Bug reproduction is a critical task in software testing, as it helps developers to identify and fix bugs in the software. While some automated reproduction tools are designed to assist developers in reproducing bugs d...
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Deep neural networks have shown their powerful potential in various fields, especially vision classification problems, even though it still has black-box property. Visualization methods for deep neural networks, as a ...
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In recent years, as the rise of edge intelligence hand tracking applications have emerged in various IoT systems and applications, such as human-computer interaction, sign language translation, and motion rehabilitati...
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User intents are ever-changing, which requires deep learning models to have the ability to classify unknown intents. Meta-learning aims to solve this problem by improving the model's generalization ability to unkn...
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User intents are ever-changing, which requires deep learning models to have the ability to classify unknown intents. Meta-learning aims to solve this problem by improving the model's generalization ability to unknown intent. However, learning on a small amount of text can easily lead to overfitting of the model. Domain adaptation can help us train a more robust model. However, most existing methods only focus on global feature alignment and ignore alignment in subdomains. Therefore, in this study, we first consider the case where the model can maintain robustness with a small amount of data and then explore and mine the higher quality transferable features. Based on these ideas, we propose Dynamic Balance Domain Adaptation Meta-learning (DBDAML), which adaptively learns higher quality transferable features in both the global domain and subdomains.(1) At the same time, we define a dynamic balance factor to enable DBDAML to dynamically focus on the global domain and subdomains. This allows the model to give different attention to different domain adaptations and prevents it from overfitting of a domain feature alignment. The dynamic balance factor is estimated by the contribution of different domain discriminators to the loss, which also makes it easy to calculate and accurate. Finally, we use the meta-learning framework to model the entire theoretical idea. Extensive experiments demonstrate that our approach achieves better performance than state-of-the-art baseline methods.
The efficiency of inverse optimization in physically based differentiable rendering heavily depends on the variance of Monte Carlo estimation. Despite recent advancements emphasizing the necessity of tailored differen...
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The efficiency of inverse optimization in physically based differentiable rendering heavily depends on the variance of Monte Carlo estimation. Despite recent advancements emphasizing the necessity of tailored differential sampling strategies, the general approaches remain unexplored. In this paper, we investigate the interplay between local sampling decisions and the estimation of light path derivatives. Considering that modern differentiable rendering algorithms share the same path for estimating differential radiance and ordinary radiance, we demonstrate that conventional guiding approaches, conditioned solely on the last vertex, cannot attain this density. Instead, a mixture of different sampling distributions is required, where the weights are conditioned on all the previously sampled vertices in the path. To embody our theory, we implement a conditional mixture path guiding that explicitly computes optimal weights on the fly. Furthermore, we show how to perform positivization to eliminate sign variance and extend to scenes with millions of parameters. To the best of our knowledge, this is the first generic framework for applying path guiding to differentiable rendering. Extensive experiments demonstrate that our method achieves nearly one order of magnitude improvements over state-of-the-art methods in terms of variance reduction in gradient estimation and errors of inverse optimization. The implementation of our proposed method is available at https://***/mollnn/conditional-mixture.
This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation. Test script generation is a vital component of software testing, enabling efficient an...
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Code completion is an integral component of modern integrated development environments, as it not only facilitates the software development process but also enhances the quality of software products. By leveraging lar...
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Although atomicity plays a key role in data operations of shared variables in parallel computation, researchers haven't treated atomicity in Python in much detail. This study provides a novel approach to integrate...
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Although atomicity plays a key role in data operations of shared variables in parallel computation, researchers haven't treated atomicity in Python in much detail. This study provides a novel approach to integrate the CPU-based atomic C APIs into Python shared variables by C Foreign Function Interface for Python (CFFI) on all major platforms and utilises Cython to optimise calculation in CPython. Evidence shows that the resulting product, Shared Atomic Enterprise (SAE), could accelerate data operations on shared data types to a large extent. These findings provide a solid evidence base for the massive utilisation of Python atomic operations in parallel computation and concurrent programming.
Malware family prediction has been mainly formulated as a multiclass classification to predict one malware family. This approach suffers from label uncertainty, which can mislead malware analysts. To render malware pr...
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Malware family prediction has been mainly formulated as a multiclass classification to predict one malware family. This approach suffers from label uncertainty, which can mislead malware analysts. To render malware prediction less susceptible to uncertainty, malware family prediction, which entails predicting one or more families, is performed in this study. In this regard, an encoder-decoder malware family prediction model, EnDePMal, with label uncertainty awareness, is proposed. EnDePMal aims to predict all malware families related to samples and preserve their priorities. It comprises a residual neural network-based encoder and a long short-term memory-based decoder with an attention mechanism. The model uses a sequence of malware family names, but not a family name, as a label. Once a visualized malware image is input into EnDePMal, its encoder extracts the important features from the image. Subsequently, its decoder generates family names, where the attention mechanism allows it to focus on relevant features by attending to the encoder's output. Experimental results show that EnDePMal can predict 77.64% of malware family sequences that preserve their priorities. Moreover, it achieves an accuracy of 93.49% and an F1-score of 0.9282 for malware families with the highest priority, rendering it comparable to the typical multiclass classification model.
Medical images play a crucial role in doctors' clinical diagnosis and treatment. However, the transmission and sharing of such private information raises security concerns. To address this issue, image hiding is u...
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Medical images play a crucial role in doctors' clinical diagnosis and treatment. However, the transmission and sharing of such private information raises security concerns. To address this issue, image hiding is used as an effective technique to protect images. To achieve large hiding capacity, lossless recovery and anti-steganalysis, we propose a novel two-stage medical image-hiding method in this article. In the first stage, a QR code for the patient diagnosis information (PDI) is generated and embedded into a secret medical image using reversible data hiding. In the second stage, the secret medical image containing PDI is hidden in a natural target image. A kind of lossless compression technique named soft compression is innovatively introduced in two hiding stages, to ensure that the reconstructed secret medical image and PDI are exactly identical to the original ones. Moreover, an adaptive n-LSB model is proposed to improve the stego image quality. Extensive experimental results show that our method achieves a PSNR of over 40 dB for the stego image at 2 BPP while recovering the PDI and secret medical image with 100% accuracy on the DIV2K, COCO and ImageNet datasets. It outperforms other state-of-the-art methods in terms of hiding invisibility, recovery accuracy and security.
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