This article proposes a vision-driven roadmap for Extended Reality (XR) in education that treats technology as infrastructure rather than spectacle. Two anchors guide the work. First, vision-led design focuses on persistent educational needs before tools. Second, the long path to adoption reminds us that mainstream impact follows decades of refinement. We adapt and apply the Computer Supported Cooperative Work (CSCW) time-space matrix to the educational context, using Germany as a case study to map current practices. The map reveals a strong bias toward same-time, same-place activities, limited use of asynchronous and distributed work, and integration barriers, including novelty effects, preparation time, infrastructure, and interoperability. We then outline development pathways across three horizons. In 10-15 years, co-located lessons mature through teacher-editable, persistent scenes and modular "nuggets" that reduce preparation cost. In 25-35 years, hybrid integration connects sites at scale, with shared environments that support both live collaboration and asynchronous work with Artificial Intelligence (AI) peers. At 50 years, balanced use of all four quadrants becomes routine, supported by safer interfaces, richer haptics, and portable AI companions that respect privacy. Throughout, we argue that XR should be judged by how well it fosters modelling and simulation literacy, collaboration across time and place, AI literacy, and embodied making, all while safely offloading teachers' routine tasks such as basic explanations, first-pass grading, and basic task sequencing.
Existing cybersecurity playbooks are often written in heterogeneous, non-machine-readable formats, which limits their automation and interoperability across Security Orchestration, Automation, and Response platforms. This paper explores the aptitude of Large Language Models with Prompt Engineering to automatically translate legacy incident response playbooks into the standardized, machine-readable CACAO format. We systematically examine various Prompt Engineering techniques and design prompts aimed at maximizing syntactic accuracy and semantic fidelity for control flow preservation. Our transformation pipeline integrates a syntax checker to ensure syntactic correctness, and it features an iterative refinement mechanism that progressively reduces syntactic errors. We evaluate our approach on a custom-generated dataset comprising diverse legacy playbooks paired with manually created CACAO references. The results demonstrate that our method significantly improves the accuracy of playbook transformation over baseline models, effectively captures complex workflow structures, and substantially reduces errors. It highlights the potential for practical deployment in automated cybersecurity playbook transformation tasks.
Process discovery algorithms traditionally linearize events, failing to capture the inherent concurrency of real-world processes. While some techniques can handle partially ordered data, they often struggle with scalability on large event logs. We introduce a novel, scalable algorithm that directly leverages partial orders in process discovery. Our approach derives partially ordered traces from event data and aggregates them into a sound-by-construction, perfectly fitting process model. Our hierarchical algorithm preserves inherent concurrency while systematically abstracting exclusive choices and loop patterns, enhancing model compactness and precision. We have implemented our technique and demonstrated its applicability on complex real-life event logs. Our work contributes a scalable solution for a more faithful representation of process behavior, especially when concurrency is prevalent in event data.
In modern power systems, edge devices serve as local hubs that collect data, perform on-site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However, as the number of monitored nodes and control loops grows, traditional edge devices face serious limits. They can become overloaded by complex signal processing and decision tasks, causing delays and higher energy use. Standard sensors hit a noise floor that prevents them from detecting miniature changes, making it harder to spot early signs of faults or instability. Meanwhile, conventional communication links struggle with bandwidth limits, security risks, and rising encryption demands, which together slow down and weaken the transfer of critical grid information. Quantum technologies have the potential to overcome these challenges. Quantum computers can deliver exponential speed-ups for optimization and machine-learning tasks that ordinary processors cannot handle. Quantum sensors can sense signals with atomic precision, giving edge devices a more precise view of grid dynamics. Quantum communication techniques, including quantum key distribution, offer methods to achieve information-theoretic security and ensure that information arrives quickly and without tampering. We explore how quantum technologies can be integrated into edge devices, highlighting both opportunities and challenges.
Canary deployment routes a fraction of traffic to a new software version, monitors metrics, and rolls back on regression. Mainstream controllers (Argo Rollouts, Spinnaker, Flagger) change the deployed system's cryptographic identity during the canary window. The drift is harmless for stateless microservices but breaks the claim that "the agent you certified is still the agent you have" for safety-critical embodied agents, forcing re-certification per canary. We present ICAN-Deploy (Identity-stable CANary Deployment), a middleware construction whose state machine holds the identity hash invariant across the canary window by separating capability names (frozen, hashed) from capability versions (mutable runtime state). We implement ICAN-Deploy inside a runtime governance layer for LLM-driven robots and verify invariance by closed-form proof, AST lint, and TLA+ model-checking, then corroborate over N=100 real canary cycles on a Franka Panda arm in MuJoCo (zero drift; entry latency 95
