Antici, F.; Borghesi, A.; Kiziltan, Z.; Domke, J.; Bartolini, A., An online algorithm for power consumption prediction of HPC workload, «FUTURE GENERATION COMPUTER SYSTEMS», 2026, 175, Article number: 108064 , pp. 1 - 14 [Scientific article]Open Access
Manhaeve, Robin; Giannini, Francesco; Ali, Mehdi; Azzolini, Damiano; Bizzarri, Alice; Borghesi, Andrea; Bortolotti, Samuele; De Raedt, Luc; Dhami, Devendra; Diligenti, Michelangelo; Dumančić, Sebastijan; Faltings, Boi; Gentili, Elisabetta; Gerevini, Alfonso; Gori, Marco; Guns, Tias; Homola, Martin; Kersting, Kristian; Lehmann, Jens; Lombardi, Michele; Lorello, Luca; Marconato, Emanuele; Melacci, Stefano; Passerini, Andrea; Paul, Debjit; Riguzzi, Fabrizio; Teso, Stefano; Yorke-Smith, Neil; Lippi, Marco, Benchmarking in Neuro-Symbolic AI, in: Lecture Notes in Computer Science, Springer Science and Business Media Deutschland GmbH, 2026, 16059 LNAI, pp. 238 - 249 (atti di: 4th International Joint Conference on Learning and Reasoning, IJCLR 2024, and 33rd International Conference on Inductive Logic Programming, ILP 2024, chn, 2024) [Contribution to conference proceedings]
Loreti, Daniela; Leone, Davide; Borghesi, Andrea, Duration-Informed Workload Scheduler, in: Lecture Notes in Computer Science, Springer Cham, 2026, 16091 LNCS, pp. 3 - 14 (atti di: 40th International Conference on High Performance Computing, ISC High Performance 2025, Hamburg, Germany, 10-13 June 2025) [Contribution to conference proceedings]
Bellatreccia, C.; Zama, D.; Dondi, A.; Pierantoni, L.; Laura, A.; Neri, I.; Lanari, M.; Borghesi, A.; Calegari, R., Addressing Bias and Data Scarcity in AI-Based Skin Disease Diagnosis with Non-Dermoscopic Images, in: CEUR Workshop Proceedings, CEUR-WS, 2025, 3961, pp. 1 - 14 (atti di: 2nd Workshop on AI Bias: Measurements, Mitigation, Explanation Strategies, AIMMES 2025, Spain, 2025) [Contribution to conference proceedings]Open Access
Ciatto, G.; Matteini, M.; Sartori, L.; Rebrean, M.; Muller, C.; Borghesi, A.; Calegari, R., AI-fairness: the FAIRBRIDGE approach to practically bridge the gap between socio-legal and technical perspectives, in: Proceedings of the Annual Hawaii International Conference on System Sciences, Piscataway, NJ, IEEE Computer Society, 2025, pp. 6504 - 6513 (atti di: 58th Hawaii International Conference on System Sciences, HICSS 2025, island o maui (USA), 2025) [Contribution to conference proceedings]Open Access
Artioli, Marcello; Borghesi, Andrea; Chinnici, Marta; Ciampolini, Anna; Colonna, Michele; De Chiara, Davide; Loreti, Daniela, C6EnPLS: A High-Performance Computing Job Dataset for the Analysis of Linear Solvers’ Power Consumption, «FUTURE INTERNET», 2025, 17, Article number: 203 , pp. 1 - 18 [Scientific article]Open Access
Farooq, Emmen; Milano, Michela; Borghesi, Andrea, Federated transfer learning for anomaly detection in HPC systems: First real-world validation on a tier-0 supercomputer, «EXPERT SYSTEMS WITH APPLICATIONS», 2025, 298, Article number: 129754 , pp. 1 - 15 [Scientific article]
Antici, F; Borghesi, A; Domke, J; Kiziltan, Z, UoPC: A User-Based Online Framework to Predict Job Power Consumption in HPC Systems, in: ISC High Performance 2025 Research Paper Proceedings (40th International Conference), 2025, pp. 1 - 12 (atti di: ISC High Performance 2025 (40th International Conference), Hamburg, Germany, 10-13 June 2025) [Contribution to conference proceedings]
Borghesi, Andrea; Calegari, Roberta, Generation of Clinical Skin Images with Pathology with Scarce Data, in: AI for Health Equity and Fairness, 2024, 1164, pp. 47 - 64 (atti di: THE 8th INTERNATIONAL WORKSHOP ON HEALTH INTELLIGENCE, Vancouver, Canada, 27-27 Febbraio 2024) [Contribution to conference proceedings]Open Access
Molan, M.; Seyedkazemi Ardebili, Mohsen; Khan, J. A.; Beneventi, F.; Cesarini, D.; Borghesi, A.; Bartolini, A., GRAAFE: GRaph anomaly anticipation framework for exascale HPC systems, «FUTURE GENERATION COMPUTER SYSTEMS», 2024, 160, pp. 644 - 653 [Scientific article]Open Access
Farooq, Emmen; Milano, Michela; Borghesi, Andrea, Harnessing federated learning for anomaly detection in supercomputer nodes, «FUTURE GENERATION COMPUTER SYSTEMS», 2024, 161, pp. 673 - 685 [Scientific article]Open Access
Farooq, Emmen; Borghesi, Andrea, LSTM-Based Unsupervised Anomaly Detection in High-Performance Computing: A Federated Learning Approach, in: 2024 IEEE International Conference on Big Data (BigData), 2024, pp. 7735 - 7744 (atti di: 2024 IEEE International Conference on Big Data (BigData), Washington, DC, USA, 15-18 December 2024) [Contribution to conference proceedings]Open Access
De Filippo A.; Di Giacomo E.; Borghesi A., Machine learning approaches to predict the execution time of the meteorological simulation software COSMO, «JOURNAL OF INTELLIGENT INFORMATION SYSTEMS», 2024, x, pp. 1 - 25 [Scientific article]
Antici, F.; Borghesi, A.; Kiziltan, Z., Online Job Failure Prediction in an HPC System, in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, Springer Science and Business Media Deutschland GmbH, 2024, 14352, pp. 167 - 179 (atti di: International workshops held at the 29th International Conference on Parallel and Distributed Computing, Euro-Par 2023, cyprus, 2023) [Contribution to conference proceedings]
Zama, D.; Borghesi, A.; Ranieri, A.; Manieri, E.; Pierantoni, L.; Andreozzi, L.; Dondi, A.; Neri, I.; Lanari, M.; Calegari, R., Perspectives and Challenges of Telemedicine and Artificial Intelligence in Pediatric Dermatology, «CHILDREN», 2024, 11, Article number: 1401 , pp. 1 - 21 [Scientific article]Open Access
