Blockchain technologies have been used to facilitate Web 3.0 and FinTech ***,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech appli...
详细信息
Blockchain technologies have been used to facilitate Web 3.0 and FinTech ***,conventional blockchain technologies suffer from long transaction delays and low transaction success rates in some Web 3.0 and FinTech applications such as Supply Chain Finance(SCF).Blockchain sharding has been proposed to improve blockchain ***,the existing sharding methods either use a static sharding strategy,which lacks the adaptability for the dynamic SCF environment,or are designed for public chains,which are not applicable to consortium blockchain-based *** address these issues,we propose an adaptive consortium blockchain sharding framework named ACSarF,which is based on the deep reinforcement learning *** proposed framework can improve consortium blockchain sharding to effectively reduce transaction delay and adaptively adjust the sharding and blockout strategies to increase the transaction success rate in a dynamic SCF ***,we propose to use a consistent hash algorithm in the ACSarF framework to ensure transaction load balancing in the adaptive sharding system to further improve the performance of blockchain sharding in dynamic SCF *** evaluate the proposed framework,we conducted extensive experiments in a typical SCF *** obtained experimental results show that the ACSarF framework achieves a more than 60%improvement in user experience compared to other state-of-the-art blockchain systems.
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achie...
详细信息
Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.
In recent years, large language models (LLMs) have gained significant traction across various domains, including education. This paper explores the application of LLMs in grading programming assignments. By leveraging...
详细信息
Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corros...
详细信息
This study presents the numerical solutions of the fractional schistosomiasis disease model (SDM) using the supervised neural networks (SNNs) and the computational scaled conjugate gradient (SCG), i.e. SNNs-SCG. The f...
详细信息
This paper introduces a new solution to DNA storage that integrates all three steps of retrieval, namely clustering, reconstruction, and error correction. DNA-correcting codes are presented as a unique solution to the...
详细信息
Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are st...
详细信息
Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.
This paper considers the fixed-/preassigned-time synchronization of complex networks via aperiodically intermittent event-triggered control. A novel intermittent control lemma is developed to ensure fixed-time synchro...
详细信息
Neural decoding plays a vital role in the interaction between the brain and the outside world. Our task in this paper is to decode the movement track of a finger directly based on the neural data. Existing neural deco...
详细信息
Attention is a vital cognitive process in the learning and memory environment, particularly in the context of online learning. Traditional methods for classifying attention states of online learners based on behaviora...
详细信息
暂无评论