This demo presents a fully immersive and interactive virtual environment (VE) - the ArcheoVR, which represents Itapeva Rocky Shelter, a prehistoric archeological site in Brazil. W workflow started with a real world data capture - laser scanners, drones and photogrammetry. Captured information was transformed into a carefully designed realistic 3D scene and interactive features that allows users to experience the virtual archeological site in real-time. The main objective of this VR model is to allow the general public to feel and explore an otherwise restricted and ephemeral site and to assess prototype tools intended for future digital archaeological exploration.

The efficient identification of hardware and software faults in parallel and distributed systems still remains a serious challenge in today's most prolific decentralized environments. System-level fault diagnosis is concerned with the detection of all faulty nodes in a set of interconnected units. This is accomplished by thoroughly examining the collection of outcomes of all tests carried out by the nodes under a particular test model. Such task has non-polynomial complexity and can be posed as a combinatorial optimization problem, whose optimal solution has been sought through bio-inspired methods like genetic algorithms, ant colonies and artificial immune systems. In this paper, we employ a swarm of artificial fireflies to quickly and reliably navigate across the search space of all feasible sets of faulty units under the invalidation and comparison test models. Our approach uses a binary encoding of the potential solutions (fireflies), an adaptive light absorption coefficient to accelerate the search and problem-specific knowledge to handle infeasible solutions. The empirical analysis confirms that the proposed algorithm outperforms existing techniques in terms of convergence speed and memory requirements, thus becoming a viable approach for real-time fault diagnosis in large-size systems.

The efficient diagnosis of hardware and software faults in parallel and distributed systems remains a challenge in today's most prolific decentralized environments. System-level fault diagnosis is concerned with the identification of all faulty components among a set of hundreds (or even thousands) of interconnected units, usually by thoroughly examining a collection of test outcomes carried out by the nodes under a specific test model. This task has non-polynomial complexity and can be posed as a combinatorial optimization problem. Here, we apply a binary version of the Particle Swarm Optimization meta-heuristic approach to solve the system-level fault diagnosis problem (BPSO-FD) under the invalidation and comparison diagnosis models. Our method is computationally simpler than those already published in literature and, according to our empirical results, BPSO-FD quickly and reliably identifies the true ensemble of faulty units and scales well for large parallel and distributed systems.