Keystroke dynamics is a behavioural biometric utilised for user identification and authentication. We propose a new set of features based on the distance between keys on the keyboard, a concept that has not been considered before in keystroke dynamics. We combine flight times, a popular metric, with the distance between keys on the keyboard and call them as Distance Enhanced Flight Time features (DEFT). This novel approach provides comprehensive insights into a person's typing behaviour, surpassing typing velocity alone. We build a DEFT model by combining DEFT features with other previously used keystroke dynamic features. The DEFT model is designed to be device-agnostic, allowing us to evaluate its effectiveness across three commonly used devices: desktop, mobile, and tablet. The DEFT model outperforms the existing state-of-the-art methods when we evaluate its effectiveness across two datasets. We obtain accuracy rates exceeding 99% and equal error rates below 10% on all three devices.

With the changing world, it is important for companies to transform accordingly. One of the most widely used technologies used these days is cloud computing. The adoption of cloud computing has been increasing rapidly. Either company has already adopted it or is moving towards adopting it. The advantages that cloud computing provides are immaculate.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Although I've had the chance now to play with these different technologies, I'm still amazed by the convenience, portability, and computing power that Big Data and Cloud Computing technologies offer, both to consumers and businesses.

Autonomous vehicles are poised to take to our roads in massive numbers in the coming years. This has been made possible due to advances in deep learning and its application to autonomous driving. In this post, we take you through a tutorial that shows you how to build a remote control (RC) vehicle that uses Amazon AI services. Typically each autonomous vehicle is stacked with a lot of sensors that provide rich telemetry. This telemetry can be used to improve the driving of the individual vehicle but also the user experience.

There are many ways to use Amazon Web Services (AWS) for Machine Learning. Amazon have expended a lot of effort to build up their offerings in this space, but I'm going to take a step back and have you build up your own machine in the Amazon cloud that we can run experiments on. If you're not familiar with AWS, best way to think of it is a bunch of different computer technologies available to rent. You only pay for the amount of time you use these technologies, which makes it a great place to try out new stuff. You can simply rent an Amazon computer, equipped with a graphics card or FPGA if you like, and run your experiments for a couple of hours.

Allstate Corporation, the second largest insurance company in United States, founded in 1931, recently launched a Machine Learning recruitment challenge in partnership with Kaggle. Allstate's objective was to predict the cost, and hence the severity, of claims. The competition organizers provide the competitors with more than 300,000 examples with masked and anonymous data consisting of more than 100 categorical and numerical attributes, thus being compliant with confidentiality constraints. The Spark/Scala script explained in this post obtains the training and test input datasets from local or Amazon's AWS S3 environment and trains a Random Forest model over it. The objective is to demonstrate the use of Spark 2.0 Machine Learning pipelines with Scala language, AWS S3 integration and some general good practices for building Machine Learning models. In order to keep this main objective, more sophisticated techniques (such as a thorough exploratory data analysis and feature engineering) are intentionally omitted. Since almost all personal computers nowadays have many Gigabytes of RAM (and it is in an accelerated growing) and powerful CPUs and GPUs, many real-world machine learning problems can be solved with a single computer and frameworks such as ScikitLearn, with no need of a distributed system.