An ocarina extension for AADL formal semantics generation

International audienceThe formal veri cation has become a recommended practice in safety-critical software engineering. The hand-written of the for- mal speci cation requires a formal expertise and may become com- plex especially with large systems. In such context, the automatic generation of the formal speci cation seems helpful and reward- ing, particularly for reused and generic mapping such as hardware representations and real-time features. In this paper, we aim to formally verify real-time systems designed by AADL language. We propose an extension AADL2LNT of the Ocarina tool suite allowing the automatic generation of an LNT speci cation to draw a gateway for the CADP formal analysis toolbox. This work is illustrated with the Pacemaker case study

Optimal Cost for Time-Aware Cloud Resource Allocation in Business Process

Cloud Computing infrastructures are being increasingly used for running business process activities due to its high performance level and low operating cost. The enterprise QoS requirements are diverse and different resources are offered by Cloud providers in various QoS-based pricing strategies. Furthermore, business process activities are constrained by hard timing constraints and if they are not executed correctly the enterprise will pay penalties costs. Therefore, finding the optimal Cloud resources allocation for a business process becomes a highly challenging problem. While optimizing the Cloud resource allocation cost, it is important to respect activities QoS requirements and temporal constraints and Cloud pricing strategies constraints. The aim of the present paper is to offer a method that assists users finding the optimal pricing strategy for Cloud resource used by business process activities. Basically, we use a binary/(0-1) linear program with an objective function under a set of constraints. In order to show its feasibility, our approach has been implemented and the results of our experiments highlight the effectiveness of our proposed solution

DiffECG: A Generalized Probabilistic Diffusion Model for ECG Signals Synthesis

In recent years, deep generative models have gained attention as a promising data augmentation solution for heart disease detection using deep learning approaches applied to ECG signals. In this paper, we introduce a novel approach based on denoising diffusion probabilistic models for ECG synthesis that covers three scenarios: heartbeat generation, partial signal completion, and full heartbeat forecasting. Our approach represents the first generalized conditional approach for ECG synthesis, and our experimental results demonstrate its effectiveness for various ECG-related tasks. Moreover, we show that our approach outperforms other state-of-the-art ECG generative models and can enhance the performance of state-of-the-art classifiers.Comment: under revie

Toward a correct and optimal time-aware cloud resource allocation to business processes

© 2020 Elsevier B.V. Cloud is an increasingly popular computing paradigm that provides on-demand services to organizations for deploying their business processes over the Internet as it reduces their needs to plan ahead for provisioning resources. Cloud providers offer competitive pricing strategies (e.g., on-demand, reserved, and spot) specified based on temporal constraints to accommodate organizations’ changing and last-minute demands. Despite their varieties and benefits to optimize business process deployment cost, using those pricing strategies can lead to violating time constraints and exceeding budget constraints due to inappropriate decisions when allocating cloud resources to business processes. In this paper, we present an approach to guarantee a correct and optimal time-aware allocation of cloud resources to business processes. Correct because time constraints on these processes are not violated. And, optimal because the deployment cost of these processes is minimized. For this purpose, our approach uses timed automata to formally verify the matching between business processes’ temporal constraints and cloud resources’ time availabilities and linear programming to optimize deployment costs. Experiments demonstrate the technical doability of our proposed approach

Leveraging Statistical Shape Priors in GAN-based ECG Synthesis

Due to the difficulty of collecting electrocardiogram (ECG) data during emergency situations, ECG data generation is an efficient solution for dealing with highly imbalanced ECG training datasets. However, due to the complex dynamics of ECG signals, the synthesis of such signals is a challenging task. In this paper, we present a novel approach for ECG signal generation based on Generative Adversarial Networks (GANs). Our approach combines GANs with statistical ECG data modeling to leverage prior knowledge about ECG dynamics in the generation process. To validate the proposed approach, we present experiments using ECG signals from the MIT-BIH arrhythmia database. The obtained results show the benefits of modeling temporal and amplitude variations of ECG signals as 2-D shapes in generating realistic signals and also improving the performance of state-of-the-art arrhythmia classification baselines.Comment: 6 figures, 26 page

A formal approach to AADL model-based software engineering

Formal methods have become a recommended practice in safety-critical software engineering. To be formally verified, a system should be specified with a specific formalism such as Petri nets, automata and process algebras, which requires a formal expertise and may become complex especially with large systems. In this paper, we report our experience in the formal verification of safety-critical real-time systems. We propose a formal mapping for a real-time task model using the LNT language, and we describe how it is used for the integration of a formal verification phase in an AADL model-based development process. We focus on real-time systems with event-driven tasks, asynchronous communication and preemptive fixed-priority scheduling. We provide a complete tool-chain for the automatic model transformation and formal verification of AADL models. Experimentation illustrates our results with the Flight control system and Line follower robot case studies

An Operational Semantics Dedicated to the Coordination of Cooperating Agents

Abstract. This paper presents a contribution towards rigourous reasoning about coordinating agents. First, it defines formal models for coordination and coordinating agents. These models enable to specify the relations between the concepts of: plan, plan proposal and resource allocation, on the one hand, and concepts of: knowledge, belief and capability, on the other hand. Second, it provides a structured coordination language enabling to specify primitives, protocols and processes of coordination. This language is defined by a precise syntax, and it is formally interpreted using a transition system leading to an operational semantics for coordinating agents

Strategic Partnerships in e-Health in Low and Lower Middle-Income Countries in Africa

Strategic partnerships are very important for the successful deployment of e-health as they play a crucial role in achieving common goals and creating an added value for the involved partners. In this paper, we will provide relevant information about strategic partnerships in e-health deployment in four African countries, namely Ethiopia, Ghana, Malawi, and Tunisia. A Partnership Assessment Tool is developed to analyze different aspects of partnerships and classify them. According to the analysis, 11 partnerships were strategic amongst the 15 identified. Findings analysis also shows that certain aspects, mainly sustainability, have to be enhanced to guarantee the impact of partnerships after the ending of its actions. Increased governmental support is required in addition to international funding resources to the successful deployment of e-health in the participating countries.publishedVersio

Best Practices and Lessons Learned in eHealth in Four Low and Lower Middle-Income Countries in Africa

Studying best practices and lessons learned is important to improve performance and avoid previous mistakes of projects and interventions. In this paper, the analysis of ehealth interventions in four African Low and Lower Middle-Income Countries, Ethiopia, Ghana, Malawi and Tunisia is performed to extract best practices and lessons learned. A two-level evaluation methodology is proposed, where the first level is based on data available on the Global Digital Health Index platform, and the second level is a qualitative analysis based on a set of criteria. The findings obtained reveal 7 best practices and associated lessons learned in the studied countries. Although the extracted best practices represent successful interventions, the analysis indicates that certain aspects represent challenges to their success, namely, sustainability, transferability, innovation and impact.publishedVersio