The ScenarioTools Play-Out of Modal Sequence Diagram Specifications with Environment Assumptions

Many software-intensive systems consist of multiple components that provide complex functionality by their interaction. The scenario-based languages LSCs and MSDs are intuitive, but precise means to specify interactions; the engineers can specify how a system can, must, or must not react to events in its environment. A key benefit of LSCs/MSDs is that they can be executed via the play-out algorithm, which allows engineers to perform an early automated analysis of the specification. However, LSCs/MSDs lack support for expressing also what can or cannot happen in the environment. This is crucial especially in embedded systems: very often, the software will only be able to satisfy its requirements if certain assumptions are made about the behavior of mechanical parts or the physical environment. We extend MSD specifications to formally express such environment assumptions, and propose a corresponding extension of the play-out algorithm. The concepts are implemented in a novel, Eclipse-based tool

Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

Efficient Dynamic Updates of Distributed Components Through Version Consistency

Modern component-based distributed software systems are increasingly required to offer non-stop service and thus their updates must be carried out at runtime. Different authors have already proposed solutions for the safe management of dynamic updates. Our contribution aims at improving their efficiency without compromising safety. We propose a new criterion, called version consistency, which defines when a dynamic update can be safely and efficiently applied to the components that execute distributed transactions. Version consistency ensures that distributed transactions be served as if they were operated on a single coherent version of the system despite possible concurrent updates. The paper presents a distributed algorithm for checking version consistency efficiently, formalizes the proposed approach by means of a graph transformation system, and verifies its correctness through model checking. The paper also presents ConUp, a novel prototype framework that supports the approach and offers a viable, concrete solution for the use of version consistency. Both the approach and ConUp are evaluated on a significant third-party application. Obtained results witness the benefits of the proposed solution with respect to both timeliness and disruption

Graph-deep-learning-based inference of fine-grained air quality from mobile IoT sensors

Internet-of-Things (IoT) technologies incorporate a large number of different sensing devices and communication technologies to collect a large amount of data for various applications. Smart cities employ IoT infrastructures to build services useful for the administration of the city and the citizens. In this article, we present an IoT pipeline for acquisition, processing, and visualization of air pollution data over the city of Antwerp, Belgium. Our system employs IoT devices mounted on vehicles as well as static reference stations to measure a variety of city parameters, such as humidity, temperature, and air pollution. Mobile measurements cover a larger area compared to static stations; however, there is a tradeoff between temporal and spatial resolution. We address this problem as a matrix completion on graphs problem and rely on variational graph autoencoders to propose a deep learning solution for the estimation of the unknown air pollution values. Our model is extended to capture the correlation among different air pollutants, leading to improved estimation. We conduct experiments at different spatial and temporal resolution and compare with state-of-the-art methods to show the efficiency of our approach. The observed and estimated air pollution values can be accessed by interested users through a Web visualization tool designed to provide an air pollution map of the city of Antwerp

Street-level air quality inference based on geographically context-aware random forest using opportunistic mobile sensor network

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