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Giorgio Lazzarinetti, Riccardo Dondi, Sara Manzoni and Italo Zoppis
Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for r...
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Adil Redaoui, Amina Belalia and Kamel Belloulata
Deep network-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structura...
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Jing Chen, Gang Zhou, Jicang Lu, Shiyu Wang and Shunhang Li
Fake news detection has become a significant topic based on the fast-spreading and detrimental effects of such news. Many methods based on deep neural networks learn clues from claim content and message propagation structure or temporal information, whic...
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Yu Dai, Yuqiao Liu, Lei Yang and Yufan Fu
Idioms are a unique class of words in the Chinese language that can be challenging for Chinese machine reading comprehension due to their formal simplicity and the potential mismatch between their literal and figurative meanings. To address this issue, t...
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Shumin Lai, Longjun Huang, Ping Li, Zhenzhen Luo, Jianzhong Wang and Yugen Yi
In this paper, we present a novel unsupervised feature selection method termed robust matrix factorization with robust adaptive structure learning (RMFRASL), which can select discriminative features from a large amount of multimedia data to improve the p...
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