Cross-lingual cross-target stance detection addresses the critical challenge of identifying textual stances (support/oppose) toward specific targets when linguistic variations and target distribution discrepancies exist across multilingual contexts. Performance degrades due to cross-lingual feature misalignment and target representation inconsistency. Furthermore, existing methods typically model text and target representations independently, overlooking that stance depends on the joint representation of the text and target. To overcome these limitations, we propose the Dual-Expert Collaborative Model (DECM), a unified framework that synergizes text-target interaction modeling with cross-lingual adaptation and target distribution alignment through three designed components: Cross-Lingual Expert Module leverages prompt-enhanced fine-tuning on mBERT with cross-lingual consistency constraints, aligning stance semantics across languages; Cross-Target Expert Module addresses target heterogeneity via joint text-target encoding, contrastive representation clustering, and feature space optimization to disentangle stance-relevant target characteristics; Collaborative Adaptation Module integrates cross-expert knowledge through pseudolabel propagation on unlabeled target-language data, reinforced by confidence-weighted prediction aggregation and unsupervised contrastive representation refinement. Extensive experiments demonstrate that DECM significantly outperforms baseline methods across multiple cross-lingual and cross-target stance detection benchmarks. It achieves 77.05
Skeleton-based gait emotion recognition has attracted significant attention due to its wide-ranging application potential. Existing approaches, however, still face several limitations: spatial graph topologies often follow physical joint connections and therefore miss semantically related long-distance coordination, temporal modules based on local convolutions have limited receptive fields, and visually similar emotions can produce ambiguous skeleton patterns. To address these issues, we propose a multi-semantic hierarchical graph-transformer network (MSH-GT) for gait emotion recognition. Central to our model is the hierarchical graph-transformer module, which integrates hierarchical graph convolutions with Transformer mechanisms. This design enhances spatial relationship modeling by adopting a centroid diffusion strategy to construct multi-scale semantic connections and leverages global temporal context to capture long-range dynamics. Additionally, a motion-pose spatio-temporal fusion module is proposed for dual-stream information fusion, and a prototype-based refinement contrastive module improves feature discriminability for ambiguous samples. Comprehensive experiments under the adopted Emotion-Gait and ELMD benchmark protocols show that MSH-GT achieves competitive state-of-the-art accuracy while using fewer parameters and FLOPs than several strong graph-based baselines. The source code is released on https://github.com/lebronble/MSH-GT .
Fuzzy β covering rough set model represents a significant expansion of covering rough set model, which can effectively handle the uncertainty and fuzziness of data. Swarm intelligence algorithms, which simulate the collaboration and interaction between individuals in biological groups, find extensive application across machine learning domains to solve complex optimization problems. In attribute reduction, swarm intelligence algorithms facilitate the efficient search for optimal attribute subsets within a vast attribute combination space. This paper studies fuzzy β covering-driven attribute reduction for hybrid data via fuzzy conditional information entropy using matrix operation and cuckoo search algorithm. Initially, a distance function is utilized to ascertain the difference or similarity between objects in hybrid data, further establishing the corresponding fuzzy β covering decision information system. Subsequently, fuzzy conditional information entropy is defined within this system to measure the classification ability of attribute subsets, and then fuzzy conditional information entropy can be viewed as an evaluation function for attribute reduction. After that, a method for computing fuzzy conditional information entropy using matrix operation is proposed. Next, an attribute reduction algorithm based on fuzzy conditional information entropy and the greedy search strategy is designed. Additionally, an attribute reduction algorithm that incorporates fuzzy conditional information entropy and the cuckoo search strategy is also designed to search for more optimal attribute subsets. Finally, experiments are conducted comparing the two designed algorithms with nine existing advanced attribute reduction algorithms. With classification accuracy and F1 score as evaluation metrics, the experimental results show that the proposed algorithms are effective and achieve competitive classification performance on the evaluated datasets and classifiers.
In recent years, multi-scale feature processing has been regarded as key to achieving high-precision pose estimation. Among these approaches, High-Resolution Networks (HRNets) have demonstrated leading performance due to their unique multi-scale parallel processing and fusion mechanisms. However, their high computational complexity and massive parameter count severely hinder real-time applications on resource-constrained devices such as mobile platforms. To address this challenge, this paper proposes a lightweight high-resolution pose estimation network, UG-HRNet. The proposed design centers on two major innovations: First, a novel lightweight deformable convolution block (LDC-Block) is introduced. This module reduces redundancy through half-channel computation and channel mixing strategies. To preserve feature expressiveness despite lightweighting, a context-guided deformable convolution variant, termed UG-DConv, is integrated into its computational branch. UG-DConv preserves the standard deformable convolution formulation while using a decomposed large-kernel guidance branch to generate more context-aware offsets and modulation masks with limited additional cost, effectively compensating for the expressiveness loss caused by lightweighting. Second, at the architectural level, HRNet’s expensive fully parallel fusion is replaced by an iterative H/L alternating fusion mechanism. This mechanism decomposes multi-scale interactions into efficient top-down (H-Only) and bottom-up (L-Only) information flows, enabling progressive feature refinement. Finally, experimental evaluations on COCO and MPII datasets demonstrate that UG-HRNet achieves a remarkable balance between efficiency and accuracy. Based on the comprehensive analysis of model complexity, computational cost, and peak memory efficiency, the results suggest that UG-HRNet possesses significant potential for deployment on resource-constrained devices. It significantly reduces model complexity while maintaining competitive performance, serving as a practical alternative to existing popular lightweight networks.
Aiming at information fuzziness, uncertain decision-makers’ preferences and the lack of social network trust relationships in complex decisions, existing picture fuzzy information-based group decision-making methods have defects including a complex consistency check process, lack of objective weight allocation basis and difficulty in quantifying decision result reliability. This paper proposes a novel picture fuzzy information group decision-making method integrating the BrowseRank algorithm and judgment matrix consistency theory. Firstly, a breakthrough is made in judgment matrix consistency theory: transformation functions are used to convert picture fuzzy preference relations into fuzzy complementary judgment matrices to simplify data and reduce computational complexity. A derived matrix and an additive consistency determination theorem are proposed, solving cumbersome traditional verification processes. Secondly, innovations are made in social networks and weight allocation: a picture fuzzy trust propagation operator complements missing trust relationships; the BrowseRank algorithm is introduced to calculate decision-makers’ objective weights by integrating multiple parameters, breaking the limitation of the ignoring social attributes. An aggregation strategy combining the weighted geometric average operator and Hadamard product ensures the consistency of the aggregated matrix, filling relevant research gaps. Thirdly, a picture fuzzy set similarity measure defines decision-maker reliability, realizing a quantitative evaluation of decision result reliability and improving the evaluation system. Finally, the method is applied to new energy vehicle battery selection; its advantages in consistency check efficiency, weight rationality and result reliability are verified via simulation and comparative analysis.
