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Profil

BRORSSON Mats Håkan

University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN

ORCID
0000-0002-9637-2065
Main Referenced Co-authors
BLANCO, Braulio  (7)
WANG, Xin Lin  (7)
KAKATI, Sangeeta  (4)
PANNER SELVAM, Karthick  (4)
RAC, Samuel  (4)
Main Referenced Keywords
Cloud computing (4); 5G (3); Edge computing (3); Finance (3); BERT (2);
Main Referenced Unit & Research Centers
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SEDAN - Service and Data Management in Distributed Systems (17)
NCER-FT - FinTech National Centre of Excellence in Research (8)
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN) (3)
ULHPC - University of Luxembourg: High Performance Computing (1)
University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > > (1)
Main Referenced Disciplines
Computer science (27)

Publications (total 27)

The most downloaded
377 downloads
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). https://hdl.handle.net/10993/41419

The most cited

9 citations (Scopus®)

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 https://hdl.handle.net/10993/57810

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.
Peer reviewed

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
Peer reviewed

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.
Peer reviewed

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
Peer reviewed
* 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
Peer reviewed
* 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.
Peer reviewed

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.
Peer reviewed

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
Peer reviewed

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
Peer reviewed

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
Peer reviewed

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
Peer reviewed

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.
Peer reviewed

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
Peer reviewed
* 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
Peer reviewed

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.
Peer reviewed

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
Peer reviewed

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
Peer reviewed

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.
Peer reviewed

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.
Peer reviewed

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).
Peer reviewed

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
Peer Reviewed verified by ORBi

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