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France's Macron Sends Clear Message to Trump: "Nationalism is a Betrayal of Patriotism"

Slate

More than 60 world leaders gathered in Paris Sunday to mark 100 years since the end of World War I, and although the general theme was unity, President Donald Trump seemed determined to stand apart. While world leaders took a bus to the Arc de Triomphe and walked side-by-side as bells tolled to mark the exact moment 100 years ago when the war ended, Trump arrived with his own motorcade. Russian President Vladimir Putin also arrived separately and walked in by himself to the ceremony that included, among others, German Chancellor Angela Merkel, Canadian Prime Minister Justin Trudeau, and Israeli Prime Minister Benjamin Netanyahu. White House press secretary Sarah Huckabee Sanders said Trump arrived separately "due to security protocols." But his insistence on standing apart didn't sit well with others, particularly after Trump drew fire for his decision to cancel his appearance at a memorial service Saturday because of rain.



Semantic Hypergraphs

arXiv.org Artificial Intelligence

Existing computational methods for the analysis of corpora of text in natural language are still far from approaching a human level of understanding. We attempt to advance the state of the art by introducing a model and algorithmic framework to transform text into recursively structured data. We apply this to the analysis of news titles extracted from a social news aggregation website. We show that a recursive ordered hypergraph is a sufficiently generic structure to represent significant number of fundamental natural language constructs, with advantages over conventional approaches such as semantic graphs. We present a pipeline of transformations from the output of conventional NLP algorithms to such hypergraphs, which we denote as semantic hypergraphs. The features of these transformations include the creation of new concepts from existing ones, the organisation of statements into regular structures of predicates followed by an arbitrary number of entities and the ability to represent statements about other statements. We demonstrate knowledge inference from the hypergraph, identifying claims and expressions of conflicts, along with their participating actors and topics. We show how this enables the actor-centric summarization of conflicts, comparison of topics of claims between actors and networks of conflicts between actors in the context of a given topic. On the whole, we propose a hypergraphic knowledge representation model that can be used to provide effective overviews of a large corpus of text in natural language.


Facebook Code Update Gone Wrong Exposes Anonymous Admins

#artificialintelligence

Recently Facebook encountered quite a bug crisis, as a bad code update going live on the night of 10th January apparently prompted the exposure of the mysterious anonymous of admins and many known personalities for a few hours. All it took to exploit' the bug was opening a target page and checking specifically the edit history of a post and Facebook erroneously showed the account or accounts that made those edits to each post, as opposed to simply displaying the edits themselves. In spite of the fact that Facebook immediately pushed a fix for this flaw, yet it wasn't quick than the word that had already got around on message boards like 4chan, where users posted screen captures that'doxed' the accounts behind prominent and rather well-known pages. Saying that it was the aftereffect of a code update, the social media giant, exposed the accounts behind the official Facebook Pages of the'pseudonymous' artist Banksy, Russian President Vladimir Putin, former US secretary of state Hillary Clinton, Canadian Prime Minister Justin Trudeau alongside the Climate activist Greta Thunberg, and rapper Snoop Dogg, among others. No data past a name and public profile link was accessible; however, for those admins running anti-regime pages under'a repressive government', even this much public exposure is also extremely alarming.


No Permanent Friends or Enemies: Tracking Relationships between Nations from News

arXiv.org Artificial Intelligence

Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nations from news articles. We extend existing models by incorporating shallow linguistics information and propose a new automatic evaluation metric that aligns relationship dynamics with manually annotated key events. As understanding international relations requires carefully analyzing complex relationships, we conduct in-person human evaluations with three groups of participants. Overall, humans prefer the outputs of our model and give insightful feedback that suggests future directions for human-centered models. Furthermore, our model reveals interesting regional differences in news coverage. For instance, with respect to US-China relations, Singaporean media focus more on "strengthening" and "purchasing", while US media focus more on "criticizing" and "denouncing".