Healthcare social networks (HSNs) are pivotal in spreading healthcare knowledge, providing support to both potential patients and medical professionals, and enhancing healthcare services. However, identifying unseen data in HSN poses a significant challenge due to their intrinsic heterogeneity, dynamic characteristics, and the scarcity of labeled data. Employing semantic knowledge transfer for class-agnostic zero-shot recognition stands out as a promising and innovative solution to this problem, but the visual-semantic gap and domain shift problems considerably hinder advancements in zero-shot recognition capabilities. Previous zero-shot models often impose constraints between vision and semantics in the loss part without explicitly injecting intermodality guidance into the feature refinement process. This article yields a novel zero-shot recognition framework for HSN, named the dual tensor prototype graph network, devoted to improving the performance of recognizing unseen objects in HSN leveraging semantic knowledge. We have developed an iterative and interactive updating strategy for dual tensor prototype graphs, explicitly leveraging the distribution information from one modality to guide the prototype graph updates of another modality. We constrain the update process of the dual prototype graphs by several tailored loss functions and episodic training, alleviating the inconsistency between semantic and visual manifolds. Extensive comparative experiments conducted on two medical imaging datasets and five zero-shot benchmarks affirm the stronger generalization ability of our proposed method compared with other advanced approaches, showing the potential of addressing zero-shot problems in HSN.

Zero-shot learning (ZSL) aims to recognize unseen classes by transferring semantic knowledge from seen categories. However, existing methods often struggle with the persistent semantic gap caused by limited semantic descriptors and rigid visual feature modeling. In particular, modeling pre-defined class-level attribute descriptions as ground truth hinders effective semantic-to-visual alignment to some extent. To mitigate these issues, we propose the Bilateral-guided Prototype Refinement Network (BPRN), a novel ZSL framework designed to refine dual prototypes across meta-domains of varying scales. Specifically, we first disentangle the relationships among class-level semantics and use them to generate corresponding pseudo-visual prototypes. Then, by leveraging distribution information across dual prototypes in different meta-domains, BPRN achieves bidirectional calibration between visual-to-semantic and semantic-to-visual modalities. Finally, a synthesized class-level representation derived from the refined dual prototypes is employed for inference, instead of relying on a single prototype. Extensive experiments conducted on five widely-used ZSL benchmark datasets demonstrate that BPRN consistently achieves competitive or even superior performance. Specifically, in the GZSL scenario, BPRN shows improvements of 2.1%, 7.3%, 6.1%, and 4.8% on AWA1, AWA2, SUN, and aPY, respectively, compared to existing embedding-based ZSL methods. Ablation studies and visualization analyses further validate the effectiveness of the proposed components.

The continuous combination of digital network technology and traditional financial services has given birth to digital financial networks, which explore massive economic data under the AI-driven models to achieve intelligent connections among financial institutions, markets, transactions, and instruments. Empirical asset pricing is a challenging task in financial analysis, which has attracted research attention. However, existing studies only focus on tackling the challenges of equity risk premium in the single stock market. Considering multiple economic linkages between the two countries, the transaction history of the US stock market as empirical knowledge is a powerful supplement to improve the prediction of equity risk premium in the China market. In this paper, we aim to fully leverage the prior information in two stock markets for empirical asset pricing models. Due to the rich financial domain knowledge, there may be various characteristic signals that partially overlap in different periods. To address these issues, we propose a framework based on long-short dual-mode knowledge distillation, termed as LSDM-KD, which incorporates US and China stock market models, and a shared characteristic signals model. The method effectively understands the relationships between assets and market behaviour, reducing reliance on expensive correlation databases and professional knowledge. Extensive experiments conducted on US and China stock market datasets demonstrate that our LSDM-KD can significantly improve the performance of empirical asset pricing.

The Internet of Things (IoT) has recently experienced a significant increase in the frequency of cyberattacks, leading to an urgent need for high security and reliability in intelligent IoT applications. Ensuring that interconnected devices within the system operate as expected and provide accurate data has become an essential concern. Reliable coverage can provide a trusted data source for the system. Comprehensively considering various factors such as node multi-state, potential intrusions, and interferences, a trust-based intrusion-tolerant coverage reliability evaluation algorithm (T-ITCR) is proposed to evaluate the coverage reliability based on the trust-based reliable confident information coverage model (T-RCIC). In T-ITCR, trust management is deeply integrated throughout the evaluation process, facilitating dynamic adjustments in node states, network connectivity, and node coverage weights. Malicious nodes are identified and excluded to guarantee the security of data sensing and transmission. Furthermore, to predict node states more accurately, a precise energy assessment mechanism is conducted based on node interaction processes. A significant number of experiments have demonstrated the performance of the proposed algorithm. Consequently, the T-ITCR algorithm demonstrates its ability to efficiently detect malicious intrusions and adjust network states, which significantly strengthens the security and reliability of the networks.