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.

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).

Pedestrian detection in intelligent community scenarios needs to accurately recognize pedestrians to address various situations. However, for persons who are occluded or at long distances, existing detectors exhibit problems such as missed detection, detection error, and large models. To address these problems, this paper proposes a pedestrian detection algorithm, Multiscale Efficient-YOLO (ME-YOLO), based on YOLOv8. An efficient feature Extraction Module (EM) is designed to improve network learning and capture pedestrian features, which reduces the number of network parameters and improves detection accuracy. The reconstructed detection head module reintegrates the detection layer to enhance the network's ability to recognize small targets and effectively detect small target pedestrians. A Bidirectional Feature Pyramid Network (BiFPN) is introduced to design a new neck network, namely the Bidirectional Dilated Residual-Feature Pyramid Network (BDR-FPN), and the expanded residual module and weighted attention mechanism expand the receptive field and learn pedestrian features with emphasis, thereby alleviating the problem of network insensitivity to occluded pedestrians. Compared with the original YOLOv8 algorithm, ME-YOLO increases the AP₅₀ by 5.6 percentage points, reduces the number of model parameters by 41%, and compresses the model size by 40% after training and verification based on the CityPersons dataset. ME-YOLO also increases the AP₅₀ by 4.1 percentage points and AP_50∶95 by 1.7 percentage points on the TinyPerson dataset. Moreover, the algorithm significantly reduces the number of model parameters and model size and effectively improves detection accuracy. This method has a considerable application value in intelligent community scenarios.

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.

Transformer-based object tracking methods are widely used in the field of computer vision and have achieved excellent results. However, object transformations, object occlusion, illumination changes, and rapid object motion can change object information during actual tracking tasks, and consequently, the underutilization of object template change information in existing methods prevents the tracking performance from improving. To solve this problem, this paper presents a Transformer object tracking method, TransTRDT, based on real-time dynamic template update. A dynamic template updating branch is attached to reflect the latest appearance and motion state of an object. The branch determines whether the template is updated through the template quality scoring header; when it identifies the possibility of an update, it passes the initial template, the dynamic template of the previous frame, and the latest prediction after cropping into the dynamic template updating network to update the dynamic template. As a result, the object can be tracked more accurately by obtaining a more reliable template. The tracking performance of TransTRDT on GOT-10k, LsSOT, and TrackingNet is superior to algorithms such as SwinTrack and StarK. It outperforms to achieve a tracking success rate of 71.9% on the OTB100 dataset, with a tracking speed of 36.82 frames per second, reaching the current leading level in the industry.

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.

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.

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.

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.

Graphics Processing Unit (GPU) is not fully utilized when processing large-scale dynamic graphs, and the limitations of GPU-oriented graph partitioning methods lead to performance bottlenecks. To improve the performance of graph computing, a Central Processing Unit (CPU)/GPU Distributed Heterogeneous Engine (DH-Engine) is proposed to improve the performance of heterogeneous processors. First, a new heterogeneous graph partitioning algorithm is proposed. It uses a streaming algorithm for graph partitioning as the core to achieve dynamic load balancing between the computing nodes and between the CPU and GPU. The greedy strategy assigns vertices based on the maximum number of neighboring vertices during the initial graph partitioning and dynamically adjusts the vertex position based on the minimum number of connected edges during the iteration. Second, the system introduces a GPU heterogeneous computing model to improve graph computing efficiency through functional parallelism. The experiment used PageRank, Connected Components(CC), Single-Source Shortest Path(SSSP), and k-core as examples to conduct comparative experiments with other graph computing systems. Compared with other graph engines, DH-Engine can better balance the computing load of each node and the load between heterogeneous processors to shorten the delay and accelerate the overall computing speed. The results show that the CPU/GPU synergy of this system tends to 1, and the heterogeneous computing has speedup ratio of 5 times compared to other graph computing systems. DH-Engine provides an improved heterogeneous graph scheme.

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.

In response to the need for intelligent curling training, a new on-site curling decision-making method that combines computer vision and deep Reinforcement Learning (RL) technologies, Artificial Intelligence (AI)-Curling, is proposed. AI-Curling comprises two components: SR-Yolo for curling detection and Global Strategy Perception-Monte Carlo Tree Search (GSP-MCTS) for strategy generation. The former is responsible for sensing the state of the curling stones at critical moments and extracting information on the location and type of stones in real scenes. To improve the detection accuracy of small targets in large scenes and prevent feature loss due to inappropriate downsampling, a Shallow Refinement Backbone Network (SRNet) is introduced to capture richer feature information by adding layers during the initial stages of the network. An Adaptive Feature Optimization Fusion (AFOF) module is further introduced into the multiscale fusion network to increase the number of effective samples in each layer, thereby preventing small-scale targets from being submerged in complex backgrounds and noise. In the strategy generation module, curling match decision analysis is implemented using a combination of the MCTS algorithm and policy value network. A GSP module is embedded into the policy value network to enhance network spatial perception by introducing a kernel function to deal with action space continuity and execution uncertainty. In the experiments, SR-Yolo achieved 0.974 mAP@0.5 on the standard Curling dataset and 0.723 mAP@0.5 on the more complex obstructed Curling_hard dataset. In addition, GSP-MCTS achieved a 62% winning percentage compared with the latest real-scene curling model Curling MCTS, indicating that GSP-MCTS has superior performance.

Traffic sign detection is crucial for assisted driving and plays a vital role in ensuring driving safety. However, in real-world traffic environments, factors such as darkness and rain create background noise that complicates the detection process. In addition, existing models often struggle to effectively detect small traffic signs from a distance. Furthermore, when a traffic sign detection model is designed, the model size must be considered for practical deployment. To address these challenges, this study proposes a lightweight traffic sign detection model based on YOLOv8 with enhanced foregrounds. First, a lightweight PC2f module is designed to replace a part of the C2f module in the original Backbone. This modification reduces the number of parameters and computational load, enriches the gradient flow, retains more shallow information, and ultimately enhances detection performance while maintaining a lightweight design. Next, the study designs a Foreground Enhancement Module (FEM) and incorporates it into the Neck position to effectively amplify the foreground information and reduce background noise. Finally, the study adds a small-target detection layer to extract shallow features from high-resolution images, thereby improving the ability of the model to detect small-target traffic signs. Experimental results show that the optimized model achieves a mAP₅₀ of 82.5% and 95.3% on the CCTSDB 2021 and GTSDB datasets, which is an improvement of 3.6 and 1 percentage points over the original model, respectively, with a reduction in model weight size by 0.22×10⁶. These results confirm the effectiveness of the proposed model for practical applications.

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

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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%.

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.

Small targets often exhibit low resolution and blurriness and are easily affected by occlusions and background interference, making accurate and real-time detection of small targets challenging. In this study, to enhance the detection performance, a super-resolution-aided small-target detection algorithm based on multi-task learning called Multi-YOLO is proposed. First, a super-resolution auxiliary branch is introduced to guide the main network in extracting effective features, thereby reducing the loss of information for small targets. Second, a collaborative supervision method is employed by combining Anchor based and Anchor free detection heads to improve the detection accuracy. Additionally, a CTR3 module is used at the end of the backbone network to strengthen the correlation between the target information and position awareness. Finally, during the inference stage, only the detection branch is used to maintain the speed of inference. Experimental results show that, compared with the baseline network, Multi-YOLO achieves performance improvement on the VEDAI, COCO MiniTrain, and SPCD datasets. Specifically, on the VEDAI dataset, this method achieves a 10.9% improvement in mean Average Precision (mAP) improvement while maintaining a model size similar to that of the baseline model. Moreover, compared with mainstream single-stage object detection networks, Multi-YOLO excels in small-target detection, maintaining a remarkable balance between accuracy and speed.

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.

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.

The exponential growth of diverse terminal devices, driven by advancements in mobile computing, 5th Generation mobile communication (5G), and Internet of Things (IoT) technologies, has generated vast volumes of semi-structured and unstructured data, as these devices connect to networks. Neural networks are widely applicable because of their unique advantages in mining data. To enhance data processing capabilities and inference accuracy, neural network models are often designed to be complex and thus consume substantial computational resources in both storage and execution. However, edge devices typically have limited computational resources and cannot satisfy the storage and inference requirements of complex neural network models. Therefore, they often rely on cloud computing centers to perform these tasks. This cloud-based collaboration can lead to increased response latency and network bandwidth consumption, incurring potential risks such as the infringement of user privacy through data leakage. To address these issues, this paper introduces Neural network Generation and Deployment (NGD), a method for rapidly generating and deploying tailored neural network models on edge devices, whereby neural network models that match the hardware configuration of edge devices and specific computational task requirements are generated and rapidly deployed onto target devices for local inference. Experiments are conducted on three typical edge devices, and the results demonstrate the effectiveness of NGD in generating and deploying models, affirming its practicality and effectiveness.

Depression, as a prevalent mental health problem, substantially impacts individual′s daily lives and well-being. Addressing the limitations of current depression detection, such as subjectivity and manual intervention, automatic detection methods based on deep learning have become a popular research direction. The primary challenge in the most accessible text modality is modelling the long-range and sequence dependencies in depressive texts. To address this problem, this paper proposes a contextual knowledge-based enhanced Transformer network model, named Robustly optimized Bidirectional Encoder Representations from Transformers approach-Bidirectional Long Short-Term Memory(RoBERTa-BiLSTM), to comprehensively extract and utilize contextual features from depressive text sequences. By combining the strengths of sequence models and Transformer architectures, the proposed model captures contextual interactions between words to provide a reference for depression category prediction and information characterization. First, the RoBERTa model is employed to embed vocabulary into a semantic vector space, and then, a BiLSTM network effectively captures long-range contextual semantics. Finally, empirical research is conducted on two large-scale datasets, DAIC-WOZ and EATD-Corpus. Experimental results demonstrate that the model achieves an accuracy exceeding 0.74 and 0.93, and a recall exceeding 0.66 and 0.56, respectively, enabling accurate depression detection.

Image fusion is the process of combining multiple input images into a unified single image. Although visible-infrared image fusion enhances target detection accuracy, its performance often fails in low-light scenarios. This study introduces a novel fusion model, namely, DAPR-Net, which features an encoder-decoder structure with cross-layer residual connections. These connections link the encoder's output to the corresponding layer in the decoder to thereby reinforce the information flow between the convolutional layers. Within the encoder, a Dual Attention Feature Extraction Module(AFEM) is designed to better distinguish the differences between the fused image and the input visible light and infrared images while retaining crucial information from both. The experimental results show that compared with the benchmark PIAFusion model, the information entropy, spatial frequency, mean gradient, standard deviation, and visual fidelity indices on the model on LLVIP and MSRS datasets increase by 0.849, 3.252, 7.634, 10.38, and 0.293, and 2.105, 2.23, 4.099, 27.938, and 0.343, respectively. In the YOLOV5 target detection network, the average mean precision, recall rate, accuracy rate, and F1 value index of the LLVIP and MSRS datasets increased by 8.8, 1.4, 1.9, and 1.5 percentage points and 7.5, 1.4, 8.8, and 1.2 percentage points, respectively, showing significant advantages compared with other fusion methods.

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.

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.

Large Language Models(LLMs) are currently undergoing vigorous development in the field of Natural Language Processing(NLP). However, significant challenges remain in their applications to educational digitization. To address the problem posed by the scarcity of domain-specific data and the instability of summarization leading to information loss or redundancy, this study introduces a lightweight idempotent model framework, Idempotent Generative Language Model(IGLM), for educational text summarization. The model first employs multisource training for adaptive augmentation to enhance data diversity. Subsequently, various fine-tuning procedures are applied to the downstream text summarization task. Concurrently, an idempotent summarization generation strategy is designed to mitigate the impact of text length. This strategy brings the summaries closer to idempotent form, constrains the model, mitigates biases resulting from uneven language corpora, and combines quantization techniques to generate more precise and fluent summaries under low-resource conditions. The experiments used Recall-Oriented Understudy for Gisting Evaluation(ROUGE) scores as the evaluation metric and validated the model on publicly available Chinese text summarization datasets Large-scale Chinese Short Text Summarization(LCSTS), EDUCATION, and Natural Language Processing and Chinese Computing(NLPCC). The results revealed significant enhancements in precision and coherence within this framework. Specifically, compared to the baseline model, the ROUGE-1/2/L scores were improved by 7.9, 7.4, and 8.7 percentage points on the LCSTS dataset. Moreover, on the EDUCATION dataset, the scores exhibited enhancements of 12.9, 15.4, and 15.7 percentage points for ROUGE-1/2/L. Similarly, on the NLPCC dataset, improvements of 12.2, 11.7, and 12.7 percentage points were observed for ROUGE-1/2/L. This validation confirms the model's effectiveness.

Knowledge Graph Completion(KGC) is an important means of improving the quality of KGs. Existing methods for KGC are mainly divided into structure- and description-based methods. The structure-based methods perform poorly in the inference of long-tailed entities commonly found in KGs, and the description-based methods are insufficient for descriptive information utilization and negative sample information learning. To address these challenges, this paper proposes a KGC method with knowledge enhancement and contrastive learning, named KEKGC. A specific template is designed to convert triples and their descriptive information into coherent natural language statements. These statements serve as input to a Pre-trained Language Model(PLM) through a manually defined template, enhancing the language model's comprehension of the structural and descriptive knowledge of each triple. On this basis, a contrastive learning framework is incorporated to improve the efficiency and accuracy of the link prediction task. This is done by building a memory bank to store entity embedding vectors, from which positive and negative samples are selected and trained with InfoNCE loss. Experimental results on WN18RR dataset show that compared with MEM-KGC, in the link prediction task, KEKGC improves Mean Reciprocal Rank(MRR) by 5.5, improves Hits@1, Hits@3, and Hits@10 metrics by 2.8, 0.7, and 4.2 percentage points, respectively, and the accuracy of the triple classification task reaches 94.1%. Hence, this method can achieve higher prediction accuracy and better generalization ability, especially for long-tailed entities, which can effectively improve graph completion.

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.