ABSTRACT We use automatic speech recognition to assess spoken English learner pronunciation based on the authentic intelligibility of the learners' spoken responses determined from support vector machine (SVM) classifier or deep learning neural network model predictions of transcription correctness. Using numeric features produced by PocketSphinx alignment mode and many recognition passes searching for the substitution and deletion of each expected phoneme and insertion of unexpected phonemes in sequence, the SVM models achieve 82% agreement with the accuracy of Amazon Mechanical Turk crowdworker transcriptions, up from 75% reported by multiple independent researchers. Using such features with SVM classifier probability prediction models can help computeraided pronunciation teaching (CAPT) systems provide intelligibility remediation. Index Terms-- phoneme alignment, pronunciation assessment, computer aided language learning, binary features 1. INTRODUCTION Authentic intelligibility, the ability of listeners to correctly transcribe recorded utterances, initially used for CAPT by [1] and [2], is a better measure of pronunciation assessment for spoken language learners compared to mispronunciations identified by expert pronunciation judges or panels of experts, because such mispronunciations are associated with only 16% of intelligibility problems, according to [3], who state: We investigated... which words are likely to be misrecognized and which words are likely to be marked as pronunciation errors. Words perceived as mispronounced remain intelligible in about half of all cases.

Data Science is one of the hottest jobs of the 21 century with an average salary of over $120,000. This course is designed learners of all backgrounds including beginners with no programming experience to experienced programmers who would like to advance to become a spatial data scientist. I will teach you programming with R to visualize, explore, and analyze your spatial data. At the end of this course, you will be able to acquire skills spatial data analysis. Enroll now in this course and start your journey of becoming a spatial data scientist!

Feeling rather good about myself as I'm writing this as I've just completed the first course of Andrew Ng's latest Deep Learning specialization on Coursera. I've been meaning to learn about Deep Learning for quite awhile now but haven't been able to wrap my heads around the theory aspect of it for longest of time. Previously, my foray into deep learning has been via Udacity's Deep Learning materials, random internet articles, and the Deep Learning textbook. Bought it from Amazon a few months ago, and am still going through the pages. Still finding it tough to find the time between going through a few pages, the day job, and sorting out the kids at night.

You may be impressed with the incredible capabilities of cameras on today's smartphones. But according to imaging experts, that's nothing compared to what may be around the corner. Researchers claim cameras of the future could have the ability to see through walls with the help of lasers, potentially leading to a new generation of'spy phones'. In this article by The Conversation, Daniele Faccio, a Professor of Quantum Technologies at the University of Glasgow, and Stephen McLaughlin, Head of the School of Engineering and Physical Sciences at Heriot-Watt University, examine the technologies that could underpin a larger revolution of camera technology. You might be really pleased with the camera technology in your latest smartphone, which can recognise your face and take slow-mo video in ultra-high definition.

This course is intended for R and data science professionals aiming to master R. Intermediate and advanced users, will both find that this course will separate them from the rest of people doing analytics with R. We don't recommend this course on beginners. We start by explaining how to work with closures, environments, dates, and more advanced topics. We then move into regex expressions and parsing html data. We explain how to write R packages, and write the proper documentation that the CRAN team expects if you want to upload your code into R's libraries. After that we introduce the necessary skills for profiling your R code.

Dinesh Verma is an IBM Fellow, the company's pre-eminent technical distinction granted in recognition of outstanding and sustained technical achievements and leadership in engineering. Dinesh has worked in IBM Research for nearly 25 years, holds more than 150 patents, is a member of the IBM Academy of Technology, and heads a team that is focused on Distributed Artificial Intelligence (AI). The IBM THINK Blog caught up with Dinesh recently to talk about his current work, as well as his career at IBM. The following is an excerpt and is part of our Perspectives series featuring stories by and about IBMers who take the "long view." THINK: Can you tell us a little bit about your role at IBM? Dinesh Verma: I lead the Distributed AI team at IBM Research at the Thomas J. Watson Research Center in Yorktown, NY.

As AI continues to advance, what future jobs are just over the horizon? Despite fears of automation taking away jobs, the need for skilled humans to operate, utilise and advance technologies will remain unequivocally necessary. While there are plenty of people who fear robots taking over their jobs, there are also many important positives to automation. This starts with having robots in the workplace to treat like robots, and revaluing human employees as actual humans with a need for purpose and work-life balance. Automation, and augmented and virtual reality (AR/VR) all lead to the idea that human workers will be valued for their uniquely human skills, such as creativity and innovative thinking.

This book was designed around major data structures, operations, and techniques in linear algebra that are directly relevant to machine learning algorithms. There are a lot of things you could learn about linear algebra, from theory to abstract concepts to APIs. My goal is to take you straight to developing an intuition for the elements you must understand with laser-focused tutorials. I designed the tutorials to focus on how to get things done with linear algebra. They give you the tools to both rapidly understand and apply each technique or operation. Each tutorial is designed to take you about one hour to read through and complete, excluding the extensions and further reading. You can choose to work through the lessons one per day, one per week, or at your own pace. I think momentum is critically important, and this book is intended to be read and used, not to sit idle. I would recommend picking a schedule and sticking to it.

So you've taken the plunge. You want to become a data scientist. There are far too many resources out there. How do you decide the starting point? Did you miss out on topics you should have studied?

An intelligent robot agent based on domain ontology, machine learning mechanism, and Fuzzy Markup Language (FML) for students and robot co-learning is presented in this paper. The machine-human co-learning model is established to help various students learn the mathematical concepts based on their learning ability and performance. Meanwhile, the robot acts as a teacher's assistant to co-learn with children in the class. The FML-based knowledge base and rule base are embedded in the robot so that the teachers can get feedback from the robot on whether students make progress or not. Next, we inferred students' learning performance based on learning content's difficulty and students' ability, concentration level, as well as teamwork sprit in the class. Experimental results show that learning with the robot is helpful for disadvantaged and below-basic children. Moreover, the accuracy of the intelligent FML-based agent for student learning is increased after machine learning mechanism.