Fake news detection methods based on writing style have achieved remarkable progress. However, as adversaries increasingly imitate the style of authentic news, the effectiveness of such approaches is gradually diminishing. Recent research has explored incorporating large language models (LLMs) to enhance fake news detection. Yet, despite their transformative potential, LLMs remain an untapped goldmine for fake news detection, with their real-world adoption hampered by shallow functionality exploration, ambiguous usability, and prohibitive inference costs. In this paper, we propose a novel fake news detection framework, dubbed FACTGUARD, that leverages LLMs to extract event-centric content, thereby reducing the impact of writing style on detection performance. Furthermore, our approach introduces a dynamic usability mechanism that identifies contradictions and ambiguous cases in factual reasoning, adaptively incorporating LLM advice to improve decision reliability. To ensure efficiency and practical deployment, we employ knowledge distillation to derive FACTGUARD-D, enabling the framework to operate effectively in cold-start and resource-constrained scenarios. Comprehensive experiments on two benchmark datasets demonstrate that our approach consistently outperforms existing methods in both robustness and accuracy, effectively addressing the challenges of style sensitivity and LLM usability in fake news detection.

Remote sensing images are becoming increasingly widespread in military, earth resource exploration. Because of the limitation of a single sensor, we can obtain high spatial resolution grayscale panchromatic (PAN) images and low spatial resolution color multispectral (MS) images. Therefore, an important issue is to obtain a color image with high spatial resolution when there is only a PAN image at the input. The existing methods improve spatial resolution using super-resolution (SR) technology and spectral recovery using colorization technology. However, the SR technique cannot improve the spectral resolution, and the colorization technique cannot improve the spatial resolution. Moreover, the pansharpening method needs two registered inputs and can not achieve SR. As a result, an integrated approach is expected. We designed a novel multi-function model (MFmamba) to realize the tasks of SR, spectral recovery, joint SR and spectral recovery through three different inputs. Firstly, MFmamba utilizes UNet++ as the backbone, and a Mamba Upsample Block (MUB) is combined with UNet++. Secondly, a Dual Pool Attention (DPA) is designed to replace the skip connection in UNet++. Finally, a Multi-scale Hybrid Cross Block (MHCB) is proposed for initial feature extraction. Many experiments show that MFmamba is competitive in evaluation metrics and visual results and performs well in the three tasks when only the input PAN image is used.

Recently, Few-shot Learning (FSL) has become a popular task that aims to recognize new classes from only a few labeled examples and has been widely applied in fields such as natural science, remote sensing, and medical images. However, most existing methods focus only on the visual modality and compute prototypes directly from raw support images, which lack comprehensive and rich multimodal information. To address these limitations, we propose a novel Multimodal Prototype Augmentation FSL framework called MPA, including LLM-based Multi-Variant Semantic Enhancement (LMSE), Hierarchical Multi-View Augmentation (HMA), and an Adaptive Uncertain Class Absorber (AUCA). LMSE leverages large language models to generate diverse paraphrased category descriptions, enriching the support set with additional semantic cues. HMA exploits both natural and multi-view augmentations to enhance feature diversity (e.g., changes in viewing distance, camera angles, and lighting conditions). AUCA models uncertainty by introducing uncertain classes via interpolation and Gaussian sampling, effectively absorbing uncertain samples. Extensive experiments on four single-domain and six cross-domain FSL benchmarks demonstrate that MPA achieves superior performance compared to existing state-of-the-art methods across most settings. Notably, MPA surpasses the second-best method by 12.29% and 24.56% in the single-domain and cross-domain setting, respectively, in the 5-way 1-shot setting.

With the continuous development of fields such as national defense and military applications, the importance of infrared small target detection (IRSTD) technology based on thermal imaging has become increasingly prominent. However, in practical application scenarios, it remains difficult to effectively extract the features of weak and small targets under low signal-to-noise ratio conditions, while simultaneously suppressing background clutter, preserving target details, and balancing detection accuracy and speed. To address the issues mentioned above, this work proposes a real-time IRSTD network (ECMSFNet) based on efficient convolution and attention-guided multi-scale feature weighting and fusion. During the feature extraction stage, a dual-branch hybrid convolution module (DBHConv) is designed to extract infrared small target features more efficiently. In the feature fusion stage, a three-branch attention-guided module (TBAG) is designed to enhance the input features from both spatial and channel dimensions. By using a three-branch parallel structure to process input features in a differentiated manner, noise is effectively filtered while target detail information is preserved. In addition, to further address the issue of missed detections, a multi-scale feature weighting and fusion module (MSFWF) is designed at the added detection head to adaptively weight the features and optimize the feature propagation path, thereby improving the model’s detection accuracy. Extensive experimental results on multiple datasets demonstrate that the method proposed in this paper outperforms other advanced approaches and achieves a real-time detection speed of 74.63 frames per second. https://github.com/liubiaohua/ECMSFNet.

Virtualization environments (e.g., containers and hypervisors) achieve isolation of multiple runtime entities but result in two mutually isolated guest and host layers. Such cross-ayer isolation could cause high latency and low throughput of the system. Previous aware scheduling and double scheduling fail to achieve bidirectional coordination between the guest and host layers. To address this challenge, we develop DCS3, a Dual-layer Co-aware Scheduler that combines stealing balance and synchronized priority. Stealing balancing migrates tasks between virtual CPU (vCPU) queues for load balance based on the workloads of physical CPUs (pCPUs). Synchronized priority dynamically adjusts the thread priorities running on the pCPUs according to the current vCPU workloads. The vCPUs and pC-PUs belong to the guest and host layers, respectively. Compared with aware scheduling, double scheduling, and DCS2 (i.e., DCS3 without synchronized priority), DCS3 has the following obvious advantages: 1) Requests Per Second (RPS) increases by up to 52%, 55%, and 2%, respectively; 2) request latency decreases by up to 72%, 71%, and 20%, respectively.

With the rapid development of deep learning techniques in mobile and embedded devices, light-weight inference engines (e.g., Paddle Lite and TensorFlow Lite) are emerged. In some real-time application scenarios, these light-weight inference engines require time acceleration and low memory consumption. Paddle Lite is a well-known open-source inference engine that is fully functional. However, Paddle Lite only supports regular OS (e.g., Linux, Windows, and iOS), making it difficult to achieve time acceleration and low memory consumption for real-time application scenarios during inference. In this brief, we propose the Paddle Lite on Zephyr solution for inference acceleration in RTOS. We first propose a modular compilation method to incorporate the most basic functions of Paddle Lite. To address the system differences between RTOS and Linux, we resolve the system-level and compilation-level issues from modular compilation. We then load the Paddle Lite model into memory as a device when the system starts up. We further design an inference method that skips third-party libraries during inference and thus obtains the same inference results as Linux. We deploy the Paddle Lite on Zephyr and conduct experiments with seven classic Convolutional Neural Network (CNN) models on a single-core CPU. The experiment results show that the average inference time on Zephyr RTOS is reduced by 7%, and the average memory consumption is reduced by 78% compared to Linux. This work has merged an upstream branch of the Paddle Lite.

The convergence of federated learning (FL) and blockchain in edge-end-cloud systems offers promising opportunities for privacy-preserving collaborative intelligence. However, existing blockchain-enhanced FL (BFL) approaches remain vulnerable to malicious participants and lack robust protection for model updates. To address these issues, we propose SEPP-FLBC, a Secure and Efficient Privacy Protection framework based on Federated Learning and Blockchain Committees. SEPP-FLBC introduces a novel blockchain committee consensus mechanism to validate model updates and defend against unreliable nodes. It further employs a refined multi-party communication paradigm to facilitate indirect and secure data interactions, reducing the risk of information leakage. Additionally, differential privacy noise is applied to model updates to enhance resistance to inference attacks. A formal convergence analysis is conducted to ensure model stability and minimize overhead. Extensive experiments on benchmark datasets demonstrate that SEPP-FLBC achieves superior accuracy while maintaining strong privacy guarantees and communication efficiency, outperforming state-of-the-art BFL methods in both security and performance.

In-vehicle networks are crucial in traffic systems, enabling moving vehicles to share real-time road condition information, enhancing traffic efficiency and safety. However, some malicious vehicles may intentionally disseminate false information to disrupt the system or mislead other drivers. Conventional trust management models exhibit limited assessment accuracy in low-density traffic scenarios, particularly under harsh weather where data scarcity persists. In this paper, We propose a blockchain-Bayesian collaborative trust management framework that integrates Bayesian Networks (BN) for dynamic credibility evaluation in the IoV. The model establishes three trust dimensions: vehicle reputation score, road historical data, and real-time environmental factors, formalized through an adaptive Directed Acyclic Graph (DAG). Concurrently, blockchain guarantees data security and consistency, fosters a trustworthy environment for information dissemination, and enables distributed certificateless authentication and reputation management for vehicles through smart contracts. The simulation results demonstrate that our mechanism effectively performs identity authentication, information accuracy assessment, and vehicle reputation evaluation.

Abstract Image preprocessing models typically serve as the initial step in advanced visual tasks, aiming to enhance the performance of subsequent tasks. For example, multi-focus image fusion technology significantly improves the performance of downstream semantic classification tasks. However, with the advancement of adversarial attack techniques, these models are facing significant challenges. Previous research has only explored the impact of adversarial attacks on the performance of individual models, lacking an in-depth investigation into the robustness of tasks involving the combination of multiple models. This study aims to delve into the robustness issues of tasks that combine multi-focus image fusion and image classification. To address this challenge, we have designed a new adversarial attack generator specifically for scenarios that combine multi-focus image fusion with image classification. This attack method uses a decision map surrogate model and a binary weight map to precisely add adversarial perturbations to the effective information parts of multi-focus images. It also incorporates attention mechanisms and Grad-CAM technology to optimize the perturbation areas, aiming to disrupt the key features of the fused image to improve the transferability of the attack. Comprehensive experimental results show that this method significantly improves the efficiency of attacks on downstream classification tasks while maintaining the effectiveness of the fusion model.

With the vigorous development of Internet technology, the scale of systems in the network has increased sharply, which provides a great opportunity for potential attacks, especially the Distributed Denial of Service (DDoS) attack. In this case, detecting DDoS attacks is critical to system security. However, current detection methods exhibit limitations, leading to compromises in accuracy and efficiency. To cope with it, three key strategies are implemented in this paper: (i) Using tensors to model large-scale and heterogeneous data in complex networks; (ii) Proposing a denoising algorithm based on the improved and distributed tensor train (IDTT) decomposition, which optimizes the tensor train(TT) decomposition in terms of parallel computation and low-rank estimation; (iii) Combining (i), (ii) and Light Gradient Boosting Machine (LightGBM) classification model, an efficient DDoS attack detection framework is proposed. Datasets CIC-DDoS2019 and NSL-KDD are used to evaluate the framework, and results demonstrate that accuracy can reach 99.19% while having the characteristics of low storage consumption and well speedup ratio.

Blockchain technology underpins the value internet, yet the isolation of blockchain systems creates "data and value islands," limiting interoperability. To address this challenge, we propose a scalable and secure cross-chain framework that eliminates reliance on relay chains and enhances performance through decentralized execution and state sharding. Each business blockchain operates as an autonomous node, collaboratively maintaining a virtual cross-chain transaction blockchain. Our framework achieves significant improvements: when state operations are executed on-chain, the framework(with a block size of 2) delivers 66% and 149% higher transactions/s compared to TCIP and BitXhub, respectively, with 20% lower latency. Furthermore, off-chain execution(with a block size of 2) boosts maximum transactions/s by 63% and reduces latency by 36%. The introduction of state sharding enhances parallel transaction execution(with a block size of 128 and a number of sharding nodes of 16), achieving an additional 108% performance improvement. This work demonstrates a novel approach to cross-chain interoperability, offering a secure, efficient, and scalable solution for complex blockchain ecosystems.

While directed acyclic graph (DAG) blockchain technologies improve scalability and throughput over traditional blockchains, they still face critical challenges, particularly in efficiently determining transaction order and ensuring verifiable transaction selection. These limitations significantly hinder their applicability in high-demand environments like IoT, where secure, high-throughput processing is essential. To address these issues, we propose a DAG-partitioned multichain architecture (DPMA), which generates distinct chains for each user node, enabling parallel transaction processing. In addition, we introduce a transaction selection algorithm with verifiable random function (TSAV), which implements a verifiable two-tier transaction selection process, ensuring secure and transparent transaction ordering. To further enhance security, we propose a dynamic transaction confidence analysis method that adjusts VRF parameters in response to network conditions. Experimental results demonstrate that our approach effectively identifies malicious behavior and improves transaction credibility. Compared to existing methods, our solution offers enhanced scalability and security, making it well-suited for large-scale decentralized applications.

Hypervisor is a VMM (Virtual Machine Monitor) that creates and runs multiple VMs (Virtual Machines) through abstracting resources from a physical machine. Hypercall is a special and crucial call used in virtualized systems as it serves as a main communication channel between VMs and the hypervisor. However, hypercall attacks occur when an attacker manipulates the communication channel, and it could cause abnormal VM status, potentially leading to the abnormal resource allocation of the host OS (Operating System) and crash of VMs. Therefore, the virtualized system should execute abnormal VM status detection to identify potential abnormal behaviors to protect VMs and the host OS; however, existing works are either for reconstructing the hypervisor or hardware isolation, not for the VM status detection for abnormal hypercall.This study develops a hypercall-oriented abnormal VM status detection system called HypercallDetector based on the following three innovations: 1) we implement a hypercall tracing based on eBPF to obtain the hypercall-related running status (including CPU usage, memory usage, network traffic, etc.) of each VM; 2) we implement a window division technology to divide the VM status into multiple status windows of the same size, and appropriate window size with balanced detection precision (95.0%) and latency (within 8.8 ms) obtained by proposing the window regulator; and 3) we implement a CS-H algorithm (Compressing Sensing for Hypercall) to distinguish whether the VM status is abnormal. HypercallDetector shows higher precision and lower latency than its opponent and consumes only 8.6% CPU of single core and 0.3% memory usage when starting 240 VMs.

Realistic 3D food creation generation plays a critical role in applications such as nutritional assessment, advertising, and virtual content creation. The existing text-to-3D models typically begin by initializing a 3D representation, which is subsequently refined using supervision from a text-to-image model to obtain the final 3D output. In this work, we present Food3D, a novel framework for 3D food generation designed to address two main limitations of current models. First, the limitation of initialization in 3D generation: poor initialization can result in the generated 3D food lacking crucial details and realism, thereby reducing its quality. To address this issue, we propose a generalized method named Food3D-G, which uses Mamba-based initialization to improve the starting point of the initialization process, thereby enhancing the visual fidelity and quality of the generated 3D food. Second, the limitation of text-to-image models: current text-to-3D models often rely on text-to-image models for supervision. However, a considerable gap persists between the generated images and real-world visuals, particularly when modeling complex food structures. These models fail to accurately capture the fine details and textures, which negatively impacts the quality and realism of the generated 3D food models. To address this limitation, we propose a customizable method for personalized 3D food generation, termed Food3D-C. This method employs a dual-branch diffusion model that effectively captures intricate details, particularly in complex food structures. Within the Food3D framework, both proposed methods incorporate 3D Gaussian splatting (3D GS) and a schedulable interval score matching (S-ISM) algorithm to enhance shape and texture generation. Extensive experiments demonstrate that Food3D achieves state-of-the-art performance, with substantial improvements in detail, shape accuracy, and overall visual realism. Project page and source codes: https://yudongjian.github.io/Food3D/.

With the rapid advancement of 5G communication technology in the era of big data, massive terminal devices connected to the Internet have dramatically increased the scale of network, generating a large amount of high-dimensional and heterogeneous information. This not only enhances the difficulty of information processing in the network, but also poses a severe challenge to data storage and calculation, which has become a big data problem to be solved urgently. To cope with it, this paper proposes an efficient information processing framework and applies it to Distributed Denial of Service (DDoS) attack detection. Overall, three major highlights are made: (i) Tensor is used to represent multi-modal information in large-scale networks; (ii) A novel denoising algorithm based on tensor train(TT) decomposition is proposed, focused on optimizing both computation and correlation; (iii) A big data-driven information processing framework is developed, which includes information preprocessing, denoising and classification. Results in case study indicate that the framework can achieve an accuracy of 99.19%, all while maintaining the great storage advantage, well speedup ratio and strong computing capabilities under the same computational complexity. It can also be generalized to other network data processing scenarios.

Deep neural networks are shown to be vulnerable to adversarial examples. Recently, various methods have been proposed to improve the transferability of adversarial examples. However, most of the existing methods add perturbations to the whole image without discrimination, causing the visual quality of the adversarial examples to degrade drastically. In addition, existing attack methods ignore the gradient information of secondary features, which affects the accuracy of generating adversarial perturbations. In this work, we propose Adaptive Attention and Gradient Purification Attack (AAGP) to address such issues. Specifically, we judge the mean and standard deviation of the gradient values to find out where the model is interested. Since different models share similar regions of attention, adding perturbations only to such areas can reduce the addition of adversarial perturbation and can also lead to better transferability of adversarial examples to other models. In addition, we disrupt the correlation of pixels at the distribution of secondary features by random discarding pixels in low-attention areas, generating more transferable perturbations through more accurate gradient information. Experimental results on ImageNet show that our method enhances the visibility of the adversarial examples and their transferability compared with several advanced baselines.

Social media offers a promising way to analyze online public opinion, which has drawn extensive attention from various sectors. In academia, most studies focus on predicting public opinion using unimodal time series methods, paying little attention to multimodal approaches. However, public opinion may be affected by various complex social factors, so it is necessary to explore multimodal elements. Based on the N-Beats model, we propose a novel model, HFN-BeatsConv, which employs a powerful modal alignment strategy, 3D-TCN. Most fuses multimodal data for public opinion prediction. The model employs a component, 3D-TCN, for modal alignment, differs from other research in that it focuses on the time at which the text appears. Subsequently, the model, HFN-BeatsConv, the N-Beats model is enhanced through the utilization of 3D-TCN, which enables the processing of multivariate time series data and the reduction of multimodal time series forecast error. To increase the usability and sustainability of research, this study provides a valuable social media dataset, as a supplementary feature of time series prediction. Through extensive experiments, the proposed method outperforms the existing methods.

Abstract In the era of 5G, billions of terminal devices achieve global interconnection and intercommunication, which leads to the generation of massive data. However, the existing cloud‐based data‐sharing mechanism faces challenges such as sensitive information leakage and data islands, which makes it difficult to achieve secure sharing across domains. In this paper, the authors propose a fine‐grained data‐sharing scheme based on blockchain and ciphertext policy attribute‐based encryption, and design a verifiable outsourced computation method to reduce the computational pressure of end users. Second, the authors comprehensively consider the user's identity privacy and transaction privacy, and propose a multi‐level privacy protection method based on ring signature and garbled bloom filter, which enhance the user's data privacy and availability, and prevent the traceability of requests. Finally, the authors design a set of interconnected smart contracts, and verify that their scheme can achieve secure and efficient data sharing through security analysis and performance testing.

Adversarial examples have the property of transferring across models, which has created a great threat for deep learning models. To reveal the shortcomings in the existing deep learning models, the method of the ensemble has been introduced to the generating of transferable adversarial examples. However, most of the model ensemble attacks directly combine the different models’ output but ignore the large differences in optimization direction of them, which severely limits the transfer attack ability. In this work, we propose a new kind of ensemble attack method called stochastic average ensemble attack. Unlike the existing approach of averaging the outputs of each model as an integrated output, we continuously optimize the ensemble gradient in an internal loop using the model history gradient and the average gradient of different models. In this way, the adversarial examples can be updated in a more appropriate direction and make the crafted adversarial examples more transferable. Experimental results on ImageNet show that our method generates highly transferable adversarial examples and outperforms existing methods.

Abstract Generative Adversarial Networks (GANs) emerged thanks to the development of deep neural networks. Forgery images generated by various variants of GANs are widely spread on the Internet, which may be damage personal credibility and cause huge property losses. Thus, numerous methods are proposed to detect forgery images, but most of them are designed to detect forgery faces. Therefore, a method to detect forgery images of various scenes is proposed. In this work, central difference convolution and vanilla convolution (CDC‐Mix) are mixed after considering the depth and width features of neural networks and analyzing the influence of attention on network performance. Based on CDC‐Mix, a separable convolution (SeparableCDC‐Mix) is proposed. The proposed method consists of three parts: (1) CDC‐Mix and SeparableCDC‐Mix are used to extract the gradient information and texture features; (2) CDCM is used to extract the multi‐scale information of the image; (3) multi‐scale fusion module (MS‐Fusion) is used to fuse the multi‐scale information from different locations of the network. A large number of experiments have been carried out on several datasets generated by GAN, and the experimental results show that the proposed method has a great improvement compared with the existing advanced methods.

Abstract Image steganography is the art of concealing secret information within images to prevent detection. In deep‐learning‐based image steganography, a common practice is to fuse the secret image with the cover image to directly generate the stego image. However, not all features are equally critical for data hiding, and some insignificant ones may lead to the appearance of residual artifacts in the stego image. In this article, a novel network architecture for image steganography with hybrid attention mechanism based on generative adversarial network is introduced. This model consists of three subnetworks: a generator for generate stego images, an extractor for extracting the secret images, and a discriminator to simulate the detection process, which aids the generator in producing more realistic stego images. A specific hybrid attention mechanism (HAM) module is designed that effectively fuses information across channel and spatial domains, facilitating adaptive feature refinement within deep image representations. The experimental results suggest that the HAM module not only enhances the image quality during both the steganography and extraction processes but also improves the model's undetectability. Stego images are mixed with varying levels of noise in the training process, which can further improve robustness. Finally, it is verified that the model outperforms current steganography approaches on three datasets and exhibits good undetectability.

Industrial big data has experienced from data silos due to its high potential value and strong security requirements, making it difficult to share securely across domains. Blockchain-based solutions allow nodes to establish access control to trusted data on unreliable or trustless networks, but still face issues such as inefficient data sharing and leakage of sensitive information. In this paper, we propose a blockchain-based access control scheme for privacy security and dynamic regulation. First, ciphertext policy attribute-based encryption (CP-ABE) is developed to gain fine-grained access to node resources, with verifiable outsourcing decryption method to significantly reduce computational pressure on end users. Second, a policy hiding method based on multi-chain architecture is proposed, which performs double hiding of attribute information and access policy information on the blockchain. Finally, a supervisory policy that incorporates dynamic trust assessment and smart contracts is proposed to achieve effective detection and hierarchical classification punishment of malicious behavior. Security analysis and experimental results show that our scheme can limit the complexity of terminal decryption at a constant level and effectively achieve secure and efficient access control in the industrial Internet environment.

Despite achieving exceptional performance, deep neural networks (DNNs) suffer from the harassment caused by adversarial examples, which are produced by corrupting clean examples with tiny perturbations. Many powerful defense methods have been presented such as training data augmentation and input reconstruction which, however, usually rely on the prior knowledge of the targeted models or attacks. A clean example and its adversarial version are very similar but have different high-level representations in a victim model. If we can obtain a space in which the representations of similar examples are also similar, then adversarial examples can be picked out by comparing the representations of input examples in this space and the high-level space of the victim model. Inspired by this, we propose a novel approach for detecting adversarial images, which can protect any pre-trained DNN classifiers and resist an endless stream of new attacks. Specifically, we first adopt a dual autoencoder to project images to a latent space. The dual autoencoder uses the self-supervised learning to ensure that small modifications to samples do not significantly alter their latent representations. Next, the mutual information neural estimation is utilized to enhance the discrimination of the latent representations. We then leverage the prior distribution matching to regularize the latent representations. To easily compare the representations of examples in the two spaces, and not rely on the prior knowledge of the targeted model, a simple fully connected neural network is used to embed the learned representations into an eigenspace, which is consistent with the output eigenspace of the targeted model. Through the distribution similarity of an input example in the two eigenspaces, we can judge whether the input example is adversarial or not. Extensive experiments on MNIST, CIFAR-10, and ImageNet show that the proposed method has superior defense performance and transferability than state-of-the-arts.

State-of-the-art image style transfer methods have achieved impressive results by using neural networks. However, neural style transfer (NST) methods either ignore the local details of the style image by using the global statistics for style modeling or cannot fully use shallow features of neural networks, leading to the synthesized image having fewer details. In this study, we proposed a new patch-based style transfer method that directly operates in the image pixel domain without using any neural networks, achieving fascinating style transfer results with rich image details. The proposed method was derived from classic texture synthesis methods. Most previous methods rely on nearest neighbor search (NNS) for patch matching. However, this greedy strategy cannot guarantee the similarity of patch distributions between the synthesized image and the style image, which limits the expressiveness of textures. We solved this problem by proposing an optimal patch matching algorithm formed on the Optimal Transport (OT) theory, which theoretically guarantees the similarity of the patch distributions and gives a flexible style modeling method. Various qualitative and quantitative experiments demonstrated that the proposed method achieves better synthesized results than state-of-the-art style transfer methods, including NST and classic methods based on texture synthesis.

The traditional electronic voting (e-voting) has the problems of dead vote, repetition and missing registration, which cannot reflect the voting result correctly, objectively and fairly. With the booming development of blockchain in recent years, blockchain technology provides a new solution in the field of e-voting. Multi-District election is an important method of election in real life, e.g. in US presidential election. Inspired by the electoral college system, we propose a blockchain-based e-voting scheme for multi-district election. First, we propose a blockchain-based voter registration method that all voters are authenticated by the blockchain system. Second, we design a two-layer blockchain architecture, where the lower layer records the votes of voters in each district and the higher layer records the votes of electors. Then we describe the voting process and evaluate the security and availability of the proposed scheme. The experimental results show that the proposed scheme can satisfy the needs of multi-district election.

As facial image manipulation techniques developed, deep fake detection attracted extensive attentions. Although researchers have made remarkable progresses in deepfake detection recently, which is still suffering from two limitations: a) current detectors achieve high accuracy in the high-quality videos and images, but it is hard to capture local and subtle artifacts in the low-quality and high-compression media; b) few of deep fake detection methods gain satisfying performance under cross-database scenario, because detector overfit to specific color textures producing by same manipulation algorithm. Inspired the above issues, this paper proposes a novel framework fusing local related features and frequency information to mine the forgery patterns. Firstly, we design multi-feature enhancement module, which amplifies implicit local disc repancies and capture spatial correlation from three shallow feature layers and high-level semantic layer guided by attention maps. Secondly, dual frequency decomposition module is proposed for disassembling high-frequency and low-frequency features, the forgery artifacts are exposed after dual cross attention block processing in the frequency spectrum. Features from the two streams are fused to the classification for the final result. Comprehensive experiments demonstrate the superior performance of our proposed approach in the low-quality benchmark database and cross-dataset sce-nario.

The state-of-the-art HotStuff operates an efficient pipeline in which a stable leader drives decisions with linear communication. However, with the unifying proposing-voting pattern, it takes two rounds of messages to produce a certified proposal, which severely limits the performance of the consensus protocol and makes it difficult for the blockchain to exert the bandwidth and concurrency of modern operating systems. Thus, this paper developed a new consensus protocol, called Multi-pipeline HotStuff, for permissioned blockchain. To the best of the authors’ knowledge, this is the first protocol that combines multiple pipelines of HotStuff to propose batches in order, such that proposals are built optimistically when a correct replica realizes that the current proposal is valid and will be certified by quorum votes in the near future. Simultaneous proposing and voting allow the protocol to produce more proposals in every two rounds of messages, it further boosts the throughput at a comparable latency with that of HotStuff. The evaluation experiment confirmed that the throughput of the proposed protocol outperformed HotStuff by approximately 60% without significantly increasing end-to-end latency under varying system sizes. Even if the protocol frequently performs view-change phase due to network asynchrony, its optimization continues to demonstrate better performance.

Abstract Deep neural networks are widely adopted powerful tools for perceptual tasks. However, recent research indicated that they are easily fooled by adversarial examples, which are produced by adding imperceptible adversarial perturbations to clean examples. Here the steganalysis rich model (SRM) is utilized to generate noise feature maps, and they are combined with RGB images to discover the difference between adversarial examples and clean examples. In particular, a two‐stream pseudo‐siamese network that fuses the subtle difference in RGB images with the noise inconsistency in noise features is proposed. The proposed method has strong detection capability and transferability, and can be combined with any model without modifying its architecture or training procedure. The extensive empirical experiments show that, compared with the state‐of‐the‐art detection methods, the proposed approach achieves excellent performance in distinguishing adversarial samples generated by popular attack methods on different real datasets. Moreover, this method has good generalization, it trained by a specific adversary can defend against other adversaries effectively.

Abstract Arbitrary style transfer aims to stylize the content image with the style image. The key problem of style transfer is how to balance the global content structure and the local style patterns. A promising method to solve this problem is the attentional style transfer method, where a learnable embedding of image features enables style patterns to be flexibly recombined with the content image, so local style patterns will be well preserved in the stylized image. However, current attentional style transfer methods cannot well preserve the global content structure. To solve this problem, a novel attentional style transfer network is proposed, that relies on Optimal Transport (OT) for computing the attention map. The proposed OT‐based attention ensures the similarity between global distributions of the synthesized image and its corresponding style image. For the optimal transport computation, a regularized formulation is used, which not only allows an unbalanced optimal transport to address the deviational distributions but also improves the robustness of stylized results. The proposed method finds a well balance between the global content structure and local style patterns. Various experiments are conducted to demonstrate the superiority of the proposed method over state‐of‐the‐art methods.

Abstract Multi‐focus image fusion technology solves the problem of limited depth of field of the optical lens. It can extract different focus parts under the same target to synthesize a full‐focus image. This paper proposes an unsupervised dense network for multi‐focus image fusion. In the network, a multi‐scale feature extraction module is employed to extract the spatial details of source images from different scales, and a convolutional block attention module is used to select the useful deep features, and a residual module is used to effectively optimize the performance of the network. By introducing these three modules, the proposed network can effectively extract the shallow and deep features of the source images. Besides, Gaussian‐based Sum‐Modified‐Laplacian (GSML) is used to calculate the activity level of the feature map to generate a decision map. The performance of the proposed method is analyzed from two aspects: visual quality and objective metrics. Experimental results show that compared with nine image fusion methods, the performance of this algorithm is better.

The intersecting cortical model (ICM), initially designed for image processing, is a special case of the biologically inspired pulse-coupled neural-network (PCNN) models. Although the ICM has been widely used, few studies concern the internal activities and firing conditions of the neuron, which may lead to an invalid model in the application. Furthermore, the lack of theoretical analysis has led to inappropriate parameter settings and consequent limitations on ICM applications. To address this deficiency, we first study the continuous firing condition of ICM neurons to determine the restrictions that exist between network parameters and the input signal. Second, we investigate the neuron pulse period to understand the neural firing mechanism. Third, we derive the relationship between the continuous firing condition and the neural pulse period, and the relationship can prove the validity of the continuous firing condition and the neural pulse period as well. A solid understanding of the neural firing mechanism is helpful in setting appropriate parameters and in providing a theoretical basis for widespread applications to use the ICM model effectively. Extensive experiments of numerical tests with a common image reveal the rationality of our theoretical results.

Remote sensing data have become increasingly vital in target detection, disaster monitoring, and military surveillance. Abundant pan-sharpening and super-resolution (SR) methods based on deep learning have been proposed and have achieved remarkable performance. However, pan-sharpening requires paired panchromatic (PAN) and multispectral (MS) images, and SR cannot increase the spectral resolution of PAN. Thus, we introduce a computational imaging-based method to recover or produce the incomplete data of single PAN or MS. This work also explores the integration of multiple tasks by a single neural network. We start with SR and colorization, study the feasibility of simultaneously finishing SR colorization, and use a model trained in SR colorization to finish pan-sharpening without MS. A generic neural network, remote sensing image improvement network (RSI-Net), is designed for remote sensing image SR, colorization, simultaneous SR colorization, and pan-sharpening. To verify its performance, RSI-Net is compared with the state-of-the-art SR and colorization methods. Experiments show that RSI-Net can be competitive in visual effects and evaluation indexes, and it performs well at simultaneous SR colorization, and RSI-Net finishes pan-sharpening and only needs to input PAN. Our experiments confirm the effect of integrating multiple tasks.

Machine learning as a service (MLaaS) brings many benefits to people's daily life. However, the service mode of MLaaS will increase the risk of users' privacy leakage. Existing works focusing on privacy-preserving based on encryption, differential privacy, and distributed framework require high computing resources or cannot be applied in MLaaS. In this paper, we propose feature dilution (FD), a noise-based desensitization algorithm to remove sensitive information in raw data. In particular, FD continuously adds raw data features to the random noise until it meets the minimum amount for an effective query, and we call this noise weak-feature noise (WFN). By fine-tuning the MLaaS architecture, we have realized that users can utilize WFN to get normal services without exposing their local private data. Meanwhile, noise addition technology is introduced by us to reduce the risk of privacy leakage caused by “weak features”. Extensive experiments have demonstrated that users can use FD to obtain effective services without exposing their private data. Finally, we conducted practical tests on weak-feature noises and found that these noises are difficult to use by malicious service providers.

Given an undirected graph, the Maximum k-plex Problem (MKP) is to find a largest induced subgraph in which each vertex has at most k−1 non-adjacent vertices. The problem arises in social network analysis and has found applications in many important areas employing graph-based data mining. Existing exact algorithms usually implement a branch-and-bound approach that requires a tight upper bound to reduce the search space. In this paper, we propose a new upper bound for MKP, which is a partitioning of the candidate vertex set with respect to the constructing solution. We implement a new branch-and-bound algorithm that employs the upper bound to reduce the number of branches. Experimental results show that the upper bound is very effective in reducing the search space. The new algorithm outperforms the state-of-the-art algorithms significantly on real-world massive graphs, DIMACS graphs and random graphs.

Deep convolutional neural networks have respectively achieved significant success in image super-resolution and colorization. The DNN has a strong capability to generate high quality images. Both colorization and super-resolution (SR) can be regarded as an independent pixel mapping problem, and this work combines these two visual problems into an integrated task. In this work, we propose an end-to-end model for accomplishing single satellite image colorization and SR simultaneously. Our model comprises two phases: features extraction network and recovery network. First, the residual receptive field block structure is introduced in features extraction network to learn better feature representations for image colorization and SR. Residual Receptive Field Block(RRFB) is improved by expanding the receptive field and enhancing the context connection from inception model. Second, the extracted features are transformed to a color high-resolution image by a recovery architecture. In this work, U-net is employed as the key structure of the recovery architecture. Besides, the squeeze-and-excitation blocks and complex residual blocks are incorporated into the proposed model to increase the reconstruction performance. To verify the performance, our method is compared with the state-of-the-art methods of SR and colorization. The experiments show that proposed method can get competitive in visual effect and evaluation index compared with the existing methods. In the end, the panchromatic dataset is also used to validate our model, and a good color high-resolution image can be obtained by giving a gray and low-resolution panchromatic image.

In fuzzy set theory, the distance and entropy measure of intuitionistic fuzzy sets (IFSs) have received extensive concern because of the capability for handling imprecise or uncertain problems. However, most of the existing modeling methods for distance and entropy measure are imperfect in teams of intelligibility and performance. In this work, we proposed a new geometric modeling method that can be simultaneously used for distance and fuzzy entropy modeling of IFSs. We used rigorously mathematical derivation to prove that the proposed distance and fuzzy entropy measures satisfy the properties of the definitions. In the experiments, we applied the proposed distance and fuzzy entropy measure into pattern recognition, medical diagnosis, and multi-attribute decision making to examine the usability of the two measures in practical situations.

In this paper, we investigate offloading scheme and resource allocation strategy for Orthogonal Frequency-Division Multiple Access (OFDMA) based multi-access edge computing (MEC) network to minimize the total system energy consumption. Partial data offloading is studied where mobile date can be computed at both local devices and the edge cloud with the consideration of time-sensitive tasks for users. Since the NP-hardness of the considered optimization problem, we propose an iterative algorithm to decide the proportion of data to offload and design the resource allocation strategy in a sequence. Simulation results show that the proposed algorithm achieves better performance than the reference schemes.

Summary Web service compositions have been widely applied in different applications. A service composition is usually implemented in either a centralized or decentralized manner. Compared with the centralized service composition, the decentralized composition has no central control component, and components interact with each other directly, thereby achieving better performance. Process partitioning is a technique to divide a process into multiple parts and has been shown that it can be successfully applied to decentralizing process‐driven service compositions. This paper proposes a new process partitioning technique for constructing decentralized service compositions. The proposed technique, which is based on typed digraphs and a graph transformation technique, is used for exploring available process partitioning solutions. For applications, this paper discusses the topology and interaction features about the partitioning solutions and summarizes a ranking method for them. Three experiments are conducted to evaluate the proposed methods in this paper. Experimental results show that the proposed methods can be applied in constructing decentralized service compositions effectively. In addition, the results also show that the decentralized compositions can have lower average response times and higher throughputs than the corresponding centralized compositions in the experiments.

With the in-depth research of data classification, multi-label classification has become a hot issue of research. Multi-label k-nearest neighbor (ML- k NN) is a classification method which predicts the unclassified instances' labels by learning the classified instances. However, this method doesn't consider the interrelationships between attributes and labels. Considering the relationships between properties and labels can improve accuracy of classification methods, but the diversities of properties and labels will present the curse of dimensionality. This problem make such methods can not be expanded under the background of big data. To solve this problem, this paper proposes three methods, called multi-label k-nearest neighbor based on principal component analysis(PML- kNN), coupled similarity multi-label k-nearest neighbor based on principal component analysis(PCSML- kNN) and coupled similarity multi-label k-nearest neighbor classification based on feature selection (FCSML- kNN), which use feature extraction and feature selection to reduce the dimensions of labels' properties. We test the ML- kNN and the three methods we proposed with two real data, the experimental results show that reduce the dimensions of labels' properties can improve the efficiency of classification methods.

A brain tumor is a growth of abnormal cells in the tissues of the brain, which is difficult for treatment and severely affects patients' cognitive ability. Recent year magnetic resonance imaging (MRI) has been widely used imaging technique to assess brain tumors. However manual segmentation and artificial extracting features block MRI's practice when facing with the huge amount of data produced by MRI. An efficient and automatic image segmentation of brain tumor is still needed. In this paper, a novel automatic segmentation framework of brain tumors, which have 5 parts and resnet-50 use as a backbone, is proposed based on convolutional neural network. A dilated convolution refine (DCR) structure is introduced to extract the local features and global features. After investigating different parameters of our framework, it is proved that DCR is an efficient and robust method in Brain Tumor Segmentation. The experiments are evaluated by Multimodal Brain Tumor Image Segmentation (BRATS 2015) dataset. The results show that our framework in complete tumor segmentation achieved excellent results with a DEC score of 0.87 and a PPV score of 0.92. (GitHub: https://github.com/wei-lab/DCR)

In recent years, tensor-based machine learning methods, in which the Support Tensor Machine (STM) is a typical technology, have gradually attracted the attention of researchers. Compared with Support Vector Machine (SVM), STM has superior generalization ability that can make full use of the structural information of data. However, it still faces many challenges due to the imperfection of its theoretical basis and model. In order to study the further development of STM, this paper provides a survey about the potential and existing problems in STM.

Group key is used to encrypt group data in group communication. Multicast key distribution scheme updates and distributes group keys safely when a member joins or leaves the group. However it has problems when arbitrary group members want to build a dynamic conference, because traditional distribution scheme uses group controller to manage the work. In fact, a n-member group will have 2n- n - 1 possible conferences. Moreover, members may need to join and/or leave the group dynamically. If a group controller deals with all the members' requests, it will be the performance bottleneck. In this paper, we propose a new stateful multicast key distribution protocol based on identity-based encryption. This protocol can distribute the group keys safely and allows the group members to build dynamic conferences by themselves. It greatly reduces the group controller's workload. In addition, compared with traditional rekeying messages contain identifier and group key's version, our protocol can reduce the size of rekeying messages by customizing the public key. We provide three algorithms (INIT, JOIN, LEAVE) for rekeying when a group membership changes and a revocation algorithm for building dynamic conferences without the group controller. We also give the security proof of our protocol in a symbolic security model and the implementation of the protocol.

With the development of next-generation sequencing (NGS), DNA/RNA sequencing has become cheaper and more efficient. Today, a whole human genome can be sequenced under $1,000, providing opportunities for large-scale bioinformatic analysis on big datasets. However, most of existing bioinformatic analysis tools are programmed for single server based computing platform and not suitable to process such big datasets. As Hadoop MapReduce and Spark are gaining popularity as cluster computing based big data processing platform, more and more bioinformatic applications start to explore cluster computing platform for large scale data analysis. In this paper we present an in-depth experimental study on deploying Spark clusters for high performance bioinformatic short sequence reconstruction. Our experimental results enable us to answer a number of challenging and yet most frequently asked questions regarding efficient management of bioinformatic data analysis services on Spark systems. Example questions include how to best split big dataset into multiple partitions, and how to distribute data partitions and bioinformatic analysis tasks on a Spark cluster for carrying out a high performance distributed analysis job? What types of memory models are effective for bioinformatic data analysis services on a Spark cluster? Why do different bioinformatic data analysis operations exhibit different throughput performance on the same Spark cluster? We conjecture that this experimental study not only demonstrates the feasibility of high performance bioinformatic data analysis on Spark platform, but also will help bioinformatic application developers to make more informed decisions on both design and configuration of Spark Cluster, managing and tuning parameters of Spark runtime system for enhancing the performance of large scale big data analytics.

Summary: With the advent of next-generation sequencing, traditional bioinformatics tools are challenged by massive raw metagenomic datasets. One of the bottlenecks of metagenomic studies is lack of large-scale and cloud computing suitable data analysis tools. In this paper, we proposed a Spark based tool, called MetaSpark, to recruit metagenomic reads to reference genomes. MetaSpark benefits from the distributed data set (RDD) of Spark, which makes it able to cache data set in memory across cluster nodes and scale well with the datasets. Compared with previous metagenomics recruitment tools, MetaSpark recruited significantly more reads than many programs such as SOAP2, BWA and LAST and increased recruited reads by ∼4% compared with FR-HIT when there were 1 million reads and 0.75 GB references. Different test cases demonstrate MetaSpark's scalability and overall high performance. Availability: https://github.com/zhouweiyg/metaspark. Contact: [email protected] , [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Workflow resource patterns focus on the various ways in which resources are represented and utilized in workflows. This paper uses Pi-calculus to model workflow resource patterns. The main goal is to explore expressive capabilities of Pi-calculus regarding business process and resource. The formalizations can be used as a foundation for pattern-based workflow system as well as a basis for future research on workflow-related patterns.

Workflow patterns contain basic features of business process. How to implement these patterns depends on the modeling languages and methods. Pi-calculus, as a kind of process algebra, can be applied in business process modeling. This paper uses Pi-calculus, as a formalizing utility, to investigate some workflow patterns that may have multiple presentation versions with BPMN. The main goal is to explore expressive capabilities of Pi-calculus regarding business process and check representations of workflow patterns in Pi-calculus.