languages applications and technologies 4th international symposium slate 2015

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Gamification Based E Learning Strategies For Computer Programming Education

Author : Alexandre Peixoto de Queirós, Ricardo
ISBN : 9781522510352
Genre : Education
File Size : 71. 4 MB
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Computer technologies are forever evolving and it is vital that computer science educators find new methods of teaching programming in order to maintain the rapid changes occurring in the field. One of the ways to increase student engagement and retention is by integrating games into the curriculum. Gamification-Based E-Learning Strategies for Computer Programming Education evaluates the different approaches and issues faced in integrating games into computer education settings. Featuring emergent trends on the application of gaming to pedagogical strategies and technological tactics, as well as new methodologies and approaches being utilized in computer programming courses, this book is an essential reference source for practitioners, researchers, computer science teachers, and students pursuing computer science.

Central European Functional Programming School

Author : Viktória Zsók
ISBN : 9783319159409
Genre : Computers
File Size : 48. 78 MB
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This volume presents the revised lecture notes of selected talks given at the Fifth Central European Functional Programming School, CEFP 2013, held in July 2013 in Cluj-Napoca, Romania. The 14 revised full papers presented were carefully reviewed and selected. The lectures cover a wide range of distributed and multicore functional programming subjects. The last 5 papers are selected papers of the PhD Workshop organized for the participants of the summer school.

Advanced Structured Prediction

Author : Sebastian Nowozin
ISBN : 9780262028370
Genre : Computers
File Size : 65. 26 MB
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The goal of structured prediction is to build machine learning models that predict relational information that itself has structure, such as being composed of multiple interrelated parts. These models, which reflect prior knowledge, task-specific relations, and constraints, are used in fields including computer vision, speech recognition, natural language processing, and computational biology. They can carry out such tasks as predicting a natural language sentence, or segmenting an image into meaningful components. These models are expressive and powerful, but exact computation is often intractable. A broad research effort in recent years has aimed at designing structured prediction models and approximate inference and learning procedures that are computationally efficient. This volume offers an overview of this recent research in order to make the work accessible to a broader research community. The chapters, by leading researchers in the field, cover a range of topics, including research trends, the linear programming relaxation approach, innovations in probabilistic modeling, recent theoretical progress, and resource-aware learning.Sebastian Nowozin is a Researcher in the Machine Learning and Perception group (MLP) at Microsoft Research, Cambridge, England. Peter V. Gehler is a Senior Researcher in the Perceiving Systems group at the Max Planck Institute for Intelligent Systems, Tübingen, Germany. Jeremy Jancsary is a Senior Research Scientist at Nuance Communications, Vienna. Christoph H. Lampert is Assistant Professor at the Institute of Science and Technology Austria, where he heads a group for Computer Vision and Machine Learning. Contributors Jonas Behr, Yutian Chen, Fernando De La Torre, Justin Domke, Peter V. Gehler, Andrew E. Gelfand, Sébastien Giguère, Amir Globerson, Fred A. Hamprecht, Minh Hoai, Tommi Jaakkola, Jeremy Jancsary, Joseph Keshet, Marius Kloft, Vladimir Kolmogorov, Christoph H. Lampert, François Laviolette, Xinghua Lou, Mario Marchand, André F. T. Martins, Ofer Meshi, Sebastian Nowozin, George Papandreou, Daniel Pruša, Gunnar Rätsch, Amélie Rolland, Bogdan Savchynskyy, Stefan Schmidt, Thomas Schoenemann, Gabriele Schweikert, Ben Taskar, Sinisa Todorovic, Max Welling, David Weiss, Thomáš Werner, Alan Yuille, Stanislav Živný

The Rational Unified Process

Author : Philippe Kruchten
ISBN : 0321197704
Genre : Computers
File Size : 78. 37 MB
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bull; Reflects all of the changes that were integrated into RUP v2003-the latest version of the very popular produc t bull; Learn the key concepts, fundamentals of structure, integral content, and motivation behind the RUP bull; Covers all phases of the software development lifecycle -from concept, to delivery, to revision

The Children S Machine

Author : Seymour Papert
ISBN : 9780465010639
Genre : Education
File Size : 49. 60 MB
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In a follow-up to Mindstorms (selling over 135,000 copies in paperback), the pioneering scientist who created the programming language Logo used in hundreds of schools nationwide now discusses why the computer revolution has failed to revolutionize eduction. "Bold and adventurous".--New York Times.

The Cambridge Handbook Of Learner Corpus Research

Author : Sylviane Granger
ISBN : 1107041198
Genre : Language Arts & Disciplines
File Size : 77. 17 MB
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The origins of learner corpus research go back to the late 1980s when large electronic collections of written or spoken data started to be collected from foreign/second language learners, with a view to advancing our understanding of the mechanisms of second language acquisition and developing tailor-made pedagogical tools. Engaging with the interdisciplinary nature of this fast-growing field, The Cambridge Handbook of Learner Corpus Research explores the diverse and extensive applications of learner corpora, with 27 chapters written by internationally renowned experts. This comprehensive work is a vital resource for students, teachers and researchers, offering fresh perspectives and a unique overview of the field. With representative studies in each chapter which provide an essential guide on how to conduct learner corpus research in a wide range of areas, this work is a cutting-edge account of learner corpus collection, annotation, methodology, theory, analysis and applications.

Optimization For Machine Learning

Author : Suvrit Sra
ISBN : 9780262016469
Genre : Computers
File Size : 76. 62 MB
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The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields.Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

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