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. |
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. Intelligent Systems and Applications, 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 Dataset: 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. 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 |