Per anno

@article{unfairinequality-scidata,
    abstract = {This paper introduces a novel benchmark dataset designed to support fairness-oriented research in artificial intelligence within the educational domain. The dataset originates from longitudinal survey data collected by the Agencia Canaria de Calidad Universitaria y Evaluación Educativa, encompassing comprehensive information from students, families, and teachers across the Canary Islands, Spain. It includes detailed student profiles and academic trajectories, covering multiple years of academic performance outcomes. The original data is characterised by a high-dimensional and sparse feature space, which presents challenges for direct application in AI workflows. To address these challenges while minimising the risk of introducing bias during preprocessing, we provide a curated version of the dataset specifically tailored for AI applications. This version preserves the statistical properties of the original data and is accompanied by detailed documentation of the preprocessing steps, including strategies for dimensionality reduction and fairness preservation. The dataset is intended as a resource for the research community, enabling studies on fairness, predictive modeling, and educational analytics. We describe its structure, content, and preparation process.},
    apice = {UnfairinequalityScidata},
    articleno = 572,
    author = {Joseph Giovanelli and Magnini, Matteo and Ciatto, Giovanni and Angel S. Marrero and Andrea Borghesi and Gustavo A. Marrero and Calegari, Roberta},
    doi = {10.1038/s41597-026-06827-x},
    issn = {2052-4463},
    journal = {Scientific Data},
    month = mar,
    note = {Data Descriptor},
    numpages = 12,
    openalex = {W7133213904},
    publisher = {Nature Portfolio / Springer Nature},
    pubmed = {41771897},
    title = {Unfair Inequality in Education: A Benchmark for AI-Fairness Research},
    url = {https://doi.org/10.1038/s41597-026-06827-x},
    urlopenaccess = {https://doi.org/10.1038/s41597-026-06827-x},
    volume = 13,
    year = 2026
}

@inproceedings{nl2unifol-skilledllm2026,
    abstract = {Automatic translation of Natural Language (NL) sentences into logic representation – such as First-Order Logic (FOL) formulae – is a task of great interest for many communities, including Knowledge Representation (KR), Natural Language Processing (NLP), normative Artificial Intelligence (AI), and AI in general. Recently, an increasing number of works have explored the use of Large Language Models (LLMs) to translate NL into FOL, with promising results. However, existing works mostly focus on the translation of a single NL sentence at a time, resulting in independent formulae that do not share a common predicate and constant name space. This work presents a new framework – namely Natural Language to Uniform First Order Logic (NL2UNIFOL) – that leverages LLMs to translate NL sentences into a uniform FOL theory, where formulae share the same vocabulary. NL2UNIFOL is an end-to-end pipeline that: (i) translates NL sentences into preliminary FOL formulae; (ii) identifies and safely merges predicate and constant names to obtain a uniform vocabulary; (iii) finally generates the final FOL theory. We use NL-FOL pairs from the MALLS dataset to validate our framework. Results show that in the majority of cases NL2UNIFOL is able to correctly create clusters of predicate and constant names to be merged together, which allows to obtain a uniform FOL theory. We also recognise that there are still a numerous amount of cases in which the represented name and the representative name semantically diverge, which motivates future work to further improve the symbol clustering process.},
    apice = {Nl2unifolSkilledllm2026},
    author = {Magnini, Matteo and Davide Liga and Luca Pasetto},
    booktitle = {Proceedings of the Joint Workshop on Statistics and Knowledge Integration for Logic, Learning, Ethical Decisions, and LLMs (SKILLED-LLMs 2026) co-located with the Federated Logic Conference 2026 (FLoC 2026), Lisbon, Portugal, July 18, 2026},
    dblp = {conf/skilled-llms/MagniniLP26},
    editor = {Ha Thanh Nguyen and Francesca Toni and Kostas Stathis and Ken Satoh and Randy Goebel and Francesco Chiariello and Yves Lespérance and Magnini, Matteo and Sabbatini, Federico and Elena Umili and Nourhan Ehab and Mervat Abu-Elkheir},
    issn = {1613-0073},
    keywords = {Natural Language Processing, First-Order Logic, Large Language Models, Knowledge Representation},
    numpages = 18,
    pages = {126--143},
    publisher = {Sun SITE Central Europe, RWTH Aachen University},
    series = {CEUR Workshop Proceedings},
    title = {NL2UNIFOL: From Natural Language Sentences to Uniform First-Order Logic Formulae},
    url = {https://ceur-ws.org/Vol-4229/paper13.pdf},
    urlopenaccess = {https://ceur-ws.org/Vol-4229/paper13.pdf},
    urlpdf = {https://ceur-ws.org/Vol-4229/paper13.pdf},
    volume = 4229,
    year = 2026
}

@inproceedings{slmmobiledevices-percomworkshops2026,
    abstract = {Large language models (LLMs) are increasingly being adopted across diverse healthcare scenarios. However, their deployment on mobile devices is hindered by significant resource demands and privacy concerns associated with cloud-based solutions. Ensuring reliable, private, and accessible healthcare support on mobile devices requires models that are both performant and lightweight. Therefore, small language models (SLMs) present a promising solution for enabling on-device healthcare support. This study explores the trade-offs between model size and performance necessary to effectively execute general medical question-answering tasks on mobile devices. To evaluate this, we present MedicoAI, a cross-platform application designed to support local SLMs inference across mobile, web, and desktop environments. We evaluated four state-of-the-art SLMs with model sizes under 1GB using two prompt templates (a standard baseline and one with medical safety constraints) and three word-limit configurations. Our findings highlight the viability of deploying SLMs for medical question-answering on mobile devices while maintaining user privacy and resource efficiency.},
    apice = {SlmmobiledevicesPercomworkshops2026},
    author = {Aqila Farahmand and Montagna, Sara and Alessandro Bogliolo and Stefano Ferretti and Magnini, Matteo},
    booktitle = {2026 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops)},
    dblp = {conf/percom/FarahmandMBFM26},
    doi = {10.1109/PerComWorkshops68308.2026.11585382},
    isbn = {979-8-3315-7615-8},
    keywords = {Small Language Models, Mobile Health, Patient Self-Management, Local Models, Privacy-preserving},
    month = {16-20 March},
    note = {5th International Workshop on Telemedicine and e-Health in the digital society (TELMED 2026)},
    numpages = 6,
    openalex = {W7167064743},
    pages = {1--6},
    publisher = {IEEE},
    title = {Performant and Small: Can We Have Both? SLMs on Mobile Devices for Healthcare Chatbots},
    url = {https://ieeexplore.ieee.org/document/11585382},
    urlpdf = {https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11585382},
    year = 2026
}

@incollection{neurosymbolic-woa25y,
    address = {Cham},
    apice = {NeurosymbolicWoa25Y},
    author = {Agiollo, Andrea and Calegari, Roberta and Ciatto, Giovanni and Magnini, Matteo and Omicini, Andrea and Sabbatini, Federico},
    booktitle = {The Agents Journey: Twenty-Five Years of Multi-agent Systems},
    chapter = 12,
    doi = {10.1007/978-3-032-22940-3_12},
    editor = {Mascardi, Viviana and Omicini, Andrea},
    eisbn = {978-3-032-22940-3},
    eissn = {1611-3349},
    iris = {11585/1061411},
    isbn = {978-3-032-22939-7},
    issn = {0302-9743},
    keywords = {Rational Agents, Logic Programming, Multi-agent Systems, Symbolic-Subsymbolic Integration},
    lens = {028-851-073-271-306},
    month = apr,
    numpages = 20,
    openalex = {W7155969644},
    pages = {320--339},
    publisher = {Springer Nature Switzerland},
    scholar = {21797340038813643},
    scopus = {2-s2.0-105041239279},
    series = {Lecture Notes in Computer Science},
    subseries = {State-of-the-Art Survey},
    title = {Intelligent Agents from Symbolic to Neurosymbolic Systems: The Quest for Integration},
    url = {https://link.springer.com/10.1007/978-3-032-22940-3_12},
    urlopenaccess = {https://link.springer.com/content/pdf/10.1007/978-3-032-22940-3_12.pdf},
    volume = 16395,
    year = 2026
}

@incollection{ageml-adbis2026,
    apice = {AgemlAdbis2026},
    author = {Matteini, Mattia and Magnini, Matteo and Francia, Matteo and Ciatto, Giovanni and Omicini, Andrea},
    booktitle = {Advances in Databases and Information Systems: 30th European Conference, ADBIS 2026, Orl\'{e}ans, France, September 28 -- October 1, 2026, Proceedings},
    chapter = 7,
    editor = {Genoveva Vargas-Solar and Kostas Stefanidis and Themis Palpanas and Patrick Marcel and Mirian Halfeld-Ferrari},
    eisbn = {978-3-032-39820-8},
    eissn = {1611-3349},
    isbn = {978-3-032-39819-2},
    issn = {0302-9743},
    keywords = {MLOps, Data-centric AI, Agentic AI, AutoML, LLMs},
    month = sep,
    numpages = 17,
    pages = {83--99},
    part = {Core Database Systems and Serialization},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    title = {Agentic Architecture for Data-Centric {AutoML}},
    url = {https://link.springer.com/chapter/10.1007/978-3-032-39820-8_7},
    urlpdf = {https://link.springer.com/content/pdf/10.1007/978-3-032-39820-8_7.pdf},
    volume = 16937,
    year = 2026
}