The ubiquity of social media platforms facilitates malicious linguistic steganography, posing significant security risks. However, detection is severely hampered by two fundamental issues during model training. Firstly, extreme class imbalance (less than 1% steganographic samples) induces a strong decision bias. Secondly, the invisibility of generative steganography means its features are nearly indistinguishable from benign text; this similarity, compounded by their extreme rarity, leads to severe feature marginalization, where faint steganographic signals are completely overwhelmed. To directly address these optimization-level challenges, we propose FADRW (Feature-Aware Modulated and Dynamically Reweighted Loss), a novel loss function framework engineered for few-shot steganalysis. FADRW employs Dynamic Reweighting to progressively counteract decision bias, and a Feature-Aware Modulation module to structurally reshape the feature space, preventing feature marginalization by enhancing the separability of these subtle features. Extensive experiments on datasets from three real-world social platforms demonstrate that FADRW significantly outperforms state-of-the-art methods, particularly in the challenging few-shot steganographic sample scenario.

Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy (cryo-EM) is a critical technique for recovering and studying the fine 3D structure of proteins and other biological macromolecules, where the primary issue is to determine the orientations of projection images with high levels of noise. This paper proposes a method to determine the orientations of cryo-EM projection images using reliable common lines and spherical embeddings. First, the reliability of common lines between projection images is evaluated using a weighted voting algorithm based on an iterative improvement technique and binarized weighting. Then, the reliable common lines are used to calculate the normal vectors and local -axis vectors of projection images after two spherical embeddings. Finally, the orientations of projection images are determined by aligning the results of the two spherical embeddings using an orthogonal constraint. Experimental results on both synthetic and real cryo-EM projection image datasets demonstrate that the proposed method can achieve higher accuracy in estimating the orientations of projection images and higher resolution in reconstructing preliminary 3D structures than some common line-based methods, indicating that the proposed method is effective in single-particle cryo-EM 3D reconstruction.

Traffic prediction plays an important role in urban planning and smart city construction. Reasonable forecasting of future traffic conditions can effectively avoid traffic congestion and allow planning time for people to travel. However, complex traffic networks and non-linear time dependence make traffic prediction very challenging, and existing methods often lack the ability to model the dynamic spatio-temporal correlation of traffic data, making forecasting results unsatisfactory. We therefore propose a dynamic spatio-temporal multi graph convolutional neural network (MGCN) based on graph convolution network and attention mechanisms to perform traffic prediction tasks. Specifically, we design a spatio-temporal graph convolution module to simultaneously capture traffic network structure information, dynamic neighbour node information and traffic variation information in the temporal dimension, and effectively fuse them to represent comprehensive and dynamic spatio-temporal correlation. Further, we transform the historical time series into a future time series representation and resolve the future traffic conditions along the temporal and spatial dimensions on two Decoders respectively, aggregating the multidimensional information. Adequate experiments were conducted on two real large scale datasets, and the experimental results demonstrate the effectiveness and superiority of our approach and achieve a better level of performance than other baseline methods.

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream technologies in the field of structural biology to determine the three-dimensional (3D) structures of biological macromolecules. Heterogeneous cryo-EM projection image classification is an effective way to discover conformational heterogeneity of biological macromolecules in different functional states. However, due to the low signal-to-noise ratio of the projection images, the classification of heterogeneous cryo-EM projection images is a very challenging task. In this paper, two novel distance measures between projection images integrating the reliability of common lines, pixel intensity and class averages are designed, and then a two-stage spectral clustering algorithm based on the two distance measures is proposed for heterogeneous cryo-EM projection image classification. In the first stage, the novel distance measure integrating common lines and pixel intensities of projection images is used to obtain preliminary classification results through spectral clustering. In the second stage, another novel distance measure integrating the first novel distance measure and class averages generated from each group of projection images is used to obtain the final classification results through spectral clustering. The proposed two-stage spectral clustering algorithm is applied on a simulated and a real cryo-EM dataset for heterogeneous reconstruction. Results show that the two novel distance measures can be used to improve the classification performance of spectral clustering, and using the proposed two-stage spectral clustering algorithm can achieve higher classification and reconstruction accuracy than using RELION and XMIPP.

A microblog recommendation method based on tag correlation and user social relation is proposed via analyzing microblog features and the deficiencies of existing microblog recommendation algorithm. Specifically, a tag retrieval strategy is established to add tags for unlabeled users and users with few tags, and the user-tag matrix is then built and user-tag weights are then obtained. In order to solve the problem of sparsity of the matrix, the correlation between the tags is investigated to update the user-tag matrix. Considering the significance of user social relation for microblog recommendation, a user-user social relation similarity matrix is constructed and a mechanism is designed to iteratively obtain user interest. Experimental results show that the algorithm is effective for microblog recommendation.

In this paper, we present a microblog recommendation algorithm based on multi-tag correlation. Firstly, a tag retrieval strategy is designed to add tags for unlabeled users, the initial user-tag matrix is then constructed and user-tag weights are set. In order to represent user interests accurately, we fully investigate the associations between the tags. Both inner and outer correlation between tags are defined to conquer the problem of sparsity of user-tag matrix. The user interests can then be decided and microblogs can be recommended to users. Experimental results show that the algorithm is effective for microblog recommendation.

In this paper, we present a semi-supervised clustering method for microblog in which both word-level and microblog (document)-level constraints are automatically generated totally based on statistical information rather than any kind of external knowledge. The key idea is first to explore term correlation data, which investigates both inter and intra correlation of words, and the initial similarity between words can therefore be deduced. And then an iterative method is established to calculate both word similarity and microblog similarity. The mechanism of incorporating dual constraints is presented based on word similarity and microblog similarity. We then formulate short text clustering problem as a non-negative matrix factorization based on dual constraints. Empirical study of two real-world dataset shows the superior performance of our framework in handling noisy and microblogs.