Graph Neural Networks (GNNs) have demonstrated remarkable performance in modeling complex relational data. However, their deployment in privacy-sensitive domains remains challenging due to the inherent tension between privacy protection and model interpretability. This paper introduces a novel Multi-Branch Dendritic Computing framework for Federated Graph Neural Networks (MBD-FGNN) that addresses this fundamental challenge. Our approach integrates biologically-inspired dendritic computing with federated learning paradigms, enabling distributed training across decentralized graph data while preserving both privacy and interpretability. The multi-branch architecture mimics dendritic computation in biological neurons,where each branch processes patterns at different receptive field scales (1-hop, 2-hop, and 4-hop neighborhoods), providing natural model explanations through branch contribution analysis.To ensure rigorous privacy protection, we develop a branch-adaptive differential privacy mechanism with gradient clipping and calibrated Gaussian noise injection, satisfying (ϵ, δ)-differential privacy. We theoretically analyze the framework’s ability to maintain interpretation stability under differential privacy constraints and derive formal guarantees on the privacy-interpretability trade-off. Extensive experiments on benchmark graph datasets demonstrate that MBD-FGNN outperforms state-of-the-art federated GNN approaches in terms of accuracy, privacy preservation, and explanation quality. Notably, our method achieves robust explanation stability even under privacy constraints, maintaining higher explanation consistency compared to conventional approaches where explanations significantly degrade as privacy protection increases. The proposed framework opens new avenues for deploying interpretable graph learning systems in privacy-critical applications such as healthcare networks, financial transaction graphs, and social network analysis. The code and dataset are publicly available at https://github.com/ZhaoGuan-nuist/MBD-FGNN.

With the rapid advancement of remote sensing technology, change detection using bitemporal remote sensing images has significant applications in land use planning and environmental monitoring. The emergence of convolutional neural networks (CNNs) has accelerated the development of deep learning-based change detection. However, existing deep learning algorithms exhibit limitations in understanding bitemporal feature relationships and accurately identifying change region boundaries. Moreover, they inadequately explore feature interactions between bitemporal images before extracting differential features. To address these issues, this article proposes a novel interactive and supervised dual-mode attention network (ISDANet). In the feature encoding stage, we employ the lightweight MobileNetV2 as the backbone to extract bitemporal features. Additionally, we design the neighbor feature aggregation module (NFAM) to aggregate semantic features from adjacent scales within the dual-branch backbone, enhancing the representation of temporal features. We further introduce the interactive attention enhancement module (IAEM), which effectively integrates self-attention and cross-attention mechanisms. This establishes deep interactions between bitemporal features, suppresses irrelevant noise, and ensures precise focus on true change regions. In the feature decoding stage, the supervised attention module (SAM) reweights differential features and leverages supervisory signals to guide the learning of attention mechanisms, significantly improving boundary detection accuracy. SAM dynamically aggregates multilevel features, balancing high-level semantics and low-level details to capture subtle changes in complex scenes. The proposed model achieves F1 scores that are 0.28%, 1.6%, and 0.76% higher than the best comparative method, spatiotemporal enhancement and interlevel fusion network (SEIFNet), on three CD datasets [LEVIR-CD, Guangzhou dataset (GZ-CD), and Sun Yat-sen University dataset (SYSU-CD)], respectively, while maintaining a lightweight design with only 6.93 M parameters and 3.46G floating-point operations (FLOPs). The code is available athttps://github.com/RenHongjin6/ISDANet.

Deep learning has significantly reshaped the landscape of remote sensing change detection (RSCD). However, detecting subtle changes remains a formidable challenge due to the simultaneous presence of three critical issues: domain shift from seasonal and illumination variations, severe class imbalance between change and background areas, and the need for high deployment efficiency. To address these multifaceted problems in a unified manner, this paper introduces SDTNet, a comprehensive framework featuring three key innovations. First, to counter domain shift, we propose the Style-Unified Temporal Difference Contrastive Learning Strategy (STDCL), a novel, fine-grained contrastive learning method that guides the model to decouple real from pseudo changes without incurring additional inference costs. Second, to mitigate severe class imbalance, we design the Adaptive Dice Focal Loss (ADFLoss), which, unlike static loss functions, introduces two novel adaptive factors to dynamically balance sample weights and implement a curriculum learning strategy. Third, for efficient feature enhancement, we present the Dual-Branch Temporal Difference Transformer (DTDFormer), an efficiency-aware architecture that adapts the differential attention mechanism for bi-temporal inputs and incorporates a sparse attention module to reduce computational complexity. Experimental results demonstrate that our integrated SDTNet framework achieves state-of-the-art (SOTA) performance across five large-scale change detection datasets. The code is publicly available at https://github.com/Baobaon/SDTNet.

Unit commitment (UC) is a critical component for the power system dispatching departments. Current methodologies for solving UC problems predominantly rely on mixed-integer linear programming and are supplemented by data-driven approaches. These methodologies have two primary limitations: first, as the scale of the power grid expands, the complexity of algorithms increases sharply. Second, they fail to fully exploit grid topology information and historical data trends. To address these limitations, this article proposes a constrained graph-temporal convolutional network, which addresses the UC problem by directly predicting power output with constraints. The algorithm takes historical load data from grid nodes as input, utilizes graph convolutional networks to capture the physical grid topology information, and employs temporal convolutional networks to extract temporal features. Ultimately, the output of the graph-temporal convolutional network is projected into the feasible domain through a linear constraint activation layer, achieving accurate power prediction. Experiments conducted on the IEEE 30-BUS and IEEE 118-BUS systems validate the feasibility and superiority of our method in terms of accuracy and computational efficiency.

In the context of vertical federated learning (VFL), agents utilize multimodal data on their edge devices to corporately train and inference with the deep learning models. However, in classical VFL, there exists three problems from the perspective of embeddings. 1) the utilization of oversimplified embedding fusion mechanism may result in suboptimal performance of the models; 2) the exchange of embeddings and their gradients poses a potential risk of private information leakage, as they inherently contain sensitive information; 3) finally, the withdrawal of some agents from cooperation disrupts the collaborative inference capabilities of the remaining agents. To mitigate these problems, this article introduces a novel VFL algorithm grounded in embedding alignment. It includes two distinct schemes: 1) performance-oriented scheme (POS) and 2) privacy-respecting scheme (PRS). Within POS, this article employs contrastive loss and joint fine-tuning to augment the expressiveness and the overall performance of models. While the PRS incorporates homomorphic-encryption-based contrastive loss and individual fine-tuning to safeguard the data security. In addition, the PRS eliminates the necessity of collaborative inference. In this article, comprehensive security analysis and proofs are conducted for PRS. Moreover, experiments demonstrate the superior performance of the proposed POS over classical VFL, showcasing a substantial performance improvement. Simultaneously, the PRS surpasses the performance of training alone, even under stringent security constraints.

The segmentation task of cloud and cloud shadow has always been one of the important tasks in remote sensing image processing. At present, cloud detection based on deep learning methods lacks generalization, which is easy to cause the loss of space and detail information, and missed detection and false detection occur from time to time. Aiming at the above problems, this paper proposes a multi-path, multi-scale attention network (MMA). In this network, Muti-scale overlapping patch embedding (MSP) is introduced to extract multi-scale semantic information with multi-path PVT, and strip convolution is used to supplement the spatial detail information of the image, so as to realize the effective aggregation of fine and rough features at the same feature level. In order to aggregate the global information, the multi-scale global aggregation module (MGAM) is used for deep feature extraction to supplement the high semantic information. In the decoding stage, aiming at the problem of small target cloud detection, the attention guided fusion module (AGFM) is proposed to focus on the important information of the image, remove the network noise and increase the detection accuracy of small targets. Contextual information fusion (CIF) decoding method is proposed for the coarse segmentation boundary problem, which fully integrates the context information and effectively helps to restore the image. The experimental results on Biome 8 Dataset, HRC-WHU Dataset and SPARCS Dataset confirm that our method is superior to the current cutting-edge cloud and cloud shadow detection technology.

Traditional building and water segmentation methods are vulnerable to noise interference, and hence they could not avoid missed and false detections in the detection process. Excessive deep learning downsampling would lead to significant loss of feature map information, and image location information offset, and the overall effect of falling apart. To address these issues, a Multi-Scale Location Attention Network (MSLA) is proposed. Location-spatial information and channel information are particularly important for edge detail segmentation in building and water cover. The network includes a Location Channel Attention Unit (LCA) to focus on tributary details of rivers and segmentation of building edge eaves. Moreover, this paper builds a Dual-Branch Multi-Scale Aggregation Unit (DBMSA) to obtain deeper multi-scale semantic information. Finally, the Multi-Scale Fusion Unit (MSF) is used to guide the information merging of multiple stages, and the boundary information is improved by splicing the acquired deep multi-scale information with the information of the relevant feature extraction layer in the downsampling. The experimental results on several datasets show that the proposed approach outperforms other methodologies in segmentation accuracy.

The biometric information contained in a face image is affected by many factors such as living environment, racial differences, and genetic diversity, this complexity leads to the nonstationary of the age estimation. In order to reduce the overlap of face features between adjacent ages and improve the accuracy of age prediction, a multi-stage feature constraints learning method is proposed for face age estimation. The proposed method gradually refines the feature through three feature constraint stages. In each stage, the algorithm continuously updates the feature center of its corresponding age range, and minimizes the distance between each age feature and feature center of the corresponding age range through feature constraint. Feature constraint makes the feature distances between different individuals in the same age feature space smaller and decrease the overlap areas between adjacent age range feature spaces. Meanwhile, the feature distance of different age range feature space is enlarged. The proposed network efficiently merges the features of three stages and optimizes the mapping of feature maps to an ordered binary comparison space. Experiments show that the proposed method is able to effectively improve the discrimination between different age features, and hence to improve the accuracy of face age estimation. In addition, the proposed algorithm is simple enough to achieve fast face age estimation.

Convolutional Neural Networks(CNNs) have achieved great success for object recognition in still images. However, CNNs can't make evident improvement for action recognition in videos, one reason is that many current network architectures are relatively shallow compared with deep models in image domain, and the other reason is that CNNs can't capture effective long-term motion information from videos. Encouraged by the good performance of Residual Network-s(ResNets) for training extremely deep models, and Long-term Recurrent Convolutional Networks(LSTMs) for dealing with tasks involving sequences, we presented an action recognition method based on a two-stream architecture, with 2D ResNets with LSTMs in one stream and designed 3D ResNets with LSTMs in the other stream, which can combine appearance and motion information better. Especially, our proposed method first learns spatiotemporal features of videos through the Residual networks, then models complex temporal dynamics by the Long-term Recurrent Convolutional networks, and with a softmax layer on the top of two streams, the final classification results can be predicted by fusing scores of each stream with weights on score distribution. Furthermore, for better reducing the influence of redundant background information in videos for recognition results, we also applied a center extraction method to generate central regions of videos instead of an entire video into a visual representation. On two video action benchmarks of UCF101 and HMDB51, our method achieved promising performance compared with state-of-the-art.

Machine learning is a subfield of artificial intelligence concerned with techniques that allow computers to improve their outputs based on previous experiences. Among numerous machine learning algorithms, Weighted Extreme Learning Machine (WELM) is one of the famous cases recently. It not only has Extreme Learning Machine (ELM)’s extremely fast training speed and better generalization performance than traditional Neuron Network (NN), but also has the merit in handling imbalance data by assigning more weight to minority class and less weight to majority class. But it still has the limitation of its weight generated according to class distribution of training data, thereby, creating dependency on input data [R. Sharma and A. S. Bist, Genetic algorithm based weighted extreme learning machine for binary imbalance learning, 2015 Int. Conf. Cognitive Computing and Information Processing (CCIP) (IEEE, 2015), pp. 1–6; N. Koutsouleris, Classification/machine learning approaches, Annu. Rev. Clin. Psychol. 13(1) (2016); G. Dudek, Extreme learning machine for function approximation–interval problem of input weights and biases, 2015 IEEE 2nd Int. Conf. Cybernetics (CYBCONF) (IEEE, 2015), pp. 62–67; N. Zhang, Y. Qu and A. Deng, Evolutionary extreme learning machine based weighted nearest-neighbor equality classification, 2015 7th Int. Conf. Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 2 (IEEE, 2015), pp. 274–279]. This leads to the lack of finding optimal weight at which good generalization performance could be achieved [R. Sharma and A. S. Bist, Genetic algorithm based weighted extreme learning machine for binary imbalance learning, 2015 Int. Conf. Cognitive Computing and Information Processing (CCIP) (IEEE, 2015), pp. 1–6; N. Koutsouleris, Classification/machine learning approaches, Annu. Rev. Clin. Psychol. 13(1) (2016); G. Dudek, Extreme learning machine for function approximation–interval problem of input weights and biases, 2015 IEEE 2nd Int. Conf. Cybernetics (CYBCONF) (IEEE, 2015), pp. 62–67; N. Zhang, Y. Qu and A. Deng, Evolutionary extreme learning machine based weighted nearest-neighbor equality classification, 2015 7th Int. Conf. Intelligent Human-Machine Systems and Cybernetics (IHMSC), Vol. 2 (IEEE, 2015), pp. 274–279]. To solve it, a hybrid algorithm which composed by WELM algorithm and Particle Swarm Optimization (PSO) is proposed. Firstly, it distributes the weight according to the number of different samples, determines weighted method; Then, it combines the ELM model and the weighted method to establish WELM model; finally it utilizes PSO to optimize WELM’s three parameters (input weight, bias, the weight of imbalanced training data). Experiment data from both prediction and recognition show that it has better performance than classical WELM algorithms.

This work addresses the problem of formation control for multiple spacecraft in a Planetary Orbital Environment (POE). Due to the diverse interferences and uncertainties in outer space, traditional control methods encounter great difficulties in this area. A new control approach inspired by human memory system is proposed, which is shown to be capable of learning from past control experience and current behavior to improve its performance and demands much less system dynamic information as compared with traditional controls. Both theoretical analysis and computer simulation verify its effectiveness.