This paper presents an interoperability architecture for data spaces (DSIA), allowing members of the different data spaces using different technologies and services to exchange data. The DSIA builds on the existing trust of data space members and extends it through shared agreements and additional federation and participant services. The DSIA consists of four components: sub-systems for data sharing across data spaces, data pipeline, deployment, and a set of supporting applications for users. The DSIA has been validated with DS2 implementations and compared to other interoperability approaches. The main benefits are minimal independence to interacting data space technologies, little governance overheads, and easy deployment for existing data space participants and authorities’ systems.

Today, the advancement of Industries 4.0 and 5.0 has enabled the digital transformation of the manufacturing domain and the creation of data-sharing ecosystems. A cornerstone of this advancement is the Internet of Things, which has enabled the realization of applications related to real-time monitoring, automation, and analytics. However, IoT-enabled data sharing scenarios across organizations have also introduced significant security, privacy, and trust challenges. This paper presents the Security Risk Modeler (SRM), a tool designed to address these challenges by providing a systematic approach to identifying, analyzing, and mitigating security risks in Industry 4.0/5.0 data sharing environments. The tool is applied to a real-world industrial waste management scenario that involves IoT enabled waste bins, cloud-based analytics, and data sharing between two companies. Through this case study, SRM demonstrates its ability to model complex systems, identify threats, and propose actionable mitigation controls. Evaluations by industry stakeholders highlighted the tool's usability, risk analysis capabilities, and potential for broader adoption in securing Industry 4.0/5.0 ecosystems.

Distributed acoustic sensing (DAS) is an optoelectronic technology that utilizes fibre optic cables to detect disturbances caused by seismic waves. Using DAS, seismologists can monitor geophysical phenomena at high spatial and temporal resolutions over long distances in inhospitable environments. Field experiments using DAS, are typically associated with large volumes of observations, requiring algorithms for efficient processing and monitoring capabilities. In this study, we present a supervised classifier trained to recognize seismic activity from other sources of hydroacoustic energy. Our classifier is based on a 2-D convolutional neural network architecture. The 55-km-long ocean-bottom fibre optic cable, located off Cape Muroto in southwest of Japan, was interrogated using DAS. Data were collected during two different monitoring time periods. Optimization of the model's hyperparameters using Gaussian Processes Regression was necessary to prevent issues associated with small sizes of training data. Using a test set of 100 labeled images, the highest-performing model achieved an average classification accuracy of 92 per cent, correctly classifying 100 per cent of instances in the geophysical class, 80 per cent in the non-geophysical class and 96 per cent in ambient noise class. This performance demonstrates the model's effectiveness in distinguishing between geophysical data, various sources of hydroacoustic energy, and ambient noise.

This paper describes a simulation-based approach for automated risk assessment of complex cyber-physical systems to support implementers of ISO 27005. The approach is based on systematic cause-and-effect modelling of threats, their causes and effects, and the ways in which the effects of one threat can lead to other threats. In this way, the approach deals with inter-dependencies within the target system, automatically finding attack paths and secondary effect cascades, which generally are very complex and the source of many challenges when implementing ISO 27005. The approach uses a knowledgebase describing classes of system assets and their possible relationships, along with the associated threats, causes and effects in a generic context. A target system can then be modelled in terms of related assets, describing the intended system structure and purpose (in the absence of any deviations). The knowledgebase is then used to identify which threats are relevant and create a cause-and-effect simulation of those threats. This allows threat likelihoods and risk levels to be found based on input concerning trust assumptions and the presence of controls in the system. The approach has been implemented by the open source Spyderisk project and validated by modelling a published case study of an attack on a steel mill. Given reasonable assumptions about security controls in place, the shortest, highest likelihood attack path found coincides with the published analysis. The case study demonstrates the strengths of the approach: transparency, reproducibility, and performance.

This paper presents an extended approach to Impact Assessment (IA) within European Union funded large-scale projects within the manufacturing domain, which may offer value to other research projects and SME organisations seeking to develop detailed organizational reporting. It details the six-phase process that forms the framework for this extended approach, demonstrating how project Outcome Indictors and impact assessment criterion can be aligned through an extensive review and integration of existing impact domains, objectives, measures and evidence sources with project documentation to provide the detailed individual impact assessment criteria for this extended IA approach. It also reports on the application of the approach in the EC-funded digital manufacturing project, European Connected Factory Platform for Agile Manufacturing (EFPF), finding that 24 of the 27 IA criteria were met or exceed, suggesting that the project made an important contribution to the EU Industry4.0 ecosystem through furthering the key priorities of Industrial Leadership, Data Integration, Uptake of New Technologies, Open Science, the Circulation of Knowledge, and a minor contribution to Climate Change Mitigation.

This paper presents an extended approach to Impact Assessment (IA) within European Union funded large-scale projects within the manufacturing domain, which may offer value to other research projects and SME organisations seeking to develop detailed organizational reporting. It details the six-phase process that forms the framework for this extended approach, demonstrating how project Outcome Indictors and impact assessment criterion can be aligned through an extensive review and integration of existing impact domains, objectives, measures and evidence sources with project documentation to provide the detailed individual impact assessment criteria for this extended IA approach. It also reports on the application of the approach in the EC-funded digital manufacturing project, European Connected Factory Platform for Agile Manufacturing (EFPF), finding that 24 of the 27 IA criteria were met or exceed, suggesting that the project made an important contribution to the EU Industry4.0 ecosystem through furthering the key priorities of Industrial Leadership, Data Integration, Uptake of New Technologies, Open Science, the Circulation of Knowledge, and a minor contribution to Climate Change Mitigation.

To reduce the risk and the required bureaucracy for accessing public funding, the EC has developed a mechanism to grant money to consortia to be used to fund to startups, SMEs and Midcaps to start, evolve or transition their offer to increase their competitivity in the market, in turn providing benefit for the entire European economy.

In this paper we present a classifier that was trained on space-time images obtained from distributed acoustic sensing, for the purpose of monitoring earthquakes. The model is capable of discriminating between actual and non-earthquake events.

New media applications and services are revolutionising social interaction and user experience in both society and in wide ranging industry sectors. The rapid emergence of pervasive human and environment sensing technologies, novel immersive presentation devices and high performance, globally connected network and cloud infrastructures is generating huge opportunities for application providers, service provider and content providers. These new applications are driving convergence across devices, clouds, networks and services, and the merging of industries, technology and society. Yet the developers of such systems face many challenges in understanding how to optimise their solutions (Quality of Service - QoS) to enhance user experience (Quality of Experience - QoE) and how their disruptive innovations can be introduced into the market with appropriate business models. In this report, we present the results of a new multi-disciplinary collaborative approach to product and service innovation that brings together users, technology and live events in a series of experiments conducted in real world settings. Through experimentation we have explored a broad range of technical, societal and economic challenges faced by technology providers each aiming to create and exploit new multimedia value chains in markets such as leisure and tourism, cultural and heritage, and sports science and training. The experiments highlight the features of multimedia systems and the future opportunities for companies, as the Internet continues to transition towards the increasingly connected world of Internet of Things and Big Data. We know that putting user values at the heart of design decisions and evaluation is the key to success, and that long term benefits to providers of technology, services and content must derive from enhanced user experience. Engaging users in real-world settings to co-design and assess how technology can be used is now more important than testing how technology will be operated. We have only scratched the surface of possibility in novel networked multimedia systems yet we believe that the individual and collective results in the report are significant as they are grounded in real?world evidence. A new way of conducting research and innovation has been created that maximises the potential for commercial exploitation and societal impact. We think this is extremely important and when adopted will lead to greater benefits for all.

The presented paper investigates the relationship between interoperability and system security. This is mainly an optimisation problem, since making a system interoperable means that some APIs need to be exposed, which can potentially open the system to malicious attacks. The paper explores the use of the System Security Modeller (SSM) tool which allows an assessment of the cost of interoperability by calculating the security risks. The security implications of interoperability are illustrated through a case study representing a smart manufacturing scenario.

ProsocialLearn is a digital pro-social games platform. The ProsocialLearn project has delivered a series of disruptive innovations for the production and distribution of pro-social digital games that engage children (7-10 years old). Additionally it has stimulated technology transfer from the games industry to the educational sector. ProsocialLearn fosters the creation of a new market for digital games aimed at increasing social inclusion and academic performance, as well as a distribution channel to deliver prosocial games to children and teachers in European schools. Furthermore, it provides a proven pro-social methodology to design digital games. The ProsocialLearn platform makes available a series of APIs, which game developers use to integrate many of the ProsocialLearn functions into games, i.e. emotion and engagement monitoring, in-game achievements, games adaptation based on Prosocial Learning Objectives (PLOs), and micro-transactions.

ProsocialLearn is a digital pro-social games platform. ProsocialLearn consortium aims at delivering a series of disruptive innovations for the production and distribution of pro-social digital games that engage children (7-10 years old), as well as stimulate technology transfer from the games industry to the educational sector. ProsocialLearn will foster the creation of a new market for digital games aimed at increasing social inclusion and academic performance, as well as a distribution channel to deliver these games to children and teachers in European schools. In addition to provide a proven pro-social methodology to design digital games the ProsocialLearn platform makes available a series of application programming interface (APIs), which game developers can use to integrate many of the ProsocialLearn functions into their games, including emotion and engagement monitoring, in-game achievements, games ed on Prosocial Learning Objectives(PLOs) , micro-transactions, etc.

The GRAVITATE project is developing techniques that bring together geometric and semantic data analysis to provide a new and more effective method of re-associating, reassembling or reunifying cultural objects that have been broken or dispersed over time. The project is driven by the needs of archaeological institutes, and the techniques are exemplified by their application to a collection of several hundred 3D-scanned fragments of large-scale terracotta statues from Salamis, Cyprus. The integration of geometrical feature extraction and matching with semantic annotation and matching into a single decision support platform will lead to more accurate reconstructions of artefacts and greater insights into history. In this paper we describe the project and its objectives, then we describe the progress made to date towards achieving those objectives: describing the datasets, requirements and analysing the state of the art. We follow this with an overview of the architecture of the integrated decision support platform and the first realisation of the user dashboard. The paper concludes with a description of the continuing work being undertaken to deliver a workable system to cultural heritage curators and researchers.

Acquiring skills for social and emotional well-being is important for inclusive societies and academic achievement. Studies have demonstrated the beneficial link between prosocial behaviours and improved results in curriculum topics. This paper describes a Prosocial Learning (PSL) process for creation and delivery of digital games for children (7-10 yrs) within educational systems that support learning of prosocial skills. The approach combines prosocial pedagogies with advanced ICT technologies and cloud delivery models to create attractive and exciting learning opportunities for children; produce novel digital game-based pedagogies and simplify deployment. Prosociality is a concept that refers to an individual's propensity towards positive social behaviours. Individuals with prosocial skills are, for example, able to join in conversations, talk nicely, identifying feelings and emotions in themselves and others, identify someone needs help and ask for help. PSL classifies these skills in terms of Friendship, Feelings and Cooperation. By using interactive digital games supported by additional instructive and reflective activities, PSL allows children to learn social skills that can be generalised to real life situations in the classroom, playground and at home. PSL is implemented through a technology platform offering systematic pedagogical support for prosocial games developed by an ecosystem of teachers and games companies. Capabilities include multi-modal sensors to observe emotional affect, game interaction and decision-making. Information is acquired through standard protocols (e.g. xAPI) and evaluated by learning analytics algorithms to provide real-time feedback on player behaviours that are be used for in-game feedback and adaptation, and by teachers to shape follow-up activities. PSL is validated through short and longitudinal studies at European schools to gather evidence for effectiveness. This paper provides early evidence from short studies that will steer larger pan-European trials to test hypotheses, promote to policy makers and to increase adoption of game-based learning in schools.

Measurement of network Quality of Service (QoS) has attracted considerable research effort over the last two decades. The recent trend towards Internet Service Providers (ISP's) offering application-specific QoS is creating possibilities for more sophisticated QoS metrics to be offered by ISP's in service level agreements. This in turn could be used for the purposes of improved network optimization and billing according to application specific QoS guarantees. We report a scalable near real-time approach using passively logged IP traffic data for classification of application latency and packet loss across a range of real-time interactive applications. We run six experiments involving Minecraft, Quake 3 Urban Terror, VLC video streaming and the commercial Wirofon VOIP application. We use a mixture of laboratory and real-world deployments, with run times ranging from hours to days, and observe a combination of real and simulated ISP latency and packet loss events. Our binary classification (i.e. classes 'OK' or 'lag') 10-fold cross validation F1 scores are between 0.80 and 0.93 depending on the application type. Our multi-class classification (i.e. classes representing discrete packet loss or latency ranges) 10-fold cross validation F1 scores for Minecraft are 0.89 for latency and 0.90 for packet loss. With new business models between ISP's and application developers being actively considered this work represents a significant contribution to the debate by providing scientific evidence relating to a novel approach to scalable QoS measurement

This document describes the evaluation strategy for the assessment of game effectiveness, market value impact and ethics procedure to drive detailed planning of technical validation, short and longitudinal studies and market viability tests

This document describes the evaluation strategy for the assessment of game effectiveness, market value impact and ethics procedure to drive detailed planning of technical validation, short and longitudinal studies and market viability tests.