With the booming of industrial internet, the demand for computing resources is increasing. The traditional cloud computing cannot adapt to the current industrial requirements for low delay of computing resources and p...
With the booming of industrial internet, the demand for computing resources is increasing. The traditional cloud computing cannot adapt to the current industrial requirements for low delay of computing resources and privacy protecting during data storage and transmission. To address these challenges, edge computing (EC) has emerged as a new paradigm, enhancing task processing and response speed while mitigating data leakage risks through decentralized processing at the edge. Since the task offloading directly affects the operation efficiency of the EC system. Unreasonable task offloading will not improve or even reduce the EC performance. However, industrial internet imposes high demands on delay and privacy. To address the task offloading issue facing industrial internet, this paper first establishes a joint task caching and privacy protecting EC task offloading model, which aims to reduce the delay while ensuring the data privacy. Secondly, to solve the offloading strategy from the established model, a task offloading algorithm based on the improved pathfinder algorithm and simulated annealing algorithm is proposed. The proposed offloading algorithm searches for the optimal solution through the improved pathfinder algorithm, in which we add a mechanism of replenishing the number of individuals in the population and a mechanism of updating individuals with low fitness to speed up the convergence of finding the near-optimal solution. Moreover, the simulated annealing algorithm is used to prevent premature convergence. Finally, the established task offloading model improves the objective value by 49.93% over the local computation, which is important for protecting data privacy and improving response speed. Simulation results show that compared with the existing offloading algorithms, the proposed algorithm is at least 1.53% faster than others, with faster optimal solution-solving capability.
Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through compu...
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Novel coronavirus disease 2019(COVID-19)is an ongoing health *** studies are related to ***,its molecular mechanism remains *** rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational *** paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine *** method obtains seed genes based on prior *** genes are mined from biomedical *** candidate genes are scored by machine learning based on the similarities measured between the seed and candidate ***,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of *** results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19.
Molecular representation learning is widely used in the field of drug discovery, due to its ability to accurately capture the complex features of compounds in high-dimensional space. However, existing molecular repres...
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Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problem...
Intellectual property transactions have shown a strong growth momentum in recent years, but the patent transaction market has been plagued by the matching degree of consumers and sellers, resulting in frequent problems such as low patent transformation efficiency and poor transaction quality. This paper proposes a method of recommending patents to consumers by experts to improve the environment of patent transactions. Through the analysis of the past transaction information of the patent, the effective path information of the target is extracted. The graph neural network is used to describe the characteristics and semantics among experts, patents and consumers, and then capture the potential weight among them through the common attention mechanism, and then dynamically integrate them to predict the occurrence of recommendation behavior. The paper makes reasonable use of social information and expert information in the transaction, which significantly improves the rationality and accuracy of expert recommendation.
Neighborhood rough set based attribute reduction, as a powerful tool to deal with numerical data, is widely used in areas such as machine learning, pattern recognition, and decision support. A neighborhood actually co...
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As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only ...
As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events.
As the number of patent applications increases yearly, the negation relation between patents has become intertwined, which makes it difficult for constructing negation relation in patent examination manually. Therefor...
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Neighborhood rough set based attribute reduction, as a powerful tool to deal with numerical data, is widely used in areas such as machine learning, pattern recognition, and decision support. A neighborhood actually co...
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Neighborhood rough set based attribute reduction, as a powerful tool to deal with numerical data, is widely used in areas such as machine learning, pattern recognition, and decision support. A neighborhood actually co...
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Automatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved significant pro...
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