With the rise of social media, the rapid spread of rumors online has resulted in numerous negative effects on society and the economy. The methods for rumor detection have attracted great interest from both academia and industry. Given the widespread effectiveness of contrastive learning, many graph contrastive learning models for rumor detection have been proposed by using the event propagation structure as graph data. However, the existing contrastive models usually treat the propagation structure of other events similar to the anchor events as negative samples. While this design choice allows for discriminative learning, on the other hand, it also inevitably pushes apart semantically similar samples and, thus, degrades model performance. In this article, we propose a novel propagation fusion model called propagation structure fusion model based on node-level contrastive learning (PFNC) for rumor detection based on node-level contrastive learning. PFNC first obtains three augmented propagation structures by masking the text of each node in the propagation structure randomly and perturbing some edges in the propagation structure based on the importance of edges. Then, PFNC applies the node-level contrastive learning method between every two augmented propagation structures to prevent the samples with similar propagation structure from far away. Finally, a convolutional neural network (CNN)-based model is proposed to capture the relevant information that is consistent and supplementary among three augmented propagation structures by regarding the propagation structure of the event as a color picture, three augmented propagation structures as color channels, and each node as a pixel. The experimental results on real datasets show that the PFNC significantly outperforms the state-of-the-art models for rumor detection.

Anomaly targets in a hyperspectral image (HSI) are often multi-pixel, rather than single-pixel, objects. Therefore, algorithms using a test point vector may ignore the spatial characteristics of the test point. In addition, hyperspectral anomaly detection (AD) algorithms usually use original spectral signatures. In a fractional Fourier transform (FrFT), the signals in the fractional Fourier domain (FrFD) possess complementary characteristics of both the original reflectance spectrum and its Fourier transform. In this paper, a tensor RX (TRX) algorithm based on FrFT (FrFT-TRX) is proposed for hyperspectral AD. First, the fractional order of FrFT is selected by fractional Fourier entropy (FrFE) maximization. Then, the HSI is transformed into the FrFD by FrFT. Next, TRX is employed in the FrFD. Finally, according to the optimal spatial dimensions of the target and background tensors, the optimal AD result is achieved by adjusting the fractional order. TRX employs a test point tensor, making better use of the spatial characteristics of the test point. TRX in the FrFD exploits the complementary advantages of the intermediate domain to increase discrimination between the target and background. Six existing algorithms are used for comparison in order to verify the AD performance of the proposed FrFT-TRX over five real HSIs. The experimental results demonstrate the superiority of the proposed algorithm.

The rise of social media has posed a challenging problem of effectively identifying rumors. With the great success of contrastive learning in many fields, many contrastive learning models for rumor detection have been proposed. However, existing models usually use the propagation structure of other events as negative samples and regard more similar samples to anchor events as hard ones across all the training processes, resulting in undesirably pushing away the samples of the same class. Thus, we propose a novel contrastive learning model (CRFB) to solve the above problem. Specifically, we employ contrastive learning between two augmented propagation structure and fit a two-component (true-false) beta mixture model (BMM) to measure the probability of negative samples being true. In addition, we propose a CNN-based model to capture the consistent and complementary information between two augmented propagation structure. The experimental results on public datasets demonstrate that our CRFB outperforms the existing state-of-the-art models for rumor detection.

Recently, using spatial–spectral information for hyperspectral anomaly detection (AD) has received extensive attention. However, the test point and its neighborhood points are usually treated equally without highlighting the test point, which is unreasonable. In this paper, improved central attention network-based tensor RX (ICAN-TRX) is designed to extract hyperspectral anomaly targets. The ICAN-TRX algorithm consists of two parts, ICAN and TRX. In ICAN, a test tensor block as a value tensor is first reconstructed by DBN to make the anomaly points more prominent. Then, in the reconstructed tensor block, the central tensor is used as a convolution kernel to perform convolution operation with its tensor block. The result tensor as a key tensor is transformed into a weight matrix. Finally, after the correlation operation between the value tensor and the weight matrix, the new test point is obtained. In ICAN, the spectral information of a test point is emphasized, and the spatial relationships between the test point and its neighborhood points reflect their similarities. TRX is used in the new HSI after ICAN, which allows more abundant spatial information to be used for AD. Five real hyperspectral datasets are selected to estimate the performance of the proposed ICAN-TRX algorithm. The detection results demonstrate that ICAN-TRX achieves superior performance compared with seven other AD algorithms.