"Security and Privacy in Social Networks" brings to the forefront innovative approaches for analyzing and enhancing the security and privacy dimensions in online social networks, and is the first comprehensive attempt dedicated entirely to this field. In order to facilitate the transition of such methods from theory to mechanisms designed and deployed in existing online social networking services, the book aspires to create a common language between the researchers and practitioners of this new area- spanning from the theory of computational social sciences to conventional security and network engineering.

This book offers a comprehensive analysis of the theory and tools needed for the development of an efficient and robust infrastructure for the design of collaborative patrolling unmanned aerial vehicle (UAV) swarms, focusing on its applications for tactical intelligence drones. It discusses frameworks for robustly and near-optimally analyzing flocks of semi-autonomous vehicles designed to efficiently perform the ongoing dynamic patrolling and scanning of pre-defined "search regions". It discusses the theoretical limitations of such systems, as well as the trade-offs between the systems' various economic and operational parameters. Current UAV systems rely mainly on human operators for the design and adaptation of drones' flying routes. However, recent technological advances have introduced new systems, comprised of a small number of self-organizing vehicles, manually guided at the swarm level by a human operator. With the growing complexity of such man-supervised architectures, it is becoming increasingly harder to guarantee a pre-defined level of performance. The use of large scale swarms of UAVs as a combat and reconnaissance platform therefore necessitates the development of an efficient optimization mechanism of their utilization, specifically in the design and maintenance of their patrolling routes. The book is intended for researchers and engineers in the fields of swarms systems and autonomous drones.

This book contains the thoroughly refereed post-proceedings of two events discussing AI for Human Computing: one Special Session during the Eighth International ACM Conference on Multimodal Interfaces 2006 and a Workshop organized in conjunction with the 20th International Joint Conference on Artificial Intelligence 2007. It covers foundational issues of human computing, sensing humans and their activities, and anthropocentric interaction models.

Recent advances in the field of computer vision are leading to novel and radical changes in the way we interact with computers. It will soon be possible to enable a computer linked to a video camera to detect the presence of users, track faces, arms and hands in real time, and analyze expressions and gestures. The implications for interface design are immense and are expected to have major repercussions for all areas where computers are used, from the work place to recreation. This book collects ideas and algorithms from the world's leading scientists, offering a glimpse of the radical changes around the corner that will alter the way we interact with computers in the near future.

"From one of the world's leading data scientists, a landmark tour of the new science of idea flow, offering revolutionary insights into the mysteries of collective intelligence and social influence"
If the Big Data revolution has a presiding genius, it is MIT's Alex "Sandy" Pentland. Over years of groundbreaking experiments, he has distilled remarkable discoveries significant enough to become the bedrock of a whole new scientific field: social physics. Humans have more in common with bees than we like to admit: We're social creatures first and foremost. Our most important habits of action--and most basic notions of common sense--are wired into us through our coordination in social groups. Social physics is about "idea flow," the way human social networks spread ideas and transform those ideas into behaviors.
Thanks to the millions of digital bread crumbs people leave behind via smartphones, GPS devices, and the Internet, the amount of new information we have about human activity is truly profound. Until now, sociologists have depended on limited data sets and surveys that tell us how people "say" they think and behave, rather than what they actually "do." As a result, we've been stuck with the same stale social structures--classes, markets--and a focus on individual actors, data snapshots, and steady states. Pentland shows that, in fact, humans respond much more powerfully to social incentives that involve rewarding others and strengthening the ties that bind than incentives that involve only their own economic self-interest.
Pentland and his teams have found that they can study "patterns "of information exchange in a social network without any knowledge of the actual "content "of the information and predict with stunning accuracy how productive and effective that network is, whether it's a business or an entire city. We can maximize a group's collective intelligence to improve performance and use social incentives to create new organizations and guide them through disruptive change in a way that maximizes the good. At every level of interaction, from small groups to large cities, social networks can be tuned to increase exploration and engagement, thus vastly improving idea flow.
"Social Physics" will change the way we think about how we learn and how our social groups work--and can be made to work better, at every level of society. Pentland leads readers to the edge of the most important revolution in the study of social behavior in a generation, an entirely new way to look at life itself.

This is the Spanish translation of "Honest signals," by Alex Pentland. Los grupos de animales toman decisiones conjuntas, comunicandose entre ellos mucha informacion por medio de actitudes, sonidos, gestos... cuando los humanos evolucionamos hasta emplear el lenguaje para compartir informacion, este otro sistema de comunicacion social no desaparecio. Las investigaciones del profesor Pentland demuestran no solo su existencia, sino tambien que este tipo de informacion tiene tanto valor para nosotros que incluso prevalece sobre la informacion linguistica, aunque no seamos conscientes de ella. Este libro cuenta como el MIT ha desarrollado herramientas y metodos para medir estas senales y convertirlas en informacion manejable. Con ella pueden establecerse patrones de comportamiento y predecirse conductas individuales y de grupos. Senales honestas es el resultado de una disciplina nueva y emergente, llamada ciencia de las redes, que intenta entender a las personas en el contexto de sus redes sociales en lugar de considerarlas como individuos aislados. Tal vez la consciencia no sea tan importante como tendemos a creer. Quiza necesitemos reexaminar nuestras suposiciones sobre el rol que juegan tanto las fuerzas conscientes como las inconscientes en la configuracion de nuestras conductas cotidianas. La informacion de nuestros sociometros apoya la idea de que gran parte de la conducta humana es automatica o esta determinada por procesos inconscientes.

How to create an Internet of Trusted Data in which insights from data can be extracted without collecting, holding, or revealing the underlying data. Trusted Data describes a data architecture that places humans and their societal values at the center of the discussion. By involving people from all parts of the ecosystem of information, this new approach allows us to realize the benefits of data-driven algorithmic decision making while minimizing the risks and unintended consequences. It proposes a software architecture and legal framework for an Internet of Trusted Data that provides safe, secure access for everyone and protects against bias, unfairness, and other unintended effects. This approach addresses issues of data privacy, security, ownership, and trust by allowing insights to be extracted from data held by different people, companies, or governments without collecting, holding, or revealing the underlying data. The software architecture, called Open Algorithms, or OPAL, sends algorithms to databases rather than copying or sharing data. The data is protected by existing firewalls; only encrypted results are shared. Data never leaves its repository. A higher security architecture, ENIGMA, built on OPAL, is fully encrypted. Contributors Michiel Bakker, Yves-Alexandre de Montjoye, Daniel Greenwood, Thomas Hardjoni, Jake Kendall, Cameron Kerry, Bruno Lepri, Alexander Lipton, Takeo Nishikata, Alejandro Noriega-Campero, Nuria Oliver, Alex Pentland, David L. Shrier, Jacopo Staiano, Guy Zyskind An MIT Connection Science and Engineering Book