Zero-shot multilingual fact-checking, which aims to discover and infer subtle clues from the retrieved relevant evidence to verify the given claim in cross-language and cross-domain scenarios, is crucial for optimizing a free, trusted, wholesome global network environment. Previous works have made enlightening and practical explorations in claim verification, while the zero-shot multilingual task faces new challenging gap issues: neglecting authenticity-dependent learning between multilingual claims, lacking heuristic checking, and a bottleneck of insufficient evidence. To alleviate these gaps, a novel Joint Prompt and Evidence Inference Network (PEINet) is proposed to verify the multilingual claim according to the human fact-checking cognitive paradigm. In detail, firstly, we leverage the language family encoding mechanism to strengthen knowledge transfer among multi-language claims. Then, the prompt turning module is designed to infer the falsity of the fact, and further, sufficient fine-grained evidence is extracted and aggregated based on a recursive graph attention network to verify the claim again. Finally, we build a unified inference framework via multi-task learning for final fact verification. The newly achieved state-of-the-art performance on the released challenging benchmark dataset that includes not only an out-of-domain test, but also a zero-shot test, proves the effectiveness of our framework, and further analysis demonstrates the superiority of our PEINet in multilingual claim verification and inference, especially in the zero-shot scenario.

Cross-modal image–text retrieval enables searching and retrieving of semantically relevant data across heterogeneous modalities, acting as a pivotal technology for interpreting massive remote sensing (RS) data. Despite recent progress, most existing methods in remote sensing cross-modal image–text retrieval (RSCIR) rely on high-dimensional real-valued embeddings, which suffer from excessive storage overhead and slow retrieval speeds, severely limiting their scalability in real-world applications. Conversely, while hashing techniques offer efficiency, traditional methods often fail to preserve the fine-grained semantic consistency required for complex RS scenes, leading to significant performance degradation. To bridge this gap, this paper proposes a novel framework named ConHash (Cross-modal Contrastive Hashing), which transfers the discriminative power of pre-trained vision–language models into a compact binary Hamming space. Specifically, ConHash comprises three synergistic components: (1) a hash module designed to project continuous embeddings into a latent discrete space while reducing information loss; (2) a hash-aware contrastive constraint that enforces cross-modal alignment directly in the hash space; and (3) a collaborative hybrid optimization strategy that jointly constrains real-valued embeddings and hash representations. Extensive experiments on RSICD and RSITMD demonstrate that ConHash achieves a favorable balance between accuracy and efficiency. Using 512-bit hash codes with L1 quantization loss as the main configuration, ConHash achieves mR values of 21.69% and 35.79% on RSICD and RSITMD, respectively. It also provides up to 3.50× retrieval speedup and a 32× theoretical storage reduction compared with 512-dimensional float32 embeddings, making it suitable for scalable remote sensing retrieval applications.

Radar radiation source recognition technology is vital in electronic countermeasures, electromagnetic control, and air traffic management. Its primary function is to identify radar signals in real time by computing and inferring the parameters of intercepted signals. With the rapid advancement of AI technology, deep learning algorithms have shown promising results in addressing the challenges of radar radiation source recognition. However, significant obstacles remain: the radar radiation source data often exhibit large-scale, unbalanced sample distribution and incomplete sample labeling, resulting in limited training data resources. Additionally, in practical applications, models must be deployed on outdoor edge computing terminals, where the storage and computing capabilities of lightweight embedded systems are limited. This paper focuses on overcoming the constraints posed by data resources and edge computing capabilities to design and deploy large-scale radar radiation source recognition algorithms. Initially, it addresses the issues related to large-scale radar radiation source samples through data analysis, preprocessing, and feature selection, extracting and forming prior knowledge information. Subsequently, a model named RIR-DA (Radar ID Recognition based on Deep Learning Autoencoder) is developed, integrating this prior knowledge. The RIR-DA model successfully identified 96 radar radiation source targets with an accuracy exceeding 95% in a dataset characterized by a highly imbalanced sample distribution. To tackle the challenges of poor migration effects and low computational efficiency on lightweight edge computing platforms, a parallel acceleration scheme based on the embedded microprocessor T4240 is designed. This approach achieved a nearly eightfold increase in computational speed while maintaining the original training performance. Furthermore, an integrated solution for a radar radiation source intelligent detection system combining PC devices and edge devices is preliminarily designed. Experimental results demonstrate that, compared to existing radar radiation source target recognition algorithms, the proposed method offers superior model performance and greater practical extensibility. This research provides an innovative exploratory solution for the industrial application of deep learning models in radar radiation source recognition.