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.

Text similarity calculation is a part of natural language processing and is used to calculate the similarity between two words, sentences, or texts in many application scenarios. Research on text similarity calculation plays an important role in the development of artificial intelligence. Text similarity calculation has conventionally been based on character string surfaces. With the introduction of word vectors, text similarity calculation can be modeled and calculated based on statistics and deep learning, in addition to combining it with pre-trained models. First, text similarity calculation methods can be divided into five categories: character string-based, word vector-based, pre-trained model-based, deep learning-based, and other methods. Each category is briefly introduced. Subsequently, according to the principles of the different text similarity calculation methods, common methods such as the edit distance, Hamming distance, bag of words model, Vector Space Model (VSM), Deep Structured Semantic Model (DSSM), and Simple Contrastive learning of Sentence Embedding (SimCSE) are discussed. Finally, commonly used data sets and evaluation criteria for text similarity calculation are sorted and analyzed, and the future development of text similarity calculation is prospected.

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.

The deep integration of knowledge graphs with education has promoted the development of smart education. However, there is a lack of literature on educational knowledge graphs currently, necessitating its improvement with regard to research normativity and content perspective. Four conclusions are presented from a systematic literature review of 55 important Chinese journal articles from the previous decade. First, the development of educational knowledge graphs requires five key technologies: ontology construction, knowledge extraction, knowledge representation, knowledge fusion, and knowledge reasoning. Deep learning methods are becoming a popular research topic in this context. Second, in the context of applicability, the educational knowledge graphs cover six application scenarios: personalized learning recommendations, intelligent Question-Answering (Q&A), teaching resource management, intelligent search, intelligent learning diagnosis, and classroom teaching analysis, and the horizon of applications is continuously expanding. Third, regarding application effects, the educational knowledge graphs promote personalized learning and fragmented ubiquitous learning of students while improving their learning performance as well as professionalism of teachers. Fourth, the education knowledge graphs suffer from several problems and challenges, such as single data modality, lack of quality datasets, low level of automation and borderline technology, high level of difficulty in knowledge modeling, insufficient competence care, lack of interoperability standards, and low rate of educational adoption. Hence, for further insight into the study, future research should refine the theory and establish standards, optimize techniques, achieve accurate modeling, and strengthen applications and lifting effects.

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.

In view of missed and false detection phenomena caused by numerous small target instances and occlusions among targets in drone images, this paper proposes a lightweight small target detection algorithm for Unmanned Aerial Vehicle (UAV) images based on an improved YOLOv8. The Triple Feature Encoder (TFE) and Scale Sequence Feature Fusion (SSFF) modules are introduced in the neck to enhance the ability of the network to extract features at different scales. Furthermore, a Small Object Detection Head (SMOH) is designed and fused with the improved neck feature extraction network, and an additional detection head is also introduced to reduce the loss of small target features and enhance the recognition ability of the network for small targets. Additionally, considering the defects of Complete Intersection over Union (CIoU), a regression loss function, Wise-Inner-MPDIoU, is proposed by combining Wise-IoU, Inner-IoU, and Minimum Point Distance based IoU (MPDIoU). Finally, to realize the lightweight application requirements of the algorithm in mobile and embedded systems, amplitude-based layer-adaptive sparse pruning is performed to further reduce the model size while ensuring model accuracy. Experimental results demonstrate that, compared to the original YOLOv8s model, the improved model proposed in this paper improves mAP@0.5 by 6.8 percentage points, while reducing the number of parameters, amount of computation, and model size by 76.4%, 17.1%, and 73.5%, respectively. The proposed model is lightweight, improves detection accuracy, and has strong practical significance.

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.

This study proposes a Common and Differential Cross-Attention Module-Bird's-Eye View (CDCAM-BEV) algorithm that combines 4D millimeter-wave radar and vision fusion to improve target detection accuracy for pedestrian and vehicle target recognition and localization in autonomous driving scenarios. First, a radar cylinder network is designed to encode the 4D radar point cloud into a pseudo image and convert the monocular image into a Bird's-Eye View (BEV) feature through Orthogonal Feature Transformation (OFT). Second, based on the cross-attention mechanism, a Common Information Extraction Module (CICAM) and a Differential Information Extraction Module (DICAM) are used to fully explore the common and differential information between radar and images. Finally, a BEV feature fusion module is designed based on CICAM and DICAM to achieve feature-level fusion of image and radar information in the BEV space. Experiments are conducted on the VOD dataset, and the CDCAM-BEV algorithm is compared with five other 3D object detection algorithms. The experimental results show that CDCAM-BEV achieves better detection performance in multiple modes. In the 3D mode, the average detection accuracy of CDCAM-BEV is 3.65 percentage points higher than that of the second ranked Part-A²; in the BEV mode, it is 5.04 percentage points higher than that of the second ranked PointPillars; in the Average Directional Similarity (AOS) mode, it is 2.62 percentage points higher than that of the second ranked Part-A². These results show that CDCAM-BEV exhibits excellent performance in all modes, effectively fusing images and 4D radar point cloud features, which significantly improves the accuracy and reliability of object detection.

Deep learning-based object detection has significantly improved the detection of medium and large targets. However, when detecting small objects, traditional algorithms often face challenges such as missed detections and false positives owing to the inherent issues of small scale and complex backgrounds. Therefore, this study aims to enhance the accuracy of small object detection by improving the YOLOv8 model. First, the convolutional module in the backbone is replaced with the RFAConv module, which enhances the ability of the model to process complex images. Second, a Mixed Local Channel Attention (MLCA) mechanism is introduced in the neck part, allowing the model to fuse features from different layers more efficiently while maintaining computational efficiency. Third, the Detect head of YOLOv8 is replaced with the Detect_FASFF head to address the inconsistency between different feature scales and improve the ability of the model to detect small objects. Finally, the Complete Intersection over Union (CIoU) loss function is replaced with the Focaler-IoU loss function, enabling the model to focus more on small objects that are difficult to locate precisely. Experimental results show that the improved model increases mAP@0.5 by 4.8 percentage points and mAP@0.5:0.95 by 3.0 percentage points on the FloW-Img dataset, which is sparse in small objects. On the VisDrone2019 dataset which has a high density of small objects, mAP@0.5 increases by 5.9 percentage points and mAP@0.5:0.95 improves by 4.0 percentage points. In addition, generalization comparison experiments are conducted on the low-altitude dataset AU-AIR and the pedestrian-dense detection dataset WiderPerson. The optimized model significantly improves the accuracy of small object detection compared with the original model and expands its applicability.

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.

To satisfy the performance requirements for robot path planning, an algorithm integrating improved A^* algorithm and improved Dynamic Window Approach(DWA) is proposed, which shortens the path length and improves the searching efficiency and path smoothness. To combat the challenges of the traditional A^* algorithm in complex scenarios, a new heuristic function is designed based on Manhattan distance and the diagonal distance. The weights are assigned dynamically, and the global shortest path and the least searching time are obtained. Next, an improved search strategy based on the 8-neighborhood is proposed, which involves dynamically assigning the optimal search direction to the current node, thus improving the searching efficiency and reducing the time consumption compared to the traditional 8-neighborhood 8-direction search method. Subsequently, the Floyd algorithm is employed to remove redundant nodes, reduce the steering times, and shorten the path distance. Additionally, the traditional DWA faces certain challenges; for instance, the path is not globally optimal, the path planning may fail, or the path length may increase. To solve these problems, a keypoint densification strategy is proposed to modify the deflective path. Finally, the proposed improved A^* algorithm and fusion algorithm are compared with existing methods. The simulation results show that the improved A^* algorithm can generate the shortest global path in complex environments, reducing the average steering time by 16.3% and shortening the average path searching time by 55.66%. For the fused algorithm, the average path length and average runtime shorten by 6.1% and 14.7% in the temporary obstacle environment, respectively, and shorten by 1.6% and 39.8%, respectively, in the moving obstacle environment.

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.

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 TAPDataset pedestrian detection dataset is used in this study to address the issues of low detection accuracy, large number of algorithm parameters, and limitations of existing public datasets for small target detection in current scenic pedestrian detection. This dataset addresses the deficiencies of existing datasets regarding small target detection. Based on the YOLOv8 algorithm, a new model with high detection accuracy and low hardware requirements, called YOLOv8-L, is proposed. First, the lightweight convolution module DepthSepConv is introduced to reduce the number of parameters and computations of the model. Second, the BiFormer attention mechanism and CARAFE upsampling operator are used to enhance the model's semantic understanding of images and information fusion capability, significantly improving detection accuracy. Finally, a small target detection layer is added to extract more shallow features, effectively improving the model's performance for small target detection. The effectiveness of the algorithm is verified using the TAPDataset, VOC 2007, and TAP+VOC datasets. The experimental results show that compared with YOLOv8, the number of model parameters is reduced by 18.06% on the TAPDataset with unchanged FPS, mAP@0.5 improves by 5.51%, and mAP@0.5∶0.95 improves by 6.03%. On the VOC 2007 dataset, the number of parameters is reduced by 13.6%, with mAP@0.5 improving by 3.96% and mAP@0.5∶0.95 improving by 6.39%. On the TAP+VOC dataset, the number of parameters is reduced by 14.02%, with mAP@0.5 improving by 4.49% and mAP@0.5∶0.95 improving by 5.68%. The improved algorithm demonstrates stronger generalization performance and can be better applied to scenic pedestrian detection tasks.

Text classification is a basic task in the field of natural language processing and plays an important role in information retrieval, machine translation, sentiment analysis, and other applications. However, most deep learning models do not fully consider the rich information in training instances during inference, resulting in inadequate text feature learning. To leverage training instance information fully, this paper proposes a text classification method based on contrastive learning and attention mechanism. First, a supervised contrastive learning training strategy is designed to optimize the retrieval of text vector representations, thereby improving the quality of the retrieved training instances during the inference process. Second, an attention mechanism is constructed to learn the attention distribution of the obtained training text features, focusing on adjacent instance information with stronger relevance and capturing more implicit similarity features. Finally, the attention mechanism is combined with the model network, fusing information from adjacent training instances to enhance the ability of the model to extract diverse features and achieve global and local feature extraction. The experimental results demonstrate that this method achieves significant improvements on various models, including Convolutional Neural Network(CNN), Bidirectional Long Short-Term Memory(BiLSTM), Graph Convolutional Network(GCN), Bidirectional Encoder Representations from Transformers(BERT), and RoBERTa. For the CNN model, the macro F1 value is increased by 4.15, 6.2, and 1.92 percentage points for the THUCNews, Toutiao, and Sogou datasets, respectively. Therefore, this method provides an effective solution for text classification tasks.

Large Language Models (LLMs) have made significant progress in dialogue, reasoning, and knowledge retention. However, they still face challenges in terms of factual accuracy, knowledge updates, and a lack of high-quality domain datasets for handling knowledge-intensive tasks in the electricity sector. This study aims to address these challenges by introducing an improved Retrieval-Augmented Generation (RAG) strategy. This strategy combines hybrid retrieval with a fine-tuned generative model for efficient knowledge capturing and updating. The Metadata-driven RAG framework (Meta-RAG) is proposed for knowledge Question Answering (QA) tasks in the electricity domain. This includes data preparation, model fine-tuning, and reasoning retrieval stages. The data-preparation stage involves document conversion, metadata extraction and enhancement, and document parsing. These processes ensure efficient indexing and structured processing of power regulation documents. The Electricity Question Answering (EleQA) dataset, consisting of 19 560 QA pairs, is constructed specifically for this sector. The model fine-tuning stage uses multi-question generation, chain-of-thought prompting, and supervised instruction fine-tuning to optimize the reasoning abilities in specific tasks. The retrieval reasoning stage employs mixed encoding and re-ranking strategies, combining retrieval and generation modules to improve answer accuracy and relevance. Experiments validate the effectiveness of Meta-RAG. Compared to baseline models such as Self-RAG, Corrective-RAG, Adaptive-RAG, and RA-ISF, Meta-RAG shows higher answer accuracy and retrieval hit rates. Meta-RAG with the Qwen1.5-14B-Chat model achieves an overall accuracy of 0.804 3, surpassing the other methods. Ablation and document recall experiments indicate that document retrieval significantly impacts the framework performance, with a 0.292 8 drop in accuracy when the retrieval capability is lost.

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.

In the context of ongoing advancements in educational informatization, constructing precise and efficient curriculum knowledge graphs has become key to promoting personalized education development. As a structured knowledge representation model, curriculum knowledge graphs reveal complex relations between curriculum content and learning objectives to optimize the allocation of educational resources, and tailoring personalized learning paths for learners. This survey presents a discussion around the techniques used to construct curriculum knowledge graphs, starting with an explanation of the basic concepts; intrinsic connections; and significant differences among general, educational, and curriculum knowledge graphs. It then delves into the key technologies used for building curriculum knowledge graphs, covering aspects such as curriculum ontology design, entity extraction, and relation extraction, and provides a detailed analysis and summary of their evolution, key features, and limitations. Furthermore, it explores the application value of curriculum knowledge graphs in scenarios such as learning resource recommendation, learner behavior profile and modeling, and multimodal curriculum knowledge graph construction. Finally, it focuses on the challenges in constructing curriculum knowledge graphs, such as data diversity and heterogeneity, difficulties in quality evaluation, and the lack of cross-curriculum integration, and provides future-oriented insights based on cutting-edge technologies such as deep learning and Large Language Models (LLMs).

Multimodal sentiment analysis aims to extract and integrate semantic information from text, images, and audio data in order to identify the emotional states of speakers in online videos. Although, multimodal fusion methods have shown definite outcomes in this research area, previous studies have not adequately addressed the distribution differences between modes and the fusion of relational knowledge. Therefore, a multimodal sentiment analysis method is recommended. In this context, this study proposes the design of a Multimodal Prompt Gate (MPG) module. The proposed module can convert nonverbal information into prompts that fuse the context, filter the noise of nonverbal signals using text information, and obtain prompts containing rich semantic information to enhance information integration between the modalities. In addition, a contrastive learning framework from instance to label is proposed. This framework is used to distinguish the different labels in latent space at the semantic level to further optimize the model output. Experiments on three large-scale sentiment analysis datasets are conducted. The results show that the binary classification accuracy of the proposed method improves by approximately 0.7% compared to the suboptimal model, and the ternary classification accuracy improves by more than 2.5%, reaching 0.671. This method can provide a reference for introducing multimodal sentiment analysis in the fields of user profiling, video understanding, and AI interviews.

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.

To accelerate the digital transformation of education, the precise analysis and empirical application of AI technology integrated into the entire process of teaching and learning behaviors have become a current research hotspot. To address the problems of low detection accuracy, high density of bonding boxes, severe overlap and occlusion, large scale variations, and imbalance of data volume in student classroom behavior detection, this paper establishes a student classroom behavior dataset (DBS Dataset). Additionally, it proposes a student classroom behavior detection algorithm VWE-YOLOv8 based on improved YOLOv8. First, it introduces the CSWin-Transformer attention mechanism to enhance the model's capability to extract global information from images. This improves the network's detection accuracy. Second, it increases the model's recognition capability on multi-scale targets by integrating the Large Separable Kernel Attention (LSKA) module into the SPPF architecture. Additionally, it incorporates an occlusion-aware attention mechanism into the design of the detection head (which modifies the original Head structure to SEAMHead) to effectively detect occluded objects. Finally, it introduces a weight adjustment function (Slide Loss) to address the issue of sample imbalance. The experimental results reveal that compared with YOLOv8, the improved VWE-YOLOv8 achieves increases of 1.16% and 1.70% in mAP@0.50 and 7.36% and 2.13% in mAP@0.50∶0.95, on the DBS Dataset and public SCB Dataset. Furthermore, it improves the precision by 4.17%, 6.74% and recall rate by 1.96% and 3.13% on these datasets, respectively. These results indicate that the improved algorithm has a higher detection accuracy and stronger generalization capability. Moreover, it is capable of detecting students' classroom behaviors. This can strongly support the application of smart education and aid the digital transformation of education.

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.

Existing trajectory prediction methods rely heavily on high-definition maps, which are time-consuming, costly, and complex to acquire. This makes it difficult for them to quickly adapt to the widespread adoption of intelligent transportation. To address the problem of vehicle trajectory prediction in map-free scenes, a trajectory prediction method based on spatio-temporal features of multi-modal data is proposed in this paper. Multiple spatio-temporal interaction graphs are constructed from the history of the trajectory, temporal and spatial attention are cross-utilized and deeply fused to model the spatio-temporal correlations between vehicles on the road. Finally, a residual network is used for a multi-objective and multi-modal trajectory generation. The model is trained and tested on the real dataset, Argoverse 2, and the experimental results show that compared with the CRAT-Pred, this model can improve minADE, minFDE and Miss Rate(MR) metrics in single-modal prediction by 3.86%, 3.89%, and 0.48%, and in multi-modal prediction by 0.78%, 0.96% and 0.42%. Hence, the proposed trajectory prediction method can efficiently capture the temporal and spatial characteristics of vehicle movement trajectories and can be effectively applied in related fields such as autonomous driving.

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.

The System of Chip (SoC) integrates multiple peripheral interfaces, the verification of which has become one of the most time-consuming steps in chip development. The PCIe protocol provides high-speed peer-to-peer serial interconnection services within the system, while supporting hot swapping, which has gradually become a universal bus protocol. When using conventional Hardware Description Languages (HDL) to validate PCIe interface designs, problems usually arise, such as difficulty in covering multiple design scenarios and boundary conditions in a short period, leading to insufficient verification. To address the above issues, this study utilizes Universal Verification Methodology (UVM) to build a PCIe interface validation platform. This platform adopts a UVM-defined framework and test classes, achieving top-level environmental integration and design of test constraints, with strong reusability and comprehensive verification. This implementation includes SoC system-level environmental integration, design, and connection of the modules to be tested, implementation of sequencer and monitor classes in the verification platform, and partial interface design. To ensure that the test cases cover as many design states and paths as possible, different functional points are divided deliberately, and constraint conditions are designed to evaluate the effectiveness and coverage of test cases using various coverage indicators. The experimental results show that the verification platform can curtail the verification cycle and increase the comprehensive coverage by more than 30%.

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 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.

Deep learning has driven the development of artificial intelligence, which is widely used in computer vision. It provides breakthroughs and remarkable results in complex tasks such as image recognition, object detection, object tracking, and face recognition, demonstrating its excellent recognition and prediction capabilities. However, vulnerabilities and loopholes in deep learning models have been gradually exposed. Deep learning techniques, represented by convolutional neural networks, are extremely sensitive to well-designed adversarial examples, which can easily affect the security and privacy of the models. This paper first summarizes the concept of adversarial attacks, reasons for generating adversarial examples, and related terms. It outlines several types of classical adversarial attack strategies in the digital and physical domains and analyzes their advantages and disadvantages. Second, it focuses on computer vision and summarizes the latest research in adversarial attacks during tasks such as object detection, face recognition, object tracking, monocular depth estimation, and optical flow estimation, from both the digital and physical domains, as well as the various datasets commonly used in the study. It also briefly introduces the current stage of adversarial example defense and detection methods, summarizes the advantages and disadvantages of these methods, and describes examples of the applications of adversarial sample defense for various visual tasks. Finally, based on the summary of adversarial attack methods, it explores and analyzes the deficiencies and challenges of existing computer vision adversarial attacks.

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.

In multi-agent game simulations, the performance of Large Language Model (LLM) has been widely studied; however, their decision-making ability to guide multi-agent cooperation in fuzzy task objectives or uncertain environments is often unreliable. To address this issue, a multi-level collaborative decision-making framework based on distributed Bayesian inferences is proposed. This framework integrates three major functional modules: decision making, peer evaluation, and supervision. It utilizes multiple LLM for collaborative decision making and has been experimentally validated in a spatial prisoner's dilemma game. The experimental results show that the framework effectively overcomes the decision-making bottleneck of LLM in fuzzy task environments and successfully promotes the emergence of multi-agent cooperative behavior. Additionally, a quantitative evaluation of the model's decision-making ability in different experimental scenarios reveals that the decision error of the model is not linearly related to the model size. Under fuzzy task instructions, the decision error of the LLaMA3 (70×10⁹) model is 16.6% higher than that of the LLaMA3 (8×10⁹) model and 7.2% higher than that of the LLaMA2 (7×10⁹) model. This indicates that in more complex environments, relying solely on the expansion of the model size does not significantly improve the decision-making performance. By contrast, LLM collaborative decision making has shown significant advantages in improving decision consistency and effectiveness. These results reveal the crucial role of multi-model collaboration in complex decision-making environments and provide important references for the future design of intelligent agent systems for uncertain tasks.

To reduce social risks caused by the abuse of deepfake technology, an active defense method against deep forgery based on a Generative Adversarial Network (GAN) is proposed. Adversarial samples are created by adding imperceptible perturbation to original images, which significantly distorts the output of multiple forgery models. The proposed model comprises an adversarial sample generation module and an adversarial sample optimization module. The adversarial-sample generation module includes a generator and discriminator. After the generator receives an original image to generate a perturbation, the spatial distribution of the perturbation is constrained through adversarial training. By reducing the visual perception of the perturbation, the authenticity of the adversarial sample is improved. The adversarial sample optimization module comprises basic adversarial watermarking, deep forgery models, and discriminators. This module simulates black-box scenarios to attack multiple deep forgery models, thereby improving the attack and migration of adversarial samples. Training and testing are conducted on commonly used deepfake datasets Celebfaces Attributes (CelebA) and Labeled Faces in the Wild (LFW). Experimental results show that compared with existing active defense methods, the proposed method achieves a defense success rate exceeding 85% based on the cross-model active defense method and generates adversarial samples. Additionally, the method improves efficiency by 20-30 times compared with those of conventional algorithms.

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).

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.

Artificial intelligence has made remarkable progress across many fields, encouraging countries to attach great importance to its research and development. However, the rapid development of artificial intelligence has also brought about a series of problems and threats, and overreliance on and blind trust in such models can lead to serious risks. Therefore, interpretable artificial intelligence has become a key element in building trusted and transparent intelligent systems, and its research and development requires immediate attention. This survey comprehensively summarizes the research progress on explainable artificial intelligence at home and abroad comprehensively from multiple dimensions and levels. Based on current research results in the industry, this survey subdivides the key technologies of explainable artificial intelligence into four categories: interpretation model, interpretation method, safety testing, and experimental verification, with the aim of clarifying the technical focus and development direction of each field. Furthermore, the survey explores specific application examples of explainable artificial intelligence across key industry sectors, including but not limited to education, healthcare, finance, autonomous driving, and justice, demonstrating the significant role it plays in enhancing decision-making transparency. Finally, this survey provides an in-depth analysis of the major technical challenges of interpretable artificial intelligence and presents future development trends, in addition to a special investigation and in-depth analysis of the interpretability of large models, which has attracted considerable attention recently.

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.

With the increasing applicability of Internet-of-Things (IoT) technology, the number and types of IoT devices and sensors are continuously increasing. In particular, IoT water quality sensors play a vital role in the field of ecological monitoring and protection. Accordingly, this study proposes an unsupervised anomaly data detection algorithm based on explainable deep learning to address the issues of large volume, high dimensionality, and lack of labeling in the monitoring data collected by IoT water quality sensors. The algorithm uses the Auto-Encoder (AE) and SHAP algorithms to detect anomalies in multi-dimensional water quality datasets. The AE model is trained to flag data with significant reconstruction errors, and SHAP is used to interpret the AE and calculate the importance of each feature in the flagged data. Based on the importance of these features, the final anomaly value is determined for the dataset. Experimental results on an IoT water quality monitoring dataset show that the algorithm can effectively detect anomalous data with an F1 value of 0.875, outperforming existing unsupervised anomaly detection algorithms. Thus, the proposed algorithm has a practical application value for processing IoT water quality monitoring data. Furthermore, the algorithm can be applied to the anomaly detection of massive IoT monitoring data in other fields, such as meteorology and the environment.

Convolutional Neural Networks (CNNs) are widely used in the field of object detection, earning widespread acclaim in scholarly circles due to their precision and scalability. It has spawned numerous notable models, including those in the Region-based Convolutional Neural Networks (R-CNNs) (such as Fast R-CNN and Faster R-CNN) and You Only Look Once (YOLO) series. After the success of Transformers in the field of natural language processing, researchers began exploring their application in computer vision, leading to the development of visual backbone networks such as Visual Transformer (ViT) and Swin Transformer. In 2020, a Facebook research team unveiled DEtection TRansformer (DETR), an end-to-end object detection algorithm based on Transformers, designed to minimize the need for prior knowledge and postprocessing in object detection tasks. Despite the promise shown by DETR in object detection, it has limitations including low convergence speed, relatively low accuracy, and the ambiguous physical significance of target queries. These issues have spurred a wave of research aimed at refining and enhancing the algorithm. This paper aims to collate, scrutinize, and synthesize the various efforts aimed at improving DETR, assessing their respective merits and demerits. Furthermore, it presents a comprehensive overview of state-of-the-art research and specialized application domains that employ DETR and concludes with a prospective analysis of the future role of DETR in the field of computer vision.

Addressing the limitations of Chinese Named Entity Recognition(NER) within specific domains, this paper proposes a model to enhance entity recognition accuracy by utilizing domain-specific Knowledge Graphs(KGs) and images. The proposed model leverages domain graphs and images to improve entity recognition accuracy in short texts related to computer science. The model employs a Bidirectional Encoder Representations from Transformers(BERT)-Bidirectional Long Short-Term Memory(BiLSTM)-Attention-based model to extract textual features, a ResNet152-based approach to extract image features, and a word segmentation tool to obtain noun entities from sentences. These noun entities are then embedded with KG nodes using BERT. The model uses cosine similarity to determine the most similar nodes in the KG for the segmented words in the sentence. It retains neighboring nodes with a distance of 1 from this node to generate an optimal matching subgraph for semantic enrichment of the sentence. A Multi-Layer Perceptron(MLP) is employed to map the textual, image, and subgraph features into the same space. A unique gating mechanism is utilized to achieve fine-grained cross-modal feature fusion between textual and image features. Finally, multimodal features are fused with subgraph features by using a cross-attention mechanism and are then fed into the decoder for entity labeling. Experimental comparisons with relevant baseline models conducted on Twitter2015, Twitter2017, and a self-constructed computer science dataset are presented. The results indicate that the proposed approach achieved precision, recall, and F1 value of 88.56%, 87.47%, and 88.01% on the domain dataset compared to the optimal baseline model, its F1 value increased by 1.36 percentage points, demonstrating the effectiveness of incorporating domain KGs for entity recognition.

High-quality annotated data are crucial for Natural Language Processing(NLP) tasks in the field of Chinese scientific literature. A method of annotation based on a Large Language Model(LLM) was proposed to address the lack of high-quality annotated corpora and the issues of inconsistent and inefficient manual annotation in Chinese scientific literature. First, a fine-grained annotation specification suitable for multi-domain Chinese scientific literature was established to clarify entity types and annotation granularity. Second, a structured text annotation prompt template and a generation parser were designed. The annotation task of Chinese scientific literature was set up as a single-stage, single-round question-and-answer process in which the annotation specifications and text to be annotated were filled into the corresponding slots of the prompt template to construct the task prompt. This prompt was then injected into the LLM to generate output text containing annotation information. Finally, the structured annotation data were obtained by the parser. Subsequently, using prompt learning based on LLM, the Annotated Chinese Scientific Literature(ACSL) entity dataset was generated, which contains 10 000 annotated documents and 72 536 annotated entities distributed across 48 disciplines. For ACSL, three baseline models based on RoBERTa-wwm-ext, a configuration of the Robustly optimized Bidirectional Encoder Representations from Transformers(RoBERT) approach, were proposed. The experimental results demonstrate that the BERT+Span model performs best on long-span entity recognition in Chinese scientific literature, achieving an F1 value of 0.335. These results serve as benchmarks for future research.

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.

The spatiotemporal fusion super-resolution reconstruction of remote sensing images extracts information from low-resolution images with high temporal density and high-resolution images with low temporal resolution to generate remote sensing images with both high temporal and spatial resolutions. This process is directly related to the implementation of subsequent tasks such as interpretation, detection, and tracking. With the rapid advancement of Convolutional Neural Network (CNN), researchers have proposed a series of CNN-based spatiotemporal fusion methods. However, because of the inherent limitations of convolution operations, these methods still face challenges with respect to global information extraction. Inspired by the global modeling capabilities of the Swin Transformer, this paper proposes a super-resolution reconstruction model based on the Swin Transformer. In the feature extraction stage, a dual-stream structure is introduced, dividing the feature extraction network into two parts to extract temporal and spatial information separately. The performance of the model is enhanced by the global capabilities of the Swin Transformer. In the feature fusion stage, a Convolutional Block Attention Module (CBAM) that combines channel and spatial attention is introduced to enhance the important features and improve the image reconstruction accuracy. Comparative experiments are conducted on the Coleambally Irrigation Area (CIA) and Lower Gwydir Catchment (LGC) datasets using various spatiotemporal fused super-resolution reconstruction models. The results show that the proposed model achieved optimal performance across all evaluation metrics, demonstrating superior performance and enhanced generalization capabilities.

To meet the requirements of low-overhead and high-performance applications in the embedded field, a 32 bit microprocessor based on the RISC-V open-source instruction set architecture has been developed. The processor adopts a five-stage pipeline structure of sequential launch, sequential execution, and out-of-order write back and realizes a combination of integer and multiply-divide instruction set modules. To cope with pipeline conflicts, the processor adopts a dynamic branch-prediction technique and designs data-correlation control and write back disorder mechanisms. The processor is designed using Verilog and builds a System on Chip (SoC) using an Advanced High-performance Bus (AHB) and an Advanced Peripheral Bus (APB) as interconnecting bus protocols. The processor logic functions are verified by writing RV32IM assembly instruction test programs in a simulation environment. Add timing constraints and physical constraints under the Vivado synthesis tool to perform logical synthesis on the processor code and analyze the utilization of processor hardware resources, and the synthesized code stream file is downloaded to the Xilinx Artix-7 (XC7A200T-2FBG484I) Field Programmable Gate Array(FPGA) development board and runs at a main frequency of 50 MHz. In the CoreMark program at 50 MHz, the CoreMark running points reaches 3.25 CoreMark/MHz. The validation results show that the processor performance running points is the same as that of the ARM Cortex-M3 series processors, and under the premise of the same technical comparison indexes, the processor running points is better than that of the RISC-V processor comparison item. The designed processor logic function is correct and uses a low hardware overhead to achieve relatively high-performance indicators suitable for cost-constrained high-performance embedded applications.

Timely and effective disinformation detection is crucial for curbing the spread of disinformation and minimizing social harm. Numerous deep learning methods have been employed for disinformation detection. Summarizing the detection principles and paradigms of existing research is essential for identifying directions for technical optimization. Therefore, this paper comprehensively reviews existing research based on the principles and implementation paths of disinformation detection, and for the first time, summarizes and compares the applications of large language models in this field. First, the relevant concepts of disinformation detection tasks are introduced and the data structures of commonly used disinformation detection datasets are summarized. Then, based on detection principles and implementation methods, the paper presents ways to detect textual and multimodal disinformation through semantic feature representation, auxiliary task design, internal knowledge inference, and fact verification, refining them into ten subcategories and summarizing the potential characteristics of detection methods for each subcategory. Finally, the paper summarizes disinformation detection paradigms based on deep neural networks and large language models, compares the detection performance of representative methods from these paradigms across seven disinformation detection datasets, and highlights the advantages and limitations of large language models in detecting disinformation. It also presents the anticipated opportunities and challenges brought about by large language models in the field of disinformation detection, providing a reference for future research.

To solve the problem of insufficient personalized monitoring in human health assessment methods and meet the demand for fine-grained health status assessment in different scenarios, a multi-scenario-based human health status assessment method is needed to achieve long-term automated monitoring. This study proposes a multi-scenario human health assessment model based on a combination of the Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM). First, health monitoring index data for the human body in four different scenarios, including exercise, rest, work/study, and recreation, are collected to construct the corresponding assessment index system. Then, the AHP and EWM weights are calculated for the assessment indicators, and the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm is used to distribute the subjective and objective weights for the AHP and EWM to ensure the objectivity of the proportion of evaluation indicators. Finally, the human health state is assessed and quantified using the fuzzy comprehensive evaluation method, and the reliability and stability of the method are verified using actual monitoring data. The experimental results show that the composite scores of the proposed method under the four scenarios (exercise, rest, work/study, and recreation) are 63.78, 59.83, 58.71, and 59.21, respectively, indicating that the model has good accuracy and stability under different scenarios. The results of the physical state evaluation of the testers are analyzed, and some health suggestions are given. The model proposed in this study can comprehensively determine the health status of the human body under different scenarios and provide scientific health guidance. Thus, it provides a scientific basis for health management and disease prevention.

Federated learning enables participants to collaboratively model without revealing their raw data, thereby effectively addressing the privacy issue of distributed data. However, as research advances, federated learning continues to face security concerns such as privacy inference attacks and malicious client poisoning attacks. Existing improvements to federated learning mainly focus on either privacy protection or against poisoning attacks without simultaneously addressing both types of attacks. To address both inference and poisoning attacks in federated learning, a privacy-preserving against poisoning federated learning scheme called APFL is proposed. This scheme involves the design of a model detection algorithm that utilizes Differential Privacy (DP) techniques to assign corresponding aggregation weights to each client based on the cosine similarity between the models. Homomorphic encryption techniques are employed for the weighted aggregation of the local models. Experimental evaluations of the MNIST and CIFAR10 datasets demonstrate that APFL effectively filters malicious models and defends against poisoning attacks while ensuring data privacy. When the poisoning ratio is no more than 50%, APFL achieves a model performance consistent with the Federated Averaging (FedAvg) scheme in a non-poisoned environment. Compared with the Krum and FLTrust schemes, APFL exhibits average reductions of 19% and 9% in model test error rate, respectively.

Spike sorting refers to the detection, clustering, and identification of distinct extracellular neuronal signals from different recording sites with the aim of reliably assigning them to different putative neurons. This crucial preprocessing is fundamental for neural decoding in neuroscience and represents a prominent research direction in high-bandwidth Brain-Computer Interface (BCI) studies. Conventional spike sorting algorithms involve various steps such as spike detection, spike alignment, feature extraction, and feature clustering. Currently, explosive growth in the number and density of neural electrodes presents significant challenges in terms of the efficiency and accuracy of spike sorting. To address issues such as limited feature extraction capabilities, low Signal-to-Noise Ratio (SNR), and signal superposition, algorithmic advancements, such as artificial intelligence approaches and big data spike sorting solutions have emerged as strategies for enhancing the comprehension of the intricate principles and functions underlying brain activity. This paper provides an overview linking invasive BCIs with neural encoding/decoding and spike sorting methods. Subsequently, it outlines the principles underlying various spike sorting algorithms, while discussing how brain neural signals map onto specific behaviors. Finally, the paper concludes by anticipating future challenges and trends in the development of high-bandwidth neural encoding and decoding.

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.

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.

As multivariate time series data become increasingly prevalent across various industries, anomaly detection methods that can ensure the stable operation and security of systems have become crucial. Owing to the inherent complexity and dynamic nature of multivariate time series data, higher demands are placed on anomaly detection algorithms. To address the inefficiencies of existing anomaly detection methods in processing high-dimensional data with complex variable relations, this study proposes an anomaly detection algorithm for multivariate time series data, based on Graph Neural Networks (GNNs) and a diffusion model, named GRD. By leveraging node embedding and graph structure learning, GRD algorithm proficiently captures the relations between variables and refines features through a Gated Recurrent Unit (GRU) and a Denoising Diffusion Probabilistic Model (DDPM), thereby facilitating precise anomaly detection. Traditional assessment methods often employ a Point-Adjustment (PA) protocol that involves pre-scoring, substantially overestimating an algorithm's capability. To reflect model performance realistically, this work adopts a new evaluation protocol along with new metrics. The GRD algorithm demonstrates F1@k scores of 0.741 4, 0.801 7, and 0.767 1 on three public datasets. These results indicate that GRD algorithm consistently outperforms existing methods, with notable advantages in the processing of high-dimensional data, thereby underscoring its practicality and robustness in real-world anomaly detection applications.