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- 저자 : ( Wanting Yu ) (검색결과 2 건)
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Internet Immunization Strategy based on Relations of Nodes
Fan Tongrang,Qin Wanting,Zhao Wenbin,Wang Qian,Yu Tao 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.11
Inspired by the biological immune system against outside invasion in nature, this paper propose a network security strategy using Agent technology. The Agents with independent behavior capacity are set for resisting network intrusion using their spontaneous coordinate organization. Based on the comparisons of existing immunization strategies, such as target immune, acquaintance immune and random immunity, it is found that the importance of nodes in network are influenced by interaction between nodes, degree of nodes, information flow, and other factors. If the nodes are more important, they have a greater influence over the whole network. When important nodes are infected by virus, there will be a higher probability of spreading of hazard information. Therefore, this paper proposes a network security model using Agent technology, where important nodes are implanted with relationship immunization strategy. Experimental results show when the network suffered from random or malicious attacks, relationship immunization strategy is more effective than others existing methods.
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KI-HABS: Key Information Guided Hierarchical Abstractive Summarization
( Mengli Zhang ),( Gang Zhou ),( Wanting Yu ),( Wenfen Liu ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.12
With the unprecedented growth of textual information on the Internet, an efficient automatic summarization system has become an urgent need. Recently, the neural network models based on the encoder-decoder with an attention mechanism have demonstrated powerful capabilities in the sentence summarization task. However, for paragraphs or longer document summarization, these models fail to mine the core information in the input text, which leads to information loss and repetitions. In this paper, we propose an abstractive document summarization method by applying guidance signals of key sentences to the encoder based on the hierarchical encoder-decoder architecture, denoted as KI-HABS. Specifically, we first train an extractor to extract key sentences in the input document by the hierarchical bidirectional GRU. Then, we encode the key sentences to the key information representation in the sentence level. Finally, we adopt key information representation guided selective encoding strategies to filter source information, which establishes a connection between the key sentences and the document. We use the CNN/Daily Mail and Gigaword datasets to evaluate our model. The experimental results demonstrate that our method generates more informative and concise summaries, achieving better performance than the competitive models.
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