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 objective of image matching is to establish correspondences between similar structures across two or more images. This task is fundamental to computer vision, with applications in robotics, remote sensing, and autonomous driving. With the advancements in deep learning in recent years, Two-Dimensional (2D) image matching algorithms based on deep learning have seen regular improvements in feature extraction, description, and matching. The performance of these algorithms in terms of matching accuracy and robustness has surpassed that of traditional algorithms, leading to significant advancements. First, this study summarizes 2D image matching algorithms based on deep learning features from the past ten years and categorizes them into three types: two-stage image matching based on local features, image matching of joint detection and description, and image matching without feature detection. Second, the study details the development processes, classification methods, and performance evaluation metrics of these three categories and summarizes their advantages and limitations. Typical application scenarios of 2D image matching algorithms are then introduced, and the effects of research progress in 2D image matching on its application domains are analyzed. Finally, the study summarizes the development trends of 2D image matching algorithms and discusses future prospects.

Electric vehicles (EV), when managed centrally by aggregators, can be utilized as flexible and adjustable resources to participate in energy market arbitrage and provide ancillary services to the grid. To optimize this potential, this study introduces an advanced decision-making algorithm for EV aggregators based on hybrid action reinforcement learning. The algorithm uses continuous actions to optimize market bidding decisions and discrete actions to manage the dynamic switching between different power disaggregation strategies, realizing a joint optimization of market bidding and power disaggregation. In addition, the study presents an EV aggregator flexibility modelling method that considers the value of unit flexibility, aiming to maximize the total daily flexibility value while ensuring that the charging demand of each vehicle is met. Simulation results show that dynamic policy switching effectively leverages the strengths of both priority decomposition and proportional decomposition strategies, helping to reduce battery degradation and maintain the flexibility of two-way battery regulation. The proposed algorithm enhances the operational economy of EV charging stations, outperforming algorithms that focus solely on optimizing the bidding decision.

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.

Due to low detection accuracy for small targets in complex environments, along with false and missed detections in mainstream traffic sign detection algorithms, an improved algorithm based on YOLOv8s is proposed. This algorithm uses Pconv convolution in the backbone network and incorporates a C2faster module to achieve a lightweight network structure while maintaining network accuracy. In addition, to better utilize the information between low- and high-level features and enhance the regional context association ability, the SPPFCSPC module is designed as a spatial pyramid pooling module based on the concept of SPPF. In addition, by adding the GAM attention mechanism, the feature extraction capability of the network is further enhanced, and the detection accuracy is effectively improved. To improve the detection ability of small targets, a four-fold downsampling branch is added at the neck of the network to optimize target positioning. In addition, the Focal-EIoU loss function is used to replace the original CIoU loss function to accurately define the aspect ratio of the prediction box, which alleviates the problem of imbalance between the positive and negative samples. Experimental results show that on the CCTSDB-2021 traffic sign dataset, the improved algorithm achieved 86.1%, 73.0%, and 81.2% precision, recall, and mAP@0.5, respectively. Compared with the original YOLOv8s algorithm, increases of 0.8%, 6.3%, and 6.9% were observed, respectively. This algorithm significantly reduces false and missed detections in complex weather and harsh environments, offering better overall detection performance than the comparison algorithm, with strong practical value.

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.

In recent years, remarkable advancements in Artificial Intelligence (AI) across unimodal domains, such as computer vision and Natural Language Processing (NLP), have highlighted the growing importance and necessity of multimodal learning. Among the emerging techniques, the Zero-Shot Transfer (ZST) method, based on visual-language pre-trained models, has garnered widespread attention from researchers worldwide. Owing to the robust generalization capabilities of pre-trained models, leveraging visual-language pre-trained models not only enhances the accuracy of zero-shot recognition tasks but also addresses certain zero-shot downstream tasks that are beyond the scope of conventional approaches. This review provides an overview of ZST methods based on vision-language pre-trained models. First, it introduces conventional approaches to Few-Shot Learning (FSL) and summarizes its main forms. It then discusses the distinctions between ZST and FSL based on vision-language pre-trained models, highlighting the new tasks that ZST can address. Subsequently, it explores the application of ZST methods in various downstream tasks, including sample recognition, object detection, semantic segmentation, and cross-modal generation. Finally, it analyzes the challenges of current ZST methods based on vision-language pre-trained models and outlines potential future research directions.

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).

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.

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.

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.

Cybersecurity threats are becoming increasingly prevalent with the rapid advancement of Internet technologies. Cyberattacks exhibiting high complexity and diversity, are posing significant challenges to existing defense mechanisms. As an emerging concept, situation awareness technology offers new approaches to enhancing cybersecurity defense. However, the current cybersecurity situation awareness methods suffer from limited data feature extraction capabilities and inadequate handling of long-term sequential data. To address these issues, this study proposes a fusion model that integrates Stack Sparse Auto-Encoder (SSAE), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention Mechanism (AM). By utilizing SSAE and CNN to extract data features and enhancing the focus on critical information through the AM in the BiGRU model, the proposed model aims to classify the attack categories of abnormal traffic. In conjunction with the network security situational quantification indicators proposed in this study, the network security situation is quantitatively evaluated and classified. The experimental results demonstrate that the proposed fusion model outperforms traditional deep learning models in various metrics, enabling an accurate perception of the network situation.

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.

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.

In intelligent situational awareness application scenarios, multi-agent angle tracking problems often occur when moving targets must be monitored and controlled. In contrast to traditional target tracking, the angle tracking task entails not only tracking the spatial coordinates of the target, but also determining the relative angles between targets. Existing control methods often exhibit unstable effects and reduced performance when addressing large-scale problems that are susceptible to environmental changes. To address this problem, the present study proposes a solution scheme based on Multi-Agent Reinforcement Learning(MARL). First, a basic model of the multi-agent angle tracking problem is established, a multi-level simulation decision-making framework is designed, and an adaptive method is proposed for this problem. As a stronger multi-agent reinforcement learning algorithm, AR-MAPPO enhances learning efficiency and model stability by dynamically adjusting the number of data reuse rounds. The experimental results show that the proposed method achieves higher convergence efficiency and better angle tracking performance than traditional methods and other reinforcement learning methods in multi-agent angle tracking tasks.

Alzheimer's Disease (AD) is an irreversible neurodegenerative disorder that leads to gradual cognitive decline. The evolution of AD symptoms can be long, with subtle changes in biomarkers in brain regions that are detectable by different neuroimaging modalities; however, early detection is challenging. Given the high complexity of neuroimaging data and the irregularity of brain networks, traditional machine learning, and deep neural network models exhibit many shortcomings, and the development of Computer-Aided Diagnostic(CAD) models based on Graph Neural Network (GNN) can be beneficial for probing biomarkers and analyzing neuroimaging patterns in non-Euclidean space. First, a detailed investigation and overview of AD prediction based on GNN classification methods is carried out. Subsequently, an analysis is conducted from the two perspectives of single- and multi-modal data, with a focus on discussing and analyzing the processes of data extraction, brain network modeling, feature learning, and information fusion within the context of single- and multi-modal data applications. A performance evaluation is provided for certain methods. Finally, the primary challenges and future research directions for the application of GNNs in AD diagnosis are outlined to provide beneficial suggestions for further research on AD-assisted diagnosis.

In the case of detection tasks in low-light environments, owing to the influence of unfavorable factors, such as low brightness, low contrast, and noise, missed and wrong detections can occur. Hence, a low-light object detection algorithm based on image adaptive enhancement is proposed. Combining conventional image processing methods with deep learning, an image adaptive enhancement network is designed, where multiple adjustable filters are combined in cascade to gradually enhance the input low-light image, and the adjustment parameters of each filter are predicted using a convolutional neural network based on the global information of the input image. The adaptive enhancement network is combined with the YOLOv5 object detection network for end-to-end joint training such that the image enhancement effect is more conducive to object detection. As the low-light object detection process is susceptible to missed detection, the channel attention mechanism SE-Net is improved, and a feature enhancement network is designed and embedded into the end of the Neck region of the YOLOv5 network to reduce the loss of information about potential target features caused by the process of fusion of network features. Experimental results show that the proposed algorithm achieves a detection accuracy of 77.3% on the low-light dataset ExDark, which is 2.1 percentage points higher than that afforded by the original YOLOv5 object detection network, and its detection speed reaches 79 frame/s, which affords real-time detection.

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.

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.

The existing Dung Beetle Optimization(DBO) algorithm has the disadvantages of poor search accuracy and insufficient global search ability, thereby easily falling into local optima. This paper proposes a multi-strategy improved dung beetle optimization algorithm that uses a chaotic opposition-based learning strategy to initialize the dung beetle population, whereby dung beetle individuals are evenly distributed in solution space and population diversity is improved. The golden sine strategy with a nonlinear weight is introduced to improve the ball-rolling behavior and coordinate the global search and local mining ability of the algorithm. Foraging behavior is improved by referring to the position update strategy of the sparrow search algorithm, which brings the population close to the optimal position and improves convergence speed and algorithmic accuracy. Stealing behavior is improved by introducing a piecewise function, which benefits the population in the full global exploration in the early iteration stages, to avoid premature convergence of the algorithm. The Cauchy-Gaussian mutation strategy with a nonlinear weight is used to randomly perturb the current optimal position and guide the algorithm to jump out of the local optimal position. The proposed algorithm is compared with five optimization algorithms using 23 benchmark functions, 12 CEC2022 test functions, and two engineering optimization problems. The experimental results show that the proposed algorithm is superior to the other algorithms and ranks first among at least 21 benchmark functions, 10 CEC2022 test functions, and two engineering optimization problems. Compared with the original dung beetle optimization algorithm, the proposed algorithm exhibits significant improvements in convergence accuracy, convergence speed, global search ability, and stability.

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.

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.

In training medical image noise annotation data, the prevailing approach involves partitioning the noise-labeled dataset based on training loss to filter out the noise-labeled samples. However, this method faces two pressing issues that require resolution: first, filtering out noise samples while retaining difficult samples with similar loss distributions as much as possible, and second, enhancing sample utilization and uncovering valuable information embedded in noise samples to alleviate model overfitting. This study proposes a Sample Distribution Guided Noise Robust Learning strategy (SGRL) comprising sample partitioning and semi-supervised contrastive classification to address these challenges. A straightforward yet effective sample selection method called a noise filter method is introduced to distinguish informative, difficult samples from detrimental noise samples more accurately. Additionally, an enhanced matching contrastive network is proposed to train using all samples, yielding a noise-robust classification model. Contrastive learning is utilized as a supplement to counter the memorization of noise labels and improve screening accuracy. The experimental results demonstrate significant performance improvement of the proposed method across dust-induced pneumoconiosis chest X-ray datasets with noise ratios of 5%, 10%, 20%, and 40%. Compared with existing state-of-the-art methods, the screening accuracy of this method increased by an average of 5.88, 7.05, 7.59, and 6.19 percentage points, validating the effectiveness of the proposed improvement method.

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.

In computer vision, Neural Radiance Fields (NeRF) define processes that use spatial coordinates or other dimensions, such as time and camera pose, as input and simulate the objective function through a Multi-Layer Perceptron (MLP) network to generate the target scalar (color and depth). NeRF reconstructs 3D scenes well but blurs or distorts different resolutions and trains them slowly. To solve these two issues, this study proposes a NeRF 3D reconstruction method based on cone tracking and network decomposition. First, the cone-tracking method is used to project a cone for each pixel; the projected cone is cut into a series of cones, characterized along the cone, and the blur or artifact effect is reduced by efficiently rendering the anti-aliasing cone. To shorten the training time, the neural network of the original NeRF receiving five-dimensional data is decomposed into two networks using the network decomposition method, which effectively shortens the training time. Experimental results show that the proposed method improves the Peak Signal-to-Noise Ratio (PSNR) by 14.4%-24.6% compared with NeRF, F²-NeRF, and other algorithms in NeRF_Synthetic, LLFF, and Multiresolution datasets. The training time is also reduced, which allows the reconstruction of richer detailed features, better visual effects, and faster training speed.

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.

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.

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.

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.

Considering the computing requirements of a large number of delay-sensitive and computation-intensive tasks in the information network environment, Mobile Edge Computing (MEC) and its computation offloading technology provide an effective solution. Therefore, a cost optimization algorithm is designed for task offloading strategies in resource-constrained mobile edge systems. First, a multi-user and multi-server network scenario is constructed based on the basic data structure of the system, and a minimum cost optimization model, including penalty terms, is established based on optimization indicators such as latency and energy consumption. An Improved Artificial Hummingbird Algorithm (IAHA) is further proposed to adaptively adjust and optimize the structure and optimization method of the original algorithm, and an emergency avoidance strategy is introduced to achieve a high degree of fit between the system model and algorithm mapping, thereby providing a fast and accurate solution to the model problem and obtaining the optimal offloading strategy for the system. Finally, the application strategy is deployed to reduce system costs and enhance user service experience. The simulation results show that the proposed improved algorithm can effectively reduce system costs and has outstanding convergence performance and optimization accuracy when solving high-dimensional complex models. Under specific experimental conditions, this improved algorithm reduced system costs by 20.79% to 65.39%, respectively, compared with some classic metaheuristic and typical new swarm intelligence algorithms, and the average system cost is 66.98% less than those of local computing strategies with the proposed task offloading algorithm.

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.

Natural Language Processing (NLP) models the Chinese Named Entity Recognition (NER) task as a sequence annotation task and maps each character in the text to a label. Each character is relatively independent and has limited information. Therefore, the addition of vocabulary information to the NER field can solve the problem of the lack of connections between characters. To address the challenges of existing Chinese NER models that require additional vocabulary construction, employ a cumbersome extraction process of vocabulary information, and have difficulties integrating information due to different sources of word-level embedding, this study proposes a Chinese NER model based on Wobert and adversarial learning named ALWAE-BiLSTM-CRF. Unlike traditional pre-training models, the Wobert pre-training model segments the text in advance (i.e., during the pre-training stage), thereby fully learning information at both the word and character levels. Accordingly, the proposed model obtains character word vectors through the Wobert pre-training model and then uses the Wobert word splitter to obtain the existing vocabulary vector in the pre-training model. The proposed model next uses the BiLSTM model to obtain the temporal information of the two. The model then utilizes a multi-head attention mechanism to integrate vocabulary-level information elements into the character word vector while simultaneously generating adversarial samples through adversarial learning attacks to enhance model generalization. Finally, the proposed model utilizes a Conditional Random Field (CRF) layer to constrain the results and obtain the best prediction sequence. The study conducted comparative and ablation experiments on the Resume and self-built Porcelain datasets in the field of porcelains, the results show that the ALWAE-BiLSTM-CRF model achieves 97.21% and 85.7% F1 values on the two datasets, proving its effectiveness in the Chinese NER task.

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.

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.

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.

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.