Accurate traffic flow prediction plays a pivotal role in intelligent transportation systems (ITS). While deep learning-based approaches have demonstrated remarkable success in this domain, their performance heavily depends on the availability of large-scale training data. However, many cities face challenges in collecting sufficient traffic flow data due to privacy concerns and substantial storage requirements. Consequently, conventional traffic flow prediction models often suffer from performance degradation when applied to cities with limited data availability, primarily due to spatially unbalanced data distributions. To overcome this limitation, we propose a novel pre-trained framework for cross-city traffic flow prediction, termed PTCC. Different from existing methods that focus solely on optimizing performance for data-rich cities, our framework innovatively transfers spatiotemporal knowledge from data-abundant cities to enhance prediction accuracy in data-scarce scenarios. The proposed PTCC framework comprises three key components: 1) A pre-trained module that learns long-term temporal patterns from traffic flow data in source cities and generates comprehensive segment-level representations; 2) A discrete graph learning structure that captures node dependencies from contextual segment-level representations; 3) A spatiotemporal prediction module that effectively transfers the acquired knowledge to facilitate accurate traffic flow forecasting in target cities. We conduct extensive experiments to validate the framework’s effectiveness, training the model on METR-LA and PEMS-BAY datasets, and evaluating its performance on PEMS04 and PEMS08 datasets. The experimental results demonstrate that our pre-trained frame-work significantly outperforms existing methods, establishing its superiority for traffic flow prediction in cities with limited data availability.

Industrial IoT (IIoT) sensing nodes for bearing monitoring often operate in high-noise environments, where acoustic and mechanical interference masks weak fault signatures and undermines diagnostic reliability. To address this challenge, we propose a lightweight multi-modal fusion framework for robust fault diagnosis under extreme noise. Our method first applies multi-resolution decomposition with selective reconstruction and adaptive enhancement to preserve fault-related components while suppressing interference. The enhanced signals are transformed from 1D time series into two-dimensional representations to jointly capture temporal dynamics and spectral characteristics. We then design a tri-branch multi-head attention architecture that integrates a multiscale recurrence plot network, a Gramian angular field network, and a lightweight residual network. Learnable attention weights enable adaptive fusion of complementary cross-modal features with low computational overhead. Extensive experiments on the CWRU benchmark show superior robustness from 0 dB to -6 dB SNR, with a mean accuracy above 99.6% and consistent gains over eight state-of-the-art methods. Additional tasks on single-domain diagnosis, cross-condition fault type recognition, and fault degree discrimination (T1–T3) confirm strong generalization and multi-scale adaptability, with average improvements of 2–4% over the second-best baseline and stable variance under noise. The compact architecture and high noise immunity indicate practical suitability for IIoT sensing nodes and edge deployment in complex industrial scenarios.

Traffic flow prediction is fundamental to traffic information services, control, and guidance. The challenge to accurately model the traffic flow is to comprehensively capture dynamic, global spatial, and local temporal correlations. To address challenges in spatiotemporal dependencies, global similarity, and local dynamics, we propose a multilayer spatiotemporal correlation-aware graph attention network (MSTC-GAT) for traffic flow prediction. Our model contains a multilayer spatial structure-aware module [spatial graph attention network (S-GAT)] using a spatial GAT with hierarchical attention masks and a path-based node correlation matrix to effectively capture local and global spatial dependencies. The temporal structure-aware module [temporal graph attention networks (T-GATs)] constructs a short-term similarity matrix of nodes for the temporal GAT to capture local dynamic temporal dependencies. Finally, a spatiotemporal Transformer (ST-Transformer) fuses weighted spatiotemporal node embeddings to capture global dynamic dependencies for accurate prediction. We conduct extensive experiments on four public benchmark datasets compared with 10 state-of-the-art models. The experimental results demonstrate that the MSTC-GAT outperforms all comparisons for short- and long-term predictions.

Panoramic Image Generation (PIG) aims to create coherent images of arbitrary lengths. Most existing methods fall in the joint diffusion paradigm, but their complex and heuristic crop connection designs often limit their ability to achieve multilevel coherence. By deconstructing this challenge into its core components, we find it naturally aligns with next-token prediction, leading us to adopt an autoregressive (AR) paradigm for PIG modeling. However, existing visual AR (VAR) models are limited to fixed-size generation, lacking the capability to produce panoramic images. In this paper, we propose PanoLlama, a novel framework that achieves endless and coherent panorama generation with the autoregressive paradigm. Our approach develops a training-free strategy that utilizes token redirection to overcome the size limitations of existing VAR models, enabling next-crop prediction in both horizontal and vertical directions. This refreshes the PIG pipeline while achieving SOTA performance in coherence (47.50%), fidelity(28.16%), and aesthetics (15%). Additionally, PanoLlama supports applications other PIG methods cannot achieve, including mask-free layout control, multi-scale and multi-guidance synthesis. To facilitate standardized evaluation, we also establish a dataset with 1,000 prompts spanning 100+ themes, providing a new testing benchmark for PIG research. The code is available at https://github.com/0606zt/PanoLlama.

Diffusion models have recently gained recognition for generating diverse and high-quality content, especially in image synthesis. These models excel not only in creating fixed-size images but also in producing panoramic images. However, existing methods often struggle with spatial layout consistency when producing high-resolution panoramas due to the lack of guidance on the global image layout. This paper introduces the Multi-Scale Diffusion (MSD), an optimized framework that extends the panoramic image generation framework to multiple resolution levels. Our method leverages gradient descent techniques to incorporate structural information from low-resolution images into high-resolution outputs. Through comprehensive qualitative and quantitative evaluations against prior work, we demonstrate that our approach significantly improves the coherence of high-resolution panorama generation.

The convergence of blockchain with Industry 5.0 technologies presents significant opportunities for healthcare data management; however, current systems face challenges related to scalability, privacy, and energy efficiency. This article introduces an innovative framework that leverages ciphertext-policy attribute-based encryption (CP-ABE), Ethereum smart contracts, and decentralized IPFS storage to address these challenges. The framework presents three main innovations: 1) a human-centric authentication system that ensures security without sacrificing cryptographic integrity; 2) post-quantum Kyber-786 algorithms paired with CP-ABE, which minimizes computational overhead by 27% while facilitating 32 ms key generation; and 3) an energy-efficient Proof of Stake (PoS) consensus mechanism that reduces energy consumption by 98% (0.05 kW/transaction) compared to traditional blockchain systems. Thorough testing demonstrates 99.5% resistance to man-in-the-middle attacks, a throughput of 15.6 MB/min at scale, and an emergency access latency of under 120 ms, which is essential for practical healthcare applications. By consolidating decentralized pseudo-identities for patient anonymity, secure audit logs, and GDPR/HIPAA-compliant data governance, this research establishes a new standard for secure, sustainable, and patient-focused health data ecosystems in the Industry 5.0 landscape.

Gland lesions affect a large global population. Accurately segmenting surface structures is crucial for assisting in the diagnosis of these diseases. In this direction, we investigate two key issues: 1) How to accurately segment gland morphology and irregular boundaries and 2) How to distinguish gland internal heterogeneity and its similarity to the background. The main results are that 1) parallel multi-scale attention (PMA) smooths the segmentation of blurred boundaries of varying sizes and improves detail accuracy. 2) Cross-dimensional attention (CDA) models the dependencies between gland channels and spatial dimensions to enhance the understanding of spatial information both inside and outside the gland, thereby more accurately distinguishing the gland from the background. Per the main results, we propose a multi-scale cross-dimensional attention network (MCANet) for gland segmentation. Extensive experiments on six real-world datasets demonstrate the superior performance of our method in gland segmentation. The source code is available athttps://github.com/yuchaozhi/MCANet.

Electroencephalogram (EEG)-based motor imagery (MI) signals are frequently used in brain-computer interfaces (BCIs) due to their wide applications in the rehabilitation field. However, cross-subject variations often result in a model trained on one participant failing when applied to another. Additionally, privacy concerns regarding sensitive health and mental information in EEG-based MI signals further complicate the situation. Source-free domain adaptation aims to address these cross-subject variations by transferring knowledge from a source domain (i.e., a previous participant) to a target domain (i.e., a new participant) without accessing sensitive source data. However, source-free unsupervised domain adaptation models often face issues with incorrect pseudo-labels, which can lead to unstable and ineffective adaptation. To address this, we propose a source-free semi-supervised domain adaptation algorithm for EEG-based MI signal classification. This algorithm tackles noise accumulation caused by incorrect pseudo-labels while effectively handling data distribution variations and privacy concerns, similar to source-free unsupervised domain adaptation models. Specifically, we train the classifier head using only a limited amount of labeled target data to prevent noise accumulation, and generate pseudo-labels for the unlabeled target data. Furthermore, we introduce an independent self-training head that learns better representations using the generated pseudo-labels, mitigating overfitting caused by the limited labeled target data. Additionally, we design an adversarial head that plays a minimax game to extract more discriminative feature representations from the unlabeled target data. Extensive experiments on three benchmark datasets, compared with eighteen state-of-the-art SFDA methods, demonstrate the superiority of our approach.

Diffusion models have emerged as effective tools for generating diverse and high-quality content. However, their capability in high-resolution image generation, particularly for panoramic images, still faces challenges such as visible seams and incoherent transitions. In this paper, we propose TwinDiffusion, an optimized framework designed to address these challenges through two key innovations: the Crop Fusion for quality enhancement and the Cross Sampling for efficiency optimization. We introduce a training-free optimizing stage to refine the similarity of adjacent image areas, as well as an interleaving sampling strategy to yield dynamic patches during the cropping process. A comprehensive evaluation is conducted to compare TwinDiffusion with the prior works, considering factors including coherence, fidelity, compatibility, and efficiency. The results demonstrate the superior performance of our approach in generating seamless and coherent panoramas, setting a new standard in quality and efficiency for panoramic image generation.

Real-time and high-precision traffic flow prediction plays a crucial role in transportation management, contributing to control dispatch and reducing traffic congestion. Due to the complex and dynamic characteristics of the traffic flow, traffic flow prediction remains challenging. Previous work often ignores some dynamic and momentary spatial information, and predicting the traffic flow of a target road segment in the long-term horizon is a difficult problem. To address these issues, we propose a novel deep learning framework, termed long short-term structural spatiotemporal information fusion graph wavelet network (LSSTF-GWN), to capture momentary dynamic spatiotemporal correlation and make long-term predictions. The LSSTF-GWN model integrates graph wavelets network (GWN) with temporal gated convolution networks into graph convolution network to construct a multigraph architecture to address the complex spatiotemporal correlations in traffic flow data. The GWN extracts the instantaneous and global features of the spatial information by designing different adjacency matrices. The LSSTF-GWN not only considers the fixed distance between graphs but also builds long-term dynamic graphs for the inner relationships of the nodes to reflect contextual and global information. The proposed method is evaluated using three metrics, i.e., mean absolute error, root-mean-square error, and mean absolute percentage error, on two real-world data sets from the Caltrans Performance Measurement System (PeMS). The experimental results demonstrate the superior performance of our method in long-term traffic flow prediction.

Data-driven machine learning is increasingly involved in human life and industrial development due to its large-scale testing and low time cost. However, existing learning algorithms are not suitable for real-world applications with data dilemmas, such as extremely high-dimension-low-sample-size problems, non-Gaussian noise, and uncertainty. In this article, we propose a novel fuzzy multikernel subspace learning (FMKSL) to address these problems, which provides a robust multikernel representation with a fuzzy constraint and sparse coding. We then develop an adaptive learner chain optimization method based on the iterative process of FMKSL to speed up learning and achieve the best performance. Different from previous methods, we also design a flexible data augmentation method, namely generalized correntropy-based adaptive data augmentation (GC-ADA), to effectively use the$\alpha$-order statistics between samples to transform the exact value prediction task into a simpler classification one. It is important that our general framework only needs an extremely small dataset to predict the related ranking of the sample since the exact label value measured by different institutions in reality varies largely. A typical scenario is the drug screening task, i.e., the inhibitory potency prediction of the nicotinamide phosphoribosyltransferase inhibitors. Extensive experiments on nine real-world datasets (four tasks) show that our framework outperforms state-of-the-art methods in prioritizing candidate samples and chemicals for experimental research and analysis via a data-driven computational approach.

Abstract Corona Virus disease 2019 (COVID‐19) has shattered people's daily lives and is spreading rapidly across the globe. Existing non‐pharmaceutical intervention solutions often require timely and precise selection of small areas of people for containment or even isolation. Although such containment has been successful in stopping or mitigating the spread of COVID‐19 in some countries, it has been criticized as inefficient or ineffective, because of the time‐delayed and sophisticated nature of the statistics on determining cases. To address these concerns, we propose a GSA‐ELM model based on a gravitational search algorithm to forecast the global number of active cases of COVID‐19. The model employs the gravitational search algorithm, which utilises the gravitational law between two particles to guide the motion of each particle to optimise the search for the global optimal solution, and utilises an extreme learning machine to address the effects of nonlinearity in the number of active cases. Extensive experiments are conducted on the statistical COVID‐19 dataset from Johns Hopkins University, the MAPE of the authors’ model is 7.79%, which corroborates the superiority of the model to state‐of‐the‐art methods.

Extensive traffic flow data are received from the loop detector networks every second, which requires us to develop an effective and efficient algorithm to predict future traffic flow. However, dynamic traffic conditions on a road are not just influenced by sequential patterns in the temporal dimension, but also by other roadways in the spatial dimension. Although many successful models have been developed in previous studies to forecast future traffic flows, most of them have shortcomings in modeling spatial and temporal dependencies. In this article, we focus on spatial-temporal factors and propose a new adaptive spatial-temporal transformer graph network (ASTTGN) to improve the accuracy of traffic forecasting by jointly modeling the spatial-temporal information of road networks. Specifically, we propose an adaptive spatial-temporal transformer module, which contains two developed adaptive transformer modules for capturing dynamic spatial dependence and temporal dependence across multiple time steps, respectively. Finally, feature fusion is performed through a gated feature aggregation layer to simulate the effect of complex spatial-temporal factors on traffic conditions. In particular, the multihead attention mechanism employed by the transformer can effectively explore the potential spatial-temporal dependence patterns in different subspaces. Experimental results on two real-world traffic data sets demonstrate the superiority of the proposed model compared to existing techniques.

In this paper, we propose a cross-channel, cross-scale, and cross-stage network (C3Net) for single image super-resolution, which effectively shares the features learned from multiple channels, multiple scales, and multiple stages. Multi-scale spatial features are extracted in each stage in an encoder-decoder fashion. The channel attention is performed after each encoder to exploit the inter-channel dependencies. After that, we design a cross-stage and cross-scale feature sharing module to accelerate the feature sharing across different scales and different stages. The whole network is optimized by multiple similar stages to reduce the number of parameters. Finally, super-resolution images of multiple resolutions are reconstructed simultaneously. We evaluate the proposed network on four benchmark datasets by comparing it with eleven state-of-the-art methods. Comprehensive experiments show the proposed network outperforms state-of-the-art methods by fewer parameters. The source code is available at https://github.com/thinkerww/SR_Version.

Early diagnosis is currently the most effective way of saving the life of patients with neuropsychiatric systemic lupus erythematosus (NPSLE). However, it is rather difficult to detect this terrible disease at the early stage, due to the subtle and elusive symptomatic signals. Recent studies show that the$^{1}$H-MRS (proton magnetic resonance spectroscopy) imaging technique can capture more information reflecting the early appearance of this disease than conventional magnetic resonance imaging techniques.$^{1}$H-MRS data, however, also presents more noises that can bring serious diagnosis bias. We hence proposed a noise-immune extreme ensemble learning technique for effectively leveraging$^{1}$H-MRS data for advancing the early diagnosis of NPSLE. Our main results are that 1) by developing generalized maximum correntropy criterion in the kernel extreme learning setting, many types of non-Gaussian noises can be distinguished, and 2) weighted recursive feature elimination, using maximal information coefficient to weight feature’s importance, helps to further alleviate the bad impact of noises on the diagnosis performance. The proposed method is assessed on a publicly available dataset with 97.5% accuracy, 95.8% sensitivity and 99.9% specificity, which well demonstrates its efficacy.

We investigate vehicle density detection from traffic surveillance. This task is rather challenging, mainly due to the low-resolution of data and large-scale variance of vehicles. The main result is that by learning cross-scale features, high-quality vehicle density maps can be attainable. Our main technical contribution is a learning model, called Adaptive Cross-Scale Network (ACSNet), that can learn cross-scale features from traffic surveillance data with low-resolution and large scale variance of vehicles. ACSNet consists of 1) a series of cross-scale feature extraction blocks with dense bypassing paths for harvesting spatial information, 2) an attention block for learning from appropriate scales, and 3) a structural similarity index for learning from occlusion scenes. We assess our ACSNet on two benchmark datasets, and extensive empirical evidence shows that our ACSNet performs favor-ably against the state-of-the-art methods.

With the boom of Initial Coin Offerings (ICO) in the financial markets, smart contracts have gained rapid popularity among consumers. Smart contract vulnerabilities however made them a prime target to malicious attacks that are leading to huge losses. The research community is thus applying various software engineering technologies to smart contracts to address them. In general, to detect vulnerabilities in smart contracts, mutation and fuzz based testing approaches have been widely studied and indeed achieved promising performance on benchmark datasets. Generating test inputs with mutation approaches essentially relies on the available test cases in a smart contract program. In our preliminary study, however, we observed that 56.4% of 218 identified open-source smart contract project repositories do not provide any test case for validation. Fuzzing test inputs leads to random values and lacks practical usefulness. Our work addresses this problem: we propose an approach, Smartgift, which generates practical inputs for testing smart contracts by learning from the transaction records of real-world smart contracts. Leveraging a collected set of over 60 thousand transaction records, Smartgift is able to generate relevant test inputs for ~77% smart contract functions, largely outperforming the traditional fuzzing approach (successful for only 60% functions). We further demonstrate the practicality of the test inputs by using them to replace the test inputs of the ContractFuzzer state of the art smart contract vulnerability detector: with inputs by Smartgift, ContractFuzzer can now detect 131 of the 154 vulnerabilities in its benchmark.

Multimodality sentiment analysis in the social Internet of Things is a developing field, which is basic to empathetic mechanisms, affective computing, and artificial intelligence. Current works in this domain do not explicitly consider the influence of contextual information fusion based on correlation coefficient and memory network with branch structure for sentiment analysis. Unlike present works, this article presents a hierarchical self-attention fusion (H-SATF) model for capturing contextual information better among utterances, a contextual self-attention temporal convolutional network (CSAT-TCN) for sentiment recognition in the social Internet of Things, and a multibranch memory (MBM) network that stores self-speaker and interspeaker sentimental states into global memories. For MOSI data sets, the hybrid H-SATF-CSAT-TCN-MBM model outperforms the state-of-the-art networks and shows 0.31%-9.93% improvement.

The purpose of this study was to implement principal component analysis (PCA) on videocapsule endoscopy (VE) images to develop a new computerized tool for celiac disease recognition. Three PCA algorithms were implemented for feature extraction and sparse representation. A novel strip PCA (SPCA) with nongreedy L1-norm maximization is proposed for VE image analysis. The extracted principal components were interpreted by a non-parametric k-nearest neighbor (k-NN) method for automated celiac disease classification. A benchmark dataset of 460 images (240 from celiac disease patients with small intestinal villous atrophy versus 220 control patients lacking villous atrophy) was constructed from the clinical VE series. It was found that the newly developed SPCA with nongreedy L1-norm maximization was most efficient for computerized celiac disease recognition, having a robust performance with an average recognition accuracy of 93.9 percent. Furthermore, SPCA also has a reduced computation time as compared with other methods. Therefore, it is likely that SPCA will be a helpful adjunct for the diagnosis of celiac disease.

In this paper, we present a novel and effective technique for short-term traffic flow forecasting. Our main contribution is an extension of Kalman filter, such that it becomes to be able to identify the noise and then filter out it; we hence named the present technique as noise-identified Kalman filter. Our epistemological perspective is that the classic Kalman filter filters out not only the noise but also useful signals. We hence develop the Kalman filter for de-noising while preserving the useful signals by devising a cost function. By conducting extensive experiments on four benchmark data sets, the proposed technique is firmly verified to be effective for short-term traffic flow forecasting, outperforming not only the classic Kalman filter but also other frequently-used parametric and non-parametric techniques.