Smart farming, as a branch of the Internet of Things (IoT), combines the recognition of agricultural economic competencies and the progress of data and information collected from connected devices with statistical analysis to characterize the essentials of the assimilated information, allowing farmers to make intelligent conclusions that will maximize the harvest benefit. However, the integration of advanced technologies requires the adoption of high-tech security approaches. In this paper, we present a framework that promises to enhance the security and privacy of smart farms by leveraging the decentralized nature of blockchain technology. The framework stores and manages data acquired from IoT devices installed in smart farms using a distributed ledger architecture, which provides secure and tamper-proof data storage and ensures the integrity and validity of the data. The study uses the AWS cloud, ESP32, the smart farm security monitoring framework, and the Ethereum Rinkeby smart contract mechanism, which enables the automated execution of pre-defined rules and regulations. As a result of a proof-of-concept implementation, the system can detect and respond to security threats in real time, and the results illustrate its usefulness in improving the security of smart farms. The number of accepted blockchain transactions on smart farming requests fell from 189,000 to 109,450 after carrying out the first three tests while the next three testing phases showed a rise in the number of blockchain transactions accepted on smart farming requests from 176,000 to 290,786. We further observed that the lesser the time taken to induce the device alarm, the higher the number of blockchain transactions accepted on smart farming requests, which demonstrates the efficacy of blockchain-based poisoning attack mitigation in smart farming.

The aim of steganography detection is to identify whether the multimedia data contain hidden information. Although many detection algorithms have been presented to solve tasks with inconsistent distributions between the source and target domains, effectively exploiting transferable correlation information across domains remains challenging. As a solution, we present a novel multiperspective progressive structure adaptation (MPSA) scheme based on active progressive learning (APL) for JPEG steganography detection across domains. First, the source and target data originating from unprocessed steganalysis features are clustered together to explore the structures in different domains, where the intradomain and interdomain structures can be captured to provide adequate information for cross-domain steganography detection. Second, the structure vectors containing the global and local modalities are exploited to reduce nonlinear distribution discrepancy based on APL in the latent representation space. In this way, the signal-to-noise ratio (SNR) of a weak stego signal can be improved by selecting suitable objects and adjusting the learning sequence. Third, the structure adaptation across multiple domains is achieved by the constraints for iterative optimization to promote the discrimination and transferability of structure knowledge. In addition, a unified framework for single-source domain adaptation (SSDA) and multiple-source domain adaptation (MSDA) in mismatched steganalysis can enhance the model’s capability to avoid a potential negative transfer. Extensive experiments on various benchmark cross-domain steganography detection tasks show the superiority of the proposed approach over the state-of-the-art methods.

Classification methods based on fine-tuning pre-trained language models often require a large number of labeled samples; therefore, few-shot text classification has attracted considerable attention. Prompt learning is an effective method for addressing few-shot text classification tasks in low-resource settings. The essence of prompt tuning is to insert tokens into the input, thereby converting a text classification task into a masked language modeling problem. However, constructing appropriate prompt templates and verbalizers remains challenging, as manual prompts often require expert knowledge, while auto-constructing prompts is time-consuming. In addition, the extensive knowledge contained in entities and relations should not be ignored. To address these issues, we propose a structured knowledge prompt tuning (SKPT) method, which is a knowledge-enhanced prompt tuning approach. Specifically, SKPT includes three components: prompt template, prompt verbalizer, and training strategies. First, we insert virtual tokens into the prompt template based on open triples to introduce external knowledge. Second, we use an improved knowledgeable verbalizer to expand and filter the label words. Finally, we use structured knowledge constraints during the training phase to optimize the model. Through extensive experiments on few-shot text classification tasks with different settings, the effectiveness of our model has been demonstrated.

Cross-scene hyperspectral image (HSI) classification remains a challenging task due to the distribution discrepancies that arise from variations in imaging sensors, geographic regions, atmospheric conditions, and other factors between the source and target domains. Recent research indicates that convolutional neural networks (CNNs) exhibit a significant tendency to prioritize image styles, which are highly sensitive to domain variations, over the actual content of the images. However, few existing domain adaptation (DA) methods for cross-scene HSI classification take into consideration the style variations both within the samples of an HSI and between the cross-scene source and target domains. Accordingly, we propose a novel Content-biased and Style-Assisted Transfer Network (CSTnet) for unsupervised domain adaptation in cross-scene HSI classification. The CSTnet introduces a Content and Style Reorganization (CSR) module that disentangles content features from style features via instance normalization, while refining useful style information as a complementary component to enhance discriminability. A content-wise reorganization loss is designed to reduce the disparity between the separated content/style representations and the output features, thereby enhancing content-level alignment across different domains. Furthermore, we incorporate batch nuclear-norm maximization (BNM) as an effective class-balancing technique that directly exploits unlabeled target data to enhance minority class representations without requiring prior knowledge or pseudo-labels, achieving better distribution alignment. Comprehensive experiments on three cross-scene HSI datasets demonstrate that the proposed CSTnet achieves state-of-the-art performance, effectively leveraging content bias and style assistance for robust domain adaptation in cross-scene HSI classification tasks. The code is available from the website: https://github.com/nbdszw/CSTnet.

Fake News (FN) prevalence on Online Social Networks (OSNs) and online websites is a worldwide issue. The previous studies on Fake News Detection (FND) have focused on rich-resource languages with limited relevance to users other than native speakers. Despite the progressing multilingual approaches, FND in low-resource languages remains obscure due to a lack of large-sized annotated corpora of real-world news. Large Language Models (LLMs) have emerged as a promising solution for multilingual FND. This study leverages the power of LLMs for an automated mechanism compared to traditional feature extraction methods. We curate the first large-sized multi-domain corpus, the Hook and Bait Urdu, with 78,409 fake and true news, and fine-tune the LLaMA 2 model for our proposed approach. We implement the curated dataset for two experiments. First, this study evaluates the dataset for unimodal text-based Urdu FND. The proposed LLaMA-based approach shows an accuracy of 0.978 and an F1-score of 0.971. For our second experiment, we fine-tuned the LLaMA 2-based framework for multilingual FND using the curated dataset (in Urdu) and the ISOT Fake News dataset (in English). Analytical and prediction performance comparisons with the previous studies validate the efficacy of the proposed framework with an accuracy of 0.984 and an F1-score of 0.980. The lightweight LoRA fine-tuning method, with 0.032% trainable parameters, ensured robust data handling, computational efficiency while leveraging early stopping and optimized hyperparameters for reliable and high-performing monolingual and multilingual FND. The real-world news dataset is publicly available for developing an automated FND mechanism to curb the threat of FN and related cybercrimes.