Abstract
Models-at different levels of abstraction and pertaining to different engineering views-are central in the design of railway networks, in particular signalling systems. The design of such systems must follow numerous strict rules, which may vary from project to project and require information from different views. This renders manual verification of railway networks costly and error-prone. This paper presents EVEREST, a tool for automating the verification of railway network models that preserves the loosely coupled nature of the design process. To achieve this goal, EVEREST first combines two different views of a railway network model-the topology provided in signalling diagrams containing the functional infrastructure, and the precise coordinates of the elements provided in technical drawings (CAD)-in a unified model stored in the railML standard format. This railML model is then verified against a set of user-defined infrastructure rules, written in a custom modal logic that simplifies the specification of spatial constraints in the network. The violated rules can be visualized both in the signalling diagrams and technical drawings, where the element(s) responsible for the violation are highlighted. EVEREST is integrated in a long-term effort of EFACEC to implement industry-strong tools to automate and formally verify the design of railway solutions. © 2022 ACM.

Abstract
Alloy is a popular language and tool for formal software design. A key factor to this popularity is its relational logic, an elegant specification language with a minimal syntax and semantics. However, many software problems nowadays involve both structural and quantitative requirements, and Alloy's relational logic is not well suited to reason about the latter. This paper introduces QAlloy, an extension of Alloy with quantitative relations that add integer quantities to associations between domain elements. Having integers internalised in relations, instead of being explicit domain elements like in standard Alloy, allows quantitative requirements to be specified in QAlloy with a similar elegance to structural requirements, with the side-effect of providing basic dimensional analysis support via the type system. The QAlloy Analyzer also implements an SMT-based engine that enables quantities to be unbounded, thus avoiding many problems that may arise with the current bounded integer semantics of Alloy.

Abstract
Based on the connection between the categorical derivation of classical programs from specifications and a category-theoretic approach to quantum information, this paper contributes to extending the laws of classical program algebra to quantum programming. This aims at building correct-by-construction quantum circuits to be deployed on quantum devices such as those available through the IBM Q Experience. Reversibility is ensured by minimal complements. Such complementation is extended inductively to encompass catamorphisms on lists (vulgo folds), giving rise to the corresponding recursion scheme in reversible computation. The same idea is then applied to the setting of quantum programming, where computation is expressed by unitary transformations. This yields the notion of 'quantamorphism', a structural form of quantum recursion implementing cycles and folds on lists with quantum control flow. By Kleisli correspondence, quantamorphisms can be written as monadic functional programs with quantum parameters. This enables the use of Haskell, a monadic functional programming language, to perform the experimental work. Such calculated quantum programs prepared in Haskell are pushed through Quipper and the Qiskit interface to IBM Q quantum devices. The generated quantum circuits - often quite large - exhibit the predicted behaviour. However, running them on real quantum devices naturally incurs a significant amount of errors. As quantum technology is rapidly evolving, an increase in reliability is likely in the future, allowing for our programs to run more accurately.

Abstract
There is an increasing demand for stateful edge computing for both complex Virtual Network Functions (VNFs) and application services in emerging 5G networks. Managing a mutable persistent state in the edge does however bring new architectural, performance, and dependability challenges. Not only it has to be integrated with existing cloud-based systems, but also cope with both operational and analytical workloads and be compatible with a variety of SQL and NoSQL database management systems. We address these challenges with AIDA-DB, a polyglot data management architecture for the edge and cloud continuum. It leverages recent development in distributed transaction processing for a reliable mutable state in operational workloads, with a flexible synchronization mechanism for efficient data collection in cloud-based analytical workloads. © 2022 IEEE.

Abstract
Wide availability of edge computing platforms, as expected in emerging 5G networks, enables a computing continuum between centralized cloud services and the edge of the network, close to end-user devices. This is particularly appealing for online analytics as data collected by devices is made available for decisionmaking. However, cloud-based parallel-distributed data processing platforms are not able to directly access data on the edge. This can be circumvented, at the expense of freshness, with data synchronization that periodically uploads data to the cloud for processing. In this work, we propose an adaptive database synchronization system that makes distributed data in edge nodes available dynamically to the cloud by balancing between reducing the amount of data that needs to be transmitted and the computational effort needed to do so at the edge. This adapts to the availability of CPU and network resources as well as to the application workload.

Abstract
We present PAID, a framework that allows developers to implement portable I/O policies and optimizations for different applications with minor modifications to their original code base. The chief insight behind PALO is that if we are able to intercept and differentiate requests as they flow through different layers of the I/O stack, we can enforce complex storage policies without significantly changing the layers themselves. PAIO adopts ideas from the Software-Defined Storage community, building data plane stages that mediate and optimize I/O requests across layers and a control plane that coordinates and fine-tunes stages according to different storage policies. We demonstrate the performance and applicability of PALO with two use cases. The first improves 99th percentile latency by 4 x in industry-standard LSM-based key-value stores. The second ensures dynamic per-application bandwidth guarantees under shared storage environments.