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Fangling Leng, Fan Li, Yubin Bao, Tiancheng Zhang and Ge Yu
As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the...
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Kexiang Qian, Hongyu Yang, Ruyu Li, Weizhe Chen, Xi Luo and Lihua Yin
With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to th...
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Thiago dos Santos Gonçalves, Harald Klammler and Luíz Rogério Bastos Leal
Aquifer properties, such as hydraulic transmissivity T and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of ge...
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Li-Na Wang, Guoqiang Zhong, Yaxin Shi and Mohamed Cheriet
Most of the dimensionality reduction algorithms assume that data are independent and identically distributed (i.i.d.). In real-world applications, however, sometimes there exist relationships between data. Some relational learning methods have been propo...
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Zhe Sun, Liyuan Dou, Zongbao Mu, Siyuan Tan, Zhi Zong, Kamal Djidjeli and Guiyong Zhang
To improve the accuracy of solving the Poisson equation and the efficiency of handling complex boundary shapes in the particle method, this paper proposes a Local Regular-distributed Background Particles (LRBP) as an alternative to traditional boundary h...
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