Symmetry

Video anomaly detection is critical for information security in IoT-based intelligent surveillance, where heterogeneous sensors continuously measure the physical environment and anomalies manifest as deviations from learned normal behavior. Existing methods, however, still struggle to jointly model long-range spatio-temporal dependencies and to fuse heterogeneous multimodal measurements. Deploying such detectors on resource-constrained edge devices remains a further practical barrier. To address these challenges, we propose E-STG-DiffNet, an edge–cloud collaborative multimodal spatio-temporal graph diffusion network that formulates secure IoT surveillance as a network measurement problem. The system employs a Video Swin Transformer to extract multi-scale visual features with linear complexity. A dynamic adaptive spatio-temporal graph convolutional network (ST-GCN) with trainable node embeddings then fuses the heterogeneous sensor measurements in non-Euclidean space, and a masked latent diffusion model (MLDM) combines masked context reconstruction with stochastic denoising for anomaly scoring. A confidence-aware edge–cloud collaborative inference mechanism, supported by knowledge distillation, enables low-latency deployment. On UCSD Ped2, CUHK Avenue, and ShanghaiTech, E-STG-DiffNet reaches frame-level micro-AUC scores of 98.1%, 90.4%, and 78.3%, respectively, competitive with the strongest recent methods on these benchmarks. All results are averaged over three random seeds. Ablation studies confirm that each core component contributes to the overall performance. In the efficiency analysis, the system sustains 26.3 FPS with a mean end-to-end latency of 43 ms (99th percentile: 88 ms), while only about 10% of frames are offloaded to the cloud as compressed latents, about 0.03% of the raw-video uplink traffic. Because the public benchmarks used in our experiments do not contain real non-visual sensor streams, the temperature, vibration, and acoustic modalities are simulated following an established protocol. The reported results therefore primarily validate the video anomaly detection capability of the framework, and validation on real multimodal sensor networks is left to future work.

In this study, the mechanical behavior of continuous-fiber-reinforced rubber composites is investigated experimentally and numerically. Uniaxial tensile tests were performed on the rubber matrix, individual reinforcement fibers, and composite laminates with different fiber orientations and layer configurations. The experimental results were used to calibrate and validate a finite element model developed in ANSYS MAPDL (release 2026 R1), in which the rubber matrix was described using an incompressible Mooney–Rivlin hyperelastic formulation, while the reinforcement was represented by embedded discrete elements with a linear-elastic or a nonlinear elastic cord model. The composite simulations capture the main influence of fiber orientation and laminate structure on the global stress–strain response. The $0^{\circ }$/${90}^{\circ }$ configurations exhibit higher stiffness because fibers aligned with the loading direction transfer load directly, whereas the ${45}^{\circ }$/$-{45}^{\circ }$ configurations show a more compliant response governed by matrix deformation and fiber reorientation. At higher deformation levels, the measured softening was not captured by the model. These differences may be attributed to damage mechanisms such as matrix degradation and fiber–matrix debonding, which are not included in the current formulation. The proposed modeling approach provides a basis for assessing the global mechanical response of such composites and for future extensions that include interfacial damage and fatigue.

Advanced persistent threats are complex, stealthy, and long-lasting attacks that can seriously compromise networked systems. System logs provide critical information for APT detection, and provenance graph analysis is commonly used for log anomaly detection. However, existing graph-based methods build provenance graphs from a single perspective, lacking adequate integration of structural and semantic information among entities. To address this issue, we propose a log anomaly detection method based on dual graph modeling and mutual knowledge distillation. The structural branch captures event interactions among entities, while the semantic branch captures associations implied by node text attributes. Relational graph convolution is applied for representation learning in both views. During training, a mutual knowledge distillation framework enables the two branches to serve as teachers and students for each other, facilitating bidirectional knowledge transfer. A reliability estimator based on noise-perturbation invariance assigns higher weights to samples with more trustworthy teacher outputs. Experiments on three public datasets demonstrate that the proposed method achieves statistically significant improvements in Recall on Wget and Cadets, and in Precision on StreamSpot, while maintaining F1-scores comparable to MAGIC across several datasets. Threshold-independent ROC-AUC evaluation further shows that the observed advantages on Wget and Cadets are also reflected in anomaly ranking performance rather than being solely attributable to threshold selection. Ablation experiments validate the contributions of dual-graph modeling, mutual distillation, and the reliability estimation mechanism.