Abstract The cybersecurity defense strategy of “proactive defense, traceability, and responsiveness” has gained increasing attention. This strategy relies on the collection and application of massive cybersecurity threat intelligence (CTI). However, in the named entity recognition task for threat intelligence processing, traditional models suffer from severe out-of-vocabulary (OOV) issues due to their over-reliance on explicit entity mention information. To address this technical bottleneck, this study designs a novel named entity recognition model–VIB-NER. The model leverages the variational information bottleneck to compress redundant features and strengthen key OOV characteristics, complemented by a mutual information dynamic balance loss. Experimental results show that this method achieves F1 score, recall, and precision of 79%, 77%, and 80% in entity extraction tasks for cybersecurity threat intelligence data, representing a 4–8% improvement over mainstream models such as E-NER. Meanwhile, in terms of training efficiency, the time consumption per training batch is reduced by 50% compared to existing models.

Topic modeling has been widely applied to analyze text data from social media platforms. Under this scenario, traditional Neural Topic Models (NTMs) encounter three primary challenges: (1) initial text representation; (2) the long-tail nature of topic distributions in social network texts; (3) approximation of Optimal Transport. Motivated by these challenges, we propose an end-to-end solution spanning from text representation to topic modeling. First, we propose SBoWC, a novel text representation method that performs dimensionality reduction while absorbing semantic information through base terms, achieved by combining word embeddings with clustering statistics. Subsequently, we propose GSWTM, a Wasserstein-based autoencoder topic model that fits the long-tail topic distribution in social network texts via Gamma priors and innovatively employs log-domain Sinkhorn to approximate Optimal Transport. Ablation studies demonstrate the transferability and effectiveness of SBoWC in text representation. GSWTM demonstrates significantly better performance than baselines in TU, C V , and the comprehensive metrics TQ across four real social network datasets of varying sizes. The log-domain Sinkhorn approximation exhibits excellent stability, allowing the regularization parameter ϵ to be reduced to 0.1–0.01, thereby approaching the original Optimal Transport.

Satellite data often exhibit spatial discontinuities, and the unstructured nature of these data makes it difficult for researchers to use structured spatial analysis methods such as convolutional neural networks (CNNs) in studies of sea surface parameters retrieval. To address these issues, we propose a dynamic graph physical residual network (DGPRN) for retrieving sea surface vector winds and sea surface temperatures (SSTs) via data from the microwave radiation imager (MWRI) onboard the Fengyun-3 (FY-3) satellite. This model consists of a position encoder, a dynamic adjacency matrix generator, a graph convolution module, and a physical residual module. The results indicate that the DGPRN model performs better than traditional retrieval algorithms under complex wind conditions. The DGPRN incorporates the radiative transfer equation as an inductive bias to convert relative wind direction information into brightness temperature residuals, thereby increasing the retrieval accuracy. Moreover, we capture the spatial distribution characteristics of sea surface wind speed (SSWS) and sea surface wind direction (SSWD) and retrieve continuous wind fields in areas with complex topography and cyclonic circulation. Extensive experiments indicate that the DGPRN model outperforms other competing methods in both retrieval accuracy and generalization performance. Specifically, DGPRN model achieves root mean square error (RMSE) values of 0.87 m/s for the wind speed, 14.80° for the wind direction, and 0.81 K for the SST, with correlation coefficients exceeding 0.93.

Text-based CAPTCHA is widely used in fields such as user identity verification during human-computer interaction in real scenarios. With the development of artificial intelligence, several technologies that automatically bypass CAPTCHAs have emerged, weakening the robustness of CAPTCHAs. In-depth study of adversarial sample technology is needed to further reduce the accuracy of automatic verification code recognition of deep learning models while retaining correct human recognition. We propose a novel method based on gradient adaptive truncation to generate adversarial text-based CAPTCHAs more efficiently. Based on the generated model, our method dynamically adjusts the gradient truncation threshold according to the progress of the perturbation attack method, thereby improving the performance of the sample generation model. On the authoritative dataset, our method is compared with the existing state-of-the-art methods. The results show that our AT-I-FGSM can more effectively reduce the accuracy of automatic recognition models to identify CAPTCHAs and improve the security of CAPTCHAs. At the same time, our method consumes less time in generating CAPTCHAs.

Summary Community detection is a fundamental research problem in social networks. However, most existing research focuses on homogeneous networks while ignoring the multitopology and attributes in social media. In this article, we propose community detection algorithms based on community kernels to detect high‐quality communities in heterogeneous social networks. It is noticed that the social community has multiple topology structures, as nodes or users in social media networks have multiple attributions. For example, users can be friends and coworkers in a research group simultaneously. Hence, we propose a multilayer and attribute combined measure (MACM), a novel measurement based on the multilayer structure and common neighboring attributes, which includes the similarity measure between nodes and the importance measure for individual node in multilayer networks. Two improved community kernel detection algorithms based on MACM are subsequently proposed. They are the MA‐Greedy, which is based on the greedy algorithm, and the MA‐WeBA, which is a weighted balanced algorithm. The multilayer structure and attributes are comprehensively considered when calculating the similarity and importance of nodes in these strategies. Extensive experimental results on two public data sets demonstrate that the multilayer structure and attribute information can be used to enhance the precision of community detection.

The paper initially extends CORBA with the concept of explicit binding, where path of communication between objects is represented as first class objects. We then introduce the concept of open bindings which support inspection and adaptation of the path of communications. An implementation of open bindings for adaptive continuous-media interaction is described using the example of adaptive video-on-demand for mobile environments. To support multimedia applications in mobile environments, it will be necessary for applications to be aware of the underlying network conditions and also to be able to adapt their behaviour and that of the underlying platform. This paper focuses on the role of middleware in supporting such adaptation. In particular, we investigate the role of open implementation and reflection in the design of middleware platforms such as CORBA.

In this paper, we give a formal definition of trust relationship with a strict mathematical structure that can not only reflect many of the commonly used extreme notions of trust but also provides a taxonomy framework where a range of useful trust relationships can be expressed and compared. Trust management and trustworthy computing are becoming increasingly significant at present. Over the recent years there have been several research works that have addressed the issue of trust management in distributed systems. However a clear and comprehensive definition that can be used to capture a range of commonly understood notions of trust is still lacking. Then we show how the proposed structure can be used to analyze both commonly used and some unique trust notions that arise in distributed environments. This proposed trust structure is currently being used in the development of the overall methodology of life cycle of trust relationships in distributed information systems.