Upper ontologies have traditionally arisen from the approach in which concepts that are common across a set of domains can be axiomatized at a general level. The rationale is that reuse across domains is to be supported through specialization of the general concepts from an upper ontology. Similarly, semantic integration between ontologies is to be achieved through the general concepts they specialize. The TUpper Ontology follows an alternative approach to the conventional upper ontology paradigm. Rather than think of an upper (...) ontology as a monolithic axiomatization centred on a taxonomy, the sideways approach considers an upper ontology to be a modular ontology composed of generic ontologies that cover concepts including those related to time, process, and space. TUpper is therefore composed of a set of generic ontologies, and each generic ontology axiomatizes a particular set of generic concepts. The TUpper Ontology is designed as a top-level ontology that contains modules from the ontologies within existing international standards. (shrink)

本书内容包括:当代军人的世界观、当代军人的时代观、当代军人的国家观、当代军人的安全观、当代军人的战争观、当代军人的使命观等。.

Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding (...) results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones. (shrink)

Ben shu yi jian ming e yao de bi chu xiang du zhe ti gong le yi fu quan jing shi de xi fang mei xue li shi hua juan.Qi nei rong han gai cong gu xi la,Luo ma zhi dao 20 shi ji de xi fang mei xue si xiang he li lun cheng guo,Qi zhong yi li lun xing tai de mei xue si xiang wei zhu gan,Tong shi she ji wen xue yi shu yi ji she hui (...) feng shang suo ti xian de shen mei yi shi. (shrink)

Meu intuito é tecer algumas considerações sobre a descoberta e relação de Voltaire com o newtonianismo, e a partir daí refletir de maneira mais geral sobre as possibilidades da aproximação entre filosofia e ciência.