Binary self-dual cyclic codes have been studied since the classical work of Sloane and Thompson published in IEEE Trans. Inf. Theory, vol. 29, 1983. Twenty five years later, an infinite family of binary self-dual cycl...
详细信息
Nowadays, three-dimensional (3D) reconstruction techniques are becoming increasingly important in the fields of architecture, game development, movie production, and more. Due to common issues in the reconstruction pr...
详细信息
Federated Learning (FL) is vulnerable to backdoor attacks - especially distributed backdoor attacks (DBA) that are more persistent and stealthy than centralized backdoor attacks. However, we observe that the attack ef...
详细信息
Machine Learning (ML), a subfield of Artificial Intelligence (AI), has been used successfully in the healthcare domain for disease diagnosis. Thyroid disorders and diabetes are two of the most prevalent and interconne...
详细信息
Quantum Learning (QL) has emerged as a promising approach to medical image classification, leveraging the principles of quantum mechanics to improve the performance and efficiency of machine learning algorithms. This ...
详细信息
Users may now create and use large amounts of data saved online thanks to e-commerce systems. Modern shoppers examine online reviews before making purchases. Evaluations are essential for both people and organizations...
详细信息
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
详细信息
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
Thermal images are crucial for object detection in surveillance, security, industrial automation, and vehicular navigation due to their ability to capture heat signatures. However, complexities like low contrast, fluc...
详细信息
The paper addresses the critical problem of application workflow offloading in a fog environment. Resource constrained mobile and Internet of Things devices may not possess specialized hardware to run complex workflow...
详细信息
If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must...
详细信息
暂无评论