Most of the JavaScript code deployed in the wild has been minified, a process in which identifier names are replaced with short, arbitrary and meaningless names. Minified code occupies less space, but also makes the code extremely difficult to manually inspect and understand. This paper presents Context2Name, a deep learningbased technique that partially reverses the effect of minification by predicting natural identifier names for minified names. The core idea is to predict from the usage context of a variable a name that captures the meaning of the variable. The approach combines a lightweight, token-based static analysis with an auto-encoder neural network that summarizes usage contexts and a recurrent neural network that predict natural names for a given usage context. We evaluate Context2Name with a large corpus of real-world JavaScript code and show that it successfully predicts 47.5% of all minified identifiers while taking only 2.9 milliseconds on average to predict a name. A comparison with the state-of-the-art tools JSNice and JSNaughty shows that our approach performs comparably in terms of accuracy while improving in terms of efficiency. Moreover, Context2Name complements the state-of-the-art by predicting 5.3% additional identifiers that are missed by both existing tools.

Research into the classification of time series has made enormous progress in the last decade. The UCR time series archive has played a significant role in challenging and guiding the development of new learners for time series classification. The largest dataset in the UCR archive holds 10 thousand time series only; which may explain why the primary research focus has been in creating algorithms that have high accuracy on relatively small datasets. This paper introduces Proximity Forest, an algorithm that learns accurate models from datasets with millions of time series, and classifies a time series in milliseconds. The models are ensembles of highly randomized Proximity Trees. Whereas conventional decision trees branch on attribute values (and usually perform poorly on time series), Proximity Trees branch on the proximity of time series to one exemplar time series or another; allowing us to leverage the decades of work into developing relevant measures for time series. Proximity Forest gains both efficiency and accuracy by stochastic selection of both exemplars and similarity measures. Our work is motivated by recent time series applications that provide orders of magnitude more time series than the UCR benchmarks. Our experiments demonstrate that Proximity Forest is highly competitive on the UCR archive: it ranks among the most accurate classifiers while being significantly faster. We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100,000 times faster than current state of the art models Elastic Ensemble and COTE.

"CIOs and technology leaders should always be scanning the market along with assessing and piloting emerging technologies to identify new business opportunities with high impact potential and strategic relevance for their business," says Gartner research vice president Mike J. Walker. In Gartner's latest Hype Cycle for Emerging Technologies, Walker reports on these must-watch technologies, listing five that will "blur the lines" between human and machine. They will profoundly create new experiences, with unrivaled intelligence, and offer platforms that allow organisations to connect with new business ecosystems, he states. AI technologies will be virtually everywhere over the next 10 years, reports Gartner. While these technologies enable early adopters to adapt to new situations and solve problems that have not been encountered previously, these technologies will become available to the masses -- democratised.

Today, Google announced that its smart assistant is now bilingual. While Google Assistant could already understand multiple languages, now you can speak two languages interchangeably and Assistant will be able to follow what you're saying. Supported languages include any pairing of English, German, French, Spanish, Italian and Japanese. More languages will be added in the next few months. Google is also expanding the availability of its smart speaker Google Home Max.

Scientists are looking to nature to inspire the next generation of robots. Here's what they've come up with. Could the Biggest Ocean Recording Ever Made Redefine Marine Science? The Swim - https://youtu.be/-GWVmk-U-kk Read More: Soft Robotics: Challenges and Perspectives https://www.sciencedirect.com/science... "There has been an increasing interest in the use of unconventional materials and morphologies in robotic systems because the underlying mechanical properties (such as body shapes, elasticity, viscosity, softness, density and stickiness) are crucial research topics for our in-depth understanding of embodied intelligence." Soft robotic fish swims alongside real ones in coral reefs http://news.mit.edu/2018/soft-robotic... "During test dives in the Rainbow Reef in Fiji, SoFi swam at depths of more than 50 feet for up to 40 minutes at once, nimbly handling currents and taking high-resolution photos and videos using (what else?) a fisheye lens." A World of "Soft Robots" Could Actually Be the Gross Future We Need https://www.inverse.com/article/35979... "Jamie Paik, one of their creators and the director of the Reconfigurable Robotics Laboratory at the Swiss Institute of Technology, tells Inverse these machines have a chance to revolutionize how humans and robots interact.

Google's most expensive speaker option is finally available in the US. It's certainly taking the tech giant some time to release Home Max in markets outside North America, but it's at least making its way to more places around the globe -- it also arrived in Australia a few weeks ago. Mountain View created Home Max to be 20 times more powerful than the ordinary Home speaker, so it can fill even large rooms with high-fidelity sound and a deep, balanced bass thanks to its 4.5 -inch high- excursion woofers. It also features Google's new AI-powered technology called "Smart Sound," which allows sounds to adapt to the speaker's environment. Just place the device wherever you want, and it'll tune itself to deliver the best sound possible.

An earthquake and its aftershocks rocked Japan's Kumamoto prefecture in 2016, causing 48 deaths.Credit: Aflo/REX/Shutterstock A machine-learning study that analysed hundreds of thousands of earthquakes beat the standard method at predicting the location of aftershocks. Scientists say that the work provides a fresh way of exploring how changes in ground stress, such as those that occur during a big earthquake, trigger the quakes that follow. It could also help researchers to develop new methods for assessing seismic risk. "We've really just scratched the surface of what machine learning may be able to do for aftershock forecasting," says Phoebe DeVries, a seismologist at Harvard University in Cambridge, Massachusetts. She and her colleagues report their findings1 on 29 August in Nature.

The 18th of March, 2018, was the day tech insiders had been dreading. That night, a new moon added almost no light to a poorly lit four-lane road in Tempe, Arizona, as a specially adapted Uber Volvo XC90 detected an object ahead. Part of the modern gold rush to develop self-driving vehicles, the SUV had been driving autonomously, with no input from its human backup driver, for 19 minutes. An array of radar and light-emitting lidar sensors allowed onboard algorithms to calculate that, given their host vehicle's steady speed of 43mph, the object was six seconds away – assuming it remained stationary. But objects in roads seldom remain stationary, so more algorithms crawled a database of recognizable mechanical and biological entities, searching for a fit from which this one's likely behavior could be inferred. At first the computer drew a blank; seconds later, it decided it was dealing with another car, expecting it to drive away and require no special action. Only at the last second was a clear identification found – a woman with a bike, shopping bags hanging confusingly from handlebars, doubtless assuming the Volvo would route around her as any ordinary vehicle would. Barred from taking evasive action on its own, the computer abruptly handed control back to its human master, but the master wasn't paying attention. Elaine Herzberg, aged 49, was struck and killed, leaving more reflective members of the tech community with two uncomfortable questions: was this algorithmic tragedy inevitable? And how used to such incidents would we, should we, be prepared to get? "In some ways we've lost agency. When programs pass into code and code passes into algorithms and then algorithms start to create new algorithms, it gets farther and farther from human agency. Software is released into a code universe which no one can fully understand."

Monash University has long been an advocate of GPU computing technologies, and has recently formed a formal partnership with NVIDIA. As part of this partnership NVIDIA have provided SensiLab with next-generation hardware for deep learning and advanced visual simulation. Vinh Nguyen, Deep Learning Solutions Architect for NVIDIA, said, 'SensiLab has established itself as an excellent interdisciplinary research center, focusing on AI in creative art, but also other areas like robotics and visualization. As part of the growing collaboration with Monash, NVIDIA will be contributing staff time and equipment to joint research projects at SensiLab.' The NVIDIA systems contributed will be used as part of research efforts in Creative AI and visualisation.

There are classification tasks that take as inputs groups of images rather than single images. In order to address such situations, we introduce a nested multi-instance deep network. The approach is generic in that it is applicable to general data instances, not just images. The network has several convolutional neural networks grouped together at different stages. This primarily differs from other previous works in that we organize instances into relevant groups that are treated differently. We also introduce a method to replace instances that are missing which successfully creates neutral input instances and consistently outperforms standard fill-in methods in real world use cases. In addition, we propose a method for manual dropout when a whole group of instances is missing that allows us to use richer training data and obtain higher accuracy at the end of training. With specific pretraining, we find that the model works to great effect on our real world and pub-lic datasets in comparison to baseline methods, justifying the different treatment among groups of instances.