Accurate object detection in aerial images is essential for a wide range of applications. However, detection performance degrades severely in hazy conditions, primarily due to the lack of realistic aerial haze datasets and the mismatch between low-level dehazing and high-level detection tasks. To address these challenges, we propose FSGD-Det, a novel framework for aerial object detection in hazy scenes that improves detection robustness through frequency–amplitude reconstruction. Specifically, we introduce a Physically-guided Haze Generator (PHG) to synthesize realistic hazy images during training, effectively narrowing the domain gap between synthetic and real-world haze. We further design a Frequency Division Dehazing (FDD) module that separately processes high- and low-frequency amplitude components to enhance dehazing quality in a frequency-aware manner. Building upon this, a Frequency Gap Amplification (FGA) module integrates structural cues from the hazy input with fine-grained details recovered from the dehazed image, reconstructing more robust frequency–amplitude representations for detection. Extensive experiments on both synthetic and real-world hazy aerial datasets demonstrate that FSGD-Det consistently outperforms state-of-the-art methods, validating its effectiveness and generalization capability.

Infrared-visible image fusion for object detection (IVIF-OD) aims to utilize complementary information in the two modalities to synthesize new images with richer information to serve object detection. Most existing works focus on how to better fuse pixel-level details while ignoring object-related information required for detection and introducing redundant and object-irrelevant information in the fused images. To address the limitations of previous studies, this paper proposes a diffusion-based denoising fusion for object detection in infrared-visible images, termed DDFD. Specifically, DDFD treats image fusion as a diffusion-based denoising process to generate fused images that are informative yet non-redundant. Since visible imaging is easily affected by adverse conditions, DDFD exploits an image-adaptive enhancement (IAE) module that adaptively improves visible images to achieve better fusion. To extract key fusion features and remove redundancy, DDFD uses an image-aware noise estimator (INE) to determine the noise in the input infrared-visible images for promoting the diffusion denoising network. To take advantage of both the fusion network and object detection network, DDFD jointly optimizes them such that the fusion network can receive object information to improve the fused images, and the improved images can provide high-quality features to enhance object detection performance. Extensive experiments on the M3FD, DroneVehicle, and VEDAI public datasets reveal the superior object detection performance of DDFD and confirm the effectiveness of IVIF-based object detection under challenging weather conditions.

In smart warehouses that use automated guided vehicles (AGVs) for goods transportation, task allocation has a great impact on operational efficiency. Currently, warehouse task allocation is typically modeled as a pickup and delivery problem (PDP), which requires vehicles to start and return from the same depot to construct several closed-loop routes. This approach increases the vehicle travel distance without load in high-throughput warehouses and results in resource wastage. Thus, we remodel the task allocation problem as an open-loop routing problem with heterogeneous starting points and name it capacitied multiagent open PDP (CMOPDP), which has more complex solution space and constraints than PDP. The solving speed of existing heuristic methods cannot meet the real-time processing demands of large-scale warehouses. And deep reinforcement learning (DRL)-based methods typically satisfy constraints through the output mask of decoders, which leads to unsatisfactory quality of solutions under complex constraints. To address these limitations, we design an DRL-based model with encoder-decoder architecture to solve the CMOPDP. Specifically, first, an encoder with heterogeneous attention is designed to fully explore constraint relationships between nodes. Second, we utilize dual decoders and information sharing to maximize vehicle-customer nodes matching. Finally, entropy rewards are introduced to enhance exploration during reinforcement learning, preventing the model from getting stuck in local optima. Extensive experiments on random datasets and various warehouse maps demonstrate that our method improves solution quality by at least 1.76% over baselines, while maintaining competitive solving time and exhibiting good generalization performance.

Student pose information can reflect learning status, which is significant in teaching management and evaluation. However, the traditional manual behavior recognition and analysis process is complex and slow, so counting massive classroom data is a difficult task. Therefore, using computer vision technology to accurately recognize student behavior is of great significance to teaching management and evaluation. Besides, the performance of existing behavior recognition methods is limited by problems such as dense objects and occlusion in classroom scenes. To address these issues, we propose an anchor-free object detector based on center keypoints for behavior recognition in classroom scenes. Specifically, we design a multiscale convolution neural networks (CNNs) module to alleviate the scale variation of objects through multiscale receptive fields. The head network is based on the anchor-free detection head architecture and integrates the keypoint heatmaps to accurately extract the center keypoint position through the center pooling module (CPM). the CPM can suppress irrelevant background information and effectively alleviate the missed detection of dense objects. Regressing the distances from the center keypoint of the positive region to the four sides can suppress low-quality bounding boxes so that the detector can accurately locate the object. Furthermore, during the inference stage, the heatmap scores of keypoints and center keypoints are linearly combined as novel confidence to mitigate inaccurate measurement of predicted bounding boxes. The extensive experimental performance comparison on the classroom behavior (CB) and SCB-dataset3 benchmark datasets demonstrate that the proposed behavior recognition method can accurately detect objects in classroom scenes. Compared with other current state-of-the-art methods, the proposed behavior recognition method based on anchor-free object detector is able to achieve good performance.

Integrating dynamic effects has shown its significance in enhancing the accuracy and robustness of Visual-Inertial Odometry (VIO) systems in dynamic scenarios. Existing methods either prune dynamic features or rely heavily on prior semantic knowledge or kinetic models, proved unfriendly to scenes with a multitude of dynamic elements. This work proposes a novel dynamic feature fusion method for monocular VIO, named DFF-VIO, which requires no prior models or scene preference. By combining IMU-predicted poses with visual clues, it initially identifies dynamic features during the tracking stage by constraints of consistency and degree of motion. Then, we innovatively design a Dynamic Transformation Operation (DTO) to separate the effect of dynamic features on multiple frames into pairwise effects and construct a Dynamic Feature Cell (DFC) to preserve the eligible information. Subsequently, we reformulate the VIO nonlinear optimization problem and construct dynamic feature residuals with the transformed DFC as a unit. Based on the proposed inter-frame model of moving features, a so-called motion compensation is developed to resolve the reprojection issue of dynamic features, allowing their effects to be incorporated into the VIO’s tight coupling optimization, thereby realizing robust positioning in dynamic scenarios. We conduct accuracy evaluations on ADVIO and VIODE, degradation tests on EuRoC dataset, as well as ablation studies to highlight the joint optimization of dynamic residuals. Results reveal that DFF-VIO outperforms state-of-the-art methods in pose accuracy and robustness across various dynamic environments.

The zero-watermarking methods provide a means of lossless, which was adopted to protect medical image copyright requiring high integrity. However, most existing studies have only focused on robustness and there has been little discussion about the analysis and experiment on discriminability. Therefore, this paper proposes a trusted robust zero-watermarking scheme for medical images based on Deep convolution neural network (DCNN) and the hyperchaotic encryption system. Firstly, the medical image is converted into several feature map matrices by the specific convolution layer of DCNN. Then, a stable Gram matrix is obtained by calculating the colinear correlation between different channels in feature map matrices. Finally, the Gram matrixes of the medical image and the feature map matrixes of the watermark image are fused by the trained DCNN to generate the zero-watermark. Meanwhile, we propose two feature evaluation criteria for finding differentiated eigenvalues. The eigenvalue is used as the explicit key to encrypt the generated zero-watermark by Lorenz hyperchaotic encryption, which enhances security and discriminability. The experimental results show that the proposed scheme can resist common image attacks and geometric attacks, and is distinguishable in experiments, being applicable for the copyright protection of medical images.

Oriented object detection typically adds an additional rotation angle to the regressed horizontal bounding box (HBB) for representing the oriented bounding box (OBB). However, existing oriented object detectors based on regression angles face inconsistency between metric and loss, boundary discontinuity or square-like problems. To solve the above problems, we propose an anchor-free oriented object detector named PRA-Det, which assigns the center region of the object to regress OBBs represented by the polar radius vectors. Specifically, the proposed PRA-Det introduces a diamond-shaped positive region of category-wise attention factor to assign positive sample points to regress polar radius vectors. PRA-Det regresses the polar radius vector of the edges from the assigned sample points as the regression target and suppresses the predicted low-quality polar radius vectors through the category-wise attention factor. The OBBs defined for different protocols are uniformly encoded by the polar radius encoding module into regression targets represented by polar radius vectors. Therefore, the regression target represented by the polar radius vector does not have angle parameters during training, thus solving the angle-sensitive boundary discontinuity and square-like problems. To optimize the predicted polar radius vector, we design a spatial geometry loss to improve the detection accuracy. Furthermore, in the inference stage, the center offset score of the polar radius vector is combined with the classification score as the confidence to alleviate the inconsistency between classification and regression. The extensive experiments on public benchmarks demonstrate that the PRA-Det is highly competitive with state-of-the-art oriented object detectors and outperforms other comparison methods.

Sharding technology achieves parallel processing of transactions by dividing the network into multiple independent parts, namely shards, significantly increasing the throughput of the blockchain system and reducing transaction processing latency, thereby improving its scalability. Although sharding technology enhances blockchain performance, it also introduces new security challenges, as an individual shard is more vulnerable to attacks compared to the entire network, potentially compromising its consensus reliability. To address these challenges, we propose BECHAIN: a sharding blockchain system with excellent Byzantine node tolerance. It incorporates a series of effective security measures, such as improved node allocation methods, enhanced inter-shard collaborative defense mechanisms, and refined malicious node monitoring strategies, to bolster the blockchain system’s defense against malicious nodes. Key measures include random node allocation, a node reputation scoring model, consensus supervision chain, and shard reconfiguration. Simulation results show that BECHAIN achieves linear scalability and enhances system security by increasing the consensus success rate.

Anonymous access is an effective method for preserving privacy in access control. This study assumes that anonymous access control requires both frameworks and policies. Numerous solutions have been proposed for anonymous access at the framework level. In this study, these solutions are analyzed and quantified using a unified attribute-based access control (ABAC) anonymous access reference framework. Anonymous access at the framework level is the first line of defense, and inappropriate policies may undermine subject anonymity. An anonymity metric is proposed at the policy level to prevent authorization authority from re-identification using specific attributes and policies. The anonymity metric evaluates the risk of re-identifying a subject due to inappropriate access requests, as well as subject attribute assignment schemes and policies. This study is the first to focus on anonymity at the policy level in ABAC. Furthermore, a formal definition of anonymity suitable for ABAC is proposed. The feasibility of the proposed anonymity metric is verified through simulations.

The application of oriented object detection in the field of aerial images has gained substantial attention and made significant progress. However, most one-stage object detectors struggle to extract rotation-invariant features of oriented objects using ordinary convolutions. And the structure of two parallel vision subtasks can result in the inconsistency between the classification and regression. In this article, we propose a Task Integration based on a Rotated convolution kernel Network (TIR-Net) consisting of three modules: selective rotation of the kernel (SRK), regression feature refinement (RFR), and task integration (TI). Specifically, an SRK module enhances classification features by applying rotated convolution kernels selectively, introducing rotational invariance to the features. An RFR module places more emphasis on feature extraction of large aspect ratio objects to improve their perceptibility. A TI module integrates classification and regression features to alleviate the inconsistency between classification and regression. Experimental evaluations on the benchmarks for oriented object detection indicate that our method achieves excellent detection performance.

Convolution and self-attention are two powerful techniques for multi-source remote sensing (RS) data fusion that have been widely adopted in Earth observation tasks. However, Convolutional Neural Networks (CNNs) are inadequate for fully mining contextual information and representing the sequence attributes of spectral signatures. Additionally, the specific self-attention mechanism often comes with high computation costs, which hinders its application in the field of RS. To overcome the above limitations, this paper proposes a unified framework called “Mixing Self-Attention and Convolution Network" for comprehensive feature extraction and efficient feature fusion. First, the proposed MACN utilizes two adaptive CNN encoders (ACEs) to extract shallow convolutional features from multi-source RS data. Secondly, taking the complexity and varying scales of RS data into account, the proposed mixing self-attention and convolution transformer (MACT) layer achieves local and global multiscale perception through an elegant integration of self-attention and convolution. MACT can extract abundant spatial and high-dimensional information (e.g., spectral and elevation information) while maintaining minimal computational overhead compared to pure convolution or self-attention counterparts. Finally, a multi-source cross-guided fusion (MCGF) module is designed to achieve deep fusion of multi-source RS data features. MCGF utilizes a carefully designed cross-modal attention mechanism to capture the interaction between multi-source data and aggregate contextual information. Extensive tests on six public RS datasets have shown that our method outperforms other multi-source fusion models, delivering state-of-the-art results on multiple RS data fusion tasks without specific tuning. The source code of the proposed method will be available publicly at https://github.com/like413/MACN.

Rapid development of smart homes in recent years has led to the production of an increasing number of smart devices that meet people’s daily needs. However, these smart devices may belong to different vendors and provide different functions and services, making it difficult to apply a centralized solution to a smart home system. Although blockchain has the potential to address the potential problems of smart homes because of its features such as nontampering, decentralization, and security, scalability remains a key challenge when integrating blockchain and smart homes, which is mainly reflected in the horizontal expansion, throughput and latency of the system. To address this challenge, Communitychain–a new scalable blockchain architecture suitable for smart home systems composing heterogeneous devices–is proposed in this study. A community model based on sharding that enables the architecture to adapt to an increase in the number of nodes is designed, which includes new miner nodes and leader node selection algorithms. Efficient cross-shard routing and sideBlock schemes are adopted to improve the throughput and latency and enhance the scalability of the system. Furthermore, lightweight authorization and authentication processes running in each community ensure secure access to device resources. The experimental results indicate that the proposed blockchain architecture can effectively improve the scalability of blockchain-based smart homes and adapt well to the system dynamics.

The emerging technologies, such assmart sensors, 5G/6G wireless communication, artificial intelligence, etc., have been maturing the future Internet of Things (IoT) by connecting the massive number of devices, which are expected to consistently collect and transmit real-time data to support business intelligence in an efficient and privacy-preserving way. The IoT can afford businesses predictive maintenance, improve field service, asset tracking, and further enhance customer satisfaction and facility management in industrial sectors. However, the privacy concern in IoT is a big challenge in IoT applications and services. This work proposed a lightweight privacy-preserving scheme based on homomorphic encryption in the context of the IoT, in which we investigated and analyzed the privacy issues between the data owners, untrustworthy third-party cloud servers, and the data users. Meanwhile, computationally efficient homomorphic algorithms are proposed to guarantee the privacy protection for the data users. Experimental results demonstrate that the proposed scheme can effectively prevent privacy breaches in IoT.

People who are to live, study and work abroad will face more challenges in the new cultural environment and suffer more acculturative stress. Virtual Reality (VR), by which an immersive learning environment can be built, may help them adapt to a foreign culture at a lower cost of time and money. In order to work out a design method for culture learning in VR, we have designed a VR application so that learners can experience and learn the typical western festival culture – Christmas culture – in an immersive environment. To evaluate the effectiveness of the VR method, 50 EFL Chinese university students were enrolled in our experiments and randomly assigned to the VR group and the non-VR group, the data was drawn from cultural knowledge questionnaire, behavior test and Intercultural Sensitivity Scale (ISS). The ANCOVA revealed no major effect for group factor on knowledge learning. Similarly, the Mixed ANOVA identified no major effect for group factor on behavior learning and attitude learning. There was no interaction effect between time and group in all experiments. Our results show that the VR method is preferred by most of the participants, but it shows no remarkable advantage over the non-VR method. Moreover, regression analysis between the culture learning and the sense of presence in VR shows that presence has the potential to improve the performance of intercultural interaction engagement. Our findings are of practical value for culture learning in VR.

Smart cities require new access control models for Internet of Things (IoT) devices that preserve user privacy while guaranteeing scalability and efficiency. Researchers believe that anonymous access can protect the private information even if the private information is not stored in authorization organization. Many attribute-based access control (ABAC) models that support anonymous access expose the attributes of the subject to the authorization organization during the authorization process, which allows the authorization organization to obtain the attributes of the subject and infer the identity of the subject. The ABAC with anonymous access proposed in this paper called ABSAC strengthens the identity-less of ABAC by combining homomorphic attribute-based signatures (HABSs) which does not send the subject attributes to the authorization organization, reducing the risk of subject identity re-identification. It is a secure anonymous access framework. Tests show that the performance of ABSAC implementation is similar to ABAC’s performance.

The Bell-LaPadula (BLP) model is a widely used access control model for the multilevel security system. The researchers proposed many modified BLP models to express privileges that cannot be expressed by the BLP model. However, these models are not compatible with the BLP model, leading to the transportation cost-prohibitive and difficult to be practically applied. In this article, an improved BLP model incorporated the break the glass (BTG) mechanism is proposed to overcome the limitations of the standard BLP and other modified BLP models. The improved model inherits some of the advantages of BTG, such as policy dynamic modification and fine-grained access control, which gives it wide availability. Additionally, in the implementation, BTG is used as an independent function attached to the original BLP; the proposed BLP model can be easily implemented in systems where BLP models have been implemented. The results of the analysis and simulations showed that the proposed BLP model improves the ability of expressing policy of BLP and achieves fine-grained access control without compromise in security. Compared with other modified BLP models, the proposed BLP model could express policy more effectively and is compatible with the original BLP model.

As a widely recognized policy language of access control, the eXtensible Access Control Markup Language (XACML) is widely used with its fine-grained and easy-to-read. With the application of XACML, researchers find that the XACML based policy evaluation and policy management methods can no longer meet the current large-scale requests for efficient access and dynamic management requirements. To improve the performance of policy evaluation based on XACML, we propose a policy evaluation method based on the matching tree to search policy efficiently and avoid the extra consumption of invalid policy participation. Furthermore, we propose a policy dynamic management method based on the matching tree to reduce the scale of the policy to be disabled for management, by adding locks in the tree node and the information mapping table. Through theoretical derivation and the factors that may affect its evaluation performance, we verify the improvement of evaluation efficiency. The simulation also shows the improvement of the evaluation engine based on the matching tree compared with OuenAz.

An important challenge for facial expression recognition (FER) is that real-world training data are usually imbalanced. Although many deep learning approaches have been proposed to enhance the discriminative power of deep expression features and enable a good predictive effect, few works have focused on the multiclass imbalance problem. When supervised by softmax loss (SL), which is widely used in FER, the classifier is often biased against minority categories (i.e., smaller interclass angular distances). In this letter, we present advanced softmax loss (ASL) to mitigate the bias induced by data imbalance and hence increase accuracy and reliability. The proposed ASL essentially magnifies the interclass diversity in the angular space to enhance discriminative power in every category. The proposed loss can easily be implemented in any deep network. Extensive experiments on the FER2013 and real-world affective faces (RAF) databases demonstrate that ASL is significantly more accurate and reliable than many state-of-the-art approaches and that it can easily be plugged into other methods and improves their performance.

Bell-LaPadula model is the most classical multilevel security access control model, however, the existence of the response blind area in Bell-LaPadula model is a great threat for system. In this paper we propose an improved Bell-LaPadula model combining with obligation mechanism. Response mechanism is also introduced in the improved model which can resolve the disadvantage of response blind area. Furthermore, the security of the improved model and covert channel is analyzed in detail.

Abstract Biba model is hard to implement because the rules are too strict to meet the flexibility of system. To enhance the flexibility, the low‐water‐mark policy based on the Biba model is proposed by supporting the dynamic change of subject tags. However, the biggest drawback of low‐water‐mark policy is that the integrity level of the subjects in a system decreases monotonously, which results that some subjects cannot access most of the objects and the system life cycle is cut down. An improved model is proposed based on the Biba model, which not only describes the infection degree of subjects by separating the subject into uninfected and infected subjects and introducing the confidence interval but also reduces the decline rate of integrity level of the subject and prolongs the life time cycle by adopting trusted computing to adjust subject tags. Theory analysis and experiment show that the improved model enhances the availability of system. Copyright © 2015 John Wiley & Sons, Ltd.