Collaborating Authors

MLitB: Machine Learning in the Browser Machine Learning

With few exceptions, the field of Machine Learning (ML) research has largely ignored the browser as a computational engine. Beyond an educational resource for ML, the browser has vast potential to not only improve the state-of-the-art in ML research, but also, inexpensively and on a massive scale, to bring sophisticated ML learning and prediction to the public at large. This paper introduces MLitB, a prototype ML framework written entirely in JavaScript, capable of performing large-scale distributed computing with heterogeneous classes of devices. The development of MLitB has been driven by several underlying objectives whose aim is to make ML learning and usage ubiquitous (by using ubiquitous compute devices), cheap and effortlessly distributed, and collaborative. This is achieved by allowing every internet capable device to run training algorithms and predictive models with no software installation and by saving models in universally readable formats. Our prototype library is capable of training deep neural networks with synchronized, distributed stochastic gradient descent. MLitB offers several important opportunities for novel ML research, including: development of distributed learning algorithms, advancement of web GPU algorithms, novel field and mobile applications, privacy preserving computing, and green grid-computing. MLitB is available as open source software.

Distributed Machine Learning on Mobile Devices: A Survey Machine Learning

In recent years, mobile devices have gained increasingly development with stronger computation capability and larger storage. Some of the computation-intensive machine learning and deep learning tasks can now be run on mobile devices. To take advantage of the resources available on mobile devices and preserve users' privacy, the idea of mobile distributed machine learning is proposed. It uses local hardware resources and local data to solve machine learning sub-problems on mobile devices, and only uploads computation results instead of original data to contribute to the optimization of the global model. This architecture can not only relieve computation and storage burden on servers, but also protect the users' sensitive information. Another benefit is the bandwidth reduction, as various kinds of local data can now participate in the training process without being uploaded to the server. In this paper, we provide a comprehensive survey on recent studies of mobile distributed machine learning. We survey a number of widely-used mobile distributed machine learning methods. We also present an in-depth discussion on the challenges and future directions in this area. We believe that this survey can demonstrate a clear overview of mobile distributed machine learning and provide guidelines on applying mobile distributed machine learning to real applications.

OpenSSH arrives in Windows 10 Spring Update


Windows 10 is becoming a useful Unix/Linux sysadmin platform. First, it has incorporated Windows Subsystem for Linux in the Windows 10 Fall Creators Update. Now, in the Windows 10 April 2018 Update, Microsoft has finally brought a native Secure Shell (SSH) to Windows. It's taken a long time. Microsoft started work on porting OpenSSH to PowerShell in 2015 because of user demand.

Hardware Acceleration with Bitfusion


The platform can share GPUs in a virtualized infrastructure, as a pool of network-accessible resources, rather than isolated resources per server. Bitfusion also supports other accelerators such as Field Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs). Bitfusion works across AI frameworks, clouds, networks, and formats such as virtual machines and containers.

Google Chrome extension that steals card numbers still available on Web Store


A malicious Google Chrome extension that can recognize and steal payment card details entered in web forms is still available on the Chrome Web Store. The extension is the work of a cyber-criminal group and has been at the heart of a malware distribution effort in the past. The website through which the extension was initially distributed is now down, but the extension is still available on the Play Store, meaning it could be used for future campaigns to infect new users. Until now, the extension has been installed by roughly 400 users, according to stats available on its official Chrome Web Store listing. The extension's name is Flash Reader.