Abstract.The advent of decentralised applications across a range of sectors has led to a growing emphasis on the research and development of methods to identify vulnerabilities in smart contracts for decentralised applications. However, current detection techniques have been found to have limitations in terms of accuracy and the number of false alarms they generate. In order to address the aforementioned issues, this paper puts forth a modular vulnerability detection model, designated as BAMC. The method initially utilises the word2vec model to derive the word vector representation of the smart … Read more →

Abstract.With the continuous progress of the fashion e-commerce industry, consumers' demand for accuracy and personalization of search results is increasing day by day. However, traditional computing methods are inadequate in dealing with big data processing and complex sorting tasks. Quantum computing, with its unique advantages in solving complex problems and parallel operations, has gradually become a research hotspot for improving search ranking optimization. This article explores various challenges encountered in applying quantum computing to the search ranking process of fashion e-commerce … Read more →

Abstract.Geographic Information Systems (GIS), as a key means of managing, storing, and analyzing spatial information, have attracted much attention in the field of GIS due to the rapid development of big data and intelligent technology. This article elaborates on the intelligent data mining process based on information collection, data storage, data processing, and data analysis, supplemented by practical application cases, including urban traffic flow research, intelligent transportation system design, and commercial network layout optimization, to demonstrate its broad prospects in practical … Read more →

Abstract.As a key achievement in the field of artificial intelligence, the Large Language Model (LLM) has great potential for development in enterprise applications. This study explores the widely used big language models and elaborates on their application capabilities in processing natural language, creating text content, and data parsing. It also analyzed the key issues that enterprises encounter when utilizing big language models, such as data security vulnerabilities, accuracy errors, system integration difficulties, and employee adaptability. A series of response strategies are proposed to … Read more →

Abstract.With the rapid development of deep learning technologies, human pose estimation has become a hot research topic in the field of computer vision. This paper provides a systematic review of the latest advances in single-person 3D human pose estimation based on deep learning, with a focus on the main challenges faced by both single-view and multi-view approaches, such as pose estimation accuracy, occlusion issues, and the effective utilization of depth information. Firstly, the paper explores single-view human pose estimation methods based on single-frame images and video data. Then, it … Read more →

Abstract.With the rapid development of Internet technology, Web database occupies the core position in modern information system. This study provides an in-depth analysis of the current popular web database construction technologies, covering the application and unique technical attributes of traditional relational databases, new non relational databases, and cloud computing databases in web applications. At first, various types of web databases were systematically divided, and then focused on key aspects of database construction, including database architecture design, query efficiency improvement, … Read more →

Abstract.The prediction of lubrication oil temperature plays a crucial role in the performance optimization and fault diagnosis of twin-screw refrigeration compressors. However, due to the complexity of operating conditions and the nonlinear characteristics of the data, traditional prediction methods still face challenges in terms of accuracy and generalization ability. This paper proposes a lubrication oil temperature prediction method based on optimization algorithms and deep learning, integrating an improved Beluga Whale Optimization (BWO) algorithm with a Temporal Convolutional Network (TCN) and an … Read more →

Abstract.In the financial field, streaming media advertising is gradually showing its indispensable position. However, traditional advertising methods have encountered difficulties such as insufficient accuracy, heavy cost burden, and low conversion efficiency. In this context, artificial intelligence (AI) technology, with its excellent data analysis capabilities, user profiling, personalized promotion, and creative content generation advantages, has brought new ideas for the precise placement of streaming media advertising in the financial field. This article deeply analyzes the specific application … Read more →

Abstract.Reconstruction-based single-image super-resolution methods, while demonstrating excellent performance, often face challenges such as training instability, artifact generation, information loss, and insufficient control over global information. To address these challenges, we propose an implicit function super-resolution reconstruction algorithm based on Group Propagation Vision Transformer (GP-ViT). This method employs GP-ViT as an encoder to efficiently capture global contextual information through a group propagation mechanism, while reducing computational complexity and memory consumption, … Read more →

Abstract.The widespread use of social media platforms has made data privacy protection issues increasingly prominent. This article aims to analyze the application of data privacy protection technologies in social media, and provide a detailed review of different types of privacy protection technologies and their characteristics. It clearly points out the challenges that social media platforms face in privacy protection, including risks of personal data leakage, difficulties in complying with regulations, and insufficient user awareness of privacy protection. By strengthening the application of … Read more →

Abstract.This article addresses embodied intelligence and reinforcement learning integration in the field of text processing, aiming to enhance text handling with more intelligence on the basis of embodied intelligence's perception and action superiority and reinforcement learning's decision optimization capability. Through detailed theoretical explanation and experimental exploration, a novel integration model is introduced. This model has been demonstrated to be very effective in a wide range of text processing tasks, validating its applicative potential. Read more →

Abstract.With the rapid development of UAV technology, intelligent control systems have become an important factor in improving their stability, efficiency and adaptability. Traditional flight control methods have limitations when dealing with complex environments and dynamic tasks, while intelligent control models based on artificial intelligence can achieve more efficient and autonomous flight control by combining technologies such as deep learning, reinforcement learning and fuzzy control. This paper first reviews the current research status of UAV intelligent control models and analyzes the … Read more →

Abstract.Link prediction is a fundamental task in network analysis with extensive applications in social networks, recommendation systems, and biological networks. In this work, we propose an improved Graph Convolutional Network (IGCN) model that leverages an encoder–decoder architecture, integrating multi-layer GCN-based aggregation of node features and local structure with adaptive hard negative sampling and contrastive learning. Our framework is evaluated on three benchmark datasets—Cora, Citeseer, and Pubmed—and achieves state-of-the-art performance when compared with traditional methods and other … Read more →

Abstract.To improve the accuracy of transformer oil temperature prediction and ensure the stability and safety of transformers during operation, this paper proposes an innovative oil temperature prediction method—an EMD-CPO-GRU hybrid model based on Empirical Mode Decomposition (EMD), Crested Porcupine Optimization (CPO) algorithm, and Gated Recurrent Unit (GRU). The method first decomposes the oil temperature data using EMD, effectively extracting the nonlinear and non-stationary characteristics of the signal, thereby providing more representative and effective features for subsequent predictions. … Read more →

Abstract.This paper explores methods and technologies for secondary development using AutoCAD in the field of drafting. By conducting an in-depth study of secondary development methods, the paper aims to enhance software customizability and scalability to meet specific design requirements across different industries. The focus is on three secondary development techniques, including programming languages and interfaces like ObjectARX, VB, and C#.NET. Specific application cases in the field of mechanical drafting are analyzed in detail. Read more →

Abstract.Singing, no doubt, is the most natural, the most instinctive expression of human emotion in the category of music art. Therefore, natural singing a good singing should have a natural singing psychological state, natural breathing state, natural vocal state and natural emotional expression. We can even say: whether "natural", is to identify the basic scale of a person singing good or bad, this paper from the Angle of psychology, from the construction of vocal psychology, vocal music performance of fear digestion mechanism, vocal performance psychological construction in six aspects to explain … Read more →

Abstract.With the rapid development of deep learning technology, anomaly detection based on deep learning has become an important research direction. This paper classifies and summarizes anomaly detection methods, covering traditional methods, machine learning-based methods, and deep learning-based methods. It particularly introduces typical deep learning models, analyzing their respective advantages and disadvantages. Experiments were conducted on two public datasets, and the performance of each model in anomaly detection was compared in detail. Read more →

Abstract.With the increasing complexity of infectious disease prevention and control and critical care emergency work, the traditional manual monitoring and early warning system has been difficult to meet the needs of modern public health management and regional medical coordination. To cope with this challenge, this paper constructs a whole-process medical intelligence optimization platform, based on cutting-edge technologies such as Internet of Things (IoT), 5G communication, artificial intelligence (AI) and blockchain, to realize the full-cycle medical management of pre-hospital emergency care, … Read more →