Securing complex and networked systems has become increasingly important as these systems play an indispensable role in modern life at the turn of the - formation age. Concurrently, security of ubiquitous communicatio...
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ISBN:
(数字)9783642171970
ISBN:
(纸本)9783642171963
Securing complex and networked systems has become increasingly important as these systems play an indispensable role in modern life at the turn of the - formation age. Concurrently, security of ubiquitous communication, data, and computing poses novel research challenges. Security is a multi-faceted problem due to the complexity of underlying hardware, software, and network inter- pendencies as well as human and social factors. It involves decision making on multiple levels and multiple time scales, given the limited resources available to both malicious attackers and administrators defending networked systems. - cision and game theory provides a rich set of analyticalmethods and approaches to address various resource allocation and decision-making problems arising in security. This edited volume contains the contributions presented at the inaugural Conference on Decision and Game Theory for Security - GameSec 2010. These 18 articles (12 full and 6 short papers) are thematically categorized into the following six sections: – “Security investments and planning” contains two articles, which present optimization methods for (security) investments when facing adversaries. – “Privacy and anonymity” has three articles discussing location privacy, - line anonymity, and economic aspects of privacy. – “Adversarial and robust control” contains three articles, which investigate security and robustness aspects of control in networks. – “Networksecurityandbotnets”hasfourarticlesfocusingondefensivestra- giesagainstbotnetsaswellasdetectionofmaliciousadversariesinnetworks. – “Authorizationandauthentication”hasanarticleonpasswordpracticesand another one presenting a game-theoretic authorization model. – “Theory and algorithms for security” containsfour articles on various th- retic and algorithmic aspects of security.
The two volume set CCIS 775 and 776 constitutes the refereed proceedings of the First International Conference on Computational Intelligence, Communications, and Business Analytics, CICBA 2017, held in Kolkata, India,...
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ISBN:
(数字)9789811064302
ISBN:
(纸本)9789811064296
The two volume set CCIS 775 and 776 constitutes the refereed proceedings of the First International Conference on Computational Intelligence, Communications, and Business Analytics, CICBA 2017, held in Kolkata, India, in March 2017.
Question generation is an important task in natural language processing that involves generating questions from a given text. This paper proposes a novel approach for dynamic question generation using a context-aware ...
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Question generation is an important task in natural language processing that involves generating questions from a given text. This paper proposes a novel approach for dynamic question generation using a context-aware auto-encoded graph neural model. Our approach involves constructing a graph representation of the input text, where each node in the graph corresponds to a word or phrase in the text, and the edges represent the relationships between them. We then use an auto-encoder model to learn a compressed representation of the graph that captures the most important information in the input text. Finally, we use the compressed graph representation to generate questions by dynamically selecting nodes and edges based on their relevance to the context of the input text. We evaluate our approach on four benchmark datasets (SQuAD, Natural Questions, TriviaQA, and QuAC) and demonstrate that it outperforms existing state-of-the-art methods for dynamic question generation. In the experimentation, to evaluate the result four performance metrics are used i.e. BLEU, ROUGE, F1-Score, and Accuracy. The result of the proposed approach yields an accuracy of 92% on the SQuAD dataset, 89% with QuAC, and 84% with TriviaQA. while on the natural questions dataset, the model gives 79% accuracy. Our results suggest that the use of graph neural networks and auto-encoder models can significantly improve the accuracy and effectiveness of question generation in NLP. Further research in this area can lead to even more sophisticated models that can generate questions that are even more contextually relevant and natural-sounding.
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