The fidelity and utility of synthetic network traffic are critically compromised by architectural mismatch across heterogeneous network datasets and prevalent scalability failure. This study addresses this challenge by establishing an Architectural Selection Framework that empirically quantifies how data structure compatibility dictates the optimal fidelity-utility trade-off. We systematically evaluate twelve generative architectures (both non-AI and AI) across two distinct data structure types: categorical-heavy NSL-KDD and continuous-flow-heavy CIC-IDS2017. Fidelity is rigorously assessed through three structural metrics (Data Structure, Correlation, and Probability Distribution Difference) to confirm structural realism before evaluating downstream utility. Our results, confirmed over twenty independent runs (N = 20), demonstrate that GAN-based models (CTGAN, CopulaGAN) exhibit superior architectural robustness, consistently achieving the optimal balance of statistical fidelity and practical utility. Conversely, the framework exposes critical failure modes, i.e., statistical methods compromise structural fidelity for utility(Compromised fidelity), and modern iterative architectures, such as Diffusion Models, face prohibitive computational barriers, rendering them impractical for large-scale security deployment. This contribution provides security practitioners with an evidence-based guide for mitigating architectural failures, thereby setting a benchmark for reliable and scalable synthetic data deployment in adaptive security solutions.

The AI marketplace ecosystem accelerates multiple modules of the AI engineering pipeline by fostering collaboration between stakeholders. However, marketplace collaborators often face a dilemma in striking a balance between sharing artifacts and protecting intellectual property (IP) rights. Thus, there is a need for a license management system within the AI marketplace to facilitate the exchange of artifacts in a trusted and secure manner. 

This work shares experiences while building such a license management system within the Bonseyes marketplace (BMP), a functional crowdsourcing AI marketplace that specializes in deploying real-time applications on edge devices. The BMP was developed, and its applicability is proven through the European H2020 project by a series of open calls and workshops, for gathering stakeholders and orchestrating the marketplace operations. 

The main contributions of this work are (i) implementation of an end-to-end license management system that deals with selecting license templates, license agreement interaction between seller and buyer, and the generation and enforcement of human- and machine-readable license files, and (ii) introduction of "Synchronization licenses'' concept from the music industry to the AI marketplace context where consumers acquire a license to integrate the artifact into another application, and a respective BMP use-case for collaborative AI engineering. 

Multi-user extended reality (XR) is an advanced technology that immerses users simultaneously into the meta-verse, aiming to provide a real-time interaction experience in different scenarios. However, different users' network conditions cause data transmission to be asynchronous. It forces users' behavior in the metaverse to go out of sync, seriously impacting the user experience. This paper presents a framework for human-centred synchronization of networks and metaverse applications, often denoted as Intelligent Realities, using Digital Twin (DT) technologies for the networks (network DTs) as well as for the individual users' XR parts (XR DTs). Our framework uses users' attention as a key input to dynamically allocate network resources and align content, thereby keeping widely distributed users in sync. To motivate this framework, we surveyed recent DT-based XR and network systems and found a significant gap: few studies work on synchronizing systems between network DTs and XR DTs. This highlights the need to explore integrated solutions for networked real-time multi-user interaction in the metaverse. 

Collaborative AI engineering is a paradigm that enables multiple stakeholders to maintain AI pipelines by exchanging artifacts, such as data, models, and software packages. It is a cost-efficient engineering process that accelerates the development of AI applications, specifically for small and medium-sized enterprises (SMEs). However, AI artifacts are often associated with inherent intellectual property (IP) or sensitive information, which hinders collaboration due to a lack of trust among stakeholders. Digital sovereignty is viewed as an ideal state where artifact owners maintain full control over their assets, such as making key decisions regarding access, storage, and interoperability. Thus, it is postulated that implementing digital sovereignty mechanisms in multi-stakeholder information systems can address the trust gap and promote collaboration. This work addresses this gap by conducting a systematic review of the literature on digital sovereignty for collaborative AI engineering. The main contributions of this work include: i) mapping digital sovereignty definitions and requirements within the context of collaborative AI engineering, ii) identifying existing technologies and concepts for implementing sovereignty features in AI engineering, and iii) analyzing existing collaborative AI platforms such as data marketplaces, data spaces, and GAIA-X. This analysis highlights their sovereignty requirements, solutions, benefits, and implementation challenges. In addition, this work iv) identifies research gaps in data pricing, confidentiality, and interoperability, and proposes future directions to enhance digital sovereignty in collaborative AI ecosystems. 

The 'Human-Centered Intelligent Realities' (HINTS) laboratory is a strategic infrastructure project aiming to support research that advances the development of immersive, user-aware, and intelligent digital environments by integrating augmented reality (AR), virtual reality (VR), extended reality (XR), artificial intelligence (AI), and machine learning (ML). By combining virtual reality and communication-computing continuums, the HINTS environment seeks to create innovative concepts, methods, and tools that empower users to engage with digital systems in novel, efficient, and effective ways. Research in the HINTS laboratory focuses on experience assessment, new digital environments and interaction techniques, visual analytics, adaptive AI, and networking. This paper presents the HINTS laboratory, ongoing activities, and opportunities and challenges for the future.

Multi-user interaction is a key feature in Human-centered Intelligent Realities (HC-IRs). Such distributed Human-centric IRs are an evolved version of Extended Reality (XR) applications aiming at very individual and deep levels of immersiveness in a virtual environment, paired with smart decision-making.

HC-IRs use a variety of novel concepts such as spatial computing, Machine Learning/ Deep Learning (ML/DL), or the latest Human-Computer Interaction (HCI) techniques. A challenge in multi-user HC-IRs is to maximize and synchronize the immersiveness across multiple users and probably multiple networking technologies. As more computational effort is needed in multi-user HC-IRs, this challenge, compared with those in single-user or single-network XR applications, is harder to address but also appealingly powerful due to the available networking-computing continuum, e.g., in advanced 5G and future 6G networks. This paper works towards a framework for adaptive multi-user synchronization in distributed HC-IRs, in which different users are interconnected through advanced networks, e.g., using SDN technologies. The framework is based on identifying the user and network control loops within such HC-IRs.

Ensuring the quality of input data is essential for building robust and explainable AI models in critical infrastructure domains such as smart grids. However, in SCADA-based intrusion detection pipelines, structural inconsistencies and latent feature drift are rarely assessed. In this preliminary study, we adapt the DQuaG framework—a graph-based reconstruction model originally developed for general tabular data—to assess data quality in a SCADA dataset based on the DNP3 protocol. Weapply the model in an unsupervised setting, using reconstructionloss to detect potential inconsistencies without labeled errors. Our results show that even within clean datasets, structural outliers can be identified, highlighting the value of structure-aware data validation. We discuss the implications for data-centric AI pipelines in SCADA cybersecurity and outline future directions for improving quality assessment and synthetic data generation.

The effectiveness of cybersecurity research for SCADA systems depends on access to high-quality network traffic data, yet such data remains scarce due to proprietary restrictions and security concerns. Synthetic data generated by machine learning models, particularly Generative Adversarial Networks (GANs), presents a promising alternative. This study provides a preliminary evaluation of GAN-based approaches for SCADA network traffic synthesis using the IEEE ITACHA DNP3 Smart Grid dataset. We compare a general-purpose GAN (CTGAN) with a network-traffic-specific GAN (NetShare) based on fidelity and statistical consistency. Initial results indicate that CTGAN generates statistically diverse synthetic data, while NetShare suffers from excessive duplication, limiting its applicability. These findings offer an early structured roadmap for selecting and refining generative models for SCADA data synthesis, supporting future research in smart grid security. 

Renewable energy sources were introduced as an alternative to fossil fuel sources to make electricity generation cleaner. However, today's renewable energy markets face a number of limitations, such as inflexible pricing models and inaccurate consumption information. These limitations can be addressed with a decentralized marketplace architecture. Such architecture requires a mechanism to guarantee that all marketplace operations are executed according to predefined rules and regulations. One of the ways to establish such a mechanism is blockchain technology. This work defines a decentralized blockchain-based peer-to-peer (P2P) energy marketplace which addresses actors' privacy and the performance of consensus mechanisms. The defined marketplace utilizes private permissioned Ethereum-based blockchain client Hyperledger Besu (HB) and its smart contracts to automate the P2P trade settlement process. Also, to make the marketplace compliant with energy trade regulations, it includes the regulator actor, which manages the issue and consumption of guarantees of origin and certifies the renewable energy sources used to generate traded electricity. Finally, the proposed marketplace incorporates privacy-preserving features, allowing it to generate private transactions and store them within a designated group of actors. Performance evaluation results of HB-based marketplace with three main consensus mechanisms for private networks, i.e., Clique, IBFT 2.0, and QBFT, demonstrate a lower throughput than another popular private permissioned blockchain platform Hyperledger Fabric (HF). However, the lower throughput is a side effect of the Byzantine Fault Tolerant characteristics of HB's consensus mechanisms, i.e., IBFT 2.0 and QBFT, which provide increased security compared to HF's Crash Fault Tolerant consensus RAFT.