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Benefiting many information retrieval applications, named entity recognition (NER) has shown impressive progress. Recently, there has been a growing trend to decompose complex NER tasks into two subtasks (e.g., entity span detection (ESD) and entity type ...

In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, brings potential security risks to our society. Existing generated image detection methods suffer from performance drops when faced with ...

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• Performance drops across generators and scenes in detecting AI-generated images.
• Artifact Purification Network with explicit purification and implicit purification.
• At least 1.7% improvement across both domains on two open ...

As a new paradigm, the Internet of drones (IoD) is making the future easy with its flexibility and wide range of applications. However, these drones are prone to security attacks during communication because of this flexibility. The traditional ...

Few-Shot Learning (FSL) targets a model to quickly discriminate new categories with limited samples. While most methods struggle to effectively utilize the knowledge learned in the base classes, resulting in models that fail to alleviate the ...

• A novel multi-domain fake news detection model is proposed for domain generalization in fake news detection.
• Entities in news articles are key to learning both domain-invariant and domain-specific news representations.
• We introduce ...

Fake news has become a significant concern in recent times, particularly during the COVID-19 pandemic, as spreading false information can pose significant public health risks. Although many models have been suggested to detect fake news, they are ...

The new classes of radar signals are increasingly difficult to acquire under non-cooperative environments, which makes it difficult to support convolutional neural network training with limited labeled samples. The few-shot learning (FSL) methods ...

• A CDPSC method is proposed for few-shot radar modulation signal recognition.
• A domain prototype similarity mapping strategy improves the accuracy of prototypes.
• The method improves feature extraction ability by extracting time-...

Face liveness detection is essential to ensuring the security of face recognition systems. Most current methods demonstrate excellent performance within the intra-domain. However, to achieve robust performance, effective methods should consider ...

With the pervasive growth of IoT, Wi-Fi CSI-based human gait recognition faces the challenge of maintaining model robustness in new environments. Current cross-domain methods often rely on symmetric data, restricting their practical utility. To ...

• Siamese GAN Module: Orders transformations, enhances signal conversion.
• Balancing realism, preserving individuality in Multi-anchor Metric Discriminator.
• Static Environmental Encoder: Enhances integration, generates more realistic ...

Gearbox fault diagnosis is a critical aspect of machinery maintenance and reliability, as it ensures the safe and efficient operation of various industrial systems. The cross-domain fault diagnosis method based on transfer learning has been ...

Vehicular Ad-Hoc Networks (VANETs) have significantly enhanced driving safety and comfort by leveraging vehicular wireless communication technology. Due to the open nature of VANETs, conditional privacy-preserving authentication protocol should ...

• We propose a conditional privacy-preserving authentication protocol based share group session key (SGSK) by integrating the self-healing key distribution technique, blockchain, and MTI/C0 protocol to instead of the time-consuming ...

Most few-shot learning(FSL) methods rely on a pre-trained network on a large annotated base dataset with a feature distribution similar to that of the target domain. Conventional transfer learning and traditional few-shot learning methods are ...

• The cross-domain network can weaken the spectral differences of the same feature.
• Zero sample extraction of winter wheat acreage was realized.
• Comparison loss ensures the diversity and reliability of the generated samples.
• ...

Accurate extraction of winter wheat area is essential for wheat yield estimation. Remotely sensed images have limited coverage, are taken at different times, from different angles and in different geographical areas, and the spectral information ...

The increasing availability of low-cost wearable devices and smartphones has significantly advanced the field of sensor-based human activity recognition (HAR), attracting considerable research interest. One of the major challenges in HAR is the domain ...

Underwater object detection (UOD) plays a significant role in aquaculture and marine environmental protection. Considering the challenges posed by low contrast and low-light conditions in underwater environments, several underwater image ...

• The underwater object detection (UOD) suffers from low contrast, blur, and color shift. We propose a Gated Cross-domain Collaborative network (GCC-Net) to address these challenges. Our method integrates the under- water image enhancement ...

Few-shot learning aims to recognize novel concepts with only few samples by using prior knowledge learned from the seen concepts. In this paper, we address the problem of few-shot learning under domain shifts. Traditional few-shot learning ...

• A novel Hierarchical Optimal Transport Network with Attention (HOTA) for CD-FSL.
• HOTA captures geometric structures at domain/feature/sample levels.
• HOTA maintains discrimination and transferability during domain alignment.
• Our ...

Automatic term extraction (ATE) is a natural language processing task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. In this paper, we treat ATE as a sequence-labeling task ...

The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain ...

Recently, few-shot learning (FSL) has exhibited remarkable performance in computer vision tasks. However, the existing FSL approaches perform poorly when facing data shortages and domain variations between the source and target datasets. This is ...

With the rise of social media users, the quick transmission of news without sufficient verification has become a common problem. The proliferation of fake news across various social media platforms poses enormous harm to society and affects the ...

Traditional deep learning fails to identify new faults when the number of faulty samples is limited. Existing meta-learning studies on cross-domain small-sample fault diagnosis do not fully account for the differences in the distribution of ...