Most existing Chinese spam detection models suffer such problems as inaccurate representation, unsatisfactory detection effect and poor practicality. To address these problems, a capsule network model combining the long-short attention mechanism is proposed here to achieve efficient Chinese spam detection. For text representation, the proposed model uses a multi-channel structure based on the long-short attention mechanism, which can capture complex text features in spam and generate contextual word vectors with more semantic information. For feature mining and classification, the model improves the structure of the traditional capsule network without compromising the classification performance and optimizes the dynamic routing algorithm, so that the model has a high accuracy without reducing the running speed. Experimental results show that the model outperformed the current mainstream methods such as TextCNN, LSTM and even BERT in characterization and detection; and it achieved an accuracy as high as 98.72% on an unbalanced dataset and 99.30% on a balanced dataset.

In recent years, the number of malicious web pages has increased dramatically, posing a great challenge to network security. While current machine learning-based detection methods have emerged as a promising alternative to traditional detection techniques. However, these methods are commonly based on single-modal features or simple stacking of classifiers built on various features. As a result, these techniques are not capable of effectively fusing features from different modalities, ultimately limiting the detection effectiveness. To address this limitation, we propose a malicious web page detection method based on multi-modal learning and pre-trained models. First, in the input stage, the raw URL and HTML tag sequences of web pages are used as input features. To help the subsequent model learn the relationship between the two modalities and avoid information confusion, modal-type encoding, and positional encoding are introduced. Next, a single-stream neural network based on the ConvBERT pre-trained model is used as the backbone classifier, and it learns the representation of multi-modal features through fine-tuning. For the output part of the model, a linear layer based on large margin softmax is applied to the decision-making. This activation function effectively increases the classification boundary and improves the robustness. In addition, a coarse-grained modal matching loss is added to the model optimization objective to assist the models in learning the cross-modal association features. Experimental results on synthetic datasets show that our proposed method outperforms traditional single-modal detection methods in general, and has advantages over baseline models in terms of accuracy and reliability.

Motion capture technology is the use of external devices to perform data recording and posture reproduction of the displacement of human structures. Deep learning algorithms are playing an increasingly important role in motion capture technology as the technology involves data that can be directly understood and processed by computers in terms of dimensional measurements, positioning of objects in physical space, and orientation determination. This paper presents an application of a convolutional neural network system, YOLO-V4, in the field of motion capture. YOLO-V4 system weight files are small and do not require high hardware requirements. It can also be implemented in PyTorch so that it can be deployed on mobile devices, enabling edge devices to run these models as well, relieving the space constraint of immovable signal capture devices and providing the advantages of high accuracy and high detection rate.

Modern physical protection systems (PPSs) play a pivotal role in safeguarding critical infrastructure and maintaining public safety. Yet increasingly complex system architectures and evolving threat landscapes pose significant vulnerability challenges to PPSs. Conventional vulnerability assessment methods predominantly rely on expert knowledge or single-path analysis, which inadequately captures complex inter-component relationships and the impact of uncertainties on PPS vulnerabilities. To bridge this gap, this paper introduces a hybrid analytical framework synergizing complex network theory with fuzzy Petri net (FPN). The proposed method operates through two integrated phases: (1) constructing topological models of PPS using complex network theory to characterize component interrelationships, and (2) incorporating FPN to establish vulnerability propagation models that simulate cascading effects and quantify overall system vulnerability. Compared with conventional methods, the proposed approach demonstrates superior effectiveness in identifying critical vulnerability points within the system, providing a scientifically grounded foundation for enhancing PPS security and implementing risk control measures.