Crowd intelligence has been highlighted as one of the five major trend directions in China's New Generation Artificial Intelligence Development Plan, and the question ″How does crowd intelligence emerge?″ was listed by Science in 2021 among 125 key scientific challenges for the future. Existing studies on intelligence emergence have largely focused on collective intelligence in biological groups (e.g., flocks of birds and schools of fish), emphasizing how simple individuals following local interaction rules can give rise to globally ordered behaviors. By contrast, crowd intelligence in human societies involves not only behavioral coordination and organization but also higher-order intelligence and richer connotations manifested in knowledge, culture, and innovation. To address this discrepancy and research gap, this study, for the first time, systematically reviews and clarifies the conceptual framework and core connotations of crowd intelligence in human societies. It conducts an in-depth analysis of seven representative emergent phenomena—crowd behavior, wisdom of crowds, consensus formation, social cooperation, social learning, knowledge and culture, and collective intelligence innovation—by summarizing their key mechanisms, major models, driving factors, and evolutionary regularities, thereby constructing a theoretical system for the emergence of crowd intelligence in human societies. Building on this foundation, the study further investigates the mapping pathways and mechanisms from human crowd intelligence to artificial crowd intelligence; extracts representative paradigms and essential implementation essentials of mechanism-driven artificial crowd intelligence systems; and provides fundamental theoretical and methodological support for the design, construction, and future development of artificial crowd intelligence systems oriented toward complex tasks.

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 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.

Owing to the heterogeneity and complexity of Android malware, traditional static analysis methods that rely on single features such as permissions or API often struggle to accurately differentiate between benign and malicious applications. To address this limitation, this study proposes a novel feature construction method based on multi-modal feature fusion based on in-depth research of Android software features such as permissions, API, bytecodes, and opcodes. The bytecode is transformed into RGB images and visual representations are extracted using the pretrained EfficientNetV2B3 model to capture the high-level characteristics of Android applications. Additionally, Locality-Sensitive Hashing (LSH) is employed to extract opcode sequence features that represent low-level, detailed characteristics of the application. These heterogeneous features are then fused using a Multimodal Factorized Bilinear pooling (MFB) algorithm to create a more discriminative representation of the malware. Building on this enhanced feature representation, a Transformer Encoder-based Android Anomaly Detection (TEAAD) model is introduced. By leveraging the transformer architecture, the TEAAD effectively learns to detect anomalies in Android malware. The experimental results demonstrate that the TEAAD model based on fused features outperforms other deep-learning models, achieving a detection accuracy of 96.87%. The MFB feature fusion method exhibits superior malware identification capabilities compared with other research methods.

In recent years, there has been significant progress in terms of accuracy and robustness of deep-learning-based algorithms for object detection that have been widely applied in industry. However, in the field of small object detection, currently used object detection algorithms suffer from high rates of missed detections and false positives. Therefore, in this study, a YOLO small object detection algorithm, viz., BS-YOLO, which is based on SCConv and BSAM attention mechanism, is developed. First, in response to the problem of the large amount of redundant information generated in the feature extraction network, a new module, viz., C3SC, is proposed to reconstruct the backbone network using SCConv. This module reduces redundant information in both spatial and channel aspects of the extracted feature maps, thereby improving the quality of the feature maps extracted by the backbone network, and in turn enhancing detection accuracy. Second, a new attention mechanism, viz., BSAM, is proposed by combining CBAM and the BiFormer self-attention mechanism, by which weights are allocated reasonably in both spatial and channel aspects, making the feature map more focused on effective information and suppressing background interference. Finally, to solve the problem of uneven distribution of difficult and easy samples in terms of small object detection, Slideloss is used to optimize the loss function, thereby improving the effectiveness of the algorithm for small object detection. The experimental results obtained using the RSOD dataset show that the BS-YOLO algorithm has a precision of 94.2%, a recall rate of 91.6%, and a mAP@0.5 of 95.9%, corresponding to improvements of 3.3, 0.1, and 3.6 percentage, respectively, compared to the original YOLOv5 algorithm. This indicates that the BS-YOLO algorithm can effectively improve the accuracy of small object detection and reduce the missed detection rate.

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 continuous integration of artificial intelligence and robotics technology has facilitated the widespread adoption of multi-rotor Unmanned Aerial Vehicles (UAVs) across various fields, demonstrating their flexibility and efficiency. However, when developing and validating flight control algorithms or solutions for multi-rotor UAVs, researchers face high costs and significant risks. To mitigate these risks and enhance the efficiency of algorithm testing and optimization, simulation platforms for multi-rotor UAVs provide a safe and controlled environment. In this regard, this paper first introduces conventional models of multi-rotor UAVs, selecting the commonly used quadrotor as the representative model. It then elaborates on dynamic models according to different levels of simulation. Subsequently, it provides an overview of the conventional system framework of multi-rotor UAV simulation platforms and discusses their evaluation methods and classification approaches. The evaluation of simulation platforms is detailed from the perspectives of function and performance. multi-rotor UAVs are classified based on whether they support an interactive learning environment and their focus areas: dynamics, sensors, and multi-UAV coordination. This paper also reviews the main solutions for existing UAV flight missions, analyzing typical multi-rotor UAV simulation platforms within the context of traditional and learning-based methods. Finally, the paper outlines future directions for multi-rotor UAV simulation platforms.

Operating Systems (OSs), which form a critical infrastructure in the information age, are widely used in core fields such as medical care, industries, and the military. Their reliability and security directly determine the operational stability of these key fields, while vulnerabilities can lead to serious consequences, including system crashes and data leakage. Hence, the construction of a systematic security assurance system would be of great theoretical and engineering value. This paper systematically reviews the research achievements in this field over the past decade based on the framework of ″formal specification—formal verification—engineering implementation″ and analyzes technical paths and practical applications. With regard to formal specification level, it clarifies the differences between model specifications that describe system functions based on mathematical structures, such as transition systems, and property specifications that define safety and liveness requirements based on Linear Temporal Logic (LTL). This analysis focuses on two key aspects: functional correctness and security attributes. Functional correctness covers task management scheduling, memory allocation and recycling, exception interrupt handling, inter-task communication, and file system read—write consistency, while security attributes focus on the BLP and BIBA models for access control, multidomain isolation in separation kernels, and noninterference and nonleakage theories of information flow. For formal verification, three core methods are considered: deductive proof, which verifies program consistency by relying on Hoare's logic; model checking, which verifies temporal properties based on LTL and Computation Tree Logic (CTL); and standardized process of property verification. Taking the seL4 microkernel, which is the first to achieve functional correctness and information flow non-interference through machine proof, as a case study, this discussion reveals the transformation from theory to engineering. With regard to engineering applications, the achievements in Controller Area Network (CAN) bus communication verification within the automotive field and robustness detection of intercomponent communication in the Android system of smartphones are summarized. This systematic review aims to establish a foundation for future research in related fields, provide dataset support for large language models, and provide a reference for the engineering implementation of these technologies.

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.

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.

Blockchain has gradually evolved into a critical infrastructure that supports the digital economy. However, its inherent characteristics such as anonymity, cross-chain interoperability, and multi-party participation have led to frequent security incidents, including fraud, money laundering, and cyberattacks, which pose serious threats to the stability and compliance of the blockchain ecosystem. Although existing analytical tools and methods have made notable progress in blockchain service security, they suffer from limited generalizability, insufficient reasoning capabilities, and poor adaptability to the evolution of complex business logic. The rapid development of generative Large Language Model (LLM) has significantly reshaped the service computing paradigm. With their strong capabilities in natural language understanding, knowledge reasoning, and multimodal integration, LLM provide new perspectives and technical pathways for research on blockchain service security. This paper systematically reviews the progress of LLM applications in three major areas: pre-event smart contract auditing, in-event anomaly detection, and post-event cross-chain behavior correlation. Further, it summarizes their advantages and limitations and highlights representative practices of LLM-enabled blockchain security. Finally, open research challenges and future directions are discussed, aiming to provide insights for building a trustworthy, interpretable, and efficient framework for blockchain service computing and governance.

Large Language Model (LLM)-based Multi-Agent System (MAS) has demonstrated significant potential in handling complex tasks. Their distributed nature and interaction uncertainty can lead to diverse anomalies that threaten system reliability. This paper presents a comprehensive review, identifying and classifying these anomalies systematically. Seven representative multi-agent systems and their corresponding datasets are selected, accounting for 13 418 operational traces, and a hybrid data analysis method is employed, combining preliminary LLM analysis with expert manual validation. A fine-grained, four-level anomaly classification framework is constructed, encompassing the following anomalies: model understanding and perception, agent interaction, task execution, and external environment. Typical cases are analyzed to reveal the underlying logic and external causes of each type of anomaly. Statistical analysis indicates that model understanding and perception anomalies account for the highest proportion, with ″context hallucination″ and ″task instruction misunderstanding″ being the primary issues. Agent interaction anomalies represent 16.8%, primarily caused by ″information concealment″. Task execution anomalies account for 27.1%, mainly characterized by ″repetitive decision errors″. External environment anomalies account for 18.3%, with ″memory conflicts″ as the predominant factor. In addition, the model perception and understanding of anomalies often act as root causes, triggering anomalies at other levels, highlighting the importance of enhancing fundamental model capabilities. These classification and root cause analyses aim to provide theoretical support and practical reference for building highly reliable LLM-based multi-agent systems.

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.

Current U-Net-based pavement crack detection methods do not fully consider the interaction between the features of each level of the encoder, causing incomplete detection results or missed detections because of information loss during the downsampling process. To address this issue, this study proposes a pavement crack detection method based on multi-level feature fusion. In the encoding stage, the features of cracks at different levels are extracted to form crack feature representations from shallow to deep layers. In the skip connection section, a cross-level fusion strategy based on an improved Channel Cross Transformer (CCT) is adopted to enhance the complementarity between features at each level and enrich the expression of crack features. In the decoding stage, the feature fusion module is used to optimize the decoder's utilization of encoder features, promote the transmission of crack features, and improve the perception ability of crack features. In a series of comparative and ablation experiments on two public datasets, DeepCrack and CRACK500, the proposed method outperforms six other methods, including DeepCrack and Swin-UNet. On DeepCrack, the proposed method increases the F1 value by 2.30 and 2.51 percentage points, respectively, compared to those of DeepCrack and Swin-UNet, while on CRACK500, it increases by 1.65 and 1.00 percentage points, respectively.

DeepFake-enabled abuse of face forgery technology has given rise to considerable security risks to society and individuals; therefore, DeepFake detection has become a hot topic of research. Current deep learning-based forgery detection techniques exhibit good results on High-Quality (HQ) datasets but show poor performance on Low-Quality (LQ) datasets and across different datasets. To improve the generalization of DeepFake detection performance, this paper proposes a Multi-Scale Dual-Stream Network (MSDSnet) for DeepFake detection. The network input is divided into a spatial-domain feature stream and a high-frequency noise feature flow. First, the Multi-Scale Fusion (MSF) module is used to capture the tampered coarse-grain facial features from images and fine-grained high-frequency noise information from forged images in different situations. The network fully integrates the dual-stream features of the spatial-domain feature stream and high-frequency noise feature flow through the MSF module. The Multi-modal Interaction Attention (MIA) module further interacts to learn the dual-stream information. Finally, a Frequency Channel Attention Network (FcaNet) is used to obtain the global information of the forged face features for complete detection and classification. Experimental results show that the proposed method achieves 98.54% accuracy on the HQ dataset Celeb-DF v2 and 93.11% on the LQ dataset FaceForensics++. Simultaneously, the experimental results are better than those obtained using other methods in cross-dataset experiments.

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.

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.

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.

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.

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.

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).

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.

Password leakage incidents often involve the leakage of user passwords and identity information. Because users are accustomed to reusing passwords across multiple network services, attackers can tweak leaked passwords to accurately attack user accounts. This is called a credential tweaking attack. By analyzing large-scale leaked passwords and the corresponding user identity information, this study finds that user strategies for creating passwords are often associated with user identity information. However, current research on credential tweaking attacks relies only on leaked password structures and ignores leaked user identity information when predicting password tweaking strategies. To improve the accuracy of credential tweaking attacks, this study designs a credential tweaking attack optimization method based on user identity information. In the preprocessing phase, username and regional information is extracted from the user identity information and the probability of users' different password creation strategies in different regions is statistically calculated. In the training phase, regional information is combined to learn users' character-level editing operations on leaked passwords. In the password generation phase, a password generation method that integrates character-level editing operations, structure-level editing operations, and username information is designed. The experimental results show that in an attack with 10³ guesses, the cracking rate of this method has a maximum improvement of 41.8% compared to the existing best method (PassBERT), highlighting the threat posed by credential tweaking attacks based on user identity information to password security.

This study presents a facial emotion recognition network based on UniRepLKNet to address the difficulty in effectively capturing feature information and preventing key facial information from occupying a more prominent position in the facial emotion recognition process. Moreover, to extract facial emotional features more accurately, the study designs a masked polarized self-attention module that combines U-Net and a polarized self-attention mechanism. This module can deeply mine the dependency between channels and spaces. It can also strengthen the influence of local key information of the face on emotion recognition through a multi-scale feature fusion strategy. The study optimizes UniRepLKNet, a universal large kernel Convolutional Neural Network (CNN), and proposes the EmoRepLKNet neural network structure. In EmoRepLKNet, the mask-polarized self-attention module enables the network to extract key information for facial emotion recognition. Combined with the wide receptive field of large kernel CNN, facial emotions can be recognized effectively. Experimental results show that on the facial emotion recognition dataset FER2013, EmoRepLKNet achieves an accuracy of 76.20%, outperforming existing comparison models and significantly improving facial emotion recognition accuracy compared to that of UniRepLKNet. Additionally, on the single-label portion of the RAF-DB dataset, the proposed method achieves an accuracy of 89.67%.

Adversarial examples can be used to perform transferable attacks on black-box models using surrogates, without knowing the internal structure and parameters of the black-box model. Previous studies have reported relatively low transferability of targeted attacks on black-box models. This study proposes a method for enhancing the transferability of image-directed targeted attacks based on feature fusion. First, adversarial examples are generated via ensemble attacks. Subsequently, using the gradient direction of existing adversarial examples as a baseline, clean features extracted from the original image are used as perturbations to fine tune the existing adversarial examples for improving the transferability of targeted attacks. For model ensembling, a gradient adaptive module is introduced based on the contribution of each model to the overall adversarial objective. To reduce the gradient differences among different models, a gradient filter is proposed for synchronously controlling the gradient direction. Using the feature fusion module, the clean features of the original image are mixed to fine tune the gradient direction of the existing adversarial examples for mitigating the issue of overfocusing on specific features. Experiments on the ImageNet-Compatible dataset reveal that, compared to the Clean Feature Mixup (CFM) method, the proposed method improves the average attack success rate by 7.7 percentage points for non-robustly trained models and by 5.3 percentage points for robustly trained and Transformer models, demonstrating the effectiveness of the method.

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.

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.

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.

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.

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.

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

In 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.

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.

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.

Accurate traffic flow prediction is a key prerequisite for realizing intelligent transportation systems, and is of great significance for strengthening system simulation and control and improving the decision-making of managers. To address the problem of most existing Graph Convolutional Network (GCN) models ignoring the dynamic spatial and temporal variations in traffic data and insufficiently employing node information, which leads to insufficient extraction of spatial and temporal correlations, a traffic flow prediction model based on multiple spatio-temporal graph fusion and dynamic attention is proposed. First, the temporal characteristics of traffic flow data in multi-temporal states are extracted by different convolutional cells. The next step involves constructing a multiple spatio-temporal graph to capture the dynamic trend and heterogeneity of nodes in spatial distribution, followed by extracting spatial characteristics through the integration of GCN. Finally, the spatial and temporal characteristics are analyzed and fused using the multi-head self-attention mechanism to output prediction results. Experimental analyses are performed on two public datasets, PeMS04 and PeMS08, and compared with the Attention Based Spatial-Temporal Graph Convolutional Network (ASTGCN), Multiview Spatial-Temporal Transformer Network (MVSTT), Dynamic Spatial-Temporal Aware Graph Neural Network (DSTAGNN) and other benchmark models that utilize spatio-temporal graph convolution. The results show that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE) of the proposed model are reduced by 7.10%, 7.22%, and 6.47%, respectively, demonstrating the proposed model′s strong adaptability and robustness.

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

The sequence recommendation algorithm dynamically models the user's historical behavior to predict the content they may be interested in. This study focuses on the application of contrastive Self Supervised Learning (SSL) in sequence recommendation, enhancing the model's representation ability in sparse data scenarios by designing effective self supervised signals. First, a personalized data augmentation method incorporating user preferences is proposed to address the issue of noise introduced by random data augmentation. This method guides the augmentation process based on user ratings and combines different augmentation methods for short and long sequences to generate augmented sequences that align with user preferences. Second, a mixed-augmentation training approach is designed to address the issue of imbalanced feature learning during training. In the early stages of training, augmentation sequences are generated using randomly selected methods to enhance the model performance and generalization. In the later stages, augmentation sequences with high similarity to the original sequences are selected to enable the model to comprehensively learn the actual preferences and behavior patterns of users. Finally, traditional sequence prediction objectives are combined with SSL objectives to infer user representations. Experimental verification is performed using the Beauty, Toys, and Sports datasets. Compared with the best result in the baseline model, the HR@5 indicator of the proposed method increases by 6.61%, 3.11%, and 3.76%, and the NDCG@5 indicator increases by 11.40%, 3.50%, and 2.16%, respectively, for the aforementioned datasets. These experimental results confirm the rationality and validity of the proposed method.

As a pivotal technology driving organization digital transformation, Robotic Process Automation (RPA) has garnered significant attention from both the academic and industrial sectors in recent years. However, current deployment strategies suffer from a lack of process analysis, leading to misguided deployment of RPA robots and resource wastage. Furthermore, existing RPA robots deployment methods based on process mining depend overly on domain-specific expertise, limiting their generality. To address these challenges, this study proposes the integration of process mining with RPA robots and presents a deployment method for RPA robots based on process mining. The method is initiated by introducing an approach to mine the global process model from event logs and extracting a Time Petri net containing temporal information. Subsequently, critical process paths are identified using a method designed to recognize key process paths. Finally, an optimization deployment strategy for RPA robots is introduced, which determines the optimal deployment node set considering the time and cost constraints. The proposed method is implemented using ProM, an open-source process mining tool platform. It is compared with four deployment methods in experiments that focus on improving time efficiency. The experimental results indicate that, compared to other deployment methods, this approach results in a time efficiency improvement ranging from 22% to 41%, and the deployment accuracy reaches 1, without relying on domain-specific expert knowledge, validating its generality and accuracy.

Deep learning has been widely applied to medical imaging. A medical image segmentation model based on an attention mechanism is one of the main methods used in current research. For the multi-organ segmentation task, most existing 2D segmentation models mainly focus on the overall segmentation effect of slices, while ignoring the loss or under-segmentation of small object feature information in slices, which limits the model′s segmentation performance. To solve this problem, this study proposes a multi-organ semantic segmentation model, DASC-Net, based on multi-scale feature fusion and an improved attention mechanism. The overall framework of the DASC-Net is based on an encoder-decoder architecture. The encoder uses the ResNet 50 network and sets a skip connection with the decoder. The attention mechanism is realized using the parallel structure of a Dual Attention Module (DAM) and a Small Object Capture (SOC) module to perform multi-scale regional feature fusion. DASC-Net not only perceives the feature information of larger objects but also retains the feature information of small objects through attention weight reconstruction, which effectively addresses the limitations of the attention module and further improves the segmentation performance of the model. The experimental results on the CHAOS dataset show that DASC-Net can obtain 83.72%, 75.79%, 87.75%, 85.63% and 77.60% on the Sensitivity, Jaccard similarity coefficient, Positivity Predictive Value (PPV), Dice similarity coefficient, and mean Intersection over Union (mIoU) indicators, respectively; the Dice similarity coefficient and 95% Hausdorff Distance (HD95) values on the Synapse dataset are 82.44% and 21.25 mm, respectively. DASC-Net performs better than the other segmentation networks on both datasets, which demonstrates its reliable and accurate segmentation performance.

The complex backgrounds, diverse target types, and significant scale variations in remote sensing images lead to target omission and false detection. To address these issues, this study proposes an improved Faster R-CNN multi-object detection model. First, the ResNet 50 backbone network is replaced with the Swin Transformer to enhance the model's feature extraction capability. Second, a Balanced Feature Pyramid (BFP) module is introduced to fuse shallow and deep semantic information, further strengthening the feature fusion effect. Finally, in the classification and regression branches, a dynamic weighting mechanism is incorporated to encourage the network to focus more on high-quality candidate boxes during training, thereby improving the precision of target localization and classification. The experimental results on the RSOD dataset show that the proposed model significantly reduces the number of Floating-Point Operations per second (FLOPs) compared to the Faster R-CNN model. The proposed model achieves 10.7 percentage points improvement in mAP@0.5 ∶0.95 and 10.6 percentage points increase in Average Recall (AR). Compared to other mainstream detection models, the proposed model achieves higher accuracy while reducing the false detection rate. These results indicate that the proposed model significantly enhances detection accuracy in remote sensing images with complex backgrounds.

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

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

A multimodal remote sensing small-target detection method, BFMYOLO, is proposed to address misdetection and omission issues in remote sensing images with complex backgrounds and less effective information. The method utilizes a pixel-level Red-Green-Blue (RGB) and infrared (IR) image fusion module, namely, the Bimodal Fusion Module (BFM), for effectively making full use of the complementarity of different modes to realize the effective fusion of information from two modalities. In addition, a full-scale adaptive updating module, AA, is introduced to resolve multitarget information conflicts during feature fusion. This module incorporates the CARAFE up-sampling operator and shallow features to enhance non-neighboring layer fusion and improve the spatial information of small targets. An Improved task decoupling Detection Head (IDHead) is designed to handle classification and regression tasks separately, thereby reducing the mutual interference between different tasks and enhancing detection performance by fusing deeper semantic features. The proposed method adopts the Normalized Wasserstein Distance (NWD) loss function as the localization regression loss function to mitigate positional bias sensitivity. Results of experiments on the VEDAI, NWPU VHR-10, and DIOR datasets demonstrate the superior performance of the model, with mean Average Precision when the threshold is set to 0.5 (mAP@0.5) of 78.6%, 95.5%, and 73.3%, respectively. The model thus outperforms other advanced models in remote sensing small-target detection.

Image segmentation is a crucial technology for environmental perception, and it is widely used in various scenarios such as autonomous driving and virtual reality. With the rapid development of technology, computer vision-based blind guiding systems are attracting increasing attention as they outperform traditional solutions in terms of accuracy and stability. The semantic segmentation of road images is an essential feature of a visual guiding system. By analyzing the output of algorithms, the guiding system can understand the current environment and aid blind people in safe navigation, which helps them avoid obstacles, move efficiently, and get the optimal moving path. Visual blind guiding systems are often used in complex environments, which require high running efficiency and segmentation accuracy. However, commonly used high-precision semantic segmentation algorithms are unsuitable for use in blind guiding systems owing to their low running speed and a large number of model parameters. To solve this problem, this paper proposes a lightweight road image segmentation algorithm based on multiscale features. Unlike existing methods, the proposed model contains two feature extraction branches, namely, the Detail Branch and Semantic Branch. The Detail Branch extracts low-level detail information from the image, while the Semantic Branch extracts high-level semantic information. Multiscale features from the two branches are processed and used by the designed feature mapping module, which can further improve the feature modeling performance. Subsequently, a simple and efficient feature fusion module is designed for the fusion of features with different scales to enhance the ability of the model in terms of encoding contextual information by fusing multiscale features. A large amount of road segmentation data suitable for blind guiding scenarios are collected and labeled, and a corresponding dataset is generated. The model is trained and tested on the dataset. The experimental results show that the mean Intersection over Union (mIoU) of the proposed method is 96.5%, which is better than that of existing image segmentation models. The proposed model can achieve a running speed of 201 frames per second on NVIDIA GTX 3090Ti, which is higher than that of existing lightweight image segmentation models. The model can be deployed on NVIDIA AGX Xavier to obtain a running speed of 53 frames per second, which can meet the requirements for practical applications.

Existing object detection algorithms suffer from low detection accuracy and poor real-time performance when detecting fall events in indoor scenes, owing to changes in angle and light. In response to this challenge, this study proposes an improved fall detection algorithm based on YOLOv8, called OEF-YOLO. The C2f module in YOLOv8 is improved by using a Omni-dimensional Dynamic Convolution (ODConv) module, optimizing the four dimensions of the kernel space to enhance feature extraction capabilities and effectively reduce computational burden. Simultaneously, to capture finer grained features, the Efficient Multi-scale Attention (EMA) module is introduced into the neck network to further aggregate pixel-level features and improve the network's processing ability in fall scenes. Integrating the Focal Loss idea into the Complete Intersection over Union (CIoU) loss function allows the model to pay more attention to difficult-to-classify samples and optimize overall model performance. Experimental results show that compared to YOLOv8n, OEF-YOLO achieves improvements of 1.5 and 1.4 percentage points in terms of mAP@0.5 and mAP@0.5∶0.95, the parameters and computational complexity are 3.1×10⁶ and 6.5 GFLOPs. Frames Per Second (FPS) increases by 44 on a Graphic Processing Unit (GPU), achieving high-precision detection of fall events while also meeting deployment requirements in low computing scenarios.

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

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

With the rapid increase in the complexity of integrated circuit design, a trend of globalization and division of labor has emerged, necessitating the involvement of an increasing number of third-party Intellectual Property (IP) core providers. The widespread use of third-party IP cores introduces risks of hardware trojans. To detect and evaluate the presence of hardware trojans and their potential functionalities in third-party IP cores, there is an urgent need to explore feasible hardware security evaluation methods for IP cores. The functional identification of digital circuit modules has garnered significant attention as a fundamental research area in hardware trojan analysis. In this study, the task of circuit function detection is transformed into a multiclassification problem. By leveraging the characteristics of the circuit and graph data structures, a gate-level circuit function classification and detection method based on Graph Attention Networks (GAT) is proposed. First, to address the lack of functional identification datasets for gate-level netlists, a representative set of Register Transfer Level (RTL) codes is collected and synthesized to generate gate-level netlists, thereby constructing a gate-level circuit dataset of appropriate scale and diversity. Subsequently, to extract and process the circuit feature information, a software tool based on text recognition is developed. This tool maps the complex interconnections of circuits into a structured and concise JSON(JavaScript Object Notation) format, thereby facilitating neural network processing. Finally, a graph attention neural network is employed to train a multiclassifier using the constructed gate-level netlist dataset. After training, the multiclassifier becomes capable of classifying and identifying unknown gate-level circuits. The experimental results demonstrate that the classifier, after learning from more than 3 000 netlists in the self-built dataset, achieves a classification accuracy of 90% for 645 netlists across six categories.

The accurate recognition of fake news is an important research topic in the online environment, where distinguishing information explosion and authenticity is difficult. Existing studies mostly use multiple deep learning models to extract multivariate semantic features to capture different levels of semantic information in the text; however, the simple splicing of these features causes information redundancy and noise, limiting detection accuracy and generalization, and effective deep fusion methods are not available. In addition, existing studies tend to ignore the impact of dual sentiments co-constructed by news content and its corresponding comments on revealing news authenticity. This paper proposes a Dual Emotion and Multi-feature Fusion based Fake News Detection (DEMF-FND) model to address these problems. First, the emotional features of news and comments are extracted by emotion analysis. The emotional difference features reflecting the correlation between the two are introduced using similarity computation, and a dual emotion feature set is constructed. Subsequently, a fusion mechanism based on multihead attention is used to deeply fuse the global and local semantic features of the news text captured by a Bidirectional Long Short-Term Memory (BiLSTM) network with a designed Integrated Static-Dynamic Embedded Convolutional Neural Network (ISDE-CNN). Eventually, the dual emotion feature set is concatenated with the semantic features obtained by deep fusion and fed into a classification layer consisting of a fully connected layer, to determine news authenticity. Experimental results show that the proposed method outperforms the baseline method in terms of benchmark metrics on three real datasets, namely Weibo20, Twitter15, and Twitter16, and achieves 2.5, 2.3, and 5.5 percentage points improvements in accuracy, respectively, highlighting the importance of dual emotion and the deep fusion of semantic features in enhancing the performance of fake news detection.

Medical Visual Question Answering (Med-VQA) requires an understanding of content related to both medical images and text-based questions. Therefore, designing effective modal representations and cross-modal fusion methods is crucial for performing well in Med-VQA tasks. Currently, Med-VQA methods focus only on the global features of medical images and the distribution of attention within a single modality, ignoring medical information in the local features of images and cross-modal interactions, thereby limiting the understanding of image content. This study proposes the Cross-Modal Attention-Guided Medical VQA (CMAG-MVQA) model. First, based on U-Net encoding, this method effectively enhances the local features of an image. Second, from the perspective of cross-modal collaboration, a selection guided attention method is proposed to introduce interactive information from other modalities. In addition, a self-attention mechanism is used to further enhance the image representation obtained by selective guided attention acquisition. Ablation and comparative experiments on the VQA-RAD medical question-answering dataset show that the proposed method performs well in Med-VQA tasks and improves feature representation performance compared to similar methods.