Corporations require screening critical information from numerous resumes with different formats and content for managerial decision-making. However, traditional manual screening methods have low accuracy to meet the ...
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Corporations require screening critical information from numerous resumes with different formats and content for managerial decision-making. However, traditional manual screening methods have low accuracy to meet the demand. Therefore, we propose a multimodal network model incorporating entity semantic graphs, ESGNet, for accurately extracting critical informa-tion from Chinese resumes. Firstly, each resume is partitioned into distinct blocks according to content while constructing an entity semantic graph according to entity categories. Then we interact with associated features within image and text modalities to capture the latent semantic information. Furthermore, we employ Transformer containing multimodal self-attention to establish relationships among modalities and incorporate supervised comparative learning concepts into the loss function for categorizing feature information. The experimental results on the real Chinese resume dataset demonstrate that ESGNet achieves the best information extraction results on all three indicators compared with other models, with the comprehensive indicator F1 score reaching 91.65%.
In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, ...
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In order to solve the challenges brought by multi-source and cross-domain scenarios to online music education, this paper designs an online music education system based on advanced artificial intelligence technology, which can provide personalized learning course resource recommendations for music online learners. The system includes four layers, consisting of user interface layer, application module layer, function module layer and data storage layer. At the application module level, this paper proposes a music recommendation algorithm based on a personalized multimodal network model. The recommendation algorithm performs music information retrieval (MIR) based on the similarity judgment of the contour of music pitch and the overall change, and constructs a multimodal network model based on the user's preference for resources to achieve personalized music recommendation. This paper crawls more than one million music score data from a well-known music platform database in China to establish a dataset to evaluate the performance of this method. The comparison results with three existing works show that the method proposed in this paper has good performance and can provide users with suitable music recommendations. The artificial intelligence technology-driven online music education mechanism proposed in this paper has good prospects.
The authors present a case study of a multimodal routing system that takes into account both dynamic and stochastic travel time information. A multimodal network model is presented that makes it possible to model the ...
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The authors present a case study of a multimodal routing system that takes into account both dynamic and stochastic travel time information. A multimodal network model is presented that makes it possible to model the travel time information of each transportation mode differently. This travel time information can either be static or dynamic, or either deterministic or stochastic. Next, a Dijkstra-based routing algorithm is presented that deals with this variety of travel time information in a uniform way. This research focuses on a practical implementation of the system, which means that a number of assumptions were made, like the modelling of the stochastic distributions, comparing these distributions, and so on. A tradeoff had to be made between the performance of the system and the accuracy of the results. Experiments have shown that the proposed system produces realistic routes in a short amount of time. It is demonstrated that routing dynamically indeed results in a travel time gain in comparison to routing statically. By making use of the additional stochastic travel time information even better (i.e. faster), more reliable routes can be calculated. Moreover, it is shown that routing in the multimodalnetwork may have its advantages over routing in a unimodal network, especially during rush hours.
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