The rapid growth of short video platforms has made multimodal fake news more prevalent.Existing detectors suffer from two major limitations: (I) global-alignment bias that overemphasizes holistic cross-modal matching and thus misses subtle, localized inconsistencies; and (II) LLM-based methods that leverage powerful generative reasoning to identify cognitive forgeries but inherently suffer from hallucinations and high inference latency.To overcome these limitations, we propose PCDD, a novel Perception-Cognition Dualdriven Detector that jointly observes the form and probes the logic for short video fake news detection.The perception stream exposes finegrained cross-modal conflicts by amplifying localized inconsistencies into explicit discrepancies.The cognition stream transfers reasoning capabilities from LLMs to a lightweight student to mine cognitive forgeries, while reducing the risk of hallucinations and eliminating reliance on LLMs at inference.Experiments on real-world datasets show that PCDD consistently outperforms baselines, while improving interpretability and robustness in data scarcity scenarios.

Federated learning enables participants to collaboratively train a global model through distributed training without sharing raw data. However, this distributed training is vulnerable to single-point failures, privacy leakage, and Byzantine attacks. To address these challenges simultaneously, we propose a privacy-enhanced federated learning algorithm empowered by blockchain and game theory (BGFL). First, we integrate blockchain technology into federated learning and establish a decentralized training paradigm, which effectively avoids the threat of single-point failures in centralized training. Second, we propose a privacy protection method that combines the Chinese Remainder Theorem and Shamir's Secret Sharing, providing dual privacy protection for participants in decentralized federated learning scenarios. Furthermore, leveraging the homomorphic properties of the proposed privacy-enhanced approach, a Byzantine attack defense mechanism is designed. Finally, a game-theoretic incentive mechanism is proposed to mitigate malicious behaviors during collaborative training, ensuring secure cooperation among all parties. Experimental results demonstrate that, compared to baseline methods, BGFL improves test accuracy by 0.93%–22.75%. Compared to other secret sharing-based federated learning schemes, BGFL requires only 5.91% of the computational overhead and 4.01% of the communication overhead at the same dataset scale. This enables efficient achievement of security and robustness objectives.

Federated learning allows users to collaboratively train models, but studies have shown that local model parameters may leak users’ privacy. Furthermore, the global model in federated learning could be compromised by Byzantine attacks. To address these issues, a robust federated learning algorithm based on chaotic encryption and dual-server detection (CDRFL) is proposed. First, CDRFL introduces a lightweight chaotic encryption method, where ciphertexts have a unique property of mutual cancellation, allowing the plaintext global model parameters to be derived without decryption. Moreover, CDRFL allows users to encrypt with different keys, further reducing the risk of privacy leakage. Next, a dual-server detection scheme is designed to defend against Byzantine attacks while preserving user privacy. Lastly, a reputation score mechanism with a forgetting function is proposed to fairly distinguish Byzantine participants. Experimental results demonstrate that, compared with popular robust aggregation schemes, CDRFL improves testing accuracy by 3.26% to 9.06%. When compared to existing chaotic encryption federated learning schemes, CDRFL enhances the ability to defend against Byzantine attacks and reduces time and communication overhead by 73.63% and 95.40%, respectively.

In recent years, unsupervised domain adaptation (UDA) based on deep learning has been widely applied to address the spectral shift problem in cross-scene hyperspectral image classification (HSIC). However, most existing UDA methods focus solely on learning from source domain (SD) or target domain (TD) features, without fully exploiting the valuable class-discriminative information in the TD, resulting in limited performance on target data. To tackle this issue, we propose a prototype-guided cross-domain cyclic self-training (PGCST) framework. Specifically, we combine domain adversarial training with prototype-guided domain adaptation (PGDA) to align both global and class-wise distributions across domains. To better exploit TD information, we introduce a mutual information maximization (MIM) strategy to enhance the compactness and discriminability of target features. Furthermore, we propose a novel pseudo-label selection method that incorporates a classification loss-based cyclic self-training (CST) mechanism to improve the model’s discriminative ability on target samples. Experimental results on two cross-scene hyperspectral datasets demonstrate that the proposed method outperforms several state-of-the-art approaches.

The remote sensing satellite system has steadily developed, and the availability of massive high-quality satellite remote sensing data has rapidly improved. The dynamic monitoring of land cover changes using hyperspectral data has received great attention. The existing change detection methods usually use the spatial correlation or spatial-spectral correlation of hyperspectral images, lacking an overall consideration of the three-dimensional temporal-spatial-spectral joint features, resulting in suboptimal change detection results. Given the considerations above, this paper proposes a SimAM multi-scale joint feature extraction network for hyperspectral image change detection. The proposed method first adopts SimAM multi-scale joint features network to consider the hyperspectral image as a whole, and extracts multi-scale temporal-spatial-spectral joint features from the network. Then, uses multi-scale feature weighted fusion module to weight different scale features after simple fusion, highlighting the change regions. Finally, adopts the batch-balance measurement module to measure the similarity of the bi-temporal fusion features and output the change detection result map. Experiments show that on two public datasets, the proposed method can achieve a good change detection effect.

Hyperspectral image classification (HSIC) is a pivotal technology in hyperspectral remote sensing, playing a widespread role in remote sensing applications. However, the limited number of labeled samples has always made hyperspectral image classification difficult. In response to this issue, researchers have delved into cross-domain classification studies. Moreover, significant progress on cross-domain HSIC has been made in recent years. Nevertheless, existing methods exhibit shortcomings, including inadequate exploitation of spectral and spatial information and a slow training speed, rendering them unsuitable for downstream application tasks. To address these challenges, this paper introduces a model of cross-domain few-shot learning with spectral-spatial split attention(S3A-CFSL). Channel attention and split attention are presented to emphasize effective spectral and spatial information for HSIC adaptively. Additionally, the ResNet variant, called ResNeSt, is employed to expedite the training speed of the model. Experimental results demonstrate notable enhancements in the proposed method's classification accuracy and model training speed across two public datasets.

The fusion of hyperspectral image (HSI) and LiDAR data for classification has gained widespread attention. However, the current fusion methods still have limitations on the use of heterogeneous data information and the interaction of heterogeneous data features. Therefore, we propose a classification method based on heterogeneous attention feature fusion network (HAFF-Net). Firstly, The convolutional neural network is employed to capture local spatial features from multi-scale inputs of heterogeneous data. Secondly, the heterogeneous feature attention module is designed to deeply fuse the extracted local spatial features and promote the interaction of heterogeneous data. Next, a transformer encoder is utilized to extract global spectral features. Finally, the two extracted features are classified by decision fusion using different classifiers. The proposed approach exhibits effectiveness through experimental results on two datasets.

There is abundant spectral and spatial information in Hyperspectral images (HSI). However, there exists a limitation of not using spatial information sufficiently in HSI classification. Besides, there is the limitation of mononuclear in feature extraction, resulting in insufficient feature extraction and insufficient utilization of data information. In view of these problems, a node similarity semi-supervised classification method of multiscale feature is proposed to break the limitation of mononuclear and achieve full extraction of spatial information. First, to extract pixel-level features, a three-dimensional (3-D) multiscale convolutional neural network (CNN) is used. Second, based on node similarity superpixel graph U-Net (NSGUNet) is proposed to extract superpixel-level features. Finally, the above two features are weighted fusing, the fused features are classified by sparse graph regularization. Experiments on three datasets illustrate that the proposed method is effective.

Deep learning has strong learning ability to extract the features from Image datasets. In recent years, deep networks especially deep convolutional neural networks have revolutionized this field. When exposed to a huge number of datasets and their labels, deep learning techniques like Convolutional Neural Networks (CNNs) can produce precise categorization results. However, employing CNNs with scant labeled data might have a number of issues, including the problem of heavy overfitting. Convolutional Neural Networks are the backbone of modern deep learning architectures for the purpose of image classification. We solved image classification problem with different architectures and compare their performances. The objective of this work lies in the approach to study the given datasets. We use domain adaptation approach to highlight the underlying characteristics of these datasets and the different parameters (activation function, weights, regularization, neural simulation etc.) associated with these architectures. In our work, we use three datasets—RS19, UC Merced, and EuroSat images—were utilized in the CNN implementation to training the suggested model. The obtained results effectively demonstrate the local representation capacity of CNNs. Furthermore, this work shows that transfer learning improves classification outcomes in optical remote sensing images, particularly when the training sample is small.

With the development of hyperspectral sensors, there is an increasing amount of accessible hyperspectral data, and the classification task for land cover categories has gained significant attention. Existing classification methods typically extract features from either the pixel or superpixel perspective. However, using a single-scale feature extraction approach fails to simultaneously consider both local and global features of land cover, leading to suboptimal classification results. To address this issue, this paper proposes a parallel graph attention network model based on pixel and superpixel feature fusion (SSPGAT) for hyperspectral image classification, which leverages the fusion of pixel-level and superpixel-level features. The proposed approach first employs spectral convolutional layers to reduce the redundant spectral dimension. Then, it utilizes graph attention network (GAT) to extract local and global features of land cover separately from the pixel and superpixel perspectives. Finally, a fully connected network is employed to classify the fused features from both branches. Experimental results on two different datasets demonstrate the effectiveness of the proposed approach.

In this paper, a new method for improving the classification performance of hyperspectral images with the aid of derivative information is investigated. First, spectral features are filtered and derivatives of different orders at different sampling intervals are computed. Then, the suitable spectral magnitude features and different derivative features are chosen by using segmented principle component analysis feature extraction method with optimal parameters, and are stacked to constitute a new feature cube. Finally, the efficacy of the spectral derivatives in improving the classification performance of the hyperspectral data is testified using support vector machine for AVIRIS hyperspectral data. The experimental results show that the proposed method can improve the classification accuracy compared to the traditional classification techniques with spectral magnitude features even on very small training samples.

In this paper, we study the computability of the initial value problem of the Combined KdV equation. It is shown that, for any integer s>2, the nonlinear solution operator which maps an initial condition data to the solution of the Combined KdV equation can be computed by a Turing machine.

Uniform nested loops are broadly used in scientific and multidimensional digital signal processing applications. Due to the amount of data handled by such applications, on-chip memory is required to improve the data access and overall system performance. In this study a static data scheduling method, carrot-hole data scheduling, is proposed for multidimensional applications, in order to control the data traffic between different levels of memory. Based on this data schedule, optimal partitioning and scheduling are selected. Experiments show that by using this technique, on-chip memory misses are significantly reduced as compared to results obtained from traditional methods. The carrot-hole data scheduling method is proven to obtain smallest on-chip memory misses compared with other linear scheduling and partitioning schemes.