The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In...
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Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture i...
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Shui manuscripts are part of the national intangible cultural heritage of China. Owing to the particularity of text reading, the level of informatization and intelligence in the protection of Shui manuscript culture is not adequate. To address this issue, this study created Shuishu＿C, the largest image dataset of Shui manuscript characters that has been reported. Furthermore, after extensive experimental validation, we proposed ShuiNet-A,a lightweight artificial neural network model based on the attention mechanism, which combines channel and spatial dimensions to extract key features and finally recognize Shui manuscript characters. The effectiveness and stability of ShuiNet-A were verified through multiple sets of experiments. Our results showed that, on the Shui manuscript dataset with 113 categories, the accuracy of ShuiN et-A was 99.8%, which is 1.5% higher than those of similar studies. The proposed model could contribute to the classification accuracy and protection of ancient Shui manuscript characters.
In recent years, with the continuous improvement of the degree of industrial automation in our country, the intelligent requirements for electronic equipment are also rising. For this reason, some scholars propose to ...
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In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise *** least squares(OLS),which selects at each ste...
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In countless applications,we need to reconstruct a K-sparse signal x∈R n from noisy measurements y=Φx+v,whereΦ∈R^(m×n)is a sensing matrix and v∈R m is a noise *** least squares(OLS),which selects at each step the column that results in the most significant decrease in the residual power,is one of the most popular sparse recovery *** this paper,we investigate the number of iterations required for recovering x with the OLS *** show that OLS provides a stable reconstruction of all K-sparse signals x in[2.8K]iterations provided thatΦsatisfies the restricted isometry property(RIP).Our result provides a better recovery bound and fewer number of required iterations than those proposed by Foucart in 2013.
Sketches have risen as promising solutions for frequency estimation, which is one of the most fundamental tasks in approximate data stream processing. In many scenarios, users have a strong demand to apply sketches un...
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Bayesian classification is a common data analysis and modeling method in data mining. In this paper, an improved ensemble method and the optimized Kernel density estimation used to Bayesian classifier. Unlike the trad...
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The learning content of the Internet of Things (IoT) professional courses is closely related to reality, and has the characteristics of comprehensiveness, intersection and application. In order to solve the limitation...
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The pre-trained models such as BERT for fake review detection have received more attention. Most of research has overlooked the role of behavioral features. Additionally, the improvements of the pre-trained models hav...
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Real-Time Strategy (RTS) games have attracted millions of players due to their characteristics of diverse scenes and flexible decision-making mechanisms. However, the mechanisms, contents, and operations of RTS games ...
Real-Time Strategy (RTS) games have attracted millions of players due to their characteristics of diverse scenes and flexible decision-making mechanisms. However, the mechanisms, contents, and operations of RTS games could be quite complex for beginners. The frustration and poor beginner experience caused by the high level of difficulty in getting started may lead novice players to give up on a specific RTS game due to a lack of sense of achievement. To address this issue and enhance the gaming experiences of beginners, this article proposes to assist beginners in familiarizing themselves with the mechanisms of the games and evaluating the opponents' abilities based on ontology. Firstly, an ontology will be built according to the properties of game units, which can provide beginners with a clear and comprehensive conceptual framework to understand game mechanisms and operations. Secondly, a knowledge-based reasoning method will be proposed to help beginners evaluate their opponents' abilities. Using a specific RTS game as a demonstration, the ontology is built and a reasoning example is proposed. By analyzing and reasoning about the abilities of their opponents' game units, this reasoning method can help beginners develop more effective strategies and tactics to enhance their experience in the game.
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