In recent years, the Siamese network has gained popularity in RGB-T tracking primarily due to its notable success in RGB object tracking. Although the speed of the existing RGB-T Siamese tracker is faster than the rea...
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As 5G networks advance, cybersecurity vulnerabilities in IoT devices are increasingly exposed. While intrusion detection systems (IDS) have progressed, there is a notable gap in IDS specifically designed for the uniqu...
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Aligning aspects and related viewpoints for aspect-specific sentiment polarity categorization is the goal of Aspect-Based Sentiment Analysis (ABSA), a fine-grained sentiment analysis endeavor. Dependency tree-based gr...
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In the human sensing system, information is transmitted, received, and processed in the form of action voltages or electrical pulses. The birth of Spiking Neural Networks (SNNs) has brought neural networks closer to t...
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Value Numbering is a fundamental technique used in optimizer compilers and model checking tools to detect equivalence between different expressions and eliminate the redundant computations. This survey reviews the evo...
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Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations r...
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Simulation is crucial for autonomous driving technology evolution. Radar, as an essential sensor in this field, significantly influences decision-making with its outputs. High-fidelity autonomous driving simulations require radar models that replicate radar outputs, including false alarms, missed alarms, and measurement errors, both in real-time and with high fidelity. The radar detection process is highly complex, and false and miss alarms add significant uncertainty to the detection results. Current radar models cannot accurately predict radar outputs. To address these issues, this study introduces a data-driven radar modeling approach. Initially, an analysis of factors influencing radar detection outcomes was conducted. Then proposes a labeling method for radar output objects, identify the corresponding scene targets, and distinguish between ghost and real objects. Following this, it introduces a modeling technique that separates radar output status and parameters, aiming to accurately predict radar outputs in the presence of false and missed alarms. It further decouples output parameters to boost prediction accuracy. Radar data is then collected to create a dataset. The radar model is developed and validated against conventional models. The model achieves a 96.5% accuracy in predicting false and missed alarms, with its predictions for radar output parameters closely approximating actual values. Compared to traditional models, there are improvements exceeding 70.60% and 93.68% respectively. Its 5-millisecond processing speed is substantially faster than actual radar speeds. This demonstrates the method's ability to create high-fidelity, real-time models. IEEE
Although deep neural networks have demonstrated exceptional performance in various fields, especially in image processing, they still face some significant unresolved challenges. Catastrophic forgetting is one of the ...
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Hacking is one of the most widespread issues that the general public faces today. Hackers essentially use some social engineering techniques, combined with the publicly available information, to crack open the social ...
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An amino acid change in the hemoglobin protein causes sickle cell disease (SCD), a prevalent hereditary illness that causes red blood cells to take on a sickle shape. These malformed cells cause severe health complica...
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Federated learning (FL) trains a model collaboratively but is susceptible to backdoor attacks for its privacy-preserving nature. Existing defenses against backdoor attacks in FL always make specific assumptions on dat...
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