To address the issue of inaccurate and incomplete reflection of sarcasm sentiment in generated sentences, which arises from insufficient visual information in image-based sarcasm sentiment description generation, we p...
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At present, the existing multi-load wireless Energy transmission (WPT) power supply system mainly adopts multiple single-transmit single-receive magnetic coupling power transmission structures based on single resonant...
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Software vulnerabilities damage the functionality of software systems. Recently, many deep learning-based approaches have been proposed to detect vulnerabilities at the function level by using one or a few different m...
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ISBN:
(纸本)9798350329964
Software vulnerabilities damage the functionality of software systems. Recently, many deep learning-based approaches have been proposed to detect vulnerabilities at the function level by using one or a few different modalities (e.g., text representation, graph-based representation) of the function and have achieved promising performance. However, some of these existing studies have not completely leveraged these diverse modalities, particularly the underutilized image modality, and the others using images to represent functions for vulnerability detection have not made adequate use of the significant graph structure underlying the images. In this paper, we propose MVulD, a multi-modal-based function-level vulnerability detection approach, which utilizes multi-modal features of the function (i.e., text representation, graph representation, and image representation) to detect vulnerabilities. Specifically, MVulD utilizes a pre-trained model (i.e., UniXcoder) to learn the semantic information of the textual source code, employs the graph neural network to distill graph-based representation, and makes use of computer vision techniques to obtain the image representation while retaining the graph structure of the function. We conducted a large-scale experiment on 25,816 functions. The experimental results show that MVulD improves four state-of-the-art baselines by 30.8%-81.3%, 12.8%-27.4%, 48.8%-115%, and 22.9%-141% in terms of F1-score, Accuracy, Precision, and PR-AUC respectively.
this paper details a method for precisely controlling the two-dimensional spatial orientation of any electrical instrument using only hand gestures. To obtain a reasonable idea of where the user's hand is in respe...
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multi-band spectrum prediction is a crucial task for spectrum management and improving spectrum utilization. Despite the complexity and spatiotemporal variability of spectrum data, which make accurate prediction chall...
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Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dy...
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Nowadays, when biometric identification is widely used, privacy protection in identification has become a very important issue. In recent years, many scholars have contributed to the biometric authentication with cryp...
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Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, ...
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ISBN:
(纸本)9798350307184
Removing moire patterns from videos recorded on screens or complex textures is known as video demoireing. It is a challenging task as both structures and textures of an image usually exhibit strong periodic patterns, which thus are easily confused with moire patterns and can be significantly erased in the removal process. By interpreting video demoireing as a multi-frame decomposition problem, we propose a compact invertible dyadic network called CIDNet that progressively decouples latent frames and the moire patterns from an input video sequence. Using a dyadic cross-scale coupling structure with coupling layers tailored for multi-scale processing, CIDNet aims at disentangling the features of image patterns from that of moire patterns at different scales, while retaining all latent image features to facilitate reconstruction. In addition, a compressed form for the networks output is introduced to reduce computational complexity and alleviate overfitting. The experiments show that CIDNet outperforms existing methods and enjoys the advantages in model size and computational efficiency.
The development of a virtual reality platform through human-computer interaction (HCI) is obtained using deep learning with a genetic algorithm. The human-computer interaction forms a bridge between users and the VR s...
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In the context of deep learning applied to single-image super-resolution, the quality of the reconstructed images is largely contingent upon the intricacy of the convolutional neural networks employed. However, this c...
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