W e investigate the political roles of "Internet trolls" in social media. Political trolls, such as the ones linked to the Russian Internet Research Agency (IRA), have recently gained enormous attention for their ability to sway public opinion and even influence elections. Analysis of the online traces of trolls has shown different behavioral patterns, which target different slices of the population. However, this analysis is manual and labor-intensive, thus making it impractical as a first-response tool for newly-discovered troll farms. In this paper, we show how to automate this analysis by using machine learning in a realistic setting. In particular, we show how to classify trolls according to their political role --left, news feed, right-- by using features extracted from social media, i.e., Twitter, in two scenarios: ( i) in a traditional supervised learning scenario, where labels for trolls are available, and ( ii) in a distant supervision scenario, where labels for trolls are not available, and we rely on more-commonly-available labels for news outlets mentioned by the trolls. Technically, we leverage the community structure and the text of the messages in the online social network of trolls represented as a graph, from which we extract several types of learned representations, i.e., embeddings, for the trolls. Experiments on the "IRA Russian Troll" dataset show that our methodology improves over the state-of-the-art in the first scenario, while providing a compelling case for the second scenario, which has not been explored in the literature thus far.

ABSTRACT We present work on deception detection, where, given a spoken claim, we aim to predict its factuality. While previous work in the speech community has relied on recordings from staged setups where people were asked to tell the truth or to lie and their statements were recorded, here we use real-world political debates. Thanks to the efforts of fact-checking organizations, it is possible to obtain annotations for statements in the context of a political discourse as true, half-true, or false. Lab, which was limited to text, we performed alignment to the corresponding videos, thus producing a multimodal dataset. We further developed a multimodal deep-learning architecture for the task of deception detection, which yielded sizable improvements over the state of the art for the CLEF-2018 Lab task 2. Our experiments show that the use of the acoustic signal consistently helped to improve the performance compared to using textual and metadata features only, based on several different evaluation measures. We release the new dataset to the research community, hoping to help advance the overall field of multimodal deception detection. Index T erms-- deception detection, fact-checking, fake news, disinformation, computational paralinguistics, multi-modality, political debates. 1. INTRODUCTION Traditionally, news media have been the gate keepers of information, as they carefully selected what was appropriate to present to the public after double-checking it.

Not all Artificial Intelligence is created equal. As we move towards a future where we lean on cybersecurity much more in our daily lives, it's important to be aware of the differences in the types of AI being used for network security. Dr. Igor, Chief Scientist and CTO at MixMode explains: Over the last decade, Machine Learning has made huge progress in technology with Supervised and Reinforcement learning, in everything from photo recognition to self-driving cars. However, Supervised Learning is limited in its network security abilities like finding threats because it only looks for specifics that it has seen or labeled before, whereas Unsupervised Learning is constantly searching the network to find anomalies. Machine Learning comes in a few forms: Supervised, Reinforcement, Unsupervised and Semi-Supervised (also known as Active Learning).

One of the biggest urban legends growing up in New York City were rumors about alligators living in the sewers. This myth even inspired a popular children's book called "The Great Escape: Or, The Sewer Story," with illustrations of reptiles crawling out of apartment toilets. To this day, city dwellers anxiously look at manholes wondering what lurks below. This curiosity was shared last month by the US Defense Department with its appeal for access to commercial underground complexes. The US military's research arm, DARPA, launched the Subterranean (or SubT) Challenge in 2017 with the expressed goal of developing systems that enhance "situational awareness capabilities" for underground missions.

Astrophysicist (Views are my own) http://rocketdatascience.org/ Are you sure you want to view these Tweets? The way you think about data needs an update, so here's a patch for your brain. This collection of blog posts from @KirkDBorne will expand AND update your thinking! RT @ScienceUnderSec " @ENERGY's #NationalLabs are using #AI and supercomputing to solve some of the world's toughest problems as well as advancing cancer research and strengthening our national security." Twitter may be over capacity or experiencing a momentary hiccup.

It will still be a while before you are able to order drone-delivered packages, however. The news: The Federal Aviation Administration has granted UPS's drone business a Part 135 certification, meaning it is treated as a full-fledged airline, able to operate as many drones in as many locations as it wishes (although there are a lot of obstacles and caveats before that can happen in reality). UPS has dubbed its new drone airline "UPS Flight Forward," and it's the first in the US to gain official recognition. Currently: UPS has been providing a drone delivery service at the WakeMed hospital and campus in Raleigh, North Carolina, since March, moving medical samples around the site about 10 times a day. This new certification means UPS can expand beyond this site.

Suicide car bomber attacks military airstrip used for U.S. drone mission; reaction and analysis from retired four-star Gen. Jack Keane, Fox News senior strategic analyst. The United States Embassy in Mogadishu, Somalia, has reopened after being closed for 28 years, officials said Wednesday. "The reestablishment of Embassy Mogadishu is another step forward in the resumption of regular U.S.-Somali relations, symbolizing the strengthening of U.S.-Somalia relations and advancement of stability, development and peace for Somalia and the region," the embassy said in a statement. The embassy closed on Jan. 5, 1991, after Somalia became engulfed in a civil war and the regime of Siad Barre was overthrown. The United States reestablished a permanent diplomatic presence in Mogadishu back in December, operated out of Nairobi, Kenya.

Editor's Note: This article was submitted in response to the call for ideas issued by the co-chairs of the National Security Commission on Artificial Intelligence, Eric Schmidt and Robert Work. It addresses the first question (part a.) on the character of war, and the third question (part d.) on the types of data that would be most useful for developing applications. Billy Beane, general manager of the struggling Oakland Athletics baseball team, faced a problem in the early 2000s. He needed to field a competitive team with one of the league's smallest budgets. Beane turned to what became known as a "moneyball" approach -- a new analytical method that valued a player's ability to get on base over traditional statistics like batting average and home runs.

Sophisticated threat actors can often maintain a long-term presence in their target environments for months at a time, without being detected. They move slowly and with caution, to evade traditional security controls and are often targeted to specific individuals and organizations. AI will also be able to learn the dominant communication channels and the best ports and protocols to use to move around a system, discretely blending in with routine activity. This ability to disguise itself amid the noise will mean that it is able to expertly spread within a digital environment, and stealthily compromise more devices than ever before. AI malware will also be able to analyse vast volumes of data at machine speed, rapidly identifying which data sets are valuable and which are not. This will save the (human) attacker a great deal of time and effort.

A team at NASA's Jet Propulsion Laboratory (JPL) is testing a 3D-printed Transformer-like new robot, Shapeshifter, which is capable of morphing into multiple configurations. And it is stated that a similar design could one day be leveraged to explore Saturn's moon Titan. Saturn's moon Titan is one of the most potential targets on any planetary scientist's list for exploration. But any mission to Titan will have to deal with an environment unlike any other – frigid temperatures, cryovolcanoes, caves, and lakes, seas, and rain of liquid hydrocarbons. However, the latest concept could encompass 12 mini robots – cobots (Collaborative Robots) – that can fly or swim, exploring caves and oceans and will go where other robots haven't been able to explore.