1.

Record Nr.

UNINA9910457236103321

Autore

Daniel John S. <1942-, >

Titolo

Mega-schools, technology, and teachers : achieving education for all / / John S. Daniel

Pubbl/distr/stampa

New York, N.Y. : , : Routledge, , 2010

ISBN

1-135-16333-2

1-282-57154-0

9786612571541

0-203-85832-8

Descrizione fisica

1 online resource (209 p.)

Collana

Open and flexible learning series

Disciplina

372.9172/4

372.91724

Soggetti

Education, Elementary - Developing countries

Educational equalization - Developing countries

Distance education - Computer-assisted instruction - Developing countries

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Book Cover; Title; Copyright; Dedication; Contents; List of Figures and Tables; Series Editor's Foreword; Acknowledgements; Glossary of Acronyms; Introduction; 1 Education for All: Unfinished Business; 2 Seeking a Silver Bullet; 3 Technology Is the Answer: What Is the Question?; 4 Open Schools and Mega-Schools; 5 Teacher Education at Scale; 6 Strategies for Success; APPENDIX 1 Profiles: Selected Open Schools and Mega-Schools; APPENDIX 2 Programmes and Mechanisms for Expanding Teacher Supply; Bibliography; Subject Index; Name Index

Sommario/riassunto

Education for All (EFA) has been a top priority for governments and intergovernmental development agencies for the last twenty years. So far the global EFA movement has placed its principal focus on providing quality universal primary education (UPE) for all children by 2015.The latest addition to The Open and Flexible Learning series, this book addresses the new challenges created by both the successes and the failures of the UPE campaign. This book advocates new approaches for



providing access to secondary education for today's rapidly growing population of childr

2.

Record Nr.

UNINA9910818966703321

Autore

Chebbi Chiheb

Titolo

Mastering machine learning for penetration testing : develop an extensive skill set to break self-learning systems using Python / / Chiheb Chebbi

Pubbl/distr/stampa

Birmingham : , : Packt, , 2018

ISBN

1-78899-311-X

Edizione

[1st edition]

Descrizione fisica

1 online resource (264 pages)

Disciplina

005.133

Soggetti

Python (Computer program language)

Penetration testing (Computer security)

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references.

Sommario/riassunto

Become a master at penetration testing using machine learning with Python About This Book Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Who This Book Is For This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary. What You Will Learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning In Detail Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it's important for pentesters and security researchers to understand how these systems work, and to



breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you've gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you'll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you'll focus on topics such as network intrusion detection and AV and IDS evasion. We'll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. Style and approach This book takes a step-by-step approach to identify the loop holes in a self-learning security system. You will be able to efficiently breach a machine learning system with the help of best practices towards the end of the book.

3.

Record Nr.

UNINA9910155271803321

Autore

Borghetti Fabio

Titolo

Tunnel Fire Testing and Modeling : The Morgex North Tunnel Experiment / / by Fabio Borghetti, Marco Derudi, Paolo Gandini, Alessio Frassoldati, Silvia Tavelli

Pubbl/distr/stampa

Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017

Edizione

[1st ed. 2017.]

Descrizione fisica

1 online resource (XIII, 97 p. 96 illus., 43 illus. in color.)

Collana

PoliMI SpringerBriefs, , 2282-2577

Disciplina

624.193

Soggetti

Fluid mechanics

Thermodynamics

Heat engineering

Heat - Transmission

Mass transfer

Quality control

Reliability

Industrial safety

Computer simulation

Civil engineering

Engineering Fluid Dynamics

Engineering Thermodynamics, Heat and Mass Transfer

Quality Control, Reliability, Safety and Risk



Simulation and Modeling

Civil Engineering

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references at the end of each chapters.

Nota di contenuto

Safety in road tunnels -- The research project and partners involved -- The fire tests in the Morgex North tunnel -- The test results -- Evaluation of the consequences on the users safety -- Conclusions.

Sommario/riassunto

This book aims to cast light on all aspects of tunnel fires, based on experimental activities and theoretical and computational fluid dynamics (CFD) analyses. In particular, the authors describe a transient full-scale fire test (~15 MW), explaining how they designed and performed the experimental activity inside the Morgex North tunnel in Italy. The entire organization of the experiment is described, from preliminary evaluations to the solutions found for management of operational difficulties and safety issues. This fire test allowed the collection of different measurements (temperature, air velocity, smoke composition, pollutant species) useful for validating and improving CFD codes and for testing the real behavior of the tunnel and its safety systems during a diesel oil fire with a significant heat release rate. Finally, the fire dynamics are compared with empirical correlations, CFD simulations, and literature measurements obtained in other similar tunnel fire tests. This book will be of interest to all engineers and public officials who are concerned with the nature, prevention, and management of tunnel fires.