WANG, X. L., & BRORSSON, M. H. (In press). Which company adjustment matter? Insights from Uplift Modeling on Financial Health [Paper presentation]. The 9th Workshop on MIning DAta for financial applicationS in conjunction with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery, Vilnius, Lithuania. ![]() |
![]() ![]() | KAKATI, S., & BRORSSON, M. H. (10 April 2025). Performance and Usability Implications of Multiplatform and WebAssembly Containers [Paper presentation]. 15th International Conference on Cloud Computing and Services Science, Porto, Portugal. doi:10.5220/0013203200003950 ![]() |
![]() ![]() | WANG, X. L., & BRORSSON, M. H. (January 2025). Can Large language model analyze financial statements well? [Paper presentation]. The Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal) In conjunction with COLING-2025, Abu Dhabi, United Arab Emirates. ![]() |
![]() ![]() | KAKATI, S.* , & BRORSSON, M. H.*. (11 November 2024). An Investigative Study of WebAssembly Performance in Cloud-to-Edge [Paper presentation]. 2024 International Symposium on Parallel Computing and Distributed Systems (PCDS), Singapore, Singapore. doi:10.1109/pcds61776.2024.10743586 ![]() * These authors have contributed equally to this work. |
![]() ![]() | WANG, X. L., Kräussl, Z., & BRORSSON, M. H. (2024). Datasets for Advanced Bankruptcy Prediction: A survey and Taxonomy. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/63483. |
![]() ![]() | KAKATI, S.* , & BRORSSON, M. H.*. (08 October 2024). A Cross-Architecture Evaluation of WebAssembly in the Cloud-Edge Continuum [Paper presentation]. IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID). doi:10.1109/ccgrid59990.2024.00046 ![]() * These authors have contributed equally to this work. |
![]() ![]() | BLANCO, B., & BRORSSON, M. H. (31 July 2024). A Novel Architecture for Long-Text Predictions Using BERT-Based Models. Lecture Notes in Networks and Systems, 2, 105–125. ![]() |
![]() ![]() | PANNER SELVAM, K., & BRORSSON, M. H. (2024). Can Tree-Based Model Improve Performance Prediction for LLMs? ARC-LG workshop at 51st International Symposium on Computer Architecture. ![]() |
![]() ![]() | WANG, X. L., & BRORSSON, M. (2024). Augmenting Bankruptcy Prediction Using Reported Behavior of Corporate Restructuring. In Intelligent Computers, Algorithms, and Applications. Springer Nature Singapore. doi:10.1007/978-981-97-0065-3_8 ![]() |
![]() ![]() | RAC, S., & BRORSSON, M. H. (2024). Cost-aware service placement and scheduling in the Edge-Cloud Continuum. Transactions on Architecture and Code Optimization. doi:10.1145/3640823 ![]() |
![]() ![]() | PANNER SELVAM, K., & BRORSSON, M. H. (2024). Can Semi-Supervised Learning Improve Prediction of Deep Learning Model Resource Consumption? International Journal of Advanced Computer Science and Applications, 15 (6), 74 - 83. doi:10.14569/IJACSA.2024.0150610 ![]() |
![]() ![]() | RAC, S., & BRORSSON, M. H. (2023). Cost-Effective Scheduling for Kubernetes in the Edge-to-Cloud Continuum. In 2023 IEEE International Conference on Cloud Engineering (IC2E) (pp. 153-160). The Institute of Electrical and Electronics Engineers. doi:10.1109/ic2e59103.2023.00025 ![]() ![]() |
![]() ![]() | BLANCO, B.* , Becerra-Sanchez, P., BRORSSON, M. H., & ZURAD, M. (Other coll.). (2023). Reducing tokenizer’s tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer. Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting, 94–103. ![]() |
![]() ![]() | KAKATI, S.* , & BRORSSON, M. H.*. (2023). WebAssembly beyond the Web: A Review for the Edge-Cloud Continuum. In 2023 3rd International Conference on Intelligent Technologies, CONIT 2023 (pp. 8). Institute of Electrical and Electronics Engineers Inc. doi:10.1109/CONIT59222.2023.10205816 ![]() * These authors have contributed equally to this work. |
![]() ![]() | WANG, X. L., Kraussl, Z., ZURAD, M., & BRORSSON, M. H. (2023). Effective Automatic Feature Engineering on Financial Statements for Bankruptcy Prediction. In X. L. WANG, Z. KRÄUSSL, M. Zurad, ... M. H. BRORSSON, Effective Automatic Feature Engineering on Financial Statements for Bankruptcy Prediction. Unknown/unspecified: IEEE Xplore. doi:10.1109/iceccme57830.2023.10252608 ![]() |
![]() ![]() | PANNER SELVAM, K., & BRORSSON, M. H. (2023). Performance Analysis and Benchmarking of a Temperature Downscaling Deep Learning Model. In 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, Naples, Italy 1-3 March 2023. ![]() |
![]() ![]() | BLANCO, B., BRORSSON, M. H., & ZURAD, M. (2023). Auto-clustering of Financial Reports Based on Formatting Style and Author’s Fingerprint. In I. Koprinska (Ed.), Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. doi:10.1007/978-3-031-23633-4_9 ![]() |
![]() ![]() | PANNER SELVAM, K., & BRORSSON, M. H. (2022). Performance Modeling of Weather Forecast Machine Learning for Efficient HPC. In International Conference on Distributed Computing Systems (ICDCS), Italy 10-13 July 2022 (42nd, pp. 1268-1269). Bologna, Italy: IEEE. doi:10.1109/ICDCS54860.2022.00127 ![]() |
![]() ![]() | BLANCO, B., & BRORSSON, M. H. (2022). PROJECT DASHBOARD. Luxembourg, Luxembourg: SnT Uni.lu. https://orbilu.uni.lu/handle/10993/51747 |
![]() ![]() | WANG, X. L., BLANCO, B., & BRORSSON, M. H. (2022). REPORT OF DATA FUSION AND EVALUATION. Luxembourg, Luxembourg: SNT uni.lu. https://orbilu.uni.lu/handle/10993/51574 |
![]() ![]() | RAC, S., Sanyal, R., & BRORSSON, M. H. (2022). A Cloud-Edge Continuum Experimental Methodology applied to a 5G Core Study. Transactions on Computational Science and Computational Intelligence. ![]() |
![]() ![]() | RAC, S., & BRORSSON, M. H. (2021). At the Edge of a Seamless Cloud Experience. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/51871. doi:10.48550/arXiv.2111.06157 |
![]() ![]() | BLANCO, B., & BRORSSON, M. H. (2021). DATA DISTRIBUTION API SPECIFICATION. Luxembourg, Luxembourg: SnT Uni.lu. https://orbilu.uni.lu/handle/10993/48696 |
![]() ![]() | WANG, X. L., BLANCO, B., & BRORSSON, M. H. (2021). REPORT OF DATA SOURCES. Luxembourg, Luxembourg: SnT Uni.lu. https://orbilu.uni.lu/handle/10993/48690 |
![]() ![]() | DU, M., HAMMERSCHMIDT, C., VARISTEAS, G., STATE, R., BRORSSON, M. H., & Zhang, Z. (2019). Time Series Modeling of Market Price in Real-Time Bidding. In 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ![]() |
![]() ![]() | DU, M., Cowen-Rivers, A. I., Wen, Y., Sakulwongtana, P., Wang, J., BRORSSON, M. H., & STATE, R. (2019). Know Your Enemies and Know Yourself in the Real-Time Bidding Function Optimisation. In Proceedings of the 19th IEEE International Conference on Data Mining Workshops (ICDMW 2019). ![]() |
![]() ![]() | Oz, I., Bhatti, M. K., Popov, K., & BRORSSON, M. H. (2019). Regression-based prediction for task-based program performance. Journal of Circuits, Systems and Computers, 28 (04), 1950060. doi:10.1142/S0218126619500609 ![]() |