Typical network traffic is characterized by high-dimensional, polymorphic and massive amounts of data, which is a consistent challenge for pattern-based intrusion detection. Most detection models suffer from low effic...
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The Internet Protocol (IP) is a critical component of computer networks, playing a crucial role in data exchange between computers. Despite the development of several IP protocols, these protocols have limitations suc...
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Agriculture is the backbone of any country as it feeds its population. Since the global population keeps increasing, it is necessary to give utmost importance to the field of agriculture. State-of-the-art techniques s...
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Guava, a valuable tropical fruit crop, faces significant defect challenges due to diseases. Manual disease checks are time- consuming and error prone, leading to delayed action. Our research focuses on image processin...
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This paper discusses a novel design of an ultrathin conformal modified octagonal ring-loaded cross dipole frequency selective surface (MORCD-FSS). The proposed FSS offers multifunctional properties such as in-band pas...
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Sindhi is an ancient language rooted in the lower Indus River Valley. Many literary works have been produced in it;however, it is considered a low-resource language for Deep Learning. The applications of Deep Learning...
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Worldwide, communication frequently involves the use of images. Numerous applications use images, such as medical imaging, remote sensing, educational imaging, and electronic commerce. In the age of information and th...
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In order to prevent medical images from being tampered in network transmission, a high fidelity anti-tampering medical image digital watermarking algorithm in DWT domain is proposed. The algorithm uses Logistic chaoti...
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Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and sto...
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Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://***/yihong-97/Source-free-IAPC. IEEE
Information technologies represented by 5G and artificial intelligence (AI) have comprehensively permeated into the educational field, driving the high-speed development of educational informatization and leading to t...
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