Text Classification is an important research area in natural language processing (NLP) that has received a considerable amount of scholarly attention in recent years. However, real Chinese online news is characterized by long text, a large amount of information and complex structure, which also reduces the accuracy of Chinese long text classification as a result. To improve the accuracy of long text classification of Chinese news, we propose a BERT-based local feature convolutional network (LFCN) model including four novel modules. First, to address the limitation of Bidirectional Encoder Representations from Transformers (BERT) on the length of the max input sequence, we propose a named Dynamic LEAD-n (DLn) method to extract short texts within the long text based on the traditional LEAD digest algorithm. In Text-Text Encoder (TTE) module, we use BERT pretrained language model to complete the sentence-level feature vector representation of a news text and to capture global features by using the attention mechanism to identify correlated words in text. After that, we propose a CNN-based local feature convolution (LFC) module to capture local features in text, such as key phrases. Finally, the feature vectors generated by the different operations over several different periods are fused and used to predict the category of a news text. Experimental results show that the new method further improves the accuracy of long text classification of Chinese news.

An application based on a microservice architecture with a set of independent, fine-grained modular services is desirable, due to its low management cost, simple deployment, and high portability. This type of container technology has been widely used in cloud computing. Several methods have been applied to container-based microservice scheduling, but they come with significant disadvantages, such as high network transmission overhead, ineffective load balancing, and low service reliability. In order to overcome these disadvantages, in this study, we present a multi-objective optimization problem for container-based microservice scheduling. Our approach is based on the particle swarm optimization algorithm, combined parallel computing, and Pareto-optimal theory. The particle swarm optimization algorithm has fast convergence speed, fewer parameters, and many other advantages. First, we detail the various resources of the physical nodes, cluster, local load balancing, failure rate, and other aspects. Then, we discuss our improvement with respect to the relevant parameters. Second, we create a multi-objective optimization model and use a multi-objective optimization parallel particle swarm optimization algorithm for container-based microservice scheduling (MOPPSO-CMS). This algorithm is based on user needs and can effectively balance the performance of the cluster. After comparative experiments, we found that the algorithm can achieve good results, in terms of load balancing, network transmission overhead, and optimization speed.

This paper proposes the YOLOv8n_H method to address issues regarding parameter redundancy, slow inference speed, and suboptimal detection precision in contemporary helmet-wearing target recognition algorithms. The YOLOv8 C2f module is enhanced with a new SC_Bottleneck structure, incorporating the SCConv module, now termed SC_C2f, to mitigate model complexity and computational costs. Additionally, the original Detect structure is substituted with the PC-Head decoupling head, leading to a significant reduction in parameter count and an enhancement in model efficiency. Moreover, the original Detect structure is replaced by the PC-Head decoupling head, significantly reducing parameter count and enhancing model efficiency. Finally, regression accuracy and convergence speed are boosted by the dynamic non-monotonic focusing mechanism introduced through the WIoU boundary loss function. Experimental results on the expanded SHWD dataset demonstrate a 46.63% reduction in model volume, a 44.19% decrease in parameter count, a 54.88% reduction in computational load, and an improvement in mean Average Precision (mAP) to 93.8% compared to the original YOLOv8 algorithm. In comparison to other algorithms, the model proposed in this paper markedly reduces model size, parameter count, and computational load while ensuring superior detection accuracy.

In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing. Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node. Then a single physical node could provide different services to user. How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays. Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality. This paper propose a Particle Swarm - Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations. In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier. The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.

To tackle the challenge of low accuracy in stock prediction within high-noise environments, this paper innovatively introduces the CED-PSO-StockNet time series model. Initially, the model decomposes raw stock data using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) technique and reconstructs the components by estimating their frequencies via the extreme point method. This process enhances component stability and mitigates noise interference. Subsequently, an Encoder-Decoder framework equipped with an attention mechanism is employed for precise prediction of the reconstructed components, facilitating more effective extraction and utilization of data features. Furthermore, this paper utilizes an Improved Particle Swarm Optimization (IPSO) algorithm to optimize the model parameters. On the Pudong Bank dataset, through ablation experiments and comparisons with baseline models, various optimization strategies incorporated into the proposed CED-PSO-StockNet model were effectively validated. Compared to the standalone LSTM model, CED-PSO-StockNet achieved a remarkable 45.59% improvement in the R² metric. To further assess the model’s generalization capability, this paper also conducted comparative experiments on the Ping An Bank dataset, and the results underscored the significant advantages of CED-PSO-StockNet in the domain of stock prediction.