Wireless power transmission has been widely used to replenish energy for wireless sensor networks, where the energy consumption rate of sensor nodes is usually time varying and indefinite. However, few works have inve...
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Forecasting Human mobility is of great significance in the simulation and control of infectious diseases like COVID-19. To get a clear picture of potential future outbreaks, it is necessary to forecast multi-step Ori...
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Quantum pseudorandom state generators (PRSGs) have stimulated exciting developments in recent years. A PRSG, on a fixed initial (e.g., all-zero) state, produces an output state that is computationally indistinguishabl...
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To improve the accuracy of steel surface defect detection, this study proposes an improved multi-directional optimization model based on the YOLOv10n algorithm. First, we introduce innovations to the convolution (C2F)...
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Sign Language Production (SLP) aims to convert text or audio sentences into sign language videos corresponding to their semantics, which is challenging due to the diversity and complexity of sign languages, and cross-...
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Ensuring the correctness of debugger toolchains is of paramount importance, as they play a vital role in understanding and resolving programming errors during software development. Bugs hidden within these toolchains ...
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ISBN:
(纸本)9798400706981
Ensuring the correctness of debugger toolchains is of paramount importance, as they play a vital role in understanding and resolving programming errors during software development. Bugs hidden within these toolchains can significantly mislead developers. Unfortunately, comprehensive testing of debugger toolchains is lacking due to the absence of effective test oracles. Existing studies on debugger toolchain validation have primarily focused on validating the debug information within optimized executables by comparing the traces between debugging optimized and unoptimized executables (i.e., different executables) in the debugger, under the assumption that the traces obtained from debugging unoptimized executables serve as a reliable oracle. However, these techniques suffer from inherent limitations, as compiler optimizations can drastically alter source code elements, variable representations, and instruction order, rendering the traces obtained from debugging different executables incomparable and failing to uncover bugs in debugger toolchains when debugging unoptimized executables. To address these limitations, we propose a novel concept called Cross-Level Debugging (CLD) for validating the debugger toolchain. CLD compares the traces obtained from debugging the same executable using source-level and instruction-level strategies within the same debugger. The core insight of CLD is that the execution traces obtained from different debugging levels for the same executable should adhere to specific relationships, regardless of whether the executable is generated with or without optimization. We formulate three key relations in CLD: reachability preservation of program locations, order preservation for reachable program locations, and value consistency at program locations, which apply to traces at different debugging levels. We implement Devil, a practical framework that employs these relations for debugger toolchain validation. We evaluate the effectiveness of Devil u
Transfer learning is a valuable tool for the effective assistance of gastroenterologists in the powerful diagnosis of medical images with fast convergence. It also intends to minimize the time and estimated effort req...
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Transfer learning is a valuable tool for the effective assistance of gastroenterologists in the powerful diagnosis of medical images with fast convergence. It also intends to minimize the time and estimated effort required for improved gastrointestinal tract (GIT) diagnosis. GIT abnormalities are widely known to be fatal disorders leading to significant mortalities. It includes both upper and lower GIT disorders. The challenges of addressing GIT issues are complex and need significant study. Multiple challenges exist regarding computer-aided diagnosis (CAD) and endoscopy including a lack of annotated images, dark backgrounds, less contrast, noisy backgrounds, and irregular patterns. Deep learning and transfer learning have assisted gastroenterologists in effective diagnosis in various ways. The goal of proposed framework is the effective classification of endoscopic GIT images with enhanced accuracy. The proposed research aims to formulate a transfer learning-based deep ensemble model, accurately classifying GIT disorders for therapeutic purposes. The proposed model is based on weighted voting ensemble of the two state-of-the-art (STA) base models, NasNet-Mobile and EfficientNet. The extraction of regions of interest, specifically the sick portions, have been performed using images captured from endoscopic procedure. Performance evaluation of the proposed model is performed with cross-dataset evaluation. The datasets utilized include the training dataset HyperKvasir and two test datasets, Kvasir v1 and Kvasir v2. However, the dataset alone cannot create a robust model due to the unequal distribution of images across categories, making transfer learning a promising approach for model development. The evaluation of the proposed framework has been conducted by cross-dataset evaluation utilizing accuracy, precision, recall, Area under curve (AUC) score and F1 score performance metrics. The proposed work outperforms much of the existing transfer learning-based models giv
A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise...
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A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing *** method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable *** the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 ***,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent *** the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 *** mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.
Understanding the Linux kernel is challenging due to its large and complex program state. While existing kernel debugging tools provide full access to kernel states at arbitrary levels of detail, developers often spen...
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Infrared target detection is now applied in many fields, such as medical imaging, military detection, autonomous driving, and environmental monitoring with drones. Due to the small size of these targets, complex envir...
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