Elevators serve as vital components in modern buildings, yet optimizing passengers' waiting time remains a crucial challenge. This study proposes a machine learning-based approach to enhance the efficiency of elev...
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Formal verification is essential but challenging: Even the best verifiers may produce wrong verification verdicts. Certifying verifiers enhance the confidence in verification results by generating a witness for other ...
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ISBN:
(纸本)9783031572555;9783031572562
Formal verification is essential but challenging: Even the best verifiers may produce wrong verification verdicts. Certifying verifiers enhance the confidence in verification results by generating a witness for other tools to validate the verdict independently. Recently, translating the hardware-modeling language BTOR2 to software, such as the programming language C or LLVM intermediate representation, has been actively studied and facilitated verifying hardware designs by software analyzers. However, it remained unknown whether witnesses produced by software verifiers contain helpful information about the original circuits and how such information can aid hardware analysis. We propose a certifying and validating framework BTOR2-Cert to verify safety properties of BTOR2 circuits, combining BTOR2-to-C translation, software verifiers, and a new witness validator BTOR2-Val, to answer the above open questions. BTOR2-Cert translates a software violation witness to a BTOR2 violation witness;As the BTOR2 language lacks a format for correctness witnesses, we encode invariants in software correctness witnesses as BTOR2 circuits. The validator BTOR2-Val checks violation witnesses by circuit simulation and correctness witnesses by validation via verification. In our evaluation, BTOR2-Cert successfully utilized software witnesses to improve quality assurance of hardware. By invoking the software verifier Cbmc on translated programs, it uniquely solved, with confirmed witnesses, 8% of the unsafe tasks for which the hardware verifier ABC failed to detect bugs.
This study is based on the exploration of ensemble regression models using Adaboost Regressor, Random Forest Regressor, Gradient Boosting Regressor, and XGBoost Regressor for predicting the compressive strength of coc...
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In this paper we developed a method for the mathematical modeling for the near-zero apparent motion (NZAM) detection of astronomical objects in series of CCD-frames using the methods of statistical and in situ modelin...
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Financial analysis is a systematic set of techniques and tools financial management uses to explore, define, optimize, and evaluate decisions. The financial position and reliance on economic statements of firms are al...
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Deep neural networks have shown remarkable efficacy for partial discharge (PD) faults classification in power systems, playing a crucial role in the maintenance of electrical equipment. However, when labeled data for ...
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Recently, fish farming's have been increasing to keep food production. Fish body size is one of the most important factors to determine an appropriate feeding rate. Thus, this data is often measured manually by di...
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CAD assembly modeling, which refers to using CAD software to design new products from a catalog of existing machine components, is important in the industrial field. The graph neural network (GNN) based recommender sy...
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ISBN:
(纸本)9798400704901
CAD assembly modeling, which refers to using CAD software to design new products from a catalog of existing machine components, is important in the industrial field. The graph neural network (GNN) based recommender system for CAD assembly modeling can help designers make decisions and speed up the design process by recommending the next required component based on the existing components in CAD software. These components can be represented as a graph naturally. However, present recommender systems for CAD assembly modeling adopt fixed GNN architectures, which may be sub-optimal for different manufacturers with different data distribution. Therefore, to customize a well-suited recommender system for different manufacturers, we propose a novel neural architecture search (NAS) framework, dubbed CusGNN, which can design data-specific GNN automatically. Specifically, we design a search space from three dimensions (i.e., aggregation, fusion, and readout functions), which contains a wide variety of GNN architectures. Then, we develop an effective differentiable search algorithm to search high-performing GNN from the search space. Experimental results show that the customized GNNs achieve 1.5-5.1% higher top-10 accuracy compared to previous manual designed methods, demonstrating the superiority of the proposed approach. Code and data are available at https://***/BUPT-GAMMA/CusGNN.
This paper presents a comprehensive review of six techniques that are widely used for voltage amplitude measurement. The performances of these techniques are investigated and compared under different test scenarios ac...
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This paper presents a study on the effect of using a smaller number of inputs in the FPGA logic block calculated according to a pre-compiled model based on Rent's rule. This rule, when applied to the FPGA logic bl...
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