Conference Series welcomes you to attend the "International Conference on Advanced Robotics, Mechatronics and Artificial Intelligence" during December 03-04, 2018, Valencia, Spain. The main theme of the conference is "Boundless implication of Automation and Control Systems in Mechatronics". We cordially invite all the participants who are interested in sharing their knowledge and research in the arena of Advanced Robotics, Mechatronics and Artificial Intelligence. Advanced Robotics 2018 anticipates more than 150 participants around the globe with thought provoking Keynote lectures, Oral and Poster presentations. Opportunity to attend the presentations delivered by eminent scientists, researchers, experts from all over the world.

Subspace clustering aims to find groups of similar objects (clusters) that exist in lower dimensional subspaces from a high dimensional dataset. It has a wide range of applications, such as analysing high dimensional sensor data or DNA sequences. However, existing algorithms have limitations in finding clusters in non-disjoint subspaces and scaling to large data, which impinge their applicability in areas such as bioinformatics and the Internet of Things. We aim to address such limitations by proposing a subspace clustering algorithm using a bottom-up strategy. Our algorithm first searches for base clusters in low dimensional subspaces. It then forms clusters in higher-dimensional subspaces using these base clusters, which we formulate as a frequent pattern mining problem. This formulation enables efficient search for clusters in higher-dimensional subspaces, which is done using FP-trees. The proposed algorithm is evaluated against traditional bottom-up clustering algorithms and state-of-the-art subspace clustering algorithms. The experimental results show that the proposed algorithm produces clusters with high accuracy, and scales well to large volumes of data. We also demonstrate the algorithm's performance using real-life data, including ten genomic datasets and a car parking occupancy dataset.

Classification, the process of assigning a label (or class) to an observation given its features, is a common task in many applications. Nonetheless in most real-life applications, the labels can not be fully explained by the observed features. Indeed there can be many factors hidden to the modellers. The unexplained variation is then treated as some random noise which is handled differently depending on the method retained by the practitioner. This work focuses on two simple and widely used supervised classification algorithms: discrete choice models and artificial neural networks in the context of binary classification. Through various numerical experiments involving continuous or discrete explanatory features, we present a comparison of the retained methods' performance in presence of missing variables. The impact of the distribution of the two classes in the training data is also investigated. The outcomes of those experiments highlight the fact that artificial neural networks outperforms the discrete choice models, except when the distribution of the classes in the training data is highly unbalanced. Finally, this work provides some guidelines for choosing the right classifier with respect to the training data.

In less than 50 years, Artificial Intelligence (AI) will match humans on traits like adaptability, creativity and emotional intelligence, an expert has predicted. Speaking at the "Festival of Dangerous Ideas" at University of New South Wales in Sydney on Sunday, Professor Toby Walsh said AI will match human intelligence by 2062. "Toby Walsh, Scientia Professor of Artificial Intelligence at UNSW Sydney, has put a date on this looming reality. "He considers 2062 the year that artificial intelligence will match human intelligence, although a fundamental shift has already occurred in the world as we know it," the university said in a statement. Walsh argued that we are already experiencing the risks of AI that seem to be so far in the future.

China now has seminars to teach other countries how to censor free speech as its'techno-dystopia' spreads, a worrying report has found. Governments worldwide are stepping up use of online tools to suppress dissent and tighten their grip on power, a human rights watchdog study found. Chinese officials have held sessions on controlling information with 36 of the 65 countries assessed, and provided telecom and surveillance equipment to a number of foreign governments, researchers said. India led the world in the number of internet shutdowns, with over 100 reported incidents in 2018 so far, claiming that the moves were needed to halt the flow of disinformation and incitement to violence. Many governments, including Saudi Arabia, are employing'troll armies' to manipulate social media and in many cases drown out the voices of dissidents.

Nearly eight months after one of its autonomous test vehicles hit and killed an Arizona pedestrian, Uber wants to resume testing on public roads. The company has filed an application with the Pennsylvania Department of Transportation to test in Pittsburgh, and it has issued a lengthy safety report pledging to put two human backup drivers in each vehicle and take a raft of other precautions to make the vehicles safe. Company officials acknowledge they have a long way to go to regain public trust after the March 18 crash in Tempe, Arizona, that killed Elaine Herzberg, 49, as she crossed a darkened road outside the lines of a crosswalk. Nearly eight months after one of its autonomous test vehicles hit and killed an Arizona pedestrian, Uber wants to resume testing on public roads. Police said Uber's backup driver in the autonomous Volvo SUV was streaming the television show'The Voice' on her phone and looking downward before crash. The National Transportation Safety Board said the autonomous driving system on the Volvo spotted Herzberg about six seconds before hitting her, but did not stop because the system used to automatically apply brakes in potentially dangerous situations had been disabled.

This artificial intelligence program, named ClaRAN, has the ability to scan images taken by radio telescopes. With the responsibility to identify radio galaxies, galaxies that emit powerful radio jets from supermassive black holes at their centres, ClaRAN is the brainchild of big data specialist Dr Chen Wu and astronomer Dr Ivy Wong, both from The University of Western Australia in partnership with the International Centre for Radio Astronomy Research (ICRAR). Wong explains: "These supermassive black holes occasionally burp out jets that can be seen with a radio telescope." "Over time, the jets can stretch a long way from their host galaxies, making it difficult for traditional computer programs to figure out where the galaxy is." "That's what we're trying to teach ClaRAN to do." Describing the origin of the artificial intelligence program, Dr Wu discusses how ClaRAN grew out of an open source version of Microsoft and Facebook's object detection software. The program was completely overhauled and trained to recognise galaxies instead of people.

Researchers have taught an artificial intelligence program used to recognise faces on Facebook to identify galaxies in deep space. The result is an AI bot named ClaRAN that scans images taken by radio telescopes. Its job is to spot radio galaxies - galaxies that emit powerful radio jets from supermassive black holes at their centres. ClaRAN is the brainchild of big data specialist Dr Chen Wu and astronomer Dr Ivy Wong, both from The University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR). Dr Wong said black holes are found at the centre of most, if not all, galaxies.

An artificial intelligence (AI) programme used to recognize faces on Facebook can also identify galaxies in deep space, scientists said Wednesday. The AI bot named ClaRAN scans images taken by radio telescopes, said researchers from the International Centre for Radio Astronomy Research (ICRAR) in Australia. Its job is to spot radio galaxies -- galaxies that emit powerful radio jets from supermassive black holes at their centers, according to the research published in the journal Monthly Notices of the Royal Astronomical Society. Black holes are found at the center of most, if not all, galaxies. "These supermassive black holes occasionally burp out jets that can be seen with a radio telescope," said Ivy Wong from The University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR).

Researchers have taught an artificial intelligence program used to recognise faces on Facebook to identify galaxies in deep space. The result is an AI bot named ClaRAN that scans images taken by radio telescopes. Its job is to spot radio galaxies--galaxies that emit powerful radio jets from supermassive black holes at their centres. ClaRAN is the brainchild of big data specialist Dr. Chen Wu and astronomer Dr. Ivy Wong, both from The University of Western Australia node of the International Centre for Radio Astronomy Research (ICRAR). Dr. Wong said black holes are found at the centre of most, if not all, galaxies.