According to the EU State of Cybersecurity report by the European Union Agency for Cybersecurity (ENISA), the number of cybersecurity-related incidents will increase by 24 percent by 2025, with ransomware and DoS/DDoS attacks being the most common. The emergence of new threats [1] and the consolidation of existing ones require doubling of efforts in proactive prevention and a decisive increase in research dedicated to cybersecurity. CyberNEMO (End-to-end Cybersecurity to NEMO meta-OS) project emerges as an evolution of the NEMO (Next Generation Meta Operating System) platform, designed to provide a secure, trustworthy, and robust execution environment across the IoT-Edge-Cloud computing continuum. Leveraging NEMO modular meta-operating system (mOS) framework, CyberNEMO introduces advanced cybersecurity and privacy-preserving mechanisms, emphasizing Zero Trust principles. This paper presents the CyberNEMO architecture, details its core innovative technologies, and describes its validation strategy through diverse living labs-including Smart Energy, Smart Water, Smart Manufacturing, Healthcare, Multimedia Distribution, and Smart Farming scenarios-demonstrating end-to-end cybersecurity and real-time threat mitigation capabilities, aligned with Europe's strategic cybersecurity goals.

As society is continuously adapting to technological change and progress, fast-moving digital transformations are the driving force for setting the necessary skillsets for the workforce. Furthermore, the advent of Industry 5.0 as a defining concept for the future, which advocates a human-centric coalescence of humans and technology or software, renders the skilled workforce the most important asset in any organization or business. The endgame of the digital transformation is to evoke the reshaping, evolution, or replacement of traditional and possibly obsolete processes at intra- or inter-organizational levels in multiple aspects, introducing innovative ways of re-defining the workforce. In this context SKILLAB will act as a smart tool for handling, honing, and widening the competencies of the personnel of companies, forecasting future skill gaps and providing European citizens with a tool for upskilling and reskilling.

Industries generate and collect a huge amount of data about their processes. Usually such data contain relevant information, which can be used to monitor and analyze the processes, but also improve them, applying optimization techniques that allow to enhance different aspects, such as machine maintenance scheduling, product quality, use of resources and so on and so forth. The Digital Twin (DT) concept has been recently applied in the context of Industry 4.0, to exploit this data, leveraging advanced physical modelling, data analysis, Artificial Intelligence (AI) algorithms for optimization and prediction, which are two key concepts in the Industry 4.0 paradigm. Currently, the major challenge in this field is to make these techniques available for the industries and ensure their ease of use for the final users. This paper presents an holistic solution that combining two open-source software - i.e., REclaim oPtimization and simuLatIon Cooperation in digitAl twin (REPLICA) and Optimization Platform for Refurbishment and Re-manufacturing (OPR2) - provides a flexible, open and easy-to-use AI environment that allows data scientists to create, test, connect and deploy their algorithms and to optimize them. This work presents a first prototype of this solution, then describes how it has been tested and validated with real industry data and finally provides the results obtained with these tests.

AI is one of the biggest megatrends towards the 4th industrial revolution. Although these technologies promise business sustainability as well as product and process quality, it seems that the ever-changing market demands, the complexity of technologies and fair concerns about privacy, impede broad application and reuse of Artificial Intelligence (AI) models across the industry. To break the entry barriers for these technologies and unleash its full potential, the knowlEdge project will develop a new generation of AI methods, systems, and data management infrastructure. Subsequently, as part of the knowlEdge project we propose several major innovations in the areas of data management, data analytics and knowledge management including (i) a set of AI services that allows the usage of edge deployments as computational and live data infrastructure as well as a continuous learning execution pipeline on the edge, (ii) a digital twin of the shop-floor able to test AI models, (iii) a data management framework deployed along the edge-to-cloud continuum ensuring data quality, privacy and confidentiality, (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system, (v) a set of standardisation mechanisms for the exchange of trained AI models from one context to another, and (vi) a knowledge marketplace platform to distribute and interchange trained AI models. In this paper, we present a short overview of the EU Project knowlEdge –Towards Artificial Intelligence powered manufacturing services, processes, and products in an edge-to-cloud-knowledge continuum for humans [in-the-loop], which is funded by the Horizon 2020 (H2020) Framework Programme of the European Commission under Grant Agreement 957331. Our overview includes a description of the project’s main concept and methodology as well as the envisioned innovations.

Imprecision and noise in the time series data may result in series with similar overall behaviors being recognized as being dissimilar because of the accumulation of many small local differences in noisy observations. While smoothing techniques can be used for eliminating such noise, the degree of smoothing that needs to be performed may vary significantly at different parts of the given time series. In this paper, we propose a content-adaptive smoothing, CA-Smooth, technique to reduce the impact of non-informative details and noise in time series by means of a data-driven approach to smoothing. The proposed smoothing process treats different parts of the time series according to local information content. We show the impact of different adaptive smoothing criteria on a number of samples from different datasets, containing series with diverse characteristics.

Since many applications rely on time-based data, visualizing temporal data and helping experts explore large time series data sets are critical in many application domains. In this interactive system preview, we argue that time series often carry structural features that can, if efficiently identified and effectively visualized, help reduce visual overload and help the user quickly focus on the relevant portions of the data sets. Relying on this observation, we introduce a novel STFMap system, which includes four innovative query- and feature-driven time series data set visualization techniques: (a) segment-maps, (b) warp-maps, (c) stretch-maps, and (d) feature-maps. These rely on the salient temporal features of the time series and their alignments with respect to the given user query to help users explore the data set in a query-driven fashion.

Many applications generate and consume temporal data and retrieval of time series is a key processing step in many application domains. Dynamic time warping (DTW) distance between time series of size N and M is computed relying on a dynamic programming approach which creates and fills an N x M grid to search for an optimal warp path . Since this can be costly, various heuristics have been proposed to cut away the potentially unproductive portions of the DTW grid. In this paper, we argue that time series often carry structural features that can be used for identifying locally relevant constraints to eliminate redundant work. Relying on this observation, we propose salient feature based sDTW algorithms which first identify robust salient features in the given time series and then find a consistent alignment of these to establish the boundaries for the warp path search. More specifically, we propose alternative fixed core&adaptive width, adaptive core&fixed width , and adaptive core&adaptive width strategies which enforce different constraints reflecting the high level structural characteristics of the series in the data set. Experiment results show that the proposed sDTW algorithms help achieve much higher accuracy in DTW computation and time series retrieval than fixed core & fixed width algorithms that do not leverage local features of the given time series.