An Algorithm Combining Latent Dirichlet Allocation and Bimodal Network for Evaluating Goal Deviation of Intellectual Property Strategy Execution in China

Abstract

China has implemented the intellectual property strategy since 2008 to support innovation-driven development. However, statistical data issued during the “12th Five-Year Plan” (2011–2015) showed that there are certain deviations between the actual and expected intellectual property strategy’s goals. To effectively diagnose the goal deviation, an algorithm combining the latent Dirichlet allocation and bimodal network based on policy text was proposed. In this method, topics in intellectual property policy texts of China’s provincial regions were extracted through the latent Dirichlet allocation model, and a bimodal network centered at provincial administration district-policy topics was constructed. Subsequently, the characteristics of the goal execution deviation of the IPS in the provincial government were explored based on the centrality of the bimodal network and singular value decomposition. Finally, some diagnosis results and conclusions were demonstrated to provide reasonable methods for evaluation of national strategic planning and promoting policy performance.

Optimized Design of English Learning Environment Based on Deep Neural Network

“…e pretraining is mainly to get a generative model structure by training RBM layer by layer; the fine-tuning is to add a softmax output layer corresponding to the target classification in the final output layer of DBN and then fine-tune the model parameters using the traditional BP algorithm [14,15]. Moreover, English teachers in colleges and universities should avoid a fill-in-the-blank teaching style and keep classroom lectures within twenty minutes, leaving sufficient time for students to discuss and analyze, and training their analytical skills and troubleshooting capabilities.…”