As critical cyber-physical systems, urban transport systems are vulnerable to natural disasters and deliberate attacks. Ensuring their resilience is crucial for sustainable operations and includes the ability to withstand, absorb and recover efficiently from disruptions. Assessing the resilience of such systems requires a comprehensive set of performance indicators covering social, economic, organisational, environmental and technical concerns. In addition, the interdependence of the different modes of transport and the resulting human activities requires the inclusion of the spatial dimension to capture potential cascading failures. Furthermore, the integration of both aleatory (data) and epistemic (modelling) uncertainties is essential for robust performance indicators.

With increasing interdependencies and evolving threats, maintaining operational continuity in critical systems has become a significant challenge. This paper presents the DYNABIC (Dynamic business continuity of critical infrastructures on top of adaptive multi-level cybersecurity) approach as a comprehensive framework to enhance the resilience of critical infrastructures. The DYNABIC approach provides the resilience enhancement through dynamic adaptation, automated response, collaboration, risk assessment, and continuous improvement. By fostering a proactive and collaborative approach to resilience, the DYNABIC framework empowers critical infrastructure sectors to effectively mitigate disruptions and recover from incidents. The paper explores the key components and architecture of the DYNABIC approach and highlights its potential to strengthen the resilience of critical infrastructures using the concept of Digital Twins in the face of evolving threats and complex operating environments involving cascading effects.

International audience

Multiple tools have emerged to support the development as well as the continuous deployment of cloud-based software systems. However, currently, there is a lack of proper tool support for the continuous orchestration and deployment of software systems spanning across the IoT, edge, and cloud space. In particular, there is a lack of languages and abstractions that can support the orchestration and deployment of software services across vastly heterogeneous IoT infrastructures. In this paper, we present a tool supported framework for the continuous orchestration and deployment of IoT systems, named GeneSIS. In particular, GeneSIS enables to cope with the heterogeneity at each of the IoT, edge, and cloud levels and allows to control the orchestration and continuous deployment of software systems that executes across IoT, edge, and cloud infrastructures.

International audience

Small and medium sized enterprises (SMEs) are a pillar of European economy. Nowadays the acquisition of new e-competences is essential to follow the digital transformation for the society and the market, and to keep an adequate level of competitiveness. Internet of Things (IoT) are producing a relevant impact in several economic sectors (industry, transportations, energy, agriculture, home automation, etc.). This technology has a high potential in terms of new business opportunities and new job positions. Several national and European policies have been set-up to train the EU companies to the adoption and diffusion of the IoT technologies. This is also the main aim of the European project IoT4SMEs. In this paper, we describe the early activity of the project, focused on an assessment of the labour market training needs related to the IoT technologies.

The complexity of the Cyber-Physical Systems (CPS) and their interactions with the physical environment makes them difficult to model completely. Once deployed, these systems are therefore subject to unexpected events that may degrade their behavior. Consequently, it becomes necessary to evaluate, at runtime, the degree of effectiveness of these systems. To address this concern, a new approach is proposed, drawing its foundations from the possibility theory and accounting for temporal constraints. The degree of effectiveness is then given as the degree of possibility. A step further, the Choquet integral operator is used for aggregating multiple evaluations, providing us with a measure of the users' satisfaction w.r.t. their preferences. Finally the proposed approach is validated through a use-case in the smart-home domain.

A lot of research has been done for human activity recognition. But most of it uses a static and immutable set of sensors known beforehand. This approach does not work when applied to a ubiquitous or mobile system, since we cannot know which sensors will be available in the users' surroundings. This is why we consider here an opportunistic approach, where each sensor individually trained are able to bring its own knowledge. Inspired by the Opportunity project, we propose to evaluate both the effectiveness of using a Random Forest (RF) classifier to train the sensors and the robustness of fusing the results using a weighted majority vote. We found that RF gave better and more robust results than the other classifiers formally tested by Opportunity.

Background. With the recent advent of the so-called connected objects, today largely present in our surroundings, software applications have an open door to the physical world through sensors and actuators. However, although it offers huge opportunities in many areas (e.g., smart-home, smart-cities, etc...), it poses a serious methodological challenge. Indeed, while classical software applications operate in the well known and delimited digital world, the so-called ambient applications operate in and through the physical world, open and subject to uncertainties that cannot be modeled accurately and entirely. These uncertainties lead the behavior of the ambient applications to potentially drift over time against requirements. In this paper, we propose a framework to estimate the behavioral drift of the ambient applications against requirements at runtime.

International audience

This paper presents an approach for the recognition of on-line handwritten mathematical expressions. The hidden Markov model (HMM) based system makes use of simultaneous segmentation and recognition capabilities, avoiding a crucial segmentation during pre-processing. With the segmentation and recognition results, obtained from the HMM recognizer it is possible to analyze and interpret the spatial two-dimensional arrangement of the symbols. We use a graph grammar approach for the structure recognition, also used in off-line recognition process, resulting in a general tree-structure of the underlying input-expression. The resulting constructed tree can be translated to any desired syntax (for example: Lisp, KT/sub E/X, and OpenMath).

The paper describes the design and the first steps of implementation of OFR (optical formula recognition), a system for extracting and understanding mathematical expressions in printed documents. Our approach clearly separate OCR step, geometrical treatments and syntactic analysis. We focus on the third part: we define a class of context sensitive graph grammars for mathematical formulas, study their properties and show how to remove their ambiguities (by adding contexts in rules) to define efficient parsing. This method is based on a "critical pairs" approach in the sense of Knuth-Bendix algorithm.