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Comunicação de atitude
Um estudo sobre comunicação e suas áreas, muito importante para o profissional de relações públicas. O livro procura analisar os aspectos relativos às inovações em comunicação e aos modos de participação do profissional de relações públicas, como criador e/ou gestor de políticas organizacionais e nos relacionamentos com os agentes de influência. Para isso, fez-se uso de um amplo referencial através de consultas bibliográficas, análises etimológicas e observações em algumas vivências e discussões, que forneceram elementos importantes para nortear a reflexão. Diversos autores trouxeram um aporte interdisciplinar para o enfrentamento das resistências e obstáculos of...
Nos rastros de Foucault
A obra objetiva tensionar os conceitos-ferramentas de Michel Foucault a partir de inquietações que visam esquadrinhar as formações discursivas, penetrar nas relações de poder e conduzir, regular e produzir subjetividades. Destarte, os sujeitos, as instituições e as práticas compõem o escopo analítico das discussões a serem travadas nesta obra.
Ontology Alignment Using Biologically-inspired Optimisation Algorithms
It is investigated how biologically-inspired optimisation methods can be used to compute alignments between ontologies. Independent of particular similarity metrics, the developed techniques demonstrate anytime behaviour and high scalability. Due to the inherent parallelisability of these population-based algorithms it is possible to exploit dynamically scalable cloud infrastructures - a step towards the provisioning of Alignment-as-a-Service solutions for future semantic applications.
Automating the Design of Data Mining Algorithms
Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the desi...
Mathematical Reviews
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Causality and Probability in the Sciences
Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics in order to infer causal relationships. However, the very foundations of causal inference are up in the air; it is by no means clear which methods of causal inference should be used, nor why they work when they do. This book brings philosophers and scientists together to tackle these important questions. The papers in this volume shed light on the relationship between causality and probability and the application of these concepts within the sciences. With its interdisciplinary perspective and its careful analysis, "Causality and Probability in the Sciences" heralds the transition of causal inference from an art to a science.
Neuroscience of Nicotine
Neuroscience of Nicotine: Mechanisms and Treatment presents the fundamental information necessary for a thorough understanding of the neurobiological underpinnings of nicotine addiction and its effects on the brain. Offering thorough coverage of all aspects of nicotine research, treatment, policy and prevention, and containing contributions from internationally recognized experts, the book provides students, early-career researchers, and investigators at all levels with a fundamental introduction to all aspects of nicotine misuse. With an estimated one billion individuals worldwide classified as tobacco users—and tobacco use often being synonymous with nicotine addiction—nicotine is one ...
Dataset Shift in Machine Learning
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. Dataset shift is present in most practical applications, for reasons ranging from the bias introduced by experimental design to the irreproducibility of the testing conditions at training time. (An example is -email spam filtering, which may fail...
