GLAZE
发表于 2025-3-21 17:15:25
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hangdog
发表于 2025-3-21 20:17:23
oduced to make up for this shortcoming. In this paper, we introduce a DL-based KT model referred to as Convolutional Attention Knowledge Tracing (CAKT) utilizing attention mechanism to augment Convolutional Neural Network (CNN) in order to enhance the ability of modeling longer range dependencies.
coagulation
发表于 2025-3-22 04:09:13
Monika FickIn this paper, we presented a sentence preprocessing method that extracts the leftmost longest common sequence to obtain the common and difference subsequences between the rumor text and its explanation text to compose samples and train a supervised model for classification between rumors and explan
土坯
发表于 2025-3-22 05:23:39
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狂乱
发表于 2025-3-22 10:58:18
Monika Fickal results conducted in this paper compare the accuracy, efficiency, and stability of various algorithms using synthetic datasets, Sushi datasets, and Irish datasets, which demonstrate the effectiveness of our proposed algorithm in real-world scenarios.
Infiltrate
发表于 2025-3-22 15:50:14
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octogenarian
发表于 2025-3-22 21:02:48
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fleeting
发表于 2025-3-22 22:01:51
Monika Fickent. Additionally, it proposes an improved Temporal Convolutional Network (TCN) named Temporal Convolutional Sparse Multilayer Perceptron Network (TCSMN). This network captures sequential structural features of cells and their surrounding neighbors, enhancing the ability to extract semantic features
反复无常
发表于 2025-3-23 03:15:14
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reject
发表于 2025-3-23 05:41:48
Monika Fickwith tournament selection. Furthermore, power-law selection outperforms UMDA and the (1+1) EA in our experiments on the .-. and . k-. problems, but yields to the .-selection, tournament selection, and the self-adaptive MOSA-EA. On the unicost set cover problems, the EA with power-law selection shows