Federated learning is an emerging distributed learning framework that facilitates the collective engagement of multiple clients in global model training without sharing raw data, thereby effectively safeguarding data privacy. However, traditional federated learning still harbors latent security vulnerabilities that are susceptible to poisoning and inference attacks. Therefore, enhancing the security and model performance of federated learning has become imperative for precisely identifying malicious client behavior by employing gradient noise as a countermeasure to prevent attackers from gaining access to client data through gradient monitoring. This study proposes a robust federated learning framework that combines mechanisms for malicious client detection with Local Differential Privacy (LDP) techniques. The algorithm initially employs gradient similarity to identify and classify potentially malicious clients, thereby minimizing their adverse impact on model training tasks. Subsequently, a dynamic privacy budget based on LDP is designed, to accommodate the sensitivity of different queries and individual privacy requirements, with the objective of achieving a balance between privacy preservation and data quality. Experimental results on the MNIST, CIFAR-10, and Movie Reviews (MR) text classification datasets demonstrate that compared to the three baseline algorithms, this algorithm results in an average 3 percentage points increase in accuracy for sP-type clients, thereby achieving a higher security level with significantly enhanced model performance within the federated learning framework.

The superior performance of Transformer in natural language processing has inspired researchers to explore their applications in computer vision tasks. The Transformer-based object detection model, Detection Transformer (DETR), treats object detection as a set prediction problem, introducing the Transformer model to address this task and eliminating the proposal generation and post-processing steps that are typical of traditional methods. The original DETR model encounters issues related to slow training convergence and inefficiency in detecting small objects. To address these challenges, researchers have implemented various improvements to enhance DETR performance. This study conducts an in-depth investigation of both the basic and enhanced modules of DETR, including modifications to the backbone architecture, query design strategies, and improvements to the attention mechanism. Furthermore, it provides a comparative analysis of various detectors and evaluates their performance and network architecture. The potential and application prospects of DETR in computer vision tasks are discussed herein, along with its current limitations and challenges. Finally, this study analyzes and summarizes related models, assesses the advantages and limitations of attention models in the context of object detection, and outlines future research directions in this field.

This study proposes an improved Artificial Potential Field (APF) algorithm (called FC-V-APF) based on Fuzzy Control (FC) and a virtual target point method to solve the local minimum trap and path redundancy issues of the APF method in robot local path planning. First, a virtual target point obstacle avoidance strategy is designed, and the V-APF algorithm is constructed to help the robot overcome local minimum traps by adding an obstacle crossing mechanism and a target point update threshold. Second, a control strategy based on the cumulative angle sum is proposed to assist the robot in exiting a multi-U complex obstacle area. Subsequently, the V-APF and FC algorithms are combined to construct the FC-V-APF algorithm. The corresponding environment is evaluated using real-time data from the radar sensor and designed weight function, and a fuzzy controller is selected to output the auxiliary force to avoid obstacles in advance. Finally, a simulation environment is built on the Robot Operating System (ROS) platform to compare the path planning performance of the FC-V-APF algorithm with that of other algorithms. Considering path length, running time, and speed curves, the designed FC-V-APF algorithm can quickly eliminate traps, reduce redundant paths, improve path smoothness, and reduce planning time.

Fingerprint recognition is one of the earliest and most mature biometric recognition technologies that is widely used in mobile payments, access control and attendance in the civilian field, and in criminal investigation to retrieve clues from suspects. Recently, deep learning technology has achieved excellent application results in the field of biometric recognition, and provided fingerprint researchers with new methods for automatic processing and the application of fusion features to effectively represent fingerprints, which have excellent application results at all stages of the fingerprint recognition process. This paper outlines the development history and application background of fingerprint recognition, expounds the main processing processes of the three stages of fingerprint recognition, which are image preprocessing, feature extraction, and fingerprint matching, summarizes the application status of deep learning technology in specific links at different stages, and compares the advantages and disadvantages of different deep neural networks in specific links, such as image segmentation, image enhancement, direction field estimation, minutiae extraction, and fingerprint matching. Finally, some of the current problems and challenges in the field of fingerprint recognition are analyzed, and future development directions, such as building public fingerprint datasets, multi-scale fingerprint feature extraction, and training end-to-end fingerprint recognition models, are prospected.

The rapid development of Artificial Intelligence (AI) has empowered numerous fields and significantly impacted society, establishing a solid technological foundation for university informatization services. This study explores the historical development of both AI and university informatization by analyzing their respective trajectories and interconnections. Although universities worldwide may focus on different aspects of AI in their digital transformation efforts, they universally demonstrate vast potential of AI in enhancing education quality and streamlining management processes. Thus, this study focuses on five core areas: teaching, learning, administration, assessment, and examination. It comprehensively summarizes typical AI-empowered application cases to demonstrate how AI effectively improves educational quality and management efficiency. In addition, this study highlights the potential challenges associated with AI applications in university informatization, such as data privacy protection, algorithmic bias, and technology dependence. Furthermore, common strategies for addressing these issues such as enhancing data security, optimizing algorithm transparency and fairness, and fostering digital literacy among both teachers and students are elaborated upon in this study. Based on these analyses, the study explores future research directions for AI in university informatization, emphasizing the balance technological innovation and ethical standards. It advocates for the establishment of interdisciplinary collaboration mechanisms to promote the healthy and sustainable development of AI in the field of university informatization.

Smartphones have become an integral part of modern daily life. The Android operating system currently holds the largest market share in the mobile operating system market owing to its open-source nature and comprehensive ecosystem. Within Android smartphones, the storage subsystem plays a pivotal role, exerting a significant influence on the user experience. However, the design of Android mobile storage systems diverges from server scenarios, necessitating the consideration of distinct factors, such as resource constraints, cost sensitivity, and foreground application prioritization. Extensive research has been conducted in this area. By summarizing and analyzing the current research status in this field, we categorize the issues experienced by users of Android smartphone storage systems into five categories: host-side writing amplification, memory swapping, file system fragmentation, flash device performance, and I/O priority inversion. Subsequently, existing works addressing these five categories of issues are classified, along with commonly used tools for testing and analyzing mobile storage systems. Finally, we conclude by examining existing techniques that ensure the user experience with Android smartphone storage systems and discuss potential avenues for future investigation.

Unmanned Aerial Vehicle (UAV) Multi-Object Tracking (MOT) technology is widely used in various fields such as traffic operation, safety monitoring, and water area inspection. However, existing MOT algorithms are primarily designed for single-UAV MOT scenarios. The perspective of a single-UAV typically has certain limitations, which can lead to tracking failures when objects are occluded, thereby causing ID switching. To address this issue, this paper proposes a Multi-UAV Multi-Object Tracking (MUMTTrack) algorithm. The MUMTTrack network adopts an MOT paradigm based on Tracking By Detection (TBD), utilizing multiple UAVs to track objects simultaneously and compensating for the perspective limitations of a single-UAV. Additionally, to effectively integrate the tracking results from multiple UAVs, an ID assignment strategy and an image matching strategy are designed based on the Speeded Up Robust Feature (SURF) algorithm for MUMTTrack. Finally, the performance of MUMTTrack is compared with that of existing widely used single-UAV MOT algorithms on the MDMT dataset. According to the comparative analysis, MUMTTrack demonstrates significant advantages in terms of MOT performance metrics, such as the Identity F1 (IDF1) value and Multi-Object Tracking Accuracy (MOTA).

Simulators play an indispensable role in an array of scientific fields involving research and development. Particularly in architectural design, simulators provide a secure and cost-effective virtual environment, enabling researchers to conduct rapid experimental analyses and evaluations. Simultaneously, simulators facilitate the acceleration of the chip design and verification processes, thereby conserving time and reducing resource expenditure. However, with the evolutionary advances in processor architectural designs—specifically, the flourishing diversifications featured in dedicated processors—the key role played by simulators in providing substantial feedback for architectural design exploration has gained prominence. This discourse provides an overview of the current developments and applications of architectural simulators, accentuating a few illustrative examples. Analyzing the techniques employed by simulators dedicated to various processors allows for a deeper understanding of the focal points and technical complexities under different architectures. Moreover, this discourse deliberates speculative assessments and critiques of vital aspects of future architectural simulator developments, aspiring to forecast their prospects in the field of processor design research.

An instance segmentation algorithm (DE-YOLO) based on the improved YOLOv8 is proposed. To decrease the effect of complex backgrounds in the images, efficient multiscale attention is introduced, and cross-dimensional interaction ensures an even spatial feature distribution within each feature group. In the backbone network, a deformable convolution using DCNv2 is combined with a C2f convolutional layer to overcome the limitations of traditional convolutions and increase flexibility. This is performed to reduce harmful gradient effects and improve the overall accuracy of the detector. The dynamic nonmonotonic Wise-Intersection-over-Union (WIoU) focusing mechanism is employed instead of the traditional Complete Intersection-over-Union (CIoU) loss function to evaluate the quality, optimize detection frame positioning, and improve segmentation accuracy. Meanwhile, Mixup data enhancement processing is enabled to enrich the training features of the dataset and improve the learning ability of the model. The experimental results demonstrate that DE-YOLO improves the mean Average Precision of mask(mAP_mask) and mAP_mask@0.5 by 2.0 and 3.2 percentage points compared with the benchmark model YOLOv8n-seg in the Cityscapes dataset of urban landscapes, respectively. Furthermore, DE-YOLO maintains an excellent detection speed and small parameter quantity while exhibiting improved accuracy, with the model requiring 2.2-31.3 percentage points fewer parameters than similar models.

Traffic signal control plays an important role in alleviating traffic congestion and improving urban commuting efficiency. In recent years, breakthroughs have been made in traffic signal control algorithms based on deep reinforcement learning using real-time traffic data as input. However, traffic data in real-world scenarios often involve data distortion. Traditional solutions use reinforcement learning algorithms to control signal lights after repairing distorted data. However, on the one hand, the dynamic phases of traffic signal introduces additional uncertainty to distortion repair, and on the other hand, distortion repair is difficult to combine with deep reinforcement learning frameworks to improve performance. To address these issues, a distorted traffic signal control model based on hidden state prediction, HCRL, is proposed. The HCRL model comprises encoding, control, and encoding prediction sub-models. By introducing a hidden state representation mechanism for signalized intersections, the HCRL model can adapt better to deep reinforcement learning frameworks and effectively express the control state of signalized intersections. In addition, the HCRL model uses a special transfer training method to avoid data distortion interference in the control sub-model. Two real datasets are used to verify the impact of data distortion on the intelligent signal light control algorithms. The experimental results show that the HCRL model outperforms the distortion-completion-based traffic signal control models in all distortion scenarios and distortion rates; further, it demonstrates strong robustness against data distortion when compared with other baseline models.

In the context of big data, the rapid advancement of fields such as scientific computing and artificial intelligence, there is an increasing demand for high computational power across various domains. The unique hardware architecture of the Graphics Processing Unit (GPU) makes it suitable for parallel computing. In recent years, the concurrent development of GPUs and fields such as artificial intelligence and scientific computing has enhanced GPU capabilities, leading to the emergence of mature General-Purpose Graphics Processing Units (GPGPUs). Currently, GPGPUs are one of the most important co-processors for Central Processing Units (CPUs). However, the fixed hardware configuration of the GPU after delivery and its limited memory capacity can significantly hinder its performance, particularly when dealing with large datasets. To address this issue, Compute Unified Device Architecture (CUDA) 6.0 introduces unified memory, allowing GPGPU and CPU to share a virtual memory space, thereby simplifying heterogeneous programming and expanding the GPGPU-accessible memory space. Unified memory offers a solution for processing large datasets on GPGPUs and alleviates the constraints of limited GPGPU memory capacity. However, the use of unified memory introduces performance issues. Effective data management within unified memory is the key to enhancing performance. This article provides an overview of the development and application of CUDA unified memory. It covers topics such as the features and evolution of unified memory, its advantages and limitations, its applications in artificial intelligence and big data processing systems, and its prospects. This article provides a valuable reference for future work on applying and optimizing CUDA unified memory.

Industrial time-series forecasting is critical for optimizing production processes and enhancing decision-making. Existing deep learning-based methods often underperform in this context due to a lack of domain knowledge. Prior studies have proposed using mechanistic models to guide deep learning; however, these approaches typically consider only a single mechanistic model, ignoring scenarios with multiple time-series prediction mechanisms in industrial processes and the inherent complexity of industrial time-series (e.g., multiscale dynamics and nonlinearity). To address this issue, this study proposes a Multi-Mechanism-guided Deep Learning for Industrial Time-series Forecasting (M-MDLITF) framework based on attention mechanisms. This framework embeds multiple mechanistic models into a deep industrial time-series prediction network to guide training and integrate the strengths of different mechanisms by focusing on final predictions. As an instantiation of the M-MDLITF, the Multi-mechanism Deep Wiener (M-DeepWiener) method employs contextual sliding windows and a Transformer-encoder architecture to capture complex patterns in industrial time-series. Experimental results from a simulated dataset and two real-world datasets demonstrate that M-DeepWiener achieves high computational efficiency and robustness. It significantly outperforms the single-mechanism Deep Wiener (DeepWiener), classical Wiener mechanistic models, and purely data-driven methods, reducing the prediction error by 20% compared to DeepWiener-M1 on the simulated dataset.

Achieving enhanced detection accuracy is a challenging task in the field of PCB defect detection. To address this problem, this study proposes a series of improvement methods based on PCB defect detection. First, a novel attention mechanism, referred to as BiFormer, is introduced. This mechanism uses dual-layer routing to achieve dynamic sparse attention, thereby reducing the amount of computation required. Second, an innovative upsampling operator called CARAFE is employed. This operator combines semantic and content information for upsampling, thereby making the upsampling process more comprehensive and efficient. Finally, a new loss function based on the MPDIoU metric, referred to as the LMPDIoU loss function, is adopted. This loss function effectively addresses unbalanced categories, small targets, and denseness problems, thereby further improving image detection performance. The experimental results reveal that the model achieves a significant improvement in mean Average Precision (mAP) with a score of 93.91%, 13.12 percentage points higher than that of the original model. In terms of recognition accuracy, the new model reached a score of 90.55%, representing an improvement of 8.74 percentage points. These results show that the introduction of the BiFormer attention mechanism, CARAFE upsampling operator, and LMPDIoU loss function effectively improves the accuracy and efficiency of PCB defect detection. Thus, the proposed methods provide valuable references for research in industrial inspection, laying the foundation for future research and applications.

Advances in computational power and network technologies have driven robots toward miniaturization, swarm intelligence, and autonomous capabilities. Robot software deployed on robotic hardware must integrate diverse modules from low-level device drivers and controls to high-level motion planning and reasoning, resulting in increasingly complex architectures. A communication and programming framework for multi-robot systems—focusing on standardization, modularization, and platformization—can alleviate the complexity of programming robotic software. The development trends in robotic software and hardware architecture show that a swarm robotic system is a multi-domain, heterogeneous, and distributed system composed of computing nodes, actuators, sensors, and other hardware devices interconnected through wired or wireless networks. The heterogeneity of hardware devices makes it difficult to integrate software components into a single framework. This survey summarizes and analyzes existing robotic communication frameworks in terms of ease of use and portability, comparing their core features, such as programming models, heterogeneous hardware support, communication and coordination mechanisms between components, and programming languages. The survey then highlights the technical trends of advanced topics such as real-time virtualization, component orchestration, and fault tolerance. Moreover, this survey focuses on building a next-generation framework on a meta Operating System (OS) foundation, aiming to build a ubiquitous and integrated multi-robot software architecture for human-machine-object interactions.

Target detection technology is advancing, but recognizing online listening behavior remains a challenge. Inaccurate identification of online classroom conduct and high model computation owing to limited human supervision and complex target detection models pose problems. To address this, we employed an upgraded YOLOv8-based method to detect and identify online listening behaviors. This approach incorporates a Bidirectional Feature Pyramid Network (BiFPN) to fuse features based on YOLOv8n, thereby enhancing feature extraction and model recognition accuracy. Second, the C3Ghost module is selected over the C2f module on the Head side to minimize the computational burden significantly. The study demonstrates that the YOLOv8n-BiFPN-C3Ghost model achieved an mAP@0.5 score of 98.6% and an mAP@0.5∶0.95 score of 92.6% on an online listening behavior dataset. The proposed model enhanced the accuracy by 4.2% and 5.7%, respectively, compared with other classroom behavior recognition models. Moreover, the required computation amount is only 6.6 GFLOPS, which is 19.5% less than that of the original model. The YOLOv8n-BiFPN-C3Ghost model is capable of detecting and recognizing online listening behavior with greater speed and accuracy while utilizing lower computing costs. This will ultimately enable the dynamic and scientific recognition of online classroom learning among students.

Video Snapshot Compressive Imaging (SCI) is a computational imaging technique that achieves efficient imaging through hybrid compression in both temporal and spatial domains. In video SCI, the sparsity of the signal and its correlations in the temporal and spatial domains can be exploited to effectively reconstruct the original video signal using appropriate video snapshot SCI algorithms. Although recent deep learning-based reconstruction algorithms have achieved state-of-the-art results in many tasks, they still face challenges related to excessive model complexity and slow reconstruction speeds. To address these issues, this research proposes a reconstruction network model for SCI based on triple self-attention, called SCT-SCI. It employs a multibranch-grouped self-attention mechanism to leverage the correlation in the spatial and temporal domains. The SCT-SCI model comprises a feature extraction module, a video reconstruction module, and a triple self-attention module, called SCT-Block. Each SCT-Block comprises a window self-attention branch, a channel self-attention branch, and a temporal self-attention branch. Additionally, it introduces a spatial fusion module, called SC-2DFusion, and a global fusion module, called SCT-3DFusion, to enhance feature fusion. The experimental results show that on the simulated video dataset, the proposed model demonstrates an advantage in low complexity. It saves 31.58% of the reconstruction time compared to the EfficientSCI model, while maintaining a similar reconstruction quality, thus improving real-time performance.

Steel surface defect detection technology in industrial scenarios is hindered by low detection accuracy and slow convergence speed. To address these issues, this study presents an improved YOLOv8 algorithm, namely a YOLOv8n-MDC. First, a Multi-scale Cross-fusion Network (MCN) is added to the backbone network. Establishing closer connections between the feature layers promotes uniform information transmission and reduces semantic information loss during cross-layer feature fusion, thereby enhancing the ability of the model to perceive steel defects. Second, deformable convolution is introduced in the module to adaptively change the shape and position of the convolution kernel, enabling a more flexible capture of the edge features of irregular defects, reducing information loss, and improving detection accuracy. Finally, a Coordinate Attention (CA) mechanism is added to embed position information into channel attention, solving the problem of position information loss and enabling the model to perceive the position and morphological features of defects, thereby enhancing detection precision and stability. Experimental results on the NEU-DET dataset show that the YOLOv8n-MDC algorithm achieves mAP@0.5 of 81.0%, which is 4.2 percentage points higher than that of the original baseline network. The algorithm has a faster convergence speed and higher accuracy; therefore, it meets the requirements of practical industrial production.

Air pollution is one of the primary challenges in urban environmental governance, with PM_2.5 being a significant contributor that affects air quality. As the traditional time-series prediction models for PM_2.5 often lack seasonal factor analysis and sufficient prediction accuracy, a fusion model based on machine learning, Seasonal Autoregressive Integrated Moving Average (SARIMA)-Support Vector Machine (SVM), is proposed in this paper. The fusion model is a tandem fusion model, which splits the data into linear and nonlinear parts. Based on the Autoregressive Integral Moving Average (ARIMA) model, the SARIMA model adds seasonal factor extraction parameters, to effectively analyze and predict the future linear seasonal trend of PM_2.5 data. Combined with the SVM model, the sliding step size prediction method is used to determine the optimal prediction step size for the residual series, thereby optimizing the residual sequence of the predicted data. The optimal model parameters are further determined through grid search, leading to the long-term predictions of PM_2.5 data and improves overall prediction accuracy. The analysis of the PM_2.5 monitoring data in Wuhan for the past five years shows that prediction accuracy of the fusion model is significantly higher than that of the single model. In the same experimental environment, the accuracy of the fusion model is improved by 99%, 99%, and 98% compared with those of ARIMA, Auto ARIMA, and SARIMA models, respectively and the stability of the model is also better, thus providing a new direction for the prediction of PM_2.5.

With the continuous development of big data and artificial intelligence technologies, knowledge graph embedding is developing rapidly, and knowledge graph applications are becoming increasingly widespread. Knowledge graph embedding improves the efficiency of knowledge representation and reasoning by representing structured knowledge into a low-dimensional vector space. This study provides a comprehensive overview of knowledge graph embedding technology, including its basic concepts, model categories, evaluation indices, and application prospects. First, the basic concepts and background of knowledge graph embedding are introduced, classifying the technology into four main categories: embedding models based on translation mechanisms, semantic- matching mechanisms, neural networks, and additional information. The core ideas, scoring functions, advantages and disadvantages, and application scenarios of the related models are meticulously sorted. Second, common datasets and evaluation indices of knowledge graph embedding are summarized, along with application prospects, such as link prediction and triple classification. The experimental results are analyzed, and downstream tasks, such as question-and-answer systems and recommenders, are introduced. Finally, the knowledge graph embedding technology is reviewed and summarized, outlining its limitations and the primary existing problems while discussing the opportunities and challenges for future knowledge graph embedding along with potential research directions.

Adversarial examples are crucial for evaluating the robustness of Deep Neural Network (DNN) and revealing their potential security risks. The adversarial example generation method based on a Generative Adversarial Network (GAN), AdvGAN, has made significant progress in generating image adversarial examples; however, the sparsity and amplitude of the perturbation generated by this method are insufficient, resulting in lower authenticity of adversarial examples. To address this issue, this study proposes an improved image adversarial example generation method based on AdvGAN, Squeeze-and-Excitation (SE)-AdvGAN. SE-AdvGAN improves the sparsity of perturbation by constructing an SE attention generator and an SE residual discriminator. The SE attention generator is used to extract the key features of an image and limit the position of perturbation generation. The SE residual discriminator guides the generator to avoid generating irrelevant perturbation. Moreover, a boundary loss based on l₂ norm is added to the loss function of the SE attention generator to limit the amplitude of perturbation, thereby improving the authenticity of adversarial examples. The experimental results indicate that in the white box attack scenario, the SE-AdvGAN method has higher sparsity and smaller amplitude of adversarial example perturbation compared to existing methods and achieves better attack performance on different target models. This indicates that the high-quality adversarial examples generated by SE-AdvGAN can more effectively evaluate the robustness of DNN.

Deep-learning models have achieved impressive results in fields such as image classification; however, they remain vulnerable to interference and threats from adversarial examples. Attackers can craft small perturbations using various attack algorithms to create adversarial examples that are visually indistinguishable yet can lead to misclassification in deep neural networks, posing significant security risks to image classification tasks. To improve the robustness of these models, we propose an adversarial-example defense method that combines adversarial detection and purification using a conditional diffusion model, while preserving the structure and parameters of the target model during detection and purification. This approach features two key modules: adversarial detection and adversarial purification. For adversarial detection, we employ an inconsistency enhancement technique, training an image restoration model that integrates both the high-dimensional features of the target model and basic image features. By comparing the inconsistencies between the initial input and the restored output, adversarial examples can be detected. An end-to-end adversarial purification method is then applied, introducing image artifacts during the denoising process. An adversarial detection and purification module is placed before the target model to ensure its accuracy. Based on detection outcomes, appropriate purification strategies are implemented to remove adversarial examples and improve model robustness. The method was compared with recent adversarial detection and purification approaches on the CIFAR10 and CIFAR100 datasets, using five adversarial attack algorithms to generate adversarial examples. It demonstrated a 5-9 percentage points improvement in detection accuracy over Argos on both datasets in a low-purification setting. Additionally, it exhibited a more stable defense performance than Adaptive Denoising Purification(ADP), with a 1.3 percentage points higher accuracy under Backwards Pass Differentiable Approximation(BPDA) attacks.

With the advent of the big data era, the proliferation of information types has increased the requirements for controlled data sharing. Decentralized Attribute-Based Encryption (DABE) has been widely studied in this context to enable fine-grained access control among multiple participants. However, the Internet of Things (IoT) data sharing scenario has become mainstream and requires more data features, such as cross-domain access, transparency, trustworthiness, and controllability, whereas traditional Attribute-Based Encryption (ABE) schemes pose a computational burden on resource-constrained IoT devices. To solve these problems, this study proposes an accountable and verifiable outsourced hierarchical attribute-based encryption scheme based on blockchain to support cross-domain data access and improve the transparency and trustworthiness of data sharing using blockchain. By introducing the concept of Verifiable Credential (VC), this scheme addresses the issue of user identity authentication and distributes the burden of complex encryption and decryption processes to outsourced computing nodes. Finally, using a hierarchical structure, fine-grained data access control is achieved. A security analysis has demonstrated that the proposed scheme can withstand chosen-plaintext attacks. Simulation results on small IoT devices with limited resources using Docker have shown that the proposed scheme has a lower computational overhead than existing schemes. For up to 30 attributes, the computation costs have not exceeded 2.5 s for any of the algorithms, and the average cost is approximately 1 s, making the scheme suitable for resource-constrained IoT devices.

In path planning for mobile robots, challenges arise when dealing with unknown and dynamically changing environments, such as high collision rates with obstacles and susceptibility to local optima. To address these issues, this paper proposes an improved Twin Delayed Deep Deterministic (TD3) algorithm, based on TD3 policy gradient, to enhance the path-planning performance of mobile robots in unknown dynamic environments. First, a Long Short-Term Memory (LSTM) neural network is introduced and combined with the TD3 algorithm. Employing gate structures, historical state information is filtered to perceive the state changes of obstacles within the sensing range for the robot to gain a better understanding of the dynamic environment and movement patterns of obstacles. This enables the mobile robot to accurately predict and respond to the behavior of dynamic obstacles, thereby reducing the collision rate with obstacles. Second, Ornstein-Uhlenbeck(OU) exploration noise is incorporated to facilitate continuous exploration of the surrounding environment, thereby enhancing the robot's random exploration capability. Additionally, a single experience pool is divided into three separate pools-success, failure, and temporary-to improve the sampling efficiency of the effective samples and reduce training time. Finally, simulation experiments are conducted for two different scenarios involving a mixture of dynamic and static obstacles for path planning. A comparative analysis of the experimental results demonstrates that in scenario 1, the proposed algorithm reduces the convergence of the model by 100-200 rounds compared with the Deep Deterministic Policy Gradient (DDPG) and TD3 algorithms. Moreover, it shortens the path length by 0.5-0.8 units and reduces the planning time by 1-4 s. In scenario 2, the proposed algorithm reduces the convergence of the model by 100-300 rounds compared to the TD3 algorithm, shortening the path length by 1-3 units and reducing the planning time by 4-8 s. However, the DDPG algorithm fails as the mobile robot is unable to reach the destination successfully. Therefore, the improved algorithm exhibits superior path planning performance.

Pedestrian detection in crowded scenes is a key technology in intelligent monitoring of public space. It enables the intelligent monitoring of crowds, using object detection methods to detect the positions and number of pedestrians in videos. This paper presents Crowd-YOLOv8, an improved version of the YOLOv8 detection model, to address the issue of pedestrians being easily missed owing to occlusion and small target size in densely populated areas. First, nostride-Conv-SPD is introduced into the backbone network to enhance its capability of extracting fine-grained information, such as small object features in images. Second, small object detection heads and the CARAFE upsampling operator are introduced into the neck part of the YOLOv8 network to fuse features at different scales and improve the detection performance in the case of small targets. Experimental results demonstrate that the proposed method achieves an mAP@0.5 of 84.3% and an mAP@0.5∶0.95 of 58.2% on a CrowdedHuman dataset, which is an improvement of 3.7 and 5.2 percentage points, respectively, compared to those of the original YOLOv8n. On the WiderPerson dataset, the proposed method achieves an mAP@0.5 of 88.4% and an mAP@0.5∶0.95 of 67.4%, which is an improvement of 1.1 and 1.5 percentage points compared to those of the original YOLOv8n.

This paper proposes a path-planning method based on hybrid A^* and modified RS curve fusion to address the issue of unmanned transfer vehicles in limited scenarios being unable to maintain a safe distance from surrounding obstacles during path planning, resulting in collisions between vehicles and obstacles. First, a distance cost function based on the KD Tree algorithm is proposed and added to the cost function of the hybrid A^* algorithm. Second, the expansion strategy of the hybrid A^* algorithm is changed by dynamically changing the node expansion distance based on the surrounding environment of the vehicle, achieving dynamic node expansion and improving the algorithm's node search efficiency. Finally, the RS curve generation mechanism of the hybrid A^* algorithm is improved to make the straight part of the generated RS curve parallel to the boundary of the surrounding obstacles to meet the requirements of road driving in the plant area. Subsequently, the local path is smoothed to ensure that it meets the continuity of path curvature changes under the conditions of vehicle kinematics constraints to improve the quality of the generated path. The experimental results show that, compared with traditional algorithms, the proposed algorithm reduces the search time by 38.06%, reduces the maximum curvature by 25.2%, and increases the closest distance from the path to the obstacle by 51.3%. Thus, the proposed method effectively improves the quality of path generation of the hybrid A^* algorithm and can operate well in limited scenarios.

Human pose estimation is widely used in multiple fields, including sports fitness, gesture control, unmanned supermarkets, and entertainment games. However, pose-estimation tasks face several challenges. Considering the current mainstream human pose-estimation networks with large parameters and complex calculations, LitePose, a lightweight pose-estimation network based on a high-resolution network, is proposed. First, Ghost convolution is used to reduce the parameters of the feature extraction network. Second, by using the Decoupled Fully Connected (DFC) attention module, the dependence relationship between pixels in the far distance space position is better captured and the loss in feature extraction due to decrease in parameters is reduced. The accuracy of human pose keypoint regression is improved, and a feature enhancement module is designed to further enhance the features extracted by the backbone network. Finally, a new coordinate decoding method is designed to reduce the error in the heatmap decoding process and improve the accuracy of keypoint regression. LitePose is validated on the human critical point detection datasets COCO and MPII and compared with current mainstream methods. The experimental results show that LitePose loses 0.2% accuracy compared to the baseline network HRNet; however, the number of parameters is less than one-third of the baseline network. LitePose can significantly reduce the number of parameters in the network model while ensuring minimal accuracy loss.

The rapid development of blockchain technology and the rise of cryptocurrency not only challenge traditional financial and asset management concepts but also bring about a series of privacy and security issues. Coin mixing technology is the primary means of privacy protection in blockchain. It is designed to improve the anonymity of transactions and protect the privacy of user identities. However, this enhanced anonymity also makes cryptocurrencies a tool for criminal activities such as money laundering, theft, and fraud. Therefore, there is an urgent need for a comprehensive review of technology for the identification, detection, and regulation of coin mixing services to support law enforcement agencies and promote cryptocurrency security compliance. This paper first systematically sorts out the technical principles and mechanism classification of coin mixing services, designs a general classification system of coin mixing tools, and discusses in detail the criminal activities that may be caused by coin mixing services. Then this paper focuses on the latest academic research progress of five coin-mixing regulatory technologies based on manual rules, address clustering, entity classification, abnormal transaction detection, and mixing service traceability, and summarizes the existing coin-mixing supervision-related tools in the industry. Finally, the challenges faced by coin mixing regulation are put forward, and future research directions are prospected.

In Unmanned Aerial Vehicle (UAV) aerial photography, targets are usually small targets with dense distribution and unobvious features, and the object scale varies greatly. Therefore, the problems of missing detection and false detection are easy to occur in object detection. In order to solve these problems, a lightweight small object detection algorithm based on improved YOLOv8n, namely PECS-YOLO, is proposed for aerial photography. By adding P2 small object detection layer in the Neck part, the algorithm combines shallow and deep feature maps to better capture details of small targets. A lightweight convolution, namely PartialConv, is introduced to a new structure of Cross Stage Partial PartialConv (CSPPC), to replace Concatenation with Fusion (C2f) in the Neck network to realized lightweight of the model. By using a model of Spatial Pyramid Pooling with Efficient Layer Aggregation Network (SPPELAN), small object features can be captured effectively. By adding Squeeze-and-Excitation (SE)attention mechanism in front of each detection head in the Neck part, the network can better focus on useful channels and reduce the interference of background noise on small object detection tasks in complex environments. Finally, EfficiCIoU is used as the boundary frame loss function, and the shape difference of the boundary frame is also taken into account, which enhances the detection ability of the model for small targets. Experimental results show that, compared YOLOv8n, the mean Average Precision at Intersection over Union (IoU) of 0.5 (mAP@0.5) and the mean Average Precision at IoU of 0.5∶0.95 (mAP@0.5∶0.95) of PECS-YOLO object detection algorithm on VisDrone2019-DET dataset are increased by 3.5% and 3.7% respectively, the number of parameters is reduced by about 25.7%, and detection speed is increased by about 65.2%. In summary, PECS-YOLO model is suitable for small object detection in UAV aerial photography.

Traditional vision Simultaneous Localization And Mapping(SLAM) systems are based on the assumption of a static environment. However, real scenes often have dynamic objects, which may lead to decreased accuracy, deterioration of robustness, and even tracking loss in SLAM position estimation and map construction. To address these issues, this study proposes a new semantic SLAM system, named YGL-SLAM, based on ORB -SLAM2. The system first uses a lightweight target detection algorithm named YOLOv8n, to track dynamic objects and obtain their semantic information. Subsequently, both point and line features are extracted from the tracking thread, and the dynamic features are culled based on the acquired semantic information using the Z-score and parapolar geometry algorithms to improve the performance of SLAM in dynamic scenes. Given that lightweight target detection algorithms suffer from missed detection in consecutive frames when tracking dynamic objects, this study designs a detection compensation method based on neighboring frames. Testing on the public datasets TUM and Bonn reveals that YGL-SLAM system improves detection performance by over 90% compared to ORB-SLAM2, while demonstrating superior accuracy and robustness compared to other dynamic SLAM.

Traditional machine learning algorithms perform well only when the training and testing sets are identically distributed. They cannot perform incremental learning for new categories or tasks that were not present in the original training set. Continual learning enables models to learn new knowledge adaptively while preventing the forgetting of old tasks. However, they still face challenges related to computation, storage overhead, and performance stability. Recent advances in pre-training models have provided new research directions for continual learning, which are promising for further performance improvements. This survey summarizes existing pre-training-based continual learning methods. According to the anti-forgetting mechanism, they are categorized into five types: methods based on prompt pools, methods with slow parameter updating, methods based on backbone branch extension, methods based on parameter regularization, and methods based on classifier design. Additionally, these methods are classified according to the number of phases, fine-tuning approaches, and use of language modalities. Subsequently, the overall challenges of continual learning methods are analyzed, and the applicable scenarios and limitations of various continual learning methods are summarized. The main characteristics and advantages of each method are also outlined. Comprehensive experiments are conducted on multiple benchmarks, followed by in-depth discussions on the performance gaps among the different methods. Finally, the survey discusses research trends in pre-training-based continual learning methods.

To address the challenges of complex and difficult relation extraction caused by long sentences and entity density in biomedical literature, this study proposes an Evidence Path Enhanced Graphical Reasoning Framework (EPE-GR). First, a graph attention mechanism that introduces structured bias (B-GAT) is established to enhance the directionality of information aggregation, combined with mention- and entity-level graph modeling to capture global and local features. Second, a heuristic search is used to focus on evidence sentences, and a path inference structure based on a mask multi-head attention mechanism is constructed to strengthen the correlation between non-neighbor evidence sentences and alleviate the complexity surge caused by fine-grained evidence encoding. Finally, global, local, and path reasoning are collaboratively used to predict semantic relations between entities. Compared to existing methods, EPE-GR demonstrates superior performance on Drug-Mutation Interaction (DMI) dataset and Chemical Induced Disease (CDR) dataset. For DMIs, the proposed framework improves accuracy by 5.65 percentage points in binary classification and 5.13 percentage points in multi-classification. For CDRs, the F1 value increases by 2.85 percentage points. These results confirm that EPE-GR is an effective document-level biomedical relationship extraction method with strong generalization ability. Further experiments highlight the effectiveness of the proposed relationship dependency modeling and evidence path inference mechanism in enhancing inter-sentence relation model inference.

Multi-dimensional time series classification is widely used in industry, medical treatment, finance and other fields; it plays an important role in industrial product quality control, disease prediction, financial risk control and so on. Aiming at the problem that time dependence and spatial dependence of multi-dimensional time series are equally important, and that traditional multi-dimensional time series models only focus on a certain dimension of time or space, this paper proposes a multi-dimensional time series classification model based on the pre-trained recursive Transformer-Mixer PRTMMTSC. The model is based on a Transformer-Mixer module that can fully learn the temporal and spatial correlations of multi-dimensional time series. To further improve the classification performance, inspired by the anomaly detection model, the proposed model combines the pre-trained hidden layer features and the residual features, and uses the PolyLoss loss function for training. To reduce the number of model training parameters, the Transformer-Mixer module in the model is constructed recursively, so that the number of multi-layer trainable parameters is only the number of single-layer Transformer-Mixer parameters. The experimental results on the UEA datasets show that the performance of the proposed model is better than that of the contrast models. Compared with the TARNet model and the RLPAM model, the accuracy of proposed model has increased by 3.03% and 4.69%, respectively. Ablation experiments on the UEA and the IF steel inclusions defect classification further illustrate the effectiveness of the proposed pre-trained method, Transformer-Mixer module, residual information, and the PolyLoss loss function.

Blockchain, as a distributed and trusted database, has gained significant attention in academic and industrial circles for its effective application in the domain of digital copyright protection. Traditional digital copyright protection technologies suffer from issues such as difficulties in tracking infringements, complexities in copyright transactions, and inadequate protection of legitimate rights, which severely hampering the development of digital copyright protection endeavors. The immutability, traceability, and decentralization inherent in blockchain technology provide a highly reliable, transparent, and secure solution to mitigate the risks associated with digital copyright infringement. This overview starts with an introduction to the fundamental principles of blockchain technology. Then, it discusses the latest research findings on the integration of blockchain with traditional copyright protection technologies to address the problems inherent in traditional copyright protection schemes. Further, an evaluation of the practical applications and potential of blockchain is conducted, emphasizing its positive impact on the copyright protection ecosystem. Finally, this overview delves into the challenges and future trends related to blockchain based copyright protection, ultimately aiming to establish a more robust and sustainable blockchain copyright protection system.

To address the issues of identification difficulties, low detection accuracy, misdetection, and missing detection of small target vehicles on traffic roads, this study proposes a road traffic small target vehicle detection model, RGGE-YOLOv8, based on the YOLOv8 algorithm with a large kernel and multi-scale gradient combination. First, the RepLayer model replaces the backbone of the YOLOv8 network, and depthwise separable convolution is introduced to expand the context information, thereby enhancing the ability of the model to capture information on small targets. Second, the Complete IoU loss (GIoU) replaces the original loss function to address the issue where the IoU cannot be optimized when there is no overlap. Subsequently, a Global Attention Mechanism (GAM) is introduced to improve the feature representation capability of the network by reducing information loss and enhancing global interactive information. Finally, CSPNet is incorporated, and the gradient combination feature pyramid is parameterized to ensure that the model achieves a large receptive field and high shape deviation. The experimental results indicate that the mAP@0.5 index of the improved algorithm on the Visdrone dataset and the custom dataset reaches 34.8% and 94.7%, respectively. The overall accuracy of the improved algorithm is 2.2 percentage points and 5.51 percentage points higher than that of the original YOLOv8n algorithm. These findings demonstrate the practicability of the RGGE-YOLOv8 model for small target vehicle detection on traffic roads.

To address the challenges of insufficient computing capacity of end users and the unbalanced distribution of computing power among edge nodes in computing power networks, this study proposes an Unmanned Aerial Vehicle (UAV)-assisted Device-to-Device (D2D) edge computing solution based on incentive mechanisms. First, under constraints involving computing resources, transmission power, and the unit pricing of computing resources, a unified optimization problem is formulated to maximize system revenue. This problem aims to optimize the task offloading ratio, computing resource constraints, UAV trajectory, as well as the transmission power and unit pricing of computing resources for both UAVs and users. The Proximal Policy Optimization (PPO) algorithm is employed to establish user offloading and purchasing strategies. In addition, an iterative strategy is implemented at each time step to solve the optimization problem and obtain the optimal solution. The simulation results demonstrate that the PPO-based system revenue maximization algorithm exhibits superior convergence and improves overall system revenue compared to the baseline algorithm.

In the modern industrial sector, the perception and analysis of text data have become essential for promoting intelligent manufacturing and optimizing production processes. However, industrial text data is typically characterized by high specialization, diversity, and complexity, along with high annotation costs, making traditional large-scale annotation methods unsuitable. Existing few-shot named entity recognition(NER) methods often use prototypical networks to classify entities, where the prototype is the average of the features of all samples belonging to the same category. These methods, however, are highly sensitive to support set data and prone to sample selection bias. To address this, we propose a few-shot named entity recognition model based on distribution calibration—DC-NER（Distribution Calibration-based Named Entity Recognition）. The model innovatively decomposes the task into two phases: span detection and entity classification. During the entity classification phase, a precise distance measurement function is employed to identify similar categories between the source domain and the target domain. Based on this, the distribution of samples in the target domain is corrected to generate more accurate class prototypes. Experimental results on both in-domain dataset (Few-NERD) and cross-domain dataset (Cross-NER) demonstrate that DC-NER significantly outperforms comparative models in terms of F1 score, validating its effectiveness in few-shot named entity recognition.

In light of the dynamic nature of user requirements in edge computing networks, as well as the communication congestion stemming from several users offloading tasks, this study proposes an admission control mechanism for an Unmanned Aerial Vehicle (UAV)-assisted edge computing system. The aim is to maximize service provider revenue while maintaining Quality of Service (QoS) for users. First, a server communication threshold structure is established based on factors such as user channel quality and base station communication bandwidth, mitigating excessively high transmission delays for tasks. Users without a connection to a base station can opt to offload tasks to a UAV or process them directly on their terminal devices. Second, an optimal threshold for UAV task reception is determined considering the limited resources and operating costs of UAVs. UAVs perform preprocessing operations on tasks and offload the preprocessed tasks to the base station to reduce task- processing delays. This stage is modeled as a birth and death process, with matrix geometry methods employed to derive the probability distribution of the system's stable state and the expected benefits for users. Subsequently, the optimal UAV task reception threshold is determined, optimal prices are set, and the UAV revenue is maximized under high task concurrency conditions. The simulation results demonstrate the significant advantages of the proposed solution algorithm in terms of revenue of service providers and user QoS.

Industrial Control System (ICS) that utilizes Digital Twin (DT) technology plays a critical role in enhancing system security, ensuring stable operations, and optimizing production efficiency. The application of DT technology in the field of industrial control security primarily focuses on two key areas: security situation awareness and industrial cyber ranges. DT-based security situation awareness facilitates real-time monitoring, anomaly detection, vulnerability analyses, and threat identification while enabling a visualized approach to managing system security. Similarly, industrial cyber ranges powered by DT technology act as strategy validation platforms, supporting attack-defense simulations for ICSs, assessing the effectiveness of security strategies, enhancing the protection of critical infrastructure, and providing robust training support for personnel. This study analyzes the current security landscape of ICS and advancements in applying DT technology to enhance ICS security situation awareness, with particular emphasis on the technology's contributions to risk assessment. Furthermore, the study explores the optimization capabilities of the DT-based industrial cyber ranges for bolstering ICS security. Through a case study of intelligent power grids, this study validates the critical role of DT technology in ICS security. Finally, the study discusses future directions for the development of DT technology within the ICS security domain.

Acoustic Scene Classification (ASC) aims to enable computers to simulate the human auditory system in the task of recognizing various acoustic environments, which is a challenging task in the field of computer audition. With rapid advancements in intelligent audio processing technologies and neural network learning algorithms, a series of new algorithms and technologies for ASC have emerged in recent years. To comprehensively present the technological development trajectory and evolution in this field, this review systematically examines both early work and recent developments in ASC, providing a thorough overview of the field. This review first describes application scenarios and the challenges encountered in ASC and then details the mainstream frameworks in ASC, with a focus on the application of deep learning algorithms in this domain. Subsequently, it systematically summarizes frontier explorations, extension tasks, and publicly available datasets in ASC and finally discusses the prospects for future development trends in ASC.

With the development of agricultural information technology, a substantial amount of rice planting-related data has been accumulated on the Internet. To address the challenges that farmers face in quickly obtaining accurate information during the planting process, an intelligent question-answering system is constructed based on a knowledge graph, specifically for rice planting. First, relevant data are obtained through manual collection as well as web crawler technology. Natural language processing techniques, such as the named entity recognition model and an intent recognition model, are built in conjunction with front- and back-end technologies to develop an intelligent question-answering system for rice planting. Experimental results show that in the named entity recognition and intent recognition modules, the F1 values of the constructed models reach 89.17% and 96.54%, respectively, which are higher than those of other conventional models. The intelligent rice planting question-answering system, based on knowledge graph, can accurately answer most inquiries farmers encounter during the process of rice planting, facilitating the management and visualization of rice planting knowledge graph data.

Sign language recognition has received widespread attention in recent years. However, existing sign language recognition models face challenges, such as long training times and high computational costs. To address this issue, this study proposes a hybrid deep learning method that integrates an attention mechanism with an Expanded Wide-kernel Deep Convolutional Neural Network (EWDCNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network based on data obtained from a wearable data glove, EWBiLSTM-ATT model. First, by widening the first convolutional layer, the model parameter count is reduced, which enhances computational speed. Subsequently, by deepening the EWDCNN convolutional layers, the model's ability to automatically extract features from sign language is improved. Second, BiLSTM is introduced as a temporal model to capture the dynamic temporal information of sign language sequential data, effectively handling temporal relationships in the sensor data. Finally, the attention mechanism is employed to map the weighted sum and learn a parameter matrix that assigns different weights to the hidden states of BiLSTM, allowing the model to automatically select key time segments related to gesture actions by calculating the attention weights for each time step. This study uses the STM32F103 as the main control module and builds a data glove sign language acquisition platform with MPU6050 and Flex Sensor 4.5 sensors as the core components. Sixteen dynamic sign language actions are selected to construct the GR-Dataset data training model. Under the same experimental conditions, compared to the CLT-net, CNN-GRU, CLA-net, and CNN-GRU-ATT models, the recognition rate of the EWBiLSTM-ATT model is 99.40%, which is increased by 10.36, 8.41, 3.87, and 3.05 percentage points, respectively. Further, the total training time is reduced to 57%, 61%, 55%, and 56% of the comparison models, respectively.

Aiming at non-affine nonlinear multi-Agent systems with full-state constraints, this study investigates an event-triggered formation control strategy with prescribed performance. The study proposes a barrier function-based nonlinear mapping technique to transform full-state constraints into the boundedness of mapped variables, thereby eliminating feasibility conditions in the controller design. Then, it introduces a shift function and a prescribed time-convergent performance function to constrain the formation tracking error. Consequently, the restriction that the initial value of the formation tracking error must be within the performance constraint range is eliminated, thus improving formation performance. The study also designs an event-triggered prescribed performance formation controller to guarantee that Agents achieve the desired formation within a prescribed time and maintain it thereafter, while significantly reducing controller—actuator signal transmissions. Lyapunov stability analysis proves that all signals in the system are semi-globally, uniformly, and ultimately bounded. The theoretical analysis rules out the possibility of Zeno behavior occurring. Finally, numerical simulations verify the effectiveness of the proposed method.

The task of Underwater Visual Object Tracking (UVOT) not only requires dealing with the common challenges in outdoor tracking but also faces many unique difficulties specific to the underwater environment, including but are not limited to, optical degradation and scattering, uneven illumination, low visibility, and hydrodynamics. In these scenarios, directly applying a large number of traditional outdoor scene object tracking methods directly to underwater scenes inevitably leads to performance degradation. To address the above issues, first, an Underwater Image Enhancement (UIE) module inspired by uncertainty is introduced, aimed at specifically improving the quality of underwater images. This method decomposes UIE into distribution estimation and consensus processes and introduces a new probability network to learn the enhancement distribution of underwater images, thereby addressing the bias problem in reference images. These are subsequently applied to an attention-based feature fusion network to propose a target tracking algorithm, called UTransT. The feature fusion network combines self- and cross-attention mechanisms to effectively fuse template and search region features. The experimental results show that on the UTB180 dataset, the success rate of UTransT is 0.8 percentage points higher than that of MixFormer, with the best performance in the comparison algorithm, and normalization accuracy is nearly 1.9 percentage points higher. On the VMAT dataset, the success rate is 1.2 percentage points higher than that of the best-performing Masked Appearance Transfer (MAT) algorithm, with 1.5 percentage points higher normalization accuracy. Moreover, UTransT facilitates real-time tracking at 65 frames per second. These experimental results validate the effectiveness and feasibility of the proposed algorithm in underwater object tracking tasks.

Compared with the task of Mispronunciation Detection and Diagnose (MDD), oral pronunciation quality evaluation requires not only the original data features but also additional features such as fluency, accuracy, and completeness. Therefore, research on oral pronunciation quality evaluations is significantly less developed than that on MDD. Current studies on oral pronunciation quality evaluation are based on a single index: the phonetic score. This study replaces the Squeezeformer model with an improved Squeezeformer-MR model based on a Transformer, which enhances the baseline model by exploiting multiple residual connections to improve the transfer of feature information across layers. In the experiments conducted, the parameter settings are consistent with the baseline model. Using the most stable 24-layer embedding layer, the Pearson Correlation Coefficient (PCC) of the comprehensive score increases by 1.96%, 6.37%, and 1.08% at the phoneme, word, and sentence levels, respectively. Building on this improvement, the WavLM and HuBERT pre-training models are employed to extract corresponding features from the training set. These pre-training features are fused with original Goodness of Pronunciation (GOP) features using a splicing method and trained likewise. The fusion features further enhance performance, with PCC improvements of 2.45%, 7.10%, and 1.89% at the phoneme-, word-, and sentence-level scores compared to the baseline model.

Considering the problem of low accuracy in existing pedestrian detection methods for dense or small target pedestrians, this study proposes a comprehensive improved algorithm model called YOLOv5_Conv-SPD_DAFPN based on You Only Look Once (YOLO) v5, a non-strided Convolution Space-to-Depth (Conv-SPD), and Double Asymptotic Feature Pyramid Network (DAFPN). First, to address the issue of feature information loss for small targets or dense pedestrians, a Conv-SPD network module is introduced into the backbone network, to replace the original skip convolution, thereby effectively mitigating the problem of feature information loss. Second, to solve the problem of low feature fusion rates caused by nonadjacent feature maps not directly merging, this study proposes DAFPN to significantly improve the accuracy and precision of pedestrian detection. Finally, based on Efficient Intersection over Union (EIoU) and Complete-IoU (CIoU) losses, this study introduces the EfficiCIoU_Loss location loss function to adjust and accelerate the frame regression rate, thereby promoting faster convergence of the network model. The algorithm model improved mAP@0.5 and mAP@0.5∶0.95 by 3.9, 5.3 and 2.1, 2.1 percentage points, respectively, compared to the original YOLOv5 model on the CrowdHuman and WiderPerson pedestrian datasets. After introducing EfficiCIoU_Loss, the model convergence speed improved by 11% and 33%, respectively. These innovative improvements have led to significant progress in dense pedestrian detection based on YOLOv5 in terms of feature information retention, multiscale fusion, and loss function optimization, thereby enhancing performance and efficiency in practical applications.