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Mohammed Suleiman Mohammed Rudwan and Jean Vincent Fonou-Dombeu
Ontology merging is an important task in ontology engineering to date. However, despite the efforts devoted to ontology merging, the incorporation of relevant features of ontologies such as axioms, individuals and annotations in the output ontologies rem...
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Xinyi Meng and Daofeng Li
The explosive growth of malware targeting Android devices has resulted in the demand for the acquisition and integration of comprehensive information to enable effective, robust, and user-friendly malware detection. In response to this challenge, this pa...
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Jing Liu and Yong Zhong
As a structural indicator of dense subgraphs, k-core has been widely used in community search due to its concise and efficient calculation. Many community search algorithms have been expanded on the basis of k-core. However, relevant algorithms often set...
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Bae-Seon Park and Hak-Tae Lee
This paper demonstrates the effectiveness of the Extended First-Come, First-Served (EFCFS) scheduler for integrated arrival and departure scheduling by comparing the scheduling results with the recorded operational data at Incheon International Airport (...
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Marcos E. González Laffitte and Peter F. Stadler
The comparison of multiple (labeled) graphs with unrelated vertex sets is an important task in diverse areas of applications. Conceptually, it is often closely related to multiple sequence alignments since one aims to determine a correspondence, or more ...
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Shaahin Angizi, Naima Ahmed Fahmi, Deniz Najafi, Wei Zhang and Deliang Fan
In this work, we present an efficient Processing in MRAM-Accelerated De Bruijn Graph-based DNA Assembly platform, named PANDA, based on an optimized and hardware-friendly genome assembly algorithm. PANDA is able to assemble large-scale DNA sequence datas...
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Ravi Goyal and Victor De Gruttola
This paper presents a general recursive formula to estimate the number of labeled graphs as well as details to evaluate the formula for the following graph properties: number of edges (graph density), degree sequence, degree distribution, classification ...
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Xuan Di, Rongye Shi, Zhaobin Mo and Yongjie Fu
For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DN...
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Xinyue Lan, Liyue Wang, Cong Wang, Gang Sun, Jinzhang Feng and Miao Zhang
In this research, we introduce a deep-learning-based framework designed for the prediction of transonic flow through a linear cascade utilizing large-scale point-cloud data. In our experimental cases, the predictions demonstrate a nearly four-fold speed ...
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Lorenzo Arsini, Barbara Caccia, Andrea Ciardiello, Stefano Giagu and Carlo Mancini Terracciano
Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs st...
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