Graph neural networks have been demonstrated as a powerful paradigm for effectively learning graph-structured data on the web and mining content from it. Current leading graph models require a large number of labeled ...
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With the rapid development of artificial intelligence, people have put forward higher requirements for robot path planning. As a more commonly used algorithm, reinforcement learning learns from experience by imitating...
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With cars becoming the primary choice of people’s daily travel, on-board OTA upgrade technology has arisen a common concern in the automotive industry. Traditional on-board OTA upgrade technology, which adopts a meth...
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As an extended version of frequent itemset patterns, periodic itemset patterns concern both the frequency and periodicity of itemsets at the same time, so they contain more information than frequent itemset patterns, ...
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As control modules in cars become intelligent and automated, electrical systems become more complex. Dozens or even hundreds of electronic control units (ECUs) are used in many cars. In this case, the information secu...
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Left ventricle (LV) segmentation in echocardiography is of paramount significance in cardiac function analysis. Recently, semi-supervised segmentation has garnered considerable attention due to its potential mitigatio...
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Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of *** a clear bone blocks boundary,CT images have gained obvious advantages in OVFs *** with CT images,X-rays ar...
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Osteoporotic Vertebral Fracture(OVFs)is a common lumbar spine disorder that severely affects the health of *** a clear bone blocks boundary,CT images have gained obvious advantages in OVFs *** with CT images,X-rays are faster and more inexpensive but often leads to misdiagnosis and miss-diagnosis because of the overlapping *** how to transfer CT imaging advantages to achieve OVFs classification in X-rays is *** this purpose,we propose a multi-modal semantic consistency network which could do well X-ray OVFs classification by transferring CT semantic consistency *** from existing methods,we introduce a feature-level mix-up module to get the domain soft labels which helps the network reduce the domain offsets between CT and *** the meanwhile,the network uses a self-rotation pretext task on both CT and X-ray domains to enhance learning the high-level semantic invariant *** employ five evaluation metrics to compare the proposed method with the state-of-the-art *** final results show that our method improves the best value of AUC from 86.32 to 92.16%.The results indicate that multi-modal semantic consistency method could use CT imaging features to improve osteoporotic vertebral fracture classification in X-rays effectively.
Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled inst...
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Positive and Unlabeled (PU) learning refers to a special case of binary classification, and technically, it aims to induce a binary classifier from a few labeled positive training instances and loads of unlabeled instances. In this paper, we derive a theorem indicating that the probability boundary of the asymmetric disambiguation-free expected risk of PU learning is controlled by its asymmetric penalty, and we further empirically evaluated this theorem. Inspired by the theorem and its empirical evaluations, we propose an easy-to-implement two-stage PU learning method, namely Positive and Unlabeled Learning with Controlled Probability Boundary Fence (PUL-CPBF). In the first stage, we train a set of weak binary classifiers concerning different probability boundaries by minimizing the asymmetric disambiguation-free empirical risks with specific asymmetric penalty values. We can interpret these induced weak binary classifiers as a probability boundary fence. For each unlabeled instance, we can use the predictions to locate its class posterior probability and generate a stochastic label. In the second stage, we train a strong binary classifier over labeled positive training instances and all unlabeled instances with stochastic labels in a self-training manner. Extensive empirical results demonstrate that PUL-CPBF can achieve competitive performance compared with the existing PU learning baselines. Copyright 2024 by the author(s)
Few-shot learning (FSL) aims to learn to new concepts based on very limited data. One of the main challenges in FSL is the use of pretrained embeddings whose dimension is too high for the small sample size. While the ...
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Mobile CrowdSensing (MCS) is a data sensing paradigm that recruits users carrying mobile terminals to collect data. As its variant, Sparse MCS has been further proposed for large-scale and fine-grained sensing task wi...
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