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Once upon a Time, in a computer engineering department ….

SIGMOD Record 2024年第4期52卷 36-38页

作者： Catarci, Tiziana Department of Computer Control and Management Engineering Sapienza University of Roma Italy

When you decide to pursue a career in the academy, you may encounter many difficulties, obstacles, and moments of discouragement, but also have great satisfaction, exaltation, and a lot of fun. By the time you get to the mid-career period, many doubts and problems are behind you (if you are still in the academy and did not give up) and a less stressful time begins with even more freedom to study, create your own group, and choose your own ventures. This is also the time when you may decide to impact the society, not just work for yourself, but do it to contribute to the creation of a better world and be a positive example for the younger generation. © 2024 Association for Computing Machinery. All rights reserved.

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Prototype-Based Interpretable Graph Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1486-1495页

作者： Ragno, Alessio Rosa, Biagio La Capobianco, Roberto Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Sony AI Zurich8952 Switzerland

Graph neural networks have proved to be a key tool for dealing with many problems and domains, such as chemistry, natural language processing, and social networks. While the structure of the layers is simple, it is difficult to identify the patterns learned by the graph neural network. Several works propose post hoc methods to explain graph predictions, but few of them try to generate interpretable models. Conversely, the topic of the interpretable models is highly investigated in image recognition. Given the similarity between image and graph domains, we analyze the adaptability of prototype-based neural networks for graph and node classification. In particular, we investigate the use of two interpretable networks, ProtoPNet and TesNet, in the graph domain. We show that the adapted networks manage to reach better or higher accuracy scores than their respective black-box models and comparable performances with state-of-the-art self-explainable models. Showing how to extract ProtoPNet and TesNet explanations on graph neural networks, we further study how to obtain global and local explanations for the trained models. We then evaluate the explanations of the interpretable models by comparing them with post hoc approaches and self-explainable models. Our findings show that the application of TesNet and ProtoPNet to the graph domain produces qualitative predictions while improving their reliability and transparency. © 2020 IEEE.

关键词： computer architecture

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A Real-Time Machine Learning Based Solution for Privacy Enforcement in Video Recordings and Live Streaming 9

A Real-Time Machine Learning Based Solution for Privacy Enfo...

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9th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2024

作者： Conforti, Pietro Manganelli Emanuele, Matteo Mandelli, Lorenzo Department of Computer Control and Management Engineering Sapienza University of Rome Italy

These past years the world had to deal with a whole new situation brought by Covid-19. Everyone’s routine changed and we started passing way more time than before on virtual meeting, virtual chats and similar. With this, many privacy problems arised from all the video data generated by a single user. Google and Zoom introduced the possibility to blur out the background while using a front face camera, but this did not solve many privacy concerns ranging from showing people in videos without their permission, to the leaking of sensible data and information from videos uploaded online. We propose a solution build over the use of computer vision techniques like image segmentation and classification for context recognition for a privacy enforcement solution capable of fitting the user’s personal need, blurring out selectively specific objects from a video based on the user’s preferences for each room in which they are. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Differential privacy

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DanteLLM: Let's Push Italian LLM Research Forward! 30

DanteLLM: Let's Push Italian LLM Research Forward!

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bacciu, Andrea Campagnano, Cesare Trappolini, Giovanni Silvestri, Fabrizio Department of Computer Control and Management Engineering Sapienza University of Rome Italy

ISBN: (纸本)9782493814104

In recent years, the dominance of Large Language Models (LLMs) in the English language has become evident. However, there remains a pronounced gap in resources and evaluation tools tailored for non-English languages, underscoring a significant disparity in the global AI landscape. This paper seeks to bridge this gap, specifically focusing on the Italian linguistic context. We introduce a novel benchmark, and an open LLM Leaderboard, designed to evaluate LLMs' performance in Italian, providing a rigorous framework for comparative analysis. In our assessment of currently available models, we highlight their respective strengths and limitations against this standard. Crucially, we propose "DanteLLM", a state-of-the-art LLM dedicated to Italian. Our empirical evaluations underscore Dante's superiority, as it emerges as the most performant model on our benchmark, with improvements by up to 6 points. This research not only marks a significant stride in Italian-centric natural language processing but also offers a blueprint for the development and evaluation of LLMs in other languages, championing a more inclusive AI paradigm. Our code at: https://***/RSTLess-research/DanteLLM. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Benchmarking

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Conversational AI for Framed Autonomy in AI-augmented Business Process management 22

Conversational AI for Framed Autonomy in AI-augmented Busine...

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Best Dissertation Award, Doctoral Consortium, and Demonstration and Resources Forum at 22nd International Conference on Business Process management, BPM-D 2024

作者： Casciani, Angelo Department of Computer Control and Management Engineering Sapienza University of Rome Italy

AI-augmented Business Process management Systems (ABPMSs) represent an emerging category of process-aware information systems driven by AI technology. These systems autonomously manage the execution flow of business processes (BPs) within predefined frames, encompassing procedural and declarative specifications, which may sometimes conflict. Despite operating autonomously within these boundaries, ABPMSs require dynamic conversations with human agents. These conversations not only respond to user queries but also initiate discussions to inform them of BP progression and provide recommendations for performance improvement. This research proposal aims to leverage Conversational AI to support the ABPMS’s framed autonomy, functioning as a Decision Support System (DSS). This involves explaining system’s choices and suggesting actions when constraints are violated. This technique enables intelligent, context-aware interactions with ABPMSs, fostering user trust. Our findings indicate that Conversational AI has the potential to significantly enhance the interpretability and usability of ABPMSs, thereby facilitating improved decision-making and process optimization. © 2024 Copyright for this paper by its authors.

关键词： Chatbots

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Unknown Input Observer Design for a Class of Semilinear Hyperbolic Systems with Dynamic Boundary Conditions

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IEEE Transactions on Automatic control 2023年第9期68卷 5721-5728页

作者： Cristofaro, Andrea Ferrante, Francesco Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy University of Perugia Department of Engineering Perugia06125 Italy

The problem of unknown input observer design is considered for coupled PDE/ODE systems subject to incremental sector bounded nonlinearities and unknown boundary inputs. Assuming available measurements at the boundary of the distributed domain, the synthesis of the unknown input observer is based on Lyapunov methods and recast in terms of some matrix inequalities. Numerical simulations support and confirm the theoretical findings, illustrating the robust estimation performances of the proposed nonlinear unknown input observer. © 1963-2012 IEEE.

关键词： Linear matrix inequalities

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AdaLightLog: Enhancing Application Logs Anomaly Detection via Adaptive Federating Learning 19th

AdaLightLog: Enhancing Application Logs Anomaly Detection vi...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Menegatti, Danilo De Santis, Emanuele Felli, Stefano Giuseppi, Alessandro Department of Computer Control and Management Engineering ‘Antonio Ruberti’ Sapienza University of Rome Rome Italy

ISBN: (纸本)9783031842597

The role of anomaly detection systems in Critical Infrastructures (CIs) is critical due to the complexity of CIs and their control systems, which are usually implemented by computer-based controllers that constantly produce logs of their activities. Moreover, many CIs, located in different locations or even belonging to different companies, may share similar application software for controlling the CIs themselves. The goal of this work is to use such logs to perform automatic anomaly detection in a federated learning (FL) paradigm, which ensures that no data is exchanged between sites to train the anomaly detection models, but each learning agent learns on its own data, leveraging the knowledge acquired by the other agents. Our proposed approach - AdaLightLog - which implements a modified FL paradigm with adaptive loss functions at local servers side and weighted averaging of local server models, so to differentiate the quality of the different local servers’ models in the global averaging, is tested against state-of-the-art methods and shows an improvement in performance in terms of accuracy, precision and recall with respect to the standard FL implementation (FedAvg). Furthermore, a comparison between different metrics for the adaptive loss functions and the dynamic weights is presented. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Green hydrogen as a sustainable operations strategy: A socio-economic perspective

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Sustainable Operations and computers 2025年第1期6卷 1-14页

作者： Bonesso, Francesco D'Adamo, Idiano Gastaldi, Massimo Giannini, Marco Sapienza University of Rome Rome Italy Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Department of Industrial and Information Engineering and Economics University of L'Aquila L'Aquila Italy

Hydrogen is an energy carrier that can support the development of sustainable and flexible energy systems. However, decarbonization can occur when green sources are used for energy production and appropriate water use is manifested. This work aims to propose a socio-economic analysis of hydrogen production from an integrated wind and electrolysis plant in southern Italy. The estimated production amounts to about 1.8 million kg and the LCOH is calculated to be 3.60 €/kg in the base scenario. Analyses of the alternative scenarios allow us to observe that with a high probability the value ranges between 3.20–4.00 €/kg and that the capacity factor is the factor that most affects the economic results. Social analysis, conducted through an online survey, shows a strong knowledge gap as only 27.5 % claim to know the difference between green and grey hydrogen. There is a slight propensity to install systems near their homes, but this tends to increase due to increased knowledge on the topic. Respondents state sustainable behaviours, and this study suggests that these aspects should also be transformed into the energy choices that are implemented every day. The study suggests information to policy-makers, businesses and citizens as it outlines that green hydrogen is an operations strategy that moves toward sustainable development. © 2024 The Author(s)

关键词： Green development

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Dynamic Mode Decomposition for Individualized Model Predictive control with Application to Type 1 Diabetes 32

Dynamic Mode Decomposition for Individualized Model Predicti...

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32nd Mediterranean Conference on control and Automation, MED 2024

作者： Becchetti, Valentina Atanasious, Mohab M.H. Menegatti, Danilo Baldisseri, Federico Giuseppi, Alessandro Sapienza University of Rome Department of Computer Control and Management Engineering 'Antonio Ruberti' Italy

ISBN: (纸本)9798350395440

Type 1 diabetes is one of the main issues in current medical research. If not properly managed, it can lead to serious long-term complications such as blindness, stroke, coma and, ultimately, death. In recent years, research endeavors have focused on the development of a fully automated system for glucose control through automated insulin injections, known as Artificial Pancreas, which, however, is not yet robust enough for practical use. Currently, the UVA/Padova simulator provides the most accurate model for effectively representing the insulin-glucose dynamics of individual diabetic patients. As it is highly complex, consisting of 18 strongly nonlinear and time-varying equations, the objective of this work is to linearize it and reduce its dimensionality exploiting Dynamic Mode Decomposition with control. This allows for the synthesis of a controller based on Linear Model Predictive control to be applied to the original model. This is justified by the need to integrate the control algorithm into portable devices with limited computational capabilities. The proposed dimensionality reduction method is validated through in-vitro simulations showing that an accurate representation of the system dynamics is effectively preserved, while conveniently reducing required computational power. © 2024 IEEE.

关键词： Dynamic mode decomposition

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Multi-dimensional networks as a tool to model, analyze, and interpret multi-subject brain connectivity in hyperscanning settings

Multi-dimensional networks as a tool to model, analyze, and ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Puxeddu, Maria Grazia Rinaldini, Greta Astolfi, Laura University of Rome La Sapienza Department of Computer Control and Management Engineering Rome Italy

ISBN: (纸本)9798350386226

Social interactions are woven in the fabric of our lives, from birth to adulthood. Studies show that when social connections are lacking, there is a high probability of developing poor mental and physical health. Conversely, some mental disorders result in a compromised ability to form these interactions, with autism being a prime example. For these reasons, understanding the neural mechanisms underlying social interactions is crucial. Hyperscanning experiments, where brain activity is simultaneously recorded from multiple subjects during social tasks, have emerged as a powerful tool for tackling this issue, and the study of brain-to-brain connectivity has provided significant insights into social neuroscience. However, no agreed-upon methods exist for analyzing and interpreting such data, and most of the analyses still rely on techniques designed for single-subject experiments. Here, we propose a framework to model brain-to-brain connectivity through multi-dimensional networks. Under this conceptualization, both intra- and inter-brain connectivity are viewed as components of a higher-order network that can be studied by extending graph theory indices to a multidimensional case. In this article, we also discuss how these indices can be interpreted in the context of multi-subject networks. Future investigations will apply this framework to hyperscanning datasets to provide new insights into the neural circuits associated with social interactions. © 2024 IEEE.

关键词： Diseases

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