AI-enabled services deployed in critical digital infrastructure are subject to governance obligations spanning transparency, accountability, fairness, and traceability. Compliance today remains documentation-centric: obligations are described in prose, audits rely on static checklists, and verification depends on manual review. Such approaches do not scale to automated AI systems. This paper introduces Ontological Knowledge Blocks (OKBs), a programmable governance infrastructure that compiles regulatory obligations into machine-checkable constraints over structured evidence graphs. We formalize an OKB as a 5-tuple that binds normative obligations to an RDF/OWL concept schema, executable SHACL validation rules, explicit evidence requirements, and PROV-O provenance links. A deterministic regulatory compiler translates structured Intermediate Representation (IR) records into composable KB modules, enabling profile-based governance reconfiguration without modifying service code. We implement two prototypes and evaluate them in an AI-assisted HPC resource allocation scenario across 24 validation runs and four governance profiles. Results demonstrate profile-sensitive validation, strictly additive violation accumulation, SHACL validation latency between 12.6 ms and 100.3 ms, and profile equivalence testing confirming Combined as the strictly most comprehensive profile. All artefacts are released as open source.

AI governance is shifting from voluntary ethics to enforceable, risk-based regulation, yet cross-jurisdictional divergence creates compliance uncertainty for operators of high-stakes AI. We present a comparative matrix for the EU, US, and China that maps (i) risk classification triggers, (ii) binding obligations, (iii) enforcement and accountability mechanisms, and (iv) the degree to which FAIR principles are operationalised in practice. We stress-test the matrix on three high-impact domains: Electroencephalography (EEG)-guided rehabilitation robotics, AI-enabled debt collection in prospective Central Bank Digital Currency (CBDC) ecosystems, and AI-driven allocation of scarce Graphics Processing Unit (GPU) resources in emerging AI Factory infrastructures. Using primary legal texts and implementation evidence, we identify three recurring gaps: weak interoperability mandates, difficult operationalisation of cross-regime obligations (AI + sector regulation + data protection), and under-specified governance for critical digital infrastructure use cases. To bridge the implementation gap, we outline Knowledge Blocks, a machine-checkable compliance artefact pattern based on Resource Description Framework/Web Ontology Language (RDF/OWL), Shapes Constraint Language (SHACL), and Provenance Ontology (PROV-O), enabling audit-ready compliance-by-design across multiple regimes.

This paper presents the DECICE project (Device Edge Cloud Intelligent Collaboration framEwork), a Horizon Europe Research and Innovation Action (Grant No. 101092582, December 2022 to November 2025) that developed an open-source framework for intelligent workload scheduling across the cloud-HPC-edge compute continuum. A consortium of 12 partners across 6 European countries organized the work into six work packages covering AI-driven scheduling, digital twin infrastructure, system architecture and integration, monitoring, use case validation, and dissemination. The two core technical contributions are an Integrated AI Scheduler (IAIS) employing RNN-based prediction and formal workflow modeling for constraint-aware workload mapping, and a Digital Twin aggregating real-time metrics with carbon intensity and anomaly prediction for energy-aware scheduling. The framework operates within Kubernetes environments, supports unified workflow ingestion from multiple formats, and bridges cloud-native and HPC orchestration through a Slurm integration layer. We present the project vision, the overall architecture, contributions from each work package, quantitative evaluation results, and the open-source release.

The convergence of IoT, edge, cloud, and HPC technologies creates a heterogeneous compute continuum requiring sophisticated workload management. Current tools like SLURM, Kubernetes, and Snakemake lack automated optimization for cross-platform resource allocation, forcing users to manually map workloads across diverse infrastructures. We present a comprehensive framework integrating heterogeneous system and workload modeling integration with Snakemake followed by different tools and techniques like Mixed Integer Linear Programming (MILP) for multi-objective optimization to automate task mapping and scheduling across the compute continuum. Our approach extends Snakemake scheduler with formal mathematical models that optimize resource utilization and makespan. Experimental evaluation demonstrates that MILP-based solution achieves optimal scheduling for small-scale workflows (5x5 tasks) in 0.02 seconds, while heuristic methods provide 99.9% faster solutions for large-scale scenarios (5000×5000 tasks) with only 5-10% deviation from optimal makespan. For parallel workflows, the optimization achieves up to 16.7% makespan reduction compared to sequential scheduling approaches.

Efficient workflow mapping and scheduling in heterogeneous HPC-Compute Continuum (HPC-CC) systems is critical for multi-objective optimization like optimizing resource utilization and minimizing makespan or energy efficiency. Existing approaches face fundamental trade-offs: Mixed-Integer Linear Programming (MILP) provides optimal solutions but becomes computationally intractable for large workflows exceeding (50x50) nodes by tasks, while heuristic methods sacrifice optimality for speed and struggle with complex constraint modeling. We present GrapheonRL, a novel Graph Neural Network (GNN) and Reinforcement Learning (RL)-based framework that can be embedded in Snakemake to model workflows as dependency-aware graphs, enabling RL agents to dynamically learn constraint-aware scheduling policies without mathematical reformulation. We evaluated GrapheonRL against MILP and heuristic baselines (HEFT, OLB) on Standard Task Graph Set workflows (11–90 tasks) and extended to synthetic workflows of up to (10,000x10,000) nodes by tasks, GrapheonRL matches MILP optimality while offering significantly improved scalability, achieving 76% faster inference with linear memory growth (0.87 MB per 1000 tasks). On complex workflows, GrapheonRL maintains optimal makespan (569) whereas heuristics degrade substantially (HEFT: 829, OLB: 1160), demonstrating that learning-based scheduling effectively bridges the optimality-scalability gap for dynamic HPC-CC environments.

Artificial Intelligence (AI) is transforming sectors such as healthcare, finance, and autonomous systems, offering powerful tools for innovation. Yet its rapid integration raises urgent ethical concerns related to data ownership, privacy, and systemic bias. Issues like opaque decision-making, misleading outputs, and unfair treatment in high-stakes domains underscore the need for transparent and accountable AI systems.This article addresses these challenges by proposing a modular ethical assessment framework built on ontological blocks of meaning—discrete, interpretable units that encode ethical principles such as fairness, accountability, and ownership. By integrating these blocks with FAIR (Findable, Accessible, Interoperable, Reusable) principles, the framework supports scalable, transparent, and legally aligned ethical evaluations, including compliance with the EU AI Act.Using a real-world use case in AI-powered investor profiling, the paper demonstrates how the framework enables dynamic, behavior-informed risk classification. The findings suggest that ontological blocks offer a promising path toward explainable and auditable AI ethics, though challenges remain in automation and probabilistic reasoning.

Convolutional Neural Networks (CNNs) are pivotal in computer vision and Big Data analytics but demand significant computational resources when trained on large-scale datasets. Conventional training via back-propagation (BP) with loss functions like Mean Squared Error or Cross-Entropy often requires extensive iterations and may converge sub-optimally. Quantum computing offers a promising alternative by leveraging superposition, tunneling, and entanglement to search complex optimization landscapes more efficiently.In this work, we propose a hybrid optimization method that combines an Unconstrained Binary Quadratic Programming (UBQP) formulation with Stochastic Gradient Descent (SGD) to accelerate CNN training. Evaluated on the MNIST dataset, our approach achieves improvement without compromising the accuracy while maintaining similar execution times. These results illustrate the potential of hybrid quantum-classical techniques in High-Performance Computing (HPC) environments for Big Data and Deep Learning. Fully realizing these benefits, however, requires a careful alignment of algorithmic structures with underlying quantum mechanisms.

Cloud computing clusters, especially those handling cloud workloads, require efficient job scheduling to optimize resource utilization and minimize completion time. Traditional approaches often fall short in dynamic cloud environments. We propose “HOSHMAND” (High-performance Open sourced AI-based Scheduling Handler for MAnaging Node Distribution), an AI-driven framework using a custom-tailored Recurrent Neural Network (RNN) to rapidly predict the most suitable nodes for cloud workload execution. A key feature of HOSHMAND is its accelerated scheduling capability, which significantly reduces the time required for job allocation compared to traditional methods. This is particularly crucial for cloud environments with fluctuating workloads and diverse computational requirements. A distinct capability of HOSHMAND is its proficiency in managing heterogeneous resources, ensuring optimal allocation regardless of varying computational capabilities or resource types. This adaptability is crucial for contemporary cloud computing en-vironments, which often comprise a diverse array of hardware configurations, to maintain high efficiency and resource utilization. Moreover, HOSHMAND mitigates the overhead associated with repetitive scheduling computations in similar scenarios by leveraging its historical knowledge. Upon recognizing a con-figuration of jobs analogous to previously encountered situations, it promptly enacts the most effective scheduling strategy without redundant recalculations. This predictive capability not only conserves computational resources but also accelerates job execution. Our approach, tested on cloud-based datasets, demonstrates remarkable improvements in scheduling speed and efficiency, validated by reduced time-to-schedule and enhanced overall system throughput. Through its innovative handling of heterogeneous resources and intelligent avoidance of unnecessary scheduling computations, HOSHMAND sets a new benchmark for AI -driven job scheduling in cloud computing environments.