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).

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.

When implementing Federated Learning (FL) in a cell-free network environment, user scheduling and resource allocation strategies are crucial for optimizing system time overhead, improving user reachability, and accelerating FL convergence rate. To address the issue of uneven resource allocation, this study designs an optimization scheme that combines user scheduling, CPU processing frequency, and power allocation. This scheme aims to achieve fair resource allocation by maximizing the minimum user rate in the system, thus enhancing FL performance. The joint optimization problem is decomposed into two subproblems: user scheduling and power allocation. For user scheduling, this study proposes a greedy scheduling algorithm based on k-means clustering to comprehensively evaluate channel conditions and data "value" of users and categorize users into different groups. Subsequently, for the resource occupation situation, a personalized CPU processing frequency allocation plan is developed for users within each group based on their resource occupancy. Finally, by independently executing user scheduling within each group, user selection is performed efficiently and precisely, and the complexity of user selection is effectively reduced via early grouping. For power allocation, this study introduces a Bisection Method-based Power Allocation (BM-PA) algorithm. This algorithm not only considers fairness among users but also prioritizes resource-constrained users to ensure that they can obtain superior resource allocation. The BM-PA algorithm achieves fast convergence of power allocation using a low-complexity iterative optimization process, significantly improving the resource utilization efficiency without deteriorating the system performance. In this study, a reasonable user scheduling strategy serves as the foundation for obtaining optimal solutions for the power allocation subproblem. This study adopts an alternating iteration method that allows independent optimization in each subproblem while considering the solution of the other subproblem. Via multiple rounds of iterative optimization, this interdependent relationship ensures that power resources are reasonably allocated to users who need them the most or are most likely to effectively utilize them, thus enhancing the overall system performance. This study realizes joint optimization solutions that significantly improve overall system performance. Simulation results show that compared with the baseline algorithm, the proposed algorithm exhibits outstanding performance in terms of downlink achievable rates-the average improvement reaches up to 103.34% under optimal conditions. Additionally, the uplink achievable rates improve by up to 102.78%. Furthermore, the proposed algorithm can save 67.44% of the FL task training time on average compared to the baseline algorithm, particularly when the FL learning model accuracy reaches 90%, wherein the time overhead of the proposed algorithm is minimal.

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

The rapid development of blockchain technology and the rise of cryptocurrencies not only challenge traditional financial and asset management concepts but also introduce significant privacy and security concerns. Coin mixing, a primary privacy protection mechanism in blockchains, is designed to enhance transaction anonymity and protect user identities. However, this enhanced anonymity also enables the misuse of cryptocurrencies for illicit activities such as money laundering, theft, and fraud. Therefore, a comprehensive review of technologies for the identification, detection, and regulation of coin mixing services is urgently required to support law enforcement and promote the secure and compliant use of cryptocurrencies. This paper first reviews the technical principles and mechanisms of coin mixing services and proposes a general classification framework for mixing tools while discussing the criminal activities associated with them. Subsequently, it focuses on recent advances in five key regulatory approaches: manual rule-based methods, address clustering, entity classification, anomalous transaction detection, and mixing service traceability. It also surveys existing industrial tools and platforms related to coin mixing regulations. Finally, it identifies current challenges in the field and suggests directions for future research.

Post-Training Quantization (PTQ) is an efficient model compression method that converts the parameters of high-precision floating-point models into low-bit integer representations without requiring retraining, using only a small amount of unlabeled calibration data. This method significantly reduces storage and computational overhead while maximizing the retention of the original model's inference accuracy; therefore, it is widely recognized and adopted in both academia and industry. This paper systematically summarizes the progress of research on PTQ from four dimensions: quantization steps, method classification, tool ecosystem, and application advancements. First, a clear framework for the quantization process is constructed, covering steps such as dynamic range statistics, quantization parameter calculation, weight and activation quantization, error optimization, and model generation. Second, a complete classification system for quantization methods is proposed, which includes quantization granularity, bit width, calibration methods, and structure-guided quantization. Third, the tool ecosystem supporting the large-scale application of PTQ is analyzed, and its value in hardware adaptation and engineering deployment is discussed. Finally, this paper summarizes the progress in the integration and application of PTQ methods and highlights practical challenges, particularly those related to cross-modal consistency, extremely low-bit semantic collapse, and hardware adaptation. These challenges not only reveal the limitations of current technologies but also provide important directions for future research. This review provides a reference framework for PTQ methods in academia and industry, thereby facilitating the widespread application of artificial intelligence in resource-constrained scenarios.

This study explores automatic term recognition in the electric power domain, addressing challenges faced during its digital transformation, such as data silos and knowledge utilization. To improve the identification of specialized and new terms, a dynamic graph-assisted method combining large and small models is proposed. The approach enhances recall and precision through candidate term extraction and term classification. An initial knowledge graph is built using existing term databases. Target text-related nodes are queried and filtered with term features. A retrieval-augmented large language model extracts candidate terms, followed by adversarial training to develop a deep learning model for term classification. The dynamic term knowledge graph is iteratively updated based on classification results, forming a positive feedback loop. Experimental results show that the method's accuracy, recall, and F1 score improve over iterations, reaching 0.8647, 0.8565, and 0.8542, respectively, demonstrating superior performance compared to other term recognition methods.

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

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.

】Multimodal sentiment analysis leverages the complementary information of speech, text, and visual modalities to enhance emotion recognition accuracy and robustness. However, existing approaches still face three major challenges: (1) the lack of unified modeling for multi-scale emotional dynamics across fast and slow temporal rhythms; (2) the difficulty in explicitly characterizing semantic dominance and subordination among modalities; and (3) the limited ability to adaptively regulate modality intensity and information contribution. To address these issues, this paper proposes a multimodal sentiment analysis framework that integrates multi-scale encoding with a polarity-aware fusion mechanism. Specifically, a Multi-Scale Mamba encoder (MS-Mamba) is introduced for visual and audio modalities to jointly capture global and local temporal dependencies; a Polarity-Aware Fusion (PAF) module is designed to explicitly model inter-modal enhancement and suppression through semantic residuals and signed weights; and a Polarity-Driven Gating (PDG) mechanism is developed to adaptively control information flow via a saliency–direction disentanglement strategy. These components collaboratively form a closed-loop structure of “temporal modeling–polarity alignment–global gating.” Experimental results on the CMU-MOSI and CMU-MOSEI datasets demonstrate that the proposed model achieves binary classification accuracies of 86.58% and 86.50%, with F1 scores of 86.59% and 86.26%, respectively—yielding an average improvement of approximately 1.3% over mainstream baselines. The results validate the effectiveness and robustness of the proposed method in semantic alignment, temporal modeling, and adaptive fusion.

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.

The Siamese tracking network is a popular target tracking framework that includes three modules: backbone, fusion, and positioning networks. The Transformer is a relatively new and effective implementation method for fusion network modules. The encoder and decoder of the Transformer use a self-attention mechanism to enhance the features of the Convolutional Neural Network (CNN). However, the self-attention mechanism can only enhance features in the spatial dimension without considering feature enhancement in the channel dimension. To enable the self-attention network of the Transformer to enhance features both in the spatial and channel dimensions and provide accurate correlation information for the target localization network, a Transformer tracker based on dual-dimensional feature enhancement is proposed to improve the Transformer fusion network. First, using the third- and fourth-stage features of the backbone network as inputs, channel dimension feature enhancement is performed via CAE-Net in the self-attention module of the Transformer encoder and decoder to enhance the importance of the channel. Subsequently, two-stage feature-weighted fusion and linear transformation are performed via SAE-Net to obtain the self-attention factors Q, K, and V. Finally, spatial dimension feature enhancement is performed via a self-attention operation. Experiments conducted on five widely used public benchmark datasets reveal that the improved Transformer feature fusion module can improve the tracking performance of the tracker with minimal reduction in speed of tracking.

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.

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 generation of High-Definition (HD) environmental semantic maps is indispensable for environmental perception and decision making in autonomous driving systems. To address the modality discrepancy between cameras and LiDARs in perception tasks, this paper proposes an innovative multimodal fusion framework, HDMapFusion, which significantly improves semantic map generation accuracy via feature-level fusion. Unlike traditional methods that directly fuse raw sensor data, our approach innovatively transforms both camera images and LiDAR point cloud features into a unified Bird's-Eye-View (BEV) representation, enabling physically interpretable fusion of multimodal information within a consistent geometric coordinate system. Specifically, this method first extracts visual features from camera images and 3D structural features from LiDAR point clouds using deep learning networks. Subsequently, a differentiable perspective transformation module converts the front-view image features into a BEV space and the LiDAR point clouds are projected into the same BEV space through voxelization. Building on this, an attention-based feature fusion module is designed to adaptively integrate the two modalities using weighted aggregation. Finally, a semantic decoder generates high-precision semantic maps containing lane lines, pedestrian crossings, road boundary lines, and other key elements. Systematic experiments conducted on the nuScenes benchmark dataset demonstrate that HDMapFusion significantly outperforms existing baseline methods in terms of HD map generation accuracy. These results validate the effectiveness and superiority of the proposed method, offering a novel solution to multimodal fusion in autonomous driving perception.

Cross-domain recommendation systems are widely applied in e-commerce and content platforms. Although the dual-target cross-domain recommendation (DTCDR) proposed in recent years has achieved a breakthrough in simultaneously improving the performance of both domains, it still faces two major challenges: 1) the generated user-item representations lack sufficient correlation and diversity; 2) the semantic noise mixed in the shared preferences leads to negative transfer problems. To address these issues, a dual-target cross-domain recommendation model based on heterogeneous graph and hierarchical preference disentanglement (HGPD-DTCDR) is proposed. Its core innovations include: 1) a heterogeneous graph collaborative learning framework is proposed to integrate user-item interactions, user social networks, and item attribute similarities, constructing a multi-relation heterogeneous graph, and generating high-order semantic representations through a relation graph convolutional network (R-GCN) to enhance the diversity and correlation of the representations; 2) a two-stage decoupling process is designed, first separating domain-specific and shared preferences through a variational graph encoder, and then introducing a semantic filtering network to optimize the quality of shared preferences. Experiments on five real cross-domain datasets show that the performance improvement of this model stems from the synergistic effect of heterogeneous graph modeling and hierarchical decoupling mechanisms. Compared with the best baseline, it achieves average improvements of 3.55%, 7.27%, and 15.57% in hit rate, normalized discounted cumulative gain, and mean reciprocal rank, respectively. In data-sparse scenarios, the performance improvement is even more significant, with an average gain of 10.35%. Ablation studies further verify the effectiveness of each technical component and their synergistic effects.

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.

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.

With the continuous evolution of network environments and the constant emergence of Internet applications, the applicability of machine learning classifiers trained on previous traffic data to new sample spaces gradually weakens. This leads to a decline in the recognition capability of classification models; thus, these models are ineffective in meeting the ever-increasing demands of network services and security. If the classifier is manually updated based on experience, it consumes a significant amount of effort, and it is difficult to ensure the generalization performance of the new classifier. Moreover, the continuous influx of new data poses severe challenges in balancing model training accuracy with the storage of computational resources. To address this issue, an incremental learning strategy employing spatial optimization techniques is proposed to achieve efficient network traffic classification. First, by optimizing the spatial distribution of old and new traffic samples, clusters of different categories (both old and new) are maintained with minimum intervals to avoid distribution conflicts arising from old and new tasks shared feature space. Within the optimized feature space, a small number of old data samples are replayed, and knowledge distillation techniques were utilized to maintain the stability of the original model parameters. Only the extended parts of the model are adjusted to update the classifier at minimal cost. Experimental results on the USTC-TFC2016 dataset demonstrate that, compared with other methods, this approach exhibits higher stability and effectiveness in terms of model accuracy, resource consumption, and performance.

In recent years, Large Language Models (LLMs) such as GPT, LLaMA, Qwen, and DeepSeek, have achieved significant breakthroughs in natural language processing, computer vision, multimodal learning, and other fields. However, constrained by factors such as their reasoning mechanisms, parameter scales, and the inherent knowledge contained within their training data, these models often suffer from issues like ″hallucinations″—characterized by inaccurate answers and even factual deviations—when handling complex tasks, addressing questions from professional domains, or generating time-sensitive content. These limitations severely hinder their application in high-reliability scenarios. The ″tool learning″ paradigm is attracting increasing attention as a promising solution to these capability bottlenecks. Its primary objective is to enable LLMs to understand and utilize external tools to complete specific tasks. By invoking external tools, such as databases, search engines, and mathematical tools, LLMs can transcend their parameterized knowledge; enhance their reasoning, decision-making, and execution capabilities; and mitigate hallucination problems. This paper systematically reviews the development context and technical advancements in LLM tool learning, analyzes the expansion of LLM capabilities through tools, summarizes tool invocation mechanisms ranging from in-context learning to fine-tuning training, and discusses key issues including performance optimization and adaptive tool generation. The paper also analyzes evaluation methods for LLM tool invocation, summarizes the current challenges in tool learning, and outlines future research directions.

World models are generally believed to understand and represent the external world and predict future states based on current world states and actions. Large models leverage massive training data and vast parameter scales to exhibit outstanding capabilities in learning, understanding, representing, and generating textual knowledge, as exemplified by language large models such as GPT-4 and LLaMA. In recent years, research on world models has attracted significant attention from both industry and academia, leading to significant research and commercial achievements in domains such as autonomous driving, social simulation, embodied intelligence, and video generation. Moreover, researchers have applied the remarkable results of various large models to world models, further enhancing their performance. This paper comprehensively reviews world models built using large models across different domains, covering both language large model- and Vision Large Model (VLM)-based approaches. Several important application areas, including embodied intelligence, smart cities, social simulation, and physical environment simulation, are selected to introduce relevant models. This paper classifies world models based on the modality of the large models used, highlighting the functional differences between world models based on different modalities. Subsequently, important open-source resources and benchmarks for world models are presented to help researchers in related fields understand and utilize world models quickly. Finally, this paper is summarized and future research directions are presented.

In Printed Circuit Board Assembly (PCBA), defect detection is key to improving production line efficiency. However, after assembly, printed circuit boards are usually inspected manually, leading to labor and time wastage, as well as missed and false detections. To address these issues, this paper proposes an improved lightweight YOLOv8s network that effectively reduces model complexity while enhancing the accuracy of PCBA defect detection. First, owing to the lack of publicly available PCBA-related datasets, a dataset called PCBA-DET is constructed for post-assembly PCBA defect detection. Various data augmentation techniques are applied to PCBA-DET to simulate real-world production scenarios and improve the dataset balance. Second, the last C2f module of the YOLOv8s backbone is replaced with a Re-parameterized Large Kernel convolution Network (RepLKNet) to reduce computational cost and increase the effective receptive field of the model. In addition, in the neck network of YOLOv8s, a P2 small object detection layer and Ghost Convolution are introduced to capture more detailed information and effectively reduce the number of model parameters. On the augmented PCBA-DET dataset, the improved model achieves an increase of 2.6 and 0.1 percentage points in terms of mean Average Precision (mAP)@0.5∶0.95 and mAP@0.5, respectively, compared with the baseline model, whereas the number of parameters is reduced by 36.8%.

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.

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.

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.

The traditional path-planning algorithm of a mobile robot usually requires a map to effectively plan the path. By contrast, path planning based on Deep Reinforcement Learning (DRL) does not require maps for navigation, owing to which it has received considerable attention. However, traditional DRL path-planning algorithms often face challenges such as low sample utilization, slow training speed, and insufficient generalization ability. To solve these issues, the Twin Delayed Deep Deterministic (TD3) policy gradient algorithm is improved to enhance its path-planning performance for mobile robots. First, to solve the problem of the TD3 algorithm having limited ability for continuous space exploration, the exploration strategy is improved and the continuous space exploration ability of the algorithm is enhanced using pink noise with time correlation. Second, the n-step method is combined with the Loss-Adjusted Approximate Actor Prioritized (LA3P) experience replay method. The n-step method can capture the long-term reward signal more accurately by expanding the immediate reward in the experience replay buffer to the n-step cumulative discount reward, whereas the LA3P method can improve the sample utilization and performance of the algorithm by efficiently using the n-step experience. Finally, three different environments are built in Gazebo for the experiments and compared with various algorithms. The experimental results show that the improved algorithm has more advantages in terms of training time, average success rate, and average distance, which proves the effectiveness of the improved algorithm.

To address the issue of unmanned vehicles being unable to efficiently reach target points in muddy and rugged terrains such as tidal flats, an improved algorithm based on the A* algorithm, Tidal-A* (TA*), is proposed to plan optimal paths for unmanned vehicles. Considering the characteristics of tidal flat environments, the quality of the generated paths is jointly evaluated using Soil Moisture Content (SMC) along the path, path height fluctuation, and path length. To address the difficulty of directly obtaining environmental information, a drone equipped with a hyperspectral sensor and LiDAR is used to scan the target area. A dimensionality reduction method combining spectral preprocessing and the Pearson correlation coefficient is proposed to train the SMC inversion model. In response to the limitations of the traditional A* algorithm, which only searches for paths based on path length, a cost function that integrates multiple constraints is proposed based on the design of the cost functions for three individual constraints. To address the issue that the traditional A* algorithm cannot change the path according to requirements, a coefficient combination is designed to control the proportion of each constraint in the cost function while solving the problem of inconsistent orders of magnitude between different constraints. To address the potential issue that the traditional A* algorithm may overlook better solutions, the calculation range of the heuristic function is improved, allowing the algorithm to trade off-path redundancy for the optimization of other constraints. The simulation results show that when using this algorithm to train the model, the determination coefficient R² is 0.784, and the Ratio of Standard Deviation (RPD) is 2.151, which are 38% and 33.8% higher, respectively, than those of the direct inversion methods. Compared to those generated by traditional algorithms, the length, SMC value, and height fluctuation of the paths generated by the TA* algorithm are reduced by 3.4%, 5.1%, and 18.7%, respectively.

The multi-view subspace clustering algorithm, a type of multi-view clustering algorithm, emphasizes discovering potential subspaces in multi-view data and clustering based on these subspaces. The Multi-view Subspace Clustering algorithm with Grouping Effects (MvSCGE) is also a type of multi-view subspace clustering algorithm. The basic principle is to learn the subspace representation of each view through smooth regularization, while ensuring cross view consistency, and ultimately learning a consistent clustering index matrix. The clustering results are obtained after processing. However, this algorithm only considers the local structure of a single view and has certain limitations. To further explore the diversity between views, this paper proposes a Diversity-induced Multi-view Subspace Clustering algorithm with Grouping Effect (DiMvSCGE). It preserves the local structure of each view while using the Hilbert-Schmidt Independence Criterion (HSIC) to measure the diversity between views, and iteratively uses alternating direction minimization on this basis. Based on the clustering index matrix obtained after the iterations, k-means clustering is performed to obtain the final result. Comparative experiments with several advanced algorithms on four public datasets show that this algorithm offers advantages, such as low parameter sensitivity and fast convergence speed, and demonstrates good performance on different datasets.

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

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.

Single-modal images are easily affected by light, weather, and other environmental conditions in all-weather ship detection. This leads to a low ship detection accuracy and high leakage rate. To address these issues, this paper proposes a ship detection method, VIF-RTDETR, which fuses visible light and infrared image information. The method fully utilizes the rich details and color information of visible images and the stable performance of infrared images in low-light environments, and constructs a four-channel input model. The complementary fusion of varied modal information is realized by designing the fusion module VIF such that it makes more reasonable use of the information from the two modalities (visible light and infrared) in the detection network. The channel attention in the backbone feature extraction network is combined to further optimize the feature extraction capability by dynamically assigning different weights to the channels, thereby enhancing the feature expression capability of the channels. To further enhance the detection performance of small targets in ship detection, a weighted bounding box loss function is designed so that the model can effectively focus on the feature expression of targets of different sizes and improve the detection accuracy under different target sizes. The experimental results show that in the visible and infrared datasets for the ships, the detection precision AP_0.5∶0.95, AP_0.5 of the model reaches 78.3% and 98.5%, respectively, reflecting improvements by 4.7 and 9.2 percentage points relative to AP_0.5∶0.95 the single-modal visible and infrared models. Further, the recall rate AR_0.5∶0.95 reaches 85.2%, reflecting improvements by 3.1 and 7.3 percentage points relative to the single-modal visible and infrared models, respectively. Thus, the findings contribute to significantly improving the precision of ship detection and reducing the leakage rate.

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.

With the development of social networks, people are increasingly expressing their emotions through multimodal data, such as audio, text, and video. Traditional sentiment analysis methods struggle to process emotional expressions in short videos effectively, and existing multimodal sentiment analysis techniques face issues such as low accuracy and insufficient interaction between modes. To address these problems, this study proposes a Multimodal Sentiment Analysis method based on Dense Co-Attention (DCA-MSA). First, it utilizes the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model, OpenFace 2.0 model, and COVAREP tool to extract features from text, video, and audio, respectively. It then employs a Bidirectional Long Short-Term Memory (BiLSTM) network to model the temporal correlations within different features separately. Finally, it integrates different features through a dense co-attention mechanism. The experimental results show that the model proposed in this paper is competitive in multimodal sentiment analysis tasks compared to some baseline models: on the CMU-MOSEI dataset, the highest increase in binary classification accuracy is 3.7 percentage points, and the highest increase in F1 value is 3.1 percentage points; on the CH-SIMS dataset, the highest increase in binary classification accuracy is 4.1 percentage points, the highest increase in three-classification accuracy is 2.8 percentage points, and the highest increase in F1 value is 3.9 percentage points.

Accurate photovoltaic power prediction is crucial for enhancing grid stability and improving energy utilization efficiency. To address the limitations of existing methods, which struggle to simultaneously consider both long-term dependencies and short-term variation patterns of photovoltaic power, this study proposes a novel photovoltaic power prediction method named Solarformer. This method integrates a Pyramid Attention Module (PAM) with a Temporal Convolutional Network (TCN) to optimize the Transformer architecture. First, multiple feature selection mechanisms are employed to screen the input features, to enhance the model′s ability to characterize photovoltaic data features. Second, a coarse-grained construction module and PAM are utilized to optimize the Transformer encoder, capturing the long-term temporal dependency features of photovoltaic power at multiple scales. Third, a constraint mechanism based on the sunrise-sunset effect of photovoltaic power and the TCN are employed to optimize the Transformer decoder, strengthening the model′s ability to capture short-term variation features of photovoltaic power and better model its short-term variation patterns. Experimental results on the Sanyo dataset from Australia demonstrate that Solarformer can effectively improve photovoltaic power forecasting accuracy. Compared with the DLinear model, it reduces the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Symmetric Mean Absolute Percentage Error (SMAPE) by approximately 7.45%, 6.99%, and 14.10%, respectively.

Driver fatigue is a major cause of traffic accidents, and driver fatigue state classification based on Electroencephalograms (EEGs) is an important task in the field of artificial intelligence. In recent years, deep learning models that incorporate attention mechanisms have been widely applied to EEG-based fatigue recognition. While these approaches have shown promise, several studies disregard the inherent features of EEG data itself. Additionally, the exploration of the mechanisms and effects of attention on the classifier is vague, which results in failure to explain the specific effects of different attention states on classification performance. Therefore, this study selects the SEED-VIG data as the research object and adopts the ReliefF feature selection algorithm to construct optimized models of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and Support Vector Machine (SVM) based on self attention, multihead attention, channel attention, and spatial attention mechanisms. Experimental results on the EEG data included in the SEED-VIG dataset show that the performance of several neural network optimization models based on multimodal attention mechanisms has improved in terms of accuracy, recall rate, F1 score, and other indicators. Among them, the Convolutional Block Attention Module (CBAM)-CNN model, which can enhance spatial and channel information, achieves the best performance with 84.7% mean accuracy with 0.66 standard deviation.

Detecting defects on unstructured roads is important for road traffic safety; however, annotated datasets required for detection is limited. This study proposes the Multi-Augmentation with Memory (MAM) semi-supervised object detection algorithm to address the lack of annotated datasets for unstructured roads and the inability of existing models to learn from unlabeled data. First, a cache mechanism is introduced to store the positions of the bounding box regression information for unannotated images and images with pseudo annotations, avoiding computational resource wastage caused by subsequent matching. Second, the study proposes a hybrid data augmentation strategy that mixes the cached pseudo-labeled images with unlabeled images inputted into the student model, to enhance the model′s generalizability to new data and balance the scale distribution of images. The MAM semi-supervised object detection algorithm is not limited by the object detection model and better maintains the consistency of object bounding boxes, thus avoiding the need to compute consistency loss. Experimental results show that the MAM algorithm is superior to other fully supervised and semi-supervised learning algorithms. On a self-built unstructured road defect dataset, called Defect, the MAM algorithm achieves improvements of 6.8, 11.1, and 6.0 percentage points in terms of mean Average Precision (mAP) compared to those of the Soft Teacher algorithm in scenarios with annotation ratios of 10%, 20%, and 30%, respectively. On a self-built unstructured road pothole dataset, called Pothole, the MAM algorithm achieves mAP improvements of 5.8 and 4.3 percentage points compared to those of the Soft Teacher algorithm in scenarios with annotation ratios of 15% and 30%, respectively.

As core data for maritime traffic, ship trajectory data can be used for trajectory prediction, early warning, and other tasks with pronounced temporal characteristics. However, owing to factors such as harsh marine environments and poor communication reliability, missing ship trajectory data is a common problem. Learning from time series containing missing data can affect the accuracy of time series analysis significantly. The current mainstream solution is to approximate the imputation of missing data, mainly based on convolutional models, to reshape the time series along a timeline to capture its local features of the time series. However, the ability to capture the global features of long time series is limited. The Transformer enhances the ability of a model to capture the global features of a time series by capturing the relationships between various time points in the time series through its core self-attention mechanism. However, because its attention is calculated through matrix multiplication, it ignores the temporal nature of the time series, and the obtained global feature weights do not have a time span dependency. Therefore, to address the issue of capturing global features in long time series, this study proposes the GANet, a variant network based on the self-attention mechanism. GANet first obtains the basic global feature weight matrix from the time series points through the self-attention mechanism and then uses gated recurrent units to forget and update this global feature weight matrix on the timeline, thereby obtaining a global feature weight matrix with time span dependency, which is then used for data reconstruction to impute the missing data. GANet combines the self-attention mechanism and gating mechanism to capture global features while considering the impact of the time span on different time points, making the captured global feature time span dependent. Experimental results show that compared with existing models, such as Autoformer and Informer, GANet achieves better interpolation performance on Trajectory, ETT, and Electricity datasets.

To optimize Quality of Service (QoS), Mobile Edge Computing (MEC) has been deeply integrated into the Internet of Vehicle (IoV) to provide geographically proximal computing resources for vehicles, thereby reducing task processing latency and energy consumption. However, traditional MEC server deployment relies primarily on terrestrial Base Stations (BSs), resulting in high deployment costs and limited coverage, making it difficult to ensure uninterrupted services for all vehicles. Air-ground collaborative IoV technology has emerged as a solution to these challenges. Unmanned Aerial Vehicles (UAVs) can dynamically assist Road-Side Units (RSUs) using their flexibility in line-of-sight links, providing more flexible computing resources for vehicular users, thereby ensuring the continuity and efficiency of in-vehicle services. Therefore, this study proposes a Dynamic Vehicular Edge Task Offloading Method (DVETOM) based on air-ground collaboration. This method adopts a vehicle-road-air architecture, establishing Vehicle-to-RSU (V2R) and Vehicle-to-UAV (V2U) links. Transmission and computation models are constructed for three modes: local execution of vehicular tasks, offloading tasks to the RSU, and offloading tasks to the UAV. An objective function is established with the joint optimization goal of minimizing system latency and energy consumption. DVETOM transforms the task offloading problem into a Markov Decision Process (MDP) and optimizes the task offloading strategy by using the Distributed Deep Deterministic Policy Gradient (D4PG) algorithm based on Deep Reinforcement Learning (DRL). Compared with 5 benchmark methods, experimental results show that DVETOM outperforms existing methods by 3.45%—23.7% in terms of reducing system latency and 5.8%—23.47% in terms of reducing system energy consumption while improving QoS for vehicular users. In conclusion, DVETOM enhances the offloading of vehicular edge computing tasks within the IoV effectively. It offers IoV users a more efficient and energy-conserving solution, showcasing its extensive potential for application in intelligent transportation systems.

Traditional Retrieval-Augmented Generation (RAG) methods predominantly focus on pure-text scenarios. In these scenarios, their retrieval and generation mechanisms encounter difficulties in effectively modeling common visual elements, spatial layouts, and structural semantics within multimodal documents. This drawback restricts their performance in tasks related to text-image hybridization, long documents, and cross-document reasoning. To tackle this issue, Multimodal Retrieval Augmented Generation (MRAG), by integrating text, image, and layout structure modeling, and incorporating multimodal evidence retrieval and scheduling during the generation process, has already developed into a core technical paradigm for Question & Answer (Q & A) and reasoning in visually-rich documents. This paper conducts a systematic review of research progress in MRAG applications for document Q & A tasks. Firstly, based on the practical requirements for multimodal document understanding, we analyze the key challenges in MRAG implementation, including multimodal alignment, long-context modeling, evidence traceability, and system robustness. Secondly, from the perspective of how MRAG systems support the generation process, we compare representative methods across four dimensions: embedding paradigms, document retrieval scope, layout-aware mechanisms, and multimodal retrieval strategies. We focus on how design choices influence generation stability, reasoning accuracy, and system complexity. Thirdly, we summarize the characteristics and limitations of existing multimodal document Q & A datasets and evaluation frameworks, and analyze the current constraints in evidence granularity and reasoning explainability. Finally, we point out that MRAG is evolving from static similarity-matching retrieval mechanisms to dynamic evidence planning paradigms centered on generation and reasoning needs, and should continuously enhance the reliability and explainability of complex document Q & A systems through collaborative multimodal modeling with multi-granularity approaches.

This study explores the use of Artificial Intelligence (AI) technology throughout the neutron scattering experiments′ lifecycle to determine how AI technology can revolutionize key aspects such as experimental apparatus, data acquisition, and data processing. The study begins by introducing the fundamental principles and experimental procedures of neutron scattering technology before focusing on the multifaceted applications of AI technology in neutron scattering experiments. These applications include optimizing experimental infrastructure, data acquisition, and imaging preprocessing, as well as characterizing experimental samples in neutron diffraction, neutron reflection, and Inelastic Neutron Scattering (INS). This study demonstrates the importance of AI technology in increasing the intelligence level of experiments, accelerating data processing, and improving the accuracy and reliability of data analyses. In addition, an in-depth discussion is held on the future application of AI technology in neutron scattering experiments, indicating that with the continuous advancement of technologies such as multimodal learning, interpretable models, large language models, and AI-Ready databases, AI technology is poised to bring revolutionary changes to neutron scattering experiments, opening up new avenues for revealing the microstructure and properties of complex material systems.

This paper proposes an improved YOLOv8-based model named CAFR-YOLO to address the issues of insufficient cross-level feature interaction and limited feature representation capability in multi-scale object detection under complex scenes. First, a novel cross-scale feature reorganization pipeline was designed, constructing the Channel Attention-guided Feature Reorganization (CAFR) module. By using a specific layer as the fusion backbone and incorporating scale alignment, attention-weighted fusion, and feature subset splicing strategies, it alleviates insufficient cross-level interaction in traditional feature pyramid structures. Secondly, at the local level, the method introduces the C2f_DCNv3 module into the backbone network, significantly enhancing the model's geometric adaptability by exploiting the dynamic sampling characteristics of deformable convolution. From a global perspective, the C2f_SAConv module is constructed by combining Switchable Atrous Convolution (SAC) with the C2f module, optimizing multi-scale semantic feature fusion through dynamic atrous rate adjustment. These two approaches enhance the model's robustness to complex scenes. Finally, SPDConv replaces traditional convolution structures, strengthening feature representation through spatial-channel reorganization while reducing computational complexity. Experimental results demonstrate that CAFR-YOLO achieves 86.3% mAP@0.5 and 67.2% mAP@0.5:0.95 on the PASCAL VOC dataset with comparable computational costs to the original model. On the MS COCO dataset, it improves mAP@0.5 and mAP@0.5:0.95 by 3.5% and 3.9%, respectively. Compared to existing state-of-the-art methods, CAFR-YOLO exhibits significant advantages across multiple metrics. The proposed CAFR-YOLO model substantially enhances multi-scale object detection accuracy and robustness while maintaining computational efficiency, providing a novel solution for real-time object detection tasks.

To address issues such as a single point of failure, risk of tampering, opaque verification, and the proliferation of false information in centralized news architectures, a blockchain-based decentralized news-retrieval and aggregation architecture is proposed for trusted storage, verifiable retrieval, and transparent governance of multi-source news data. This architecture integrates blockchain, smart contracts, and distributed storage to form an integrated trust system of "chain-contract-storage." The consensus mechanism ensures the credibility of the data sources, whereas smart contracts automatically enforce governance rules for traceability and credibility. A Multi-Valued modulo Function (MVF) robust task allocation algorithm and a Key/Transaction Merkle-Patricia Tree (KMPT/TMPT) dual-layer verifiable indexing mechanism are proposed to optimize task scheduling and index verification, thereby enhancing retrieval and verification efficiency. The system incorporates Merkle Hash Tree (MHT) integrity verification and a multi-source reputation weighting mechanism to achieve adaptive adjustment of the source reputation, thereby enhancing retrieval accuracy and system robustness. The system is deployed in a private cloud environment on an OpenStack and experimentally validated using 106 532 news data collected in 2024. Experimental results show that compared to traditional solutions, the credibility verification accuracy of this architecture is improved by 15.6% (P < 0.01), the tamper resistance detection success rate reaches 99.6%, and the false news suppression rate is 92.4%. Deep integration of the retrieval and verification processes enhances the comprehensive effectiveness of trusted retrieval by 22.3% (P < 0.05). This study verifies the feasibility and engineering effectiveness of blockchain in trusted retrieval and data governance, providing theoretical support and an engineering reference for building a highly transparent and traceable news ecosystem.

Real-world data often follow a long-tail distribution. Federated learning methods that assume a balanced global data distribution struggle to classify tail-class data within long-tail data accurately. Researchers typically focus on retraining a balanced classifier for the global model, to mitigate the impact of long-tail data. However, this approach does not consider the feature extractor of the balanced model or how the model's feature extractor can be enabled to learn high-quality image features, leading to the poor performance of the global model. To enable the model to learn high-quality image features without bias during the feature learning stage, this study proposes a federated learning method combining rotational self-supervision and Contrastive Language-Image Pre-training (CLIP) guidance. This method uses rotational self-supervision to guide the training of local client models, thereby reducing the impact of long-tail data on the client models and enabling the model to learn high-quality image features. Simultaneously, CLIP is utilized to guide both the normal training of the model and the rotated images, transferring rich knowledge from CLIP to the client model and further enhancing the performance of the feature extractor. In experiments on the CIFAR-10 and CIFAR-100 datasets under different long-tail distributions, the proposed approach improves the global model's classification accuracy by 2.35 to 4.72 percentage points, respectively, compared with other federated learning methods.

To address the issue of interference factors such as seasonal changes, climate, and illumination that affect high-resolution remote sensing images of the same geographical space but different temporal phases, a remote sensing image building Change Detection (CD) method based on a multi-temporal ChangeFormer is proposed. This method uses multiple remote sensing images from different temporal phases and fuses the latest temporal remote sensing image with multiple prechange remote sensing images at different scales for feature difference extraction. Additionally, it focuses on both the comprehensive semantic features of the images and the details of the semantic information between the images. This approach helps reduce false detections caused by changes in factors such as season and illumination. Additionally, the method fuses the feature differences of multiple prechange remote sensing images from different temporal phases and introduces a regularization term as a loss function. This eliminates interference from nonbuilding changes and illumination shadows in the nonchanging areas of buildings, thereby enhancing the generalization ability of the model. A three-temporal remote sensing image dataset covering changes from agricultural land to construction land is constructed. The experimental results show that, compared to the current optimal BIT method, the multi-temporal ChangeFormer method improves the F1 value, Intersection over Union (IoU), precision, and recall by 9.04%, 9.87%, 15.27%, and 3.4%, respectively, thus significantly enhancing detection accuracy. Furthermore, it outperforms classical CD methods in terms of detailed information processing.

In recommender systems, sequential recommendations aim to predict the future interests of a user based on historical interaction sequences. However, existing deep learning models typically focus on capturing users' long-term behavior patterns while neglecting the fine-grained modeling of temporal information, which limits improvements in recommendation performance. To address this issue, this study proposes a method that combines the global and local self-attention mechanisms. This method processes user interaction sequences in chunks and introduces a weight decay mechanism that assigns differentiated weights to different sequence blocks based on temporal distance, thereby capturing changes in users' short- and long-term interests more accurately. However, this approach introduces additional parameters, thereby increasing the model complexity. While Stochastic Shared Embedding (SSE) technology can reduce the parameter count and mitigate overfitting, its random embedding approach may introduce noisy data, affecting the accuracy of the recommendations. To solve this problem, this study proposes a strategy that combines a Generative Adversarial Network (GAN) with SSE. By using GAN to generate high-quality interaction data that align with user interest distributions and combining this with the random replacement mechanism in SSE for data augmentation, the data are enhanced by randomly selecting generated data, thus reducing the risk of noise introduction while retaining SSE's advantage in reducing overfitting. Experiments are conducted on three public datasets: MovieLens-1M, Amazon Beauty, and Yahoo Music. The results show that the proposed method performs well in terms of Normalized Discounted Cumulative Gain (NDCG), Hit Rate (HR), and Mean Reciprocal Rank (MRR).

Cyberattacks are becoming increasingly diverse, and traditional intrusion detection methods exhibit limitations in capturing the spatio-temporal characteristics of complex network traffic. Most traditional methods rely primarily on static feature analysis, making it difficult to adapt to the ever-changing intrusion behaviors in dynamic network environments. When analyzing network traffic, existing deep learning methods often overlook the topological structure between network nodes and the temporal dynamics of traffic. To address these issues, this paper proposes a novel intrusion detection method based on a dynamic spatio-temporal Graph Neural Network (GNN), named DSTG-IDS. By segmenting network traffic through time windows, data packets within each time period are modeled as nodes in a graph, and connections are established based on the relationship between the source and destination IP, thereby constructing a sequence of dynamic graphs over time. To better capture the temporal characteristics of traffic, graph data are encoded with a temporal position to enhance the temporal information expression ability of the nodes within different time periods. In terms of model design, first, Graph Convolutional Network (GCN) are utilized to extract the spatial features of network traffic, and Graph Attention Network (GAT) are incorporated to enhance the focus on key node information. Second, Bidirectional Gated Recurrent Unit (Bidirectional GRU) are employed to model the temporal sequence of traffic, effectively capturing the dynamic characteristics of data changes over time. Finally, a multi-head attention mechanism is utilized to fuse spatio-temporal features and perform classification. Experiments on three widely used datasets—BoT-IoT, ToN-IoT, and NF-CSE-CIC-IDS2018—demonstrate that DSTG-IDS achieves accuracies of 99.69%, 98.61%, and 93.26%, respectively. Compared with other intrusion detection methods, DSTG-IDS exhibits significant advantages in terms of accuracy, recall, False Alarm Rate (FAR), and F1 value.

As a mainstream tool for container orchestration, Kubernetes can support automatic deployment, service discovery, and load balancing. It is known for its high availability and performance. However, scheduling strategies such as the best adaptation algorithm or the minimum negative cut method ignore the heterogeneity and energy differences of nodes. In addition, Kubernetes tools only consider CPU and memory resources and set a unified weight mechanism in advance, which can easily lead to problems such as load imbalance, performance degradation, and the inability to satisfy refined scheduling. To address these existing problems, this study proposes a heterogeneous task scheduling algorithm based on multi-dimensional resources, namely A-KCSS. The A-KCSS algorithm is based on the heterogeneous computing resources of a cluster. It adds disk Input/Output (I/O), network I/O load, and GPU resources as evaluation indicators for filtering and screening, and it more comprehensively considers the heterogeneity of nodes. This study also introduces a weight calculation model based on multi-dimensional resource factors. Based on the resource requirements of the task to be scheduled, the weight value of each dimension of the resource factor of the task to be scheduled is calculated, and the score of each node is calculated based on the real-time resource utilization of the cluster. Nodes are prioritized according to the score, and the node with the highest priority is selected for scheduling. The performance of the A-KCSS algorithm is experimentally verified on the Kubernetes cluster. Compared with the default scheduling algorithm and the KCSS algorithm, the average response time is reduced by 10% and 4%, the throughput is increased by 30% and 15%, the availability is improved by 40% and 30%, and the load balancing performance is increased by 23% and 18%, respectively, thereby improving the overall cluster performance.

This study proposes a skin melanoma segmentation algorithm, YOLOv8-Skin, designed to address the issue of imprecise results in existing algorithms caused by diverse shapes and blurred edges. YOLOv8-Skin combines multiscale feature extraction and enhanced edge segmentation based on YOLOv8. First, the backbone network CSPDarkNet53 of YOLOv8 is replaced with U-Net v2, which is more suitable for medical image segmentation. This change introduces rich semantic information into low-level features and refines high-level features, enabling precise delineation of lesion boundaries and effective extraction of small structures in melanoma images. Second, a Deformable-Large Kernel Attention (D-LKA) mechanism is introduced into the neck's C2f, enhancing the model's ability to capture irregular image structures through deformable convolutions and improving multilevel feature fusion using large kernel convolutions. Finally, a Diverse Branch Block (DBB) is incorporated into the head, forming a new segmentation head that enhances the representation capability of single convolutions by combining diverse branches of different scales and complexities. This enriches the feature space and improves feature extraction. Experiments conducted on the ISIC2017, ISIC2018, and PH² datasets verify the algorithm's effectiveness. On the ISIC2017 dataset, the Dice coefficient, Specificity, Sensitivity, and Accuracy reach 88.86%, 91.34%, 97.24%, and 96.29%, respectively. On the ISIC2018 dataset, they reach 91.64%, 95.42%, 96.69%, and 95.83%, respectively. On the PH² dataset, they reach 95.92%, 95.43%, 97.02%, and 96.13%, respectively. The algorithm demonstrates stronger segmentation performance and is better suited for melanoma segmentation tasks compared to existing methods.

Traditional face recognition systems use various bionic algorithms combined with Support Vector Machines (SVM) to form a corresponding face recognition model for the final face classification problem. This method selects the optimal SVM parameters through algorithm iteration. However, this strategy is hindered by low classification accuracy, long training time, and the possibility of easily falling into the local optimal solution. This paper proposes a face recognition method using an improved Artificial Hummingbird Algorithm (AHA) to optimize SVM. First, AHA is improved by introducing a chaotic sequence of Tent mapping so that the hummingbird population is initialized more uniformly and the algorithm does not fall into the local optimal solution; second, the improved AHA algorithm is introduced in the method of face recognition using SVM. By setting a certain number of iterations for the algorithm, the optimal relevant parameters used to optimize SVM are selected to improve face recognition accuracy. The improved AHA is compared to the Grey Wolf Optimizer (GWO), Sparrow Search Algorithm (SSA), and Whale Optimization Algorithm (WOA). The improved AHA has a faster convergence speed in solving the benchmark function. Simultaneously, in a face recognition experiment on the ORL face database, the improved AHA combined with SVM is compared to GWO, SSA and WOA combined with SVM. In face recognition tasks, the improved AHA combined with SVM achieves higher accuracy and recall rate, with a faster inference speed.

Blood pressure is a key indicator of cardiovascular health. To monitor daily blood pressure at home, researchers continue to use non-end-to-end measurement methods, such as combining multiple physiological signals, in addition to traditional blood pressure meters. These methods present some disadvantages: collecting multiple physiological signals is difficult and expensive, and maintaining the time synchronization of signal collection is challenging. Currently, the end-to-end method, which uses face videos to predict blood pressure, has widened the application areas to an extent; however, in most methods, selecting the areas of interest and limited prediction accuracy remain unresolved issues. To solve these problems, this paper proposes a remote blood pressure prediction method based on a multi-scale attention structure in visible light, called EBP-Net. First, each face video candidate window is classified, the effective skin region is extracted by regression, the remote Photoplethysmography (rPPG) signal is extracted from the continuous effective face region by optical flow-based technology, and the complete rPPG signal is filtered by wavelet transform. Robust rPPG signals are extracted using methods such as detrending. Second, EBP-Net introduces a new Efficient Multi-scale Attention (EMA) module and Multi-Scale Fusion (MSF) module, which can enhance the features of depth vision representation without reducing the channel dimension and significantly improve the model's ability to understand and predict physiological signals by capturing and hierarchically expressing multi-scale features. In experiments, Systolic Blood Pressure (SBP) is categorized as grade C according to the British Hypertension Society (BHS) on both datasets, while Diastolic Blood Pressure (DBP) is categorized as grade B. The Mean Absolute Error (MAE) values are 6.82 and 5.17 mmHg for systolic and DBP, respectively, which are lower than those in recent comparable studies. Compared with other models, this method has better generalization ability, a lower error rate, and provides effective methods and suggestions for the detection blood pressure level from face videos.