Leonardo Scommegna (2023). Taming Runtime Dependencies across Transient Stateful Components in the Business Logic of Software Architectures. (Tutor: Enrico Vicario)

  Carnevali L.; Paolieri M.; Reali R.; Scommegna L.; Tammaro F.; Vicario E. (2023). Using the ORIS Tool and the SIRIO Library for Model-Driven Engineering of Quantitative Analytics. In: 18th European Workshop on Computer Performance Engineering, EPEW 2022, esp, 2022, Springer Science and Business Media Deutschland GmbH, vol. 13659, pp. 200-215, ISBN:978-3-031-25048-4 DOI  

  Picano B, Scommegna L, Vicario E, Fantacci R. (2023). Echo State Learning for User Trajectory Prediction to Minimize Online Game Breaks in 6G Terahertz Networks. JOURNAL OF SENSOR AND ACTUATOR NETWORKS, pp. 1-10, ISSN:2224-2708 DOI  

  Carnevali L.; Paolieri M.; Reali R.; Scommegna L.; Vicario E. (2022). A Markov Regenerative Model of Software Rejuvenation Beyond the Enabling Restriction. In: 33rd IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2022, USA, 2022, Institute of Electrical and Electronics Engineers Inc., pp. 138-145, ISBN:978-1-6654-7679-9 DOI  

  Capobianco, Samuele*; Scommegna, Leonardo; Marinai, Simone (2018). Historical handwritten document segmentation by using a weighted loss. In: 8th IAPR TC3 workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2018, ita, 2018, Springer Verlag, vol. 11081, pp. 395-406, ISBN:9783319999777 DOI   Accesso ONLINE all'editore