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Sheikh Bahaei, S. & Gallina, B. (2024). Assessing risk of AR and organizational changes factors in socio-technical robotic manufacturing. Robotics and Computer-Integrated Manufacturing, 88, Article ID 102731.
Open this publication in new window or tab >>Assessing risk of AR and organizational changes factors in socio-technical robotic manufacturing
2024 (English)In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 88, article id 102731Article in journal (Refereed) Published
Abstract [en]

Technological changes such as the use of Augmented Reality (AR) along with the advent of new organizational changes such as digitalization are on the one hand positively changing the way of working but on the other hand they are introducing new risks, potentially leading to not only normal but also post-normal accidents. In our previous work, we have incrementally proposed a novel framework, called FRAAR, for risk assessment of AR-equipped socio-technical systems (i.e., systems integrating human, organizational and technical entities (such as AR)). We have also partly evaluated our framework via an industrial automotive study and by providing comparison and positioning with respect to other related works in a systematic literature review. In this paper, we conduct a new study to evaluate the applicability and effectiveness of our framework in a different domain. To do that, we choose a digitalized socio-technical factory system, focusing on the human–robot collaboration for a realistic diesel engine assembly task using AR-based user interface in an organization affected by organizational changes. Then, we design and execute our study to apply our framework and we discuss about the extent the conceptualizations provided by the framework are effective to capture the essential information for risk assessment in socio-technical robotic manufacturing, the extent the robotic safety standards are supported (to demonstrate the applicability of the framework in the robotic domain) and the extent of effectiveness of the risk assessment with respect to AR and organizational changes. Finally, we discuss about validity of our work and we provide our findings and intended future work.

Place, publisher, year, edition, pages
Elsevier Ltd, 2024
Keywords
Augmented reality, Human robot collaboration, Organizational factors, Risk assessment, Socio-technical systems, Robots, Safety engineering, User interfaces, Automotives, Human-robot collaboration, Organisational, Organizational change, Related works, Risks assessments, Sociotechnical, Sociotechnical systems, Technological change
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-65956 (URN)10.1016/j.rcim.2024.102731 (DOI)001170745300001 ()2-s2.0-85183184391 (Scopus ID)
Available from: 2024-02-07 Created: 2024-02-07 Last updated: 2024-03-13Bibliographically approved
Gallina, B., Olesen, T. Y., Parajdi, E. & Aarup, M. (2023). A Knowledge Management Strategy for Seamless Compliance with the Machinery Regulation. In: Commun. Comput. Info. Sci.: . Paper presented at Communications in Computer and Information Science (pp. 220-234). Springer Science and Business Media Deutschland GmbH
Open this publication in new window or tab >>A Knowledge Management Strategy for Seamless Compliance with the Machinery Regulation
2023 (English)In: Commun. Comput. Info. Sci., Springer Science and Business Media Deutschland GmbH , 2023, p. 220-234Conference paper, Published paper (Refereed)
Abstract [en]

To ensure safety, the machinery sector has to comply with the machinery directive. Recently, this directive has been not only revised to include requirements concerning other concerns e.g., safety-relevant cybersecurity and machine learning-based safety-relevant reliable self-evolving behaviour but also transformed into a regulation to avoid divergences in interpretation derived from transposition. To be able to seamlessly and continuously comply with the regulation by 2027, it is fundamental to establish a strategy for knowledge management, aimed at enabling traceability and variability management where chunks of conformity demonstration can be traced, included/excluded based on the machinery characteristics and ultimately queried in order to co-generate the technical evidence for compliance. Currently, no such strategy is available. In this paper, we contribute to the establishment of such a strategy. Specifically, we build our strategy on top of the notion of multi-concern assurance, variability modelling via feature diagrams, and ontology-based modelling. We illustrate our proposed strategy by considering the requirements for the risk management process for generic machineries, refined into sub-sector-specific requirements in the case of centrifugal pumps. We also briefly discuss about our findings and the relationship of our work with the SPI manifesto. Finally, we provide our concluding remarks and sketch future work.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH, 2023
Keywords
Artificial Intelligence Act, Centrifugal pumps, Cyber Resilience Act, Cyber Security Act, EN 809:1998+A1, Machinery Directive, Machinery Regulation, Seamless and Continuous Compliance, Artificial intelligence, Cybersecurity, Knowledge management, Risk management, Safety engineering, Cybe resilience act, Cybe security act, Cyber security, Knowledge management strategy, Machinery sector, Ontology
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-64426 (URN)10.1007/978-3-031-42307-9_17 (DOI)2-s2.0-85172113145 (Scopus ID)9783031423062 (ISBN)
Conference
Communications in Computer and Information Science
Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2023-10-09Bibliographically approved
Bibbo, D., Mariajoseph, M., Gallina, B. & Carli, M. (2022). A Novel Physiological-Based System to Assess Drivers’ Stress during Earth Moving Simulated Activities. Electronics, 11(24), Article ID 4074.
Open this publication in new window or tab >>A Novel Physiological-Based System to Assess Drivers’ Stress during Earth Moving Simulated Activities
2022 (English)In: Electronics, E-ISSN 2079-9292, Vol. 11, no 24, article id 4074Article in journal (Refereed) Published
Abstract [en]

Earth-moving vehicles (EMVs) are vital in numerous industries, including construction, forestry, mining, cleaning, and agriculture. The changing nature of the off-road environment in which they operate makes situational awareness for readiness and, consequently, mental stress crucial for drivers and requires a high level of controllability. Therefore, the monitoring of drivers’ acute stress patterns may be used as an input in identifying various levels of attentiveness. This research presents an experimental evaluation of a physiological-based system that can be useful to evaluate the readiness of a driver in different conditions. For the experimental validation, physiological signals such as electrocardiogram (ECG), galvanic skin response (GSR) and speech data were collected from nine participants throughout driving experiments of increasing complexity on a specific simulator. The experimental results show that the identified parameters derived from the acquired physiological signals can help us understand the driver status when performing different tasks, the engagement of which is related to different road environments. This multi-parameter approach can provide more reliable information compared to single parameter approaches (e.g., eye monitoring with a camera) and identify driver status variations, from relaxed to stressed or drowsy. The use of these signals allows for the development of a smart driving cockpit, which could communicate to the vehicle the driver’s status, to set up an innovative protection system aiming to increase road safety. 

Place, publisher, year, edition, pages
MDPI, 2022
Keywords
autonomic nervous system, earth-moving vehicle, galvanic skin response, heart rate variability, stress estimation
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:mdh:diva-61425 (URN)10.3390/electronics11244074 (DOI)000902688000001 ()2-s2.0-85144870337 (Scopus ID)
Available from: 2023-01-04 Created: 2023-01-04 Last updated: 2023-01-25Bibliographically approved
Cârlan, C., Gauerhof, L., Gallina, B. & Burton, S. (2022). Automating Safety Argument Change Impact Analysis for Machine Learning Components. In: Proc. IEEE Pac. Rim Int. Symp. Dependable Comput., PRDC: . Paper presented at Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC, Online, 28 November - 1 December, 2022 (pp. 43-53). IEEE Computer Society
Open this publication in new window or tab >>Automating Safety Argument Change Impact Analysis for Machine Learning Components
2022 (English)In: Proc. IEEE Pac. Rim Int. Symp. Dependable Comput., PRDC, IEEE Computer Society , 2022, p. 43-53Conference paper, Published paper (Refereed)
Abstract [en]

The need to make sense of complex input data within a vast variety of unpredictable scenarios has been a key driver for the use of machine learning (ML), for example in Automated Driving Systems (ADS). Such systems are usually safety-critical, and therefore they need to be safety assured. In order to consider the results of the safety assurance activities (scoping uncovering previously unknown hazardous scenarios), a continuous approach to arguing safety is required, whilst iteratively improving ML-specific safety-relevant properties, such as robustness and prediction certainty. Such a continuous safety life cycle will only be practical with an efficient and effective approach to analyzing the impact of system changes on the safety case. In this paper, we propose a semi-automated approach for accurately identifying the impact of changes on safety arguments. We focus on arguments that reason about the sufficiency of the data used for the development of ML components. The approach qualitatively and quantitatively analyses the impact of changes in the input space of the considered ML component on other artifacts created during the execution of the safety life cycle, such as datasets and performance requirements and makes recommendations to safety engineers for handling the identified impact. We implement the proposed approach in a model-based safety engineering environment called FASTEN, and we demonstrate its application for an ML-based pedestrian detection component of an ADS.

Place, publisher, year, edition, pages
IEEE Computer Society, 2022
Keywords
Learning systems, Life cycle, Machine components, Pedestrian safety, Automated driving systems, Change impact analyse, Change impact analysis, Design domains, Machine learning, Machine-learning, Operational design, Operational design domain, Safety arguments, Safety case, Change Impact Analysis (CIA), Machine Learning (ML), Operational Design Domain (ODD), Safety Cases
National Category
Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-61962 (URN)10.1109/PRDC55274.2022.00019 (DOI)000965064800005 ()2-s2.0-85147854756 (Scopus ID)9781665485555 (ISBN)
Conference
Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC, Online, 28 November - 1 December, 2022
Available from: 2023-02-22 Created: 2023-02-22 Last updated: 2023-05-17Bibliographically approved
Castellanos Ardila, J. P., Gallina, B. & UL Muram, F. (2022). Compliance checking of software processes: A systematic literature review. Journal of Software: Evolution and Process, 34(5), Article ID e2440.
Open this publication in new window or tab >>Compliance checking of software processes: A systematic literature review
2022 (English)In: Journal of Software: Evolution and Process, ISSN 2047-7473, E-ISSN 2047-7481, Vol. 34, no 5, article id e2440Article, review/survey (Refereed) Published
Abstract [en]

The processes used to develop software need to comply with normative requirements (e.g., standards and regulations) to align with the market and the law. Manual compliance checking is challenging because there are numerous requirements with changing nature and different purposes. Despite the importance of automated techniques, there is not any systematic study in this field. This lack may hinder organizations from moving toward automated compliance checking practices. In this paper, we characterize the methods for automatic compliance checking of software processes, including used techniques, potential impacts, and challenges. For this, we undertake a systematic literature review (SLR) of studies reporting methods in this field. As a result, we identify solutions that use different techniques (e.g., anthologies and metamodels) to represent processes and their artifacts (e.g., tasks and roles). Various languages, which have diverse capabilities for managing competing and changing norms, and agile strategies, are also used to represent normative requirements. Most solutions require tool-support concretization and enhanced capabilities to handle processes and normative diversity. Our findings outline compelling areas for future research. In particular, there is a need to select suitable languages for consolidating a generic and normative-agnostic solution, increase automation levels, tool support, and boost the application in practice by improving usability aspects.

Place, publisher, year, edition, pages
WILEY, 2022
Keywords
compliance checking, normative frameworks, software processes, systematic literature review
National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-57706 (URN)10.1002/smr.2440 (DOI)000768555100001 ()2-s2.0-85126225058 (Scopus ID)
Available from: 2022-03-30 Created: 2022-03-30 Last updated: 2022-06-07Bibliographically approved
Mirzaei, E., Carmen, C., Carsten, T. & Gallina, B. (2022). Design-time specification of dynamic modular safety cases in support of run-time safety assessment. In: : . Paper presented at Thirtieth Safety-Critical Systems Symposium, february 2022.
Open this publication in new window or tab >>Design-time specification of dynamic modular safety cases in support of run-time safety assessment
2022 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Open Adaptive Complex Systems – such as road vehicle platoons or fleets of cooperative robots – may use dynamic reconfiguration to adapt to system or environment changes. One approach enabling this feature is Service-oriented Reconfiguration, where new configurations are created by composing the available services in an unconstrained manner. Due to the high number of possible service compositions, not all configurations can be pre-assured at design-time. Despite recent progress, there is no satisfactory approach for specifying safety cases in support of their re-evaluation at run-time, after system reconfiguration. To this end, in previous work, we introduced Dynamic Modular Safety Cases (DMSC). A DMSC is a modular safety case, which can be dynamically re-constructed and re-assessed given service reconfiguration. In continuation of the previous work, in this paper we provide guidelines for specifying safety cases at design-time, whose modular structure mirrors the system service decomposition, to enable their re-construction and re-evaluation at run-time in the event of a system reconfiguration. Aiming to support the specification of DMSC, we extend FASTEN, an engineering tool for the design and verification of safety-critical systems. We exemplify the specification of DMSCs in FASTEN for an illustrative example from the smart factory domain.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-61279 (URN)
Conference
Thirtieth Safety-Critical Systems Symposium, february 2022
Available from: 2022-12-15 Created: 2022-12-15 Last updated: 2022-12-15Bibliographically approved
Gallina, B., Montecchi, L., de Oliveira, A. L. & Bressan, L. (2022). Multiconcern, Dependability-Centered Assurance Via a Qualitative and Quantitative Coanalysis. IEEE Software, 39(4), 39-47
Open this publication in new window or tab >>Multiconcern, Dependability-Centered Assurance Via a Qualitative and Quantitative Coanalysis
2022 (English)In: IEEE Software, ISSN 0740-7459, E-ISSN 1937-4194, Vol. 39, no 4, p. 39-47Article in journal (Refereed) Published
Abstract [en]

To contribute to multiconcern assurance, we focus on system design and present a high-level process that builds on top of the synergy between qualitative and quantitative dependability analysis techniques, which have been used for mono- as well as multiconcern analysis.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Keywords
Quality assurance, ISO Standards, Computer security, Certification, Statistical analysis, Safety, Multi-concern assurance, Functional safety, Cybersecurity, ISO 26262, ISO 21434, ISO, IEC, IEEE 42010, Multiconcern qualitative and quantitative co-analysis
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-59525 (URN)10.1109/MS.2022.3167370 (DOI)000814619100018 ()2-s2.0-85128613618 (Scopus ID)
Available from: 2022-07-06 Created: 2022-07-06 Last updated: 2022-08-30Bibliographically approved
Sheikh Bahaei, S. & Gallina, B. (2022). Technical Report on Assessing Risk of AR and Organizational Changes Factors in Socio-technical Robotic Manufacturing.
Open this publication in new window or tab >>Technical Report on Assessing Risk of AR and Organizational Changes Factors in Socio-technical Robotic Manufacturing
2022 (English)Report (Other academic)
Series
MRTC Report ; MDH-MRTC-346/2022-1-SE
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-61301 (URN)
Available from: 2022-12-15 Created: 2022-12-15 Last updated: 2023-03-10Bibliographically approved
Sheikh Bahaei, S. & Gallina, B. (2022). Technical report on risk assessment of safety-critical socio-technical systems: a systematic literature review.
Open this publication in new window or tab >>Technical report on risk assessment of safety-critical socio-technical systems: a systematic literature review
2022 (English)Report (Other academic)
Series
MRTC Report ; MDH-MRTC-345/2022-1-SE
National Category
Other Engineering and Technologies
Identifiers
urn:nbn:se:mdh:diva-61298 (URN)
Available from: 2022-12-15 Created: 2022-12-15 Last updated: 2023-03-10Bibliographically approved
Sheikh Bahaei, S., Gallina, B. & Vidovic, M. (2021). A case study for risk assessment in AR-equipped socio-technical systems. Journal of systems architecture, 119, Article ID 102250.
Open this publication in new window or tab >>A case study for risk assessment in AR-equipped socio-technical systems
2021 (English)In: Journal of systems architecture, ISSN 1383-7621, E-ISSN 1873-6165, Vol. 119, article id 102250Article in journal (Refereed) Published
Abstract [en]

Augmented Reality (AR) technologies are used as human-machine interface within various types of safety critical systems. Several studies have shown that AR improves human performance. However, the introduction of AR might introduce risks due to new types of dependability threats. In order to avoid unreasonable risk, it is required to detect new types of dependability threats (faults, errors, failures). In our previous work, we have designed extensions for the SafeConcert metamodel (a metamodel for modeling socio-technical systems) to capture AR-related dependability threats (focusing on faults and failures). Despite the availability of various modeling techniques, there has been no detailed investigation of providing an integrated framework for risk assessment in AR-equipped socio-technical systems. Hence, in this paper, we provide an integrated framework based on our previously proposed extensions. In addition, in cooperation with our industrial partners, active in the automotive domain, we design and execute a case study. We aim at verifying the modeling and analysis capabilities of our framework and finding out if the proposed extensions are helpful in capturing system risks caused by new AR-related dependability threats. Our conducted qualitative analysis is based on the Concerto-FLA analysis technique, which is included in the CHESS toolset and targets socio-technical systems.

National Category
Computer Systems
Identifiers
urn:nbn:se:mdh:diva-56204 (URN)10.1016/j.sysarc.2021.102250 (DOI)000701678200005 ()2-s2.0-85112660178 (Scopus ID)
Available from: 2021-10-14 Created: 2021-10-14 Last updated: 2023-03-10Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-6952-1053

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