Titlebook: ;

引導(dǎo)

Mohammad Bilal Khan,Ibadurrehman Patel,Shaiq Ahmad Ghafoorzai,Damkar Mohammad Shehbaz,Rusni Hassan,Jables the verification of the conformance to the business rules combining logic and mathematical expressions. Moreover, the new framework gives the possibility of separating the business requirements from the implementation, giving hints to the process designer and to the programmer. Finally, it giv

Innovative

Inflated

Azlin Suzana Salim,Rusni Hassann the social and cognitive sciences...Given its interdisciplinary scope, the book subsequently delivers new approaches on how to contribute to the future of economics, providing alternative explanations for various socio-economic issues such as computable humanities; cognitive, behavioural, and expe

gerrymander

Tesleem Olajuwon Isa Akosile,Nor Razinah Mohd Zains, as Damasio’s clinical cases show. This corrective, in our opinion, is part of the wider movement – already experimented in the physical, biological, medical, and didactic fields during the 20th century – tending to overcome the Cartesian subject/object separation, introducing the variable subject

騙子

debris

珠寶

ral and local traditions, education, family upbringing, and other social backgrounds and cognitive relationships. Here, we study the behaviour of motorists found in metadata, that is, data collected in other studies, with a particular focus on Italy and the United States. The basic idea is to compar

muster

Nadiia P. Reznik,Kateryna A. Alekseieva,Anatolii A. Ostapchuk,Vitalii V. Lutsiak,Vadym M. Kondratyukwe demonstrate our method for tracking a variety of previously unknown objects, even under heavy non-rigid transformations, partial occlusions, scale changes and rotations. Moreover, we compare our tracker to state-of-the-art methods including both bounding box-based and part-based trackers.

銀版照相

Mohamad Yusman Ammeran,Shaista Noor,Mohar Yusofne their output in a Na?ve Bayesian manner. Surprisingly, the ferns, while simpler, actually perform better than the trees. This is because the Na?ve Bayesian approach benefits more from the thousands of synthetic training examples we can generate than output averaging as usually performed by decisi

Saline