"Wait till you read this book. It blew me away. . . . This is a must-read book. . . . This will be a movie." -Sean Hannity "George Papadopoulos was the whole reason for the Trump Russia investigation." -Mark Meadows, White House Chief of Staff A shocking account of international spy games and a disturbing eyewitness report on a secret double government-the Deep State-intent on destroying lives and a presidency George Papadopoulos became a national figure when he was called in Special Counsel Robert Mueller's investigation into Russian interference in the 2016 election. His global network and clandestine meetings about Hillary Clinton's hacked emails made him the first target of Spygate, the Mueller Investigation, and the Russian Collusion Hoax. And it was a hoax, as validated by the Mueller Hearings-but first exposed here in Papadopoulos' historic account. As he explains in Deep State Target, American and allied intelligence services set out to destroy a Trump presidency before it even started. Papadopoulos faced a rogues' gallery of infamous figures employed by agents from the US, Britain, and Australia. Here, he gives the play-by-play of how operatives like Professor Joseph Mifsud, Sergei Millian, Alexander Downer, and Stefan Halper worked to invent a Russian conspiracy that would damage the Trump administration.

This book constitutes the proceedings of the 21st International Conference on Discovery Science, DS 2018, held in Limassol, Cyprus, in October 2018, co-located with the International Symposium on Methodologies for Intelligent Systems, ISMIS 2018. The 30 full papers presented together with 5 abstracts of invited talks in this volume were carefully reviewed and selected from 71 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Classification; meta-learning; reinforcement learning; streams and time series; subgroup and subgraph discovery; text mining; and applications.