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Meanwhile, the Entity is being held captive by a group of scientists in a secret headquarters. They torture The Entity in order to find out what it knows about the Berzelios but are shocked when The Entity tells them that the Berzelios want to kill everybody and turn the city into a total, self-sufficient, virtual city. The Entity tells them that they must stop the Berzelios and save the city. The scientists are shocked to hear what The Entity says. The Entity then says that after it was frozen it came back to life and found out what it was. The scientists then ask if it wants to be brought to the Berzelios. The Entity says that it's ready to go to the Berzelios and kill them and whoever else the Berzelios want. The scientists then ask The Entity if it wants to go back to the Berzelios with them. The Entity says that it wants to kill the Berzelios.
On the other end of the studio, the Berzelios are told that the entity is being brought to them. They are afraid that they might have to kill The Entity and others to protect the city. Nonetheless, they decide to welcome it and allow it to come to them. They ask the scientists to bring The Entity to the Berzelios.
At the studio, Carla tells Phil that she's glad he's alive and not in that place anymore. Phil tells her that she was the one who turned him into the entity and that he's really sorry. Carla says she's glad to hear it. Phil then says he loves her, and they kiss.
Collaborative Ambient Intelligence: In the era of big data, a large amount of unstructured text data springs up every day. Entity linking involves relating the mentions found in the texts to the corresponding entities, which stand for objective things in the real world, in a knowledge base. This task can help computers understand semantics in the texts correctly. Although there have been numerous approaches employed in research such as this, some challenges are still unresolved. Most current approaches utilize neural models to learn important features of the entity and mention context. However, the topic coherence among the referred entities is frequently ignored, which leads to a clear preference for popular entities but poor accuracy for less popular ones. Moreover, the graph-based models face much noise information and high computational complexity. To solve the problems above, the paper puts forward an entity linking algorithm derived from the asymmetric graph convolutional network and the contextualized semantic relevance, which can make full use of the neighboring node information as well as deal with unnecessary noise in the graph. The semantic vector of the candidate entity is obtained by continuously iterating and aggregating the information from neighboring nodes. The contextualized relevance model is a symmetrical structure that is designed to realize the deep semantic measurement between the mentions and the entities. The experimental results show that the proposed algorithm can fully explore the topology information of the graph and dramatically improve the effect of entity linking compared with the baselines.Keywords: symmetry and asymmetry; collaborative computing; entity linking; graph convolutional network; distilled BERT; contextualized semantic relevance 827ec27edc