Titlebook: Engineering of Complex Computer Systems; 28th International C Guangdong Bai,Fuyuki Ishikawa,George A. Papadopoul Conference proceedings 202

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https://doi.org/10.1007/978-3-319-58100-2ral Networks (DNN) has significantly improved detection accuracy in NIDS. Nonetheless, the inherent data imbalance between malicious and normal network traffic impairs the efficacy of DNN-based methods. Traditional approaches employ Generative Adversarial Networks (GANs) to mitigate this by generati

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https://doi.org/10.1057/9780230288256rnal product configuration system to model its vehicle diversity. This system is based on the well-known knowledge compilation approach and is associated with a set of parameters. Different input parameters have a strong influence on the system’s performance. The parameters actually used are determi

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https://doi.org/10.1007/978-3-662-68448-1ion methods employing branch and bound technique have shown excellent performance for this task and are widely adopted to provide robustness assurance. A key component in branch and bound is the branching strategy, which determines how to split the feasible region. A good branching strategy can redu

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Migrant Masculinities in Women’s Writingility in DNNs, ensuring their prediction accuracy through robustness verification becomes imperative before deploying them in safety-critical applications. Neural Network Verification (NNV) approaches can broadly be categorized into exact and approximate solutions. Exact solutions are complete but t

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https://doi.org/10.1007/978-3-030-74369-7Linux operating system distribution images and packages, developers need to build and adapt the packages. Due to the complexity of software packages and the diversity of developer experience levels, the success of software package construction is uncertain. Existing research lacks automatic classifi

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International Political Economy Seriesy. This can lead to a lack of a comprehensive overview of the test architecture, hampering the reuse of test functions when implementing new test cases. To address this challenge, we propose ., an automated test architecture generation approach, which employs an optimization algorithm to retrieve hi

TEM

Anna Triandafyllidou,Thanos Maroukisaches have shown promising results in TD prediction, but the imbalanced TD datasets can have a negative impact on ML model performance. Although previous TD studies have investigated various oversampling techniques that generates minority class instances to mitigate the imbalance, potentials of unde

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Sustainable Development Goals Seriese led to the loss of millions of dollars worth of assets. Since smart contract code cannot be updated to patch security flaws, reasoning about smart contract correctness to ensure the absence of vulnerabilities before their deployment is of the utmost importance. In this paper, we present a formal a

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