Modeling an ontology is a hard and time-consuming task. Although methodologies are useful for ontologists to create good ontologies, they do not help with the task of evaluating the quality of the ontology to be reused. For these reasons, it is imperative to evaluate the quality of the ontology after constructing it or before reusing it. Few studies usually present only a set of criteria and questions, but no guidelines to evaluate the ontology. The effort to evaluate an ontology is very high as there is a huge dependence on the evaluator's expertise to understand the criteria and questions in depth. Moreover, the evaluation is still very subjective. This study presents a novel methodology for ontology evaluation, taking into account three fundamental principles: i) it is based on the Goal, Question, Metric approach for empirical evaluation; ii) the goals of the methodologies are based on the roles of knowledge representations combined with specific evaluation criteria; iii) each ontology is evaluated according to the type of ontology. The methodology was empirically evaluated using different ontologists and ontologies of the same domain. The main contributions of this study are: i) defining a step-by-step approach to evaluate the quality of an ontology; ii) proposing an evaluation based on the roles of knowledge representations; iii) the explicit difference of the evaluation according to the type of the ontology iii) a questionnaire to evaluate the ontologies; iv) a statistical model that automatically calculates the quality of the ontologies.

Why invent something you can borrow from nature? Creating the right motion for a robot to move well through sand or snow is a tricky problem, but one that nature solved long ago. By borrowing from biology, a new robot with a rotating coiled tail can move through loose powders at a good clip, making it useful for search and rescue missions or exploration. Many bacteria use rotation to help them move through gooey fluids, powered by propeller-like tails. Similarly, seeds of some plants such as geraniums have a coiled appendage called an awn that helps push them deeper into the soil. It is 12 centimetres long, with a hemispherical head and a helical tail.

The data torrent unleashed by current and upcoming instruments requires scalable analysis methods. Machine Learning approaches scale well. However, separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is usually an afterthought. Classic forward-folding analyses with Markov Chain Monte Carlo or Nested Sampling enable parameter estimation and model comparison, even for complex and slow-to-evaluate physical models. However, these approaches require independent runs for each data set, implying an unfeasible number of model evaluations in the Big Data regime. Here we present a new algorithm, collaborative nested sampling, for deriving parameter probability distributions for each observation. Importantly, in our method the number of physical model evaluations scales sub-linearly with the number of data sets, and we make no assumptions about homogeneous errors, Gaussianity, the form of the model or heterogeneity/completeness of the observations. Collaborative nested sampling has immediate application in speeding up analyses of large surveys, integral-field-unit observations, and Monte Carlo simulations.

Back in January, Google rolled out a "parking difficulty" icon in Maps which, as its name suggests, explains how hard it will be to find a spot for your vehicle. While useful, it was only available in 25 US cities, including New York and San Francisco. Today, it's being expanded to a further 25 locations around the globe: Alicante, Amsterdam, Copenhagen, Barcelona, Cologne, Darmstadt, Dusseldorf, London, Madrid, Malaga, Manchester, Milan, Montreal, Moscow, Munich, Paris, Prague, Rio de Janeiro, Rome, Sao Paulo, Stockholm, Stuttgart, Toronto, Valencia and Vancouver. Just pop in your destination and you'll see a limited, medium or easy symbol near the bottom of the screen, next to the estimated journey time. The ratings are based on "historical parking data," according to Google, and a smidge of "machine learning magic."

Gaming is an addiction but for cyber criminals, it is a lucrative business. IT security researchers at ESET have discovered a new malware targeting gamers around the world. Dubbed "Joao" by researchers; the malware exists in third party websites offering malicious setups for Aeria games. The malware works in such a way that once executed it can install other malicious codes on a targeted device. Furthermore, Joao takes advantage of "Massively multiplayer online role-playing games (MMORPGs)," a platform for role-playing video games and massively multiplayer online games where a large number of gamers get together to interact. The attackers behind Joao have developed the malware in such a way that when a victim executes the game launcher, it silently launches itself in the background and sends device information to the attackers including its operating system, name and what privileges a user has on that device.

Convolutional dictionary learning (CDL or sparsifying CDL) has many applications in image processing and computer vision. There has been growing interest in developing efficient algorithms for CDL, mostly relying on the augmented Lagrangian (AL) method or the variant alternating direction method of multipliers (ADMM). When their parameters are properly tuned, AL methods have shown fast convergence in CDL. However, the parameter tuning process is not trivial due to its data dependence and, in practice, the convergence of AL methods depends on the AL parameters for nonconvex CDL problems. To moderate these problems, this paper proposes a new practically feasible and convergent Block Proximal Gradient method using a Majorizer (BPG-M) for CDL. The BPG-M-based CDL is investigated with different block updating schemes and majorization matrix designs, and further accelerated by incorporating some momentum coefficient formulas and restarting techniques. All of the methods investigated incorporate a boundary artifacts removal (or, more generally, sampling) operator in the learning model. Numerical experiments show that, without needing any parameter tuning process, the proposed BPG-M approach converges more stably to desirable solutions of lower objective values than the existing state-of-the-art ADMM algorithm and its memory-efficient variant do. Compared to the ADMM approaches, the BPG-M method using a multi-block updating scheme is particularly useful in single-threaded CDL algorithm handling large datasets, due to its lower memory requirement and no polynomial computational complexity. Image denoising experiments show that, for relatively strong additive white Gaussian noise, the filters learned by BPG-M-based CDL outperform those trained by the ADMM approach.

On the very first day of Wolfram Camp, I called Stephen Wolfram "Steve." "It's Stephen, actually," said the world's most controversial physicist in his crisp-yet-droll British accent. In another life, the creator of Wolfram Alpha would have made an excellent BBC Radio News announcer. "No one under the age of 50 calls me Steve," he added. Katie Orenstein is a New York City-based writer, programmer, and thespian who moonlights as a high school senior.

Science fiction gets a bad rap in some circles for being a genre with nothing but a bunch of spaceships and robots. It's also got drama, action, and even some romance. It's also full of spaceships and robots and if you're not really into those things, well, the door is over there. If you do adore droids and contraptions that fly through the cosmos, though, we have something here that's just for you. Below is a selection of the best sci-fi movies you can currently stream on Netflix, Hulu, and Amazon.

Participants run ahead of Puerto de San Lorenzo's fighting bulls during the third bull run of the San Fermin festival in Pamplona, northern Spain. Each day at 8:00 am hundreds of people race with six bulls, charging along a winding, 848.6-metre (more than half a mile) course through narrow streets to the city's bull ring, where the animals are killed in a bullfight or corrida, during this festival, immortalised in Ernest Hemingway's 1926 novel "The Sun Also Rises" and dating back to medieval times and also featuring religious processions, folk dancing, concerts and round-the-clock drinking. Iraqi women, who fled the fighting between government forces and Islamic State (IS) group jihadists in the Old City of Mosul, cry as they stand in the city's western industrial district awaiting to be relocated

There have been many kinds of books, with many kinds of meanings. This one book was special because it was the first fictional story produced via artificial intelligence. It was the first book in the sense that its contents made sense. Before this book, all other attempts of letting an AI write a book had produced things that were pastiches of randomness. A couples of sentences here and there surrounded by text that made no sense.