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Alma universitas studiorum parmensis A.D. 962 - Università di Parma Alma universitas studiorum parmensis A.D. 962 - Università di Parma
Department of
Engineering for Industrial Systems and Technologies
Università degli Studi di Parma

SoWIDE Group & IBIS Lab

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The research group operates at the Department of Engineering and Architecture, University of Parma. The group’s research activity is carried out in two laboratories: SoWIDE (Social web, intelligent and distributed systems engineering) and IBISLab (Intelligent Bio-Inspired Systems Laboratory, http://ibislab.ce.unipr.it). The group members do research on: Big Data, Artificial Intelligence, with particular regard to Distributed Systems, Social Network and Sentiment Analysis (SoWide); Biologically-inspired Computational Paradigms for the Optimization and Design of Artificial Intelligence systems, and Explainable AI (IBISLab). In particular, current research work deals with the following fields: 

  • Big data and social media analysis: natural language processing, sentiment analysis, emotion detection, information retrieval, troll detection, social network analysis, graph analytics, data mining.
  • Artificial intelligence: machine learning, swarm intelligence, evolutionary computing, neural network, semantic web, computer vision, pattern recognition, explainable Artificial Intelligence.
  • Distributed systems: multi-agent and actor-based systems, agent-based modelling and simulation, large-scale graph processing and simulations, peer-to-peer social networks, trust management. 
  • Software development: agent-oriented and object-oriented programming, paradigms and languages, computational thinking, high performance computing.
  • Applications of soft computing: optimization and design of pattern recognition systems based on evolutionary/co-evolutionary computing and swarm intelligence paradigms
  • Performance optimization of metaheuristics based on High-Performance/GPU computing.

Recent Research Projects

  • Studio, progettazione e sviluppo di innovativa selezionatrice per la cernita del prodotto (ortaggi e frutta) in base a forma e colore, 2015-2018, finanziamento privato (PROTEC).
  • NOAH, 2016-2018, UE, http://www.noahproject.eu/
  • ENSAFE, 2015-2017, UE, https://www.ensafe-aal.com/home/
  • Supercomputing Unified Platform Emilia-Romagna (SUPER), 2019-2022, POR-FESR Emilia Romagna
  • Analisi dati, processi e tempi intraoperatori per l’ottimizzazione dei processi ospedalieri - ML-MED Tracking, 2019-2021, FIL 2019 (quota incentivante).
  • Generative adversarial networks and competitive co-Evolutionary algorithms for Image Synthesis, 2022-2024, FIL 2020 (quota incentivante) e Fondazione Cariparma.
  • Identification of Prognostic and Predictive Radio-Immune-Genomic Signatures in Small Cell Lung Cancer (SCLC) and Malignant Pleural Mesothelioma (MPM), 2022-2024, Bando di Ateneo 2021 per la ricerca - Action: A - Progetti di ricerca di consolidamento o scouting.

Selected publications

  1. Lombardo, G., Pellegrino, M., Tomaiuolo, M., Cagnoni, S., Mordonini, M., Giacobini, M., & Poggi, A. (2022). Fine-Grained Agent-Based Modeling to Predict Covid-19 Spreading and Effect of Policies in Large-Scale Scenarios. IEEE Journal of Biomedical and Health Informatics., vol. 26, no. 5, pp. 2052-2062, doi: 10.1109/JBHI.2022.3160243.
  2. Lombardo, G., Poggi, A., & Tomaiuolo, M. (2022). Continual representation learning for node classification in power-law graphs. Future Generation Computer Systems, 128, 420-428.
  3. Lombardo, G., Tomaiuolo, M., Mordonini, M., Codeluppi, G., & Poggi, A. (2022). Mobility in Unsupervised Word Embeddings for Knowledge Extraction—The Scholars’ Trajectories across Research Topics. Future Internet, 14(1), 25.
  4. Pellegrino, M. Lombardo, G. Cagnoni, S., & Poggi, A. (2022).  High-Performance Computing and ABMS for High-Resolution COVID-19 Spreading Simulation. Future Internet 2022, 14, 83.
  5. Bacardit, J., Brownlee, A., Cagnoni, S., Lacca, G., McCall, J., & Walker, D. (2022). The intersection of Evolutionary Computation and Explainable AI, Genetic and Evolutionary Computation Conference: GECCO'22.
  6. Adosoglou, G., Lombardo, G., & Pardalos, P.M. (2021). Neural network embeddings on corporate annual filings for portfolio selection. Expert Systems with Applications, 164, art. no. 114053. DOI: 10.1016/j.eswa.2020.114053
  7. Petrosino, G., Bergenti, F., Lombardo, G., Mordonini, M., Poggi, A., Tomaiuolo, M., & Cagnoni, S. (2021). Island model in ActoDatA: an actor-based implementation of a classical distributed evolutionary computation paradigm, Genetic and Evolutionary Computation Conference 2021, Companion Proceedings, pp. 1801-1808.
  8. Cagnoni, S., Cozzini, L., Lombardo, G., Mordonini, M., Poggi, A., & Tomaiuolo, M. (2020). Emotion-based analysis of programming languages on Stack Overflow. ICT Express, 6 (3)., pp. 238-242. DOI: 10.1016/j.icte.2020.07.002
  9. Tomaiuolo, M., Lombardo, G., Mordonini, M., Cagnoni, S., & Poggi, A. (2020). A survey on troll detection. Future Internet, 12 (2)., art. no. 31. DOI: 10.3390/fi12020031
  10. Bergenti, F., Caire, G., Monica, S., & Poggi, A. (2020). The first twenty years of agent-based software development with JADE. Autonomous Agents and Multi-Agent Systems, 34(2), 1-19. DOI: 10.1007/s10458-020-09460-z
  11. Tomaiuolo, M. (2020). Applicability of artificial intelligence models. Neural Computing and Applications, 32(19), 15279-15280. DOI: 10.1007/s00521-020-05265-z
  12. Bellini, V., Guzzon, M., Bigliardi, B., Mordonini, M., Filippelli, S., & Bignami, E. (2020). Artificial intelligence: a new tool in operating room management. Role of machine learning models in operating room optimization. Journal of medical systems, 44(1), 1-10. DOI: 10.1007/s10916-019-1512-1
  13. Magliani, F., Sani, L., Cagnoni, S., & Prati, A. (2020). Diffusion Parameters Analysis in a Content-Based Image Retrieval Task for Mobile Vision. Sensors, 20(16), 4449. DOI: 10.3390/s20164449.
  14. Ugolotti, R., Sani, L., & Cagnoni, S. (2019). What can we learn from multi-objective meta-optimization of Evolutionary Algorithms in continuous domains? Mathematics, 7(3), 232. DOI: 10.3390/math7030232
  15. Sani, L., Pecori, R., Mordonini, M., & Cagnoni, S. (2019). From complex system analysis to pattern recognition: Experimental assessment of an unsupervised feature extraction method based on the Relevance Index metrics. Computation, 7(3), 39. DOI: 10.3390/computation7030039 
  16. Bianchi, V., Bassoli, M., Lombardo, G., Fornacciari, P., Mordonini, M., & De Munari, I. (2019). IoT Wearable Sensor and Deep Learning: An Integrated Approach for Personalized Human Activity Recognition in a Smart Home Environment. IEEE Internet of Things Journal, 6 (5)., art. no. 8727452, pp. 8553-8562. DOI: 10.1109/JIOT.2019.2920283
  17. Lombardo, G., Fornacciari, P., Mordonini, M., Sani, L., & Tomaiuolo, M. (2019). A combined approach for the analysis of support groups on Facebook - the case of patients of hidradenitis suppurativa. Multimedia Tools and Applications, 78 (3), pp. 3321-3339. DOI: 10.1007/s11042-018-6512-5  
  18. Lombardo, G., Fornacciari, P., Mordonini, M., Tomaiuolo, M., & Poggi, A. (2019). A multi-agent architecture for data analysis. Future Internet, 11 (2)., art. no. 49. DOI: 10.3390/fi11020049
  19. Fornacciari, P., Mordonini, M., Poggi, A., Sani, L., & Tomaiuolo, M. (2018). A holistic system for troll detection on Twitter. Computers in Human Behavior, 89, pp. 258-268. DOI: 10.1016/j.chb.2018.08.008
  20. Villani M., Sani L., Pecori R., Amoretti M., Roli A., Mordonini M., Serra R., & Cagnoni S. (2018). An iterative information-theoretic approach to the detection of structures in complex systems. Complexity, 2018, art. no. 3687839. DOI: 10.1155/2018/3687839
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