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Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Roc Boronat, 138, 08018 Barcelona and Spain

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6 Papers

Efficient Modelling with Logic-Labelled Finite-State Machines of IEC 61499 Function Blocks: Simulation, Execution and Verification

Topics: Model Transformation; Model-Based Testing and Validation; Model-Based Software Development; Model-Based Development of Cyber Physical Systems

Authors: Vladimir Estivill-Castro ¹ , Miguel Carrillo ² and David Rosenblueth ²

Affiliations: ¹ Department of Engineering, Pompeu Fabra University, Roc Boronat 138, Barcelona 08018, Spain , ² Instituto de Investigaciones en Matemáaticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Apdo. 20-126, Ciudad de M˙exico 01000, Mexico

Digital Object Identifier: 10.5220/0013095200003896

Proceedings of MODELSWARD 2025, ISBN: 978-989-758-729-0

Quick Abstract

As automation grows, so does the complexity of software systems. Hence, the urgent and pressing need for software verification, particularly for distributed systems, as they are notoriously difficult to verify. The widespread of verification techniques, such as model checking, however, have been hindered by requiring a significant level of expertise. In the realm of industrial automation, on the other hand, the IEC 61499 function block architecture has gained prominence for modelling intricate distributed automation systems, especially in demanding scenarios such as process control. However, it suffers from being event-driven, forcing semantic interpretations and the use of timed events by a central clock, to produce input for model checkers. We argue that this situation can be remedied by logic-labelled finite-state machines and control-status messages. This is the first time that these concepts have been used for producing executable and verifiable models of distributed systems for industrial automation with communication delays as is the current environment of application of the IEC 61499.

LLFSMs to TLA+: A Model-to-Text Transformation of Executable Models Enabling Specification and Verification of Multi-Threaded and Concurrent Systems

Topics: Model Transformation; Reasoning about Models; Model-Based Testing and Validation; Software and Systems Engineering ; Model-Based Software Development

Authors: Vladimir Estivill-Castro ¹ , Miguel Carrillo ² and David Rosenblueth ²

Digital Object Identifier: 10.5220/0013094700003896

Proceedings of MODELSWARD 2025, ISBN: 978-989-758-729-0

Quick Abstract

As complexity of software systems increases, ensuring reliability becomes ever more crucial. Despite advances, behaviour-modelling techniques still face challenges due to semantic gaps. This work focuses on translating Logic-Labelled Finite-State Machines (LLFSMs) to the Temporal Logic of Actions (TLA), bridging the gap between a time-triggered formalism and common temporal logic for model checking. The translation is innovative as multi-threaded and distributed systems can now be designed using LLFSMs. We illustrate the translation with Fischer’s protocol (for multi-threaded systems), and release tools with examples for distributed systems. The approach addresses semantic gaps from three sources: differing finite-state machine semantics, variations in translating to executable models versus models for checking, and discrepancies between abstract and executable model translations.

Verifiable Executable Models for Decomposable Real-time Systems

Topics: Model Execution and Simulation; Reasoning about Models; Systems Engineering; Model Quality; Model-Driven Development of Cyber Physical Systems; Model-Driven Software and Services Infrastructures

Authors: Callum McColl ¹ , Vladimir Estivill-Castro ² , Morgan McColl ¹ and René Hexel ¹

Affiliations: ¹ School of Information and Communication Technology, Griffith University, Brisbane, Australia , ² Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Barcelona 08018, Spain

Digital Object Identifier: 10.5220/0010812200003119

Proceedings of MODELSWARD 2022, ISBN: 978-989-758-550-0

Quick Abstract

Formally verifiable, executable models allow the high-level design, implementation, execution, and validation of reliable systems. But, unbounded complexity, semantic gaps, and combinatorial state explosion have drastically reduced the use of model-driven software engineering for even moderately complex real-time systems. We introduce a new solution that enables high level, executable models of decomposable real-time systems. Our novel approach allows verification in both the time domain and the value domain. We show that through 1) the use of a static, worst-case execution time, and 2) our time-triggered deterministic scheduling of arrangements of logic-labelled finite-state machines (LLFSMs), we can create succinct Kripke structures that are fit for formal verification, including verification of timing properties. We leap further and enable parallel, non-preemptive scheduling of LLFSMs where verification is feasible as the faithful Kripke structure has bounded size. We evaluate our approach through a case study where we fully apply a model-driven approach to a hard time-critical system of parallel sonar sensors.

A System Dynamics Model Approach for Simulating Hyper-inflammation in Different COVID-19 Patient Scenarios

Topics: Decision Support Systems; Case Studies; Biological and Social Systems Simulation; Biologically and Nature-Inspired Systems Simulation

Authors: Vladimir Estivill-Castro ¹ , Enrique Hernández-Jiménez ^{2

;

3} and David F. Nettleton ¹

Affiliations: ¹ Department of Information and Communications Technology (DTIC), Universitat Pompeu Fabra, Spain , ² Institut d’Investigació Biomèdica de Bellvitge, Barcelona, Spain , ³ Loop Diagnostics S.L., Barcelona, Spain

Digital Object Identifier: 10.5220/0010600301410153

Proceedings of SIMULTECH 2021, ISBN: 978-989-758-528-9

Quick Abstract

The exceptionally high virulence of COVID-19 and the patients’ precondition seem to constitute primary factors in how pro-inflammatory cytokines production evolves during the course of an infection. We present a System Dynamics Model approach for simulating the patient reaction using two key control parameters (i) virulence, which can be “moderate” or “high” and (ii) patient precondition, which can be “healthy”, “not so healthy” or “serious preconditions”. In particular, we study the behaviour of Inflammatory (M1) Alveolar Macrophages, IL6 and Active Adaptive Immune system as indicators of the immune system response, together with the COVID viral load over time. The results show that it is possible to build an initial model of the system to explore the behaviour of the key attributes involved in the patient condition, virulence and response. The model suggests aspects that need further study so that it can then assist in choosing the correct immunomodulatory treatment, for instance the regime of application of an Interleukin 6 (IL-6) inhibitor (tocilizumab) that corresponds to the projected immune status of the patients. We introduce machine learning techniques to corroborate aspects of the model and propose that a dynamic model and machine learning techniques could provide a decision support tool to ICU physicians.

Panel of Attribute Selection Methods to Rank Features Drastically Improves Accuracy in Filtering Web-pages Suitable for Education

Topic: Authoring Tools and Content Development

Authors: Vladimir Estivill-Castro ¹ , Matteo Lombardi ² and Alessandro Marani ²

Affiliations: ¹ Department of TIC, University Popeu Fabra, Barcelona 08018 and Spain , ² School of ICT, Griffith University, Nathan Campus, Brisbane 4111 and Australia

Digital Object Identifier: 10.5220/0007676300480057

Proceedings of CSEDU 2019, ISBN: 978-989-758-367-4

Quick Abstract

Search engines and recommender system take advantage of user queries, characteristics, preferences or perceived needs for filtering results. In contexts such as education, considering the purpose of a resource is also fundamental. A document not suitable for learning, although well related to the query, should never be recommended to a student. However, users are currently obliged to spend additional time and effort for matching the machine-filtered results to their purpose. This paper presents a method for automatically filtering web-pages according to their educational usefulness. Our ground truth is a dataset where items are web-pages classified as relevant for education or not. Then, we present a new feature selection method for lowering the number of attributes of the items. We build a committee of feature selection methods, but do not use it as an ensemble. A comprehensive evaluation of our approach against current practices in feature selection and feature reduction demonstrates that our proposal 1) enables state-of-the-art classifiers to perform a significantly faster, yet very accurate, automatic filtering of educational resources, and 2) such filtering meaningfully considers the usefulness of the resource for educational tasks.

Related Ontology Subjects/Areas/Topics (4)

Authoring Tools and Content Development		e-Learning		Information Technologies Supporting Learning
Computer-Supported Education

Privacy Preservation of Social Network Users Against Attribute Inference Attacks via Malicious Data Mining

Topic: Privacy, Security and Trust in Social Media

Authors: Khondker Jahid Reza ¹ , Md Zahidul Islam ¹ and Vladimir Estivill-Castro ²

Affiliations: ¹ School of Computing and Mathematics, Charles Sturt University, Panorama Avenue, Bathurst 2795, NSW and Australia , ² Departament de Tecnologies de la Informació i les Comunicacions, Universitat Pompeu Fabra, Roc Boronat, 138, 08018 Barcelona and Spain

Digital Object Identifier: 10.5220/0007390404120420

Proceedings of ICISSP 2019, ISBN: 978-989-758-359-9

Quick Abstract

Online social networks (OSNs) are currently a popular platform for social interactions among people. Usually, OSN users upload various contents including personal information on their profiles. The ability to infer users’ hidden information or information that has not been even uploaded (i.e. private/sensitive information) by an unauthorised agent is commonly known as attribute inference problem. In this paper, we propose 3LP+, a privacy-preserving technique, to protect users’ sensitive information leakage. We apply 3LP+ on a synthetically generated OSN data set and demonstrate the superiority of 3LP+ over an existing privacy-preserving technique.