnetism, Electrical Bioimpedance, and Electrical Impedance Tomography, pp. 191-194.

Walton, S. P., Vincalek, J., Rahat, A. A. M., Stovold, J., Evans, B.J. (2023) Genetic Car Designer: A Large-Scale User Study of a Mixed-Initiative Design Tool, Proceedings of the AISB Convention 2023, ISBN 978-1-908187-85-7, pp.115-121

Stovold, J. (2023) Neural Cellular Automata Can Respond to Signals. Proceedings of ALIFE 2023: Ghost in the Machine, 2023 Artificial Life Conference. pp. 5-14, Sapporo, Japan

Ferrando, A., Cardoso, R. C., Farrell, M., Luckcuck, M., Papacchini, F., Fisher, M., & Mascardi, V. (2022). Bridging the gap between single-and multi-model predictive runtime verification. Formal Methods in System Design, 1-33.

Cardoso, R. C., Ferrando, A., Papacchini, F., Askarpour, M., & Dennis, L. A. (2022). Proceedings of the Second Workshop on Agents and Robots for reliable Engineered Autonomy. arXiv preprint arXiv:2207.09058.

Papacchini, F., Nalon, C., Hustadt, U., & Dixon, C. (2022). Local is Best: Efficient Reductions to Modal Logic K. Journal of Automated Reasoning, 1-28.

Nalon, C., Hustadt, U., Papacchini, F., & Dixon, C. (2022). Local Reductions for the Modal Cube. In International Joint Conference on Automated Reasoning (pp. 486-505). Springer, Cham.

Hu, J., Reed, M., Thomos, N., Al-Naday, M. F., & Yang, K. (2022). A Dynamic Service Trading in a DLT-Assisted Industrial IoT Marketplace. IEEE Transactions on Network and Service Management.

Basu, A., Stapleton, G., Linker, S., Legg, C., Manalo, E., & Viana, P. (Eds.). (2021). Diagrammatic Representation and Inference: 12th International Conference, Diagrams 2021, Virtual, September 28–30, 2021, Proceedings (Vol. 12909). Springer Nature.

Linker, S. (2021, September). Natural Deduction for Intuitionistic Euler-Venn Diagrams. In International Conference on Theory and Application of Diagrams (pp. 529-533). Springer, Cham.

Basu, A., Stapleton, G., Linker, S., Legg, C., Manalo, E., & Viana, P. (Eds.). (2021). Diagrammatic Representation and Inference: 12th International Conference, Diagrams 2021, Virtual, September 28–30, 2021, Proceedings (Vol. 12909). Springer Nature.

Linker, S., Papacchini, F., & Sevegnani, M. (2021). Finite Models for a Spatial Logic with Discrete and Topological Path Operators. Leibniz International Proceedings in Informatics, LIPIcs, 202.

Cardoso, R. C., Ferrando, A., Papacchini, F., Luckcuck, M., Linker, S., & Payne, T. R. (2021). MLFC: From 10 to 50 Planners in the Multi-Agent Programming Contest. In Multi-Agent Progamming Contest (pp. 82-107). Springer, Cham.

Schmid, T., Grosse, F. (2022) Extracting Knowledge with Constructivist Machine Learning: Conceptual and Procedural Models. In: Proceedings of the AAAI 2022 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2022), Stanford University, Palo Alto, California, USA.

Schindler, B., Günzel, D., Schmid, T. (2022) Neural Noise Module: Automated Error Modeling using Adversarial Neural Networks. In: Proceedings of the International Conference of Bioelectromagnetism, Electrical Bioimpedance, and Electrical Impedance Tomography (ICBEM) (2022)

Schindler, B., Günzel, B., Schmid, T. (2021) Transcending Two-Path Impedance Spectroscopy with Machine Learning: A Computational Study on Modeling and Quantifying Electric Bipolarity of Epithelia. International Journal on Advances in Life Sciences 13(3-4), pp. 134-148

Böhm, M., Schmid, T. (2021) An Algorithmic Approach to Establish a Lower Bound for the Size of Semiring Neural Networks. Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 311–316,

Schmid, T., Hildesheim, W., Holoyad, T., Schumacher, K. (2021) The AI Methods, Capabilities and Criticality Grid – A Three-Dimensional Classification Scheme for Artificial Intelligence Applications. Künstliche Intelligenz. https://doi.org/10.1007/s13218-021-00736-4

Schmid, T. (2021) Batch-like Online Learning for More Robust Hybrid Artificial Intelligence: Deconstruction as a Machine Learning Process. Proceedings of the AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021). Stanford University, Palo Alto, California, USA, March 22-24, 2021.

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