Titlebook: Big Data Technologies and Applications; 13th EAI Internation Zhiyuan Tan,Yulei Wu,Min Xu Conference proceedings 2024 ICST Institute for Com

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978-3-031-52264-2ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024

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Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engihttp://image.papertrans.cn/b/image/185662.jpg

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CTL-I: Infrared Few-Shot Learning via?Omnidirectional Compatible Class-Incrementalures learned from the base class. Our empirical results demonstrate that our approach outperforms existing few-shot incremental learning methods on various benchmark datasets, even with extremely limited instances per class. Our work offers a promising direction for addressing the challenges of few-

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BigText-QA: Question Answering over?a?Large-Scale Hybrid Knowledge Graphkground KB (such as YAGO or Wikidata), as well as an . providing highly diversified relational paraphrases with rich context information. Our experimental results demonstrate that BigText-QA outperforms DrQA, a neural-network-based QA system, and achieves competitive results to QUEST, a graph-based

licence

STLGCN: Spatial-Temporal Graph Convolutional Network for?Long Term Traffic Forecastingd filtering out the irrelevant ones, which significantly optimizes the process of spatial information aggregation. Extensive experimental results on two real public traffic datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.

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Image Forgery Detection Using Cryptography and?Deep Learninge of CNN algorithm on the hashing image dataset gives a prediction accuracy of 72.70% at 0.09?s. Furthermore, the result of CNN on the raw image dataset gives a prediction accuracy of 89.08% at 2?s. The result shows that although a higher prediction accuracy is obtained when the CNN algorithm is use

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Revocable Attribute-Based Encryption Scheme with?Cryptographic Reverse Firewalls leaking secret information. The CRF has been deployed in many cryptographic systems, but its application in the ABE field has been relatively limited. This paper presents a novel attribute-based encryption scheme which incorporates attribute revocation, hidden policy components, and CRF mechanism t

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Karsten Lehn,Merijam Gotzes,Frank Klawonnures learned from the base class. Our empirical results demonstrate that our approach outperforms existing few-shot incremental learning methods on various benchmark datasets, even with extremely limited instances per class. Our work offers a promising direction for addressing the challenges of few-