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Publications (10 of 136) Show all publications
Möller, T., Singh, I., Bucaioni, A. & Cicchetti, A. (2024). Automating Data Extraction from Semi-Structured Industrial Documents: The Alstom Experience. In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA: . Paper presented at 29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024, Padova 10 September 2024 through 13 September 2024. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Automating Data Extraction from Semi-Structured Industrial Documents: The Alstom Experience
2024 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc. , 2024Conference paper, Published paper (Refereed)
Abstract [en]

In the system development of modern railroad vehicles, engineers frequently use a plethora of diverse notations to specify various systems, subsystems, and their associated concerns. The use of diverse notations introduces complex challenges linked with their management and integration. Conventional practices, which rely on manual revisions and translations, prove to be both time-intensive and cost-prohibitive. In addition, they carry substantial risks of human error, thereby potentially introducing faults into the system. Such practices are deemed inadequate for the railway industry, which is safety-critical in its nature and places paramount importance on the assurance of reliability and data integrity. To address these challenges, we developed a regular expression-based system facilitating the automatic translation of semi-structured texts into structured data, with a particular focus on ensuring data integrity and reliability. We have defined the system capitalizing on the insights and practical experience of our industrial partner, Alstom Rail Sweden AB, and validated it within their development process. The validation demonstrated the practicality of the system in a real-world context and highlighted valuable lessons learned throughout the process. Building on these insights, we applied model-driven engineering principles to generalize the system, providing an automated solution to the data extraction challenge from tender documents in the railway domain. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2024
Keywords
automation, data extraction, MDE, railroad, tender documents, Vehicular embedded software systems, Industrial locomotives, Interlocking signals, Network security, Steganography, Cost prohibitive, Data integrity, Embedded software systems, Railroad vehicles, Semi-structured, System development, Vehicular embedded software system
National Category
Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-69006 (URN)10.1109/ETFA61755.2024.10711023 (DOI)2-s2.0-85207849123 (Scopus ID)9798350361230 (ISBN)
Conference
29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024, Padova 10 September 2024 through 13 September 2024
Available from: 2024-11-13 Created: 2024-11-13 Last updated: 2024-11-13Bibliographically approved
Beqiri, L., Montero, C. S., Cicchetti, A. & Kruglyak, A. (2024). Classifying Ambiguous Requirements: An Explainable Approach in Railway Industry. In: Proc. - IEEE Int. Requir. Eng. Conf. Workshops, REW: . Paper presented at 32nd IEEE International Requirements Engineering Conference Workshops, REW 2024. Reykjavik24 June 2024 through 28 June 2024. Code 201933 (pp. 12-21). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Classifying Ambiguous Requirements: An Explainable Approach in Railway Industry
2024 (English)In: Proc. - IEEE Int. Requir. Eng. Conf. Workshops, REW, Institute of Electrical and Electronics Engineers Inc. , 2024, p. 12-21Conference paper, Published paper (Refereed)
Abstract [en]

A clear understanding of customers' requirements is fundamental towards developing products that behave as intended. Customers commonly use natural language (NL) to specify their requirements. As NL is innately ambiguous and an industrial project could contain thousands of specifications., requirement analysis becomes a highly demanding and time-consuming task. One of the goals in industry is, therefore., to minimise the amount of time spent manually analysing requirements. This article presents a natural language processing (NLP) approach to automatically classify rail domain requirements based on the presence of ambiguity., and to provide textual explanations regarding the reason behind the classification. Traditional machine learning (ML) classification models are trained using lexical features from requirements and experts' comments concerning ambiguity on annotated real-world data. 10-fold cross-validation results show an F-score up to 0.87., with a recall up to 0.88. Furthermore., a validation of the model with 100 additional requirements achieved an accuracy of 0.78., with 76% match between the model's and expert's classification. The provided explanations are important for the expert in understanding the key decision terms involved in the classification., as well as provided insights on the presence of ambiguities in requirements. Ours is among the first works that uses explainability to tackle ambiguity in textual requirements., employing NLP and ML.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2024
Keywords
ambiguity, explainability, natural language processing, requirements analysis, Requirements engineering, Customer requirements, Developing product, Industrial programs, Language processing, Natural languages, Railway industry, Requirement analysis, Natural language processing systems
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-68414 (URN)10.1109/REW61692.2024.00007 (DOI)001304537500003 ()2-s2.0-85203106701 (Scopus ID)9798350395518 (ISBN)
Conference
32nd IEEE International Requirements Engineering Conference Workshops, REW 2024. Reykjavik24 June 2024 through 28 June 2024. Code 201933
Available from: 2024-09-11 Created: 2024-09-11 Last updated: 2024-11-06Bibliographically approved
Heip Hong, T., Sirjani, M., Moradi, F., Cicchetti, A. & Ciccozzi, F. (2024). Combining model-based development and formal verification of a complex ROS2 multi-robots system using Timed Rebeca. In: International Workshop on Reliability Engineering Methods for Autonomous Robots – REMARO 2024: . Paper presented at International Workshop on Reliability Engineering Methods for Autonomous Robots – REMARO 2024.
Open this publication in new window or tab >>Combining model-based development and formal verification of a complex ROS2 multi-robots system using Timed Rebeca
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2024 (English)In: International Workshop on Reliability Engineering Methods for Autonomous Robots – REMARO 2024, 2024Conference paper, Oral presentation with published abstract (Other academic)
Abstract [en]

ROS2 is an increasingly popular middleware framework for developing robotic applications. A ROS2 application is composed of nodes that run concurrently, coordinate with each other through asynchronous interfaces, and can be deployed in a distributed manner. Rebeca is an actor-based language for modelling asynchronous and concurrent systems. Timed Rebeca adds timing features to deal with timing requirements of real-time systems. The similarities in concurrency and message-based asynchronous interactions of reactive nodes, and the ability of modelling timed behaviours justify using Timed Rebeca models to assist the development and verification of ROS2 applications. Model-based development and model-checking techniques allow faster prototyping and earlier detection of system errors before developing the entire real system. However, there are challenges in bridging the gap between continuous behaviours of robotic systems and discrete states in a model for automatic verification, and between complex robotic computations and inequivalent programming facilities in a modelling language like Timed Rebeca. We investigated the problem systematically and have succeeded in modelling a realistic multiple autonomous mobile robots (AMR) system using Timed Rebeca, creating corresponding ROS2 demo code, and showing the match between the model and the program. Based on experiments, we demonstrated the value of the model in development and automatic formal verification of correctness properties (target reachability, collision freedom, and deadlock freedom). Our model-checking results show that certain system problems are not always detected through simulation. The modelling principles, modelling and implementation techniques that are created and used in this work can be generalized for many other cases. 

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-69525 (URN)
Conference
International Workshop on Reliability Engineering Methods for Autonomous Robots – REMARO 2024
Available from: 2024-12-12 Created: 2024-12-12 Last updated: 2025-01-08Bibliographically approved
Bucchiarone, A., Cicchetti, A., Vazquez-Ingelmo, A., Adami, F., Schiavo, G., Garcia-Holgado, A. & Garcia-Penalvo, F. J. (2024). Designing and Generating Lesson Plans combining Open Educational Content and Generative AI. In: ACM/IEEE 27TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS: COMPANION PROCEEDINGS, MODELS 2024. Paper presented at ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS), SEP 22-27, 2024, Linz, AUSTRIA (pp. 78-86). ASSOC COMPUTING MACHINERY
Open this publication in new window or tab >>Designing and Generating Lesson Plans combining Open Educational Content and Generative AI
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2024 (English)In: ACM/IEEE 27TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS: COMPANION PROCEEDINGS, MODELS 2024, ASSOC COMPUTING MACHINERY , 2024, p. 78-86Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose an approach for assisting educators in deriving lesson plans for complex learning subjects like ModelDriven Engineering (MDE) from existing educational materials, leveraging generative AI techniques. Our method focuses on guiding teachers in defining learning objectives and suggesting concrete learning activities for students. Central to our approach is the development of a metamodel that characterizes the methodology and serves as the foundation for implementing supporting tools. By utilizing available Open Educational Resources (OERs) and incorporating them into specific learning activities, our method provides a general framework for supporting educators in designing lesson plans. We present the methodology to generate lesson plans, the metamodel conceptualizing plans ingredients, and demonstrate their application through supporting tools, illustrating the potential of our approach in facilitating the development of MDE teaching materials.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY, 2024
Keywords
Open Educational Resources, OERs, Model-driven engineering, MDE, Generative AI, Educational Paradigms, Tailored Learning Activities, Customizable Learning Content
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-69429 (URN)10.1145/3652620.3687773 (DOI)001351589800017 ()979-8-4007-0622-6 (ISBN)
Conference
ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS), SEP 22-27, 2024, Linz, AUSTRIA
Available from: 2024-12-10 Created: 2024-12-10 Last updated: 2024-12-10Bibliographically approved
Bonetti, F., Bucchiarone, A., Michael, J., Cicchetti, A., Marconi, A. & Rumpe, B. (2024). Digital Twins of Socio-Technical Ecosystems to Drive Societal Change. In: ACM/IEEE 27TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS: COMPANION PROCEEDINGS, MODELS 2024. Paper presented at ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS), SEP 22-27, 2024, Linz, AUSTRIA (pp. 275-286). ASSOC COMPUTING MACHINERY
Open this publication in new window or tab >>Digital Twins of Socio-Technical Ecosystems to Drive Societal Change
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2024 (English)In: ACM/IEEE 27TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS: COMPANION PROCEEDINGS, MODELS 2024, ASSOC COMPUTING MACHINERY , 2024, p. 275-286Conference paper, Published paper (Refereed)
Abstract [en]

While the engineering of digital twins (DTs) of cyber-physical systems already faces a number of challenges, DTs of socio-technical systems are made even more complex by human and social factors, and a comprehensive representation of their internal relations is currently lacking. DTs for socio-technical systems could open up new ways of achieving common societal goals by i) providing an understanding of complex interactions and processes, and by ii) facilitating the design of and participation in collective actions. In this context, dynamic adaptation and motivational strategies would be required to swiftly address sub-optimal system behavior. To enable the model-driven engineering of DTs responding to such requirements, we propose a conceptual model of socio-technical systems and discuss it with use-case scenarios. The presented approach supports our vision of future DT-based model-driven interventions, empowering citizens and stakeholders in driving societal change and increasing community resilience.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY, 2024
Keywords
Digital Twin, Modeling, Socio-Technical System, Model-Driven Engineering, System Engineering
National Category
Mechanical Engineering
Identifiers
urn:nbn:se:mdh:diva-69428 (URN)10.1145/3652620.3686248 (DOI)001351589800046 ()979-8-4007-0622-6 (ISBN)
Conference
ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS), SEP 22-27, 2024, Linz, AUSTRIA
Available from: 2024-12-10 Created: 2024-12-10 Last updated: 2024-12-10Bibliographically approved
Cederbladh, J., Cicchetti, A. & Suryadevara, J. (2024). Early Validation and Verification of System Behaviour in Model-Based Systems Engineering: A Systematic Literature Review. ACM Transactions on Software Engineering and Methodology, 33(3), Article ID 81.
Open this publication in new window or tab >>Early Validation and Verification of System Behaviour in Model-Based Systems Engineering: A Systematic Literature Review
2024 (English)In: ACM Transactions on Software Engineering and Methodology, ISSN 1049-331X, E-ISSN 1557-7392, Vol. 33, no 3, article id 81Article in journal (Refereed) Published
Abstract [en]

In the Systems Engineering (SE) domain there has been a paradigm shift from document-based to model-based system development artefacts; in fact, new methodologies are emerging to meet the increasing complexity of current systems and the corresponding growing need of digital workflows. In this regard, Model-Based Systems Engineering (MBSE) is considered as a key enabler by many central players of the SE community. MBSE has reached an adequate level of maturity and there exist documented success stories in its adoption in industry. In particular, one significant benefit of utilising MBSE when compared to the traditional manual and document-centric workflows is that models are available from early phases of systems development; these enable a multitude of analyses prior any implementation effort together with other relevant capabilities, like the automation of development tasks. Nonetheless, it is noticeable there is a lack of a common understanding for how formal analyses for the verification and validation (V&V) of systems behaviour, specifically in the early phases of development, could be placed in an MBSE setting.

In this article, we report on the planning, execution, and results of a systematic literature review regarding the early V&V of systems behaviour in the context of model-based systems engineering. The review aims to provide a structured representation of the state-of-the-art with respect to motivations, proposed solutions, and limitations. From an initial set of potentially relevant 701 peer-reviewed publications we selected 149 primary studies, which we analysed according to a rigorous data extraction, analysis, and synthesis process. Based on our results, early V&V has usually the goal of checking the quality of a system design to avoid discovering flaws when parts are being concretely realised; SysML is a de facto standard for describing the system under study, while the solutions for the analyses tend to be varied; also V&V analyses tend to target varied properties with a slight predominance of functional concerns, and following the variation mentioned so far the proposed solutions are largely context specific; the proposed approaches are usually presented without explicit limitations, while when limitations are discussed, readiness of the solutions, handling of analyses simplifications/assumptions, and languages/tools integration are among the most frequently mentioned issues.

Based on the survey results and the standard SE practices, we discuss how the current state-of-the-art MBSE supports early V&V of systems behaviour with a special focus on industrial adoption, and identify relevant challenges to be researched further.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY, 2024
Keywords
MBSE, validation, verification, system behaviour, systematic literature review
National Category
Software Engineering
Identifiers
urn:nbn:se:mdh:diva-65935 (URN)10.1145/3631976 (DOI)001208684200012 ()2-s2.0-85191732317 (Scopus ID)
Available from: 2024-02-07 Created: 2024-02-07 Last updated: 2024-05-15Bibliographically approved
Cederbladh, J., Krems, D. & Cicchetti, A. (2024). Extending MagicGrid to Support Virtual Prototyping for Early System Performance Validation and Verification. In: MODELS Companion '24: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems: . Paper presented at MODELS Companion '24: ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, Linz, Austria, September 22-27, 2024 (pp. 287-298).
Open this publication in new window or tab >>Extending MagicGrid to Support Virtual Prototyping for Early System Performance Validation and Verification
2024 (English)In: MODELS Companion '24: Proceedings of the ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, 2024, p. 287-298Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we propose an extension to the MagicGrid framework to support virtual prototyping for early system performance Validation & Verification (V&V). Model-Based Systems Engineering (MBSE) is at a maturity where V&V of system performance is expected to be automated for mature analysis. However, current practices do not adequately cover nor describe how this can be enabled in standard MBSE processes using SysML models as the baseline for the system descriptions and knowledge capture. Therefore we propose an extension of the industrially accepted MagicGrid framework to cover virtual V&V in a tool and process agnostic method, supporting practitioners to develop and use models in MBSE for this purpose without a specific vendor or method lock-in. The framework extension is discussed for each new cell in the grid, and we provide guidelines and best practice discussions for how V&V should be enabled for each cell. Specifically, we discuss simulating/analysing SysML directly or through (co-)simulation. Automotive development is used as a running use case.

National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-68799 (URN)10.1145/3652620.3686249 (DOI)001351589800047 ()979-8-4007-0622-6 (ISBN)
Conference
MODELS Companion '24: ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, Linz, Austria, September 22-27, 2024
Available from: 2024-11-05 Created: 2024-11-05 Last updated: 2024-12-11Bibliographically approved
Bucchiarone, A., Panciera, M., Cicchetti, A., Mana, N., Castelluccio, C. & Stott, L. (2024). PromptDeck: A No-Code Platform for Modular Prompt Engineering. In: ACM/IEEE 27TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS: COMPANION PROCEEDINGS, MODELS 2024. Paper presented at ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS), SEP 22-27, 2024, Linz, AUSTRIA (pp. 895-904). ASSOC COMPUTING MACHINERY
Open this publication in new window or tab >>PromptDeck: A No-Code Platform for Modular Prompt Engineering
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2024 (English)In: ACM/IEEE 27TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS: COMPANION PROCEEDINGS, MODELS 2024, ASSOC COMPUTING MACHINERY , 2024, p. 895-904Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces a no-code platform for modular prompt engineering, designed to democratize access to generative AI for non-developers. By integrating advanced technologies such as Node.js, Express, MongoDB, and Azure OpenAI services, the platform provides a robust and flexible environment for creating and managing AI-driven tasks. The intuitive frontend, built with React and TypeScript, enables users with minimal coding expertise to design, execute, and evaluate complex AI workflows. A key feature of the platform is its extensible plugin system, which allows users to easily incorporate additional functionalities to meet their specific needs. This no-code approach empowers a broader audience to harness the power of generative AI, fostering innovation and enabling diverse applications across various fields. By lowering the technical barriers, the platform paves the way for widespread adoption of AI technologies, driving the future of AI-enhanced solutions.

Place, publisher, year, edition, pages
ASSOC COMPUTING MACHINERY, 2024
Keywords
Low-Code Development Platforms, No-Code, Generative AI, Prompt Engineering, Modularization
National Category
Information Systems, Social aspects
Identifiers
urn:nbn:se:mdh:diva-69430 (URN)10.1145/3652620.3688336 (DOI)001351589800122 ()979-8-4007-0622-6 (ISBN)
Conference
ACM/IEEE 27th International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings (MODELS), SEP 22-27, 2024, Linz, AUSTRIA
Available from: 2024-12-10 Created: 2024-12-10 Last updated: 2024-12-10Bibliographically approved
Latifaj, M., Ciccozzi, F. & Cicchetti, A. (2024). Role-Based Access Control for Collaborative Modeling Environments.
Open this publication in new window or tab >>Role-Based Access Control for Collaborative Modeling Environments
2024 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Collaborative model-driven software engineering fosters efficient cooperation among stakeholders who collaborate on shared models. Yet, the involvement of multiple parties brings forth valid concerns about the confidentiality and integrity of shared information. Unrestricted access to such information, especially when not pertinent to individual responsibilities, poses significant risks, including unauthorized information exposure and potential harm to information integrity. This work proposes a dual-layered solution implemented as an open-source Eclipse plugin that leverages the role-based access control policy to ensure the confidentiality and integrity of model information in collaborative modeling environments. The first layer limits stakeholders' access to the shared model based on their specific roles, while the second layer refines this access by restricting manipulations to individual model elements. By ensuring that stakeholders access only the information pertinent to their roles and are authorized to manipulate such information in accordance with their expertise and responsibilities, this approach ensures the confidentiality and integrity of shared model information. Furthermore, it alleviates information overload for stakeholders by enabling them to focus only on the model information relevant to their specific roles, thereby enhancing the collaborative efforts. 

Keywords
model-driven engineering, role-base access control, multi-view modeling, collaborative modeling
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-66540 (URN)
Available from: 2024-05-06 Created: 2024-05-06 Last updated: 2024-05-07Bibliographically approved
Cederbladh, J., Berardinelli, L., Bruneliere, H., Cicchetti, A., Dehghani, M., Di Sipio, C., . . . Suryadevara, J. (2024). Towards Automating Model-Based Systems Engineering in Industry: An Experience Report. In: SysCon 2024 - 18th Annual IEEE International Systems Conference, Proceedings: . Paper presented at SysCon 2024 - 18th Annual IEEE International Systems Conference, Montreal, Canada, 15-18th April, 2024. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Towards Automating Model-Based Systems Engineering in Industry: An Experience Report
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2024 (English)In: SysCon 2024 - 18th Annual IEEE International Systems Conference, Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2024Conference paper, Published paper (Refereed)
Abstract [en]

Designing modern Cyber-Physical Systems (CPSs) is posing new challenges to both industrial practitioners and academics. In this context, adopting cutting-edge paradigms, such as Model-Based Systems Engineering (MBSE), DevOps, and Artificial Intelligence (AI), can offer new opportunities for improving CPS design automation. While such paradigms are already jointly used in the research community to support system design activities, there is a need to fill the gap between academia and industrial practitioners. Indeed, system specification is still mainly performed manually in many industrial projects. In this paper, we present a collaboration between industrial and academic partners of the AIDOaRt European project towards a model-based approach for CPS engineering applied in one of the project use cases. We identify key challenges and corresponding solutions to enhance the automation of CPS design processes. Notably, we consider a combination of prescriptive modeling, model transformations, model views, modeling process mining, and AI-based modeling recommendations. As an initial evaluation, the proposed approach is applied to a practical industrial case study.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Artificial Intelligence, Cyber-Physical Systems, DevOps, Model-Based Systems Engineering, Computer aided design, Embedded systems, Industrial research, Specifications, Cutting edges, Cybe-physical systems, Design activity, Design automations, Experience report, Industrial practitioners, Model-based system engineerings, Research communities, Support systems, Cyber Physical System
National Category
Software Engineering
Identifiers
urn:nbn:se:mdh:diva-68049 (URN)10.1109/SysCon61195.2024.10553610 (DOI)001259228200125 ()2-s2.0-85197357111 (Scopus ID)9798350358803 (ISBN)
Conference
SysCon 2024 - 18th Annual IEEE International Systems Conference, Montreal, Canada, 15-18th April, 2024
Available from: 2024-07-12 Created: 2024-07-12 Last updated: 2024-08-07Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0416-1787

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