Planning air missions for a team flying in hostile environments is a complex task, since multiple interrelated goals need to be considered, e.g., performing the mission tasks and avoiding enemy fire. The target assignment and route planning for the team should therefore be performed in a coordinated way. The mission planner suggested in this work combines genetic algorithms and particle swarm optimization in order to solve these two problems in an interconnected manner. Simulations are used for testing and analyzing the approach. It is concluded that the mission planner is able to suggest suitable plans in complex scenarios with three interrelated objectives: low risk exposure, high mission effectiveness and short route length.

Wikipedia is one of the most popular sources of free data on the Internet and subject to extensive use in numerous areas of research. Wikidata on the other hand, the knowledge base behind Wikipedia, is less popular as a source of data, despite having the "data" already in its name, and despite the fact that many applications in Natural Language Processing in general and Information Extraction in particular benefit immensely from the integration of knowledge bases. In part, this imbalance is owed to the younger age of Wikidata, which launched over a decade after Wikipedia. However, this is also owed to challenges posed by the still evolving properties of Wikidata that make its content more difficult to consume for third parties than is desirable. In this article, we analzye the causes of these challenges from the viewpoint of a data consumer and discuss possible avenues of research and advancement that both the scientific and the Wikidata community can collaborate on to turn the knowledge base into the invaluable asset that it is uniquely positioned to become.

We examine the question of whether we can automatically classify the sentiment of individual tweets in Farsi, to determine their changing sentiments over time toward a number of trending political topics. Examining tweets in Farsi adds challenges such as the lack of a sentiment lexicon and part-of-speech taggers, frequent use of colloquial words, and unique orthography and morphology characteristics. We have collected over 1 million Tweets on political topics in the Farsi language, with an annotated data set of over 3,000 tweets. We find that an SVM classifier with Brown clustering for feature selection yields a median accuracy of 56% and accuracy as high as 70%. We use this classifier to track dynamic sentiment during a key period of Irans negotiations over its nuclear program.

Discourse on social media platforms is often plagued by acute polarization, with different camps promoting different perspectives on the issue at hand—compare, for example, the differences in the liberal and conservative discourse on the U.S. immigration debate. A large body of research has studied this phenomenon by focusing on the affiliation of groups and individuals. We propose a new finer-grained perspective: studying the impartiality of individual messages. While the notion of message impartiality is quite intuitive, the lack of an objective definition and of a way to measure it directly has largely obstructed scientific examination. In this work we operationalize message impartiality in terms of how discernible the affiliation of its author is, and introduce a methodology for quantifying it automatically. Unlike a supervised machine learning approach, our method can be used in the context of emerging events where impartiality labels are not immediately available. Our framework enables us to study the effects of (im)partiality on social media discussions at scale. We show that this phenomenon is highly consequential, with partial messages being twice more likely to spread than impartial ones, even after controlling for author and topic. By taking this fine-grained approach to polarization, we also provide new insights into the temporal evolution of online discussions centered around major political and sporting events.

The White House is doing a lot more thinking about the arrival of automated decision-making -- super-intelligent or otherwise. No one in government is yet screaming "Skynet," but in two actions this week the concerns about our artificial intelligence future were sketched out. The big risks of A.I. are well-known (a robot takeover), but the more immediate worries are about the subtle, or not-so-subtle, decisions made by badly coded and designed algorithms. President Barack Obama's administration released a report this week that examines the problem associated with poorly designed systems that, increasingly, are being used in automated decision making. Algorithmic systems can affect employment, education, access to credit -- anything that relies on computer-assisted decisions.

Donald Trump knows many words. He has the best words. He's going to find the best people to help him run the country. He knows many smart people. His presidency will be classy, huge, even.

Donald Trump knows many words. He has the best words. He's going to find the best people to help him run the country. He knows many smart people. His presidency will be classy, huge, even.

Every once in a while an innocuous technical term suddenly enters public discourse with a bizarrely negative connotation. I first noticed the phenomenon some years ago, when I saw a Republican politician accusing Hillary Clinton of "parsing." From the disgust with which he said it, he clearly seemed to feel that parsing was morally equivalent to puppy-drowning. It seemed quite odd to me, since I'd only ever heard the word "parse" used to refer to the computer analysis of sentence structures. The most recent word to suddenly find itself stigmatized by Republicans (yes, it does somehow always seem to be Republican politicians who are involved in this particular kind of linguistic bullshittery) is "encryption."

With Congress revisiting how Pentagon units share authority under the 1986 Goldwater-Nichols Act, the intelligence agencies under the next presidential administration should likewise review their own unity of effort to become more agile and able to integrate, the top Defense intelligence official said Thursday. "The integration of intelligence of the past 15 years is a journey that is not finished," said Marcel Lettre, undersecretary of Defense for intelligence, at a banquet for agency and industry professionals in the nonprofit Intelligence and National Security Alliance. "I hope the new administration finds clear progress from the last 15 years and takes it on with a mantle of seriousness, or even sees an opportunity to redouble the effort." Lettre, who was sworn in in December to preside over a 17 billion budget, eight components and 110,000 employees, said he also hopes the next administration will "institutionalize and make irreversible" the intelligence community's digital data sharing modernization effort known as the Intelligence Community Information Technology Enterprise (pronounced "eyesight"). "Key critical data sets are the coin of the realm for the intel community," he said.

I wasn't planning to chip in this time, but I stumbled upon an article so compelling I felt I ought to. Uncanny Valley, published by Anna Weiner on N 1 Magazine, is a short literary piece which lives up to its title. Weiner's nameless narrator is going through most commonplaces of the Valley, both physically and mentally, without really being a part of it. "I learn the bare minimum, code-wise, to be able to do my job well -- to ask questions only when I'm truly in over my head," she confesses. She is going from being the start-up's inaugural customer support rep to become a "success manger", a title so corny for her she can't stand having it on her email signature.