Titlebook: Computer Supported Cooperative Work and Social Computing; 15th CCF Conference, Yuqing Sun,Dongning Liu,Liping Gao Conference proceedings 20

antibody

書目名稱Computer Supported Cooperative Work and Social Computing影響因子(影響力)




書目名稱Computer Supported Cooperative Work and Social Computing影響因子(影響力)學科排名




書目名稱Computer Supported Cooperative Work and Social Computing網(wǎng)絡公開度




書目名稱Computer Supported Cooperative Work and Social Computing網(wǎng)絡公開度學科排名




書目名稱Computer Supported Cooperative Work and Social Computing被引頻次




書目名稱Computer Supported Cooperative Work and Social Computing被引頻次學科排名




書目名稱Computer Supported Cooperative Work and Social Computing年度引用




書目名稱Computer Supported Cooperative Work and Social Computing年度引用學科排名




書目名稱Computer Supported Cooperative Work and Social Computing讀者反饋




書目名稱Computer Supported Cooperative Work and Social Computing讀者反饋學科排名

PRE

The Praxis Dimension of Literary Criticism,ntralized, fair, efficient and collaborative knowledge management. Additionally, we present the workflow of WikiChain by smart contracts and adopt IPFS to store large-scale data. Finally, we implement a prototype on Ethereum and evaluate the efficiency of WikiChain. The experimental results show the utility and scalability of WikiChain.

accomplishment

WikiChain: A Blockchain-Based Decentralized Wiki Frameworkntralized, fair, efficient and collaborative knowledge management. Additionally, we present the workflow of WikiChain by smart contracts and adopt IPFS to store large-scale data. Finally, we implement a prototype on Ethereum and evaluate the efficiency of WikiChain. The experimental results show the utility and scalability of WikiChain.

Innocence

使高興

Ostrich

CLENet: A Context and Location Enhanced Transformer Network for Target-Oriented Sentiment Classificai-target scenarios on limited datasets. This paper proposes a context and location enhanced transformer network that improves both the modeling of negation relations and its classification accuracy on multi-target scenarios. Experimental results on SemEval 2014 and Twitter datasets confirm the effectiveness of our model.

Ostrich

挖掘

An Efficient Framework of Convention Emergence Based on Multiple-Local Informationn table (.-table) collected from neighbors. Extensive agent-based simulations verify that our algorithm outperforms the advanced algorithms in terms of convergence efficiency and robustness in various networks.

剛毅

小木槌