Unmanned Aerial Vehicles (UAVs) present unique security challenges due to their distributed nature and susceptibility to evolving threats. Current Intrusion Detection Systems (IDS) struggle to keep pace with real-time...
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A three port DC-DC converter that employs push-pull topology with isolation is presented. The converter transfers power from two DC input sources using a multi-winding transformer to the load. The high gain of the con...
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This study introduces a new probabilistic approach to the scheduling of transmission line inspection missions using unmanned aerial vehicles (UAV). Decisions are being made based on the risk obtained by means of proba...
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Aiming at the problem of arc image with blurred edges and inhomogeneous greyscale, a multi-feature-based active contour model for arc image segmentation is proposed in this paper. Firstly, based on the greyscale featu...
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With the rapid development of intelligent systems, multi-Agent Systems (MAS) have shown unique advantages in solving complex decision-making problems. Particularly in the field of multi-Agent Reinforcement Learning (M...
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Underwater acoustic target recognition (UATR) has gained significant attention across various fields in recent years. However, traditional UATR methods, which primarily rely on data from a single sensor, have limitati...
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Real-time grid analysis is made possible by the electric digital twin grid, which combines history and present data to convey system status and project future circumstances. Cooperative smart agents that can solve pro...
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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.
The current algorithms for K-anonymity are inadequate for clustering multi-dimensional data and cannot adapt to changes in anonymized datasets. To address these challenges, this paper introduces a Dynamic multi-dimens...
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In this work, a multi-level based neutral-point-clamped (NPC) inverter is recommended for grid-connected solar photovoltaic (PV) system and battery storage. For a three-phase, three-level inverter, a new and streamlin...
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