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Abstract The healthcare sectors have constantly faced significant challenge due to the rapid rise of cyber threats. These threats can pose any potential risk within the system context and disrupt the critical healthcare service delivery. It is therefore necessary for the healthcare organisations to understand and tackle the threats to ensure overall security and resilience. However, threats are continuously evolved and there is large amount of unstructured security related textual information is available. This makes the threat assessment and management task very challenging. There are a number of existing works that consider Machine Learning models for detection and prediction of cyber attack but they lack of focus on the Natural Language Processing (NLP) to extract the threat information from unstructured security related text. To this end, this work proposes a novel method to assess and manage threats by adopting natural language processing. The proposed method has been tailored for the healthcare ecosystem and allows to identify and assess the possible threats within healthcare information infrastructure so that appropriate control and mitigation actions can be taken into consideration to tackle the threat. In detail, NLP techniques are used to extract the useful threat information related to specific assets of the healthcare ecosystems from the largely available security related information on Internet (e.g., cyber security news), to evaluate the level of the identified threats and to select the required mitigation actions. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for Biomedical Engineering (IBMT), considering in particular three different healthcare scenarios, namely implantable medical devices, wearables, and biobank, with the purpose of demonstrating the feasibility of our approach, which is able to provide a realistic manner to identify and assess the threats, evaluate the threat level and suggest the required mitigation actions.

Il contributo propone alcuni criteri di valutazione per l'analisi degli approcci all'editing testuale. Dopo averli presentati brevemente, si mostra come sono stati applicati alle attività finora svolte nel contesto del progetto ERC-885222 GreekSchools per quanto riguarda la progettazione e lo sviluppo di una piattaforma web collaborativa che fa uso di linguaggi specifici di dominio (DSL) finalizzata allo studio scientifico e alla pubblicazione di testi papiracei mediante edizioni critiche digitali.

Within the ERC AdG 885222-GreekSchools we aim to manage the edit of multiple papyrologic texts: diplomatic and literary editions and the corresponding apparatuses and their translations. To endow scholars with automatic consistency and coherence of editorial choices and to support the whole editing process, we leverage Domain Specific Languages (DSLs): a formal language definition in a bounded domain. Digital text editing can be handled in multiple ways depending on the editorial purpose. We identify four possible editing approaches to digital textual scholarship: (1) word processor; (2) structured text (e.g. XML); (3) GUI-centric; (4) domain specific language (DSL). Each of them has pros and cons. In particular we analyse five dimensions: familiarity, compactness, completeness, data elaboration support, and the need of technical training. With familiarity we refer to how much the scholar can avoid shifting his established working paradigm/environment. Compactness is the ratio between quantity of information and formalisation size. Completeness refers to the information the content represents. The capability to extract or deduce information from the data is addressed by data elaboration support. Finally, we consider important to evaluate the amount of technical training for text editing. For example, structured texts grant completeness of information, while requiring extensive technical training. In this context only the DSL approach encompasses all these dimensions while the other approaches compromises on some of them. We propose a DSL-based editor that will support and improve the editing workflow in the context of the ERC project.

Presentazione del team di lavoro e delle attività svolte in seno al progetto ERC 885222-GreekSchools durante il convegno di Papirologia Ercolanese organizzato dal 5 al 9 settembre 2022 presso il Castello Pasquini di Castiglioncello (Livorno).

A roadmap is proposed that defines a systematic approach for craft preservation and its evaluation. The proposed roadmap aims to deepen craft understanding so that blueprints of appropriate tools that support craft documentation, education, and training can be designed while achieving preservation through the stimulation and diversification of practitioner income. In addition to this roadmap, an evaluation strategy is proposed to validate the efficacy of the developed results and provide a benchmark for the efficacy of craft preservation approaches. The proposed contribution aims at the catalyzation of craft education and training with digital aids, widening access and engagement to crafts, economizing learning, increasing exercisability, and relaxing remoteness constraints in craft learning. Heritage, 6 (7) ISSN:2571-9408

L’articolo racconta l’esperienza del D4GNA - Dataset per il Geoportale Nazionale dell’Archeologia nato nell’ambito del progetto ARIADNEplus (Advanced Research Infrastructure for Archaeological Dataset Networking in Europe - plus), conclusosi lo scorso 31 dicembre. Il contributo parte dal contesto più ampio del progetto europeo per addentrarsi nel mondo dei dati archeologici italiani; la soluzione tecnologica, la standardizzazione, la dematerializzazione e la condivisione in rete dei dati sono i temi toccati in questo percorso che ci illustra il procedere verso un obiettivo virtuoso: il Geoportale Nazionale per l’Archeologia (GNA). Il GNA, realizzato dall’Istituto Centrale per l’Archeologia (ICA) e che sarà in rete dal 10 luglio 2023, è il punto di accesso nazionale per accogliere e consultare gli interventi archeologici svolti sotto la direzione scientifica del Ministero della Cultura (MiC), le indagini archeologiche condotte da università e altri enti di ricerca, nonché altre banche dati territoriali.

AbstractSeveral types of (mostly) blue-green glass beads from Iron-Age archaeological sites in Central Italy were studied using a range of spectroscopic techniques: portable X-Ray Fluorescence spectrometry, Fibre Optics Reflectance Spectroscopy, Scanning Electron Microscopy coupled with Energy Dispersive X-ray Spectrometry, micro-Raman spectroscopy and Laser Ablation Inductively Coupled Plasma Mass Spectrometry. Complementary information was gathered from each technique and discussed in the frame of the archaeological typology of the objects. The systematic evaluation of the results allowed us to draw some conclusions on the raw materials employed for primary production and to highlight some provenance indicators in the glass. Some of the beads found in the Iron Age (IA) contexts were preliminarily attributed to the Final Bronze Age (FBA) production based on their typology, and the compositional data obtained in this work confirmed that they were low magnesium high potassium (LMHK) glass, typical of FBA in the Italian peninsula. Other beads were assigned to low magnesium glass (LMG) or high magnesium glass (HMG), thus giving further information on the fluxing agents employed in the Early Iron Age (EIA) and beyond. Colour variations among the beads reflected their chemical composition, with different bead typologies coloured in a specific way. In some instances, it was possible to establish different origins for the colouring raw materials. The provenance of the samples was difficult to place, but the chemical evidence suggested a subdivision within the raw glass used to produce the beads: for one set of samples, a local origin of the glass could be hypothesised, whereas several production sites in the Near East were suggested for most of the beads considered in this study. Some preliminary clues for the local working of imported glass were also highlighted for one typological group.