Search Results (71)

Addressing the challenges of dense conceptual content and intricate knowledge relations in computer science textbooks, where traditional pipeline-based information extraction suffers from error propagation and semantic decoupling, this paper proposes a concept-driven joint extraction method termed CDE (Concept-Driven Extraction).First, the model’s ability to focus on domain-specific terminology is enhanced through conceptual priors and attention re-weighting. This is integrated with a predefined schema and structured instruction templates to achieve normalized output for both entities and relations. Second, efficient domain knowledge transfer for computer science textbooks is realized by performing Low-Rank Adaptation (LoRA) fine-tuning on the Qwen3-4B large language model. Finally, the construction of the computer science textbook knowledge graph is accomplished using the Neo4j graph database. On a self-constructed instruction dataset of computer science textbooks, CDE achieves an F1 score of 81.83%, representing an improvement of approximately 2.47 percentage points over the LKD-KGC baseline. This performance significantly surpasses that of traditional pipeline models and existing joint extraction approaches. Experimental results demonstrate that CDE can effectively improve knowledge extraction accuracy in the textbook domain, thereby providing a novel research avenue for the rapid construction of knowledge graphs for computer science educational materials. Full article

Federated data spaces, established through initiatives such as IDSA and GAIA-X, enable organizations to share and monetize datasets under contractual terms. However, enforcing these contracts—particularly detecting unauthorized reuse or modification of datasets—remains an open challenge. We present the Off-Platform Contract Inspector, a component of the PISTIS framework, that implements a modular similarity-detection pipeline combining path-value Jaccard similarity, field-aware type-specific comparisons, and sentence-embedding-based semantic analysis across structured, semi-structured, and unstructured datasets. This contributes as follows: (i) an Inverse Document Frequency (IDF)-weighted structural similarity mechanism that discounts common domain vocabulary via Inverse Document Frequency weighting over the data space catalog, combined with a schema-evidence-gated fusion that reduces false positives from domain vocabulary overlap; (ii) an adaptive threshold optimization mechanism that learns modality-specific fusion weights and decision thresholds via cross-validated grid search; and (iii) a privacy-preserving similarity layer based on MinHash Locality-Sensitive Hashing signatures, Bloom filters with OR folding alignment, and Laplace noise for differential privacy, enabling cross-organizational dataset comparison without exposing raw data. Further, we contribute a threat taxonomy of seven dataset modification types ordered by detection difficulty, and evaluate the system on dataset pairs derived from real-world datasets across three smart-city application domains (Mobility, Energy, Automotive), with controlled augmentations applied to model adversarial behaviors. The IDF-weighted pipeline achieves high precision on intra-domain hard negatives—pairs of different tables from the same data space that share domain vocabulary—where text-similarity baselines produce false positives. The adaptive scheme learns per-modality fusion weights via cross-validated grid search. The privacy-preserving mode operates without accessing raw data and runs noticeably faster than the full pipeline, enabling screening while preserving data confidentiality. Full article

Hypergraph Neural Networks (HGNNs) have demonstrated exceptional capability in modeling high-order correlations; however, their vulnerability to adversarial attacks remains inadequately addressed due to the limited scope of existing security investigations. The prevailing white-box structural attack, HyperAttack, relies exclusively on gradient-derived information and overlooks the semantic affinities between nodes and hyperedges. This oversight limits attack efficacy because gradient signals can be noisy or ambiguous under certain conditions (e.g., saturated regions or local optima), whereas semantic similarities provide complementary cues that help identify hyperedges whose perturbation more reliably alters the target node’s representation. To mitigate this limitation, this paper introduces a semantic enhanced adversarial attack framework for hypergraph neural networks, termed SE-HyperAttack. Specifically, hyperedge features are first aggregated, and semantic similarity scores are computed based on the feature similarity between target nodes and their incident hyperedges to capture latent semantic correlations. These semantic similarity scores are subsequently integrated with integrated gradient scores via a weighted summation scheme, refining the precision of hyperedge selection. Extensive experiments on two datasets demonstrate that the proposed SE-HyperAttack achieves an optimal average attack success rate (ASR) of 79.4%, showing an improvement of 2.6% over HyperAttack. Ablation studies further ascertain that a semantic weight of 30% yields peak performance, beyond which degradation is observed. Notably, the proposed approach preserves computational efficiency commensurate with HyperAttack, incurring negligible additional overhead. These findings substantiate that the integration of semantic information effectively enhances adversarial attack effectiveness on hypergraph neural networks without compromising efficiency. Full article

Automated defect detection in precision agriculture serves as a critical technology for enhancing the quality of agricultural products. Although supervised-only semantic segmentation has demonstrated remarkable performance in citrus surface defect detection, it relies heavily on training with large-scale labeled data, which results in prohibitive acquisition costs. Semi-supervised learning mitigates reliance on labeled data by generating pseudo-labels. However, existing semi-supervised segmentation methods still face challenges. On the one hand, the instability of pseudo-labels and the propagation of noise can mislead the training of semi-supervised models. On the other hand, due to the lack of semantic constraints in feature learning, models often suffer from insufficient feature discriminability when handling complex samples, such as citrus surface defects characterized by similar textures and blurred boundaries. Therefore, this study proposes UP-ETS, a dual-guided semi-supervised semantic segmentation model based on the Mean Teacher–Student framework, specifically designed for the segmentation of complex citrus surface defects. UP-ETS employs Uncertainty Estimation (UE) based on Kullback–Leibler (KL) divergence to quantify the prediction discrepancy between the teacher and student models on blurred and ambiguous pixels. This mechanism guides the model to dynamically adjust weights, thereby reducing noise propagation and enhancing pseudo-label stability under complex citrus surface textures. Prototype Contrastive Learning (PCL) is utilized to align pixel-level features of difficult samples with class prototypes, optimizing the feature discriminability for complex citrus surfaces. Experimental results demonstrate that the UP-ETS model exhibits superior semi-supervised segmentation performance. Notably, at a labeled data ratio of only 1/16, the dice improved from 85.57% to 87.76% compared to the supervised-only baseline. Furthermore, the model shows significant performance enhancements in segmenting difficult samples, such as small targets, complex boundaries, and blurred regions. The results of ablation studies and t-SNE visualization prove the effectiveness of the proposed UE and PCL. These two methods synergistically guide the model to construct a feature space that is better structured and highly discriminative. Furthermore, UP-ETS outperforms various representative semi-supervised segmentation models in terms of segmentation performance, parameters, and inference speed. In cross-dataset validation, the model exhibits robust generalization capabilities, achieving performance comparable to supervised-only methods trained on the full augmented dataset. Consequently, the framework introduced in this study effectively mitigates the heavy dependency on annotated datasets, providing significant practical value for agricultural deployment. Full article

Semantic segmentation of street-view imagery has become an important computational tool for urban morphological analysis and the evaluation of street spatial quality. However, existing methods still struggle in complex urban environments. Major challenges include large variations in building façade scales, degradation of boundary information, and severe class imbalance. These issues limit the ability of current models to capture structurally meaningful urban forms. To address these challenges, this study proposes a high-resolution street-view segmentation framework, termed HieraWaveSeg. The model aims not only to improve pixel-level segmentation accuracy but also to enhance the interpretability of urban morphology through structured representations of street space. Specifically, a Hiera Transformer backbone is employed to capture hierarchical spatial semantics. A Path Aggregation Network is further introduced to strengthen cross-scale feature interaction and improve structural consistency in complex scenes. In addition, a Wave Fusion module based on the Haar wavelet transform is incorporated to preserve fine-grained architectural details by enhancing high-frequency boundary and texture information during decoding. Unlike conventional segmentation approaches that primarily focus on object recognition, this study introduces a morphology-oriented semantic reconfiguration strategy. This strategy reorganizes original categories into functionally meaningful urban units. As a result, the segmentation outputs can be more directly linked to urban morphological indicators, such as façade continuity, spatial enclosure, and interface permeability, thereby improving interpretability in architectural and urban design contexts. To further address class imbalance, a composite loss function combining weighted cross-entropy and Dice loss is adopted, together with a median frequency balancing strategy. Experimental results on the CamVid and Cityscapes datasets demonstrate that the proposed method consistently outperforms several state-of-the-art baselines in both segmentation accuracy and structural preservation. Beyond quantitative improvements, the results indicate that the proposed framework generates more coherent and morphologically meaningful urban representations, supporting further quantitative analysis in urban morphology and architectural studies. Full article

Speech-based non-invasive screening offers a cost-effective and scalable approach for the early detection of Alzheimer’s disease (AD). However, the clinical utility of deep learning models remains severely constrained by the scarcity of labeled speech data in low-resource languages, necessitating cross-lingual transfer learning. Conventional domain adaptation paradigms typically assume semantically consistent feature domains and focus heavily on aligning marginal distributions; however, they suffer catastrophic performance degradation when applied to cross-lingual pathologic speech. By analyzing disease-associated representation vectors within a self-supervised HuBERT space, we uncover a systematic mechanism driving this failure, a phenomenon we term cross-lingual polarity flip, where the direction of disease-relative-to-control feature offsets fundamentally reverses between languages. While prior multilingual studies have largely discarded such dimensional inconsistencies as ungeneralizable noise, a 500-round Monte Carlo stability analysis demonstrates that these flips occur in a highly stable, structural manner across 18.3% of top discriminative dimensions. Leveraging this insight, we introduce Monte Carlo Polarity Flip Calibration (MC-PFC), a few-shot framework designed to explicitly rectify flip orientations. Requiring only five labeled support samples per class from the target domain, MC-PFC robustly estimates direction flips via a separability-weighted ensemble voting mechanism. Evaluated on a strictly held-out Chinese blind test set, MC-PFC achieves an area under the receiver operating characteristic curve (AUC) of 0.871, recovering 99.5% of the performance achieved by a full in-domain trained upper bound (AUC = 0.875). Ablation experiments confirm that direction calibration yields a substantial +0.361 AUC gain, vastly outperforming standard distribution alignment (+0.081). This work establishes a data-efficient paradigm for cross-lingual medical analysis, shifting the clinical AI focus from discarding cross-lingual discrepancies to actively modeling and calibrating them. Full article

In response to the challenges of drastic illumination variations, large differences in fruit scale, and severe occlusion in real-field environments, this paper proposes a lightweight end-to-end detection model, termed EMBS-DETR, for tomato maturity detection. The proposed method is built upon the RT-DETR-R18 baseline framework, retaining the advantages of global modeling and end-to-end detection enabled by the Transformer architecture, while introducing targeted improvements in feature extraction and multi-scale feature fusion. In the feature extraction stage, a C2f-FDConv module is incorporated to enhance the modeling capability of high-frequency fine-grained features, such as the surface texture and color gradients of tomatoes, while reducing redundant parameter overhead. For high-level semantic representation, an improved parameter-free attention mechanism, SimAM-TF, is designed. By jointly modeling neuron energy functions and color-aware modulation, it effectively enhances feature representation under complex lighting and occlusion conditions. For multi-scale feature fusion, a novel EMBS-FPN structure is proposed. Based on bidirectional feature flow and a multi-scale weighted fusion mechanism, this structure integrates multi-branch receptive field modeling with an efficient upsampling strategy, enabling adaptive fusion of P3–P5 feature layers. This design significantly improves representation stability for objects of varying scales while maintaining model lightweight characteristics. To evaluate the proposed method, a real-field tomato maturity dataset was constructed, consisting of 2327 images collected from facility-grown pink large-fruit tomato varieties widely cultivated in North China. According to agricultural industry standards and physicochemical properties, the dataset is categorized into three classes: immature (796 images), turning stage (718 images), and mature (813 images). Experiments were conducted on an Ubuntu 20.04 platform with an NVIDIA GeForce RTX 3080 Ti GPU. The input resolution was set to 640 × 640. Standard evaluation metrics, including Precision, Recall, mAP@0.5, mAP@0.5:0.95, as well as Params, GFLOPs, and Model Size, were used for comprehensive assessment. The experimental results demonstrate that EMBS-DETR achieves 90.9% Precision, 85.7% Recall, 89.9% mAP@0.5, and 79.8% mAP@0.5:0.95. Meanwhile, with only 37.03 M parameters, 25.2 GFLOPs computational cost, and a model size of 46.3 MB, the proposed model maintains low computational and storage overhead, achieving a favorable balance between accuracy and efficiency. Compared with mainstream YOLO-based models, the proposed method demonstrates superior overall performance in complex field environments, providing effective technical support for automated tomato maturity perception and intelligent visual understanding in precision agriculture. Full article

Due to low storage cost and fast query efficiency, cross-modal hashing has attracted considerable interest in multi-modal data retrieval. However, existing hashing methods face several challenges: one major challenge arises from the neglect of both local and non-local neighborhood structural relationships within multi-modal information, which makes it difficult to establish fine-grained semantic consistency associations between heterogeneous modalities. Additionally, the imbalance in the number of training samples limits the improvement of retrieval performance. To address these challenges, a Deep Neighborhood-similarity Preservation Hashing (DNsPH) method is proposed for cross-modal retrieval. To obtain the high-order statistical features of images, we first design a Context-aware Cross-layer Bilinear Fusion Network (C2BF-Net), which uses Long Short-Term Memory (LSTM) to model the context-dependent features of different convolutional layers. Furthermore, the image, text, and semantic labels information are fused through an adaptive weighting strategy to reconstruct the joint semantic similarity matrix to explore the fine-grained neighborhood structure between different modalities. Finally, we introduce a multi-similarity loss based on an adaptive margin to mining and weighting informative sample pairs, to alleviate the impact of sample imbalance on model training, and thereby generate more discriminative hash codes. Extensive experiments performed on the MIRFLICKR-25K and NUS-WIDE datasets demonstrate that DNsPH outperforms state-of-the-art cross-modal retrieval applications. Full article

Underwater image enhancement is critical for marine robotic perception, yet existing methods often face a persistent trade-off between restoration quality, structural reliability, and deployment efficiency. Although lightweight enhancement networks are attractive for resource-constrained underwater platforms, many of them mainly rely on empirical architectural simplification and appearance-oriented objectives, with limited mathematical analysis of complexity reduction, semantic regularization, and optimization coordination. To address this issue, this paper proposes MobileGAN, a lightweight underwater image enhancement framework equipped with dual-reference regularization and a theoretical analysis module. The proposed generator adopts a compact encoder–bottleneck–decoder architecture based on depthwise separable convolutions, which substantially reduces convolutional redundancy while preserving effective restoration capability. A dual-reference feature consistency formulation is introduced to simultaneously constrain the enhanced image toward the high-quality target representation and the degraded-input semantic anchor. In addition, an edge-aware regularization term and a stage-wise dynamic weighting mechanism are incorporated to improve local structure recovery and multi-objective optimization behavior. Beyond architectural design, we provide a mathematical analysis of the proposed framework from three aspects: computational complexity reduction, geometric interpretation of dual-reference regularization, and piecewise optimization properties of stage-wise weighted training. Extensive experiments on the UIEB benchmark demonstrate that MobileGAN achieves a favorable trade-off between enhancement quality and computational efficiency. The proposed method maintains real-time inference with a compact model size while providing competitive structural consistency and detail restoration. These results indicate that MobileGAN is not only a practical deployment-oriented enhancement framework but also an interpretable optimization model with analyzable structural properties. Full article

Accurate individual tree crown (ITC) segmentation from unmanned aerial vehicle (UAV) imagery is important for fine-scale forest inventory, plantation management, and ecological monitoring. However, delineating ITCs in dense plantation environments remains difficult because crowns are strongly adjacent, canopy structures are highly homogeneous, and crown boundaries are often blurred, making it hard for existing methods to preserve both regional integrity and boundary continuity. This study proposes the Perceptual Segment-Anything Model with Multi-head Cross-Parallel Attention (Per-SAM-MCPA), a lightweight and effective framework for fine-grained ITC segmentation in dense plantation scenes. Based on a compact ResNet-50 backbone, the framework integrates perceptual target-aware representation, multi-scale detail enhancement, global contextual modeling, and semantic-boundary collaborative refinement to improve crown discrimination and structural consistency. A perceptual relation module is used to strengthen pixel-level semantic dependency modeling, and a Multi-head Cross-Parallel Attention (MCPA) mechanism is designed to capture long-range contextual interactions through orthogonally decomposed spatial attention, improving global geometric consistency with limited computational overhead. A Composite Constraint Loss (CCL) that combines a weighted cross-entropy loss, a structural similarity loss, and a boundary term based on Hausdorff distance is introduced to jointly optimize region-level segmentation quality and boundary fidelity. Experiments on the Catalpa bungei UAV dataset show that the proposed method achieves an intersection over union (IoU) of 87.3% and an F1-score of 91.0%, outperforming representative baseline methods such as SAM and Mask R-CNN while maintaining an inference speed of 35.7 FPS on a single GPU. These results indicate that Per-SAM-MCPA offers an accurate, efficient, and practical solution for ITC segmentation in dense plantation environments. Full article

The scientific data of the Earth surface system is characterized by multi-source heterogeneity and dynamic correlation, so constructing an efficient data association network and enabling intelligent knowledge services is a hot topic. Nevertheless, confronted with the existing challenges of onerous data acquisition, inadequate precision of data recommendation, excessive time and labor consumption, as well as insufficient semantic reasoning in intelligent question-and-answer (Q&A) systems, we propose an intelligent framework that integrates dynamic optimization and retrieval-augmented generation (RAG) technology to address the problems of strong subjectivity in the setting of edge weight thresholds in association networks and insufficient semantic inference in intelligent Q&A. First, a multidimensional association network is constructed based on metadata features, redundant edge pruning is achieved through dynamic threshold analysis, and key nodes are identified by combining complex network centrality theory to optimize network structure and storage efficiency. Secondly, the RAG-based intelligent Q&A model is designed to transform the association triples into a paragraph-based knowledge base, generate a domain Q&A dataset using a large language model GPT-4o, and fine-tune the word embedding model to improve the semantic representation accuracy. Experiments show that the number of network edges is reduced by about 70% after optimization, and the node importance analysis accurately identifies key data nodes; the fine-tuned model improves each index by 6% on average in the retrieval task, and the Q&A system significantly outperforms the traditional method in terms of indexes such as relevance and completeness. This study provides innovative solutions for the intelligent service of scientific data in Earth surface systems and promotes the deep integration of association networks and generative AI. Full article

The proliferation of generative artificial intelligence (AI) as an autonomous recommendation agent fundamentally challenges traditional paradigms of marketing communication. As AI systems increasingly mediate consumer–brand relationships, understanding how artificial agents construct persuasive discourse—distinct from human communicators—becomes critical for developing effective dual-channel marketing strategies. Grounded in Source Credibility Theory and the Computers Are Social Actors (CASA) paradigm, this study investigates the semantic and structural divergence between AI-generated product recommendations and human influencer marketing messages in social commerce contexts. Employing a mixed-methods computational approach integrating term frequency analysis, TF-IDF weighting, Latent Dirichlet Allocation (LDA) topic modeling, and BERT-based contextualized semantic embedding analysis (KR-SBERT), we examined 330 Instagram influencer posts and 541 AI-generated responses concerning inner beauty enzyme products—a hybrid category combining functional health claims with hedonic beauty appeals—in the Korean social commerce market. AI-generated responses were collected through a systematically designed query protocol with empirically grounded prompts derived from actual consumer search behaviors, and analytical robustness was verified through sensitivity analyses across multiple parameter thresholds. Our findings reveal a fundamental divergence in persuasive architecture: human influencers construct experiential narratives exhibiting message characteristics typically associated with peripheral-route cues (sensory descriptions, emotional testimonials, social context), while AI recommendations employ systematic, evidence-based discourse exhibiting message characteristics typically associated with central-route argumentation (functional mechanisms, ingredient specifications, objective criteria). Topic modeling identified four distinct thematic clusters for each source type: human discourse centers on embodied experience and relational consumption, whereas AI discourse organizes around informational utility and rational decision support. Jensen–Shannon Divergence analysis (JSD = 0.213 bits) confirmed moderate distributional divergence, while chi-square testing (χ² = 847.23, p < 0.001) and Cramér’s V (0.312, indicating a medium-to-large effect) demonstrated statistically significant and substantively meaningful differences. These findings extend CASA theory by demonstrating that AI recommendation agents develop a characteristic “AI communication signature” distinguishable from human persuasion patterns. We propose an integrated Dual-Agent Persuasion Proposition—synthesizing CASA, ELM, and Source Credibility perspectives—suggesting that AI and human recommenders serve complementary functions across different stages of the consumer decision journey—a proposition whose predictions regarding sequential persuasive effectiveness and consumer processing routes await experimental validation. These findings carry implications for AI content strategy optimization, platform design, and emerging regulatory frameworks for AI-generated content labeling. Full article

With the widespread adoption of container technology, its shared kernel architecture has made abnormal file access behavior a key precursor to container escape and lateral attacks, necessitating precise and efficient runtime detection mechanisms. However, existing monitoring methods typically suffer from issues such as insufficient granularity in data collection, limited path semantic modeling capabilities, and low anomaly detection accuracy. To address these challenges, this paper proposes an eBPF-based method for detecting abnormal file access in containers. A lightweight kernel-level monitoring mechanism is constructed to capture access behavior in real time at the system call level, effectively enhancing both the granularity of data collection and the completeness of context. At the feature modeling layer, a multimodal path semantic representation method is designed, combining risk-layer rules and semantic vectorization strategies to enhance the hierarchical expression of path structures and improve context modeling ability. In the detection layer, an attention-enhanced autoencoder model is introduced, achieving high-precision identification of abnormal access behavior and low false-positive monitoring under unsupervised conditions through a path segment attention mechanism and weighted reconstruction loss function. Experiments in real container environments show that the proposed method achieves a recall rate of $82.0\%$, a false-positive rate of $0.79\%$, and a Matthews correlation coefficient of 0.852, significantly outperforming mainstream unsupervised detection methods such as Isolation Forest, One-Class SVM, and Local Outlier Factor. These results verify the advantages of the proposed method in terms of detection accuracy, real-time performance, and system friendliness, providing an efficient and feasible solution for enhancing the detection of unknown attacks in container runtimes. Full article

Text-to-music generation aims to automatically produce audio content with semantic consistency and coherent musical structure based on natural language descriptions. However, existing methods still face challenges in terms of style diversity, rhythmic consistency, and long-term structural modeling. To address these issues, we propose a novel text-to-music generation model, termed MusicDiffusionNet (MDN), which integrates diffusion models with the WaveNet architecture to jointly model musical semantics and temporal structure in a continuous latent space. By decoupling high-level semantic conditioning from low-level audio generation, MDN enhances its ability to model long-range musical structure while improving semantic alignment between text and generated music with stable generation behavior. Building upon this framework, we further design two complementary mixing strategies to improve generation quality and structural coherence. Adaptive Style Mixing (ASM) performs weighted interpolation among stylistically similar music samples in the style embedding space, incorporating key and harmonic compatibility constraints to expand the style distribution while avoiding dissonance. Multi-scale Temporal Mixing (MTM) adopts beat-aware temporal decomposition, mixing, and reorganization across multiple time scales, thereby enhancing the modeling of both local and global temporal variations while preserving rhythmic periodicity and musical groove. Both strategies are integrated into the diffusion process as conditional augmentation mechanisms, contributing to improved learning stability and representational capacity under limited data conditions. Experimental results on the Audiostock dataset demonstrate that MDN and its mixing strategies achieve consistent improvements across multiple objective metrics, including generation quality, style diversity, and rhythmic coherence, validating the effectiveness of the proposed approach for text-to-music generation. Full article

Accurate segmentation of the intercondylar notch of the femur is of great significance for the diagnosis of knee joint diseases, surgical planning, and anterior cruciate ligament (ACL) reconstruction. Among them, the obvious anatomical heterogeneity, the interference of structurally similar tissues, and the blurred boundaries in MRI images make the segmentation of the intercondylar notch challenging. The segmentation of the intercondylar notch is often regarded as a standard semantic segmentation problem, but doing so leaves the inherent high-order internal variation and low-contrast features of its anatomical structure unresolved. We proposed a new Wavelet Multi-scale Contextual Attention Network (WMCA-Net). We have coordinated the Shallow High-frequency Feature Dense Extraction Block (SHFDEB) and Wavelet Split and Fusion Block (WSFB) modules with each other. The SHFDEB intensively extracts high-frequency detailed features at the shallowest layer of the network, while the WSFB effectively splits and fuses features at various resolutions, suppressing noise while better preserving the high-frequency detailed structural information we need. The Multi-scale Depth-wise Convolution Block (MDCB) captures cross-scale features from the narrow intercondylar notch (5–8 mm wide) to the surrounding femoral structure (approximately 50 mm diameter), dynamically adapting to different morphologies, including pathological changes caused by osteophyte formation. The Contextual-Weighted Attention Module (CWAM) establishes long-term semantic associations between fuzzy regions and clear anatomical landmarks by precisely locating uncertain regions through foreground and background decomposition. The Dice Similarity Coefficient of WMCA-Net on the intercondylar notch dataset is 93.16%, and the 95% Hausdorff Distance is 1.42 mm, demonstrating its advanced segmentation performance and good anatomical adaptability. Full article