Titlebook: Network and Parallel Computing; 15th IFIP WG 10.3 In Feng Zhang,Jidong Zhai,Mateo Valero Conference proceedings 2018 IFIP International Fed

弄碎

書目名稱Network and Parallel Computing影響因子(影響力)




書目名稱Network and Parallel Computing影響因子(影響力)學科排名




書目名稱Network and Parallel Computing網(wǎng)絡(luò)公開度




書目名稱Network and Parallel Computing網(wǎng)絡(luò)公開度學科排名




書目名稱Network and Parallel Computing被引頻次




書目名稱Network and Parallel Computing被引頻次學科排名




書目名稱Network and Parallel Computing年度引用




書目名稱Network and Parallel Computing年度引用學科排名




書目名稱Network and Parallel Computing讀者反饋




書目名稱Network and Parallel Computing讀者反饋學科排名

Pathogen

妨礙議事

Eviction

尖叫

CNLoc: Channel State Information Assisted Indoor WLAN Localization Using Nomadic Access Points,ncertainty of nomadic APs. Our implementation and evaluation show that CNLoc can improve the accuracy with unknown location information of nomadic APs. We also discuss some open issues and new possibilities in future nomadic AP based indoor localization.

頌揚國家

ALOR: Adaptive Layout Optimization of Raft Groups for Heterogeneous Distributed Key-Value Stores,. and .. We conducted experiments on a practical heterogeneous cluster, and the results indicate that, on average, ALOR improves throughput by 36.89%, reduces latency and 99th percentile tail latency by 24.54% and 21.32%, respectively.

notion

prosthesis

GPU-Accelerated Clique Tree Propagation for Pouch Latent Tree Models,each model structure during PLTM training. Our experiments with real-world data sets show that the GPU-accelerated implementation procedure can achieve up?to 52x speedup over the sequential implementation running on CPUs. The experiment results signify promising potential for further improvement on the full training of PLTMs with GPUs.

minaret

Basal-Ganglia

Data Fine-Pruning: A Simple Way to Accelerate Neural Network Training,g approach. Extensive experiments with different neural networks are conducted to verify the effectiveness of our method. The experimental results show that applying the data fine-pruning approach can reduce the training time by around 14.29% while maintaining the accuracy of the neural network.