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1. |
Record Nr. |
UNINA9910788843203321 |
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Autore |
Mitrea Dorina <1965-> |
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Titolo |
Layer potentials, the Hodge Laplacian, and global boundary problems in nonsmooth Riemannian manifolds / / Dorina Mitrea, Marius Mitrea, Michael Taylor |
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Pubbl/distr/stampa |
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Providence, Rhode Island : , : American Mathematical Society, , 2001 |
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ISBN |
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Descrizione fisica |
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1 online resource (137 p.) |
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Collana |
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Memoirs of the American Mathematical Society, , 0065-9266 ; ; number 713 |
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Disciplina |
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Soggetti |
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Riemannian manifolds |
Boundary value problems |
Differential equations, Elliptic - Numerical solutions |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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"Volume 150, number 713 (fourth of 5 numbers)." |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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""Contents""; ""Introduction""; ""Chapter 1. Singular integrals on Lipschitz submanifolds of codimension one""; ""Chapter 2. Estimates on fundamental solutions""; ""Chapter 3. General second-order strongly elliptic systems""; ""Chapter 4. The Dirichlet problem for the Hodge Laplacian and related operators""; ""Chapter 5. Natural boundary problems for the Hodge Laplacian in Lipschitz domains""; ""Chapter 6. Layer potential operators on Lipschitz domains""; ""Chapter 7. Rellich type estimates for differential forms"" |
""Chapter 8. Fredholm properties of boundary integral operators on regular spaces""""Chapter 9. Weak extensions of boundary derivative operators""; ""Chapter 10. Localization arguments and the end of the proof of Theorem 6.2""; ""Chapter 11. Harmonic fields on Lipschitz domains""; ""Chapter 12. The proofs of the Theorems 5.1-5.5""; ""Chapter 13. The proofs of the auxiliary lemmas""; ""Chapter 14. Applications to Maxwell's equations on Lipschitz domains""; ""Appendix A. Analysis on Lipschitz manifolds""; ""Appendix B. The connection between dâ??and dâ??Ω""; ""Bibliography"" |
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2. |
Record Nr. |
UNINA9910484465603321 |
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Autore |
Iqbal Farkhund |
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Titolo |
Machine Learning for Authorship Attribution and Cyber Forensics / / by Farkhund Iqbal, Mourad Debbabi, Benjamin C. M. Fung |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (IX, 158 p. 38 illus., 28 illus. in color.) |
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Collana |
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International Series on Computer, Entertainment and Media Technology, , 2364-9488 |
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Disciplina |
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Soggetti |
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Data mining |
Machine learning |
Computer crimes |
Data Mining and Knowledge Discovery |
Machine Learning |
Cybercrime |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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1. Cybersecurity And Cybercrime Investigation -- 2. Machine Learning Framework For Messaging Forensics -- 3. Header-Level Investigation And Analyzing Network Information -- 4. Authorship Analysis Approaches -- 5. Authorship Analysis - Writeprint Mining For Authorship Attribution -- 6. Authorship Attribution With Few Training Samples -- 7. Authorship Characterization -- 8. Authorship Verification -- 9. Authorship Attribution Using Customized Associative Classification -- 10. Criminal Information Mining -- 11. Artificial Intelligence And Digital Forensics. |
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Sommario/riassunto |
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The book first explores the cybersecurity’s landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the |
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said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potentialsuspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law. . |
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