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[null,"1.","Record Nr.",null,"UNINA9910783494203321"]
[null,null,"Titolo",null,"Brill's companion to Ovid [[electronic resource] /] / edited by Barbara Weiden Boyd"]
[null,null,"Pubbl/distr/stampa",null,"Leiden ; ; Boston, : Brill, 2002"]
[null,null,"ISBN",null,"1-280-46478-X"]
[{"$nulls":4},"9786610464784"]
[{"$nulls":4},"1-4175-3669-1"]
[{"$nulls":4},"90-474-0095-X"]
[null,null,"Descrizione fisica",null,"1 online resource (550 p.)"]
[null,null,"Collana",null,"Brill's Companions in Classical Studies"]
[null,null,"Disciplina",null,"871/.01"]
[null,null,"Soggetti",null,"Epistolary poetry, Latin - History and criticism"]
[{"$nulls":4},"Didactic poetry, Latin - History and criticism"]
[{"$nulls":4},"Love poetry, Latin - History and criticism"]
[{"$nulls":4},"Mythology, Classical, in literature"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (p. [485]-512) and indexes."]
[null,null,"Nota di contenuto",null,"Ovid and the Augustan milieu / Peter White -- Ovid's language and style / E.J. Kenney -- The Amores: the invention of Ovid / Barbara Weiden Boyd -- The Heroides: elegiac voices / Peter E. Knox -- Praecepta amoris: Ovid's didactic elegy / Patricia Watson -- The Fasti: style, structure, and time / John F. Miller -- Ovid's Fasti: politics, history, and religion / Elaine Fantham -- Sources and genres in Ovid's Metamorphoses 1-5 / Alison Keith -- Narrative techniques and narrative structures in the Metamorphoses / Gianpiero Rosati -- The house of fame: Roman history and Augustan politics in Metamorphoses 11-15 / Garth Tissol -- Ovid's exilic poetry: worlds apart / Gareth Williams -- Siquid habent ueri uatum praesagia: Ovid in the 1st-5th centuries A.D. / Michael Dewar -- Ovid in the Middle Ages: exile, mythographer, lover / Ralph Hexter -- Manuscript traditions and the transmission of Ovid's works / John Richmond"]
[null,null,null,"Sommario/riassunto","This volume on the Roman poet Ovid (43 BCE – 17 CE) comprises articles by an international group of fourteen scholars. Their contributions cover a wide range of topics, including a biographical essay, a survey of the major manuscripts and textual traditions, and a comprehensive discussion of Ovid’s style. The remaining chapters are devoted to focused studies of each of Ovid’s major works, with "]
[{"$nulls":4},"emphasis given where appropriate to the poet’s interest in genre and narrative techniques, his engagement with the poetry that preceded his oeuvre, his response to the political, religious, and social realities of Augustan Rome, and his enduring legacy in the European literary traditions of the first 1300 years after his death. Brill's Companion to Ovid combines close analysis of each of Ovid’s major works with a comprehensive overview of scholarly trends in the study of Latin poetry and Roman literary culture. It will be a valuable resource for students and scholars of Latin literature alike."]
[null,"2.","Record Nr.",null,"UNINA9910427668503321"]
[null,null,"Titolo",null,"Federated Learning : Privacy and Incentive / / edited by Qiang Yang, Lixin Fan, Han Yu"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020"]
[null,null,"ISBN",null,"3-030-63076-5"]
[null,null,"Edizione",null,"[1st ed. 2020.]"]
[null,null,"Descrizione fisica",null,"1 online resource (X, 286 p. 94 illus., 82 illus. in color.)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 12500"]
[null,null,"Disciplina",null,"006.31"]
[null,null,"Soggetti",null,"Artificial intelligence"]
[{"$nulls":4},"Data protection"]
[{"$nulls":4},"Computer networks"]
[{"$nulls":4},"Social sciences - Data processing"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Data and Information Security"]
[{"$nulls":4},"Computer Communication Networks"]
[{"$nulls":4},"Computer Application in Social and Behavioral Sciences"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Privacy -- Threats to Federated Learning -- Rethinking Gradients Safety in Federated Learning -- Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks -- Task-Agnostic Privacy-Preserving Representation Learning via Federated Learning -- Large-Scale Kernel Method for Vertical Federated Learning -- Towards Byzantine-resilient Federated Learning via Group-wise Robust Aggregation -- Federated Soft Gradient Boosting Machine for Streaming Data -- Dealing with Label Quality Disparity In Federated Learning -- Incentive -- FedCoin: A Peer-to-Peer Payment System for Federated Learning -- Efficient and Fair Data Valuation for Horizontal Federated Learning -- A Principled Approach to Data Valuation for Federated Learning -- A Gamified Research Tool for Incentive Mechanism Design in Federated Learning -- Budget-bounded Incentives for Federated Learning -- Collaborative Fairness in Federated Learning -- A Game-Theoretic Framework for Incentive Mechanism Design in Federated Learning -- Applications -- Federated "]
[{"$nulls":4},"Recommendation Systems -- Federated Learning for Open Banking -- Building ICU In-hospital Mortality Prediction Model with Federated Learning -- Privacy-preserving Stacking with Application to Cross-organizational Diabetes Prediction. ."]
[null,null,null,"Sommario/riassunto","This book provides a comprehensive and self-contained introduction to Federated Learning, ranging from the basic knowledge and theories to various key applications, and the privacy and incentive factors are the focus of the whole book. This book is timely needed since Federated Learning is getting popular after the release of the General Data Protection Regulation (GDPR). As 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. First, it introduces different privacy-preserving methods for protecting a Federated Learning model against different types of attacks such as Data Leakage and/or Data Poisoning. Second, the book presents incentive mechanisms which aim to encourage individuals to participate in the Federated Learning ecosystems. Last but not the least, this book also describeshow Federated Learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both academia and industries, who would like to learn federated learning from scratch, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing are preferred."]