With the vigorous promotion of railway infrastructure construction in China, higher requirements have been put forward for railway running safety. Railway signal monitoring equipment, as the key equipment to ensure dr...
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To improve the reliability of conventional protection using fault travelling wave, this paper proposes a line traveling wave protection method based on improved Hausdorff distance comparison. The traveling waves at th...
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Brain-controlled system has been used in assisting disabilities, and how to enable such groups to have the ability to communicate with the surroundings has become the focus of research. However, due to the individual ...
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The traditional experimental teaching method is single, and students lack learning initiative and creativity, which leads to the problem of insufficient teaching quality. Therefore, this paper proposes a virtual exper...
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In recent years, with the continuous progress of China's high-speed railway construction, railways play an important role in the country's economic development and provide a strong driving force for people to ...
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In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language repres...
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ISBN:
(数字)9798331506612
ISBN:
(纸本)9798331506629
In the research methods of sentence similarity, sentence similarity is often calculated from the semantic aspect, while the influence of syntactic structure is ignored. We propose an enhanced knowledge language representation model (ExtKBRCNN) based on CNN and Bi-GRU, which effectively uses the fine-grained word relations in the knowledge base to evaluate semantic similarity and models the relationship between knowledge structure and text structure. In order to make full use of the syntactic information of the sentence, we also propose a dependency tree kernel-based method (Dep-SIF), which combines syntactic information and semantic features to evaluate syntactic similarity. Finally, we propose a comprehensive model that integrates semantic and syntactic information to comprehensively evaluate sentence similarity. Experimental results show that the accuracy of the model on the MRPC dataset is 77.63% and the F1 value is 83.90%.
Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to str...
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ISBN:
(数字)9798350368284
ISBN:
(纸本)9798350368291
Knowledge Tracing (KT), a technique for modeling students' knowledge levels and predicting their future question-answering performance based on their historical answer data, is one of the key research areas to strengthen the ability of personalized education. In recent years, memory networks have received more and more attention and application in the field of KT, however, the current KT model based on memory networks ignores the effect of students' learning sequence on the level of forgetting, and fails to model the forgetting behavior of students in the process of learning by using the characteristics of time intervals in the interaction data. Therefore, in this paper, we propose a dynamic student classification with forgetting mechanisms on memory networks model (DSCFMN), which enhance the existing model by dynamically classifying students similar to their learning abilities at specified time intervals, incorporating forgetting factors and introducing a weight decay strategy. Experiments show that our model performs well on online education datasets, and the proposed model achieves better prediction results than existing knowledge tracking methods.
With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an ...
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ISBN:
(数字)9798350369151
ISBN:
(纸本)9798350369168
With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an extremely beneficial instrument in this regard. Among them, Convolutional Knowledge Tracking (CKT) has demonstrated excellent performance in numerous KT tasks, while it does not take into account the phenomenon of forgetting and the interconnection between knowledge points. In order to tackle these problems, we propose a model called FGE-CKT, which integrates forgetting behavior and graph embedding with CKT. Our model addresses the drawbacks of forgetting characteristics by modelling three key factors affecting students' forgetting behaviors through a fully connected neural net: repeated knowledge learning times, adjacent learning interval and interval between visits to repeat knowledge. At the same time, convolutional neural networks are used to simultaneously extract and incorporate connections between knowledge points into the model's input portion. It was confirmed by experiments on two well-established datasets that FGE-CKT achieved significant improvements in AUC performance.
Knowledge tracing is fundamental to intelligent educational systems, as it predicts future learning outcomes by analyzing learners' historical performance related to specific knowledge concepts. While advancements...
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ISBN:
(数字)9798331528829
ISBN:
(纸本)9798331528836
Knowledge tracing is fundamental to intelligent educational systems, as it predicts future learning outcomes by analyzing learners' historical performance related to specific knowledge concepts. While advancements have been made through deep learning techniques in the knowledge tracing domain, traditional research faces two significant limitations: insufficient consideration of temporal factors, which impacts the accurate depiction of knowledge retention and mastery dynamics; and the assumption of a one-to-one correspondence between practice and a single knowledge concept, disregarding the complexity of coexisting multiple knowledge concepts. To address these issues, this paper proposes the MulTKT model, which integrates response intervals, response durations, and the synergistic effects of multiple knowledge concepts, while incorporating a temporal decay factor into the self-attention mechanism to precisely capture learning dynamics. Experimental results indicate that MulTKT more accurately represents knowledge states and predicts student performance.
In order to solve the problem of insufficient samples of near-shore synthetic aperture radar data in ship detection, a ship synthetic aperture radar (SAR) image data augmentation model based on generative adversarial ...
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