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Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including image recognition, speech recognition, natural language processing (NLP), and even in the field of genomics, where they have been utilized to classify DNA sequences. This paper provides a comprehensive overview of CNNs and their applications in image recognition tasks. It first introduces the fundamentals of CNNs, including the layers of CNNs, convolution operation (Conv_Op), Feat_Maps, activation functions (Activ_Func), and training methods. It then discusses several popular CNN architectures such as LeNet, AlexNet, VGG, ResNet, and InceptionNet, and compares their performance. It also examines when to use CNNs, their advantages and limitations, and provides recommendations for developers and data scientists, including preprocessing the data, choosing appropriate hyperparameters (Hyper_Param), and evaluating model performance. It further explores the existing platforms and libraries for CNNs such as TensorFlow, Keras, PyTorch, Caffe, and MXNet, and compares their features and functionalities. Moreover, it estimates the cost of using CNNs and discusses potential cost-saving strategies. Finally, it reviews recent developments in CNNs, including attention mechanisms, capsule networks, transfer learning, adversarial training, quantization and compression, and enhancing the reliability and efficiency of CNNs through formal methods. The paper is concluded by summarizing the key takeaways and discussing the future directions of CNN research and development.

The growing trend of interconnected subsystems has brought significant interest in designing smart systems with the ability to build fully controlled attention upon them. Designing and verifying a smart building system presents unique challenges, including ensuring security for sensitive areas, managing diverse user access levels, and integrating multiple subsystems while maintaining system integrity and performance. There is also a need to work and focus on the goal of a smart, safe, and secure courtroom environment for all stakeholders, particularly in developing countries, such as Pakistan. There are also problems of security, safety, and energy saving in the infrastructure of the smart building systems. This study presents a proposed model for a secure, safe, and energy-saving courtroom building, and the model utilizes Unified Modeling Language (UML) diagrams to visualize the system’s components or objects and interaction sequences of actions. Additionally, formal methods are used, specifically Temporal Logic of Actions (TLA+), to write specifications as per the requirements of specifications and to rigorously verify the system’s properties to ensure its correctness. In the context of the smart courtroom building, security measures, such as password-protected entry, user access level, and safety, are implemented for the courtroom building. The system also manages building-wide lighting and temperature control while incorporating a subsystem of smoke detection to enhance safety during a fire in the courtroom. The model’s properties, such as correctness, are rigorously verified using the TLA+ toolbox and built-in Model Checker (TLC) after applying the specification as per the requirements of the infrastructure of a building. So, we have developed a novel integration of UML modeling and TLA+ specification for comprehensive system representation and verification. This is a robust set of formal specifications that address the unique security and functionality requirements of a courtroom environment. We have successfully verified the system properties using the TLC model checker, ensuring the correctness and safety of the proposed smart courtroom building system.

Shift2Rail is a joint undertaking funded by the EU via its Horizon 2020 program and by main railway stakeholders. Several Shift2Rail projects aim to investigate the application of formal methods to new ERTMS/ETCS railway signalling systems that promise to move European railway forward by guaranteeing high capacity, low cost and improved reliability. We explore the ERTMS/ETCS level 3 full moving block specifications stemming from different Shift2Rail projects using Uppaal and statistical model checking. The results range from novel rigorously formalised requirements to an operational model formally verified against scenarios with multiple trains on a single railway line. From the gained experience, we have distilled future research goals to improve the formal specification and verification of real-time systems, and we discuss some barriers concerning a possible uptake of formal methods and tools in the railway industry.

Recently, formal methods have been used in large industrial organisations (including AWS, Facebook/Meta, and Microsoft) and have proved to be an effective part of a software engineering process finding important bugs. Perhaps because of that, practitioners are interested in using them more often. Nevertheless, formal methods are far less applied than expected, particularly for safety-critical systems where they are strongly recommended and have the most significant potential. We hypothesise that formal methods still seem not applicable enough or ready for their intended use in such areas. In critical software engineering, what do we mean when we speak of a formal method? And what does it mean for such a method to be applicable both from a scientific and practical viewpoint? Based on what the literature tells about the first question, with this manifesto, we identify key challenges and lay out a set of guiding principles that, when followed by a formal method, give rise to its mature applicability in a given scope. Rather than exercising criticism of past developments, this manifesto strives to foster increased use of formal methods in any appropriate context to the maximum benefit.

Argument-based assurance cases , often represented and organized using graphical argument structures , are increasingly being used in practice to provide assurance to stakeholders, e.g., regulatory authorities, that a system is acceptable for its intended use with respect to dependability and safety concerns. In general, comprehensive system-wide assurance arguments aggregate a substantial amount of diverse information, such as the results of safety analysis, requirements analysis, design, verification and other engineering activities. Although a variety of assurance case tools exist, many desirable operations on argument structures such as hierarchical and modular abstraction, argument pattern instantiation, and inclusion/extraction of richly structured information have limited to no automation support. To close this automation gap, over the past four years we have been developing a toolset for assurance case automation, AdvoCATE, at the NASA Ames Research Center. This paper describes how AdvoCATE is being engineered atop formal foundations for assurance case argument structures, to provide unique capabilities for: ( a ) automated creation and assembly of assurance arguments, ( b ) integration of formal methods into wider assurance arguments, ( c ) automated pattern instantiation, ( d ) hierarchical abstraction, ( e ) queries and views, and ( f ) verification of arguments. We (and our colleagues) have used AdvoCATE in real projects for safety assurance, in the context of unmanned aircraft systems.

The requirements engineering of Industrial Cyber-Physical Systems is extremely challenging due to large system sizes, component heterogeneity, involvement of multi-discipline stakeholders and machines, and continuous evolution. Formal and semi-formal languages, techniques, tools and frameworks can assist by providing repeatable and rigorous structures for eliciting, specifying, analysing, verifying and maintaining requirements. Various approaches have been proposed, but a contemporary and comprehensive study providing a landscape of the state-of-the-art is currently missing. This article reports a systematic mapping study covering 93 primary studies published between 2009 and October 2020. We categorise surveyed studies by current research directions in the use of semi-formal and formal methods for Requirements Engineering phases for Industrial Cyber-Physical Systems. We also identify gaps in current research and develop a novel conceptual model capturing the relationship between available formalisms and Requirements Engineering activities. We find that extensive work has been carried out on the formal analysis and verification of safety and timings requirements. However, the use of semi-formal notations, works on key phases like requirements elicitation and management, and the adoption of industrial standards are largely missing. Moreover, we find no literature providing methods to handle privacy and trust requirements, which have become critical concerns in this area.

ContextEnsuring safety for any sophisticated system is getting more complex due to the rising number of features and functionalities. This calls for formal methods to entrust confidence in such systems. Nevertheless, using formal methods in industry is demanding because of their lack of usability and the difficulty of understanding verification results.ObjectiveWe evaluate the acceptance of formal methods by Bosch automotive engineers, particularly whether the difficulty of understanding verification results can be reduced.MethodWe perform two different exploratory studies. First, we conduct a user survey to explore challenges in identifying inconsistent specifications and using formal methods by Bosch automotive engineers. Second, we perform a one-group pretest-posttest experiment to collect impressions from Bosch engineers familiar with formal methods to evaluate whether understanding verification results is simplified by our counterexample explanation approach.ResultsThe results from the user survey indicate that identifying refinement inconsistencies, understanding formal notations, and interpreting verification results are challenging. Nevertheless, engineers are still interested in using formal methods in real-world development processes because it could reduce the manual effort for verification. Additionally, they also believe formal methods could make the system safer. Furthermore, the one-group pretest-posttest experiment results indicate that engineers are more comfortable understanding the counterexample explanation than the raw model checker output.LimitationsThe main limitation of this study is the generalizability beyond the target group of Bosch automotive engineers.

The increasing complexity and connectivity of automotive systems have raised concerns about their vulnerability to security breaches. As a result, the integration of formal methods and validation techniques has become crucial in ensuring the security of automotive systems. This survey research paper aims to provide a comprehensive overview of the current state-of-the-art formal methods and validation techniques employed in the automotive industry for system security. The paper begins by discussing the challenges associated with automotive system security and the potential consequences of security breaches. Then, it explores various formal methods, such as model checking, theorem proving, and abstract interpretation, which have been widely used to analyze and verify the security properties of automotive systems. Additionally, the survey highlights the validation techniques employed to ensure the effectiveness of security measures, including penetration testing, fault injection, and fuzz testing. Furthermore, the paper examines the integration of formal methods and validation techniques within the automotive development lifecycle, including requirements engineering, design, implementation, and testing phases. It discusses the benefits and limitations of these approaches, considering factors such as scalability, efficiency, and applicability to real-world automotive systems. Through an extensive review of relevant literature and case studies, this survey provides insights into the current research trends, challenges, and open research questions in the field of formal methods and validation techniques for automotive system security. The findings of this survey can serve as a valuable resource for researchers, practitioners, and policymakers involved in the design, development, and evaluation of secure automotive systems.

A defect prevention is a part of manufacturing company practice. Paper proposes a formal approach for solving scheduling problems with unexpected events as extension of general frameworks for Zero-Defect Manufacturing (ZDM) strategy. ZDM aims to improve the process efficiency and the product quality while eliminating defects and minimizing process errors. However, most of ZDM applications focus on using the technological achievements of Industry 4.0 to detect and predict defects, forgetting to optimize the schedule on the production line. We propose formal method to create predictive-reactive schedule for problems with defect detection and repair. Our proposal is based on the formal Algebraic-Logical Meta-Model (ALMM). In particular, it uses the model switching method and combines defect detection, heuristics construction and decision support containing predictions of disturbances in the production process and enabling their prevention. Production defects are detected and repaired, and consequently, production delivers components without defects, and in the shortest possible time. Moreover, the collection and analysis of data related to the occurrence of disturbances in the production process helps the management board in making decisions based on analysis gathered and stored data. Thus, the proposed method includes strategies such as detection, repair, prediction and prevention for defect-free production. We illustrate the proposed method on the example of a flow-shop system with different types of product defect problem.

Formal methods for industrial critical systems are essential because they provide mathematically rigorous techniques to specify, design, and verify system behavior. This reduces the risk of failures in safety- and security-critical domains such as aerospace, automotive, and healthcare. This special issue of Software Tools for Technology Transfer contains four papers presenting recent advances in tools target the use of formal methods for critical systems in industry. The papers are revised and extended versions of selected conference papers from the 29th International Conference on Formal Methods for Industrial Critical Systems (FMICS 2024).