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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters.

Machine learning.

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What distinguishes an adventure novel from a historical novel? Can the same text belong to several genres? More to one than to another? Have some existing genres been overlooked? To answer these and similar questions, José Calvo Tello combines methods from Linguistics (lexicography), Literary Studies (genre theory), and Computer Science (machine learning, natural language processing). Located in the interdisciplinary field of Digital Humanities, this study analyzes a newly developed corpus of 358 Spanish novels of the silver age (1880-1939), which includes authors like Baroja, Pardo Bazán, or Valle-Inclán. Calvo Tello's key result is a graph-based model of literarygenre that reconciles recent theoretical approaches.

Machine learning.

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Traditionally associated with craftmanship and manual work, knitwear seems a quite unusual subject of investigation for scientific research. This book places it as an integrative part of the industrial design culture where the dialogue between a productive system of excellence and the design discipline taught in universities becomes a topic of central concern. From an industrial standpoint, knitwear is a fertile ground of technological experimentation while being at the same time one of the most traditional sectors of Made in Italy. The complexity of a long and fragmented production chain is an interesting challenge for designers but affects the training and the knowledge transfer inside companies. On the academic side, the presence of such an industry creates the urgency for higher education to understand how to train knit designers as new professionals, and thus the opportunity for knitwear to be recognized as a discipline deserving specific teaching strategies and a focused scientific research. The present book reports an experimentation conducted in the unique conditions of the Italian industrial design culture, that defined tools and methods to train knit designers not as artists, but with the technical and cultural knowledge and the project-oriented mindset that is typical of industrial design disciplines. These contents are of interest for the academy, as they constitute a tool to design teaching experiences oriented to such a specific industrial sector; for those approaching knitwear design, as it is a pool of information on the complexity of knitwear, a map of the background knowledge, collected and rearranged, and a compass to be guided in building one's own skills; for professionals, who will find here their history, the opinions of colleagues, the opportunity to integrate their knowledge and to learn more about the in-depth experimental, technical and design work that takes place at Politecnico di Milano.

Machine knitting

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Language learning and translation have always been complementary pillars of multilingualism in the European Union. Both have been affected by the increasing availability of machine translation (MT): language learners now make use of free online MT to help them both understand and produce texts in a second language, but there are fears that uninformed use of the technology could undermine effective language learning. At the same time, MT is promoted as a technology that will change the face of professional translation, but the technical opacity of contemporary approaches, and the legal and ethical issues they raise, can make the participation of human translators in contemporary MT workflows particularly complicated. Against this background, this book attempts to promote teaching and learning about MT among a broad range of readers, including language learners, language teachers, trainee translators, translation teachers, and professional translators. It presents a rationale for learning about MT, and provides both a basic introduction to contemporary machine-learning based MT, and a more advanced discussion of neural MT. It explores the ethical issues that increased use of MT raises, and provides advice on its application in language learning. It also shows how users can make the most of MT through pre-editing, post-editing and customization of the technology.

Machine translating

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What kind of Work-Related activities/programmes do Italian teachers propose to their students? What are common elements considered in the designing of Work-Related activities by those proposing them? A multiple case study research programme was chosen as a method to explore the current Italian situation regarding Work-Related teaching and learning methods in Higher Education (Coll et al. 2008; Dirkx 2011) and to define some possible methodological recommendations to encourage the design of Work-Related Learning activities/programmes.

Machine learning.