GiusBERTo: Italy’s AI-Based Judicial Transformation: A Teaching Case

In an age when open access to law enforcement files and judicial documents can erode individual privacy and confidentiality, miscreants can abuse this open access to personal information for blackmail, misinformation, and even social engineering. Yet, limiting access to law enforcement and court cases is a freedom-of-information violation. To address this tension, this collaborative action-research-based teaching case exemplifies how Italy’s Corte dei Conti (Court of Auditors) used artificial intelligence in the automated deidentification and anonymization of court documents in Italy’s public sector. This teaching case is aimed at undergraduate and graduate students learning about Artificial Intelligence (AI), Large Language Model (LLM) (e.g., ChatGPT) evolution, development, and operations. The case will help students learn the origin and evolution of AI transformer models and architectures, and discusses the GiusBERTo operation and process, highlighting opportunities and challenges. GiusBERTo, Italy’s custom-AI model, offers an innovative approach that walks a tightrope between anonymizing Italy’s judicial court documents without sacrificing context or information loss. The case ends with a series of questions, challenges, and potential for LLMs in data anonymization

I progressi dell’ICT per sistemi ferroviari basati su computer

La digitalizzazione dell'industria ferroviaria è favorita dalle nuove tecnologie ICT, le quali hanno un impatto significativo sui sistemi informatici ferroviari. Il lavoro di questa tesi approfondisce, su differenti livelli, tre aspetti legati alla digitalizzazione dei sistemi ferroviari basati su computer. Il primo argomento esamina l’introduzione dei metodi formali nel ciclo di sviluppo di sistemi safety-critical, partendo da un processo di sviluppo basato su logica a relè. Nella tesi, vengono discussi i limiti dell’attuale processo di progettazione, e viene esaminata una metodologia per l’introduzione di metodi formali nel processo di sviluppo per il sottosistema Interlocking. Per la progettazione del sistema, questa metodologia adotta i modelli Statechart e il linguaggio Temporal Logic for Actions (TLA +) per la specifica e verifica formale. Lo strumento BLExtractor proposto, produce codice eseguibile in forma booleana, a partire dai modelli Statechart. Le sperimentazioni sono state condotte applicando la metodologia proposta su casi di studio reali per la progettazione di logiche del sistema di interlocking. Il secondo aspetto è legato alla caratterizzazione delle tecnologie necessarie per l’abilitazione della prossima generazione di Fabbrica del Futuro nel contesto dell'Industria 4.0. La tesi riporta un confronto tra Virtual Factory, Digital Factory e Cloud Manufacturing che esamina l'interoperabilità, i processi e le tecnologie dei paradigmi per abilitarne la produzione in rete. Inoltre, viene riportato uno studio sulla perdita della Qualità del Servizio (QoS) nella composizione del servizio cloud per il Cloud Manufacturing con l’obiettivo di stimare un indice di compromesso tra l'ottimalità della QoS e i vincoli di produzione in cloud. Quindi, sono riviste le applicazioni allo stato dell’arte dei sistemi basati su agenti studiandone il grado di maturità per l’applicazione nell’ambito della fabbrica digitale. L'ultimo argomento riguarda l'applicazione delle tecnologie Big Data per l'analisi di dati IoT ferroviari. La tesi illustra la proposta di un'infrastruttura Big Data per raccogliere, elaborare e analizzare i dati prodotti dagli oggetti che comandano il piano ferroviario. L'architettura proposta è stata implementata utilizzando containers. Le sperimentazioni e le valutazioni dei modelli utilizzano i dati raccolti da una linea ferroviaria esistente. Viene, quindi, proposto un modello di failure prediction per rilevare e prevedere i guasti dei punti di scambio ferroviario.The railway industry's digitalization is enabled by new ICT trends, which significantly impact traditional railway computer-based systems. The work of this thesis covers three aspects related to the digitalization of railways systems at different levels. The first topic introduces formal methods for developing railways safety-critical systems, starting from a relay-based development process. The challenges that emerged from changing the development process model are discussed in the thesis; thus, a methodology for introducing formal methods into an existing development process of an interlocking system is examined. This methodology adopts Statechart models for system design and the Temporal Logic for Actions (TLA+) language for formal verification. The proposed BLExtractor tool produces executable code in the boolean form, starting from Statechart models. The second aspect is related to the characterization of core technologies to enable the “Factory of The Future” in the context of Industry 4.0. The thesis reports a comparison between Virtual Factory, Digital Factory, and Cloud Manufacturing examining paradigms' interoperability, processes, and technologies to enable networked manufacturing. Moreover, a study on QoS loss in cloud service composition for Cloud Manufacturing with the aim to measure a trade-off between QoS optimality and manufacturing constraints on the cloud is included. In addition, the state-of-art applications of agent-based systems are reviewed by studying its maturity for the applicability into the digital factory context. The last topic regards the application of Big Data technologies for the analysis of railway IoT data. The thesis illustrates the Big Data infrastructure that has been built to collect, process, and analyze data produced by objects composing the railway yard. The proposed architecture has been deployed using containers. Experimentations and model evaluations employ data collected from an existing railway line. A failure prediction model is, then, proposed for detecting and predicting failures of railway switch points

The Future of Factories: Different Trends

The technological advancements promote the rise of the fourth industrial revolution, where key terms are efficiency, innovation, and enterprises’ digitalization. Market globalization, product mass customization, and more complex products need to reflect on changing the actual design methods and developing business processes and methodologies that have to be data-driven, AI-assisted, smart, and service-oriented. Therefore, there is a great interest in experimenting with emerging technologies and evaluating how they impact the actual business processes. This paper reports a comparison among the major trends in the digitalization of a Factory of the Future, in conjunction with the two major strategic programs of Industry 4.0 and China 2025. We have focused on these two programs because we have had experience with them in the context of the FIRST H2020 project. European industrialists identify the radical change in the traditional manufacturing production process as the rise of Industry 4.0. Conversely, China mainland launched its strategic plan in China 2025 to promote smart manufacturing to digitalize traditional manufacturing processes. The main contribution of this review paper is to report about a study, conducted and part of the aforementioned FIRST project, which aimed to investigate major trends in applying for both programs in terms of technologies and their applications for the factory’s digitalization. In particular, our analysis consists of the comparison between Digital Factory, Virtual Factory, Smart Manufacturing, and Cloud Manufacturing. We analyzed their essential characteristics, the operational boundaries, the employed technologies, and the interoperability offered at each factory level for each paradigm. Based on this analysis, we report the building blocks in terms of essential technologies required to develop the next generation of a factory of the future, as well as some of the interoperability challenges at a different scale, for enabling inter-factories communications between heterogeneous entities

Induction of Autophagy Promotes Clearance of RHOP23H Aggregates and Protects From Retinal Degeneration

Autophagy is a critical metabolic process that acts as a major self-digestion and recycling pathway contributing to maintain cellular homeostasis. An emerging field of research supports the therapeutic modulation of autophagy for treating human neurodegenerative disorders, in which toxic aggregates are accumulated in neurons. Our previous study identified Ezrin protein as an inhibitor of autophagy and lysosomal functions in the retina; thus, in turn, identifying it as a potential pharmacological target for increasing retinal cell clearance to treat inherited retinal dystrophies in which misfolded proteins have accumulated. This study aimed to verify the therapeutic inhibition of Ezrin to induce clearance of toxic aggregates in a mouse model for a dominant form of retinitis pigmentosa (i.e., RHOP23H/+). We found that daily inhibition of Ezrin significantly decreased the accumulation of misfolded RHOP23H aggregates. Remarkably, induction of autophagy, by a drug-mediated pulsatile inhibition of Ezrin, promoted the lysosomal clearance of disease-linked RHOP23H aggregates. This was accompanied with a reduction of endoplasmic reticulum (ER)-stress, robust decrease of photoreceptors' cell death, amelioration in both retinal morphology and function culminating in a better preservation of vision. Our study opens new perspectives for a pulsatile pharmacological induction of autophagy as a mutation-independent therapy paving the way toward a more effective therapeutic strategy to treat these devastating retinal disorders due to an accumulation of intracellular toxic aggregates