我不明白
发表于 2025-3-28 16:40:01
Xiaoshun He,Maogen Chen in the reputation system and fed back to a new round of transactions and consensus. We implement distributed reputation management and enable users to append new reputation evaluations to the transaction that has previously evaluated. Meanwhile, we demonstrated that the scheme can defend against ex
晚间
发表于 2025-3-28 19:55:55
Xiaoshun He,Maogen Chenery subtle but important details. In the modulating process the carrier wave is generated by the LO circuit, and then combined with the baseband signal inside the mixer. In the demodulating process, however, the carrier signal is already contained in the incoming modulated signal and it can be recov
一再遛
发表于 2025-3-29 00:33:25
Guixing Xu,Zimeng Liuerformance-distribution-based framework for estimation and optimization of workflow critical paths. The proposed method dynamically generates the workflow scheduling plan according to the accumulated stochastic distributions of tasks. In order to prove the effectiveness of our proposed method, we co
CHANT
发表于 2025-3-29 06:47:06
ZhiYong Guo intelligent system for Web API searches based on natural language queries by using a two-step transfer learning. To train the model, we collect a significant amount of sentences from crowdsourcing and utilize an ensemble deep learning model to predict the correct description sentences for an API an
florid
发表于 2025-3-29 09:51:57
http://reply.papertrans.cn/71/7038/703741/703741_45.png
LEER
发表于 2025-3-29 14:11:44
http://reply.papertrans.cn/71/7038/703741/703741_46.png
Obstacle
发表于 2025-3-29 19:28:14
Xiaomin Shis 30 min, pairing programming is helpful for students to solve difficult problems, and it has a positive impact on the solution of subsequent problems after experiencing the process of solving difficult problems. When the periodic role switching interval is 20 min, students have a positive attitude
gorgeous
发表于 2025-3-29 23:30:58
http://reply.papertrans.cn/71/7038/703741/703741_48.png
情感
发表于 2025-3-30 03:12:49
Bing Liao,Wenfang Chens paper, we propose the improved Knowledge-aware Graph Neural Networks with Label Smoothness Regularization (iKGNN-LS) model, which makes two improvements to KGNN-LS: (1) In iKGNN-LS, by introducing user-specific entity scoring functions, the edge weights are determined jointly by personalized user
漂白
发表于 2025-3-30 05:41:55
http://reply.papertrans.cn/71/7038/703741/703741_50.png