The study, which featured a University of Queensland researcher, used a slow motion camera to capture the nuanced movement of eight species of Australian agamid lizards that run on two legs -- an action known as'bipedal' movement. UQ School of Biological Sciences researcher Nicholas Wu said the study's findings challenged existing mathematical models based on the animals' movement. "There was an existing understanding that the backwards shift in these lizards' centre of mass, combined with quick bursts of acceleration, caused them to start running on two legs at a certain point," he said. "What we found though is that some lizards run bipedally sooner than expected, by moving their body back and winging their tail up. "This means that they could run bipedally for longer, perhaps to overcome obstacles in their path." Lead author Christofer Clemente from the University of the Sunshine Coast said these results may have important implications for the design of bio-inspired robotic devices. "We're still teasing out why these species have evolved to run like this in the first place, but as we learn more, it's clear that these lessons from nature may be able to be integrated into robotics," Dr. Clemente said. "It's been suggested that this movement might have something to do with increasing vision in moments of urgency, by elevating the head at the same time and helping to navigate over obstacles.

Gone are the days when banks and credit unions could compete on product or price. Today, the best way to differentiate a brand is through hyper-personalized financial recommendations driven by data and advanced decision tools. Subscribe to The Financial Brand via email for FREE!According to the WSJ, the number of branches in the U.S. are declining at the fastest pace on record. Although that may be understandable given a shifting preference for digital banking, this is coupled with findings by J.D. Power that fully digital bank customers are the least satisfied – even Millennials. Is this because digital banking apps are poorly designed or because digital banking consumers are more demanding … or both?

At the industry's main professional drone event, InterDrone (Las Vegas, USA, Sept. 5-7, 2018), Parrot, the leading European drone group, presented two new professional drone platforms: senseFly eBee X and Parrot ANAFI Work. Designed by teams of engineers in Paris and Switzerland, brought together within Parrot Business Solutions, the two new professional drone platforms have further strengthened the Group's range of solutions for the business market. Both complementary and differentiating, the eBee X and ANAFI Work provide precision data to work more efficiently, reduce professional risks and costs, and make decisions based on detailed precision information. Far more than just a drone, eBee X is a solution designed to optimize operator efficiency and minimize risk when collecting data. Its High-Precision on Demand (RTK/PPK) feature delivers absolute accuracy of down to 3 cm (1.2 in), without ground control points.

At 4ft-nothing, with orb-like eyes, SoftBank's humanoid robot, Pepper, is designed to look friendly. But imagine if Pepper -- a powerful machine crammed with cameras, sensors and motors -- hurtled towards you at top speed? Or stood in your home, secretly recording your life? In 2017, Lucas Apa and Cesar Cerrudo, security researchers with the consultancy IOActive, showed that the version 2.5.5 of Pepper could be hacked through its software because of vulnerabilities that were discovered when it was connected to a network. They demonstrated that the robot could be controlled remotely, its limbs manipulated and its cameras used to spy on users. Yet more than a year later, SoftBank has not patched the software, according to an analysis of its change logs by Mr Apa.

Recruiting cyber security experts is becoming a challenge for companies, as skilled staff are in short supply -- but artificial intelligence is coming to the rescue. If there are enough experts around to make the AI work, that is. While AI is already taking the pressure off overstretched cyber security teams, the skills required from professionals are changing rapidly as the technology evolves. This is forcing companies to choose between retraining existing staff or hiring fresh talent. Machine learning -- which enables computers to identify and predict anomalies from previously observed patterns in large amounts of information -- is ideally suited to monitoring growing volumes of data for potential security breaches and cutting cyber security teams' workloads.

Our eyes wander as we read text, and not just in the figurative sense -- between a series of rapid motions called saccades, eyes remain still for just 200-300 milliseconds on average. Those movements are rich with subtext -- they're driven by cognitive processes involving vision, attention, language, and motor control -- and according to new research from the University of Potsdam, Weizenbaum Institute for the Networked Society, and Leibniz Institute for Agricultural Engineering and Bioeconomy, they're enough to identify a person pretty accurately. A paper published on the preprint server Arxiv.org "Identification based on eye movements during reading may offer several advantages in many application areas," the researchers wrote. "Users can be identified unobtrusively while having access to a document they would read anyway, which saves time and attention."

Recently, super typhoon Mangkhut ravaged Asia, holding millions of people at a standstill across Guam, Philippines, Taiwan, Hong Kong, Macau, and mainland China. It was the strongest typhoon to hit the Philippines in five years, and the strongest to hit Hong Kong since 1983. Barely a week afterward, the US suffered from a natural disaster: Hurricane Florence. Natural disasters are inevitable, and while we cannot stop them altogether, technology is opening up more sophisticated and efficient ways to minimize the damage they cause. Consensus AI is a powerful ally for governments in several aspects of operations, including predicting the outcome of an impending calamity and speeding up disaster response time, ultimately ensuring fast responses during critical times. Although intimidating for some, governments can easily make the transition and reap the benefits of blockchain technology and artificial intelligence without having to suffer from technical complexities and arduous requisites.

If you are a senior data scientist or pro in predictive analytics, you would probably be using both R & Python, and maybe other tools like SAS, SQL etc. But, what if you are a beginner or just thinking about to start a career in data science, machine learning, and business analytics? Which one should you learn – R or Python? It has always been a topic of great debate among data scientists, researchers and analytics professionals. In this article, we will discuss R vs Python – usability, popularity index, advantages & limitations, job opportunities, and salaries. R is a statistical and visualization language which is deep and huge and mathematical.

Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture complex structure relationships inside networks. However, existing network representation methods are commonly designed for homogeneous information networks where all the nodes (entities) of a network are of the same type, e.g., papers in a citation network. In this paper, we propose a universal network representation approach (UNRA), that represents different types of nodes in heterogeneous information networks in a continuous and common vector space. The UNRA is built on our latest mutually updated neural language module, which simultaneously captures inter-relationship among homogeneous nodes and node-content correlation. Relationships between different types of nodes are also assembled and learned in a unified framework. Experiments validate that the UNRA achieves outstanding performance, compared to six other state-of-the-art algorithms, in node representation, node classification, and network visualization. In node classification, the UNRA achieves a 3\% to 132\% performance improvement in terms of accuracy.

Britain has successfully trialled using AI to scan for hidden attackers in a mock urban battlefield environment in Montreal, Canada. The AI, called SAPIENT, was developed in the UK with the aim of using sensors to detect potential unseen dangers to soldiers. SAPIENT is more efficient than manually scanning live feeds and frees up more soldiers to be used for operational means elsewhere. Canada and the UK maintain a close security partnership as part of the so-called'Five Eyes' alliance which also includes Australia, New Zealand, and the United States. SAPIENT was tested alongside other high-end military technologies including exoskeleton suits and new surveillance and night vision equipment.