Africa
Terra Incognita: Africa's Crypto Boom Is Just Getting Started Finance Magnates
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U.S. soldier killed, four wounded during operation against al-Shabab Islamists in Somalia
WASHINGTON – One U.S. special operations soldier was killed and four U.S. service members were wounded in an "enemy attack" Friday in Somalia, the U.S. military said -- casualties that are likely to put renewed scrutiny on America's counterterrorism operations in Africa. It was the first public announcement of a U.S. military combat death on the continent since four U.S. service members were killed in a militant ambush in the west African nation of Niger in October. President Donald Trump paid tribute on Twitter on Friday night, offering "thoughts and prayers" to the families of the soldier who was killed and those who were wounded. "They are truly all HEROES," he tweeted. U.S. Africa Command said in a statement that U.S. troops with Somali and Kenyan forces came under mortar and small-arms fire in Jubaland, Somalia, at around 2:45 p.m.
Feature and TV films
Mr. Smith Goes to Washington 1939 TCM Tue. 7 p.m. Mean Streets 1973 Cinemax Sun. 6 a.m. Batman Begins 2005 AMC Sun. Throw Momma From the Train 1987 EPIX Sun. Die Hard 1988 IFC Sun. I Know What You Did Last Summer 1997 Starz Tue. Gone in 60 Seconds 2000 CMT Wed. 8 p.m., Thur. Total Recall 1990 Encore Thur. 2 a.m. A Fish Called Wanda 1988 Encore Thur. 2 p.m., 9 p.m. The World Is Not Enough 1999 EPIX Sat. 4 p.m. Look Who's Talking 1989 OVA Sun. Die Hard With a Vengeance 1995 IFC Thur. Oil-platform workers, including an estranged couple, and a Navy SEAL make a startling deep-sea discovery. A clueless politician falls in love with a waitress whose erratic behavior is caused by a nail stuck in her head. After glimpsing his future, an ambitious politician battles the agents of Fate itself to be with the woman he loves. To help a friend, a suburban baby sitter drives into downtown Chicago with her two charges and a neighbor. Two teenage baby sitters and a group of children spend a wild night ...
World Cup 2018: Does form matter for teams competing in Russia?
England fans know the drill all too well - the national team heads into a major international football tournament having qualified with a near-perfect record. Hopes are high, but then… well, you know what happens - lacklustre performances or penalty shoot-out heartbreak, followed by an early flight home. So what really determines the success or failure of a team going into a major international football tournament like the World Cup? Is it a side's quality (class) or its recent performances (form)? Reality Check has teamed up with the BBC's statistics department to try to answer one of the biggest debates in football - how much does form matter? To do this, we built a computer program that predicts football results by analysing ratings data.
Google Has Dropped Out for Now, But Lethal AI 'Inevitable'
On Thursday, Google released a document entitled "Artificial Intelligence at Google: Our Principles," vowing to avoid Pentagon projects to develop AI weapons. However, they'll continue to work with the US military on a host of other projects, including AI projects, so long as they don't include surveillance that runs counter to human rights. "Google is in a spot at the moment," Wallis, editor-at-large for Digital Journal and author of more than a dozen books, told Loud & Clear hosts John Kiriakou and Nicole Roussell. "This is gold rush time for artificial intelligence; everybody wants a piece of it, and we're not talking about the sort of generic type of artificial intelligence -- we're talking about possibly thousands or millions of different kinds of artificial intelligence." In other words, the advent of artificially intelligent weapons is "inevitable," Wallis said.
Google Has Dropped Out for Now, But Lethal AI 'Inevitable'
On Thursday, Google released a document entitled "Artificial Intelligence at Google: Our Principles," vowing to avoid Pentagon projects to develop AI weapons. However, they'll continue to work with the US military on a host of other projects, including AI projects, so long as they don't include surveillance that runs counter to human rights. "Google is in a spot at the moment," Wallis, editor-at-large for Digital Journal and author of more than a dozen books, told Loud & Clear hosts John Kiriakou and Nicole Roussell. "This is gold rush time for artificial intelligence; everybody wants a piece of it, and we're not talking about the sort of generic type of artificial intelligence -- we're talking about possibly thousands or millions of different kinds of artificial intelligence." In other words, the advent of artificially intelligent weapons is "inevitable," Wallis said.
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data
Wang, Nan, Wang, Hongning, Jia, Yiling, Yin, Yue
Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation} and \textit{opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.
A Taxonomy and Survey of Intrusion Detection System Design Techniques, Network Threats and Datasets
Hindy, Hanan, Brosset, David, Bayne, Ethan, Seeam, Amar, Tachtatzis, Christos, Atkinson, Robert, Bellekens, Xavier
With the world moving towards being increasingly dependent on computers and automation, one of the main challenges in the current decade has been to build secure applications, systems and networks. Alongside these challenges, the number of threats is rising exponentially due to the attack surface increasing through numerous interfaces offered for each service. To alleviate the impact of these threats, researchers have proposed numerous solutions; however, current tools often fail to adapt to ever-changing architectures, associated threats and 0-days. This manuscript aims to provide researchers with a taxonomy and survey of current dataset composition and current Intrusion Detection Systems (IDS) capabilities and assets. These taxonomies and surveys aim to improve both the efficiency of IDS and the creation of datasets to build the next generation IDS as well as to reflect networks threats more accurately in future datasets. To this end, this manuscript also provides a taxonomy and survey or network threats and associated tools. The manuscript highlights that current IDS only cover 25% of our threat taxonomy, while current datasets demonstrate clear lack of real-network threats and attack representation, but rather include a large number of deprecated threats, hence limiting the accuracy of current machine learning IDS. Moreover, the taxonomies are open-sourced to allow public contributions through a Github repository.
What Knowledge is Needed to Solve the RTE5 Textual Entailment Challenge?
This document gives a knowledge-oriented analysis of about 20 interesting Recognizing Textual Entailment (RTE) examples, drawn from the 2005 RTE5 competition test set. The analysis ignores shallow statistical matching techniques between T and H, and rather asks: What would it take to reasonably infer that T implies H? What world knowledge would be needed for this task? Although such knowledge-intensive techniques have not had much success in RTE evaluations, ultimately an intelligent system should be expected to know and deploy this kind of world knowledge required to perform this kind of reasoning. The selected examples are typically ones which our RTE system (called BLUE) got wrong and ones which require world knowledge to answer. In particular, the analysis covers cases where there was near-perfect lexical overlap between T and H, yet the entailment was NO, i.e., examples that most likely all current RTE systems will have got wrong. A nice example is #341 (page 26), that requires inferring from "a river floods" that "a river overflows its banks". Seems it should be easy, right? Enjoy!