The propagation of online rumors has evolved from single-modal text to composite forms incorporating multimodal information such as images and videos. Leveraging stronger visual impact and emotional appeal, multimodal content significantly accelerates the dissemination speed and expands the coverage of false information, posing severe challenges to cyberspace governance. This paper proposes a detection model based on feature enhancement and dual-path decision fusion. The overall architecture consists of three core modules: lightweight visual feature enhancement, dual-path collaborative discrimination, and adaptive decision fusion. First, a Lightweight Parallel Multi-scale Feature Enhancer (LPMFE) is designed, which employs three parallel branches—global self-attention, spatial depthwise separable convolution, and temporal 1D convolution—to capture features from different dimensions. A learnable weight vector dynamically fuses these multi-scale outputs, while a gated residual mechanism and feedforward network refine local image features while preserving the integrity of the backbone representation. Second, a dual-path collaborative discrimination mechanism is constructed. The generative path first aligns the visual feature sequence into the word embedding space of a large language model (LLM) via a cross-attention based image projector. The projected visual tokens are then inserted into the text sequence and fed into the LLM for autoregressive inference. The hidden state of the last token is extracted and mapped through a linear classification head to logits over the vocabulary, which are finally converted into a generative prior probability via a Softmax function, leveraging the model’s parametric knowledge for macroscopic logical reasoning. The discriminative path explicitly verifies image–text semantic consistency by sharing the deep hidden states from the same LLM and aggregating them via masking to obtain a global text vector; meanwhile, the enhanced visual features are pooled. Both modalities are projected into a shared 256-dimensional semantic space. A supervised contrastive loss is introduced to optimize the discriminability of modal features. The absolute difference and element-wise product of the projected features are computed to capture fine-grained semantic conflicts. The original projected features and the two interaction features are concatenated along the channel dimension and fed into a multilayer perceptron to output a consistency discrimination score. Finally, at the decision fusion level, a learnable weight parameter is introduced to adaptively fuse the dual-path output probabilities. A two-threshold mechanism is set to form a three-interval decision logic, categorizing samples into high-confidence rumors, high-confidence non-rumors, and ambiguous cases. A boundary penalty term is added to the optimization objective to prevent threshold collapse and ensure stable convergence of the fusion strategy. Experiments are conducted on the Weibo, Fakeddit, and FineFake datasets. On Weibo, the method achieves an accuracy of 93.23%, with precision/recall for non-rumors at 95.72%/90.50% and for rumors at 90.99%/95.95%. On Fakeddit, the accuracy reaches 91.56%, with F1-scores of 92.98% for non-rumor and 89.42% for rumor, a difference of 3.56 percentage points. On FineFake, the accuracy is 81.21%, with non-rumor/rumor F1-scores of 83.42%/78.31%. Ablation studies demonstrate that removing LPMFE decreases FineFake accuracy to 79.20% and rumor recall by 5.32 percentage points; replacing the cross-attention projector with a linear mapping reduces Fakeddit rumor F1 by 6.01 points; and removing the discriminative branch lowers Weibo accuracy to 86.59% and rumor recall by 16.27 points. This study demonstrates that incorporating a visual feature enhancement mechanism to suppress information redundancy and complex scene interference, combined with an LLM-based parametric commonsense reasoning and explicit cross-modal consistency verification via a generative-discriminative dual-path collaborative design, overcomes the limitations of single-paradigm detection models, effectively mitigates LLM hallucinations and unimodal semantic bias, and substantially improves the overall accuracy of multimodal rumor detection.

To address the key challenges faced by existing graph neural network (GNN) based multi‑criteria recommendation algorithms under data‑sparse conditions—namely the long‑tail effect, the difficulty in synergistically integrating graph attention networks (GAT) and graph convolutional networks (GCN) , and the ambiguous discrimination between positive and negative samples caused by the difficulty of contrastive learning anchor selection in multi‑criteria rating scenarios—this paper proposes a multi‑criteria recommendation algorithm that fuses noisy graph neural networks with contrastive learning, termed NGCLR. The research objective is to systematically mitigate node embedding degradation, enhance cross‑criteria representation consistency, and optimize contrastive learning effectiveness by introducing dynamic noise augmentation, a progressive fusion mechanism, and a neighbor‑similarity‑based anchor selection strategy, thereby significantly improving the rating prediction accuracy and generalization capability of multi‑criteria recommender systems in sparse interaction environments. On the technical implementation side, the model first constructs a user item bipartite graph for each evaluation criterion independently and normalizes the adjacency relationships using a layer wise trained bipartite graph neural network matrix, so as to preserve the independent rating distribution characteristics of each criterion. To tackle the degradation phenomenon where long tail items' embeddings converge towards head items due to sparse neighborhoods, a Gaussian noise enhanced LightGCN module is designed: during each layer of graph convolution propagation, dynamic Gaussian noise with an initial intensity of 0.2 and an exponential decay rate of 0.5 per training epoch is injected into the current layer's node embeddings. This perturbation does not alter the graph topology but acts solely on the feature space, thereby breaking feature homogenization and encouraging a more uniform embedding distribution for long tail items. To fuse the complementary advantages of graph attention networks in capturing fine grained local interactions and graph convolutional networks in perceiving global structural patterns, a progressive fusion mechanism is proposed. At each propagation layer, the multi head attention outputs from GAT and the neighborhood aggregation outputs from LightGCN are concatenated, and a learnable parameter adaptively controls the contribution of each layer, achieving hierarchical fusion from low level local details to high level global semantics. To overcome the difficulty of reliably selecting contrastive learning anchors caused by the inconsistency between rating criteria and samples in multi criteria data, an anchor selection strategy based on average neighbor cosine similarity is designed: within each criterion view, the average similarity between each node's embedding and those of all its neighbors is computed, and the node with the highest similarity is selected as the anchor for that view, serving as a stable contrastive benchmark. On this basis, a dual contrastive learning task is constructed—the local contrastive loss maximizes the consistency of positive sample embeddings of the same node across different criteria while pushing away negative samples, and the global contrastive loss enhances cross criterion alignment among global embeddings (node means) of each criterion. These two losses are jointly optimized, and the total loss function combines the local loss, global loss, and an L₂ regularization term, balancing node level discriminability and distribution level consistency. To validate the model's effectiveness, experiments are conducted on three publicly available real‑world datasets: Yahoo!Movies, BeerAdvocate, and TripAdvisor. Yahoo!Movies contains 6,078 users, 976 movies, and 758,405 four‑criterion ratings; BeerAdvocate comprises 8,831 users, 2,698 beers, and 3,880,359 four‑criterion ratings; TripAdvisor includes 15,000 users, 1,325 hotels, and 20,830 seven‑criterion ratings, with a high sparsity level. Using mean absolute error and root mean square error as evaluation metrics, the proposed NGCLR is compared against more than ten baseline methods, including BMF, MSVD, MLR, SVR, CIC, DMCF, CFM, DNN‑MF, MCAE‑FADNN, and the best‑performing baseline D‑MGAC. Experimental results show that NGCLR achieves MAE and RMSE of 0.6028 and 0.8211 on Yahoo!Movies, corresponding to relative reductions of 2.94% and 3.79% over D‑MGAC; on BeerAdvocate, the values are 0.3931 and 0.5380, with relative reductions of 4.93% and 2.73%; and on TripAdvisor, they are 0.7338 and 0.9724, with relative reductions of 2.04% and 1.96%. Ablation studies demonstrate that removing the noise augmentation module, the progressive fusion mechanism, or the contrastive learning module leads to varying degrees of increase in both MAE and RMSE. In particular, the variant adopting a random anchor selection strategy yields MAE values that are 2.86%, 2.21%, and 0.69% higher than the full model on the three datasets, respectively, confirming the independent contributions and synergistic gains of each core component. Hyperparameter analysis indicates that the optimal performance is achieved with an embedding dimension of 256, 8 attention heads, and equal weights of 0.5 for both local and global contrastive losses; the dynamic decay noise strategy outperforms fixed‑noise and standard‑adaptive‑noise strategies, and the neighbor‑similarity‑based anchor selection surpasses both global‑average‑similarity and PageRank‑based strategies. Head‑vs‑tail group experiments show that the model achieves significantly greater MAE improvements on tail item groups than on head groups, and noise enhancement visualization further confirms that the embedding distribution of long‑tail items shifts from clustering towards dispersion. In summary, NGCLR effectively addresses the three major challenges in multi‑criteria recommendation—long‑tail degradation, insufficient fusion of graph modules, and anchor bias in contrastive learning—achieving consistently superior prediction accuracy across multiple real datasets, and thus provides an efficient and robust solution for multi‑criteria recommender systems.

In high-concurrency access scenarios with massive small files, frequent context switches caused by metadata operations become a major performance bottleneck for user-space file systems. Existing caching and prefetching mechanisms find it difficult to strike an effective balance among memory overhead, adaptability to random access patterns, and performance. To reduce the per-component lookup overhead during multi-level path resolution in the FUSE (Filesystem in Userspace) framework, this paper proposes a path-aware batch metadata prefetching scheme named Lookupplus. The core design philosophy of Lookupplus is to change the request interaction granularity between the kernel and the user-space daemon. In native FUSE, path resolution follows a serial per-component processing mode: whenever the directory entry cache of a component expires, the FUSE kernel module issues an independent lookup request, and each expired component incurs two context switches. As directory depth increases, the cumulative overhead of multiple round trips significantly amplifies metadata access latency. Lookupplus observes that when the cache of an intermediate component expires, the remaining subsequent components on that path will also require revalidation within a very short time. Based on this observation, when the first expired component triggers a lookup request, Lookupplus captures the complete remaining path from the current process control block, attaches it to the current lookup request, and transmits it to the user-space daemon. The daemon parses each component in the remaining path and returns their metadata entries, allowing the kernel to update the attributes and validity periods of all child directory entries in a batch, thereby enabling subsequent components to be verified through the fast path. The user-space file system does not need to be aware of the batch prefetching logic or maintain additional cache consistency states. The additional memory overhead comes only from temporary path copying and batch response entries, and is released immediately after request processing completes. The prototype system of Lookupplus is implemented on the Linux 5.2.2 kernel and evaluated against state-of-the-art schemes including ExtFUSE and RFUSE. Single-threaded metadata performance tests show that at a directory depth of 10, the average latency of create operations in Lookupplus drops to 32% of that in native FUSE. During continuous runs across depths from 1 to 10, the total number of lookup requests falls from 65 in native FUSE to 20 in Lookupplus. Thirty-two-thread micro-benchmarks cover directory widths ranging from 10 to 1,200, with 80% of the file set pre-created. Results show that Lookupplus achieves less than 5% performance improvement in concurrent access to a shared file set. Meanwhile, ExtFUSE performs approximately 20% lower than native FUSE. RFUSE shows significantly lower throughput under narrow directory configurations due to queue pressure, but gradually recovers to a high throughput level as directory width increases. In typical real-world macro-benchmarks, under the file server workload, Lookupplus improves throughput by 7.4% over native FUSE; under the web server workload, Lookupplus achieves the highest throughput, with a 9.7% improvement over native FUSE and a 12.9% improvement over ExtFUSE; under the mail server workload, due to the flat directory structure and significant synchronization overhead, Lookupplus achieves only a marginal improvement. Memory overhead evaluation shows that the Slab increment of Lookupplus under various workloads remains close to that of native FUSE, with the vast majority being kernel-reclaimable memory. In summary, Lookupplus effectively reduces the context-switch overhead of FUSE in deep-directory scenarios through a path-level batch metadata prefetching primitive with minimal architectural changes. It does not rely on complex cache consistency protocols or prediction models, nor does it introduce new kernel data structures. The extra memory overhead remains controllable and reclaimable. The benefits are most pronounced in deep-directory, cold-start, and low-reuse scenarios, making Lookupplus a lightweight and feasible optimization path for building high-performance user-space file systems in metadata-intensive environments.

Real-time, high-precision perception and 3D reconstruction of road potholes are core technologies for ensuring driving safety and active suspension control in autonomous driving. Existing road pothole perception methods primarily rely on a single visual modality or static cross-modal fusion strategies. Single visual methods are susceptible to drastic illumination changes, texture-less road surfaces, and shadow occlusions, making it difficult to stably acquire robust depth information. Meanwhile, 3D point cloud-based methods are limited by the beam distribution of LiDAR sensors, resulting in extremely sparse point clouds in long-distance or high-speed driving scenarios, which leads to insufficient expression of the microscopic geometric features at pothole boundaries. Existing cross-modal fusion schemes mostly adopt global static concatenation, which fails to fully consider the non-linear spatial registration errors between heterogeneous data. This easily leads to feature contamination or the drowning of valid geometric information when sensors suffer from spatio-temporal misalignment, thereby limiting the 3D reconstruction accuracy of pothole boundaries. To address these problems, this paper proposes a centroid-guided road pothole perception and 3D reconstruction method based on the cross-modal fusion of LiDAR and depth camera, termed DeepRoad. The proposed method constructs an end-to-end deep neural network, which mainly comprises three core technical stages. In the first stage, multi-source heterogeneous feature rough alignment and multi-scale extraction are performed. The system concurrently inputs 2D images captured by the depth camera and spatial point clouds generated by the LiDAR. A nearest-neighbor adaptive rough matching module is utilized to establish a preliminary correlation between the pixel coordinate system and the point cloud projection matrix across spatio-temporal scales, and the underlying representations of both modalities are captured via a multi-scale feature extraction network. In the second stage, centroid-guided non-linear feature fine fusion is executed. To handle feature drift caused by spatial registration errors, a centroid guidance mechanism is innovatively introduced. A lightweight prediction network is designed to localize the geometric centroid of candidate pothole regions. Taking this centroid as a spatial anchor, a 3D asymmetric attention network is constructed to dynamically adjust the interaction weights of cross-modal features, achieving adaptive aggregation and deep fusion of high-frequency image textures and sparse point cloud microscopic geometry in high-value local regions. In the third stage, Bird's-Eye-View (BEV) space mapping and edge semantic consistency constraints are applied. To resolve the boundary blurring and topological distortion issues caused by downsampling operations, DeepRoad projects the fused cross-modal high-dimensional features uniformly into the BEV space to preserve the global topological structure, and designs a novel edge semantic consistency regularization loss function. By explicitly enforcing spatial continuity and semantic opposition between the physical boundaries of potholes and the surrounding normal road surface, this loss function effectively suppresses reconstruction artifacts and substantially improves the fitting precision of pothole edges. To verify the effectiveness and robustness of the DeepRoad method, extensive comparative and ablation experiments were conducted on the synthetic dataset SYN-UDTIRI and the real-world road dataset RSRD. In the road pothole segmentation comparative experiments on the SYN-UDTIRI dataset, DeepRoad achieves the optimal performance across all key metrics: its maximum F1-score (MaxF) reaches 97.58%, Average Precision (AP) reaches 94.04%, Precision (Pre) is 97.53%, and Recall (Rec) reaches 97.86%, comprehensively outperforming mainstream cross-modal fusion methods such as RoadFormer and SNE-RoadSeg+, demonstrating a significant recall advantage particularly in depicting boundaries and fine details. Structural module ablation experiments indicate that progressively introducing modules such as centroid guidance into the baseline cross-modal fusion network validates the effectiveness and complementarity of each module. In the candidate region construction strategy ablation, the model reflecting excellent structural independence and stability. Furthermore, in the generalization and robustness tests on the real road dataset RSRD, DeepRoad maintains stable geometric estimation capabilities under sensor noise and sparse point cloud interference, achieving an absolute error of 0.479 cm and a root mean square error (RMSE) of 0.568 cm, with the proportion of large errors (>0.5 cm) restricted to only 35.1%. Compared with the state-of-the-art RoadBEV model, all metrics of the proposed method are superior, extreme deviations are significantly reduced, and no error amplification occurs. In conclusion, the DeepRoad method proposed in this paper successfully overcomes the technical bottlenecks of blurred 3D perception boundaries and difficult alignment of heterogeneous features for road potholes in complex scenarios through the centroid-guidance mechanism. It forms a systematic end-to-end perception and reconstruction solution, providing high-precision data support for intelligent obstacle avoidance planning in autonomous driving, and possessing important theoretical innovation and engineering application value. Future research will focus on network model lightweight pruning and edge-side real-time deployment, as well as exploring the deep integration of DeepRoad with multi-modal Vision-Language Models (VLMs) to drive the intelligent decision-making and reliable evolution of autonomous driving technology under all-weather and all-scene conditions.

To address the problems of underwater images suffering from color shift, contrast attenuation, and loss of high-frequency details due to selective light absorption and scattering by suspended particles, as well as the insufficient boundary recognition capability and limited segmentation accuracy of existing general-purpose semantic segmentation models for high-value rare objects, this study aims to improve the fine-grained segmentation performance for blurred underwater targets from two aspects: feature representation and loss supervision, with particular emphasis on enhancing the reconstruction and discrimination of edge contours of rare categories.Technically, this work adopts the UWSegFormer model, which has demonstrated excellent performance in underwater scenes, as the baseline architecture. While retaining its underwater image quality attention module and multi-scale aggregation attention module, we propose targeted improvements from two perspectives: frequency-domain enhancement and edge supervision. On one hand, we design a Frequency-Spatial Multi-scale Aggregation Attention (FS‑MAA) module. This module introduces Two-Dimensional Real-valued Fast Fourier Transform (2D‑RFFT) during multi-scale feature fusion in the decoder, explicitly mapping spatial-domain features to the frequency domain. It leverages the decoupling property of the frequency domain to separate low‑frequency components, which represent global semantics, from high‑frequency components, which characterize textural details. A fixed empirical gain coefficient is applied to the high‑frequency components to compensate for high‑frequency loss caused by water scattering. The enhanced frequency-domain features are then restored to the spatial domain via inverse 2D Fourier transform and finally aggregated with the original spatial features through residual connection, thereby injecting high‑frequency detail information while preserving global contextual semantics. On the other hand, we construct an Online Hazy Edge Mining‑based Edge Learning Loss (OHEM‑ELL). During training, this loss computes edge response maps for both predicted masks and ground‑truth labels using Scharr operators, obtaining the prediction error for each boundary pixel. The errors are then sorted in descending order, and the top K% boundary pixels with the largest losses are selected as hard examples; only these pixels are used for gradient backpropagation and parameter updates. This mechanism dynamically focuses the network’s learning attention on the most difficult‑to‑recognize blurred edge regions with negligible additional computational cost, effectively alleviating the dominance of easily classified pixels (such as background water) in the loss. The overall loss function is a linear weighted combination of the standard semantic segmentation loss and the proposed OHEM‑ELL, enabling joint supervision of pixel‑wise classification and edge optimization.Experiments are conducted on the SUIM underwater semantic segmentation benchmark dataset and the DUT‑USEG real‑world underwater image dataset, using MiT‑B0 as the encoder with ImageNet‑1K pretrained weights. On the six major categories of the SUIM dataset, the proposed method achieves a mean Intersection over Union (mIoU) of 86.92%, which is 1.86 percentage points higher than the baseline UWSegFormer (85.06%). Ablation studies demonstrate that the combination of both modules yields the best performance, confirming their synergistic effect in improving segmentation accuracy. On the DUT‑USEG dataset, our method improves mIoU from the baseline of 68.12% to 68.75%, verifying its good cross‑dataset generalization ability. The model has 16.81M parameters and a computational complexity of 6.48 GFLOPs, almost identical to the baseline, indicating that the proposed improvements introduce no significant extra computational burden. Visualization results further show that the FS‑MAA module effectively recovers lost textural details in turbid underwater environments, while the OHEM‑ELL loss produces more coherent and sharper object boundaries; the model integrating both modules achieves the best visual quality. Based on the above results, this study draws the following conclusions: frequency‑domain feature decoupling with selective high‑frequency enhancement can effectively compensate for detail attenuation caused by scattering during underwater imaging, enabling the network to obtain more complete structural information at the feature level; and the online hard edge mining strategy can guide the network to concentrate its limited parameters and gradient updates on the most discriminatively challenging boundary regions, significantly improving the contour delineation capability for rare objects.

To address the difficulty of bounding end-to-end latency in high-load mixed-traffic scenarios, the degradation of transmission determinism caused by priority inversion and head-of-line blocking, and the large model size and low solving efficiency of joint scheduling over dual-homed redundant topologies, a deterministic transmission architecture and scheduling method based on Time-Sensitive Networking (TSN) is proposed for train-control services. In terms of architecture, an eight-car dual-homed ring network is constructed according to the hierarchical structure of the train backbone and consist networks. A/B-plane isolation and end-to-end cross-layer mapping are employed to support differentiated transmission and redundant-path switching for services with different priorities. In terms of scheduling, a two-stage optimization model combining spatial routing and temporal gating is established based on the time-aware shaping mechanism. The first stage determines transmission paths over the two planes, whereas the second generates conflict-free transmission phases within each scheduling period. To address the rapid growth of disjunctive constraints with the number of flows in the full-scale model, binary routing reduction, a forward-list heuristic, and accelerated binary search are applied sequentially. These strategies discretize routing decisions into plane selections, fix the transmission order of frames competing for the same port, and perform a bounded search for the end-to-end latency upper bound. Under the monotonic-feasibility condition, the binary search terminates within a finite number of steps. For cases in which the demand of critical flows exceeds the available network capacity, a two-stage overload admission mechanism is designed. A priority-based bandwidth-feasibility precheck is first performed, after which the reduced model is solved for the flow set that passes the precheck. If the flow set is infeasible, the lowest-priority flow is removed and the model is solved again until a feasible solution is obtained or a rejection result is produced. Simulations are conducted on an eight-car dual-homed ring topology with background loads ranging from 20% to 95% and with scenarios containing 10 and 20 highest-priority flows. The proposed method is compared with Strict Priority (SP) scheduling, routing-only optimization, and the Online Scalable Scheduling and Routing (OSSR) algorithm. The results show that the proposed method limits the maximum end-to-end latency of critical control flows to less than 0.6ms. For the evaluated static flow sets, it reduces the maximum end-to-end latency by 33.6%–51.9%, 38.6%–48.2%, and 37.5%–43.6% compared with SP scheduling, routing-only optimization, and OSSR, respectively. With 20 highest-priority flows, the three-stage reduction decreases the number of disjunctive constraints from 738 to 0, limits peak memory consumption to approximately 4% of that required by the full-scale model, and achieves a cumulative solving time of approximately 0.5s and a speedup of approximately 1.56 fold. The binary search converges after six feasibility checks. Under overload conditions, the method admits critical flows according to capacity constraints and service priorities while rejecting requests that exceed the available capacity, thereby providing deterministic transmission guarantees for safety-critical train-control services.

To address target feature capture difficulties from extreme organ scale variations, noise interference from shallow-deep semantic gaps, boundary geometric information loss during fixed-grid upsampling, and boundary ambiguity from adjacent organ adhesion in existing medical image segmentation, this paper proposes a Cross-Scale Geometric Calibration Network. First, utilizing PVTv2 as the backbone encoder, the network employs a self-attention mechanism to effectively extract image contextual information and establish global dependencies of anatomical structures. Building on this, a Multi-Scale Collaborative Attention Block is designed. By integrating a Multi-Scale Feature Regulation Module, Shareable Multi-Semantic Spatial Attention, and Progressive Channel-wise Self-Attention, this block constructs multiple receptive fields to flexibly perceive organ size fluctuations from patient differences. Meanwhile, a channel-spatial dual attention mechanism filters key channel semantics and focuses on salient spatial regions, effectively suppressing background noise, collaboratively enhancing multi-granularity features, and improving model sensitivity to complex organ structures. Second, a Dual-path Adaptive Fusion Module is designed. Unlike traditional simple concatenation of high- and low-dimensional features, it uses learnable weights to evaluate feature importance across dimensions, dynamically adjusting the combination ratio of high-resolution encoder spatial details and low-resolution decoder semantics to alleviate the shallow-deep semantic gap. Then, a Dynamic Upsample Block is introduced. Unlike the non-geometrically adaptive stretching of linear interpolation upsampling, this block learns grouped independent spatial offset fields to perform adaptive geometric calibration on upsampling coordinates based on a fixed initial geometric grid. This mechanism enables dynamic model deformation matching real anatomical topology, improving aliasing effects from proportional enlargement during upsampling, and achieving sub-pixel reconstruction for irregular boundaries. Finally, a Foreground Orthogonal Loss is incorporated during supervised training. By penalizing spatial overlaps of predicted probabilities among foreground categories, it effectively disentangles adhered boundaries of adjacent organs, further improving overall segmentation accuracy. The proposed method is compared with advanced networks like ECA-GC-Unet, DCAU-Net, and MCT-Net on three public datasets. Notably, the standard version CSGC-Net-B has only 28.44M of parameters, far fewer than the 91.30M of the second-best model in the main experiments. Regarding accuracy, on the abdominal multi-organ dataset Synapse, CSGC-Net-B outperforms the second-best model by increasing DSC by 0.33% and decreasing HD95 by 1.14, achieving optimal accuracy for the gallbladder, pancreas, and stomach. On the cardiac MRI dataset ACDC, its DSC improves by 0.3%, securing optimal results in the myocardial region. On the intestinal polyp endoscopic dataset CVC-ColonDB, it improves DSC by 1.21%, mIoU by 0.98%, and significantly decreases HD95 by 3.04 compared to the second-best model. Moreover, the lightweight version CSGC-Net-S is reduced to just 4.33M in parameters, yet still achieves DSCs of 82.18%, 91.74%, and 91.93% on the Synapse, ACDC, and CVC-ColonDB datasets, respectively. Overall results demonstrate that compared with existing mainstream networks, CSGC-Net improves segmentation accuracy while maintaining a lightweight architecture, exhibiting excellent generalization.

Aiming at the strong nonlinearity and non-stationarity of microgrid load series under high penetration of distributed energy resources, as well as core drawbacks of existing forecasting models including accuracy degradation in abrupt load regions and distorted extreme value prediction caused by the smoothing effect of global modeling, this paper proposes a multi-scale feature short-term load forecasting model with an error-aware mechanism (MAG-TL). Existing standalone deep learning models cannot simultaneously extract multi-scale features covering long-term periodic patterns and local transient fluctuations; hybrid forecasting models generally rely on manually tuned decomposition parameters, adopt simplistic feature fusion, and lack error correction mechanisms for high-volatility segments, and notably, Transformer-based models with outstanding long-term sequence modeling capability exhibit a smoothing effect due to weighted feature aggregation under drastic load fluctuations, resulting in notable peak and valley prediction errors and failing to support refined microgrid dispatch. The model establishes a four-stage collaborative forecasting pipeline of multi-scale feature decomposition, global dependency modeling, targeted error correction and global residual compensation: first, an improved whale optimization algorithm (IWOA) with adaptive inertia weight is employed to minimize envelope entropy, balancing global exploration and local exploitation to adaptively optimize variational mode decomposition (VMD) parameters, so that the original non-stationary load sequence is decoupled into physically interpretable intrinsic mode functions (IMFs), and a multi-scale input feature set is constructed with selected meteorological correlation features; second, parallel 1D convolutional neural networks extract deep features from each IMF, and an adaptive attention module dynamically assigns modal feature weights for adaptive fusion, enhancing error-sensitive features while preserving global baseline information; third, the fused features are fed into a Transformer encoder to capture long-range temporal dependencies, where a lightweight error-aware projection layer quantifies per-step prediction risk via hidden feature spatial offset, and a Top-K relative ranking mechanism dynamically screens high-error anchor points to autonomously locate load mutation regions without ground-truth labels; finally, an error-aware enhanced LSTM decoder integrates anchor features, local error scores and global error background via residual fusion, generates multiplicative gains for targeted amplitude correction in anchor regions with a dynamic threshold to avoid over-compensation in stationary periods, and a lightweight fully connected residual network is introduced to output global additive compensation, forming a hierarchical correction mechanism of multiplicative mutation amplitude correction and additive systematic bias compensation. Verification is conducted on 18-month field data of a high-renewable-penetration microgrid in Guiyang, with the dataset split into training, validation and test sets at a 7:2:1 ratio, and multi-dimensional performance comparisons are carried out against 3 standalone models and 4 hybrid models. The results show that on the Guiyang dataset, the proposed model achieves a root mean square error (RMSE) of 10.71 MW, a mean absolute error (MAE) of 4.78 MW, a mean absolute percentage error (MAPE) of 0.77%, and a coefficient of determination (R⊃2;) of 0.992; compared with the best-performing hybrid baseline model, its RMSE is reduced by 12.23 MW, and the maximum RMSE reduction reaches 34.81 MW compared with standalone forecasting models. Ablation experiments verify the independent contribution of each core module, and the error-aware mechanism alone reduces RMSE by 5.71 MW; the Pearson correlation coefficient between error-aware scores and actual prediction residuals reaches 0.8071, confirming the high precision of the risk quantification mechanism. The single-sample inference latency of the model is 2.34 ms, which can fully meet the real-time requirement of microgrid online closed-loop dispatch. The proposed model effectively alleviates the inherent smoothing bottleneck of global modeling, improves forecasting accuracy with controllable computational overhead, and can accurately capture the transient variation characteristics of load fluctuation errors, providing reliable data support and technical reference for precise microgrid dispatch under high renewable penetration scenarios.

To address the insufficient distinction between origin and destination roles, inadequate utilization of regional-pair interactions, and weak structural constraints on predicted matrices in inter-regional origin-destination (OD) traffic flow forecasting, this paper proposes BiHOD-Net, an OD traffic flow prediction method based on an origin-destination role bipartite graph. Since the same region reflects traffic generation when acting as an origin and traffic attraction when acting as a destination, using a unified node representation may mix these two different traffic semantics. Moreover, optimizing only the prediction error of individual OD edges may cause local errors to accumulate along the row and column dimensions of the OD matrix. To address these issues, BiHOD-Net first extracts the outflow and inflow distributions of each region toward different destinations and from different origins from historical OD matrices, respectively, and constructs origin-role and destination-role sequences. A patch-based time series Transformer (Patch Time Series Transformer, PatchTST) is then employed to independently encode the two role sequences, capturing temporal variations within local patches and dependencies across different patches. These temporal features are further combined with regional static attributes to generate origin-role and destination-role representations with distinct traffic semantics. Subsequently, the two types of role nodes are placed on opposite sides of a bipartite graph to construct an origin-destination role bipartite graph according to historical OD connections. Origin-to-destination propagation is normalized according to historical outflow relationships, whereas destination-to-origin propagation is normalized according to historical inflow relationships. Through bidirectional message propagation, the model captures directional and asymmetric interactions between the traffic generation and attraction sides. At the edge-level prediction stage, the propagated origin-role and destination-role representations are integrated with their absolute difference, element-wise product, and edge historical features derived from recent flow, mean flow, maximum flow, standard deviation, and activity ratio, forming an explicit regional-pair representation that characterizes role differences, matching relationships, and the historical movement patterns of individual OD edges. On this basis, a gated linear unit (Gated Linear Unit, GLU) is employed to adaptively filter different types of information and predict future edge-level traffic flows. Meanwhile, an OD matrix marginal consistency constraint is introduced in addition to the edge-level traffic flow prediction loss. By using the row and column marginals of the ground-truth OD matrix as supervision signals, this constraint reduces deviations in regional total outflow and inflow caused by accumulated local edge-level prediction errors, thereby balancing the prediction accuracy of individual regional pairs and the overall structure of the OD matrix. Experiments are conducted on four taxi OD traffic flow datasets from Beijing, Chengdu, Nanjing, and NYC-Taxi, with prediction intervals of 20, 40, and 60 min. BiHOD-Net is compared under a unified protocol with historical average, support vector regression, deep temporal models, spatio-temporal graph neural networks, and multiple OD demand and OD matrix forecasting methods. Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are adopted as evaluation metrics. The results show that, among the 36 evaluation results formed by four datasets and three prediction intervals, BiHOD-Net achieves 28 best and 6 second-best results, with 94.4% of the results ranking among the top two. In particular, it achieves the best performance for all nine evaluation results on both the Beijing and NYC-Taxi datasets. For the 60 min prediction task, the MAE, RMSE, and MAPE of BiHOD-Net are 1.0389, 1.5087, and 47.6338%, respectively, on the Beijing dataset, and 0.8499, 0.8822, and 42.4931%, respectively, on the NYC-Taxi dataset. Ablation experiments show that, for the 60 min prediction task on the Beijing dataset, removing edge historical features, origin-destination role separation, O-D bipartite graph propagation, and the matrix marginal consistency constraint increases the MAE from 1.0389 for the complete model to 1.3728, 1.1084, 1.1048, and 1.0990, respectively. Additional key ablation experiments on the Chengdu, Nanjing, and NYC-Taxi datasets further demonstrate that these core designs provide stable improvements under different regional partition schemes and OD network structures. Repeated experiments using three random seeds, 42, 123, and 2024, also show that the prediction results remain generally stable across all datasets. These results demonstrate that BiHOD-Net can effectively characterize directional inter-regional OD traffic demand through origin-destination role separation, bidirectional bipartite graph propagation, explicit regional-pair interaction, and OD matrix marginal consistency constraints, providing favorable overall prediction performance, stability, and cross-dataset adaptability for dynamically sparse OD traffic flow forecasting.

As the number of training jobs in distributed deep neural network clusters continues to grow, communication overhead has become a significant factor affecting training completion time, making network sharing among multiple concurrent training jobs increasingly critical. Existing studies have mainly focused on distributed training communication optimization and network resource scheduling by employing task-level scheduling or single-job communication optimization to reduce communication overhead. However, task-level scheduling cannot differentiate the priorities of communication flows within the same training job, while single-job communication optimization fails to adequately address the competition among communication flows from multiple concurrent training jobs. Consequently, these approaches are unable to effectively mitigate the straggler effect caused by delayed critical communication flows during synchronous communication, leading to inefficient utilization of network resources. To address these challenges, a multi-job communication flow scheduling method based on flow-granularity priority assignment and path selection joint optimization is proposed. First, the network resource competition problem among multiple Ring-AllReduce training jobs is formulated as a mathematical optimization model. The model takes the priority assignment and path selection of all communication flows as input and estimates the throughput of each flow under the assumption of equal bandwidth sharing among flows within the same priority queue. The communication completion time of each training job is then estimated, and the overall communication-phase duration of all training jobs is minimized as the optimization objective. Since jointly optimizing the priority and routing configurations of all communication flows leads to exponential computational complexity, a heuristic solution combining local optimal scheduling and a runtime feedback mechanism is developed. By integrating binary search and backtracking techniques, the proposed algorithm significantly reduces computational complexity while maintaining high scheduling accuracy. During execution, the runtime feedback mechanism dynamically adjusts flow priorities according to detected straggler flows, thereby further alleviating the straggler effect in synchronous communication. To evaluate the effectiveness of the proposed method, a distributed training simulation platform based on container technology and virtual switches is developed to emulate both computing nodes and network switches. A communication flow scheduling system consisting of a centralized scheduler, node agents, and runtime monitoring modules is implemented. The centralized scheduler computes the priority assignment and routing decisions for communication flows and dynamically updates node traffic control policies and switch forwarding rules through remote procedure calls (RPC) and the OpenFlow protocol, respectively. Meanwhile, the runtime monitoring module continuously detects straggler communication flows during training and reports the monitoring results to the centralized scheduler, enabling adaptive optimization of scheduling decisions. Simulation experiments compare the proposed method with the priority assignment strategies of Crux and Gsched using communication-phase duration and cluster computational resource utilization as evaluation metrics. Experimental results demonstrate that, compared with Crux, the proposed method reduces communication-phase duration by 18.4% and improves cluster computational resource utilization by 10.4%. Compared with Gsched, it reduces communication-phase duration by 13.5% and improves computational resource utilization by 6.3%. In addition, ablation studies, approximation ratio analysis, and straggler-threshold sensitivity analysis further verify the effectiveness of the proposed flow priority assignment, path selection, and runtime feedback mechanisms. The results also indicate that a straggler threshold of ρ = 20% achieves the relatively good overall scheduling performance.

Intravoxel Incoherent Motion (IVIM) is a diffusion-weighted magnetic resonance imaging (DWI) analysis technique that enables noninvasive assessment of the tissue diffusion coefficient (D) and perfusion-related parameters (F, D*) without contrast agents, which are clinically important for the diagnosis and treatment of diseases such as liver cancer and brain tumors. Existing studies mainly focus on capturing spatially similar tissue features from DWI signals to assist IVIM parameter estimation and thus improve robustness to noise. However, they often ignore the inherent limitations of the DWI signals themselves: when the signals are severely contaminated by noise, the physiological characteristics contained in the signals are damaged, making it difficult to accurately capture the similarity relationships among features. Inspired by the principle of Deep Image Prior (DIP), this study identifies a phenomenon of Differences in Signal Quality (DSQ) in DWI signals during training, and further proposes a strategy for improving the quality of the training set during the training process based on this phenomenon. Specifically, the DSQ phenomenon indicates that, as training progresses, the overall quality of some fitted signals gradually becomes better than that of the original training signals. Therefore, the fitted signals are used as guidance to optimize the original training signals. Meanwhile, to further enhance the effect of data optimization, an iterative strategy is introduced to regulate the data optimization process, progressively improving the quality of the training signals and reducing the influence of noise on the fitting process. In addition, similarity among features is also widely present in both local neighborhoods and global space. Existing convolution-based methods are limited by the size of the receptive field and thus have difficulty in effectively capturing global information, whereas self-attention-based methods can perceive global correlations among voxels but often sacrifice local high-frequency details. Considering that features belonging to the same tissue usually contain similar physiological information and exhibit higher similarity, a tissue-prior-guided similar feature extraction scheme is proposed in this study. This scheme improves the quality of the fitted signals, thereby enhancing the effectiveness of the iterative optimization strategy and further improving the accuracy of IVIM parameter estimation. Based on the above improvements, a Data Iterative Neural Network (DI-NN) is proposed. To evaluate the performance of the model, the proposed DI-NN is compared with the traditional, voxel-based deep learning, and spatial-correlation-based deep learning methods. Root Mean Square Error (RMSE), Structural Similarity Index Measure (SSIM), and the Mann-Whitney U test are used as evaluation metrics. RMSE results show that across experiments with SNRs ranging from 8 to 50, DI-NN consistently achieves lower RMSE values and more accurate parameter estimation. For example, at SNR 15, the RMSEs of IVIM parameters F, D, and D* are reduced by 37.51%, 18.36%, and 30.92%, respectively, compared with the suboptimal method. SSIM results indicate that DI-NN preserves structural features better than other methods. The Mann-Whitney U test further demonstrates that DI-NN has a stronger capability to distinguish lesions.

Unsupervised anomaly detection identifies samples deviating from dominant distributions or local structural patterns without label information, yet it faces inherent challenges including scarce anomalies, feature overlap between normal and abnormal samples, and unstable anomaly patterns. Existing methods constructing anomaly scores from distance, density or fuzzy similarity still have notable limitations in complex scenarios: uniform attribute weighting weakens the effect of discriminative features, instance-level similarity computation is susceptible to isolated noise and local fluctuations, and there lacks effective integration between global fuzzy similarity information and local aggregation structures, making it hard to capture both global deviation and local sparsity of samples. To address these issues, this paper proposes FEGBAD, an unsupervised anomaly detection algorithm integrating fuzzy entropy and granular-ball density. It organizes original samples into granular-ball structures to build a connection between sample-level and local-region information, derives attribute weights via fuzzy entropy to measure the contribution of different attributes to anomaly identification, and aggregates sample-level fuzzy probabilities into granular-ball density to characterize the similarity between local regions and the dominant distribution, finally fusing attribute weights and granular-ball density to generate comprehensive anomaly scores. Comparative experiments on 18 public benchmark datasets against 8 representative methods demonstrate that FEGBAD achieves 10 best or tied-best AUC results and 11 best or tied-best AP results, with an average AUC of 0.9149 and an average AP of 0.6423, outperforming all comparative algorithms. Ablation studies verify that both core modules contribute positively to detection performance, with the granular-ball density module exerting a more significant effect on improving anomaly detection accuracy. Runtime analysis shows FEGBAD has an average running time of 59.889 seconds, which is longer than some lightweight baselines but significantly shorter than other complex fuzzy rough set algorithms. Overall, FEGBAD achieves a favorable trade-off between detection performance and computational cost by suppressing interference from low-information attributes and enhancing the characterization of local distribution structures; it is applicable to static tabular data scenarios, while its adaptability to strongly nonlinear structures, streaming data and graph-structured data still needs further improvement.

Aiming at the core bottlenecks in intelligent generation of Chinese calligraphy characters under few-shot scenarios, including the lack of detailed stroke features, distorted character structures, insufficient style expression and unstable readability of generated contents, this study proposes a refined controllable generation method for multi-style Chinese calligraphy (MSCDiff: A Controllable Generation Method for Multi-Style Chinese Calligraphy). The core research purpose is to break through the limitations of existing generation models in modeling traditional calligraphic artistic features under few-shot conditions, realize the automatic generation of multi-style calligraphy characters with both artistic expression and standard glyph structure, and provide an efficient and feasible technical solution for the digital inheritance, intelligent creation and stylized text rendering of Chinese calligraphy. In terms of implementation methods and key technologies, the proposed method takes the conditional diffusion model as the basic architecture and abandons the single conditional constraint mode. A multi-dimensional collaborative conditioning injection mechanism is constructed, which takes the target character content semantics, reference calligraphy style features and high-frequency stroke detail features as joint generation constraints, so as to enhance the model’s capability of style transfer, structural constraint and detail depiction for calligraphy characters from the bottom layer. To accurately capture fine-grained stylistic features such as flying white, stroke transitions and edge sharpness in calligraphy, a high-frequency feature extraction module is specially designed. Through high-frequency prior construction, learnable feature encoding and detail feature enhancement strategies, it strengthens the model’s representation and restoration of key artistic features such as stroke edges, flying white textures and local stroke transitions, solving the problems of vague fine artistic details and flattened style expression in traditional models. To ensure the rigor and stability of Chinese character structures and overcome the defects of structural dislocation and component distortion under few-shot conditions, a standard font library structure mapping mechanism and a dedicated content encoder are introduced to accurately extract the skeleton and layout features of target characters. On this basis, a multi-level feature fusion mechanism is built to hierarchically couple and adaptively fuse content structure features, global style features and high-frequency stroke features, and inject the integrated constraint information into the denoising generation process of the diffusion model in an orderly manner, realizing the collaborative regulation of structural, stylistic and detail constraints. To further improve the semantic accuracy and readability of generated results and avoid normative defects such as wrong characters, missing strokes and structural deformations, a character error correction module is innovatively introduced. A semantic supervision feedback mechanism is constructed based on the Connectionist Temporal Classification (CTC) loss, which takes the semantic compliance of characters as a reverse optimization signal to guide the iterative optimization of the generation network, significantly improving the content accuracy and practical reliability of generated characters from the semantic level. To support model training and comprehensive verification, this study independently constructs a high-quality dedicated Chinese calligraphy dataset Calli, which covers the representative writing styles of 11 calligraphers and contains more than 70,000 high-precision and standardized single-character images with standardized annotations and comprehensive style coverage, providing a high-quality experimental basis for few-shot multi-style calligraphy generation tasks. Multiple comparative and ablation experiments are carried out on the self-built Calli dataset and the public IAM handwriting dataset, with comprehensive evaluation from the dimensions of subjective visual effect, objective quantitative indicators and generalization performance. The experimental results show that the proposed MSCDiff method can stably generate Chinese calligraphy characters with complete stroke details, rigorous glyph structures, distinct calligraphic styles and excellent readability. Under the few-shot setting, the model achieves superior comprehensive performance in multiple aspects, including style fidelity, structural accuracy, detail restoration, and semantic consistency. It outperforms existing mainstream generation methods on most evaluation metrics, effectively alleviating the issues of missing details, structural misalignment, and insufficient style expression in calligraphy generation under few-shot scenarios, demonstrating excellent generation effectiveness, generalization ability, and practical value.This method not only achieves high-precision controllable generation of multi-style calligraphy characters, but also provides a referable technical path for the fields of traditional art digitization, intelligent content generation and few-shot visual modeling.

To address the degradation of fine-grained forgery traces, high-frequency information, and boundary artifacts caused by platform transcoding, H.264 compression, JPEG compression, and other forms of degradation during network transmission, which may further lead to local discriminative attention drift, global representation shift, and insufficient cross-quality generalization in deepfake detection models, a Compression-Paired Hyperspherical Consistency (CPHC) learning framework is proposed. Instead of simply treating samples with different compression qualities as ordinary data augmentation and mixing them during training, CPHC organizes the Raw, C23, and C40 versions of the same face frame into source-aligned multi-quality views and explicitly exploits the cross-quality correspondences along the compression degradation chain to learn stable forgery-discriminative representations. At the local spatial-response level, a Local Compressed Attention Consistency (LCAC) module is constructed to characterize discriminative regions under different compression views based on Class Activation Maps (CAMs). Pairwise CAM alignment between adjacent quality views and a shared CAM reference across the three views are employed to guide low-quality views to preserve stable local discriminative evidence, thereby suppressing interference from background noise, compression block artifacts, and irrelevant regions. At the global representation level, a Global Hyperspherical Consistency Constraint (GHCC) is designed to normalize image features onto a unit hypersphere. The von Mises–Fisher (vMF) distribution is further employed to constrain the consistency of class centers and feature distributions for the same real/fake label across different compression qualities, thereby alleviating global class-structure shifts caused by compression degradation. Experimental results show that CPHC achieves an average ACC of 87.02%±0.34% and an average AUC of 91.30%±0.32% on the C40 strongly compressed test set of FaceForensics++, outperforming the baseline by 4.50 and 5.69 percentage points, respectively, and achieving a higher average AUC than seven mainstream methods. Further evaluations under unseen JPEG75, JPEG50, and JPEG25 compression conditions, as well as on the cross-dataset CDF-v2, DFDC, and DeeperForensics benchmarks, demonstrate favorable generalization performance. In the cross-domain evaluation of diffusion-generated forgeries, with FF++ as the source domain and DiFF as the target domain, CPHC achieves an average AUC of 83.13% across four subsets. Ablation studies further show that LCAC and GHCC play complementary roles in stabilizing local discriminative regions and maintaining global class-structure consistency, respectively, and their joint use further improves detection performance under strong compression. These results demonstrate that CPHC effectively mitigates local response drift and global representation shift caused by compression degradation while maintaining stable detection and generalization performance under strong compression, unseen compression, and cross-domain scenarios.

Distributed photovoltaic (PV) stations exhibit decentralized data storage and significant non-independent and identically distributed (Non-IID) characteristics, causing conventional global federated learning methods to suffer from gradient conflicts during cross-site training. Meanwhile, newly joined stations face severe cold-start problems due to scarce historical data. To address these challenges, this paper proposes an Adaptive Granular-Ball Federated Learning (GB-FL) method that organically integrates Granular Computing (GrC) theory with a personalized federated learning framework, enabling adaptive grouping of heterogeneous sites and intra-granular collaborative modeling. First, a 45-dimensional site behavior characterization vector is constructed based on the statistical features of PV power curves to capture differences in power generation patterns across sites. Specifically, this includes 5-dimensional basic statistical features (daily average energy, peak power, peak time index, sunrise index, and sunset indices), 4-dimensional dynamic characteristics (ramp-up rate, ramp-down rate, intraday fluctuation intensity, and clipping ratio), as well as 24-dimensional daily average curve profile and 12-dimensional intraday fluctuation profile features. All features are processed via z-score standardization for subsequent granular-ball partitioning. On this basis, a three-stage adaptive granular-ball construction algorithm comprising "heterogeneity-driven splitting–proximity-based merging–micro-ball fusion" is designed. Specifically, during the heterogeneity-driven splitting stage, intra-granular average Euclidean distance is employed to measure heterogeneity, and a weighted scoring mechanism combined with a "worst-first" greedy strategy is introduced for recursive bisection. In the proximity-based merging stage, a dynamic threshold is defined by the median of inter-granular center distances to merge over-fragmented granular balls. In the micro-ball fusion stage, residual single-site granular balls are incorporated into neighboring granular balls satisfying heterogeneity constraints. The synergy of these three stages achieves adaptive partitioning without pre-specified cluster numbers, dividing 32 sites into 5 granular-ball structures with intra-granular homogeneity and inter-granular mutual exclusivity. Building upon the granular-ball partitioning results, each granular ball independently executes personalized federated learning. The model adopts a two-layer gated recurrent unit (GRU) with a hidden dimension of 64 as a shared temporal encoder to extract common dynamic patterns, while retaining a personalized linear prediction head for each site to fit local characteristics. Local training employs the smooth Huber loss function, and the server performs weighted aggregation of shared encoder parameters based on sample sizes. Model parameters across different granular balls remain mutually isolated, thereby effectively alleviating gradient conflicts induced by heterogeneous data. To address the cold-start problem for new stations, a prior initialization strategy based on the nearest granular ball is further proposed. Specifically, a new station is first assigned to the most similar granular ball according to its behavioral characterization, inherits the shared encoder parameters of that granular ball, and achieves rapid adaptation through few-shot fine-tuning. Experiments are conducted on data from 32 actual PV stations in Shandong Province (each containing 35,040 15-min sampling points) and the La Trobe UNISOLAR public dataset (32 stations, approximately 76,000 fifteen-minute sampling points). Data are partitioned into training, validation, and test sets in a 40%/20%/40% ratio, and experiments are repeated with 5 fixed random seeds to ensure result stability. The results demonstrate that, on the Shandong dataset, GB-FL achieves a 3.8% reduction in root mean square error (RMSE) compared with Personalized Federated Learning (PFL) (15.896 kW vs. 16.517 kW) and a 7.4% reduction in mean absolute error (MAE); a 6.0% reduction in RMSE compared with FedProx; and 2.2% and 4.6% reductions in RMSE and MAE, respectively, compared with ClusterFL. In the cold-start scenario, compared with local independent training (Local), RMSE is reduced by 7.2% (8.791 kW vs. 9.475 kW) and MAE by 11.7%. On the UNISOLAR dataset, GB-FL outperforms FeDaL in both MAE (2.161 kW) and the coefficient of determination (R2) (0.796), while its MAE standard deviation (0.049) is significantly lower than that of FeDaL (0.100), validating the method's cross-dataset generalization capability and robustness. Ablation experiments further indicate that removing the granular-ball grouping mechanism leads to a 3.91% increase in RMSE, demonstrating that the adaptive granularization strategy is the key factor underlying the performance improvement.

To address the difficulty of coordinating local sequence reconstruction with global distribution fitting in generative multivariate time-series anomaly detection, as well as the tendency of such models to reconstruct anomalous signals with high fidelity, thereby narrowing the reconstruction-error gap between normal and anomalous samples, this paper proposes a Progressive Latent Diffusion-Driven Anomaly Detection framework for multivariate time series, termed PLDAD. The framework adopts a three-stage progressive training paradigm consisting of Masked Function Autoencoder pretraining, Latent Prior Learning, and end-to-end Joint Fine-Tuning, which progressively accomplishes basic feature representation, normal-prior modeling, and global collaborative optimization. First, during the Masked Function Autoencoder (MFAE) stage, element-wise independent masking is applied to the input sequence, and a dual-branch encoding structure is employed to extract inter-variable correlations and multiscale temporal dependencies, respectively. The resulting spatiotemporal features are subsequently fused through a self-attention mechanism, and the complete sequence is reconstructed using a cross-attention-based continuous-time decoder, thereby constructing a smooth and compact latent feature space. Second, during the Latent Prior Learning (LPL) stage, the pretrained encoder is frozen, and the complete unmasked sequence is mapped into initial latent features. A forward noising process and a reverse denoising process are then established in the latent space. Unlike conventional diffusion models that take Gaussian noise as the prediction target, the proposed Latent Feature Diffusion Model (LFDM) directly predicts generated latent features, guiding perturbed representations toward the normal feature manifold under the constraint of the normal-data prior. This reduces the likelihood that anomalous information is propagated to the decoder and reconstructed with high fidelity. Finally, during the Joint Fine-Tuning (JFT) stage, the encoder, LFDM, and decoder are unfrozen and optimized end to end using a joint objective composed of reconstruction loss and diffusion loss, enabling local sequence recovery and global prior constraints to be coordinated. During inference, a point-wise anomaly score is calculated from the squared error between the actual observations and their reconstructed values. Experiments are conducted on six public benchmark datasets, namely ASD, MSL, PSM, SMAP, SMD, and SWaT, comprising more than 3.4 million observations, and the proposed method is compared with 11 representative approaches. Model performance is comprehensively evaluated using the F1-score and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The results show that PLDAD achieves the best F1-score and AUC-ROC results on all six datasets, with an average F1-score of 94.31%, demonstrating stable detection performance across different data distributions and anomaly patterns. Its F1-scores on ASD, MSL, PSM, SMAP, SMD, and SWaT reach 95.12%, 94.01%, 96.51%, 88.12%, 95.67%, and 96.40%, respectively, while its AUC-ROC values on ASD, PSM, and SMD reach 88.13%, 80.12%, and 83.27%, respectively, indicating favorable overall performance in both anomaly-segment detection and the global discrimination of anomaly scores. Statistical significance analysis shows that PLDAD obtains the highest average rank of 1.0000, further confirming its stable advantages over the baseline methods. Ablation experiments demonstrate that progressive training, latent prior learning, LFDM, joint fine-tuning, and the feature purification mechanism all contribute to improved detection performance. Latent-feature visualization further reveals a clearer distribution boundary between normal and anomalous samples in the complete model. Time-efficiency experiments show that PLDAD maintains acceptable computational overhead while preserving high detection accuracy. In summary, PLDAD can alleviate the optimization conflict between local sequence reconstruction and global distribution fitting, reduce the tendency to reconstruct anomalous signals with high fidelity, and achieve favorable detection accuracy, stability, and application potential in complex multivariate time-series scenarios.

To address large layout variations, confusion between similar fields, unstable image quality, weak representation of two-dimensional spatial relations after one-dimensional serialization of image patches, and the lack of region constraints in text-only outputs, this paper proposes a logistics certificate information extraction method that combines decoupled two-dimensional positional encoding, coordinate-supervised extraction, and multi-task instruction mixing. Before the downsampled two-dimensional visual features are projected into the language model embedding space, separate learnable positional embeddings are introduced for the row and column directions. The row and column position parameters are maintained independently and concatenated along the channel dimension to form a two-dimensional positional prior. This design supplements row-column relations, local adjacency, and layout structure with low parameter overhead, reduces the loss of spatial topology caused by image patch flattening, and improves the model’s ability to distinguish elongated text regions, dense fields, and adjacent areas. During information extraction, the conventional text-only question-answering task is reformulated as the joint generation of field text and region coordinates. The model outputs field content and normalized bounding boxes in a structured sequence, thereby establishing an explicit correspondence between field semantics and image regions and improving the spatial traceability and verifiability of the extraction results. To reduce the possible optimization conflict between text generation and coordinate prediction caused by direct coordinate supervision, three instruction tasks are further constructed, including joint extraction, visual localization, and text-only extraction. These tasks are randomly mixed at preset ratios, allowing the model to learn spatial localization while maintaining stable text recognition and instruction understanding. A logistics certificate dataset containing 4,000 images was constructed, covering driving licenses, qualification certificates, road transport permits, and vehicle licenses. The dataset includes multiple layouts and image acquisition conditions, with characteristics such as dense fields, elongated text regions, adjacent similar fields, and seal occlusion. Text content and corresponding bounding boxes were annotated for key fields and converted into instruction samples for text extraction, visual localization, and joint text-coordinate extraction. Public datasets, including DocVQA, XFUND-ZH, LSVT, and LLaVA-Instruct-ZH, were further used for mixed training to improve the model’s adaptability to general document structures, Chinese layouts, and complex visual interference. InternVL2.5-2B was used as the main backbone, while InternVL3-2B and InternVL3-8B were used for extended evaluation. Average Normalized Levenshtein Similarity (ANLS), Exact Match (EM), Mean Intersection over Union (Mean IoU), and Grounded Accuracy were adopted to evaluate text recognition, region localization, and text-coordinate consistency. The experimental results show that the proposed method clearly improves field localization while maintaining high text recognition performance. Compared with the InternVL2.5-2B baseline, the macro-average ANLS and Mean IoU over the four logistics certificate categories increase by 3.11 and 11.23 percentage points, respectively. On the road transport permit dataset, Mean IoU increases from 13.42% to 48.39%, while Grounded Accuracy increases from 17.32% to 62.09%. On the driving license dataset, ANLS and Grounded Accuracy reach 98.00% and 48.97%, respectively. Results from three independent runs on InternVL3-8B show macro-average improvements of 0.31, 1.00, 1.28, and 1.46 percentage points in ANLS, EM, Mean IoU, and Grounded Accuracy, respectively. The standard deviations remain close to those of the baseline, indicating stable gains at a larger model scale. Ablation experiments show that direct coordinate supervision alone makes it difficult to balance text generation and spatial localization. Adding multi-task instruction mixing or decoupled two-dimensional positional encoding clearly improves both Mean IoU and Grounded Accuracy, while their combination achieves the best overall performance. Evaluation on XFUND-ZH and DocVQA further shows that the proposed method improves spatial modeling for structured documents without weakening general document question-answering ability. These results confirm the effectiveness of jointly modeling explicit two-dimensional spatial priors and coordinate supervision for complex logistics certificate information extraction and provide technical support for traceable information extraction and structured document understanding in real-world applications.

This paper proposes a security defense method based on the coordination of statistical and geometric domains to cut off the confusion space association in order to address the problem of severe privacy leakage risks in the target oriented adversarial samples generated by image editing technologies such as generative adversarial networks, while achieving pixel level manipulation at the semantic level under high-dimensional chaotic spatial disturbances. The research aims to solve the core bottleneck of existing single encryption or scrambling methods that are difficult to completely separate the two types of orthogonal spatial correlations that attackers rely on to reverse restore sensitive information, namely the local grayscale statistical correlation between pixel values and the global geometric layout correlation between pixel coordinates. This is to construct a full link privacy protection mechanism from encryption to transmission. In terms of technical implementation, this method consists of three layers of collaborative modules. The first layer is the statistical domain association pruning module: using the hash value of adversarial sensitive images to drive logistic mapping to generate content related dynamic S-boxes, and dynamically constructing a 16 × 16 nonlinear replacement table based on the image's own characteristics; Using the Chen hyper chaotic system to generate four sets of chaotic sequences, combined with the coordinate scrambling matrix generated by the two-dimensional Logistic chaotic system, position scrambling and multi-stage bidirectional diffusion are performed on image pixels to homogenize high-dimensional perturbation energy to the global grayscale distribution, thereby cutting off the correlation between neighboring grayscale distribution trajectories and local structural statistical patterns. The second layer is the geometric domain correlation cutting module: the encrypted image in the statistical domain is subjected to two-dimensional integer wavelet decomposition to obtain a sparse coefficient matrix, and a compressed sensing measurement matrix driven by Iterative Chaotic Map (ICM) is introduced for dimension compression; Introducing the Non Periodic Affine Nonlinear (FAN) transformation, a generalized affine scrambling mechanism that includes an infinite number of transformations, to globally remap compressed images, breaking down the spatial topology of pixels, supplemented by a closed bidirectional diffusion link driven by ICM chaotic mapping, allowing any single bit change to diffuse uniformly to the entire matrix, thereby eliminating global layout correlations within the geometric domain. The third layer is the covert transmission module: the encrypted matrix processed in the first two layers is segmented and embedded into the red, green, and blue (RGB) channels of the ordinary carrier image, as well as all 8 bits of the alpha transparent channel. The embedding position map is dynamically generated through a chaotic sequence, so that the ciphertext is distributed discretely and uniformly in space, achieving covert transmission of encrypted data without visual loss. The experimental results show that in low, medium, and high disturbance intensity scenarios, the attack success rates of our method are 0.782%, 3.469%, and 5.887%, respectively, which are 79.9%, 60.4%, and 61.6% lower than the optimal comparison method; The grayscale histogram of the encrypted image shows an absolutely flat distribution, with an information entropy of 7.988. The correlation coefficients between adjacent pixels in the horizontal, vertical, and diagonal directions are all below 0.004, indicating that the statistical independence between pixels has reached an ideal level; The Number of Pixels Change Rate (NPCR) and Unified Average Changing Intensity (UACI) are 99.61% and 33.28%, respectively, which are highly consistent with the theoretical ideal values and demonstrate strong resistance to differential cryptanalysis; In the key avalanche effect test, a 1-bit key flip caused a 99.58% change in ciphertext pixels, approaching the theoretical extreme value, verifying extremely high key sensitivity. In terms of concealment, the Peak Signal-to-Noise Ratio (PSNR) of the carrier image after steganography reached 49.2 dB, and the Structural Similarity Index (SSIM) was 0.9978, making it difficult for the human eye to distinguish between the original carrier and the steganographic carrier; The ablation experiment further quantified the independent contributions of each component: after completely removing the statistical or geometric domain cuts, the attack success rates soared to 15.342% and 18.674%, respectively; After removing dynamic S-boxes, multi-stage diffusion, FAN transformation, and bidirectional diffusion separately, the success rates of attacks increased to 9.113%, 11.456%, 12.237%, and 8.641%, respectively, verifying the irreplaceability of the modules and sub components designed in this paper in the overall defense system. In summary, this article theoretically elucidates the orthogonal independence of the correlation between the statistical domain and the geometric domain. Based on this, a targeted collaborative disconnection mechanism can achieve full link privacy protection for adversarial sensitive images from encryption to transmission while maintaining computational efficiency, providing an effective technical solution for image security defense in open network environments.

Business process remaining time prediction is a core task in predictive process monitoring. Its accuracy provides critical decision support for process optimization, resource scheduling, anomaly early warning, and Quality of Service (QoS) guarantee. However, existing methods generally suffer from two major limitations. First, deep learning models based on Long Short-Term Memory (LSTM) or Transformer, while capable of learning complex temporal dependencies, often rely on recurrent structures or self-attention mechanisms. These models involve large parameter scales and high computational complexity, leading to significant inference latency for long sequences and hindering real-time deployment in computational resource-constrained environments. Second, the utilization of temporal information remains coarse-grained in current research. Most methods rely on single temporal attributes such as timestamps or event intervals, ignoring the importance variations of multi-dimensional temporal semantics—including duration, cumulative execution time, and waiting time—across different process stages. This failure to fully exploit key driving factors limits overall prediction performance. To address these issues, this paper proposes a lightweight business process remaining time prediction method based on adaptive time-weighting, termed LTW-Mixer. First, a multi-dimensional temporal feature system is systematically constructed, covering duration, cumulative execution time, waiting time, resource response time, and periodic information (e.g., day of the week and hour of the day). Building on this, an adaptive time-weighting mechanism is designed to dynamically adjust the contribution of each temporal feature based on the process context. This mechanism enhances key temporal information while suppressing redundant features, thereby improving the model's ability to capture critical time-driven factors. Finally, to balance prediction accuracy and model efficiency, a lightweight backbone based on MLP-Mixer is developed. Sequence mixing and channel mixing are employed to replace the computationally intensive self-attention mechanism, achieving efficient interaction across both temporal and feature dimensions. Additionally, a gated fusion mechanism is introduced to dynamically integrate activity and temporal semantics, allowing the model to adaptively regulate the contribution ratios of heterogeneous features and effectively capture long-range dependencies while maintaining low computational overhead. Experimental results on six public real-world business process datasets demonstrate that LTW-Mixer achieves the lowest Mean Absolute Error (MAE) across all datasets. Specifically, for the BPIC2015_2 and BPIC2015_5 datasets, the MAE is reduced by approximately 26.7% and 39.5%, respectively, compared to the state-of-the-art baseline models. Ablation studies confirm that the multi-dimensional temporal features, adaptive weighting mechanism, gated fusion, and MLP-Mixer backbone all contribute significantly to the model's performance. Furthermore, feature weight analysis and sensitivity experiments show that the model can dynamically prioritize key factors such as waiting time and resource response time across different business scenarios, demonstrating strong interpretability. In terms of lightweight performance, LTW-Mixer requires only 0.19 M parameters and 0.71 MB of storage, with a peak GPU memory usage of 57.15 MB and a per-sample inference time of only 0.023 ms. These results outperform all comparison models, proving that LTW-Mixer significantly reduces storage and computational costs while ensuring high accuracy, thus exhibiting excellent real-time responsiveness and potential for industrial deployment.

Hierarchical Classification (HC) exploits label hierarchies to model multi-granularity semantic dependencies and has important applications in remote-sensing and natural-image analysis. However, real-world degradations, such as noise, occlusion, blur, and low resolution, weaken fine-grained discriminative cues, making a fixed top-down prediction paradigm that always terminates at a leaf node prone to over-refinement, cross-level error propagation, and structural inconsistency. Existing methods primarily emphasize parent–child subsumption consistency, while rarely modeling mutual exclusion among categories at the same level explicitly, thereby limiting the global consistency and robustness of hierarchical predictions. To address the dynamic variation in recognizable granularity under image degradation, the separation between hierarchical-depth selection and category-path prediction, and the inability of local node scores to guarantee globally valid paths, this work further formulates hierarchical classification of degraded images as a granularity-adaptive level–path joint structured inference task. The objective is to avoid forcing every sample to a leaf node, align the predicted depth with the currently recognizable granularity, and simultaneously satisfy parent–child consistency and same-level mutual-exclusion constraints. To this end, we propose VS-HESS, a structurally consistent hierarchical classification framework for degraded scenarios. First, VS-HESS introduces an exclusion-enhanced level–category hybrid prediction paradigm that jointly predicts the deepest level at which a sample can be reliably recognized and its corresponding category path within a unified framework. Second, it incorporates a Hierarchical Exclusion Constraint Graph (HECG) and a state-space matrix to explicitly model same-level category exclusion in addition to parent–child subsumption constraints, and performs consistency-aware inference through structured normalization and marginalization. Third, a hierarchy-semantic-guided visual–semantic cross-attention module uses label-semantic embeddings to guide visual-token aggregation and learns category-specific, cross-granularity discriminative representations. Specifically, a Vision Transformer first performs global visual encoding of a degraded image. Hierarchical label embeddings are then used as queries to aggregate visual tokens in a category-conditioned manner, producing semantic-aware representations and node compatibility scores for categories at different levels. A hierarchical-depth prediction head estimates the deepest semantic level supported by the current visual evidence. On this basis, the HECG jointly represents parent–child subsumption edges and same-level exclusion edges, while the set of structurally valid paths is encoded into a state-space matrix. Consequently, the model no longer makes decisions solely from mutually independent local node scores, but instead performs structured normalization, marginal-probability computation, and final level–path joint selection within the valid path space. The model is optimized end to end using hierarchical-depth supervision, path supervision, and structural-consistency supervision, thereby unifying granularity-adaptive decision making, local category discrimination, and global structured inference in a single training process. On the HRSC-Deg and CUB-Deg degraded hierarchical datasets, VS-HESS achieves the best numerical results across all reported metrics. On HRSC-Deg, the inverse symmetric difference metric, hierarchical precision, hierarchical recall, overall level accuracy, coarse-level accuracy, and fine-level accuracy reach (72.89%), (91.53%), (91.48%), (76.05%), (99.11%), and (90.42%), respectively. On CUB-Deg, the corresponding four overall metrics and the order-, family-, and species-level accuracies reach (65.72%), (90.80%), (92.94%), (73.71%), (98.96%), (92.11%), and (81.12%), respectively. These results demonstrate that visual–semantic evidence encoding, dynamic level selection, and exclusion-enhanced valid-path inference can work synergistically to improve hierarchical path matching and medium- to fine-grained recognition under image degradation. By selecting an appropriate prediction granularity according to the semantic information retained in a degraded image and making structurally consistent decisions within a path space that satisfies both parent–child subsumption and same-level exclusion relations, VS-HESS provides an effective modeling framework for structurally consistent and granularity-adaptive hierarchical image classification across application domains such as remote-sensing ship recognition and natural-image analysis.

With the widespread adoption of Transformer models in computer vision, natural language processing, and related domains, inference workloads have grown rapidly, placing increasingly stringent demands on inference throughput. Existing inference frameworks primarily improve local execution efficiency through operator fusion, kernel generation, and graph-level optimization. However, operators in Transformer models exhibit distinct resource preferences with respect to hardware resources such as Tensor Cores, CUDA Cores, and memory bandwidth. Constrained by data dependencies, these operators are typically executed in topological order, making it difficult to achieve sufficient overlap among heterogeneous hardware resources and thereby limiting further improvements in inference throughput. To address this issue, this paper proposes a batch-partitioned pipelined inference method for Transformer models. Transformer computation consists of diverse operators, including matrix multiplication, reduction, element-wise computation, and tensor reshaping. Based on operator resource-occupancy characteristics and offline performance profiling, we classify Transformer operators into three categories: Tensor Core-intensive, CUDA Core-intensive, and memory-bandwidth-intensive operators. To overcome the limited overlap among operators with different resource preferences under conventional execution, the original inference batch is partitioned into three sub-batches with adjustable sizes and scheduled concurrently while preserving the computational-graph dependencies within each sub-batch. By allowing different sub-batches to progress through different computation stages, operators with complementary resource preferences can be overlapped in a pipelined manner. This reduces contention for the same type of hardware resource, mitigates pipeline bubbles, and improves overall hardware utilization. Furthermore, we formulate the With the widespread adoption of Transformer models in computer vision, natural language processing, and related domains, inference workloads have grown rapidly, placing increasingly stringent demands on inference throughput. Existing inference frameworks primarily improve local execution efficiency through operator fusion, kernel generation, and graph-level optimization. However, operators in Transformer models exhibit distinct resource preferences with respect to hardware resources such as Tensor Cores, CUDA Cores, and memory bandwidth. Constrained by data dependencies, these operators are typically executed in topological order, making it difficult to achieve sufficient overlap among heterogeneous hardware resources and thereby limiting further improvements in inference throughput. To address this issue, this paper proposes a batch-partitioned pipelined inference method for Transformer models. Transformer computation consists of diverse operators, including matrix multiplication, reduction, element-wise computation, and tensor reshaping. Based on operator resource-occupancy characteristics and offline performance profiling, we classify Transformer operators into three categories: Tensor Core-intensive, CUDA Core-intensive, and memory-bandwidth-intensive operators. To overcome the limited overlap among operators with different resource preferences under conventional execution, the original inference batch is partitioned into three sub-batches with adjustable sizes and scheduled concurrently while preserving the computational-graph dependencies within each sub-batch. By allowing different sub-batches to progress through different computation stages, operators with complementary resource preferences can be overlapped in a pipelined manner. This reduces contention for the same type of hardware resource, mitigates pipeline bubbles, and improves overall hardware utilization. Furthermore, we formulate the

Abstract: The Common Vulnerability Scoring System (CVSS) serves as the core foundation for vulnerability prioritization, remediation decision-making, and compliance auditing. In real-world Security Operations Centers (SOCs), semi-structured data—such as ports, protocols, services, banners, and version numbers—captured by scanners often arrives prior to the formal Common Vulnerabilities and Exposures (CVE) descriptions. Existing automated scoring methods largely rely on structured vulnerability description text, making them ill-suited for workflows where scanner output precedes vulnerability disclosure. Furthermore, the CVSS base vector comprises eight distinct metrics—Attack Vector, Attack Complexity, Privileges Required, User Interaction, Scope, and the impact on Confidentiality, Integrity, and Availability. A misjudgment in any single field can significantly alter the risk prioritization of a vulnerability. Consequently, a model must not only predict individual labels but also generate a semantically consistent and verifiable complete base vector in a single pass.To address these challenges, we propose VulXbert, an interpretable multi-label prediction model for CVSS v3.1 base vectors. The model employs a dual-channel semantic alignment framework to encode vulnerability description text and host scan outputs independently, leveraging the security-domain pre-trained language model SecureBERT to enhance the representation of vulnerability terminology, service names, port protocols, and semi-structured symbols. During the training phase, the model utilizes temperature-scaled cross-entropy contrastive learning to map inputs from both description and scan channels into a unified embedding space, thereby narrowing the expression gap between public vulnerability disclosures and raw scan logs. During the prediction phase, the model utilizes the shared representation as input to a shared architecture with eight task-specific softmax classification heads, enabling the output of all CVSS v3.1 base vector fields in a single forward pass.To mitigate the impact of class imbalance on minority label performance, we introduce category-weighted cross-entropy, with weights normalized by label frequency in the training set. Furthermore, by incorporating input-gradient saliency analysis, the model provides token-level key evidence for each prediction. This allows security analysts to pinpoint the specific fields supporting judgments on attack vectors, privileges required, or scope, thereby improving the transparency, auditability, and efficiency of human review. Experiments were conducted using approximately 60,000 records from the National Vulnerability Database (NVD) (2016–2024) containing complete CVSS v3.1 base vectors, augmented with real-world scan data. Compared to existing methods such as CVSS-BERT, BERT-multi, Costa et al.’s methods, Kühn et al.’s open-source intelligence-based approach, and AutoCVSS, VulXbert achieved a Macro-F1 of 0.89, Micro-F1 of 0.91, Hamming Loss of 0.054, and Exact Match Accuracy of 68%. Metric-specific analysis demonstrates that VulXbert achieves more stable performance across Attack Vector, Privileges Required, Scope, and the three impact metrics. Ablation studies confirm that the security-domain pre-trained encoder, cross-source semantic alignment, and the joint multi-head prediction structure each contribute significantly to model performance. In summary, by utilizing cross-source semantic alignment, joint multi-label prediction, and evidence-based interpretability, VulXbert provides a scalable solution for automated, interpretable CVSS base vector generation in environments where scan logs arrive ahead of vulnerability descriptions.

In the digital economy era, digital finance plays a crucial role in alleviating financing constraints for SMEs and serving the real economy. However, SMEs generally suffer from irregular financial practices and a lack of collateral, leading to severe information asymmetry in credit risk assessment. Traditional risk control models heavily rely on static quarterly or annual financial statements, resulting in significant data lag and making it difficult to objectively and in real-time depict the true operational dynamics of enterprises. In recent years, high-frequency power load data has gradually become a core alternative risk control data source due to its ability to objectively, continuously, and tamper-resistantly reflect the actual production status of enterprises (such as production line operating rates and equipment utilization rates). Existing credit assessment methods based on power data mainly rely on expert rule engines or purely data-driven deep learning models. However, these methods have significant limitations in practical financial business applications: traditional expert rules are too rigid and struggle to capture hidden anomalies in massive nonlinear time series; while purely data-driven models are prone to falling into a "black box" dilemma when faced with lengthy high-frequency sequences and are highly susceptible to interference from normal fluctuations such as sudden changes in weather conditions and short-term shutdowns (such as shift changes and routine maintenance). This results in a lack of ability for the model to accurately focus on core default risks and an inability to provide a traceable chain of evidence that meets the requirements of financial compliance audits. To address these issues, this paper proposes a joint research approach for electricity load forecasting and credit risk early warning based on multi-source heterogeneous data, aiming to bridge the mapping mechanism from underlying physical power fluctuations to high-level credit defaults. First, in the cross-frequency feature perception stage, this paper utilizes a zero-order hold mechanism to achieve precise alignment between daily macro-meteorological covariates and 15-minute high-frequency power consumption sequences, establishing a dynamic prediction baseline and removing operational dynamic residuals after excluding environmental temperature interference, thereby physically decoupling and extracting real micro-level operational anomalies. Second, in the knowledge representation stage, this paper abandons the simple variable concatenation method and instead uses multi-hot encoding and feature projection techniques to deeply characterize enterprise business semantics (industry category), qualification labels (such as litigation default status), and quantitative capital (such as registered capital and contract capacity) into a dense heterogeneous knowledge matrix, constructing a unique priori risk control profile for each enterprise. Finally, this paper designs a knowledge-guided multi-head cross-attention mechanism, using the enterprise profile knowledge matrix as a prior query filter to perform deep noise reduction and feature purification on high-frequency residual sequences. This mechanism can guide the network to adaptively ignore load drops caused by normal production rhythms and accurately amplify structurally abnormal power outage signals caused by precipitous order shrinkage or broken capital chains. Furthermore, to collaboratively optimize prediction and recognition tasks within the same framework, this paper introduces a multi-task adaptive dynamic loss function based on homoscedastic uncertainty. This mechanism, combined with multi-classification focus loss, effectively addresses the inherently extremely unbalanced nature of the four risk levels. Simultaneously, the model is supplemented with electrical physical capacity upper limit constraints and risk monotonicity logic constraints, significantly improving joint discrimination ability and internal interpretability while ensuring high-precision load forecasting. We conducted large-scale experiments on real business datasets of leading global power companies. Experimental results show that the proposed method outperforms baseline models in both load forecasting and credit rating tasks, achieving state-of-the-art performance.

Visual and acoustic modalities provide important nonverbal cues for multimodal sentiment analysis, but they are also easily affected by environmental noise, individual expression differences, and modality-quality variations. When unreliable nonverbal information is directly introduced into multimodal fusion, it may cause unstable cross-modal interactions and biased sentiment prediction. To address this problem, this paper proposes a visual-acoustic residual compensation method for multimodal sentiment analysis. Instead of redesigning the whole multimodal fusion framework, the proposed method constructs a controlled residual compensation path on top of a language-focused multimodal fusion model, aiming to calibrate the remaining prediction bias of the base multimodal prediction. Specifically, textual, visual, and acoustic modalities are first encoded to generate a base multimodal prediction through a language-focused fusion structure. Then, the language-focused visual and acoustic representations are concatenated and fed into a residual compensation branch composed of fully connected mappings, nonlinear activation, dropout, shortcut connection, and a scalar output layer. This branch produces a visual-acoustic compensation value for output-level incremental calibration. To avoid excessive correction caused by auxiliary modalities, a forward compensation weight, a residual clipping threshold, a compensation amplitude regularization term, and a residual supervision objective are introduced. In this way, the compensation branch is encouraged to learn the remaining bias between the base prediction and the ground-truth sentiment label, rather than directly fitting the complete sentiment target. Considering that the stability of base prediction and residual patterns may vary under different data conditions, two residual-training paths are designed. For CMU-MOSEI, the model adopts a Residual+Stage path, in which the base prediction branch is trained first, the residual compensation branch is then optimized, and the whole model is finally fine-tuned jointly. For CMU-MOSI, the model adopts a Residual-only path, where the base prediction branch and the residual compensation branch are optimized end-to-end. To reduce the confounding factors caused by cross-dataset comparison, controlled down-sampling experiments with different training scales are further conducted on CMU-MOSEI while keeping the validation set, test set, modality features, and evaluation scripts unchanged. Experimental results show that the proposed method outperforms the DLF baseline on all six evaluation metrics on CMU-MOSEI and CMU-MOSI. On CMU-MOSEI, the proposed method achieves 55.26% Acc-7, 57.04% Acc-5, 86.91% Acc-2, and 86.88% F1, improving over DLF by 1.36, 1.34, 1.49, and 1.61 percentage points, respectively. The correlation coefficient increases from 0.764 to 0.780, and the mean absolute error decreases from 0.536 to 0.530. On CMU-MOSI, the proposed method achieves 48.19% Acc-7, 54.50% Acc-5, 86.67% Acc-2, and 87.70% F1, improving over DLF by 1.11, 2.17, 1.61, and 2.66 percentage points, respectively. The correlation coefficient increases from 0.781 to 0.800, and the mean absolute error decreases from 0.731 to 0.721. Ablation results show that different residual-training paths are suitable for different data conditions. On CMU-MOSEI, Residual+Stage achieves the best overall performance, while on CMU-MOSI, Residual-only performs better. The controlled training-scale experiments on CMU-MOSEI further show that Residual-only is more stable when only 10% or 25% of the training data is used, whereas Residual+Stage gradually becomes more effective as the training ratio increases to 50%, 75%, and 100%. This indicates that training sample size affects the stability of base prediction error patterns and the adaptability of residual-training paths. The compensation-value analysis shows that the average absolute compensation values on CMU-MOSEI and CMU-MOSI are only 0.018 and 0.021, respectively, and the proportions of samples close to the compensation boundary are 2.6% and 3.1%. These results indicate that the compensation branch does not largely rewrite the base prediction, but performs controlled calibration within a small range. The computational cost analysis further shows that the residual compensation branch introduces only about 2.03×104 additional parameters, increasing the total parameter size from 111.23M to 111.25M, with only slight increases in training and inference time. Overall, the proposed method provides an effective output-level calibration strategy for controlled use of visual and acoustic modalities, and improves multimodal sentiment prediction with limited additional computational cost.

Braille is a fundamental textual medium through which blind and visually impaired people access information, receive education, and participate in social life. Automatic and accurate Chinese-to-Braille translation provides important technical support for promoting information accessibility and information equity. However, several technical challenges remain in automatic Chinese-to-Braille conversion. Traditional rule- and dictionary-based methods rely heavily on manually designed pinyin mappings, Braille word segmentation and concatenation rules, and punctuation conversion rules. Owing to their limited ability to understand deep contextual semantics, these methods tend to achieve relatively low accuracy in scenarios involving polyphone disambiguation, ambiguous sentence structures, and long-distance semantic dependencies. Although general neural machine translation models provide end-to-end sequence transformation capabilities, they are susceptible to insufficient generalization and sequence alignment errors under low-resource Chinese–Braille parallel corpus conditions. Moreover, most existing neural network models employ a unified feedforward structure, making it difficult to handle differentiated linguistic phenomena, including semantic understanding, Braille word segmentation, and symbol generation, in a specialized manner. Consequently, these models may produce insufficiently fluent translations and fail to maintain consistency with Braille rules when processing complex texts. To address these limitations, this paper proposes a Chinese-to-Braille translation model that integrates a pretrained language model with a Mixture-of-Experts (MoE) network, thereby constructing an end-to-end translation architecture that combines contextual semantic understanding with task-specific feature representation. The proposed model adopts an encoder–decoder architecture. In the encoder, a pretrained Bidirectional Encoder Representations from Transformers (BERT) model is employed as the core semantic feature extraction module. Its bidirectional contextual encoding capability is used to improve the identification of Chinese polyphones, ambiguous structures, and long-distance semantic dependencies. An MoE enhancement layer is introduced after the BERT output layer. A learnable gating network computes the routing weights of different experts and dynamically integrates their outputs, thereby improving the model’s ability to process differentiated linguistic information related to semantic features, Braille word segmentation boundaries, punctuation marks, and special symbols. The decoder consists of six stacked Transformer Decoder layers. Conditioned on the enhanced semantic representations produced by the encoder, the decoder combines target Braille sequence embeddings with positional encoding information and generates the target Braille symbol sequence autoregressively. To support model training and evaluation, a Chinese–Braille parallel corpus was compiled in collaboration with a school for blind students. The corpus covers several common text categories, including news reports, literary works, science education materials, public service documents, and daily communication texts. Data cleaning, duplicate removal, and Braille symbol normalization were performed to improve the consistency and usability of the corpus. The model was evaluated using the Bilingual Evaluation Understudy (BLEU), Character Error Rate (CER), Character Accuracy (CharAcc), and Exact Match (EM). These metrics respectively measure overall n-gram consistency, character-level editing errors, character-level correctness, and complete sequence consistency. Under the same training data, data split, preprocessing procedure, and evaluation settings, the proposed model achieved BLEU, CER, CharAcc, and EM scores of 98.15%, 1.46%, 98.54%, and 63.99%, respectively, on the self-built test set. Compared with the standard Transformer baseline under the same experimental conditions, the proposed model improved BLEU by 11.64 percentage points and reduced CER by 17.24 percentage points. The ablation results showed that introducing the MoE network alone increased BLEU from 86.51% to 92.12% and reduced CER from 18.70% to 10.17%. Introducing the BERT encoder alone increased BLEU to 97.02% and reduced CER to 1.84%. When the BERT encoder and the MoE network were used together, the model achieved the best results among the ablation models evaluated in this study, indicating that the two components provide complementary benefits. A subjective experiment involving visually impaired users was also conducted to assess the practical reading quality of the generated Braille texts. The results showed that the translation method had a significant effect on Braille reading scores. The proposed model received relatively high ratings for reading fluency, content comprehensibility, and semantic accuracy across daily-life, science education, and literary texts, while its rating distributions were relatively concentrated. The combined results of automatic evaluation, ablation experiments, and the user study indicate that BERT-based semantic encoding enhances the contextual representation of Chinese inputs, whereas the MoE mechanism dynamically integrates expert outputs according to input features. Their combination helps reduce character-level Braille errors and improve the exact-match rate of complete sequences. At a medium model scale, the proposed method achieves favorable Chinese-to-Braille translation performance and can provide technical support for Braille publishing, accessible conversion of digital reading materials, intelligent assistive devices, and public Braille information services.

In response to the problems in image tampering detection, such as the insufficient perception ability of tampered regions with small scales and blurred edges, which easily leads to missed detections; the fusion mechanisms of multi - domain features in the spatial and frequency domains often adopt simple splicing or serial attention, failing to fully explore the complementarity and collaborative discriminative ability of cross - domain features; complex models usually have a large number of parameters and high computational costs, making it difficult to achieve a balance between accuracy and efficiency in actual deployment scenarios with limited resources. This paper proposes an image tampering detection network FDU - Net that integrates dual - stream features and an attention aggregation mechanism. Based on the U - shaped encoder - decoder architecture, this method constructs a parallel input mechanism for RGB and JPEG dual - streams. The RGB stream directly processes the original image, aiming to extract spatial - domain apparent features such as color, texture, and structure to capture the differences in visual consistency between the tampered region and the background. The JPEG stream maps the image to the frequency - domain representation through a carefully designed artifact extraction module and uses multi - layer convolution to capture hidden traces in the compression domain, such as block effects, quantization errors, and spectral anomalies left by tampering operations, thus effectively compensating for the lack of robustness of single - spatial - domain features to post - processing operations.In terms of the backbone network, to balance feature expression efficiency and computational economy, this paper uses the lightweight MobileNetV4 as the shared backbone for the dual - streams. Considering the special sensitivity of the tampering detection task to high - frequency edge information, this paper proposes to use two general inverted bottleneck module variants, UIB - 1 and UIB - 2, for differential combined configuration. UIB - 1 alleviates the loss of high - frequency information during the down - sampling process through pre - depth convolution pre - processing, and UIB - 2 enhances the receptive field and channel interaction through post - depth convolution, thus outputting feature maps rich in multi - scale semantics and details at five scales.In the feature fusion stage, this paper designs a dual - stream attention feature fusion module. This module breaks through the limitations of traditional serial or single - dimensional attention mechanisms, models the long - range dependence between RGB and JPEG features in parallel in the channel and spatial dimensions through cross - domain multi - head self - attention, and introduces a linear compression layer to reduce computational complexity, achieving in - depth interaction and adaptive purification of dual - stream features and effectively solving the problem of insufficient multi - domain feature fusion.In addition, to improve the positioning accuracy of tampering boundaries, this paper introduces an accuracy detection head module, which combines the dual mechanisms of channel attention and spatial attention. Channel attention filters discriminative feature channels through global pooling and fully connected layers, and spatial attention fuses the results of average pooling and maximum pooling to strengthen the boundary response. The synergistic effect of the two significantly enhances the model's sensitivity to blurred - edge and small tampered regions.This paper conducts a large number of experiments on multiple public and self - built datasets, such as CASIAV1, CASIAV2, COVERAGE, IMD2020, and the self - made VOC2012TAMPER based on PASCAL VOC2012. The experimental results show that the F1 score of the model reaches 0.8921, and it outperforms existing mainstream algorithms in key indicators such as F1, MCC, and AUC. The proposed method performs excellently in terms of detection accuracy, robustness, and generalization ability, and the model has a relatively low number of parameters, demonstrating good practicality and deployability.

In high-load edge environments, bursty task arrivals and limited multidimensional resources at edge nodes, including CPU, I/O, bandwidth, and memory, can cause queue congestion and resource fragmentation, thereby affecting scheduling metrics such as resource utilization, running time, and waiting time. Existing learning-based scheduling methods often struggle to capture higher-order resource competition between tasks and servers, and their continuous outputs cannot be directly converted into discrete placement decisions that satisfy capacity constraints. This paper proposes EdgeHypOp, an edge task scheduling method based on hypergraph modeling and bin-packing decoding. EdgeHypOp jointly optimizes resource utilization, running time, and waiting time. For each scheduling window, it constructs a bipartite hypergraph over task and server nodes with four hyperedge types. Capacity, task-server, latency, and utilization hyperedges represent shared resource budgets, candidate placement relations, task waiting states, and the overall system load, respectively. To limit online scheduling overhead under high-load conditions, EdgeHypOp calculates task urgency from waiting time and execution duration and bounds the candidate batch size per round. A multilayer hypergraph neural network propagates information between nodes and hyperedges to produce task and server representations that encode resource demands, remaining capacities, and higher-order competition relations. Bilinear matching then generates a task-server score matrix. Since this matrix does not directly satisfy discrete capacity constraints, EdgeHypOp applies a Tetris-inspired multi-resource bin-packing decoder. Among feasible candidates, the decoder considers the learned score, the match between task demands and remaining server resources, task waiting priority, and execution duration. It places tasks iteratively, updates server states after each placement, then applies lightweight local optimization to reorder tasks within each server and reduce the blocking of short tasks by long tasks. Experiments are conducted on three real-world datasets, Google Cluster Trace, Alibaba Cluster Trace, and EUA, with FCFS, HRRN, RLPNet, PSNet, HyperJet, and aRL as baselines. All methods use the same task scales, server scales, and test instances, and results are reported as means and standard deviations over 10 runs. Under a high-load setting with 2,000 tasks and 5 servers, EdgeHypOp achieves the highest resource utilization on all three datasets. Compared with the strongest baseline for each metric, it improves resource utilization by 3.93 percentage points and reduces running time and waiting time by 4.18% and 14.03%, respectively, on Google Cluster Trace. On Alibaba Cluster Trace, the changes are an increase of 2.01 percentage points and reductions of 2.25% and 8.16%; on EUA, they are an increase of 2.10 percentage points and reductions of 2.19% and 0.65%. Scale experiments further show that EdgeHypOp maintains more consistent advantages in resource utilization and waiting time as the number of tasks increases or server resources become constrained, while the performance gaps among methods narrow under low-load or resource-abundant conditions. Ablation experiments show that removing bipartite hypergraph modeling or multi-resource bin-packing decoding increases the composite objective value by 7.89% and 4.29%, respectively, indicating that higher-order relation representation and constraint-aware decoding are the main sources of performance gains. Removing local optimization increases the composite objective value by 2.46%, and this component provides greater improvements for CPU-intensive and memory-intensive tasks. Online overhead tests show that when the task scale increases from 500 to 2,000, inference time rises from 704.49 ms to 2,178.42 ms, while peak CPU and GPU memory usage increases only slightly. Model training is performed offline. During online scheduling, EdgeHypOp performs only batch selection, hypergraph construction, forward propagation, scoring, bin-packing decoding, and local optimization, without gradient updates. The results show that EdgeHypOp combines higher-order resource competition representation, multidimensional capacity constraints, and discrete task placement in a unified scheduling process. It is suitable for edge task scheduling scenarios with high task concurrency and pronounced resource competition and fragmentation, while low-load settings, single-resource bottlenecks, and workloads with strong task dependencies require further extensions.

The large-scale deployment of the Industrial Internet of Things (IIoT) exposes industrial networks to increasingly complex security threats, making network intrusion detection a key measure for ensuring the safe operation of IIoT systems. However, significant disparities exist in sample sizes among attack categories in IIoT traffic, resulting in insufficient recognition capability of existing detection models for high-risk minority-class attacks. Meanwhile, methods based on complex architectures such as Transformers incur large parameter counts and high computational overhead, making them difficult to deploy under the resource constraints of edge devices. To this end, a Hierarchical Layer-Adaptive Feature Network (HLAF-Net) for intrusion detection is proposed to achieve high-precision and low-latency detection for class-imbalanced traffic. At the data-processing level, certain continuous features in IIoT traffic follow long-tailed or sparse distributions, whose discriminative information is prone to being dominated by extreme values or diluted by a large number of zero entries during direct normalization. A logarithmic transformation is applied to such features to compress the order-of-magnitude gap between extreme and ordinary values, so that the discriminative distances among samples are preserved after normalization. At the structural-design level, the classification task is decomposed into several levels according to the sample counts of attack categories. Since each level only needs to process the sample subset associated with that level, some features used to distinguish shallow-level categories become constant within this subset and no longer provide discriminative information; a zero-variance criterion is therefore introduced to remove such redundant features level by level, and a level-specific feature subspace is constructed for each level. At the network-architecture level, each level first adopts a gated attention mechanism to adaptively recalibrate the input features, and then employs a lightweight Multi-Layer Perceptron (MLP) in parallel with an Explicit Feature Interaction Branch (EFIB) to extract semantic representations and high-order interaction features, respectively, which are fused and passed through a classification output layer to obtain level-wise predictions. During inference, all levels output their prediction probabilities in parallel, and a cascaded level-wise decision logic determines the final category. HLAF-Net is experimentally validated on two public datasets, Edge-IIoTset and X-IIoTID. Ablation studies show that the logarithmic transformation yields the most significant gain, raising the Macro-F1 score from 77.16% to 91.04%; the hierarchical architecture, supported by high-quality input features, further improves the Macro-F1 score to 95.06%; and the gated attention mechanism enhances the convergence stability of the deeper levels during training. On the Edge-IIoTset dataset, HLAF-Net achieves a detection accuracy of 98.89%, a Weighted-F1 score of 98.91%, and a single-sample inference time of only 0.0979 ms, meeting the real-time requirements of edge deployment while maintaining detection accuracy. Across five independent runs, the standard deviations of accuracy and Weighted-F1 score remain below 0.0002, indicating stable performance that does not depend on a specific random initialization. Compared with models based on the same hierarchical divide-and-conquer paradigm, HLAF-Net improves accuracy and Weighted-F1 score by 2.18% and 2.70%, respectively, verifying the effectiveness of the level-specific feature subspaces and the network branch design. On the X-IIoTID dataset, both accuracy and Weighted-F1 score reach 98.98%, further demonstrating the effectiveness and robustness of the model across different IIoT environments.

Integrated Sensing and Communication (ISAC) has been recognized as one of the key enabling technologies for future wireless networks. Reconfigurable Intelligent Surface (RIS)-assisted multi-Unmanned Aerial Vehicle (UAV) cooperative ISAC systems can simultaneously exploit the advantages of wireless propagation environment reconfiguration and aerial mobility. However, such systems face significant challenges, including strong coupling among optimization variables, high-dimensional decision spaces, and the difficulty of jointly enhancing communication and sensing performance. To address these issues, this paper proposes a joint optimization framework for RIS-assisted multi-UAV cooperative ISAC systems. A three-dimensional discrete-time system model consisting of multiple UAVs, multiple ground base stations, a sensing target, and an RIS is established, where UAV mobility constraints, RIS reflection mechanisms, air-to-ground communication processes, and target sensing processes are jointly characterized. A non-convex optimization problem is formulated to maximize a weighted utility comprising the system sum communication rate and a sensing performance metric based on the log-determinant of a dimensionless Fisher Information Matrix (FIM), with transmit beamforming, UAV trajectories, and RIS phase shifts jointly optimized. The sensing utility is derived from the cumulative FIM contributions of both direct sensing links and RIS-assisted sensing links, which explicitly captures the impacts of UAV spatial deployment, RIS configuration, and target observation geometry on sensing performance. To tackle the non-convex objective function, unit-modulus constraints, and the strong coupling between communication and sensing components, an Alternating Optimization (AO) framework is developed. Specifically, the original problem is decomposed into three subproblems: transmit beamforming optimization, UAV trajectory optimization, and RIS phase-shift optimization. For transmit beamforming optimization, the Weighted Minimum Mean Square Error (WMMSE) method is employed to transform the weighted sum-rate maximization problem into an equivalent iterative minimization problem, while incorporating sensing-aware weighting for resource allocation. For UAV trajectory optimization, the Successive Convex Approximation (SCA) method is adopted to locally convexify the communication rate term, sensing utility term, and safety-distance constraints, and trust-region and smoothing regularization techniques are introduced to improve trajectory feasibility and numerical stability. For RIS phase-shift optimization, the non-convex quadratically constrained quadratic programming problem under unit-modulus constraints is transformed into a Semidefinite Relaxation (SDR) problem, and a grouping strategy combined with Gaussian randomization is employed to reduce the complexity of high-dimensional RIS phase recovery. Simulation results demonstrate that the proposed algorithm exhibits stable convergence behavior under different system parameters and network geometries, typically reaching convergence within 8–15 iterations. Statistical results obtained from 100 independent Monte Carlo trials show that, compared with the non-RIS scheme, the proposed RIS-assisted joint optimization method improves the average system communication rate by 24.83%, indicating that RIS-enabled propagation environment reconfiguration can effectively enhance air-to-ground communication link quality. Compared with the communication-only optimization scheme, incorporating the FIM-based sensing utility increases the average flight altitude of multiple UAVs by 16.58%, demonstrating that the proposed joint objective can guide UAVs to form more favorable spatial observation geometries for target localization while simultaneously satisfying multi-UAV safety constraints. Furthermore, sensitivity analysis with respect to communication-sensing weighting factors and experiments involving different numbers of UAVs reveal a pronounced synergistic effect among RIS-enabled environment reconfiguration, multi-UAV spatial mobility, and FIM-based sensing information modeling. Overall, the proposed method achieves communication rate enhancement, sensing geometry improvement, and comprehensive utility maximization while satisfying UAV safety-flight constraints and RIS unit-modulus constraints, providing an effective solution for joint resource allocation and trajectory design in complex air-ground ISAC networks.

In the task of insulator defect (e.g., breakage and flashover) detection in transmission lines, traditional object detection models suffer from receptive field mismatches, semantic gaps in feature fusion, parameter redundancy in detection heads, and high missed detection rates for minute defects. This is primarily due to the strong background interference in aerial images, the minute size and extreme scale variations of the targets, and the geometric anisotropy of insulators, which exhibit elongated structures and arbitrary spatial orientations. To satisfy the strict constraints of edge computing devices such as unmanned aerial vehicles (UAVs) regarding model storage overhead and real-time inference, this study proposes DDE-YOLO, a lightweight and efficient insulator detection algorithm based on an improved YOLO11n. The proposed algorithm reconstructs the network across three dimensions: feature extraction, feature fusion, and prediction regression. First, a Dynamic Inception Mixer Block (C3K2-DIMB) is innovatively designed in the backbone network. Its internal Dynamic Inception Depthwise Separable Convolution employs parallel square, horizontal strip, and vertical strip convolution kernels to extract multi-directional features. Combined with a channel-level dynamic weighting mechanism, the network adaptively perceives the elongated distribution characteristics and spatial orientations of insulators, effectively filtering background noise and enhancing valid feature representation. Second, addressing the information loss problem during multi-scale feature fusion, a Dynamic Adaptive Double Fusion Network (DAFPN) is constructed. Utilizing a Dynamic Attention Gated Fusion (DAGF) module, this network dynamically filters multi-level semantic information through grouped interactions and a bidirectional gating mechanism. By generating feature weights based on global contexts, it significantly suppresses background interference, bridges the semantic gap between deep semantics and shallow details, and strengthens the capability to capture minute defect textures in complex backgrounds. Third, an Efficient Shared Detail Head (ESDH) is designed, which implements cross-scale sharing of feature extraction and regression weights among multi-scale detection layers and adopts Group Normalization (GN) to improve training stability. To mitigate the degradation of scale sensitivity caused by parameter sharing, a learnable scale calibration factor is introduced at the end of the regression branch. Furthermore, Detail Enhancement Convolution (DEConv) is employed to strengthen high-frequency edge detail extraction via differential convolution branches during the training phase. In the inference phase, structural re-parameterization technology losslessly merges these branches into a single-layer standard convolution, achieving performance improvement with zero additional inference overhead. Comprehensive experiments on the insulator dataset demonstrate that DDE-YOLO delivers exceptional detection performance, achieving a precision of 93.67%, a recall of 89.75%, an mAP50 of 93.86%, and an mAP50:95 of 65.01%. Compared with the baseline YOLO11n, the mAP50 and mAP50:95 increase by 4.74 and 5.73 percentage points, respectively. Meanwhile, the total parameter count is only 2.35 M, representing a decrease of approximately 9.3% from the baseline, with computational complexity controlled at 7.0 GFLOPs. Ablation studies confirm that the C3K2-DIMB module improves localization quality while reducing parameters by 6.6%, the DAFPN significantly increases the AP of broken and flashover defects by 4.29% and 3.47%, respectively, and the ESDH alone reduces the parameter count by 12.4%. Moreover, generalization validation on the public Unmanned Aerial Vehicle Power Line Insulator Dataset (UPID) yields an mAP50 of 87.64%, a 1.62% improvement over the baseline. On the cross-domain NEU-DET steel surface defect dataset, the mAP50 increases by 3.31% to 79.27%, fully proving its outstanding cross-domain generalization capability. The proposed algorithm substantially enhances the detection precision of minute breakages and flashovers while effectively pruning parameter redundancy and reducing computational complexity, exhibiting excellent robustness.

To address the challenges of existing task chain deduction methods in capturing deep semantics and long-range dependencies under complex environments, which lead to insufficient accuracy and stability, this paper proposes a knowledge graph-enhanced large language model for combat task chain deduction method to improve the accuracy and stability of task sequence prediction in complex battlefield scenarios. The method first employs a bidirectional encoder-based entity linking module to accurately link combat elements in task texts with external general knowledge graphs for disambiguation, obtaining semantic descriptions of the elements. A task composition graph is then constructed, and TransE graph representation learning is applied to capture complex relationships among elements, yielding knowledge-augmented vector representations. Subsequently, a self-attention mechanism integrates the task chain context with candidate task sets to generate knowledge-level task state representations, which are concatenated with chain-level temporal representations to form a comprehensive state infused with external knowledge. Finally, task chain deduction is formulated as a reinforcement learning problem, and a multi-level reward mechanism combining chain-level rewards (BLEU) and knowledge-level rewards (cosine similarity) is designed to guide multi-step deduction. Experimental results on four public datasets (NYT, Finance, ICEWS14, and ICEWS15) show that the proposed method achieves one-step prediction accuracies of 76.23%, 74.39%, 72.11%, and 70.09%, respectively, outperforming state-of-the-art baselines by an average of approximately 19 percentage points. For four-step task chain prediction, the accuracies reach 71.76%, 70.39%, 68.97%, and 69.35%, significantly surpassing all baseline models. Furthermore, three ranking metrics—MRR, Hits@3, and Hits@5—are introduced for multi-dimensional evaluation, and the proposed method is compared with recent representative approaches including SPARK, ThinkTank-ME, and the zero-shot reasoning baseline of the DeepSeek-V4-Flash large language model. The proposed method achieves optimal or near-optimal results across all four metrics, with MRR reaching 78.64%, 73.15%, and 72.83% on NYT, ICEWS14, and ICEWS15, respectively. Ablation studies confirm the critical roles of the knowledge fusion mechanism and reward design in performance improvement. This study validates the effectiveness of knowledge graphs in enhancing semantic understanding and deduction performance, providing technical support for intelligent combat command and decision-making systems.

Federated Prototype Learning (FPL) significantly reduces communication overhead but faces critical security risks in open networks, including identity forgery and poisoning attacks. Existing statistical defenses struggle to distinguish malicious perturbations from benign heterogeneous features under non-independent and identically distributed (Non-IID) settings, often wrongfully eliminating honest long-tail clients. To address these vulnerabilities, this paper proposes a lattice trapdoor-based controllable identity tracing scheme for FPL, termed Federated Lattice Hidden Trapdoor (FedLHT). Shifting from probabilistic statistical detection to deterministic mathematical computation, FedLHT establishes a lightweight framework for pre-training anonymity, real-time authentication, and post-attack accountability. It constructs a certificateless signature system via lattice cryptography, utilizing preimage sampling to bind client identities with private keys. Traceable tags and rejection sampling guarantee information-theoretic anonymity. During aggregation, nonlinear group operations are linearized over lattices for lightweight batch verification. For targeted tracing, regulators utilize the lattice trapdoor master basis to solve the bounded distance decoding problem, accurately recovering malicious identities from abnormal noise. FedLHT's security is rigorously reduced to average-case Learning With Errors (LWE) and Short Integer Solution (SIS) problems, ensuring post-quantum security. Furthermore, utilizing the linear additive homomorphism of prototypes, the server seamlessly erases malicious historical contributions via lightweight vector subtraction for rapid model self-recovery. Compared with state-of-the-art defenses, such as ShieldFL (Mitigating Poisoning Attacks in Privacy-Preserving Federated Learning) and FLTrust (Byzantine-robust Federated Learning via Trust Bootstrapping), the proposed mechanism maintains a global model accuracy above 84.65% on the CIFAR-10 dataset. Furthermore, in a severe Non-IID setting with a heterogeneity coefficient of 1.0, it achieves a 98.75% retention rate for honest long-tail data, realizing zero-misjudgment response. For 40 concurrent clients, local computing and server-side authentication overheads are merely 1.22 ms and 6.42 ms, effectively overcoming computational bottlenecks of traditional pairing-based schemes for resource-constrained Internet of Things (IoT) scenarios.

As core carriers of human civilization and Red (revolutionary) culture, historical documents harbor densely packed spatiotemporal clues and intricately interwoven entity associations within their texts. However, conventional knowledge-organization methods in the digital humanities have long been constrained by bottlenecks such as the fragmentation of multi-source historical materials, the heterogeneity of narrative styles, and referential ambiguity among persons, places, times, and events. When general-purpose large language models (LLMs) are applied directly to historical text processing, the absence of domain-specific prior knowledge readily leads to inaccurate identification of entity boundaries. At the same time, the pervasive phenomena of homonymous entities (identical names denoting distinct entities) and synonymous entities (distinct names denoting an identical entity) cause traditional entity-alignment algorithms to confront a cost crisis in which computational complexity grows quadratically with scale during the comparison of massive numbers of graph nodes. Moreover, conventional retrieval-augmented generation (RAG) approaches overlook the implicit spatiotemporal topological constraints among texts and therefore frequently produce logical distortions when addressing narrative-style question answering, such as cross-period trajectory reconstruction and complex spatiotemporal transitions. In view of these challenges, this paper proposes a spatiotemporal narrative enhancement framework for historical documents that integrates large language models with knowledge graphs, aiming to achieve efficient structured extraction, high-precision association fusion, and deep spatiotemporal reasoning-based question answering over large-scale historical texts. The proposed framework adopts a hierarchical, collaborative design comprising three progressive core modules: knowledge extraction, entity alignment, and spatiotemporal narrative intelligent question answering. In the knowledge-extraction stage, in light of the dense proper nouns and idiosyncratic grammar characteristic of historical texts, a history-constrained prompt-chain framework is designed that decomposes the single extraction task into three cascaded stages—step-by-step reasoning prompting, domain prior-knowledge injection, and structured-output constraint correction. By introducing military terminology, historical place names, and a Ganzhi (sexagenary-cycle) chronology reference table into the context stream, the framework achieves precise, high-volume conversion of unstructured text into graph-structured data. In the entity-alignment stage, to eliminate the computational bottleneck in cross-source, multi-graph merging, a two-stage entity-alignment algorithm is proposed. The first stage introduces an index-optimized filtering mechanism that constructs a four-dimensional hybrid index covering textual aliases, life cycles, geographical-hierarchy mappings, and social topological relations; by means of a bucketing strategy, it converges the search space of nodes awaiting alignment to single digits, thereby compressing the overall time complexity of the algorithm from quadratic to linear. The second stage performs LLM-enhanced semantic discrimination, fusing the static and dynamic attribute features of candidate entities with multi-hop association networks to compute a composite similarity, and establishes a tiered, human-machine collaborative closed-loop decision process via a confidence-evaluation mechanism. In the question-answering stage, a spatiotemporal narrative graph RAG module is designed; through instruction fine-tuning or semantic parsing, natural-language queries are mapped into structured constraint conditions with explicit spatiotemporal boundaries, and a graph-database query language is dynamically generated to retrieve multi-hop associated spatiotemporal subgraphs from the global knowledge graph. The temporal evolution features and spatial topological structure are thereby conveyed in full to the generative model, driving it to produce spatiotemporal narrative text with rigorous logical chains and high interpretability. To validate the robustness and effectiveness of the proposed framework in practical settings, multi-dimensional empirical evaluations were conducted on the self-constructed Red historical-document dataset N4A-Hist, the publicly available historical dataset CHisIEC, and the multilingual general-purpose dataset DBP15K. Experimental results show that, on the knowledge-extraction task for unstructured text, the prompt-chain framework attains F1 scores of 94.8% and 92.1% on N4A-Hist and CHisIEC, respectively—improvements of 16.3% and 14.0% over the baseline of direct extraction by the base model—while ablation experiments confirm the decisive role of stepwise constraints in mitigating format hallucination. In cross-source entity-alignment testing, the two-stage algorithm achieves a Hits@1 of 91.3% and an MRR of 0.962 on N4A-Hist, with a total online runtime of only 0.8 hours; compared with the state-of-the-art LLM-based alignment methods ChatEA and LLM4EA and the traditional method EasyEA, it reduces runtime cost by more than 75% while maintaining comparable or higher accuracy. On the general-purpose datasetDBP15K, it likewise sustains a Hits@1 of 98.3% with a rapid convergence time of 4.2 hours, fully corroborating the superiority of the four-dimensional index in curbing the expansion of computational scale. In a question-answering test comprising 530 complex reasoning instructions, the spatiotemporal narrative graph RAG module raises answer accuracy on spatial-displacement questions and cross-period comprehensive-reasoning questions by substantial margins of 47% and 54%, respectively, relative to traditional vector-database retrieval. In summary, this study demonstrates that organically integrating the deep semantic representation capabilities of large language models with the rigorous topological structural knowledge of knowledge graphs constitutes an effective means of overcoming the bottlenecks of high ambiguity and strong spatiotemporal correlation in digital-humanities research on historical documents. While safeguarding the efficiency of automated processing workflows, the proposed framework mitigates the uncertainty risks of practical deployment through confidence filtering and multi-dimensional constraint mechanisms, thereby providing a highly feasible and academically valuable system-level solution for the digital reconstruction, deep semantic association, and intelligent knowledge services of massive, heterogeneous historical-document resources.

Small target detection in low-altitude UAV imagery is challenged by small object scales, dense spatial distributions, strong background interference, and complex imaging conditions. Compared with general objects, small targets occupy only a limited number of pixels, making their edges, textures, and shape cues prone to being overwhelmed by background responses during feature extraction. Existing detection methods usually obtain high-level semantic features through stacked convolutions and progressive downsampling. However, this process rapidly reduces the spatial resolution of feature maps, causing the local structures and pixel-level details of small targets to be weakened or even lost. Although feature pyramids and attention mechanisms can alleviate scale variation and background interference to some extent, most methods focus mainly on multi-scale semantic fusion and provide limited direct constraints on the fine-grained spatial information loss caused by downsampling. Meanwhile, insufficient collaborative modeling among channel features makes it difficult to integrate small-target cues scattered across different channels into stable and discriminative representations. To address these problems, this paper proposes RFINet, a small target detection network based on recalibrated fine-grained downsampling and inter-group collaborative modeling. First, a recalibrated fine-grained downsampling (RFD) module is constructed, which replaces conventional downsampling with spatial slicing and channel reorganization. By mapping local spatial information into the channel dimension, RFD preserves fine-grained structures of small targets while reducing the feature map size. To suppress redundant responses and background noise introduced by channel expansion, RFD further introduces a cross-channel adaptive recalibration mechanism that reallocates weights according to aggregated channel responses, thereby enhancing target-related channels and suppressing irrelevant background information. Compared with stride convolution or pooling, RFD does not simply discard spatial sampling points; instead, it transfers information that would otherwise be compressed into the channel domain through structured rearrangement, providing more complete local context for subsequent feature extraction. Second, an inter-group collaborative modeling (ICM) module is designed to divide input features into multiple channel subspaces, extract locally salient information within each group, and promote intra-group enhancement and inter-group interaction through weight redistribution, residual connection, and channel concatenation. This design preserves the fine-grained local modeling ability of grouped features while alleviating information isolation caused by fixed grouping, enabling the network to integrate small-target cues distributed across different channels more effectively. Finally, object classification and bounding box regression are performed by the detection head to achieve small target detection in complex low-altitude scenes. To verify the effectiveness of the proposed modules, experiments are conducted from multiple perspectives, including different input resolutions, ablation studies, model complexity, detection accuracy, and cross-dataset generalization, so as to evaluate the overall performance of RFINet under dense small-target distributions, significant scale variation, and strong background interference. Experimental results on the VisDrone2019 dataset show that RFD effectively preserves fine-grained spatial information of small targets and suppresses redundant background noise, while ICM reduces redundant background responses and strengthens the model’s focus on key feature regions. The combination of the two modules brings stable performance gains under different input resolutions. RFINet achieves mAP@0.5 scores of 40.8% and 47.5% with input sizes of 640×640 and 960×960, respectively, outperforming the compared mainstream two-stage detectors, one-stage detectors, and lightweight detection models. The results demonstrate that the proposed method enhances the representation of small targets in dense, occluded, and cluttered scenes while maintaining high detection accuracy, providing a more targeted feature modeling scheme for low-altitude UAV visual perception tasks.

Existing point cloud completion networks mainly rely on the spatial coordinates and local neighborhood features of the single-modal point cloud for shape reasoning. However, for some point clouds, there are usually problems such as sparse point distribution and insufficient global contour information. These results in the appearance of missing details and discontinuous topological structures in the completed structure. Previous cross-modal point cloud completion methods strictly rely on paired input multimodal data. The collection and alignment of multimodal data also increase the cost of dataset construction. This limits its direct application in real engineering projects. To address these issues, this paper proposes a network named Dual-State Guided Cross-Modal Point Completion Network (DSG-Net). It aims to make up for the inherent limitations of single-modal features. DSG-Net only uses the partial point cloud input as the original data. During the data preprocessing stage, it generates the corresponding depth map through the preset pinhole camera model for projection and rendering to assist in the completion of the single-modal point cloud. Thus, it overcomes the dependence of traditional cross-modal methods on the strict pairing of multimodal data. The designed Cross-Modal Fusion Generation (CMFG) module utilizes convolution to map the extracted point cloud features and depth map features to the same feature space for concatenation and fusion. Then, the multi-head self-attention mechanism models long-range dependencies in the fused features. And the modeled complementary features are split point by point to generate the initial coarse point cloud. Therefinement stage introduces the Dual-State Guided Refinement (DSGR) module. It comprises two parallel processing channels: structural analysis and similarity comparison. The structural analysis channel measures the spatial correlation between the generated point cloud and the input partial point cloud. This guides the network to focus on structural differences in the missing regions and enhances the continuity of the overall contour and local structures. The similarity comparison channel focuses on mining potential similarity patterns among different local regions. It guides topological refinement in the missing regions, thereby improving the shape consistency and detail completeness of the completed point cloud. The State Flow Block (SFBlock) is integrated within the DSGR module. It enables dynamic feature-state updating and effective information propagation during the iterative refinement process. This module adaptively controls the retention ratios of historical accumulated states and current input features via a dual-gating unit. This mechanism dynamically adjusts local details and positional deviations during the refinement process. It also prevents the recovered shape information from being weakened or redundantly propagated across multiple iterations. To verify the effectiveness of DSG-Net, experiments are conducted on two public datasets, PCN and ShapeNet55, and a self-built Terracotta Warrior dataset. The experimental results show that DSG-Net achieves an average CD-L1 of 6.72 on the public PCN dataset. This corresponds to a relative reduction of 19.81% compared with PoinTr’s average CD-L1 of 8.38. Meanwhile, DSG-Net outperforms the latest network, 3DMambaComplete, in terms of CD-L1 across all eight categories. These results further demonstrate that DSG-Net can effectively improve the structural completion accuracy of missing regions. On the ShapeNet55 dataset, DSG-Net achieves an average CD-L2 of 0.89 across three tasks with different difficulty levels. Meanwhile, among the five common categories, DSG-Net obtains the best completion performance in three of them. On the self-built Terracotta Warriors dataset, DSG-Net achieves an average CD-L1 of 5.23. This corresponds to a relative reduction of 10.45% compared with PoinTr’s average CD-L1 of 5.84. This indicates that DSG-Net has strong generalization ability on non-public data. In summary, DSG-Net achieves favorable completion performance on both public and self-built datasets. This indicates that it can not only effectively restore the shape structure of complex objects, but also exhibit strong cross-dataset adaptability. Therefore, it can provide valuable technical reference for future cultural heritage digital modeling and incomplete artifact restoration.

Aiming at the problems that electric load series are highly non-stationary, exhibit significant multi-scale fluctuations, and that periodic information is difficult to model stably and is prone to attenuation and phase shift in multi-step forecasting, as well as the complex coupling relationships between exogenous variables such as temperature, humidity, electricity price and load, this paper proposes a short-term load forecasting method based on time-frequency fusion and periodic residual coupling (TFF-PRC). The proposed method aims to improve the joint representation capability of the model for long-term trends, local fluctuations and periodic patterns of load series, while enhancing the effective utilization of exogenous information in multivariate scenarios, thereby improving forecasting accuracy and stability under different prediction horizons. Existing deep learning methods mainly rely on time-domain modeling or global dependency modeling, and often fail to fully exploit low-frequency trends, high-frequency disturbances and explicit periodic information. Meanwhile, in multivariate modeling, premature mixing of exogenous variables may introduce redundant noise, whereas complete channel independence may weaken effective interactions. Therefore, this paper constructs the forecasting framework from three aspects: time-frequency collaborative modeling, multi-period residual compensation and load-centered channel interaction.First, in the time domain, moving average decomposition is adopted to extract the trend and seasonal components. In the frequency domain, multi-level discrete wavelet transform (DWT) is employed to obtain the low-frequency approximation component and multi-scale high-frequency detail components. Through a directional fusion mechanism, the low-frequency information is used to enhance the trend component, while the high-frequency information is used to enhance the seasonal component. In this way, frequency-domain information participates in the construction of time-domain components in a residual manner, thereby strengthening the model’s ability to characterize long-term trends and local disturbances. Subsequently, the enhanced trend and seasonal components are separately fed into a channel-independent temporal patch feature extraction network. Convolution is used to capture local patterns, and multi-layer LSTM is employed to model long- and short-term dependencies, so as to obtain stable and clean high-dimensional representations.To address periodic attenuation and phase shift in multi-step forecasting, the proposed method identifies the dominant periods of load series based on fast Fourier transform (FFT), extracts historical load segments that are phase-aligned with the prediction interval, and generates periodic compensation features through sample-level adaptive weighting. These features are then incorporated into the backbone forecasting results in a residual form. Furthermore, the load-centered channel interaction module takes load as the query center and selectively aggregates information from exogenous variables such as temperature, humidity and electricity price through the query-key-value mechanism. This design preserves the multivariate modeling capability while effectively suppressing noise interference among channels.Experiments are conducted on two public electric load datasets, namely New South Wales (NSW), Australia, and Spain. The input window is set to 48 time steps, and the forecasting horizons are set to 1, 2, 24 and 48 time steps. Compared with mainstream baseline models, TFF-PRC reduces the average MAE by 11.03%~20.11% and the average RMSE by 11.41%~18.53% on the NSW dataset. On the Spain dataset, the average MAE is reduced by 3.99%~5.23%, and the average RMSE is reduced by 4.05%~4.10%, indicating that the proposed model has good generalization capability. Ablation experiments show that after removing the TFF, PRC or LCA module, MAE increases by 7.54%, 10.63% and 7.00%, respectively, while RMSE increases by 7.03%, 8.18% and 8.10%, respectively, verifying the contribution of each module to forecasting performance.In summary, TFF-PRC effectively alleviates non-stationary fluctuations, periodic attenuation and multivariate noise interference through time-frequency directional fusion, multi-period residual coupling and load-centered channel interaction. The proposed method achieves high accuracy and stability across different datasets and forecasting horizons.

Existing vehicle-infrastructure cooperative perception task scheduling methods face two major challenges: insufficient characterization of computational resource hard constraints, and the difficulty of meeting scheduling decision efficiency in dynamic environments. Furthermore, during the task scheduling process, bidirectional data transmission exists between Connected Autonomous Vehicles (CAVs) and Roadside Units (RSUs), where RSUs send perception data to CAVs, and CAVs offload tasks to RSUs and receive the results. In this process, the RSU-side computational delay in the task completion delay model is inversely proportional to the allocated computational resources, which introduces fractional nonlinear terms into the delay model and further increases the mathematical complexity of the problem. To address the above issues, this paper considers the scenario where both RSUs and CAVs are subject to computational resource upper bound constraints, and constructs a vehicle-infrastructure cooperative perception task scheduling model aimed at minimizing the total system delay. At the communication level, the model adopts Orthogonal Frequency Division Multiple Access (OFDMA) as the multiple access scheme, where the RSU allocates orthogonal subcarriers to each vehicle to ensure equal communication bandwidth between CAVs and the RSU. Meanwhile, considering the Doppler shift and Inter-Carrier Interference (ICI) caused by high-speed vehicle mobility, a quantitative relationship between vehicle speed and data transmission rate is established. At the computational resource allocation level, the model explicitly characterizes four types of constraints: the RSU total computational resource upper bound, the per-task computational resource upper bound, the RSU maximum number of parallel tasks, and the CAV onboard computational resource upper bound. From the RSU perspective, the core decision is to select which CAVs' tasks to admit and to allocate computational resources to them, thereby coupling task offloading decisions with computational resource allocation. On this basis, this paper takes minimizing the sum of task completion delays of all CAVs in the system as the objective function, takes the binary task offloading decision variable, the continuous RSU computational resource allocation variable, and the continuous CAV local computational resource allocation variable as the core optimization variables, and introduces the task completion delay as an auxiliary variable, to construct a mixed-integer nonlinear programming model with fractional nonlinear constraints. To solve the difficulty caused by nonlinear constraints, this paper proposes a customized solution algorithm based on the outer approximation method. The algorithm exploits the convex separable property of the delay function, performs a first-order Taylor expansion on the delay expression of RSU task processing to generate outer approximation cuts, and transforms the original nonlinear model into a mixed-integer linear programming problem. To address the shortcomings of the traditional outer approximation method, namely its sensitivity to initial points and insufficient convergence stability, this paper proposes an initial point selection strategy: taking the maximum computational resource allowable to a single task as the initial point, to improve the solution quality and efficiency of the model. To evaluate model performance, experiments compare the proposed model with two baseline schemes: Baseline 1: all CAVs process tasks locally without using RSU resources; Baseline 2: CAVs prioritize offloading tasks to the RSU, with tasks exceeding the RSU's capacity processed locally by CAVs, and the RSU allocates equal computational resources to each offloaded task. Experimental results demonstrate that, compared with Baseline 2, the proposed model reduces the total task completion delay by 15.6% and achieves a maximum reduction of 32.4% in the task completion delay for a single CAV. This paper also compares the proposed algorithm with the Genetic Algorithm and the Adaptive Large Neighborhood Search Algorithm. Experimental results demonstrate that, in terms of solution efficiency, the proposed initial point selection strategy enables the algorithm to converge in 2.55 rounds on average, with the solution time controlled between 0.91 and 2.86 seconds. Compared with the Genetic Algorithm, the proposed algorithm achieves an average saving of 56.2% in solution time. These results indicate that the proposed vehicle-infrastructure cooperative perception task scheduling model and method under computational resource constraints can effectively reduce the total system delay and improve the efficiency of scheduling decisions.

To address significant heterogeneity among modality representations, redundant noise introduced by non-textual modalities such as vision and audio, and the difficulty of dynamically modeling sample-specific modality contributions in multimodal sentiment analysis, this paper proposes a Text-Guided and Dual-Gated Adaptive Graph Fusion model, namely TGAGF. In video-based sentiment analysis, textual, visual, and acoustic modalities provide sentiment cues from semantic content, facial expressions, and acoustic variations, respectively. However, these three modalities differ significantly in representation formats, information density, and noise distributions. The textual modality usually has strong semantic representation capability and can more directly reflect the speaker’s opinions and sentiment orientation. Visual and acoustic modalities can provide complementary nonverbal information, such as facial expressions, head poses, intonation, and speaking rate, but they are also susceptible to individual differences, environmental interference, and acquisition quality. If multimodal features are directly fused, weakly relevant information and redundant noise from non-textual modalities may be introduced into the joint representation and further propagated or amplified during cross-modal interaction, thereby weakening the model’s discriminative ability for sentiment polarity, fine-grained sentiment levels, and continuous sentiment intensity. To this end, TGAGF takes the textual modality as semantic guidance to enhance the representation capability of effective sentiment cues in visual and acoustic modalities during cross-modal fusion, while suppressing the influence of redundant noise on sentiment prediction. First, the model performs feature extraction and dimensional alignment on textual, visual, and acoustic modalities, mapping features from different modalities into a unified semantic space to provide structurally consistent input representations for subsequent fusion. Then, a shared-specific dual-branch disentanglement network is introduced to separate cross-modal consistent information from modality-specific information. Specifically, the shared branch extracts sentiment-related consistent semantic information across different modalities, while the modality-specific branch preserves complementary discriminative information within each modality. Meanwhile, reconstruction consistency, modality-specific consistency, metric constraint, and orthogonality constraint are jointly employed to constrain the disentangled representations from the perspectives of semantic recovery, modality-specific stability, cross-modal consistency, and subspace independence, thereby reducing semantic entanglement between shared and specific features. Second, a text-guided dual-stage gating mechanism is adopted to suppress redundant noise in visual and acoustic modalities before graph-based relational reasoning and to reweight modality representations after graph propagation. The first-stage gate pre-selects the modality information entering the graph reasoning module, reducing the diffusion of invalid information during relational propagation. The second-stage gate readjusts the contribution of each modality according to the cross-modal interaction results after graph propagation, enabling the model to dynamically allocate modality weights according to the sentiment expression characteristics of different samples. On this basis, textual, visual, and acoustic modalities are regarded as graph nodes, and a learnable adjacency matrix is used to model inter-modal dependencies. In this way, semantic correlations among different modalities are explicitly captured at the modality level, and complementary information across modalities is enhanced through graph message propagation. Finally, a cascaded multi-task prediction strategy is employed to jointly model sentiment polarity, fine-grained sentiment levels, and continuous sentiment intensity, allowing the model to simultaneously perform sentiment direction discrimination, sentiment level classification, and continuous sentiment intensity estimation. Experimental results on the CMU-MOSI and CMU-MOSEI datasets show that the proposed model achieves competitive performance on multiple evaluation metrics. Specifically, the Acc7, Acc5, Acc2, and F1 scores on CMU-MOSI are 48.4%, 56.1%, 85.3%, and 85.3%, respectively; the Acc2, F1, and MAE on CMU-MOSEI are 85.2%, 85.1%, and 0.534, respectively. Compared with various classical methods and recent representative methods, TGAGF demonstrates strong competitiveness in both sentiment classification and sentiment intensity prediction tasks. The experimental results verify the effectiveness of the proposed method in alleviating modality heterogeneity, suppressing redundant noise interference, dynamically modeling modality contributions, and improving the stability of multimodal sentiment recognition.

Trusted multimodal classification, which integrates heterogeneous data streams and quantifies predictive uncertainty, is of great significance in high risk domains such as medical diagnosis and autonomous driving. However, when handling multimodal conflicts, most existing methods still suffer from three core issues: mechanical noise processing leads to overconfidence when evidence is scarce; the unified fusion paradigm causes modality specific deep semantics to be lost due to “feature assimilation”; and uncertainty estimates are not utilized as a dynamic feedback signal to guide fusion decisions in a closed loop manner. To address these challenges, this paper proposes a Joint Adversarial like Learning and Dynamic Fusion (JALDF) method for trustworthy multimodal classification. The method aims to systematically improve the accuracy, noise robustness, and trustworthiness of multimodal classification through the synergistic optimization of feature alignment, semantic preservation, and adaptive fusion. In terms of technical implementation, JALDF consists of three core innovative modules. First, the cross modal class similarity module applies sigmoid sparsification and L₁ regularization to each modality’s features to select highly discriminative subsets, then extracts samples by class and uses the Wasserstein distance to measure the distribution discrepancy of the same class across different modalities. By minimizing this distance, the network is forced to learn a shared discriminative representation across modalities. This module leverages optimal transport theory to precisely characterize the minimal transport cost between distributions. Compared with symmetric measures such as KL divergence, the Wasserstein distance provides stable gradient guidance even when there is a large distribution shift in the feature space, thus ensuring effective cross modal alignment. Second, to prevent excessive modal convergence caused by the above constraint, the semantic adversarial module treats each modality as an independent domain, assigns modality origin labels to samples, and trains a domain classifier. This classifier forms an adversarial game with the similarity module: the similarity module strives to confuse modal differences, while the domain classifier tries to distinguish the source of each modality. As a result, while keeping inter class distributions close, the model precisely preserves the unique deep information of each modality. This adversarial design is introduced for the first time in the field of trustworthy multimodal classification and effectively resolves the conflict between feature collapse and information loss.Finally, the confidence supervised self attention fusion module maps each modality’s features into queries, keys, and values, computes inter modal dependency weights via multi head self attention, and uses the True Class Probability (TCP) as a trustworthy supervision signal to train a confidence estimation branch, enabling the model to dynamically evaluate the evidence quality of each modality during testing without requiring labels. The fusion loss combines classification cross entropy and confidence mean square error, driving the attention weights to be adjusted in real time according to current confidence, thereby enhancing high quality modalities and suppressing low quality or noisy ones. This closed loop supervision mechanism allows uncertainty measures to directly participate in the generation of fusion weights, breaking the limitations of traditional static fusion. Experiments on five public multimodal datasets (Cub, Caltech101, Scene15, Animal, and PIE) demonstrate that JALDF achieves leading accuracy results across all datasets. Specifically, it attains 81.40% on Scene15, outperforming the suboptimal RMVC by 8.35 percentage points, and reaches 95.14% on PIE, significantly surpassing existing methods. Additional experiments on hyperparameter analysis, conflict sensitivity, uncertainty estimation, ablation, and visualization further validate the effectiveness and superiority of JALDF from multiple perspectives. In summary, through the organic synergy of adversarial like learning and dynamic fusion, JALDF effectively solves the three major problems of overconfidence, feature assimilation, and rigid fusion in multimodal classification, offering a highly accurate and robust technical solution for trustworthy multimodal classification.

Cognitive diagnosis ( CD ) aims to accurately identify students ' mastery of different knowledge points based on their response records in online teaching platforms. It is an important basis for personalized learning recommendation, learning path planning and teaching intervention. However, the real education data generally have obvious long tail distribution characteristics : a small number of head students have rich answer interaction, which can provide sufficient supervision signals for the model ; however, a large number of tail students only have a small number of answering records, and their cognitive state is difficult to be stably estimated, which is prone to representational aliasing, mean regression and even representational collapse. Most of the existing cognitive diagnosis models rely on student ID embedding and limited interaction records, and the relatively complete cognitive models in head students are not fully utilized, which is difficult to effectively alleviate the cold start and sparse representation of tail students. At the same time, some models do not fully distinguish the cognitive semantic differences behind the correct and wrong answers, resulting in insufficient mining of discriminative information in sparse interaction. In view of the above problems, this paper proposes a student cognitive diagnosis framework CFECD based on conditional manifold enhancement and positive and negative two-view contrastive learning. In the design of the method, CFECD first introduces the semantic enhancement feature initialization module, which integrates the semantics of the exercise text, knowledge point information and ID embedding to improve the generalization ability of node representation in sparse scenes. Then, LightGCN is used to encode the high-order interaction between students, exercises and knowledge points to obtain structured cognitive representations. On this basis, the conditional manifold enhancement module is constructed to store the high-quality cognitive representation of the head students through the global memory library, and the reverse projection, latent space interpolation and forward generation of the tail students in the continuous probability space are carried out by using the reversible mapping ability of the manifold to realize the distribution completion and feature calibration of the sparse representation of the tail students. In order to further improve the reliability of the enhanced features, the model combines the original graph features, manifold generation features, peer reference features and context interaction features to form a multi-source collaborative tail student representation. At the same time, aiming at the different cognitive meanings reflected by the correct and wrong behaviors of the answers, this paper constructs a positive and negative dual-view contrastive learning mechanism, decouples the interaction graph into a correct-response mastery view and an incorrect-response deficiency view, and enhances the separability of different cognitive states in the hidden space by comparing the constraints. Finally, the model designs a diagnostic prediction function with scaling factor to moderately enlarge the marginal difference between students ' ability and the difficulty of exercises, and combines the adaptive loss weight to generate the network dynamic balance prediction loss, sorting loss, consistency loss and contrast learning loss to improve the stability of the joint optimization process. The experimental results on the three public education datasets of ASSIST2009, Junyi and XES3G5M show that CFECD is superior to various baseline models in terms of prediction accuracy and ranking ability, especially for tail students. The ablation experiment and parameter sensitivity analysis further show that the conditional manifold enhancement can effectively complement the tail student representation, the positive and negative two-view contrast learning can improve the semantic discrimination of the hidden space, and the moderate manifold constraint, network depth, contrast learning weight and scaling factor help to achieve a balance between representation enhancement and main task prediction. In summary, CFECD can generate more stable and interpretable portraits of students ' knowledge mastery in the long-tail online teaching scene, especially for the early diagnosis of low-active students, newly registered students and students with insufficient interactive records. The model output can further serve the identification of weak knowledge points, personalized exercise recommendation, learning path planning, teacher teaching early warning and class learning situation analysis. The experimental results verify the effectiveness, robustness and practical teaching application potential of the proposed method in the cognitive diagnosis task of long tail students.

Low-light image enhancement aims to recover clear, natural, and detail-preserving images from scenes with insufficient illumination, low contrast, noise degradation, and color distortion. Existing methods can improve image brightness to some extent, but they often suffer from noise amplification, unstable color restoration, texture loss, and over-enhancement in complex dark scenes. This is mainly because noise and textures may show similar high-frequency responses in the spatial domain, while luminance and chrominance information are highly coupled in the RGB space. To address these problems, this paper proposes SFSNet, a spatial-frequency synergistic network based on color space decomposition. The input image is first transformed into the HVI color space, where the intensity and chrominance components are separated to reduce the mutual interference between brightness recovery and color correction. Before spatial encoding, a frequency denoising module is introduced to suppress noise-related frequency disturbances and provide cleaner features for subsequent restoration. Then, a dual-branch backbone is constructed to process intensity and chrominance features separately, while a multi-scale convolution module is used to capture local textures, directional structures, and contextual information. To improve the consistency between brightness and color restoration, a cross-branch feature collaboration mechanism is designed to promote information interaction between the two branches. In addition, a deep interaction module and an uncertainty-based adaptive weighting strategy are employed to enhance deep feature representation and improve training stability. Experiments are conducted on LOL-v1, LOL-v2-real, and LOL-v2-syn, which provide paired normal-light reference images, and further evaluated on real unpaired datasets including DICM, LIME, MEF, and VV to verify the generalization ability of the proposed method. Compared with representative low-light enhancement methods, the proposed method can better improve dark-region brightness, suppress noise amplification, maintain natural color appearance, and preserve local structural details. Ablation studies further demonstrate the effectiveness of frequency denoising, multi-scale spatial modeling, cross-branch feature collaboration, and deep interaction supervision.

Vision-Language Models (VLMs) have demonstrated exceptional capabilities in cross-modal understanding by aligning Large Language Models (LLMs) with visual encoders. However, cross-modal hallucination remains a critical bottleneck that limits their deployment in accuracy-sensitive scenarios. The fundamental cause lies in the fact that the underlying LLM base, pre-trained on massive text corpora, establishes strong language priors that often override authentic visual features during autoregressive decoding. A detailed analysis of the internal attention mechanism during decoding reveals significant hierarchical disparities in visual feature evolution: shallow layers exhibit highly diffuse attention containing substantial redundant noise, which easily triggers attention drift, while deep layers suffer from severe attention decay due to language dominance, effectively severing the link between text generation and visual facts. Existing decoding-time intervention methods typically adopt global, coarse-grained strategies, failing to systematically address the contradiction between shallow-layer diffusion and deep-layer decay. Consequently, this paper proposes a Multi-layer Cumulative Visual Prior (MCVP) method, which reconstructs feature distributions at the decoding stage through three synergistic mechanisms. First, a sparsified focusing strategy is introduced to dynamically evaluate shallow-layer responses and filter background noise to extract high-confidence features, fundamentally preventing attention drift. Second, a layer-wise recursive accumulation mechanism is designed to construct a temporally robust global visual prior by applying weighted aggregation to preceding shallow-layer sparse features via a sliding window. Finally, an adaptive soft injection strategy is proposed to calculate the real-time visual dependency of the current generation step, dynamically incorporating the robust prior into deep-layer cross-attention modules to rebalance modal weights and enforce strict alignment with visual facts. Extensive empirical evaluations on LLaVA-1.5, MiniGPT4, and DeepseekVL, based on CHAIR and POPE benchmarks, demonstrate that MCVP significantly outperforms baseline sampling and state-of-the-art intervention methods. In the CHAIR benchmark, MCVP achieves a relative improvement of 18.8% in hallucination suppression performance; specifically, it reduces the sentence-level hallucination rate of LLaVA-1.5 from 48.4% to 38.2% while maintaining a high recall of 77.7%, successfully overcoming the tendency of traditional exploratory strategies to over-fit language probabilities and achieving an optimal balance between precision and coverage. In POPE testing, MCVP achieves a state-of-the-art F1 score of 88.45% on MiniGPT4 and effectively corrects the over-prediction issues caused by shallow-layer diffusion in DeepseekVL. Comprehensive ablation and visualization analyses confirm that these mechanisms effectively prevent deep-layer feature decay and refocus attention onto target regions. In summary, MCVP accurately captures the evolutionary patterns of hierarchical attention and provides an efficient, training-free approach to suppressing cross-modal hallucinations without compromising general reasoning capabilities, establishing a new theoretical and practical paradigm for white-box hierarchical intervention in large model optimization.

Brain tumor MRI images usually exhibit large variations in tumor scale, strong morphological heterogeneity, and blurred lesion boundaries that are often intertwined with normal brain tissues. Existing detection models still suffer from insufficient local detail extraction, global semantic modeling, and cross-scale feature fusion, which may lead to inaccurate boundary localization, increased missed and false detections, and high deployment costs. To address these issues, this paper proposes GDC-DETR, a high-accuracy and lightweight brain tumor detection model based on RTDETR-r18. The proposed model aims to improve the detection accuracy of complex lesions while reducing the number of parameters and computational cost, making it more suitable for clinical computer-aided diagnosis and lightweight deployment. GDC-DETR is improved from three aspects: backbone enhancement, feature fusion, and efficient feature interaction. First, a gated enhancement module with contextual awareness, named GLCA-CG, is designed. It extracts edge and texture details and long-range contextual information through local and global dual branches, respectively, and adaptively selects discriminative features using a convolutional gating mechanism. This enhances the model’s ability to distinguish small lesions, weak boundaries, and complex backgrounds. Second, a hierarchical local-global attention fusion module, named HLGA-FM, is constructed. By employing parallel patch-aware attention at local and global scales, this module achieves semantic alignment and adaptive fusion between high-level and low-level features, alleviating information dilution, channel redundancy, and semantic deviation caused by traditional feature concatenation, thereby improving cross-scale lesion localization. Finally, a low-resolution lightweight attention-based intra-scale feature interaction module, named LR-AIFI, is proposed. High-resolution features are first mapped into a low-resolution space through anti-aliasing downsampling for attention interaction, and then spatial details are compensated through upsampling and depthwise convolution refinement. As a result, the computational complexity of the main attention term is reduced to approximately 1/16 of the original strategy, achieving a better balance between global modeling capability and computational efficiency. Experiments are conducted on a public brain tumor MRI dataset containing three types of tumors: glioma, meningioma, and pituitary tumor. Under unified training parameters, evaluation metrics, and experimental environments, GDC-DETR is compared with RTDETR series models, YOLO series models, and several brain tumor detection methods. The experimental results show that GDC-DETR achieves an mAP50 of 92.9%, which is 2.7 percentage points higher than the RTDETR-r18 baseline. The precision and recall reach 91.4% and 91.1%, respectively. Meanwhile, the number of parameters is reduced to 15.6 M, representing a 21.6% decrease compared with the baseline, and the computational cost is 47.8 GFLOPs, maintaining a relatively low computational overhead among the mainstream comparison models. Visualization results indicate that, for challenging samples with blurred boundaries, irregular shapes, and high interference between lesions and surrounding tissues, GDC-DETR can more accurately focus on the tumor core region, reduce invalid responses in background areas, and obtain more stable bounding-box localization results. Ablation experiments demonstrate the effectiveness of each proposed module. Introducing GLCA-CG alone improves mAP50 by 1.2 percentage points and significantly reduces the parameter scale. Combining HLGA-FM with GLCA-CG further improves mAP50 to 92.4%. LR-AIFI reduces the computational burden while maintaining stable detection accuracy. The best overall performance is achieved when the three modules are jointly integrated, indicating that detail enhancement, multi-scale semantic fusion, and low-resolution efficient interaction are complementary to each other. Generalization experiments further show that the proposed model achieves mAP50 values of 94.9% and 92.9% on two external MRI datasets, respectively. In the zero-annotation transfer scenario, the mAP50 reaches 73.5%, and after fine-tuning with only 10% and 20% of target-domain annotations, it increases to 86.5% and 88.6%, respectively, demonstrating favorable cross-domain adaptability and few-shot transfer capability. In summary, GDC-DETR effectively alleviates the trade-off between high accuracy and low computational cost in brain tumor MRI detection, improves the sensitivity and localization ability for complex lesions, and reduces the dependence on hardware resources, providing a lightweight reference solution for clinical auxiliary brain tumor detection.

With the rapid development of Large Language Model (LLM), LLM has shown promising application prospects in text classification tasks. Existing text classification methods based on prompt engineering heavily rely on the generative capability of LLM, and often suffer from insufficient generation ability and poor reasoning stability in lightweight LLM scenarios, making it difficult to achieve satisfactory performance. In addition, different hidden layers of LLM exhibit significant differences in representation capability, and intermediate layer representations often contain richer semantic information, even outperforming the last layer outputs in certain tasks. To address these issues, a multi-level semantic fusion for text classification based on LLM is proposed. By extracting and fusing text embedding representations from different hidden layers of lightweight LLM, the proposed method achieves efficient and stable classification performance without fine-tuning LLM. Firstly, prompt engineering is introduced into the construction process of text embedding representations. By designing prompt templates suitable for text classification tasks, original sentences are inserted into specific contextual semantics to guide the model in adjusting the attention distribution when generating hidden layer representations, thereby enhancing the discriminative capability of text embedding representations. Secondly, curvature is employed to characterize the geometric smoothness of hidden layer representations. Hidden layer representations with lower curvature correspond to smoother semantic trajectories and are more suitable as text embedding representations for text classification tasks. By calculating the average curvature of each hidden layer, several hidden layer representations with lower curvature are selected as feature sources for subsequent fusion. Thirdly, intra-layer position-weighted pooling and inter-layer multi-scale convolution fusion are used to obtain more expressive sentence embeddings. The intra-layer position-weighted pooling assigns different weights according to the relative positions of tokens in the input sequence, preserving important semantic information at the tail while also considering contextual information at the beginning of the sequence, thereby generating more complete and stable text embedding representations. The inter-layer multi-scale convolution fusion models relationships among different hidden layers through convolution operations to achieve the fusion of multi-level semantic information. Finally, the classifier is trained using a joint loss function, simultaneously improving classification accuracy, the learning capability for hard samples, and the discriminative of the representation space, thereby optimizing model parameters from multiple perspectives to obtain the final prediction results. Experimental results demonstrate that the proposed method achieves accuracies of 96.11%, 93.48%, 89.92%, 74.15%, and 96.33% on the SST-2, MR, 20NG, Ohsumed, and R52 datasets, respectively, outperforming existing mainstream baseline methods. In addition, Macro-F1 is introduced as a supplementary evaluation metric on the relatively imbalanced Ohsumed and R52 datasets, where the proposed method achieves 62.93% and 73.12%, respectively, demonstrating its effectiveness under complex data distribution conditions. Furthermore, ablation studies, different intra-layer pooling methods analysis, different prompt templates analysis, and different numbers of layers analysis are conducted to verify the rationality and effectiveness of each module design. Sensitivity analysis of loss weights is performed to evaluate the influence of different loss weight variations on classification performance. Different layers performance analysis is performed to explore the relationship between different hidden layer representations and classification performance. In conclusion, the proposed method can effectively extract and fuse LLM embedding representations for text classification, significantly reducing computational overhead while improving the representation quality and discriminative capability, demonstrating strong generalizability and practical applicability.

In scenarios related to human-agent collaboration, the explicit/legible representation and accurate recognition of agent intent serve as vital means to enhance collaborative safety and efficiency, and constitute the foundation for establishing and improving human trust in agents. The core lies in enhancing the legibility of agent behaviors to facilitate effective human understanding. Existing research primarily conveys intent by introducing differentiated behaviors that deviate from the optimal path. While effective in certain contexts, such schemes inherently sacrifice partial execution efficiency. In highly confusing scenarios, they face two critical challenges: 1) In environments characterized by overlapping and dense routes, sacrificing efficiency yields diminishing returns in intent conveyance; 2) Overlapping routes obscure the informational value of the agents' original behavioral sequences, making it difficult for observers to discern their true intentions. Addressing these issues, this study investigates key factors influencing behavioral legibility in complex human collaboration, focusing on action posture, velocity, and distance. Without altering the agents' original action sequences, this paper proposes a multi-dimensional fusion representation mechanism through the multi-dimensional modeling and integration of key state information, including posture, velocity, and distance. Specifically, we design a posture mapping function aligned with human reasoning, utilize Gaussian kernel functions to transform and map velocity and distance representations, and integrate these features via fusion parameters to enhance behavioral legibility. Furthermore, corresponding to this mechanism, we propose an efficient intent recognition scheme based on Bayesian update. By fully leveraging the fused state information, the scheme achieves rapid and accurate identification of agent intent. Consequently, the proposed method—which integrates the mechanism and the scheme—not only enhances the explicit expressive capability of overall behaviors regarding their intent but also improves task execution efficiency by reducing the number of differentiated behaviors required. The method is comprehensively validated across three experimental scenarios: unconstrained, complex obstacle-laden, and highly confusing environments. Experimental results demonstrate that, compared to baseline methods, our approach incorporates additional dimensions of state information, improving the accuracy of agent intent recognition by an average of 44.47%. Moreover, by conveying sufficient intent information through shorter behavioral sequences, the method significantly boosts task execution efficiency, reducing agent execution costs by an average of 42.86%. Ablation studies verify the positive contributions of posture, velocity, and distance to legibility enhancement, with posture identified as the most significant factor. Sensitivity analysis determines the optimal value ranges for color and velocity factor parameters, ensuring stable performance. Additionally, experiments on continuous control tasks confirm the method's applicability in complex engines and real-world engineering. On the algorithmic side, complexity analysis reveals that the inference time increases by only 2.89 milliseconds compared to the base method, satisfying real-time interaction requirements. This study provides a novel pathway for reducing redundant behaviors while enhancing legibility, balancing belief transmission and execution efficiency without disrupting original action sequences. The method can be directly embedded into various baseline approaches to achieve varying degrees of improvement in recognition accuracy. Future wor

Efficient cross-domain resource allocation is the foundation for achieving optimized resource distribution and ensuring service quality in the Space-Air-Ground Integrated Network (SAGIN). However, constrained by the inherent multi-layer highly complex topology of SAGIN and a massive number of differentiated network slicing service requests, traditional flat architectures and static mathematical optimization models struggle to achieve efficient and dynamic cross-domain resource allocation in the face of dynamic and complex problems. Therefore, this paper proposes a hierarchical cross-domain resource allocation method based on deep reinforcement learning (DLCDS), aiming to maximize system utility while ensuring the efficient utilization of underlying physical resources. Firstly, this paper designs a hierarchical collaborative cross-domain framework, establishing a dual-layer collaborative mechanism between the global domain layer and the local domain layer. Secondly, at the global domain layer, a service level agreement (SLA) decomposition algorithm based on the Twin Delayed Deep Deterministic Policy Gradient is proposed. This algorithm leverages deterministic policies within continuous action spaces to achieve the planning and decomposition of SLA constraints across subdomains. At the local domain level, a resource allocation algorithm based on Proximal Policy Optimization is designed. This algorithm constructs an SLA mapping model to achieve the conversion from service objectives to resource requirements, and employs the exploratory advantage of stochastic policies to enhance resource allocation efficiency within the domain. Finally, experimental results demonstrate that, compared to other benchmark algorithms, this method achieves an average 12% improvement in resource utilization and a 19% increase in system utility.

To address the problems of low detection accuracy and high missed detection rate caused by the small size,weak texture features,and severe background interference of unmanned aerial vehicle (UAV) targets in low-altitude complex environments,a lightweight UAV object detection method named EF-YOLO (Efficient Fusion YOLO) based on YOLOv11 is proposed. Considering the limitations of existing models in complex scenarios,including insufficient deep semantic representation, multi-scale feature fusion mismatch, and serious background interference, the YOLOv11 network architecture is systematically optimized. First, a C3K2-MSFE module is introduced into the deep layers of the backbone network. By combining grouped convolution with multi-scale convolution kernels, the module enhances the discriminative ability of weak deep target features while maintaining the stability of the main semantic flow through a linear branch, thereby improving the feature representation capability of small UAV targets in complex backgrounds. Second, an improved multi-scale feature fusion structure named PSG-FPN is proposed. A Soft Nearest Neighbor Interpolation mechanism is adopted during the upsampling stage to alleviate semantic coverage problems in cross-scale feature fusion through adaptive soft coefficients. Meanwhile,GSConvE is employed to optimize the downsampling process,and an additional P2 detection layer is introduced to enhance the perception ability for small-scale UAV targets, thereby improving multi-scale feature alignment. Furthermore, to reduce parameter redundancy and computational overhead in traditional detection heads, a lightweight shared-feature detection head named SFE-Head is designed. By adopting a shared convolutional stem and grouped convolution structure, classification and regression branches can share features,which significantly reduces model parameters and computational complexity while maintaining detection accuracy. Finally,the original C2PSA module in YOLOv11 is improved by introducing a frequency-domain enhanced feedforward structure. Through frequency-domain feature modeling, redundant texture noise in complex backgrounds can be effectively suppressed, thereby improving the response capability and anti-interference performance for weak UAV targets. Experiments are conducted on the DUT Anti-UAV and Det-Fly datasets. The results show that, on the DUT Anti-UAV dataset, EF-YOLO achieves Precision, Recall, mAP50, and mAP50-95 values of 96.76%, 88.16%, 92.59%, and 62.41%, respectively, representing improvements of 4.01%, 3.44%, 3.04%, and 6.18% compared with the original YOLOv11. Meanwhile, the number of model parameters is reduced from 2.58M to 2.27M, and the model size decreases from 5.3 MB to 4.8 MB, while maintaining favorable real-time detection performance under relatively low computational complexity. On the Det-Fly dataset, EF-YOLO achieves mAP50 and mAP50-95 values of 96.4% and 68.06%, respectively, which are 2.39% and 2.16% higher than those of YOLOv11, demonstrating excellent generalization ability and stability under different low-altitude complex scenarios. In addition, ablation experiments verify that the proposed modules provide significant collaborative benefits. Specifically, the C3K2-MSFE module effectively enhances weak deep feature representation, PSG-FPN improves cross-scale semantic alignment, SFE-Head reduces model complexity while maintaining detection accuracy, and the frequency-domain enhancement structure further suppresses complex texture noise. Compared with conventional object detection models, EF-YOLO exhibits more stable detection performance in complex backgrounds, long-distance target scenarios, and low-contrast environments, effectively reducing missed detections and false detections while preserving model lightweight characteristics. Overall, EF-YOLO achieves a favorable balance among detection accuracy, lightweight design, and real-time performance, showing strong engineering deployment potential and providing reliable technical support for low-altitude airspace supervision, edge intelligent monitoring, and UAV security applications.

Reinforcement Learning from Human Feedback (RLHF) is a key technology for achieving professional value alignment in large legal language models. However, existing reviews lack systematic analysis of the specific adaptation mechanisms and challenges of legal scenarios. This paper aims to fill this gap, systematically construct a panoramic view of the research and application of RLHF in legal large language models, and provide a reference for method selection in engineering practice. This article takes the six-dimensional framework of "technical foundation - algorithm evolution - task migration - evaluation system - application challenges - future path" as the main analytical thread, systematically reviewing 80 core papers from top conferences and journals such as NeurIPS, ICML, ICLR, and ACL from 2017 to 2025. Analyze the complete evolution logic of RLHF in the legal field from algorithm adaptation to evaluation system. Analysis shows that the core predicament of legal RLHF lies in the inaccurate reward modeling caused by the conflict of multi-dimensional rewards and the sparsity of expert preferences. The PPO algorithm has insufficient stability in the generation of long text laws, while new algorithms such as DPO and GRPO simplify the process but face the bottleneck of preference data quality. Further research reveals that different legal tasks vary significantly in terms of risk levels, reasoning complexity, and feedback acquisition costs. Therefore, there is no unified optimal solution for the RLHF algorithm. For structured tasks such as the generation of legal documents and summaries of regulations, DPO has good applicability due to its advantages such as a simple training process and relatively high stability. For tasks such as legal reasoning and case analysis that require multi-path argumentation, GRPO uses intra-group relative ordering to construct learning signals and shows great potential in handling complex legal arguments. In high-risk scenarios such as judicial auxiliary decision-making and sentencing recommendations, it is necessary to combine expert feedback, rule constraints, and knowledge retrieval mechanisms to achieve reliable alignment. The evaluation system is evolving from automated metrics to process interpretability and bias auditing. RLHF provides a key evolutionary path for legal large language models from usability to credibility. However, its implementation relies on endogenous rewards and hybrid feedback mechanisms to reduce annotation costs, and the development of reward traceability and process evaluation to meet judicial transparency requirements. Meanwhile, with the development of new methods such as direct preference optimization and group relative strategy optimization, legal RLHF is gradually evolving from the traditional reward model-driven mode to lightweight preference optimization and relative ranking optimization, providing a new technical path for reducing training complexity and enhancing the adaptability to legal scenarios. Existing research indicates that relying solely on a single reward model is insufficient to fully depict the complex trade-off relationships within the legal value system. In the future, it is necessary to further integrate rule knowledge, expert experience, and process feedback information to construct a more stable and interpretable legal alignment mechanism. This paper not only constructs a six-dimensional analysis framework for legal RLHF research, but also further proposes a legal task-oriented RLHF adaptability analysis system, systematically comparing the applicable boundaries and failure mechanisms of algorithms such as PPO, DPO, and GRPO in different tasks such as legal document generation, legal reasoning, and compliance review. In addition, this paper conducts a horizontal comparison of different RLHF methods by combining the risk levels of legal tasks and the characteristics of algorithms, providing a reference basis for algorithm selection and engineering deployment in legal intelligent systems. Research shows that the core contradiction of legal RLHF is not merely the insufficiency of algorithm performance, but rather a structural contradiction among multi-dimensional reward conflicts in legal scenarios, inconsistent expert preferences, and the difficulty in quantifying judicial value. This paper, for the first time, comprehensively reveals the entire process logic of legal RLHF from algorithm adaptation to engineering implementation from four aspects: technological evolution, reward modeling, evaluation system, and judicial governance, providing a systematic research framework and practical guidance for the trustworthy construction of legal large language models.

Globally escalating marine ecological pollution necessitates efficient and precise automated intelligent monitoring and cleaning technologies for underwater litter leveraging autonomous underwater vehicles (AUVs) or underwater robots, which has become an urgent academic consensus in the field of marine environmental protection. To address the inherent physical degradation of underwater visible light imaging—such as color casts, blurring, and low contrast—as well as the bottlenecks of cross-domain heterogeneity caused by varying optical properties of water bodies across different sea areas and the severe scarcity of high-quality fine-grained annotated samples, this study aims to overcome the feature shift and negative transfer phenomena induced by deploying models in unknown water environments. To this end, it proposes an underwater litter object detection framework that merges pixel-level image enhancement with a two-stage robust cross-domain adaptive learning scheme. This framework adopts a design strategy of "step-by-step construction and dynamic alignment," decoupling the pipeline construction into two mutually collaborative training stages. The first stage aims to construct an interference-resistant baseline perception architecture, establishing a solid feature foundation of deep representations for the entire network. To maintain high computational efficiency while circumventing the limitations of complex deep generative networks—which easily introduce spurious edges and grid-like artifacts under small-sample conditions—a low-cost pixel-level physical alignment pipeline is implemented at the data loading layer. Specifically, the input blue-green (BGR) raw images are first transformed into the LAB color space, where the L channel is extracted and subjected to adaptive pixel-level linear stretching to maximize the restoration of edge contrast and fine details. Subsequently, a Channel Stabilization Module compensates for color casts in the A and B channels to robustly suppress underwater-specific cyan-blue artifacts, before inversely mapping the calibrated feature maps back to the standard red-green-blue (RGB) space. On this basis, 30,000 iterations of supervised perceptual training are executed on the source dataset (TrashCan Instance), enabling the RetinaNet detector to acquire high-quality low-level general geometric textures and high-level robust semantic features, thereby establishing a solid perception baseline for multi-source scenarios. The workflow then naturally proceeds to the second stage, which focuses on domain alignment and precision enhancement for cross-domain deployment. To eliminate deep feature shift and response distortion triggered by unknown new water domains, a lightweight Feature Calibration Module (FCM) is symmetrically injected after the multi-scale output layers of the pre-trained backbone network. A differential initial assignment based on the Kaiming normal distribution is directly applied to the internal parameters of the FCM at the very beginning of the fine-tuning process to explicitly induce adaptive dynamic calibration. During the subsequent 10,000 iterations of cross-domain adaptive fine-tuning, to avoid catastrophic forgetting under low-data regimes, this stage completely freezes the feature extractor parameters of the backbone network, serving as a locked low-level feature stabilizer. Concurrently, a differential asymmetrical dual-learning-rate mechanism is deployed, assigning high gradient sensitivity (initial learning rate 0.01) to the newly injected FCM while allocating a smaller step size (initial learning rate 0.002) to the top-level Feature Pyramid Network (FPN) and detection head. Furthermore, an Exponential Moving Average (EMA) technique with a fixed decay rate (λ = 0.9999) is introduced throughout the entire fine-tuning process. It automatically switches to the smoothed EMA weights during the single complete evaluation performed every 1,000 iterations, leveraging a smooth weight loss plane to shield the model from training noise brought by extremely small samples and ensuring a lossless migration of feature space alignment. To guarantee the scientific rigor of the experimental design and its practical deployment reference value, this study introduces completely independent third-party cross-domain underwater datasets for blind generalization validation. Quantitative results demonstrate that while maintaining a lightweight and compact architecture with only 55.86 million parameters, the proposed method achieves a real-time inference speed of up to 44.61 FPS on a single NVIDIA RTX 4080 SUPER GPU, showcasing immense potential for edge-side practical deployment. In in-domain closed-loop testing, the framework significantly improves the bounding box Average Precision (AP) by 5.33 percentage points compared to the traditional RetinaNet baseline model, with a notable jump of 8.97 percentage points in the highly challenging task of detecting small targets characterized by blurred edges and low visibility underwater. In the most rigorous cross-water and cross-dataset generalization blind tests, the proposed method demonstrates remarkable performance gains: on the completely unseen independent J_Litter dataset, the AP_50 of the baseline model is as low as 0.31% due to multi-source interference, whereas the proposed method catapults the AP_50 to 28.66% through dynamic feature calibration; on the DeepPlastic deep underwater dataset, the method successfully elevates the baseline AP_50 from 3.30% to a stable 12.81%. These outcomes robustly validate the scientific validity and synergetic efficiency of the "physical foundation construction" and "dynamic feature calibration" within the two-stage architecture, proving its capability to effectively overcome negative transfer in cross-water detection and highlighting its strong practical generalization potential for real-world engineering deployment in automated marine litter removal.

As a core research task within the fields of artificial intelligence and multimodal understanding, Visual Question Answering (VQA) requires models to jointly comprehend the visual content of images and natural language questions in order to generate accurate answers. Addressing the limitations of existing VQA methods—which typically lack large-scale pre-training and suffer from interference by weakly correlated background elements and redundant regions during multimodal feature modeling—this paper proposes a multi-module collaborative VQA method based on multimodal filtering and spatial-channel attention. Specifically, the proposed method first introduces a Multimodal Filtering Module. This module extracts question feature vectors from specific positions to serve as a global semantic representation; it then performs a Hadamard product interaction with aligned visual features and utilizes a perceptron combined with an activation function to generate normalized filtering gate weights. By element-wise scaling the image features prior to deep-level interaction, this process explicitly suppresses redundant background information, thereby providing a "cleaner" visual input for subsequent deep interactions. Subsequently, a Spatial-Channel Self-Attention Unit is introduced to replace the traditional self-attention architecture. Within a six-layer stacked encoder-decoder framework, this unit reshapes features into 2D maps along the spatial dimension; by combining multi-scale convolutional kernels with global pooling, it generates a spatial attention distribution that effectively captures spatial dependencies between different image regions. Simultaneously, along the channel dimension, it employs pooling and fully connected networks to dynamically adjust the semantic importance of individual channels. Finally, a gating mechanism is utilized to perform a weighted fusion of the spatial-channel-modulated local features with the global context captured by multi-head self-attention, thereby effectively enhancing the discriminative power of the visual features. Building upon this foundation, a Dynamic Modality Fusion Module is constructed to move beyond static fusion mechanisms. After multiple layers of interaction, the model generates attention maps for both visual and textual features, concatenates them, and feeds them into a fully connected classification network (incorporating dropout) to adaptively learn 2D dynamic fusion weight factors. This process ultimately achieves a dynamic weighted integration of multimodal features, with the overall model parameters optimized using a binary cross-entropy loss function. Extensive quantitative experiments conducted on the VQA-v2 and GQA datasets demonstrate that the proposed method achieves significant performance improvements, even without relying on massive-scale cross-modal pre-training data. Specifically, on the VQA-v2 test-dev split, the proposed method achieved an overall accuracy of 71.85%, representing a significant improvement of 1.22% and 2.33% over comparable baseline models—MCAN and BAN—respectively. Notably, the accuracy rates for binary "Yes/No" questions and "Other" question types reached as high as 88.03% and 62.39%, respectively. On the GQA test set—which emphasizes compositional reasoning and relational understanding—the model achieved an overall accuracy of 57.92%, with binary "Yes/No" questions attaining an accuracy of 76.68%. Furthermore, rigorous ablation studies quantitatively validated the efficacy of each core module; the introduction of the multi-modal filtering, spatial-channel attention, and dynamic modality fusion modules yielded absolute performance gains of 0.63%, 0.69%, and 0.52%, respectively. Additionally, an analysis of the impact of model depth confirmed that feature interactions are most comprehensive—and overall model performance reaches its peak—when both the encoder and decoder consist of six stacked layers. In summary, by employing fine-grained feature modeling and dynamic interactions, the proposed method successfully mitigates issues such as severe noise interference, isolated spatial-channel processing, and the poor adaptability of static fusion mechanisms, thereby offering an improved solution for visual-language multi-modal understanding and deep feature representation within specific vertical domains characterized by limited computational resources.