Chemistry, Theater, Information and Communications Technology

Theater play that shows how textiles, make up, hair dyes and music players has changed along the last six decades, especially due to polymers. It is a play that has a historic, chemical and musical side that introduces the use of ICT （information and communications technology）, such as Faeebook, Moodle platform, Web sites, E-mail, Word, Publisher, PowerPoint, Videos and Digital music. Results in the conduct of the students that participated： assertiveness, self-esteem, tolerance, cooperation, responsibility and teamwork, all of them significantly contributed a meaningful learning about the polymers.

Towards a multi-agent reinforcement learning approach for joint sensing and sharing in cognitive radio networks

The adoption of the Fifth Generation(5G)and beyond 5G networks is driving the demand for learning approaches that enable users to co-exist harmoniously in a multi-user distributed environment.Although resource-constrained,the Cognitive Radio(CR)has been identified as a key enabler of distributed 5G and beyond networks due to its cognitive abilities and ability to access idle spectrum opportunistically.Reinforcement learning is well suited to meet the demand for learning in 5G and beyond 5G networks because it does not require the learning agent to have prior information about the environment in which it operates.Intuitively,CRs should be enabled to implement reinforcement learning to efficiently gain opportunistic access to spectrum and co-exist with each other.However,the application of reinforcement learning is straightforward in a single-agent environment and complex and resource intensive in a multi-agent and multi-objective learning environment.In this paper,(1)we present a brief history and overview of reinforcement learning and its limitations;(2)we provide a review of recent multi-agent learning methods proposed and multi-agent learning algorithms applied in Cognitive Radio(CR)networks;and(3)we further present a novel framework for multi-CR reinforcement learning and conclude with a synopsis of future research directions and recommendations.

Layer-wise domain correction for unsupervised domain adaptation

Deep neural networks have been successfully applied to numerous machine learning tasks because of their impressive feature abstraction capabilities.However,conventional deep networks assume that the training and test data are sampled from the same distribution,and this assumption is often violated in real-world scenarios.To address the domain shift or data bias problems,we introduce layer-wise domain correction(LDC),a new unsupervised domain adaptation algorithm which adapts an existing deep network through additive correction layers spaced throughout the network.Through the additive layers,the representations of source and target domains can be perfectly aligned.The corrections that are trained via maximum mean discrepancy,adapt to the target domain while increasing the representational capacity of the network.LDC requires no target labels,achieves state-of-the-art performance across several adaptation benchmarks,and requires significantly less training time than existing adaptation methods.