Autonomous Robotics, a subsidiary of listed company Thalassa, is to collaborate with Robert Gordon University to conduct research on swarm technology of autonomous underwater vehicles in the North Sea. The work is supported by the Oil & Gas Innovation Centre. The purpose of this research is to further enhance the capability of the'flying node' system and further reduce the cost and time for ocean bottom seismic surveys. The Swarm Technology research will be performed by Dr Wai-keung Fung and Mr Adham Sabra, who are with the Communications and Autonomous Systems Group within the School of Engineering, with results are expected within 12 months. Chairman Dave Grant said: "ARL are working with RGU to research and create a practical localisation system for the flying node system which will allow the flying nodes to operate in a swarm and move from their initial seabed position to a new seabed location.

Text analytics based on supervised machine learning classifiers has shown great promise in a multitude of domains, but has yet to be applied to Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) on a seismological corpus of 100 articles related to the topic of precursory accelerating seismicity, spanning from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor whether explained by critical processes (i.e., cascade triggering) or by other processes (such as signature of main fault loading). We investigate rather the classification process can be automatized to help analyze larger corpora in order to better understand trends in earthquake predictability research. We find that the Naive Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies of 86% for binary classification. For a refined multiclass classification ('non-critical process' < 'agnostic' < 'critical process assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy. Prediction on a dozen of articles published since 2011 shows however a weak generalization with a F1-score of 60%, only slightly better than a random classifier, which can be explained by a change of authorship and use of different terminologies. Yet, the model shows F1-scores greater than 80% for the two multiclass extremes ('non-critical process' versus 'critical process demonstrated') while it falls to random classifier results (around 25%) for papers labelled 'agnostic' or 'critical process assumed'. Those results are encouraging in view of the small size of the corpus and of the high degree of abstraction of the labelling. Domain knowledge engineering remains essential but can be made transparent by an investigation of Naive Bayes keyword posterior probabilities.

Merging beliefs requires the plausibility of the sources of the information to be merged. They are typically assumed equally reliable when nothing suggests otherwise. A recent line of research has spun from the idea of deriving this information from the revision process itself. In particular, the history of previous revisions and previous merging examples provide information for performing subsequent merging operations. Yet, no examples or previous revisions may be available. In spite of the apparent lack of information, something can still be inferred by a try-and-check approach: a relative reliability ordering is assumed, the sources are integrated according to it and the result is compared with the original information. The final check may contradict the original ordering, like when the result of merging implies the negation of a formula coming from a source initially assumed reliable, or it implies a formula coming from a source assumed unreliable. In such cases, the reliability ordering assumed in the first place can be excluded from consideration. Such a scenario is proved real under the classifications of source reliability and definitions of belief integration considered in this article: sources divided in two, three or multiple reliability classes; integration is mostly by maximal consistent subsets but also weighted distance is considered.

For the operators of large helicopters, the principle of big data is nothing new. For years, these companies and their associated MRO operations have been collecting and analyzing vibration data from onboard health and usage monitoring systems (HUMS), looking for potential issues within an aircraft's dynamic systems as well as clues to potential maintenance problems. However, in the current era of analytics, artificial intelligence (AI) and algorithms, new uses for the data coming off the helicopters are being enabled and helping to democratize the use of systems like HUMS. "Today in the helicopter world, a lot of things are being done in the maintenance world as they would have been 40-50 years ago," says Matthieu Louvot, executive vice president for customer support and services at Airbus Helicopters. "Now is the time to digitize."

Two tiny robots have landed safely on an asteroid after a Japanese spacecraft dropped them there on Friday. The scientists behind the historic mission expressed their delight as the rovers sent back the first images from the surface of the space rock Ryugu. Dubbed MINERVA-II1, the robotic explorers are the first of their kind to be successfully landed on an asteroid. The Japanese space agency JAXA announced that both units were operational after a period of silence between the unmanned spacecraft Hayabusa-2 depositing them and connection being established with the team on Earth. "I cannot find words to express how happy I am that we were able to realise mobile exploration on the surface of an asteroid," said Hayabusa-2 project manager Dr Yuichi Tsuda.

A Japanese spacecraft has dropped two small rovers onto the surface of an asteroid zooming through space. If they land safely, the unmanned Hayabusa-2 would be the first spacecraft to ever successfully place robotic rovers onto a space rock. Japan's space agency (JAXA) hopes that the mission will provide clues about the origin of the solar system. The agency is expecting to receive data from the rovers at some point on Saturday confirming whether or not the mission has been a success. Hayabusa-2 first arrived near the asteroid, known as Ryugu and situated 280 million km (170 million miles) from Earth, in June.

The three college-age defendants behind the creation of the Mirai botnet--an online tool that wreaked destruction across the internet in the fall of 2016 with unprecedentedly powerful distributed denial of service attacks--will stand in an Alaska courtroom Tuesday and ask for a novel ruling from a federal judge: They hope to be sentenced to work for the FBI. Josiah White, Paras Jha, and Dalton Norman, who were all between 18 and 20 years old when they built and launched Mirai, pleaded guilty last December to creating the malware that hijacked hundreds of thousands of Internet of Things devices, uniting them as a digital army that began as a way to attack rival Minecraft video game hosts, and evolved into an online tsunami of nefarious traffic that knocked entire web hosting companies offline. At the time, the attacks raised fears amid the presidential election targeted online by Russia that an unknown adversary was preparing to lay waste to the internet. The original creators, panicking as they realized their invention was more powerful than they had imagined, released the code--a common tactic by hackers to ensure that if and when authorities catch them, they don't possess any code that isn't already publicly known that can help finger them as the inventors. That release in turn lead to attacks by others throughout the fall, including one that made much of the internet unusable for the East Coast of the United States on an October Friday.

The El Niño/La Niña pattern in the Pacific Ocean is notorious for its long-distance effects on weather as far away as Africa and the Midwestern United States. But climate experts also know of several other such patterns, known as "teleconnections," and believe that there are many more to be discovered. The new TRIPODS Climate project, a collaboration among the University of Chicago, University of Wisconsin-Madison and the University of California-Irvine, will develop novel data science tools to sniff out these hidden patterns, improving weather forecasts and scientific understanding of global climate. Researchers will apply data science methods such as machine learning, network analysis and predictive modeling to the growing flood of climate data. "There are fundamental challenges pervasive in data science that are epitomized in the climate science setting, making this collaboration a nice opportunity for advances on a number of fronts," said Rebecca Willett, professor of computer science and statistics at UChicago.

Soul Machines has discussed services for the elderly with prospective clients but has not announced any partnerships on that subject to date, says chief business officer Greg Cross. Soul Machines envisions a future in which digital instructors educate students without access to quality human teachers, and in which famous deceased artists are digitally resurrected to discuss their works in museums. Robot companions for the infirm, then, are not too far a leap. Nor is the prospect of a future in which a family converses with the lively AI recreation of a person suffering from dementia, while a caregiver--robot or human--tends to their ailing body in another room. The potential for deception is already here. A few years ago, Brent Lawson, the president of 1 AM Dolls, a manufacturer of life-sized rubber sex dolls, was on the phone with a client who wanted a specific doll he'd seen on the company's website. The man was particularly concerned that the doll's hair was just so, and peppered Lawson with questions about the color and style, Lawson told Quartz.

Hurricane Harvey, shown in 2017. A new data project hopes to sniff out weather patterns. The El Nino and La Nina patterns in the Pacific Ocean are notorious for their long-distance effects on weather as far away as Africa and the Midwestern United States. But climate experts also know of several other such patterns, known as teleconnections, and believe that there are many more to be discovered. The new TRIPODS Climate project, a collaboration among the University of Wisconsin–Madison, the University of Chicago, and the University of California, Irvine, will develop novel data science tools to sniff out these hidden patterns, improving weather forecasts and scientific understanding of global climate.