Detecting outliers has become an increasing challenge in many areas, such as network intrusion detection, fraud detection, medical anomaly detection, and failure detection, as it was described by Chandola [1]. An outlier is basically a data that is significantly different from the normal behavior. In addition, several anomalies do not necessarily exhibit similar characteristics. Hence, detecting anomalies must be done by defining the normal behavior in the first place. Then, the deviation measured between an individual and the normal behavior gives good indications of anomalousness. However, as noticed in the same paper [1], defining a normal region that encompasses all the possible normal behaviors is sometimes really difficult. Moreover, an anomaly does not appear necessarily on all the explanatory variables, especially when the data is high-dimensional.

In this paper we propose and study a generic variance reduction approach. The proposed method is based on minimization of the empirical variance over a suitable class of zero mean control functionals. We present the corresponding convergence analysis and analyze complexity. Finally some numerical results showing efficiency of the proposed approach are presented.

Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood $p($data$|$parameters$)$. To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC^2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.

Artificial intelligence (AI) has been widely considered as the buzzword of the tech industry in 2017. The UK government has even dedicated £75m of funding for AI, including up to £45m to build AI capability and knowledge by increasing the number of AI PhD students to 200 a year. With governments launching dedicated funding and organisations from all walks of life racing to reposition themselves as tech companies - from launching chatbots to deploying big data analysis and deep learning - AI is transforming the way we work and how we interact with brands. In today's economic climate, organisations are seeking alterative methods and technologies to serve a larger volume of people, keeping customer service standards up and ensuring costs are managed. It is in this area that the explosion of AI-powered chatbots has disrupted the very meaning of customer service.

From babysitting children to beating the world champion at Go, robots are slowly but surely developing more and more advanced capabilities. And many scientists, including Professor Stephen Hawking, suggest it may only be a matter of time before machines gain consciousness. In a new article for The Conversation, Professor Subhash Kak, Regents Professor of Electrical and Computer Engineering at Oklahoma State University explains the possible consequences if artificial intelligence gains consciousness. In a new article for The Conversation, Professor Subhash Kak explains the possible consequences if artificial intelligence gains consciousness. Most computer scientists think that consciousness is a characteristic that will emerge as technology develops. Some believe that consciousness involves accepting new information, storing and retrieving old information and cognitive processing of it all into perceptions and actions.

Expert system can be made use of for photo recognition, like in this screen at a current modern technology conference. Researchers are rushing to recognize the prospective ramifications of A.I. Credit scores Saul Loeb/Agence France-Presse– Getty Images There are basically 3 huge inquiries about artificial intelligence and its impact on the economic climate: Just what can it do? As well as just how quickly will it spread out? Three new records incorporate to suggest these answers: It can possibly do less today compared to you believe. However it will ultimately do more than you possibly believe, in even more locations than you possibly assume, as well as will possibly evolve faster than powerful modern technologies have in the past.

Last month, the ScanPyramids project, led by a team of researchers from the University of Cairo's Faculty of Engineering in Egypt and the HIP Institute in France, announced that they'd used muon imaging to discover a large void hidden deep inside the Khufu's Pyramid (also known as the Great Pyramid of Giza, since it's the big one). Nobody knows what's inside, or if there's anything inside at all, or even if maybe that's where the Stargate is stashed. Obviously there's a lot of interest in what may or may not be hiding out in here, and it could help solve mysteries like how and why exactly the pyramids were built. The problem is that (understandably) we're not going to just start blowing holes in the Great Pyramid to see what's going on. In 2002, Egyptologists used a custom exploration robot (made by iRobot, in fact) to explore a small shaft leading out of the Queen's Chamber in the Great Pyramid that was sealed by a door. Rather than try to open the door, likely destroying it in the process, the robot drilled a tiny hole just large enough to poke a camera through in an effort to do the minimum amount of irreversible damage to the only wonder of the ancient world that we've got left.

Tech, software development and Data Science is currently thriving in Dublin. To meet the demands of that crowd, PyConIE, Ireland's largest indigenous software development conference, was run late October from the Radisson Blue hotel, Dublin. With speakers and attendees flying in from all over the world, the PyConIE conference got to see some big names and share that knowledge. PyConIE has been running in Dublin for eight years now, and 2017 was the biggest year yet, with over 40 speakers, nearly 400 attendees, two tracks and two workshops covering everything from Data Science to developing in Python, to tools and even to tech culture. Speakers were represented from around the world, and from Ireland's tech scene – where there were speakers from the Dublin tech scene, and even winners from the DatSci awards.

A fraudster who met her victim on a dating website for naturists scammed him out of £50,000. Moira Etchells, 45, met Ian Chatting-Tonks in 2013 and persuaded him to lend her the cash to start a business artificially inseminating cows. Swansea Crown Court heard she spent £35,000 on a new Land Rover and banked the rest. Etchells, of Llanelli, Carmarthenshire, admitted fraud and got an 18-month sentence, suspended for two years. Widower Mr Chatting-Tonks, from Norfolk, went online to search for a new partner who was also interested in naturism, which is when he found Etchells' profile.

In January, Christina Ausset, a 24-year-old Maroon 5 fan in France, spotted an enticing Twitter post from another of the band's followers: "I just had a conversation with Maroon 5! Awesome!" The interaction, it turned out, had been conducted on Facebook Messenger with Maroon 5's chatbot -- an automated program designed to respond to basic commands. Not exactly a conversation with Adam Levine, Ms. Ausset noted, but it didn't matter. She now happily talks to the bot, too. "Having Maroon 5 on Messenger," she wrote in an email, "makes you feel really close to your favorite artists!"