Given the complex design requirements of future satellites and the need to comply with ECSS standards, the METASAT project introduces an innovative design approach that uses model-based engineering and is supported by open architecture hardware. The project highlights the importance of software virtualisation layers, such as hypervisors, in achieving compliance with standards on high-performance computing plat-forms. The project is dedicated to creating a specialised toolchain for these advanced hardware and software layers. We believe that without such innovations, the satellite industry could face increased and unsustainable development costs and timelines, potentially compromising its competitiveness and dependability. This paper provides a general overview of the METASAT project and introduces the approaches being used to add support for the XtratuM hypervisor and code generation for hardware accelerators in a model-based engineering workflow.

Recently there is an increasing interest in the use of artificial intelligence for on-board processing as indicated by the latest space missions, which cannot be satisfied by existing low-performance space-qualified processors. Although COTS AI accelerators can provide the required performance, they are not designed to meet space requirements. In this work, we co-design a low-cost SIMD micro-architecture integrated in a space qualified processor, which can significantly increase its performance. Our solution has no impact on the processor's 100 MHz frequency and consumes minimal area thanks to its innovative design compared to conventional vector micro-architectures. For the minimum configuration of our baseline space processor, our results indicate a performance boost of up to 9.3× for commonly used AI-related and image processing algorithms and 5.5× faster for a complex, space-relevant inference application with just 30% area increase.

Performing diagnostics in IT systems is an increasingly complicated task, and it is not doable in satisfactory time by even the most skillful operators. Systems and their architecture change very rapidly in response to business and user demand. Many organizations see value in the maintenance and management model of NoOps that stands for No Operations. One of the implementations of this model is a system that is maintained automatically without any human intervention. The path to NoOps involves not only precise and fast diagnostics but also reusing as much knowledge as possible after the system is reconfigured or changed. The biggest challenge is to leverage knowledge on one IT system and reuse this knowledge for diagnostics of another, different system. We propose a framework of weighted graphs which can transfer knowledge, and perform high-quality diagnostics of IT systems. We encode all possible data in a graph representation of a system state and automatically calculate weights of these graphs. Then, thanks to the evaluation of similarity between graphs, we transfer knowledge about failures from one system to another and use it for diagnostics. We successfully evaluate the proposed approach on Spark, Hadoop, Kafka and Cassandra systems.

Although crowdsourcing has been proven efficient as a mechanism to solve independent tasks for on-line production, it is still unclear how to define and manage workflows in complex tasks that require the participation and coordination of different workers. Despite the existence of different frameworks to define workflows, we still lack a commonly accepted solution that is able to describe the most common workflows in current and future platforms. In this paper, we propose CrowdWON, a new graphical framework to describe and monitor crowd processes, the proposed language is able to represent the workflow of most well-known existing applications, extend previous modelling frameworks, and assist in the future generation of crowdsourcing platforms. Beyond previous proposals, CrowdWON allows for the formal definition of adaptative workflows, that depend on the skills of the crowd workers and/or process deadlines. CrowdWON also allows expressing constraints on workers based on previous individual contributions. Finally, we show how our proposal can be used to describe well known crowdsourcing workflows.

The increasing adoption of crowdsourcing for commercial and industrial purposes rises the need for creating sophisticated mechanisms in crowd-based digital platforms for efficient worker management. One of the main challenges in this area is worker motivation and skill set control and its impact on the output quality. The quality delivered by the workers in the crowd depends on different aspects such as their skills, experience, commitment, etc. The lack of generic and detailed proposals to incentive workers and the need for creating ad-hoc solutions depending on the domain make it difficult to evaluate the best rewarding functions in each scenario. In this paper, we make a step further in this direction and propose the use of aggregation functions to evaluate the professional skills of crowd-workers based on the quality of their past tasks. Additionally, we present a real industrial crowdsourcing solution for software localisation in which the proposed solutions are put into practice with real text translations quality measures.

A central problem in the area of Process Mining is to obtain a formal model that represents the processes that are conducted in a system. If realized, this simple motivation allows for powerful techniques that can be used to formally analyze and optimize a system, without the need to resort to its semiformal and sometimes inaccurate specification. The problem addressed in this paper is known as Process Discovery: to obtain a formal model from a set of system executions. The theory of regions is a valuable tool in process discovery: it aims at learning a formal model (Petri nets) from a set of traces. On its genuine form, the theory is applied on an automaton and therefore one should convert the traces into an acyclic automaton in order to apply these techniques. Given that the complexity of the region-based techniques depends on the size of the input automata, revealing the underlying cycles and folding the initial automaton can incur in a significant complexity alleviation of the region-based techniques. In this paper, we follow this idea by incorporating region information in the cycle detection algorithm, enabling the identification of complex cycles that cannot be obtained efficiently with state-of-the-art techniques. The experimental results obtained by the devised tool suggest that the techniques presented in this paper are a big step into widening the application of the theory of regions in Process Mining for industrial scenarios.

Different methods and paradigms to protect data sets containing sensitive statistical information have been proposed and studied. The idea is to publish a perturbed version of the data set that does not leak confidential information, but that still allows users to obtain meaningful statistical values about the original data. The two main paradigms for data set protection are the classical one and the synthetic one. Recently, the possibility of combining the two paradigms, leading to a hybrid paradigm, has been considered. In this work, we first analyze the security of some synthetic and (partially) hybrid methods that have been proposed in the last years, and we conclude that they suffer from a high interval disclosure risk. We then propose the first fully hybrid SDC methods; unfortunately, they also suffer from a quite high interval disclosure risk. To mitigate this, we propose a postprocessing technique that can be applied to any data set protected with a synthetic method, with the goal of reducing its interval disclosure risk. We describe through the paper a set of experiments performed on reference data sets that support our claims.

A central problem in the area of Process Mining is to obtain a formal model that represents selected behavior of a system. The theory of regions has been applied to address this problem, enabling the derivation of a Petri net whose language includes

Comparison functions for sequences (of symbols) are important components of many applications, for example, clustering, data cleansing, and integration. For years, many efforts have been made to improve the performance of such comparison functions. Improvements have been done either at the cost of reducing the accuracy of the comparison, or by compromising certain basic characteristics of the functions, such as the triangular inequality. In this paper, we propose a new distance for sequences of symbols (or strings) called Optimal Symbol Alignment distance (OSA distance, for short). This distance has a very low cost in practice, which makes it a suitable candidate for computing distances in applications with large amounts of (very long) sequences. After providing a mathematical proof that the OSA distance is a real distance, we present some experiments for different scenarios (DNA sequences, record linkage, etc.), showing that the proposed distance outperforms, in terms of execution time and/or accuracy, other well-known comparison functions such as the Edit or Jaro-Winkler distances.