Relation extraction is a crucial task in natural language processing, aimed at identifying entities and their relationships from text, and holds particular significance in the cryptography domain due to its involvement with complex multi-entity interactions. This paper addresses the challenges of relation extraction in cryptography, such as intricate terminology, implicit relationships, data scarcity, and the demand for high precision, by proposing a method based on retrieval augmentation and chain-of-thought optimization. By integrating chain-of-thought prompt optimization, example retrieval enhancement, and dependency analysis techniques, this approach significantly improves extraction performance in low-resource environments. Additionally, a high-quality cryptography-domain dataset containing 24 entity relationship categories is constructed, providing robust support for related research.

Tailored to the unique characteristics of the cryptography domain, this paper first designs a chain-of-thought-based prompt template with a self-checking mechanism to guide the model through step-by-step reasoning, enhancing the accuracy of extracting complex relationships. Second, it employs S-BERT embeddings along with a weighted ranking strategy using cosine similarity and the DICE coefficient to dynamically retrieve and inject the most relevant examples into the prompt template, improving the model’s adaptability in data-scarce scenarios. Then, a rule-based retrieval method precisely injects necessary background knowledge. Finally, StanfordCoreNLP is utilized to extract dependency relationships as structured knowledge, further optimizing the model’s understanding of implicit relationships.

Experimental results demonstrate that this method, implemented on the ChatGLM4-Plus model, achieves an F1-score of 70.62%, significantly outperforming traditional methods and unoptimized large language model baselines. Ablation studies confirm the effectiveness of each module and their synergistic contributions, underscoring the method’s value in enhancing the precision and robustness of relation extraction in the cryptography domain. This approach not only provides an efficient tool for knowledge graph construction and information retrieval in cryptography but also offers a replicable optimization strategy for relation extraction tasks in other highly specialized domains.et

As a key component of intelligent transportation systems, Vehicular Ad Hoc Network (VANET) has been widely used to improve traffic congestion, optimize driving paths, improve driving safety, and provide diverse entertainment services. However, due to its open communication architecture, the Internet of Vehicles faces many security challenges in message transmission and user identity protection. First, when vehicles communicate with other vehicles (Vehicle-to-Vehicle, V2V) or infrastructure (Vehicle-to-Infrastructure, V2I), information is vulnerable to security threats such as forgery, eavesdropping, message replay, and denial of service attacks. This may lead to driver misjudgment and safety accidents. Secondly, the Internet of Vehicles also faces problems in user privacy protection, such as location privacy leaks and identity information abuse. Compared with existing PKI and identity-based schemes, the certificateless architecture proposed in this paper has significant advantages and benefits in the following aspects: efficiency, fault tolerance, privacy protection, scalability, and security. Through security analysis and performance evaluation, the results show that this solution is superior to existing methods in terms of computing efficiency, storage requirements, communication overhead, etc., and on the basis of ensuring privacy protection and data integrity, it achieves high-dynamic environment Secure authentication and efficient communication.

Path planning is a critical problem in robotics, aimed at generating efficient and smooth trajectories for robots. However, traditional algorithms like A* often suffer from low search efficiency, redundant nodes, and discontinuities in velocity and acceleration at path corners. To address these issues, this paper presents a path planning framework that combines the Optimized Bidirectional A* algorithm, the Critical Node Retention algorithm, and the Minimum Snap algorithm. The framework enhances the path planning process in three stages: First, the Bidirectional A* search is optimized by utilizing a weighted heuristic function to improve search efficiency and reduce unnecessary node expansions. Second, the Critical Node Retention algorithm refines the path by retaining only critical nodes, reducing redundancy and shortening the path length. Finally, the Minimum Snap algorithm smooths the path, ensuring continuous velocity and acceleration, thereby generating safer and smoother trajectories. Simulation experiments on grid maps of various sizes demonstrate that the proposed framework effectively reduces path length, improves search efficiency, and generates smooth, safe trajectories, providing an efficient solution to path planning.

With the continuous development of deep learning in recent years, the field of image aesthetic caption has gradually become a popular research direction, which significantly impacts various applications, such as advanced semantic understanding of images and the promotion of artistic images. Image aesthetic caption generation is a cutting-edge direction for integrating computer vision and natural language processing. Unlike traditional image caption that focuses on outputting basic facts of images, the core goal of image aesthetic caption research is to generate image aesthetic description texts that combine semantic accuracy and artistic expression through deep learning algorithms. In response to the problem that there is no review article in this field, this article systematically reviews the technical development context in the field of image aesthetic caption, focusing on analyzing the technical route of image aesthetic caption based on traditional deep learning architecture, namely convolutional neural networks and recurrent neural networks, and also introduces the technical application of multimodal models in the field of image aesthetic caption in recent years. This article covers the main technical methods, data sets, evaluation indicators, and future development trends in the field of image aesthetic caption and analyzes the challenges and opportunities of current research. We hope that our review can be a reference for future research in the field of image aesthetic caption.

Accelerators for pose estimation have a wide range of application scenarios at the edge. However, the complexity and variability of the edge environment and the limited power consumption restrict the application and deployment of conventional devices such as GPUs at the edge. In response, this paper proposes a low-power bit-pose estimation accelerator architecture suitable for application in multiple scenarios, and this design supports the mapping of different pose estimation networks to accelerators. In addition, we also develop an accompanying operator library to support new pose estimation networks. In order to cope with the resource- and power-scarce scenarios at the edge, the number of operators in the library can be flexibly configured before accelerator deployment, and the power consumption can be dynamically adjusted by dynamically adjusting the operating frequency of the accelerators in real applications. Finally, we deployed the proposed architecture on Virtex UltraScale+ VU9P and tested it using PVNet as well as MobileNetV2. The experimental results show that the proposed architecture can achieve performance similar to that of current state-of-the-art dedicated accelerators with lower power consumption and greater versatility while ensuring real-time performance and accuracy.