Private conversations and moments might not be so private anymore following CIA's latest plan. A CIA-linked firm is reportedly joining forces with Amazon. This is to spy on earth in an unprecedented detail. The firm known to be closely associated with the US Intelligence agency, CosmiQ Works, is working with Amazon and DigitalGlobe, a satellite mapping firm. The trio will be training artificial intelligence with an algorithm to find out what exactly is happening on the surface of the earth.

The Department of Defense is looking for a few good creators to help bring a brand new biohazard suit to life. The Chembio Suit Challenge is being held by the Chemical and Biological Defense division within the US military, and there's a 250,000 prize hanging in the balance for ideas on how to create a better protective suit. The current suit, according to the contest, has hindrances in the form of weight and bulkiness that greatly restrict soldiers' range of motion, agility and maneuverability. Those judging the contest will look for specific improvements in expedience, mobility and other areas. Basically, making the suit a more svelte and less totally cumbersome piece of equipment -- and we know a robot that's ready to test that out -- is key here.

Why can't computers watch the Earth from above and automatically map our roads, buildings, and trash heaps? Satellite operator DigitalGlobe is teaming up with Amazon, the venture arm of the CIA, and chipmaker Nvidia to try to make it happen. In a joint project, DigitalGlobe today released satellite imagery depicting the whole of Rio de Janeiro to a resolution of 50 centimeters. The outlines of 200,000 buildings inside the city's roughly 1,900 square kilometers have been manually marked on the photos. The SpaceNet data set, as it is called, is intended to spark efforts to train machine-learning algorithms to interpret high-resolution satellite photos by themselves.

Overall, the inside risks Viewpoint "The Risks of Self-Auditing Systems" by Rebecca T. Mercuri and Peter G. Neumann (June 2016) was excellent, and we applaud its call for auditing systems by independent entities to ensure correctness and trustworthiness. However, with respect to voting, it said, "Some research has been devoted to end-to-end cryptographic verification that would allow voters to demonstrate their choices were correctly recorded and accurately counted. However, this concept (as with Internet voting) enables possibilities of vote buying and selling." While Internet voting (like any remote-voting method) is indeed vulnerable to vote buying and selling, end-to-end verifiable voting is not. Poll-site-based end-to-end verifiable voting systems use cryptographic methods to ensure voters can verify their own votes are correctly recorded and tallied while (paradoxically) not enabling them to demonstrate how they voted to anyone else.

Spectral clustering methods which are frequently used in clustering and community detection applications are sensitive to the specific graph constructions particularly when imbalanced clusters are present. We show that ratio cut (RCut) or normalized cut (NCut) objectives are not tailored to imbalanced cluster sizes since they tend to emphasize cut sizes over cut values. We propose a graph partitioning problem that seeks minimum cut partitions under minimum size constraints on partitions to deal with imbalanced cluster sizes. Our approach parameterizes a family of graphs by adaptively modulating node degrees on a fixed node set, yielding a set of parameter dependent cuts reflecting varying levels of imbalance. The solution to our problem is then obtained by optimizing over these parameters. We present rigorous limit cut analysis results to justify our approach and demonstrate the superiority of our method through experiments on synthetic and real datasets for data clustering, semi-supervised learning and community detection.

This article provides a thorough meta-analysis of the anomaly detection problem. To accomplish this we first identify approaches to benchmarking anomaly detection algorithms across the literature and produce a large corpus of anomaly detection benchmarks that vary in their construction across several dimensions we deem important to real-world applications: (a) point difficulty, (b) relative frequency of anomalies, (c) clusteredness of anomalies, and (d) relevance of features. We apply a representative set of anomaly detection algorithms to this corpus, yielding a very large collection of experimental results. We analyze these results to understand many phenomena observed in previous work. First we observe the effects of experimental design on experimental results. Second, results are evaluated with two metrics, ROC Area Under the Curve and Average Precision. We employ statistical hypothesis testing to demonstrate the value (or lack thereof) of our benchmarks. We then offer several approaches to summarizing our experimental results, drawing several conclusions about the impact of our methodology as well as the strengths and weaknesses of some algorithms. Last, we compare results against a trivial solution as an alternate means of normalizing the reported performance of algorithms. The intended contributions of this article are many; in addition to providing a large publicly-available corpus of anomaly detection benchmarks, we provide an ontology for describing anomaly detection contexts, a methodology for controlling various aspects of benchmark creation, guidelines for future experimental design and a discussion of the many potential pitfalls of trying to measure success in this field.

NuTonomy, an autonomous taxi startup, has been working with the Singapore government in developing self-driving technology for quite some time now. They have been rather silent about progress, up until they broke it with a big reveal. Beating competitors like Uber and Google, NuTonomy has been able to place self-driving taxis on Singaporean roads for public, revealing the service earlier today. The public trials are starting small--six vehicles for now, with up to a dozen by year's end. The cars, mostly Renault Zoe and Mitsubishi i-MiEV electric cars, are debuting in the (primarily tech) business district area, one-north.

The revelation that a private company has been conducting secret aerial surveillance on behalf of the Baltimore Police Department -- collecting and storing footage from city neighborhoods in the process -- sparked confusion and outrage Wednesday among elected officials and civil liberties advocates. Some demanded an immediate halt to the program pending a full, public accounting of its capabilities and its use in the city to date, including in the prosecution of criminal defendants. Some called it "astounding" in its ability to intrude on individual privacy rights, and legally questionable in terms of constitutional law. Others did not fault the program but said it should have been disclosed publicly before it began in January. The program -- in which Ohio-based Persistent Surveillance Systems has for months been testing sophisticated surveillance cameras aboard a small Cessna airplane flying high above the city -- was first disclosed by Bloomberg Businessweek.

Why can't computers watch the Earth from above and automatically map our roads, buildings, and trash heaps? Satellite operator DigitalGlobe is teaming up with Amazon, the venture arm of the CIA, and chipmaker Nvidia to try to make it happen. In a joint project, DigitalGlobe today released satellite imagery depicting the whole of Rio de Janeiro to a resolution of 50 centimeters. The outlines of 200,000 buildings inside the city's roughly 1,900 square kilometers have been manually marked on the photos. The SpaceNet data set, as it is called, is intended to spark efforts to train machine-learning algorithms to interpret high-resolution satellite photos by themselves.

WORLD superpowers are engaged in a feverish "arms race" to develop the first killer robots completely removed from human control, the Sun Online can reveal. These machines will mark a dramatic escalation in computer AI from the drones and robots currently in use, all of which still require a human to press the "kill button". In a series of exclusive interviews, leading experts told The Sun Online machines making life or death decisions will likely be developed within the next 10 years. Fears are now growing about the implications of creating such smart machines, as are concerns they will fall into the hands of terrorist groups such as ISIS. Locked in this new race for military supremacy is Britain, the US, China, Russia and Israel – all of which have robot programmes of varying advancement.