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PROJETOS INTERDISCIPLINARES E A FORMAÇÃO INICIAL DE PROFESSORES/AS DE CIÊNCIAS
As ciências da natureza são carentes de diálogo entre si porque pouco o faz e por diferentes razões. Buscando um repensar, quatro formadores de uma IES Federal, em ação conjunta na disciplina Projetos Interdisciplinares IV, apresenta aqui um trabalho que envolveu formadores Doutores e graduandos das três áreas: Química, Física e Biologia. O que subsidiou a escrita foi a execução, os relatos e as experiências, frutos de um projeto de extensão que semestralmente tem chegado à escola, levado experimentação interdisciplinar e contribuído para a formação inicial e continuada.
The EU General Data Protection Regulation (GDPR)
This new book provides an article-by-article commentary on the new EU General Data Protection Regulation. Adopted in April 2016 and applicable from May 2018, the GDPR is the centrepiece of the recent reform of the EU regulatory framework for protection of personal data. It replaces the 1995 EU Data Protection Directive and has become the most significant piece of data protection legislation anywhere in the world. The book is edited by three leading authorities and written by a team of expert specialists in the field from around the EU and representing different sectors (including academia, the EU institutions, data protection authorities, and the private sector), thus providing a pan-Europea...
Defending Checks and Balances in EU Member States
This open access book deals with Article 7 TEU measures, court proceedings, financial sanctions and the EU Rule of Law Framework to protect EU values with a particular focus on checks and balances in EU Member States. It analyses substantive standards, powers, procedures as well as the consequences and implications of the various instruments. It combines the analysis of the European level, be it the EU or the Council of Europe, with that of the national level, in particular in Hungary and Poland. The LM judgment of the European Court of Justice is made subject to detailed scrutiny.
Illustrators Annual 2020
Illustrators Annual 2020 is the 2020 edition of Chronicle Books' yearly publication celebrating artists featured at the Bologna Children's Book Fair. Selected by the year's jury at the fair, these illustrators represent the most daring, exciting artistic minds working across the world. Celebrating debut and storied talent from around the world--talent poised to engage a whole new generation of book lovers--this glorious compendium can be read cover-to-cover or browsed through at random. * An annual publication that brings groundbreaking art from around the world to the English-speaking market * Inspires readers to marvel at the brilliance of the gifts shared by children's book illustrators *...
Customs Law of the European Union
Today, global competition obliges companies dealing in international trade to modernize their procedures of delivery in order to minimize the customs burden and simplify the relation with customs authorities. Customs planning is the current option to be effective in the worldwide marketplace. However, customs officials are facing new challenges: they must ensure the smooth flow of trade while applying necessary controls on the one hand, while protecting the health and safety of the Community's citizens on the other. To achieve and maintain the correct balance between these demands, control methods are constantly evolving raising major challenges to those charged with planning and compliance....
Federated Learning
This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated lear...
Metric Learning
Similarity between objects plays an important role in both human cognitive processes and artificial systems for recognition and categorization. How to appropriately measure such similarities for a given task is crucial to the performance of many machine learning, pattern recognition and data mining methods. This book is devoted to metric learning, a set of techniques to automatically learn similarity and distance functions from data that has attracted a lot of interest in machine learning and related fields in the past ten years. In this book, we provide a thorough review of the metric learning literature that covers algorithms, theory and applications for both numerical and structured data....
Machine Learning, Optimization, and Data Science
This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
Dual Learning
Many AI (and machine learning) tasks present in dual forms, e.g., English-to-Chinese translation vs. Chinese-to-English translation, speech recognition vs. speech synthesis,question answering vs. question generation, and image classification vs. image generation. Dual learning is a new learning framework that leverages the primal-dual structure of AI tasks to obtain effective feedback or regularization signals in order to enhance the learning/inference process. Since it was first introduced four years ago, the concept has attracted considerable attention in multiple fields, and been proven effective in numerous applications, such as machine translation, image-to-image translation, speech syn...
Graph Representation Learning
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical sy...
