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Sentiment analysis is widely applied in voice of the customer (VOC) applications. In this article, authors discuss NLP-based Sentiment Analysis based on machine learning (ML) and lexicon-based approaches using KNIME data analysis tools. Source: infoq.com

Microservices have become mainstream in the enterprise. This proliferation of microservices applications generates new problems, which requires a new approach to managing problems. A microservice is a small, independently deployable, and independently scalable software service that is designed to encapsulate a specific semantic function in the larger applicationl. This article explores several approaches to deploying tools to debug microservices applications on a Kubernetes platform like Red Hat OpenShift, includingOpenTracing, Squash, Telepresence, and creating a Squash Operator in Red Hat Ansible Automation.

Firecracker is an eye-catching virtual machine from AWS. It is lightweight, secure, and uses Linux. Forget bloat, minimalism is the future of serverless. Firecracker is a new virtualization technology that enables customers to deploy lightweight micro Virtual Machines or microVMs. Firecracker microVMs combine the security and workload isolation properties of traditional VMs with the speed, agility and resource efficiency enabled by containers. They provide a secure, trusted environment for multitenant services, while maintaining minimal overhead.

We’ve recently become aware of a set of malware targeting Consul nodes with a specific configuration which allows remote code execution. Members of our community also (responsibly) reported incidents caused by this malware, and worked with us to include a patch in a recent version of Consul that protects from this threat in the wild. This post details how this malware may affect users, depending on their configuration, as well as outlines the steps we’ve taken to backport a patch for versions 1.

In deep reinforcement learning (RL), solving the Atari games Montezuma’s Revenge and Pitfall has been a grand challenge. These games represent a broad class of challenging, real-world problems called “hard-exploration problems,” where an agent has to learn complex tasks with very infrequent or deceptive feedback. The state-of-the-art algorithm on Montezuma’s Revenge gets an average score of 11,347, a max score of 17,500, and solved the first level at one point in one of ten tries.

Facebook AI Research (FAIR) and NYU School of Medicine’s Center for Advanced Imaging Innovation and Research (CAI²R) are sharing new open source tools and data as part of fastMRI, a joint research project to spur development of AI systems to speed MRI scans by up to 10x. Today’s releases include new AI models and baselines for this task(as described in our paper here). It also includes the first large-scale MRI data set of its kind, which can serve as a benchmark for future research.

Last Christmas, I went through the most frustrating experience as a consumer. I was doing some last minute holiday shopping and after standing in a long line, I finally reached the blessed register only to find out that my debit card was blocked. I could sense the old lady at the register judging me with her narrowed eyes. Feeling thoroughly embarrassed, I called my bank right away. To my horror, they told me that my savings account was hacked and thousands of dollars were already gone!

It’s a good question, and before you know the answer, Docker images can seem pretty mysterious. Not only do I want to give you the answer, but I want to show you how I got there. Let’s start at the beginning. Hopefully you’re all familiar with a Dockerfile – the instructions on how Docker will build an image for you. Here’s a simple example. Each of these lines are instructions to Docker on how to build an image.

Radiology and DeepLearningDetecting pneumonia opacities from chest X-Ray images using deep learning. One day back in August, I was catching up with my best friend from high school who is now a radiology resident. One thing led to another, and we started talking about our interests in artificial intelligence and machine learning and its possible applications in radiology. A couple of months after our talk, I stumbled upon a Kaggle challenge hosted by the Radiological Society of North American (RSNA).

TL;DR—As far as I can tell, there’s currently no way of providing documentation for specific containers that we could fairly call canonical, “best practice,” or even all that widely used. This blog post suggests some currently available (but sadly not-great) workarounds but also points to what I think could be a fundamentally better path. Source: medium.com