Recent advances in deep learning have significantly improved the accuracy of skin lesion classification models, supporting medical diagnoses and promoting equitable healthcare. However, concerns remain about potential biases related to skin color, which can impact diagnostic outcomes. Ensuring fairness is challenging due to difficulties in classifying skin tones, high computational demands, and the complexity of objectively verifying fairness, given the continuous and context-dependent nature of skin tone and the dependence of fairness conclusions on metric choice and subgroup representation. To address these challenges, we propose a fairness algorithm for skin lesion classification that overcomes the challenges associated with achieving diagnostic fairness across varying skin tones. By calculating the skewness of the feature map in the convolution layer of the Visual Geometry Group network (VGG) and the patches and the heads of the Vision Transformer (ViT), our method reduces unnecessary channels related to skin tone, focusing instead on the lesion area. Application on VGG11 and ViT-B16, showed improved fairness metrics by 15-20% on average while maintaining accuracy and F1-score within 0.01 of the baseline. Additionally, the method reduced model size by 16% for VGG11 and decreased memory footprint for ViT-B16, without requiring skin tone labels at inference. Thus, the approach lowers computational costs and mitigates bias without relying on conventional statistical methods. It potentially reduces model size while maintaining fairness, making it more practical for real-world applications.

Unmanned Aerial Vehicles (UAVs) have become essential in search and rescue operations, especially in disaster management scenarios. Their effective navigation and the integration of a plethora of sensors assist in efficient person detection, making them an essential technological tool to first responders. Multi-UAV systems extend these benefits by using coordinated strategies to cover large areas efficiently, reducing overall mission response time and enhancing its success. Despite these advantages, challenges remain in ensuring the safety, security, and dependability of (mutli-)UAV missions. Issues such as navigation risks, potential cyber threats, and hardware-/software-related reliability issues can impact the mission results. Additionally, UAVs are highly constrained devices with limited battery capacity, requiring the use of lightweight technologies. In this paper, we present part of the results of the SESAME project, an EU multi-partner project that aims to develop safe and secure multi-robot Systems. In particular, we present some of the developed SESAME Executable Digital Dependability Identities (EDDI) technologies based on Markov models, statistical distance measures, and other advanced approaches for enhancing safety, security and dependability of the UAV platform and underlying models. These EDDI technologies are seamlessly integrated using the ConSerts framework in a multi-UAV platform and tested using search and rescue scenarios. The results demonstrate significant improvements in multi-UAV safety, with an availability rate of 91% and a search and rescue algorithmic accuracy of 99.8%. Additionally, the system achieves precise detection of spoofing attacks, using collaborative localization as a mitigation technique to guide the UAV to a safe landing, even in the absence of GPS signals,

Safety is a key factor in the development of critical systems, encompassing both conventional types, such as aircraft, and modern technologies, such as autonomous vehicles. Failures during their operation can be potentially far-reaching and impact people and the environment. To certify these systems and enable their employment, regulatory bodies require, among others, a safety case. However, the growing complexity of modern systems and iterative nature of development pose significant challenges to the traditional approaches for creating safety cases that are still used in practice. Furthermore, safety cases are often generated in an ad-hoc manner and remain disconnected from system models and related artefacts. Without these connections it is difficult to construct the proper infrastructure for producing and maintaining safety cases in a structured manner throughout the system lifecycle. This paper presents our innovative method, Andromeda, and its underpinning metamodel, which establish connections between safety cases, system models, safety assessment activities aligned with international safety standards, and argument patterns. Automation is applied across various stages of the production of argument structures that support safety assurance and certification activities. Andromeda is complemented by tool-support designed to facilitate its application, and we demonstrate our work through a case study from the aviation industry. • A metamodel linking systems/analyses/patterns to aid algorithmic argument synthesis. • Patterns elicited by commonalities in standards used to construct safety arguments. • Optimal SILs allocation automated via metaheuristics to support safety arguments.

Digital transformation fundamentally changes established practices in public and private sector. Hence, it represents an opportunity to improve the value creation processes (e.g., “industry 4.0”) and to rethink how to address customers' needs such as “data-driven business models” and “Mobility-as-a-Service”. Dependable, collaborative and autonomous systems are playing a central role in this transformation process. Furthermore, the emergence of data-driven approaches combined with autonomous systems will lead to new business models and market dynamics. Innovative approaches to reorganise the value creation ecosystem, to enable distributed engineering of dependable systems and to answer urgent questions such as liability will be required. Consequently, digital transformation requires a comprehensive multi-stakeholder approach which properly balances technology, ecosystem and business innovation. Targets of this paper are (a) to introduce digital transformation and the role of / opportunities provided by autonomous systems, (b) to introduce Digital Depednability Identities (DDI) - a technology for dependability engineering of collaborative, autonomous CPS, and (c) to propose an appropriate agile approach for innovation management based on business model innovation and co-entrepreneurship.

In engineering practice, multiple repair actions are considered carefully by designers, and their success or failure defines further control actions and the evolution of the system state. Such treatment is not fully supported by the current state-of-the-art in dependability analysis. We propose a novel approach for explicit modelling and analysis of repairable systems, and describe an implementation, which builds on HiP-HOPS, a method and tool for model-based synthesis of dependability evaluation models. HiP-HOPS is augmented with Pandora, a temporal logic for the qualitative analysis of Temporal Fault Trees (TFTs), and capabilities for quantitative dependability analysis via Stochastic Activity Networks (SAN). Dependability prediction is achieved via explicit modelling of local failure and repair events in a system model and then by: (i) propagation of local effects through the model and synthesis of repair-aware TFTs for the system, (ii) qualitative analysis of TFTs that respects both failure and repair logic and (iii) quantification of dependability via translation of repair-aware TFTs into SAN. The approach provides insight into the effects of multiple and alternative failure and repair scenarios, and can thus be useful in reconfigurable systems that typically employ software to utilise functional redundancies in a variety of ways.

Abstract Safety‐critical systems are of paramount importance for many application domains, where safety properties are a key driver to engineer critical aspects and avoid system failures. For the benefits of large‐scale reuse, software product lines (SPL) have been adopted in critical systems industry. However, the integration of safety analysis in the SPL development process is nontrivial. Also, the different usage contexts of safety‐critical systems complicates component fault modeling tasks and the identification of potential hazards. In this light, better methods become necessary to estimate the impact of dependability properties during Hazard Analysis and Risk Assessment. Existing methods incorporating the analysis of safety properties in SPL are limited as they do not include hazard analysis and component fault modeling. In this paper, we present the novel DEPendable Software Product Line Engineering (DEPendable‐SPLE) approach, which extends traditional SPL processes to support the reuse of safety assets. We also present a detailed analysis of the impact of product and context features on the SPL design, safety analysis, and safety requirements. We applied DEPendable‐SPLE to a realistic case study from the aerospace domain to illustrate how to model and reuse safety properties. DEPendable‐SPLE reduced the effort of safety analysis for certifying system variants.

The use of average data for dependability assessments results in an outdated system-level dependability estimation, which can lead to incorrect design decisions. With increasing availability of online data, there is room to improve traditional dependability assessment techniques. Namely, prognostics is an emerging field, which provides asset-specific failure information that can be reused to improve the system-level failure estimation. This paper presents a framework for prognostics-updated dynamic dependability assessment. The dynamic behavior comes from runtime updated information, asset interdependencies, and time-dependent system behavior. A case study from the power generation industry is analyzed, and results confirm the validity of the approach for improved near real-time unavailability estimations.

Cyber-Physical Systems (CPS) provide enormous potential for innovation but a precondition for this is that the issue of dependability has been addressed. This paper presents the concept of a Digital Dependability Identity (DDI) of a component or system as foundation for assuring the dependability of CPS. A DDI is an analyzable and potentially executable model of information about the dependability of a component or system. We argue that DDIs must fulfill a number of properties including being universally useful across supply chains, enabling off-line certification of systems where possible, and providing capabilities for in-field certification of safety of CPS. In this paper, we focus on system safety as one integral part of dependability and as a practical demonstration of the concept, we present an initial implementation of DDIs in the form of Conditional Safety Certificates (also known as ConSerts). We explain ConSerts and their practical operationalization based on an illustrative example.

Dependability analysis of a dynamic system which is embedded with several complex interrelated components raises two main problems. First, it is difficult to represent in a single coherent and complete picture how the system and its constituent parts behave in conditions of failure. Second, the analysis can be unmanageable due to a considerable number of failure events which increases with the number of components involved. To remedy this problem, in this paper we outline a scalable analysis approach that converts failure behavioural models - state machines (SMs) - to temporal fault trees (TFTs), which can then be analysed using Pandora (a recent technique for introducing temporal logic to fault trees). The improved scalability of the approach stems from a compositional synthesis of the TFTs (generated from the individual component SMs) for Pandora analysis. We show, by using a Generic Triple Redundant (GTR) system, how the approach enables a more accurate and full analysis of an increasingly complex system.

Abstract New processes for the design of dependable systems must address both cost and dependability concerns. They should also maximize the potential for automation to address the problem of increasing technological complexity and the potentially immense design spaces that need to be explored. In this paper we show a design process that integrates system modelling, automated dependability analysis and evolutionary optimization techniques to achieve the optimization of designs with respect to dependability and cost from the early stages. Computerized support is provided for difficult aspects of fault tolerant design, such as decision making on the type and location of fault detection and fault tolerant strategies. The process is supported by HiP‐HOPS, a scalable automated dependability analysis and optimization tool. The process was applied to a Pre‐collision system for vehicles at an early stage of its design. The study shows that HiP‐HOPS can overcome the limitations of earlier work based on Reliability Block Diagrams by enabling dependability analysis and optimization of architectures that may have a network topology and exhibit multiple failure modes. Copyright © 2011 John Wiley & Sons, Ltd.

Abstract Components in programmable systems often exhibit patterns of failure that are independent of function or system context. In this paper, we show that it is possible to capture, and reuse where appropriate, such patterns for the purposes of system safety analysis. We describe a language that enables abstract specification of failure behaviour and define the syntax and semantics of this language. The language extends concepts originally defined in HiP‐HOPS, a technique that enables a largely automated form of compositional system safety analysis. The paper describes how this language can be used to describe component failure patterns and demonstrates how it can be applied using a simple fuel system example. The approach is evaluated on a set of retrospective industrial case studies, where data‐mining and reverse engineering techniques are applied in order to identify hidden patterns in legacy safety analyses. Results show clear potential for practical use of patterns in HiP‐HOPS. We argue that careful specification and reuse of failure patterns in conjunction with a tool that automates Fault Tree and Failure Modes and Effects Analysis can help to simplify complex safety assessments. Copyright © 2010 John Wiley & Sons, Ltd.

Recent growth in the area of model-based product development in the automotive industry means that system models in electronic form become increasingly more available as they progressively find more applications in the course of the development process. Such models, Matlab-Simulink models for example, are currently used for simulation, fault injection and source code generation. In our work, the utility of such models is pushed further into a hitherto unexplored area that of safety and reliability analysis. We propose a design process in which techniques for semi-automatic safety and reliability analysis of such models are combined with multi-criteria optimization techniques to assist the gradual development of designs that can meet set reliability and safety targets within pragmatic cost constraints. The proposed process relies on tools to automate some aspects of the design that we believe could be automated and thus simplified without any loss of the creative input in the process by designers.

We outline a new approach to safety analysis in which concepts of computer HAZOP are fused with the idea of software fault tree analysis to enable a continuous assessment of an evolving programmable design developed in Matlab-Simulink. We also discuss the architecture of a tool that we have developed to support the new method and enable its application in complex environments. We show that the method and the tool enable the integrated hardware and software analysis of a programmable system and that in the course of that analysis they automate and simplify the development of fault trees for the system. Finally, we propose a demonstration of the method and the tool and we outline the experimental platform and aims of that demonstration.