Titlebook: On the Move to Meaningful Internet Systems 2004: OTM 2004 Workshops; OTM Confederated Int Robert Meersman,Zahir Tari,Angelo Corsaro Confere

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Manuele Kirsch-Pinheiro,Marlène Villanova-Oliver,Jérôme Gensel,Hervé Martinrly literature on women and leadership in religions such as Christianity and African Traditional Religion, there is a dearth of research on Muslim women and leadership in Zimbabwe, hence the thrust of this chapter. Using liberal feminism for its theoretical framework, the chapter explores the extent

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Yongsun Choi,J. Leon Zhao. The classifier model in data stream mining must be self-adjustable to the concept drift, otherwise it will get terrible classification results. To detect concept drift timely and accurately, this paper proposes an unsupervised online .oncept .rift .etection algorithm based on Jensen-Shannon .iverg

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Ilia Petrov,Stefan Jablonski,Marc Holze graph neural network methods are mostly based on message passing neural network(MPNN), which aggregates messages from neighbor nodes to update representations of target nodes. The framework follows the assumption of homophily that nodes linked by edges are similar and share the same labels. In the

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Iain Stalker,Nikolay Mehandjiev,Georg Weichhart,Kurt Fessl energy consumption of nodes and prolong network lifetime, this paper proposes an energy-efficient routing protocol DKBDCERP based on two-layer clustering. It uses an improved DPC-MND clustering algorithm to cluster nodes at first layer. Then, in each cluster, first-level cluster heads (CHs) are ele

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Judi Thomson,Andrew Cowell,Patrick Paulson,Scott Butner,Mark Whiting energy consumption of nodes and prolong network lifetime, this paper proposes an energy-efficient routing protocol DKBDCERP based on two-layer clustering. It uses an improved DPC-MND clustering algorithm to cluster nodes at first layer. Then, in each cluster, first-level cluster heads (CHs) are ele

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Timo Weithöner,Thorsten Liebig,Günther Spechtsingly. However, data often has the characteristics of high dimension for big data analytics. As the dimension of data increases, the performance of the optimization algorithm degrades dramatically. The Yin-Yang-Pair Optimization (YYPO) is a lightweight single-objective optimization algorithm, which

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Nacera Bennacer,Marie-Aude Aufaure,Nadine Cullot,Anastasiya Sotnykova,Christelle Vangenot graph neural network methods are mostly based on message passing neural network(MPNN), which aggregates messages from neighbor nodes to update representations of target nodes. The framework follows the assumption of homophily that nodes linked by edges are similar and share the same labels. In the