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Titlebook: Computer Safety, Reliability, and Security; 38th International C Alexander Romanovsky,Elena Troubitsyna,Friedemann Conference proceedings

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Increasing Trust in Data-Driven Model Validationn at the object, context, and sensor levels. To provide realistic augmentation and meta-data for existing image datasets, known context information and conditional probabilities are processed. First applications on the GTSRB dataset show promising results. The augmentation of datasets facilitates a
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BACRank: Ranking Building Automation and Control System Components by Business Continuity Impactapplicability of our approach on a real BACS deployed at a 5-story building hosting 375 employees on an international university campus. The experimental evaluation confirms that the proposed methodology successfully prioritizes the most relevant components of the system with respect to the business
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Model-Based Run-Time Synthesis of Architectural Configurations for Adaptive MILS Systemss, monitoring properties and the related alarms. Supporting tools have been developed for the run-time synthesis of new architectural configurations that preserve safety and security properties formalized in terms of invariants and information flow.
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Getting Started: Identifying Your Needsng scenarios, and based on the running example values, the FH is estimated. We conclude our work with a summary of the estimated FH for each of the specific driving scenarios at different road conditions and the recommended road speed limits. Such a scientific way of deciding optimal bounds on the F
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Getting Started: Identifying Your Needsulink models and even to design reliable control systems at the model level..In this paper, we propose a method for the reliability evaluation of Simulink models at the model level, extended with the assembly-level evaluation. More specifically, we transform the Simulink model into a stochastic dual
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Getting Started: Identifying Your Needsn at the object, context, and sensor levels. To provide realistic augmentation and meta-data for existing image datasets, known context information and conditional probabilities are processed. First applications on the GTSRB dataset show promising results. The augmentation of datasets facilitates a
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The Roadmap for a Better Futureapplicability of our approach on a real BACS deployed at a 5-story building hosting 375 employees on an international university campus. The experimental evaluation confirms that the proposed methodology successfully prioritizes the most relevant components of the system with respect to the business
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The Roadmap for a Better Futures, monitoring properties and the related alarms. Supporting tools have been developed for the run-time synthesis of new architectural configurations that preserve safety and security properties formalized in terms of invariants and information flow.
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